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Property Effects in Inductive Inference

A dissertation presented

by

Nadezda Vasilyeva

to

The Department of Psychology

In partial fulfillment of the requirements for the degree of

Doctor of Philosophy

in the field of

Psychology

Northeastern University

Boston, Massachusetts

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Abstract

This project examined how people generate inductive inferences – probabilistic

hypotheses rendered plausible by the given evidence but not guaranteed by it. The hypotheses

people generate are often fine-tuned to specific features of the problem. For example, when

one is asked to project a property from a known case to unknown – e.g., given that ducks have

gene X, what else is likely to share the gene? – the nature of the projected property can have a

profound effect on the generated hypotheses. Intrinsic properties, such as having a gene, tend

to be projected to members of the same class – sparrows, other birds. Contextual, or

environmentally-transmitted properties, such as having a parasite – tend to be projected to

entities that interact or co-occur with the known case – otters, other aquatic animals;

predators of ducks. Such changes in inductive inferences based on the projected property –

“property effects” - are ubiquitous in induction, but the theoretical accounts of induction

capable of addressing the underlying psychological mechanism are lacking.

To address this gap, I proposed a simple two-component model of inductive inference,

consisting of retrieval of knowledge about the premise category (duck) and property (gene),

and generation of an inductive hypothesis. I hypothesized that property effects stem from

selective retrieval of knowledge about premise categories in the context of different

properties. One set of results suggested that the knowledge about premise category and

property is combined interactively in order to form an inductive hypothesis: the relationship

between participants’ knowledge about animals and the inferences they generated about these

animals varied depending on the property. For example, salient ecological knowledge about

animals promoted ecological inferences about them (projections of property to ecologically

related species), but only when participants were reasoning about a contextual property.

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Likewise, salient categorical knowledge about animals suppressed ecological inferences, but

only when participants were reasoning about intrinsic properties. However, a study examining

the time course of early knowledge activation in the context of inductive inference found no

evidence for interactive retrieval: despite systematic differences in the use of knowledge in

inferences, the activation of knowledge about the premise category was not affected by the

property.

These results suggest that premise category and property knowledge are combined

interactively, but not during retrieval. Because these results were not compatible with the

proposed retrieval-based model of property effects, they required a revision of the model. The

revised proposal introduced explanation of the evidence to the model. On this account,

inference generation involves first, explaining the evidence provided by the premise (the

combination of premise category and the property) by identifying a larger regularity that it

belongs to, and second, formulating a guess about other entities that might share the property

by virtue of belonging to same regularity. Within this account, different properties can affect

inferences by triggering different types of explanations – formal, based on category

membership, or causal, based on a sequence of enabling events, or teleological, based on ends

and goals. Different explanations, or identifying an observation as a part of different types of

regularities can in turn lead to different generalization hypotheses. Preliminary examination of

explanations for premise information that participants spontaneously generated during the

inference-generation task showed that the dominant type of explanation indeed varied with

the property participants were reasoning about, providing the first support for the explanation-

based account of property effects.

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In sum, this project was the first to examine the processing details of property-

sensitive induction. It contributed towards understanding the mechanism of property effects in

induction in two ways. First, it suggested how property effects do not work, by demonstrating

that property does not influence retrieval of knowledge about premise categories. Second, it

introduced property-driven explanations as a possible source of property effects and provided

preliminary evidence for this proposal, opening up a new promising line of research.

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Table of Contents Abstract 2 Table of Contents 5 Chapter I. Property Effects: Overview 7 Introduction 7 Roadmap 8 What is induction, and (how) does it differ from deduction? 9 Property effects in evaluation of inductive arguments 13

Projectable vs. non-projectable properties: role of category homogeneity 14 Projectable properties: flexible reasoning and the role of structured knowledge 17 Other types of property effects 25

Argument evaluation vs. inference generation 28 Test knowledge domain: folkbiology 31 Chapter II. Theories and models of induction 33 Existing theories and models of Induction 33

Similarity-based models 33 Hypothesis-based models 35 Bayesian models 36

A (minimalistic) computational account of inference generation 39 Ingredients: sources and types of knowledge 40 Recipe, or mechanism 43

Knowledge retrieval vs. hypothesis generation: where do we start looking for property effects?

43

Model of induction, v.1.0: knowledge retrieval as a locus of property effects 45 Overview of the Studies 50 Chapter III. “Ingredients”: Knowledge types and sources 52 Study 1. Property knowledge: distributional beliefs 52

Method 56 Results 58 Discussion 60

Study 2. Premise category knowledge 62 Method 66 Results 70 Discussion 73

Summary 73 Chapter IV. Study 3. Property Effects and Knowledge Recruitment in Induction 75 Background 75 Relations among property, knowledge, and inference 77

Property only 77 Independent recruitment of property and premise knowledge 78 Interactive recruitment of property and premise knowledge 79

Method 81 Results 85

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Discussion 97 Taxonomic vs. ecological inferences 97 Property effects on inferences 98 Recruitment of property knowledge 99 Recruitment of premise category knowledge 99 Recruitment of property and premise category knowledge: independence or interaction? 103

Chapter V. Study 4. Property Effects over the Time Course of Premise Category Knowledge Retrieval in Induction.

106

Study 4 106 Property effects on knowledge retrieval: how do you know them when you see them? 109 Non-monotonicity in knowledge retrieval 113 Method 118 Results 122 Discussion 141

Chapter VI. Revising the Model of Induction: Explaining Evidence as the Engine of Property Effects in Induction (Study 5)

146

What is an explanation, and what does it have to do with induction? 147 Hypothesis generation as a locus of property effects 150 Study 5: Property effects on spontaneous explanations 152

Coding 153 Results 156 Discussion 159

Chapter VII. General discussion 167 Overview 167 Model 1.0: Property effects via knowledge retrieval 167 The Ingredients 169 The Recipe 170 Implications for Model 1.0 171 Model 2.1: Property effects via explaining the evidence 172 Conclusions and Implications 173 Future Directions 176 References 178 Appendix A. General Instructions Used in Study 1. 185 Appendix B. Selection of Animal Stimuli for Studies 2 and 3: Animal Typicality Ratings

186

Appendix C. Stimuli Used in Study 4. 189 Appendix D. Study 4: Association Norming. 191 Appendix E. Full text of instructions for the Study 4. 194

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Chapter I. Property Effects: Overview

Introduction

Imagine you are watching the news and the presenter announces in a dramatic voice

that a certain deadly virus was found in chicken, and the numbers of chicken casualties are

growing. What have you learned? On the face of it, exactly that – there is a virus affecting

chickens. But the cognitive consequences of learning this fact can go far beyond chickens.

You may start hypothesizing about the implications of this news. Should you give a call to

your auntie who has a little chicken coop and ask her how she’s been feeling lately? Should

you be worried yourself – after all, you had that big bowl of chicken soup just a couple of

days ago; is it time to get a health check up, just in case? Should you nix your plan of cooking

corn chowder for dinner – in the end, corn is in everything, including chicken food, maybe

that’s where chicken got the virus from? Should you stay away from farms and farm animals

on the coming weekend trip to the countryside?

Generating such hypotheses about uncertain outcomes from limited evidence –

inductive inference - is a pervasive cognitive activity. We rely on it heavily both in everyday

life and science, we tend to engage in it spontaneously, and we do it well enough to avoid

persistent failures1 in navigating a world of uncertainty.

Although seemingly effortless2, this is a sophisticated cognitive activity: the

hypotheses we generate are often fine-tuned to specific aspects of the problem. For example,

had you heard the news presenter announce, perhaps in a less dramatic voice, that instead of a

virus a certain genetic disorder is found in chickens, you would be much less likely to worry

that you’ve got the problem gene now after eating that bowl of soup, or call your auntie to                                                                                                                1 With some individual variability 2 Perhaps somewhat less so in science than in everyday life

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check on her in case she caught the gene cleaning the coop. Instead, you might speculate that

other birds – pigeons out in the street, or your grandpa’s parrot (who, by the way, has never

seen a single chicken in his life) – might share the defective gene.

Such sensitivity of inferences to the specifics of the property (virus vs. genetic

disorder) has been documented in many natural and laboratory contexts, across many

domains, in many test paradigms. Despite that, little is known about the psychological

underpinnings of property’s influence on inferences. The goal of this project is to fill the gap

by proposing and evaluating the mechanism and processing details of property effects in

hypothesis generation.

Roadmap

Chapter 1 introduces the notions of induction as well as property effects in induction

and reviews related previous work. The bottom line of the review is that the phenomenon of

property effects in induction is well-established, but the theoretical work addressing the

underlying psychological mechanism is lacking.

In Chapter II, we review existing models and theories of induction, and evaluate their

(in)ability to account for property effects. To address the lack of adequate mechanistic

account of property effects, we propose a model of induction based on the claim that property

affects inferences by moderating retrieval of knowledge about premise categories.

Chapter III prepares the ground for testing the proposed model. It reports results of

two studies that describe the sources and types of knowledge available at the outset of

generating an inference (Studies 1 and 2).

Chapters IV and V present two studies testing the main claim of the model, that

property moderates knowledge retrieval. Study 3 provides initial constraints on knowledge

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retrieval mechanism by examining relation between available salient knowledge and

outcome inferences. Study 4 examines knowledge retrieval in a more direct manner, by

mapping out the activation of different knowledge types across time. Chapter V concludes

with the discussion of possible practical and theoretical issues with the Study 4.

In Chapter VI, we discuss a possibility that property affects inferences by triggering

different types of explanation of the initial evidence provided by the inductive problem. We

propose a revised model of induction, report a preliminary analysis that supports it (Study 5),

and suggest further ways to test it.

Chapter VII reviews the findings as well as absence thereof from the conducted

studies, and summarizes what we have learned about the mechanism of property effects in

induction.

What is Induction, and (How) Does it Differ from Deduction?

Induction is classically defined by contrast to deduction. There are two major

approaches to distinguishing induction from deduction. The Problem approach defines

induction vs. deduction in terms of corresponding reasoning problems, assuming that it should

be possible to classify a question or an argument as deductive or inductive based on their

formal properties. The Process approach treats induction and deduction as two possible kinds

of psychological processes, assuming that people could approach any given problem using

one or the other, or a mixture of both (Heit, 2007).

There are problems with each of these approaches. Under the Problem approach, one

attempt to define inductive and deductive problems contrasts them in terms of the direction of

reasoning – general to specific (deduction) vs. specific to general (induction); however, there

are plenty of exceptions to this rule (see Heit, 2007, for review). Another attempt that falls

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under the Problem approach is to define deductively valid arguments as following the rules

of a well-specified logic, and inductive arguments as everything else; although this defines

deduction and it can do the job of distinguishing the two, it does not say much about what

induction is (see also Mill, 1963, for a philosophical discussion of deduction-induction

distinction).

Designating problems or tasks as deductive or inductive cannot guarantee that people

will reason deductively about one and inductively about the other. According to the Process

approach, deduction and induction should be specified in terms of underlying psychological

processes. On some accounts, both induction and deduction are handled by the same type of

reasoning (Johnson-Laird, 1994; Oaksford & Hahn, 2007; Osherson, Smith, Wilkie, Lopez, &

Shafir, 1990; Rips, 2001; Sloman, 1993). In contrast, two-process accounts emphasize the

distinction between two kinds of reasoning – one deliberative and analytic or rule-based, and

another one fast, intuitive and heavily influenced by context and associations. As Heit (2007)

points out, these two systems may not directly correspond to deduction and induction, and in

fact, this traditional distinction may not be optimal for psychology. However the two systems

are defined, there is some evidence that asking people to make deductive vs. inductive

judgments may lead to qualitatively different results, supporting the idea that induction and

deduction may involve different processes (Goel, Gold, Kapur, & Houle, 1997; Rips, 2001;

Rotello & Heit, 2009). Yet, although this proposal makes a claim about the number of

involved processes (>1, it appears), at the moment it is not very well developed and needs

more work in terms of specifying the underlying processes and deriving specific predictions

(Heit, 2007).

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Thus, as of now, the distinction between induction and deduction appears to be a

useful approximation. There seem to exist two clusters of problems and corresponding

approaches to solving them – deductive and inductive -- and it is not entirely clear whether

they are handled by two distinct reasoning processes, or they could both be reduced to one;

and if there are two processes, it is not clear what kind of processes they are, and where to

draw a precise line between them.

At this point, it may be reasonable to focus on describing regularities in deductive and

inductive reasoning, using some (imperfect) combination of problem- and process-

approaches to operationally distinguish between the two. Accumulated descriptions of such

regularities may either help to distinguish them further and to refine the definitions, or it may

demonstrate that the distinction between induction and deduction is superficial and suggest

that they should be combined under one name.

Nowadays, the term “induction” is used in reference to a variety of cognitive

activities, including concept learning, analogy, generation and testing of hypotheses, and

many others. An element of inductive reasoning, using the known to predict unknown, is

found in a wide range of activities, “from problem solving to social interaction to motor

control” (Heit, 2000, p. 569). Our proposal focuses on a narrower range of phenomena,

investigating how people generate inferences based on given premise information, and how

the nature of projected property affects the kinds of inferences they generate.

As a tentative basis for an operational definition of induction for our studies, we

propose that, in addition to the probabilistic nature of inductive inference, the set of relevant

knowledge for solving an inductive problem is not clearly defined. In a deductive argument,

in contrast, the relevant knowledge is restricted to the premise(s) and logical rules. All the

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other knowledge a person may have is, strictly speaking, irrelevant. For example, in the

case of a valid but a non sound deductive argument “All elephants can fly. Socrates is an

elephant. | Socrates can fly”, the person’s prior knowledge about elephants, flying and

Socrates is irrelevant for determining the argument’s validity3. In contrast, in the argument

“Adam is interested in the distinction between deduction and induction | Nancy is interested

in the distinction between deduction and induction”, the set of relevant knowledge is not

clearly defined (is it the common profession that matters? Gender? Age? Nationality?

Political affiliation? Something else?), and argument strength heavily depends on what subset

of knowledge is recruited to inform a particular judgment.

Based on this definition, the property effects – different conclusions or different

strength of a given conclusion as a function of projected property – should only be observed

when people reason inductively (i.e. assume that relevance of any given piece of knowledge is

a matter of their subjective opinion), but not when they reason deductively (i.e. only rely on

given information and logical rules). In a deductive problem, varying a property should not

affect validity judgments (imagine replacing “flying” with “sleeping” or “writing poems” in

the Socrates argument above – the deductive validity should not be affected). Whereas in an

inductive argument, varying a property may affect what knowledge is deemed relevant

leading to changes in perceived argument strength (imagine replacing “is interested in the

distinction between induction and deduction” with “supports new Massachusetts law about

primary school education” or “likes peanut butter”).

                                                                                                               

3  Not  all  background  knowledge  is  irrelevant  to  estimating  validity  of  a  deductive  argument  –  a  person  at  least  should  be  able  to  understand  the  language  and  may  need  to  know  something  about  category  membership  of  objects  that  figure  in  an  argument.  The  claim  is  that  such  set  of  relevant  knowledge  is  relatively  well-­‐defined  in  deduction.  

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Within the limits of this project, we are focusing on studying property effects in

induction only. The format of the questions in the presented studies (e.g. “Flu X5 is found in

ducks. What else is likely to have flu X5? Why?”) invites participants to use their background

knowledge to generate novel hypotheses, and the guesses they generate are inherently

probabilistic. This gives us reasons to believe that participants in these studies will be using

inductive reasoning, if such a thing exists.

By describing in some detail property effects in induction we hope to make a small but

novel contribution to the body of knowledge about regularities that govern inductive

reasoning. Further studies may show that our findings (if any) are not restricted to induction –

but that will require either a replication of property effects with deductive problems that

clearly define the set of relevant knowledge, or a demonstration that the questions in our

studies were non-inductive.

Property Effects in Evaluation of Inductive Arguments

A vast body of empirical evidence demonstrates that people make systematically

different inferences when they project different properties. A classical method of studying

property effects involves evaluation of complete inductive arguments. Typically, participants

are presented with one or more premises in which a property is attributed to a category, along

with a conclusion in which the property is attributed to a different or superordinate conclusion

category, and asked to evaluate the argument, or rate the likelihood that the conclusion is true

given that the premises are true. Within this paradigm, property effects are defined as

qualitative changes in selected conclusions, or quantitative changes in evaluated strength of a

complete argument, associated with manipulation of a projected property.

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The properties used in studies of inductive reasoning can be roughly divided into

three classes: first, idiosyncratic or non-projectable, such as fell on the floor this morning, has

a drop of water on it. Then, there are two kinds of projectable properties: uninformative, or

blank properties, e.g. has property X or has sarca, and informative biasing properties, that are

systematically projected based on a particular relation (e.g., anatomically biasing property has

a two-chambered liver; behaviorally biasing property travels in a zig-zag path; or a property

biasing towards ecological interaction relation sick with disease X).

Most research on property effects falls into one of two broad categories. One subset of

work examines how property generalization is rooted in people’s beliefs about homogeneity

of members of the conclusion category (comparisons of projectable vs. non-projectable

properties). Another subset of studies demonstrates that different projectable properties may

tap into different subsets of knowledge, or different ways to define similarity between

premises and conclusions.

Projectable vs. non-projectable properties: role of category homogeneity. Some

properties appears to be more projectable, or generalizable from one category to another, than

others. To borrow an example from Heit (2000), if one home in the neighborhood is

burglarized, it increases the perceived probability that the home next to it might be as well

(i.e. the property of being broken into generalizes from one home to the next). But if one

home is painted blue, it does not increase the perceived probability that a home next to it

would be painted blue as well (i.e. the property of being painted blue does not generalize).

The discussion of what makes a property projectable or not has a long history. Nelson

Goodman (1955) proposed that in order to be able to generalize at all, people must possess

overhypotheses, or abstract beliefs describing the scope of properties. Goodman explains this

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term on the example of bags containing marbles. If one examines a series of bags with

marbles, and notes that some contain all black marbles, others contain all white marbles, one

could form an overhypothesis about color distribution: each bag contains marbles uniform in

color. When a new bag is encountered, and a single marble drawn from it turns out to be blue,

the overhypothesis about color distribution in bags allows formulating a specific hypothesis,

an inductive guess about the bag content based on a single novel observation: the rest of the

marbles in the new bag are all blue. Having an overhypothesis about uniform color

distribution is crucial for making such a prediction; without it, after extracting a single blue

marble from the bag, a person would have no basis for a preference for any one of such

hypotheses as “all blue”, “one is blue and all the others are white”, “one black, one white, one

red, three green..”, etc. An overhypothesis provides abstract knowledge that sets up a

hypothesis space at a less abstract level (Kemp, Perfors & Tenenbaum, 2007), constraining it

to such possible specific hypotheses as “all blue”, “all white”, “all black”, etc.

Translating the marble examples to real world categories, having an overhypothesis

that homes in close proximity are likely to be under same kinds of threat justifies

generalization of a “burglary threat” property from one home to other homes belonging to the

“same bag”. And if one does not have an overhypotheses that houses in the same

neighborhood tend to be of the same color, they would not generalize blue paint on one house

to others.

One of the early pieces of empirical evidence for differences in property projectability

comes from work by Nisbett, Krantz, Jepson, & Kunda (1983). They showed that adults treat

some properties (e.g. skin color) as more generalizable than others (e.g. obesity). For example,

after being told about skin color of one member of a certain tribe, participants expected over

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90% of that tribe to have the same skin color; but when they were told about one obese

tribe member, their estimate of obese people in the tribe was only 40%. Participants justified

their responses saying that tribe members are likely to be homogeneous in skin color but

heterogeneous with respect to body weight. Thus, the beliefs about homogeneity of properties

determined their projectability: for a given category, homogeneous properties were expected

to be more projectable than heterogeneous properties.

Developmental work shows that children, like adults, have systematic expectations

about how projectable different properties are. As demonstrated by Gelman (1988), by 4 years

of age children distinguish between generalizable (permanent and intrinsic, such as made of

cellulose, eats alfalfa) and non-generalizable properties (transient, accidental and

idiosyncratic, such as fell on the floor this morning, has gum stuck on the bottom, is dirty). In

this study, children projected generalizable properties dependent on the similarity between

premises and conclusions, and did not project non-generalizable properties regardless of the

similarity. Similarly, in a study by Heyman & Gelman (2000) young children were willing to

project novel behavioral properties such as likes to play tibbits, relying on shared personality

traits between characters, but did not project transient properties such as feeling thirsty.

There is evidence that such discrimination between properties is likely based on

children’s expectations about homogeneity among the members of a category. As shown by

Gelman & Coley (1990), for 2 ½ -year-old children a common category label (“is a bird”)

applied to premise and conclusion promoted projections of a behavioral property (says tweet-

tweet) between them. In contrast, when the objects were told to share a transient attribute (is

dirty / hungry / far away), children were not any more willing to project a behavioral property

between them than in the absence of any common label. Presumably, common category

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information conveyed to children that the premise and conclusion were “from the same

bag”, like marbles, triggering their intuitions about category homogeneity. But a transient

common label does not bring about such an overhypothesis that would promote generalization

(See also Gutheil & Gelman, 1997; Macario, Shipley, & Billman, 1990; Waxman, Lynch,

Casey, & Baer, 1997).

In a study by Springer (1992) children were asked to make inferences between

biologically related animals (a mother and a child) and between perceptually and socially

related animals (playmates). Children were at chance for idiosyncratic properties (very dirty

from playing in mud), but tended to project biological properties (hairy ears) between

biologically related animals more than between perceptually and socially related pairs,

suggesting that not only can children distinguish between projectable and non-projectable

properties, but they also have expectations about relations appropriate for projection of a

particular property – the topic that we turn to in the next section.

Projectable properties: flexible reasoning and the role of structured knowledge.

The studies reviewed in the previous section suggest that “not all properties are created

equal”: some generalize easily even from one observation, others are seen as non-

generalizable. However, the picture is more complex and interesting than some properties

simply being inherently more projectable than others. Depending on a specific pairing of the

premise and conclusion properties, one and the same property can behave either as

projectable, or non-projectable. In other words, different properties can be projectable to

different kinds of categories; what matters is the match between the property and category

type.

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Going back to Goodman’s overhypotheses, the example of bags with marbles has

its limits. The categorical structure of human knowledge is notably more complex than bags

of marbles. Every entity belongs to “multiple bags”, multiple cross-cutting categories, that in

turn belong to cross-cutting category systems (also known as knowledge structures). A duck

belongs to a category of birds, which is a part of a taxonomic classification of animals. A

duck is also an aquatic animal, one of the classes of contextual classification of animals.

There is evidence that induction is sensitive to the match between property type and category

system, or, more broadly, a type of relation between premise and conclusion.

A classic demonstration of this effect comes from the study by Heit & Rubinstein

(1994). Participants were shown pairs of animals related anatomically, such as a bat and a

mouse (two mammals) or behaviorally, such as a sparrow and a bat (both fly), and asked to

estimate the probability that they might share an anatomical (has a liver with two chambers

that act as one) or behavioral (travels in back and forth, or zig-zag, trajectory) property. They

found that the strength of the argument was determined by the agreement between property

and the relation shared by the premise and conclusion. Arguments in which the premise and

conclusion were anatomically similar were judged stronger for an anatomical property than

for a behavioral property, whereas arguments with a behaviorally similar premise and

conclusion were judged stronger for behavioral than anatomical properties.

Heit and Rubinstein proposed that strength of an argument depends on a flexible

measure of similarity between premises and conclusions, and the relevant dimensions of

assessing similarity are determined by the projected property. A single similarity metric

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would be unable to account for the results of this study4. In support of their claim, a

separate analysis showed that reasoning about different properties was related to different sets

of similarity ratings: reasoning about anatomical properties was predicted by anatomic

similarity alone, whereas reasoning about behavioral properties was predicted by both

anatomic and behavioral similarity.

This study, although one of the earliest systematic investigations of property effects,

came closest to outlining a possible mechanism: Heit & Rubinstein proposed that “the

property being considered influences which features [of the categories] are important for

induction” (p. 411) and, on a different occasion, mention context-dependent retrieval of

information from semantic memory (Barsalou, 1989). Unfortunately, with regards to

specifying the mechanism of property effects, this is as specific as it gets.

Work of Shafto & Coley (2003) on expertise effects in induction highlighted the

interaction between property and structured knowledge, and, most revealingly, took the idea

of relying on different relations beyond similarity. In this study, the participants - biological

novices (undergraduate students) and experts (commercial fishermen) – were tested in an

induction task about marine creatures. When the projected property was uninformative

(property X), experts and novices in marine biology alike relied on taxonomic, similarity-

based relations. When, however, the property was informative and ecologically-biasing

(disease X), although novices continued to rely heavily on taxonomic relations, experts

changed their strategy and relied on causal and ecological relations between marine species.

                                                                                                               4  Relatedly, Goodman (1972) argued that induction based on unconstrained similarity is not possible at all, since any category has a potentially infinite set of features and can be infinitely similar to any other category. Thus it is a necessary logical requirement for inductive inference to impose some initial constraints on a subset of relevant features.    

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Thus, in the absence of detailed knowledge of alternative knowledge structures, or in the

absence of ability to match a particular property with the appropriate distribution, people may

rely on default distributions of properties across highly available knowledge structures such as

taxonomic categories. But when both knowledge of alternative relations and ability to

selectively match certain properties to these relations are available, the reasoning becomes

flexible, and we start seeing property effects in induction.

Ross and Murphy (1999) extended the study of property effects to the domain of food.

They compared taxonomic categories of food based on shared features or composition (such

as fruit or dairy) to script categories (breakfast foods), which are determined by time, location,

or setting in which the foods are eaten. Participants were presented with triads consisting of a

target food (e.g. cereal) and two alternatives, one taxonomic match (noodles, another member

of the “breads & grains” category), and one script match (milk, another “breakfast food”).

They were taught that the target food (cereal) had a biochemical property (a novel enzyme) or

a situational property (eaten in a novel culture at a particular ceremony), and asked to project

that property to one of the alternatives. Participants, like in Heit & Rubinstein (1994) study,

showed inductive selectivity: they preferred taxonomic relations when projecting biochemical

properties, and script relations when projecting situational properties. The authors emphasize

how this finding arises from the complex organization of knowledge based on two cross-

cutting systems of categories – taxonomic and script.

However, Ross and Murphy do not discuss the mechanism of property effects beyond

its general reliance on structured knowledge. Such treatment of property effects is, in fact,

fairly characteristic of the reviewed work (with a few exceptions). A vast majority of studies

treat property effects as a useful tool to examine other aspects of cognitive functioning, such

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as cross-classification, reliance on statistical information, or development of early

categorical representations and understanding of domain differences – but they do not ask the

question of how this tool actually works.

Vitkin, Coley & Feigin (2005) replicated and extended the findings of Ross and

Murphy (1999) using a task in which taxonomic and script inferences were not mutually

exclusive. In their study, participants were asked to confirm or disconfirm projections to foods

whose taxonomic or script relations to the premise were manipulated orthogonally.

Participants reasoned about a compositional property (has enzyme X) intended to render

taxonomic categories relevant, or about a situational property (appears on the menu at X’s

new restaurant), intended to render relational (script) categories relevant. Vitkin et al. (2005)

found that taxonomic relations supported inferences broadly: inferences to taxonomic targets

were strong for both compositional and situational properties. In contrast, script categories

supported inferences more narrowly: inferences to script targets were strong for situational

properties but weak for compositional properties. These results suggest that script categories

were selectively recruited to reason about menus, whereas taxonomic categories supported

reasoning about both properties. The fact that taxonomic and relational categories were

differentially susceptible to the property manipulation demonstrates the interplay between

baseline availability of knowledge (taxonomic knowledge being more available) and property

guiding selective recruitment of knowledge.

In addition, analysis of projections to foods related to premises via both taxonomic

and script categories showed an interesting asymmetry: common taxonomic categories had a

much larger influence on relational inferences than script categories had on taxonomic

inferences. Specifically, the increase in inductive likelihood conferred by taxonomic category

  22  

membership in the menu condition was consistently stronger than the parallel increase

conferred by script category membership in the enzyme condition. Likewise, according to

regression analyses, taxonomic category membership was more predictive of responses in the

menu condition than script category membership was of responses in the enzyme condition.

This asymmetry may be viewed as taxonomic categories having a stronger effect on relational

inferences than relational categories have on taxonomic inferences; but it can also be viewed

as another level of property effects on induction: property could cue not only the kind of

category (taxonomic or relational) exclusively relevant to a particular inference, but also

whether a single category system or multiple category systems might be relevant.

And again, this ability to differentiate between different types of biasing properties (in

addition to the ability to distinguish between projectable and non-projectable properties) can

be observed at a young age. Using an experimental paradigm similar to Ross and Murphy

(1999), Nguyen and Murphy (2003) showed that property information guides children’s

inferences about food: at the age of 7, they make taxonomic inferences when projecting a

biochemical property (e.g. they expect a novel enzyme found in cereal to be also found in

noodles, another member of “breads & grains” category), but they prefer contextual

inferences when they reason about a situational property (e.g., they expect two breakfast

foods – cereal and milk - to appear together at a novel ceremony).

Coley and colleagues (Coley, 2005; Coley, Vitkin, Seaton & Yopchick, 2005; Coley,

in press) examined the development of property-sensitive reasoning about the biological

world. School-aged children were presented with biological triads of a target species and two

alternatives. A taxonomic alternative was a species from the same superordinate class but

ecologically unrelated to the target; an ecological alternative was related to the target via

  23  

habitat or predation, but belonged to a different taxonomic class. An example of a triad

could be a banana tree (target), a calla lily (taxonomic match) and a monkey (ecological

match). Children were taught either a taxonomically or ecologically biasing property (stuff

inside, an internal and plausibly stable property, or a disease, a property transmittable through

an interaction in an ecosystem) about the target and asked to pick one of the alternatives that

shared the property. Children were clearly sensitive to the type of property they were asked

about: they picked taxonomic matches for stuff inside and ecological matches for diseases at

above-chance levels. Importantly, follow-up work by Coley (2009) and Coley, Muratore, &

Vasilyeva (2009) relating children’s and adult inferences to adult beliefs about relatedness of

animals in the triads showed that ecological inferences about a disease property were

predicted by the salience of ecological relations in the triad, but unrelated to the salience of

taxonomic relations. Inferences about the stuff inside were sensitive to salience of taxonomic

and ecological relations in a triad, although this pattern varied with age and amount of

experience with nature. This again suggests that different properties lead to recruitment of

different knowledge structures.

Developmental evidence on property effects is not restricted to reasoning about

animals and food. For example, Kalish & Gelman (1992) presented 4-year-old children with

novel artifacts that had double labels consisting of a material and an object kind, such as

“glass scissors”, and asked to generalize different novel properties to other items, such as

metal scissors and a glass bottle. When a property was dispositional (will get fractured if put

in really cold water), 4-year-old children’s judgments were based on the material part of the

label; when the property was functional (used for partitioning), children relied on the object

  24  

kind part of the label. These results showed that young children have expectations about

what specific information about objects is relevant for generalizing different kinds of

properties.

Mandler &McDonough (1996, 1998) reported sensitivity to property at an even

younger age. Fourteen-month old children distinguished between actions appropriate for

animals (being given a drink) and for vehicles (being open with a key), and were less willing

to imitate actions that were demonstrated on a “wrong” item than when the actions matched

the items – another piece of evidence showing that from an early age, children distinguish

between different kinds of properties and associate them, in this case, with different

knowledge domains (or, as shown above, with different knowledge structure within a single

domain).

Importantly, in addition to argument evaluation and triad selection, property effects

have been observed in an open-ended experimental paradigm. When people are provided with

the premises and the property to be projected, and need to generate conclusions on their own,

the type of conclusions and related justifications depend heavily on the property. For example,

Coley and colleagues (Vitkin, Coley & Hu, 2005; Vitkin, Vasilyeva & Coley, 2007;

Vasilyeva & Coley, 2009) taught school-age children that a pair of animals had a novel stuff

inside or a novel disease, and were asked to make guesses about what other things might

share the property and explain why. Children’s responses were coded as taxonomic (based on

similarity or common kind membership) or ecological (based on interaction or shared

ecosystem). When children were thinking about internal substances, their responses were

more category and similarity based; when they were considering diseases, their responses

tended to be based on ecological interaction. Thus, even when children were not restricted to

  25  

only a few alternatives provided by the experimenter, their open-ended responses were

guided by different kinds of knowledge based on what property they were reasoning about.

Coley & Vasilyeva (2010; see also Coley, Vasilyeva & Muratore, 2008) took the

open-ended induction paradigm one step further beyond documenting property effects, and

examined how salient relations between animals in paired premises predicted inferences. This

study demonstrated that salient relations can promote inferences of a corresponding type, and

in some cases they can inhibit inferences of mismatching types. With regard to property, not

only did different properties produce different inferences from the very same pair of animals,

but they also influenced to what extent relations among premise categories were driving

inferences. Although no consistent pattern of how exactly property moderates recruitment of

premise relations emerged, and authors acknowledged that “any detailed explanation of this

pattern of results would be a speculation” (p. 215), these results established that property can

systematically affect open-ended inference generation, not just evaluation of complete

arguments, and emphasized the need for a more detailed account of the interplay between

attributes of premise categories and projected properties.

Other types of property effects. A separate line of research focused on studying

inductive problems that involve revising beliefs about properties that are associated with

certain levels of strength or intensity of related attributes, such as can bite through wire

(expected to require certain amount of strength). Smith, Shafir, & Osherson (1993) showed

that many principles describing reasoning about blank properties – premise-conclusion

similarity and premise typicality associated with higher projection likelihood estimates – can

be violated when people reason about certain types of familiar properties. In their study,

participants were more willing to project such property as can bite through wire from a

  26  

weaker atypical dog species to a stronger one (from toy poodles to German shepherds) than

vice versa or between similar highly typical dogs of roughly equal strength (from Dobermans

to German shepherds). Presumably, since people have an initial belief about how hard it is to

bite through wire, when they are presented with the fact that even weak dogs can do it, this

requires some belief revision to cover the discovered “gap” between the premise and the

property: “biting through wire must not require that much strength, after all”. This finding

adds to the evidence demonstrating that different properties can yield different reasoning

patterns, and shows that inductive inference is a highly interactive process in which a person

may strive to bring all elements of the problem – premises, property, conclusions – to a

maximum congruence, reinterpreting and modifying them to have a coherent story justifying

an inference. However, this line of research departs in many respects from the other work on

property effects, since it focuses on revision of beliefs about the property itself, and the

findings are very specific to the types of properties and arguments used. Acknowledging that

interpretation of any given property may not always be fixed, we will assume for the purposes

of this project that there are cases that do not involve such revision of belief, and that

description of general processing mechanism based on such cases will generalize to other

cases as well.

Another somewhat stand-alone perspective on property effects is represented by work

of Sloman (1994), who focused on properties that allowed for rather elaborate reasoning

involving construction of multiple explanations for a property, and examined how such

explanations influence adults’ estimates of projection likelihood. When there was a single

likely explanation of a property being true of both premise and conclusion, the premise

increased belief in the conclusion (relative to evaluating the conclusion statement in isolation;

  27  

for example, “P: “many furniture movers have a hard time financing a house”, C: “many

secretaries have a hard time financing a house” [in both cases, likely due to low income]). But

when the conclusion statement suggested a different explanation for the property, its

subjective likelihood decreased (P: “many furniture movers have bad backs” [as a result of

heavy lifting], C: “many secretaries have bad backs” [from sitting over a desk for too long]).

Sloman discusses this effect in the context of explanation discounting – the idea that people

prefer a single explanation for a given phenomenon; when alternative explanations are

present, they compete with each other and mutually decrease each other’s credibility. For the

purpose of our project, however, we would like to point out that Sloman’s work demonstrates

that explanation of the evidence may play an important role in judgments of argument

strength, although the exact role of explanation is not clear. We will come back to this idea

in Chapter 6.

To summarize this section, property effects have been demonstrated in reasoning

about a wide range of domains: biological, social, food, artifacts. Both adults and children

seem to have a notion that properties come in different kinds, and that the relevant criteria for

projecting these properties differ accordingly. Whether a property is considered non-

projectable, or projectable but biasing toward a particular projection direction has a

considerable effect on the outcome of inference. When a property is idiosyncratic and is not

systematically distributed across any known system of categories, information about one

instance having that property does not create the expectation that the same property is true of

other entities as well. However, when a property is expected to be homogeneous within

classes of a certain category system, information about one instance of a class having that

property promotes the generalization to other members of the class. Moreover, different

  28  

properties can generalize to different classes: based on property, the projections from one

and the same entity can vary dramatically.

Although property effects are pervasive, there has not been much research on the

underlying mechanism. A large majority of studies involving property effects use them as a

tool to study other cognitive phenomena, without questioning how this tool works. The few

studies that discuss the workings of property effects make general proposals about property

affecting recruitment of different subsets of knowledge (Coley, Muratore, & Vasilyeva, 2009;

Coley & Vasilyeva, 2010; Heit & Rubinstein, 1994), with a general reference that it might

have something to do with context-dependent knowledge retrieval (Heit & Rubinstein, 1994).

However, these ideas have not been tied together in a testable proposal about the mechanism

of property effects.

Argument Evaluation vs. Inference Generation

As the above review illustrates, most previous research in inductive reasoning has

involved the evaluation of complete inductive arguments in one form or another. Relatively

recent work started exploring another way of studying induction – by using an open-ended

task of hypothesis generation.

Argument evaluation differs from hypothesis generation in a number of important

ways. In an argument evaluation task participants evaluate hypotheses given to them. This is

akin to a recognition memory task or a multiple-choice exam where people choose the best

answer from a pool of prefabricated answers. In the open-ended paradigm, participants are

presented with the premise and asked to generate their own conclusions (e.g., As have

property X. What else do you think would have property X, and why?). This approach is more

like a recall memory task or an essay exam, and it has a number of advantages.

  29  

First, we would like to argue that adding the open-ended format to the toolbox of

inferential tasks increases ecological validity of research on induction. The context mimicked

by argument evaluation tasks is not so frequent in everyday life: how often do we have

someone providing us with possible conclusions that can be drawn from the given evidence?

When one day you got sick from eating a cheesecake, did anyone approach you with a survey

packet and ask you to make judgments about potential foods to avoid: “Carrots? Ice-cream?

How about garlic salt?” Such situations are, at best, infrequent. Typically, it is our own task to

generate these hypotheses from the evidence we have. Studying induction exclusively via

argument evaluation potentially misses a large part of the spectrum of induction.

Second, interpretation of argument evaluation results depends on the participant

recognizing and evaluating specific hypotheses intended by the experimenter. In contrast,

open-ended inferences provide greater opportunity to participants to draw on any knowledge

they spontaneously deem relevant. In addition, it allows participants to base their inferences

on multiple salient relations, instead of restraining participants to choosing one or two of

them.

Third, as demonstrated by Coley & Vasilyeva (2010) this approach allows participants

to utilize relatively abstract or inchoate knowledge to guide inductive inference. As Keil

(2003) has shown, our explanatory knowledge and understanding of causal mechanisms are

often much more superficial and vague than we know or would like to admit. Nevertheless,

inductive inferences can bridge gaps in specific factual knowledge—indeed, making uncertain

guesses about the unknown is what induction is all about. Many researchers have suggested

that fairly abstract principles (domain theories, schemata) provide inductive constraints in

concept learning (e.g., Heibeck & Markman, 1987; Keil, 1981) language acquisition

  30  

(Chomsky, 1980), and inductive inference (e.g., Coley, Hayes, Lawson, & Moloney, 2004;

Coley, Medin, & Atran, 1997; Goodman, 1955). As demonstrated by Kemp, Perfors, and

Tenenbaum (2007), it is possible to learn abstract knowledge from observations before

acquiring specific knowledge at lower levels of abstraction. If so, inductive reasoning may be

especially likely to rely on abstract ideas in the absence of specific knowledge.

For example, if a participant is told that macaws are sick with a certain disease, she

might guess that anything that eats them might be sick with the same disease, even if she has

no idea what might do so. In an open-ended task, she might still be able to articulate an

inference – “Anything that preys on macaws can get this disease from eating the contaminated

meat”. But if she is asked to judge the strength of a complete argument “Macaws have disease

X, therefore monitor lizards have disease X”, she may not know that monitor lizards prey on

macaws, and lizards and macaws don’t seem otherwise similar, so she may rate the argument

as relatively unlikely. Because the participant was unable to apply her relatively abstract

knowledge about disease transmission, she rated the argument as weak when in fact she

believed it to be strong, but just didn’t realize it.

The experimenter in this case would note that arguments based on ecological relations

get low ratings, and may to conclude that it is similarity that drives this participant’s

inferences. Indeed, when Coley & Vasilyeva (2010) used an open-ended format that did not

constrain participants by lack of specific knowledge nor, indeed, “by facts or reality” (p. 220),

the inferences generated by participants showed notable increase in reliance on ecological

relations relative to argument-evaluation studies, which typically demonstrate that taxonomic,

or similarity-based relations dominate in induction. If anything, salient ecological relations

among premises had a stronger effect on taxonomic inferences than vice versa. A sizeable

  31  

proportion of ecological inferences came from vague, generic inferences. E.g., projecting a

novel substance from the premise pair “leafcutter ant & anteater”, one participant said “an

animal that is a predator to an anteater. I can’t think of any ‘cause I’m not an animal expert.

Anteater eats ants, and they both have this substance. So I assume whatever eats an anteater

will have it too or receive it by eating it.” (p. 218). This example illustrates how people can

generate sophisticated inferences based on general, framework theories, even in the absence

of specific knowledge. The authors argued that “the relative paucity of ecological and causal

reasoning among folk-biologically naïve participants in previous research may be due in part

to the fact that they were being asked to recognize such relations, rather than generate them”

(p. 220).

In sum, this inference generation task is a relatively sensitive measurement instrument

that is able to detect participants’ spontaneous use of different kinds of knowledge that they

deem relevant, in the context of an ecologically valid inductive problem. If our goal is to

examine how property moderates knowledge recruitment in the course of induction, and if the

recruited knowledge is likely to be abstract and/or vague, we may want to prefer a task that is

sensitive to these types of knowledge rather than ignores or masks them. Thus, in this project

we focus on open-ended inference generation as a more promising context for investigating

the underlying mechanism of property effects.

Test Knowledge Domain: Folkbiology

As prior work illustrates, property effects have been documented in multiple

knowledge domains. We have selected one of them as a field for studying property effects -

the domain of folkbiological reasoning, i.e. non-expert, everyday thinking about plants and

animals. This domain is organized by two relatively salient cross-cutting knowledge

  32  

structures – a taxonomic classification of living things (mammals, birds, reptiles, insects,

fish, etc.) and an orthogonal system of ecological classes (based on habitats – forest animals,

aquatic creatures – or on ecological interaction – predation chains, symbiotic groups). These

two knowledge structures have been shown to be both distinct and psychologically real: as

early as five years of age, children can sort animals into taxonomic groups as well as based on

habitat (Vitkin, Coley & Kane, 2005), and time pressure selectively impairs recruitment of

ecological but not taxonomic knowledge (Shafto et al., 2007) suggesting that these are indeed

two distinct kinds of knowledge that differ both in content and accessibility. Presence of a

limited number of clearly identifiable knowledge structures that cover a large proportion of

knowledge about this domain5, and existence of prior work on property effects in reasoning

about the natural world make folkbiology an attractive domain for investigating the

mechanism of property effects.

Next, we turn to the discussion of existing models and theories of induction and of

how they (fail to) account for the vast evidence on property effects that we have just

reviewed.

                                                                                                               5 See Study 2 for additional empirical support for this claim

  33  

Chapter II. Theories and models of induction

The biggest gap in our understanding of inductive inference concerns its mechanism.

What happens between the time an inductive premise is presented and the moment an

inductive inference is eagerly spit out by a cooperative participant? A number of models of

induction have been proposed, mainly designed to account for basic induction phenomena

such as premise-conclusion similarity (higher similarity between premises and conclusions is

associated with higher perceived argument strength), typicality (premises typical in a lowest

superordinate category that includes premises and conclusion provide better evidence for

conclusion), diversity (diverse premises provide stronger support to a conclusion than non-

diverse premises), and other phenomena (for a review, see Heit, 1997, 2007, 2008).

Unfortunately, until recently, property effects have not been on the list of the phenomena a

model of induction should be able to account for. And even taking into account the most

recent developments, the proposed models are predominantly designed to model inference

outcomes rather than process, and none of them address the question of how property-

sensitive inductive hypotheses are generated. In what follows, we briefly review some

prominent induction models, focusing on their take on property effects, and where available,

review related theory.

Existing Theories and Models of Induction

Similarity-based models. The first formal model of induction was proposed by Rips

in 1975. The model was based on a multidimensional scaling solution for a set of animal

categories and derived similarity and typicality measures that were used to predict strength of

inductive arguments with these animal categories figuring as premises and conclusions. The

model does not specifically address property effects; in fact, since the model relies on a fixed

  34  

multidimensional scaling solution, the derived similarity measure is also fixed, and cannot

vary with the projected property (as discussed in Heit, 2000).

Osherson, Smith, Wilkie, Lopez, & Shafir (1990) proposed the Similarity-Coverage

Model of induction (SCM), which has been very influential in guiding research on inductive

inference. The SCM describes the strength of an argument as determined by two factors:

similarity between premise and conclusion categories, and the degree of “coverage” provided

by the premises with respect to the lowest-level category that includes both premise and

conclusion categories. The SCM does not consider knowledge structures other than similarity

(such as causal, interaction-based relations), and does not model generalization with different

property types, offering no account of property’s potential to promote or limit generalization.

Sloman’s (1993) Feature-Based Induction (FBI) model also relies on similarity

between premise and conclusion category as the basis for induction. However, it sidesteps the

retrieval of a superordinate category for premises and conclusions and describes projection of

a novel property in terms of calculating featural overlap between known features of premises

and conclusions. Generalization of a novel property is proportional to the number of

conclusion features also observed in the premises. It can account for varying argument

strength if shared causal features are assumed to have higher weights, however, as proposed,

it has no mechanism for flexible computation of similarity based on the nature of projected

property.

Sloutsky & Fisher’s (2004) Similarity, Induction, Naming, and Categorization (SINC)

model is another similarity-based proposal, in which similarity is based both on perceptual

features and common labels shared by premise and conclusion terms. Like other similarity-

  35  

based models, it does not provide any account of how property knowledge could influence

induction.

Hypothesis-based models. The Hypothesis-Assessment Model of Categorical

Argument Strength by McDonald, Samuels, & Rispoli (1996) proposes that people “actively

construct hypotheses in response to uncertain conditions and evaluate the plausibility of these

hypotheses in light of available evidence” (p. 202). Premises of an inductive argument are

viewed as evidence, and the conclusion as a hypothesis. The authors propose that the strength

of an inductive argument is determined by the number of competing hypotheses brought to

mind by the evidence: the more alternatives are available, the weaker is any given hypothesis.

Although this model is open to the possibility of property effects in induction (presumably by

virtue of affecting the number of hypotheses generated from the evidence), it does not provide

a specific explanation of how property effects would arise. It does, however, emphasize the

role of hypothesis generation in induction (see also Feeney, Coley, & Crisp, 2010, for

supporting evidence that people spontaneously construct and evaluate hypotheses as they are

processing premises of an inductive argument).

Medin, Coley, Storms, & Hayes (2003) proposed the Relevance Theory of Induction

based on the idea that people assume premises to be relevant to the conclusion(s), and treat

distinctive properties of premise categories as likely bases for induction. In contrast to

similarity-based models relying on fixed notion of similarity that assume reasoning about

blank predicates (property X), the idea of flexible reliance on variable relevant subsets of

knowledge about premise categories, be it similarity-based or causal or of some other kind,

lies at the core of this proposal. In fact, it suggests that even with uninformative properties,

people might seek to interpret them and extract some meaning for the property from the

  36  

salient features of premise categories. Thus, this theory has everything necessary to

accommodate reasoning about informative predicates, although it does not provide a process-

level description for it.

Bayesian models. A class of Bayesian models makes a useful contribution by

explicitly distinguishing prior knowledge that needs to be in place for property-sensitive

induction to take place from the operations over that knowledge.

The key assumption of a Bayesian model proposed in Heit (1998) is that in projecting

a novel property people rely on prior knowledge about distribution of familiar properties. If a

large number of shared features is already known for the premise and conclusion in question,

this supplies high prior probability to a hypothesis that a novel property is shared as well. The

information about premises in an argument is treated as a new piece of evidence, which is

used to revise prior beliefs using Bayes’ theorem. After the beliefs are revised, the plausibility

of the conclusion given the premise is evaluated.

In contrast to the similarity-based models reviewed above, this Bayesian model allows

for property effects, although it does not specifically model them. Heit proposed the following

mechanism for property effects: the projected property serves as a cue for retrieving familiar

properties. Retrieving different subsets of properties from long-term memory provides

different priors, potentially resulting in different inferences. For example, if a projected

property sounds vaguely biological and internal (“has an omentum inside” or “has a two-

chambered heart”, as in Heit & Rubinstein, 1994), the retrieved familiar properties will also

be of the same nature, and inferences to biologically related conclusions (from hawks to

chickens) will become likely – because people already know a relatively large number of

biological internal properties true of both hawks and chickens. In contrast, if the projected

  37  

property is behavioral, such as “prefers to feed at night”, the prior hypotheses will be

drawn from distribution of familiar behavioral properties, promoting inferences to

behaviorally similar animals, such as from hawks to tigers, again, because people already

know of other behavioral properties shared by tigers and hawks.

Although driven by the goal to explain patterns observed in human data, this model, as

noted by Heit, was designed to provide a computational-level analysis of normative reasoning

with a hypothesis space, rather than a processing account of inductive inference.

The model proposed by Tenenbaum, Kemp & Shafto (2007) takes the Bayesian

approach a step further by focusing on the contribution of structured knowledge. This

modeling work explicitly specifies how a single general-purpose Bayesian reasoning

mechanism combined with different knowledge structures can lead to different patterns of

generalization behavior in different inductive contexts. This proposal emphasizes the role of

different structural representations, or relational systems of categories such as taxonomic

hierarchies or food webs in folkbiology. Knowledge in this form provides prior probabilities

for a domain-general Bayesian inference engine which drives inductive inference. Since

people can draw on different prior knowledge structures within a single domain, depending on

which of these knowledge structures is triggered, very different patterns of inference arise.

Tenenbaum et al. demonstrated that their model can strongly predict people’s

judgments in reasoning about two different types of properties, but only when the model is

given an appropriate knowledge structure matched with the property. To predict people’s

reasoning about generic biological (anatomical and physiological) properties, the Bayesian

inference engine had to draw upon a taxonomic knowledge base; while when the same

general purpose inferential engine was given an ecological knowledge base, it predicted

  38  

people’s reasoning about diseases. Kemp, Perfors & Tenenbaum (2007) extended this work

by demonstrating that the multiple knowledge structures do not have to be specified in

advance but can be learned by a hierarchical Bayesian model from data. However, as Kemp et

al. (2007) mention, the explanation provided by such modeling work is at the level of

computational theory (Marr, 1982), and does not specify the psychological mechanisms by

which the model could be implemented.

In sum, similarity-based models like SCM, FBI or SINC focus on accounting for a

limited set of phenomena and ignore property knowledge: it is not clear how such knowledge

could be incorporated into these models without major revisions. Hypothesis-based

approaches allow for property effects, but do not specify how they can be implemented.

Bayesian models offer a promising perspective and allow property to moderate induction by

positing separable knowledge structures that make contact with informative properties,

yielding different reasoning in different induction contexts (Tenenbaum et al., 2007, Kemp et

al., 2007). However, Heit (1998), Tenenbaum et al. (2007) and Kemp et al. (2007) are explicit

in that their models are computational-level models that make no commitments about

processing details such as the kind and time-course of involved psychological processes.

Tenenbaum et al. (2007) write that it remains an open question how theories “are acquired

and selected for use in the particular context” (p.200), and “the most immediate gap in our

model is that we have not specified how to decide which theory is appropriate for a given

argument” (p.200). These gaps and open questions represent the current state of

understanding of context-sensitive inductive inference.

This absence of process models of inference generation, and of proposals about

psychological mechanisms giving rise to property effects in inference generation is the

  39  

starting point for this project. The following is an attempt to fill these gaps. We propose a

model of inductive inference generation and use it to guide investigation of processing

underlying property effects.

The main question of this project is how does property exert its effect on inferences?

What is the relevant mechanism? We tackle this question at two levels. First, starting from the

higher level of description, we propose a (fairly minimalistic) computational account of

hypothesis-generation, addressing the question of what tasks need to be accomplished in order

to generate an inductive inference. Second, we examine the mechanism, i.e. describe how

these tasks are accomplished by the cognitive system. We break this step into two sub-tasks:

first, to catalog the “ingredients” that go into preparing an inference, i.e. available knowledge,

its sources and types; second, describe the “recipe”, i.e. propose and evaluate operations over

this knowledge.

A (Minimalistic) Computational Account of Inference Generation

What goals need to be accomplished in order to generate an inductive inference?

When a person is presented with some starting evidence, e.g. that a certain parasite is found in

ducks (a certain property is true of a certain premise category), and asked what else might

have the disease and why, what tasks does the cognitive system need to solve in order to

arrive to a formulated inductive hypothesis at the output: “foxes, because they eat ducks”, or

“eagles, because they are also birds”? This proposal is based on the starting assumption that

the hypotheses are related in a systematic way to the inductive problems (see Coley &

Vasilyeva, 2010 for supporting evidence). This assumption shapes the first task for the

inference-generating system: it needs to understand the incoming evidence (what “duck”

means; what “parasite” means; etc.). Second, in order for the outcome to count as a novel

  40  

hypothesis rather than a restatement of the question, it needs to go beyond the given

information, i.e. to attribute the property in question to an entity or set of entities not already

claimed to have this property in the premise (“foxes”, or “eagles”, or “other birds”). These

two goals define two major components of the proposal: knowledge retrieval and hypothesis

generation (see Figure 1).

Figure 1. Tasks that a cognitive system needs to accomplish in order to generate an inductive inference6.

Before examining the mechanism, we will describe the “ingredients” that go into

preparing an inference, i.e. available knowledge, its sources and types. Next, we will describe

the “recipe”, i.e. propose and evaluate operations over this knowledge. At this stage, we will

make more specific claims about how “knowledge retrieval” and “hypothesis generation” may

be accomplished.

Ingredients: Sources and Types of knowledge

Hypothesis generation is knowledge driven; when one learns that A has a novel

property X, one uses what they know about A and its relations to other things to form guesses

                                                                                                               6 This part of the proposal makes no claims about the mechanism (stages, order, sequential or parallel processing, feedback, etc).

  41  

about what else is likely to have the property. As soon as the “A” in this example is

replaced with a familiar term: duck, lawyer, or bacon – large amounts of information become

available as potential guides for inductive inference. In other words, one source of knowledge

that serves as input to inductive inference is knowledge about premise categories. When such

categorical knowledge is accessed, a probabilistically determined subset of features and

relations that comprise the representation of that concept becomes available as a raw material

for the inference. For example, if A turned out to be a duck, such features as “is a bird”,

“flies”, “lives in ponds”, “quacks”, “poops around Fenway”, “eaten by foxes”, “tasty when

grilled with apples” may come to mind.

Although there are many different kinds of knowledge, researchers hav divided

knowledge about living things into two broad classes: taxonomic categories based on intrinsic

similarity of members, e.g. “birds”, and contextual categories, based on extrinsic relations

between members and other entities (including extrinsic similarity and causal interactions),

e.g. “aquatic animal”, “prey”. For example, ducks at the same time belong to a taxonomic

category of birds and contextual categories of aquatic animals and prey to their predators.

Each of these types of knowledge can serve as a basis for projection from ducks – to other

birds, or other aquatic animals, or things that eat ducks. In fact, the problem is typically not

that there are not enough bases for inferences, but that there are too many, and describing

selection among many possible bases for projection is one of the major issues in psychology

of induction. (Goodman, 1955, 1972; Keil, 1981).

In addition to the premise category, knowledge about the property can also serve as a

source of information. If X in the example above is replaced with an informative property –

having a certain disease, personality trait, or fat content – this adds more information to the

  42  

problem. Independently of what we know about ducks, we also know something about

diseases: what they are like, how they are transmitted, etc. Somewhat counterintuitively, this

does not make the problem worse but helps to constrain the set of relevant information. The

critical point here is that this information comes from a different source, and different sources

of information can constrain each other, if they match in type. In keeping with the existing

literature, we will classify properties as taxonomic or contextual. As such, the property type

(e.g. disease, an ecological property) could indicate which subset of knowledge about ducks is

most relevant for inference generation.

As mentioned above, existing similarity-based models do not take into account

property as an additional source of knowledge, and/or ignore that knowledge comes in

different types (e.g. the Osherson et al. SCM addresses premise category knowledge only, and

only one type of premise knowledge, taxonomic; Sloman’s FBI and Sloutsky & Fisher’s

SINC do not account for different types of knowledge).

In sum, we propose to expand the focus of attention and to consider two orthogonal

attributes of knowledge: first, their source (premise category vs. property), and second, their

type (taxonomic vs. contextual).

Recipe, or Mechanism

Knowledge retrieval vs. Hypothesis generation: where do we start looking for

property effects? The two tasks identified earlier - knowledge retrieval and hypothesis

generation – offer two possible loci of property effects (not mutually exclusive). On the first

look, hypothesis generation is an appealing candidate - after all, the property effects are

observed in the produced hypotheses. However on the other hand, the state of knowledge

about the process of hypothesis generation can be most optimistically described as “close to

  43  

nothing”. This is not to claim that there has been no work done on hypothesis generation –

there has been. However, the specific ways the questions are framed and addressed in this

research make it less applicable to our case of interest. In most cases, what is studied under

the label of “hypothesis generation” actually applies to rule discovery in contexts with limited

set of applicable rules and a correct answer, such as the Wason selection task (e.g., Farris &

Revlin, 1989). Other questions studied under the rubric of “hypothesis generation” include

choice of hypothesis testing strategies and a related topic of confirmation bias, hypothesis

evaluation, subjects’ meta judgments about exhaustiveness of their sampling of hypothesis

space, completeness of generated hypotheses, subjects’ confidence judgments (Gettys &

Fisher, 1979; Klayman & Ha, 1989; McDonald, 1990; Mehle, 1982; Mehle, Gettys, Manning,

Baca, & Fisher, 1981) - pretty much everything except the process of hypothesis generation

itself. This leaves the hypothesis generation component of our model a rather mysterious

entity, and makes it less attractive in terms of its explanatory potential.

In contrast, knowledge retrieval is known to be affected by context. As McElree,

Murphy & Ochoa (2006) discuss, it is impossible to retrieve all known facts about every word

every time it is encountered; and even if it were possible, most of this information would be

irrelevant in any given situation and could impede reasoning rather than aid it. Indeed, is has

been shown that people tend to retrieve conceptual information selectively, based on the

context.

For example, Tabossi & Johnson-Laird (1980) presented participants with sentences

that emphasized different aspects of meaning of an embedded word: The goldsmith cut the

glass with the diamond or The mirror dispersed the light from the diamond. After reading one

of the sentences, participants were presented with a feature-verification task. Context-relevant

  44  

features (such as hard as a feature of diamonds, after reading the first sentence) were

verified faster than other true, but context-irrelevant features (e.g. brilliant).

In a study of text memory by Barclay, Bransford, Franks, McCarrell & Nitsch (1974),

after participants had been presented with such sentences as The man lifted the piano or The

man tuned the piano, in a cued memory test, something heavy was a better cue for the first

sentence than was something with a nice sound, and the reverse was true when the second

sentence was cued. This suggests that during encoding of sentences, one context caused

participants to selectively retrieve the weight of the piano, and the other context caused them

to retrieve the sound.

Work by Barsalou (1982) demonstrated context effects on knowledge retrieval in tasks

involving making judgments about categories, such as attribute verification and similarity

judgments. Results showed that people retrieve a subset of that category’s features, and this

subset is determined by the context. For example, roofs do have an attribute “can be walked

upon”, but whether it is retrieved or not when a person hears “roof” largely depends on the

context in which roof is discussed, as suggested by the attribute verification times varying

with the context.

Such context-sensitive retrieval of information has been well-documented in a variety

of experimental paradigms: sentence verification (McKoon & Ratcliff, 1988; Tabossi, 1982;

Tabossi & Johnson-Laird, 1980, Glucksberg & Estes, 2000), lexical decision (Tabossi, 1988;

Swinney, 1979) and naming (Hess, Foss, & Carroll, 1995), all showing that context focuses

memory access on relevant features of a concept.

In an inductive problem that consists of a premise category(-ies) and a to-be-projected

property, property could be viewed as context for the premise(s). A premise category label, as

  45  

any word, is connected to a vast amount of conceptual knowledge. When the information

about a premise category is retrieved from long-term memory, this process may be no

exception to context effects described above. This opens a possibility that property effects, or

differential projections dependent on the property, are an outcome of property-guided

selective retrieval of information about premises. For example, when an inductive problem

presents “duck” in the context of a taxonomic property (“ducks have gene X”), one may be

more likely to retrieve taxonomic knowledge about ducks (bird, have feathers); while in the

context of an ecological property (“ducks have parasite X”), one may be more likely to

retrieve ecological knowledge about ducks (aquatic, prey to foxes).

Thus, in the quest for the mechanism of property effects, our first-choice strategy is to

avoid attributing any major explanatory role to some mysterious processes taking place inside

the black box of hypothesis generation, and try to formulate and test a minimal account in

which property exerts its effect on inference by moderating knowledge retrieval. This

proposal is consistent with the intuitions outlined by Heit & Rubinstein (1994) and the general

idea of the Bayesian model proposed by Heit (1998). We hope to advance our understanding

of property effects by making our proposal more specific than that of Heit & Rubinstein

(1994) and stating and testing it in the context of a descriptive process model of inductive

reasoning.

Model of induction, v.1.0: Knowledge retrieval as a locus of property effects.

Knowledge retrieval. As discussed above, when the premise is presented, one needs to

understand it enough to generate a hypothesis. Understanding requires retrieval of prior

knowledge about premise categories and/or properties from long-term memory. By retrieval

of knowledge about a premise category or property we mean activation of a corresponding

  46  

lexical node and, via spreading activation, subsequent activation of nodes representing

related knowledge, with the likelihood of passing the activation threshold determined by the

strength of connection to the starting lexical node, and, as outlined above, the context of

retrieval. The specifics of context-moderated retrieval – via changes in baseline activation of

context-congruent nodes or inhibition of context-incongruent nodes, or both – will be

examined in this project, as discussed below.

In the following, we discuss both what is retrieved and how knowledge retrieval may

proceed.

Presumably, one would attend to both components of the premise statement and

retrieve both premise category and property knowledge. In this case, the retrieval of premise

and property knowledge may proceed either independently or interactively.

In the independent retrieval, the way the premise category knowledge is accessed does

not depend on the accompanying property, and vice versa. In this case, the same knowledge

about ducks would be accessed regardless of whether they were said to have a flu, a gene,

some unspecified property X, or were presented in isolation, and the same knowledge about

flu would be accessed regardless of whether it was stated true of ducks, elephants, people or

crickets, or were presented in isolation. The outcome of retrieval can be described by a simple

additive function combining salient knowledge about premise, and the salient knowledge

about property.

In contrast, in interactive retrieval, accessing knowledge about premise category is

moderated by the property, and/or vice versa. E.g., the extrinsic and ecologically transmitted

property of having a flu may direct one more towards selective retrieval of knowledge about

duck’s habitat and interaction with other animals than the intrinsic property of having a

  47  

certain gene would. Moreover, the interaction could involve inhibition of incongruent

knowledge (e.g. taxonomic vs. contextual). In this case, the outcome of retrieval will be

described by a multiplicative function reflecting potential mutual enhancement between

congruent property and premise knowledge or attenuation between incongruent property and

premise knowledge (knowledge about premise x knowledge about property).

Alternatively, one may retrieve property knowledge only (what they know about flu

infections), ignoring the premise. Such retrieval strategy would yield property effects in

inferences, but inferences would be unrelated to knowledge about specific premise categories.

Finally, in principle, even incomplete understanding of the premise may be sufficient

for inference generation. That is, if one told that a flu is found in ducks and asked to guess

what else may have it, one may focus on premise category knowledge only and retrieve what

they know about ducks, ignoring the property. If participants were consistently doing this, we

would observe inference showing no signs of property effects. Since extensive evidence

suggest that inferences are sensitive to property, and since our goal here is to propose a model

that would be able to account for property effects, we are not going to consider this scenario

here.

At this point, we need to make a distinction between knowledge recruitment and

knowledge retrieval. Knowledge recruitment describes what knowledge actually ended up

being used in the inference. It describes the relation between the input and the outcome.

Knowledge retrieval, in contrast, refers to the process of activating knowledge relative to

baseline, regardless of the consequences this knowledge had (or did not have) on the outcome

hypotheses. This distinction is supported by work showing that effects of constraining context

on knowledge retrieval can be delayed, resulting in initial activation and later discarding of

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knowledge subsets (Swinney, 1979). The scenarios above are about knowledge retrieval.

Knowledge retrieval, however, necessarily constrains knowledge recruitment. Thus, in the

following studies, we examine both knowledge recruitment (Study 3) and retrieval (Study 4)

in order to determine which retrieval strategy is used.

The knowledge retrieval step leaves one with some active (although possibly not

detailed and/or vague) knowledge about premise features (a duck is a bird with a yellow bill,

and is eaten by foxes), the property (a flu is an infection that spreads via contact; a gene is

some tiny thing inside that determines how one looks), or both. This knowledge is then used

to generate an inference.

Hypothesis generation. In section 4.1 we stated that our first-choice approach is to

look for property effects in knowledge retrieval, rather than in hypothesis generation. In other

words, our current proposal is that there is not much action during hypothesis generation that

can enlighten us about the mechanism of property effects. At this point it may be a good time

to clarify a. what exactly we mean by “not much action”, and b. that there are alternatives that

we are aware of but not considering (yet).

Generating a hypothesis involves going beyond what is given by making a prediction

about the unknown. In our minimal account, this may be achieved without much extra work –

the active knowledge about the premise and/or property can be directly translated into an

inference using a formula “whatever else has the same known features as the premise” (other

birds; other things with webbed feet), or “whatever can be related to the premise in the

property-relevant way” (“things that come in contact with ducks”, “animals that eat ducks”).

When hypotheses are generated by such “direct translation”, we expect the resulting

inferences to reflect the knowledge about the premise retrieved earlier. If both the property

  49  

and premise category knowledge has been retrieved, we expect the inferences to be a

function of both components of the premise – additive or interactive. If property knowledge

only has been retrieved, we expect the inferences to be predominantly generic, i.e. not specific

to the given premise category (e.g. “whatever eats them or come in contact with them”).

These three possible strategies of knowledge retrieval in the course of induction are shown in

Figure 2.

Figure 2. Model of induction, v.1.0: Knowledge retrieval as a locus of property effects. (The “premise category only” retrieval strategy is not shown, since it would not yield property effects).

When it comes to alternatives to simple “direct translation” during hypothesis

generation, the way in which retrieved knowledge is used to generate a hypothesis could be

rather complicated. As suggested by Sloman’s (1994) work, explanation of evidence may play

an important role in evaluating inductive arguments. Generation of inductive inferences may

not be an exception. But we will save this discussion until Chapter VI.

The core two studies of this project investigating property effects in induction –

Studies 3 & 4 - were driven by the proposed model stating that inductive hypotheses are

  50  

produced in two steps: first knowledge is retrieved, and then it is used to formulated the

hypotheses. With regard to property effects, the main claim of the model is that they take

place in knowledge retrieval: property moderates what subset of knowledge about premise

categories gets retrieved, and then this biased set of retrieved knowledge is used to generate

an inference in a relatively straightforward way via direct translation. Within this model, we

distinguish three possible scenarios of knowledge retrieval: only property knowledge is

retrieved, both property and premise knowledge is retrieved independently, both property and

premise knowledge is retrieved interactively. Staying within the limits of the model, we are

going to test all three scenarios.

The overall approach is to see how far we can push this relatively simple model

(plausibly localizing the source of property effects in knowledge retrieval, and postulating the

simplest possible mechanism of hypothesis generation) in accounting for property effects in

inference generation – until it either provides a satisfactory description of the phenomenon, or

fails to do so, which would require revision of the theory.

Overview of the Studies

Studies 1 and 2 together provide a detailed description of the knowledge recruited by

the inferences in terms of its sources (property, premise category) and type (taxonomic,

contextual (ecological)). These two studies address the question “What knowledge serves as

input to inference-generation processes?”.

Study 3 documents property effects in inference generation and examines the

relationship between the knowledge about premises (input) and the generated inferences

(outcome). In other words, the goal of this study is to specify the function relating stimuli to

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responses (knowledge recruitment), which would provide initial constraints on the retrieval

strategy that could have produced such a function.

Study 4 examines the time-course of knowledge retrieval in the course of inference

generation, focusing on premise category knowledge.

Study 5 subjects to test a revised proposal about the locus of property effects.

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Chapter III. “Ingredients”: Knowledge types and sources

This chapter addresses the question “What knowledge serves as an input to inference-

generation processes?” Studies 1 and 2 describe the ingredients available at the outset of an

inference: the types and sources of knowledge brought about by initial evidence, i.e. the

inductive problem. Because we focus here on inference generation, this will consist of a

premise made up of a category and a property, for example “Ducks have gene X”. The goal is

to provide a detailed description of knowledge recruited by the inferences in terms of its

sources (property, premise category) and type (taxonomic, ecological).

Study 1. Property knowledge: distributional beliefs

Prior research reviewed in Chapter I shows that people tend to reason differently about

such properties as possessing a gene vs. disease: genes tend to be projected to other members

of taxonomic categories, while diseases tend to be projected to ecologically related targets.

Based on such inference patterns, properties like gene have been labeled as taxonomic, while

properties like disease have been labeled ecological, or contextual. However, such definitions

are circular: it is not clear what makes a property taxonomic or ecological, besides the

inferences people make about them. What is the source of the taxonomic or ecological bias

brought about by the property?

To clarify from the beginning, we are not talking about the cases when knowing the

property would give away the answer; if a person happens to know whether pigeons actually

have a liver with two chambers that act as one or not, we are not dealing with inductive

inference, or reasoning under uncertainty, anymore. The cases we are interested in are when a

person knows something about the property, but does not know the answer to the inductive

question.

  53  

Another possibility we would like to exclude from the outset is that property is

likely to suggest potential projection targets directly. In other words, if the property of having

a gene gets projected from a duck to a pigeon, and the property of having a disease gets

projected from a duck to an otter, we claim that in the majority of such cases, the reason is not

that gene was directly associated with pigeons, and diseases were directly associated with

otters.

Instead, we would like to test the possibility that what property brings to the table is

information about its distribution. And crucially, people’s distributional beliefs about different

properties can tie them to different category systems, such as a classification of animals that

groups them into species, taxa, etc., and an ecological classification of animals that groups

animals from the same habitat and/or animals that participate in the same food chain into the

same category. For example, people can believe that genes are homogeneous within

taxonomic classes (fish tend to have similar genes), while diseases are homogeneous within

ecological classes (jungle animals tend to share diseases, or predators and prey tend to share

diseases). This association between properties and classification systems can, in turn, be the

source of bias that properties exert on inferences.

One part of this idea – about properties being associated with distributional beliefs –

comes from Goodman’s (1955) proposal about overhypotheses, or abstract beliefs describing

the scope of properties (see Chapter I for a more detailed discussion). According to

Goodman, having a belief that category members tend to be homogeneous with respect to

some property is a prerequisite for generalization. Nisbett, Crantz, Jepson, & Kunda (1983)

provided empirical support for this claim, by demonstrating that when people believe that a

category is homogeneous with respect to one property type, they generalize willingly from

  54  

one observation (e.g. skin color is generalized to the entire tribe based on one observation

of a brown-skinned tribesman), but they treat a property as non-projectable if the absence of a

belief that such property would be homogeneous within a given category (e.g. they do not

generalize the property of being obese from one observation to the whole tribe). This,

however, speaks only to what makes a property projectable or not, but not to how different

projectable properties can be biased towards one type of categories (e.g. taxonomic classes)

rather than others (e.g. ecological categories).

Yet, in principle, could overhypotheses explain a taxonomic or ecological bias among

different projectable properties? Yes, if we allow for multiple-category membership - which is

characteristic of most categorical knowledge. For example, a duck belongs to a category

birds, which is a part of a taxonomic classification of animals. A duck is also an aquatic

animal, which is an ecological class. If we allow for a belief that some properties are more

homogeneous within taxonomic classes, and other properties are more homogeneous within

ecological classes, such distributional beliefs would provide a bridge between properties and

inductive selectivity in inferences.

Indeed, the relatively recent modeling work of Tenenbaum, Kemp & Shafto (2007)

reviewed in Chapter I suggested that people might draw upon different subsets of their

categorical knowledge in reasoning about different properties. When an inductive problem

contains an informative property, it may serve as a cue towards the appropriate distributional

overhypothesis, indicating which knowledge structure is relevant for projection. As

Tenenbaum et al. report, a general-purpose Bayesian model provided with a taxonomic

category system succeeded in predicting human reasoning about a gene (an intrinsic property)

but failed to predict human reasoning about a disease (a causally transmitted property),

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whereas a model provided with an ecological system of categories based on food-web

relations between the very same set of animal species succeeded in predicting human

reasoning about a disease but failed to predict reasoning about gene. Kemp, Perfors, &

Tenenbaum (2007) demonstrated that orthogonal knowledge structures can be learned by a

hierarchical Bayesian model from observations, and upon learning such model was able to

accurately predict property-sensitive reasoning of participants making inferences about such

properties of social categories as skin color vs. body weight (data reported in Nisbett, Krantz,

Jepson & Kunda (1983)). Critically, the learning success of the model was conditional on its

hierarchical component, having an extra layer of hypotheses – the overhypothesis level. An

alternative model that was unable to incorporate overhypotheses was making identical

inferences about skin color and body weight.

In sum, we have theoretical description of what sort of information provides a basis

for property projectability (Goodman’s “overhypotheses”) with initial supporting evidence

(Nisbett et al., 1983), and we have a set of modeling results suggesting that human reasoning

can be modeled if different properties are allowed to be associated with different

distributional beliefs. However, the latter claim is an inference from a modeling result that has

not been previously demonstrated directly.

This study addresses this gap by empirically testing whether people have specific

beliefs about the distribution of hypothetical properties and whether properties differ in terms

of their expected distribution across taxonomic and ecological category systems. To address

these questions, we presented participants with a range of novel properties. Participants were

told that a certain (unspecified) animal species has one of these properties, and asked to

estimate a) the percentage of other animals belonging to the same biological family, and b)

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the percentage of other animals living in the same area that are likely to share that property.

We chose to phrase the ecological estimate question broadly in terms of shared habitat, rather

than in terms of a variety of more specific ecological relations – predation-based, symbiotic,

parasitic, etc. For most ecological relations between animals, common location is a

prerequisite; thus whichever specific ecological relations affect estimates of ecological

distribution of properties, the shared habitat is likely to capture them.

If people believe that distribution of a particular property corresponds to taxonomic

categories (i.e. members of a given biological class tend to be relatively uniform in their

properties), then upon learning about one species possessing a property, participants should

estimate that a high percentage of other animals in the same biological family have this

property as well. If people believe that a particular property clusters within eco-systems (i.e.

animals within the same eco-system tend to share properties), then participants should

estimate a high percentage of animals in the same area to share the property. If people believe

that properties can differ systematically in their distribution, with some properties being

taxonomically-biased (distributed predominantly along taxonomic category system), while

other properties are ecologically-biased (distributed predominantly along ecological category

system), the former should receive higher estimates of being shared by animals in the same

biological family than animals living in the same area, and the pattern should be reversed for

the latter.

Method

Participants. Twenty-six Northeastern University undergraduates were recruited from

introductory psychology classes and participated in exchange for course credit.

  57  

Materials. We selected a range of properties that could be plausibly attributed to

animals (since we limited the domain of investigation to reasoning about biology). Some of

these properties were predicted to be taxonomic (either based on prior research or on

subjective judgment of the experimenters), some were predicted to be ecological, and some

neutral. There was a total of 13 properties: 4 expected to be taxonomic (has gene X, has X-

cells, has enzyme X, has hormone X), 6 expected to be ecological (has disease X, has bacteria

X, has flu X, as infection X, has parasite X, has virus X) and 3 neutral, or non-biasing (has

property X, has substance X in the bloodstream, a fact X is true of [one animal species]).

Design. Every participant was presented with all 13 properties, each combined with an

arbitrary letter-digit code, such as Gene X5, Disease Z7, Property A3. Participants provided

one estimate of taxonomic and one estimate of ecological distribution for each property. The

order of properties and the estimates was randomized for every participant.

Procedure. The experiment was presented as a computerized survey. General

instructions introduced participants to the format of the task, explained main terms used in the

questions (species, biological family, area where an animal lives), and instructed to treat each

question as independent. In the main part of the experiment, participants were presented with

26 questions, one at a time. Every property was presented twice: once in the context of a

taxonomic estimate question, once in an ecological estimate question. The questions were

phrased as follows: “Imagine that one animal species has gene Z8. What percentage of

animals IN THE SAME BIOLOGICAL FAMILY is likely to have gene Z8? Enter a number

between 0 & 100.”; “Imagine that one animal species has Q1-cells. What percentage of

animals LIVING IN THE SAME AREA is likely to have Q1-cells? Enter a number between 0

& 100.”, etc (for the general instructions introducing participants to the format of the task and

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clarifying the main terms – animal species, biological family, area where an animal lives,

see Appendix A). Total time to complete the experiment was 10-15 minutes.

Results

This study examined distributional beliefs about properties. The main question was

whether people believe that some properties are relatively homogeneous within taxonomic

categories, while other properties are relatively homogeneous within ecological categories. To

address this question, we calculated two average percentage estimates for every property, one

indicating the percentage of animals in the same biological family and the other one the

percentage of animals living in the same area likely to share that property. The average

percentage estimates for all properties are shown in Table 1.

Table 1. Mean percentage estimates of animals in the same biological category and same habitat that share provided properties (*p<.05, **p<.01, ***p<.001, ****p<.0001). Comparisons to 50% (⇑ or ⇓ - above or below 50%, p<.05; ↑ - above 50%, p<.08)   % shared by

animals in the same biological

family  

% shared by animals living in

the same area  

 

Taxonomically distributed properties  Gene   67.6⇑   31.4⇓   ****  Cells   69.5⇑   33.0⇓   ****  Hormone   67.5⇑   34.5⇓   ****  Enzyme   66.6⇑   44.4   **  Fact   56.9   37.7⇓   *          Ecologically distributed properties  Flu   30.0⇓   60.2↑   ****  Infection   30.0⇓   58.9↑   ****  Parasite   33.3⇓   56.4   ***  Virus   32.1⇓   53.9   ***  Bacteria   38.1   57.8↑   **  Disease   33.2⇓   49.3   *          Non-biased properties  Property   59.2↑   49.0   n.s.  Substance   52.5   44.4   n.s.  

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As one way to determine whether for each property there was a systematic bias of

higher homogeneity estimates for taxonomic categories than ecological or vice versa, we

compared the two estimates for each property in a series of paired tests. For the following five

properties, taxonomic estimates were higher than ecological: having a gene (t(25)=5.58,

p<.0001, Cohen’s d=1.1), having cells (t(25)=7.54, p<.0001, Cohen’s d=1.48), having a

hormone (t(25)=5.36, p<.0001, Cohen’s d=1.05), having an enzyme (t(25)=2.96, p=.007,

Cohen’s d=.58), and, surprisingly, a fact true of a given species (t(25)=2.69, p=.013, Cohen’s

d=.53). For six properties, ecological estimates were higher than taxonomic: having a flu

(t(25)=6.78, p<.0001, Cohen’s d=1.33), having an infection (t(25)=5.22, p<.0001, Cohen’s

d=1.02), having a parasite (t(25)=4.34, p=.0002, Cohen’s d=.85), having a virus (t(25)=3.93,

p=.0006, Cohen’s d=.77), having bacteria (t(25)=3.42, p=.002, Cohen’s d=.67), and having a

disease (t(25)=2.63, p=.015, Cohen’s d=.52). And finally, for two properties the biological

family estimates did not differ from habitat estimates: having a property (t(25)=1.41, p=.17,

Cohen’s d=.28), having a substance in the bloodstream (t(25)=1.07, p=.29, Cohen’s d=.21).

As another way to determine taxonomic or ecological bias, we compared the estimates

to 50% mark. Of course, there is no a priori chance level of property occurrence in any given

biological category. Depending on within-category variability, even the most prevalent

properties may not be present in 100% of all members. However, since 50% can serve as a

psychological marker for being present in a majority of category members, we took 50% as a

reasonable anchor to compare assigned percentages to. The overall pattern of the comparisons

to 50% was consistent with the paired tests reported above (although some comparisons did

not reach significance). As shown in Table 1, among the five properties that had higher

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taxonomic than ecological estimates – having certain cells, gene, hormone, enzyme, or a

fact true of a species – 4 out of 5 properties received taxonomic estimates above 50% and the

ecological estimates below 50%. Among the six properties that had higher ecological than

taxonomic estimates – having a flu, infection, parasite, virus, bacteria or disease – 5 out of 6

were had taxonomic estimates below 50% and 3 out of 6 had ecological estimates marginally

above 50%. The two properties that received non-differing taxonomic and ecological

estimates – having an unspecified property and having a substance in the bloodstream – had

estimates that did not differ from 50%, although an unspecified property was believed to be

shared by 59% of animals in the same biological family, which was  marginally above 50%

(one sample t-test, t(25)=1.87, p=.07, Cohen’s d=.367).

Based on these two analyses, we categorized one set of properties as taxonomically

biased, or more likely to be shared by the animals in the same biological family (>50%) than

by animals in the same habitat (<50%): having certain cells, gene, hormone, enzyme, or a fact

true of a species. Another set of properties was categorized as ecologically biased, or more

likely to be shared by the animals in the same habitat (>50%) than in the same biological

family (<50%): having a flu, infection, parasite, virus, bacteria or disease. Finally, two

properties were categorized as neutral, or non-biasing, i.e. equally likely to be distributed

along taxonomic as well as ecological category systems (for each category, expected to be

shared by about half the category members): having an unspecified property and having a

substance in the bloodstream.

Discussion

The main goal of this experiment was to describe one source of knowledge available

at a starting point of generating an inference: property. The central question was what

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information can properties bring to the table when they are presented as a part of an

inductive problem. A theoretically plausible candidate for the knowledge that a property

might contribute is a set of beliefs about property distribution. To address this possibility, we

examined people’ beliefs about distribution of a range of properties by asking participants to

estimate percentage of animals in the same biological family or percentage of animals living

in the same area sharing a given property.

Three classes of properties emerged from the analyses of the estimates. For one set,

scores for “same biological family” were above 50%, higher than the scores for “same

habitat”, which were below 50%. This suggests that participants have strong a priori beliefs

that these properties tend to be taxonomically distributed. For another set, scores for “same

habitat” were marginally above 50%, higher than the scores for the “same biological family”,

which were below 50%. This suggests that participants believe these properties to be

ecologically distributed. Finally, for the last set of properties, the “same family” and “same

habitat” did not differ from each other or from 50%, suggesting that these properties are

neutral, or non-biased with respect to either taxonomic or ecological distribution.

Presence of such overhypothesis-like beliefs had been suggested by the modeling

work of Tenenbaum and colleagues (2007), but has not been previously demonstrated

directly. This study demonstrated that people do indeed possess beliefs that map distribution

of different biological properties on different category systems (taxonomic and ecological, in

this case). Given that nearly every object belongs to multiple categories, limiting the number

of relevant categories can provide necessary constraints on hypothesis space required in order

to generalize meaningfully. These findings are compatible with Goodman’s overhypotheses

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and suggest to us that his proposal can be extended to distributional beliefs over multiple

category systems, which, as we argue, underlie property effects in induction.

Based on the findings of this study, we argue that the nature of the property may bias

people towards attending to a limited subset of relations or category systems most relevant for

reasoning about that specific property by virtue of activating a specific set of distributional

beliefs. Specifically, in the context of an inductive problem, the properties we classified as

taxonomic might activate the taxonomic distributional beliefs, which in turn may bias people

towards attending selectively to taxonomic relations of the premise category. In contrast, the

properties we classified as ecological may activate ecological distributional beliefs and guide

attention to the ecological relations the premise category participates in.

Study 2. Premise category knowledge

Study 1 examined the contribution of property to the inductive problem. It

demonstrated that knowledge about properties includes distributional beliefs, which could

serve as a potential source of a taxonomic or ecological bias in induction. The other basic

input to an inductive inference discussed in Chapter II is knowledge about premise category.

The main goal of this study is to describe two types of knowledge about premise categories:

taxonomic and ecological.

First, to clarify the terms, in these studies we use “knowledge” in the sense of “belief

that something is true”, regardless of veridicality of the belief. Since true and false statements

are phenomenologically indistinguishable from the perspective of a person who believes them

both to be true, we do not expect such statements to have different cognitive effects.

Next, by taxonomic knowledge about animals we mean beliefs about membership in

taxonomic categories (“..is a bird”) and inherent attributes of an animal, such as intrinsic

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qualities, behaviors, abilities and parts, whether observable or not (“is yellow”, “has a

beak”, “has a cold blood”, “walks on four legs”). By ecological knowledge about animals we

mean beliefs about animals’ typical environment, dietary habits, interactions with other

entities in the environment via predation and non-predatory behaviors (“lives in water”, “eats

gazelles”, “pollinates flowers”).

In this study, we aim to describe the knowledge about animals that is likely to come to

a person’s mind when she is presented with the animal name. Since our model of property

effects makes claims about moderating effect of property on retrieval of knowledge about

premise categories, it is important to describe the initial “playfield” for the property, i.e. the

state of affairs with category knowledge when it is not affected by the property. When

property moderates retrieval of knowledge, we cannot assume that it acts upon a “level

ground”, with all pieces of knowledge having equal baseline probability of coming to mind. If

we were to assume such “level ground” as a starting point, we would always expect biasing

properties to introduce inequalities by promoting activation of some subsets of knowledge

and suppressing activation of others. However, if we know from the outset which subsets of

knowledge are more available than others, we will be able to adjust our predictions about

specific effects of biasing properties on retrieval of that knowledge: for example, if we knew

that ecological knowledge is initially less likely to come to mind than taxonomic, we could

expect the facilitating effect of an ecological property to reduce the difference between

reliance on ecological and taxonomic knowledge in induction.

There are many factors that can affect availability of knowledge about a category. One

established view of the representation of categorical knowledge describes categories as sets of

features organized by theories specifying how the features are related to each other (Murphy,

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2002; Murphy & Medin, 1985). We assume that when knowledge about a category is

accessed, the likelihood of retrieving any given feature may be affected by a large number of

chronic and acute factors, ranging from feature importance within that category’s theory to

recency of last access, emotional significance, etc. (Ahn, Kim, Lassaline, & Dennis, 2000;

Buchanan, 2007; Ebbinghaus, 1962; Shafto, Coley, & Vitkin, 2007). Here we are focusing on

the type of knowledge – taxonomic, describing intrinsic properties, and ecological, or

contextual, describing animal’s relations to other entities – as a potential source of differences

in availability.

Prior work demonstrates that in many reasoning tasks taxonomic knowledge

dominates ecological, serving as a default kind of knowledge (see Shafto, Coley, & Vitkin,

2007, for review; however, see Coley & Vasilyeva, 2010, for counter-evidence). For example,

priming ecological relations can increases similarity ratings for ecologically related items, but

priming taxonomic relations does not affect similarity ratings for ecologically related items,

suggesting that taxonomic relations are chronically more available (Ross & Murphy, 1999;

Shafto, Coley, & Vitkin, 2007). Likewise, time pressure in induction selectively impairs

reliance on ecological knowledge but not on taxonomic knowledge, consistent with the idea

that ecological knowledge is less spontaneously available, and accessing it is a more fragile

process than accessing taxonomic knowledge (Shafto, Coley, & Baldwin, 2007). Importantly,

however, such demonstrations of differences in knowledge availability are inferences based

on the outcomes of inductive or other reasoning tasks. If our aim is to describe knowledge that

serves as an input to induction, we would like to use a more direct measure of available

knowledge, that does not involve a reasoning task. This determined our choice of the

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measurement instrument of amount of salient taxonomic and ecological knowledge about

animals – a feature listing task.

In order to measure salient available knowledge about animal categories, we presented

participants with a set of animal names and asked them to write all the attributes of each

animal that came to their mind. The mean number of taxonomic and ecological features they

listed per animal was taken as a measure of amount of salient taxonomic and ecological

knowledge about the animal.

This measure (mean number of listed features) is likely to tap both into knowledge

amount (number of known facts or beliefs) and its salience (likelihood with which a given

piece of knowledge comes to mind). Knowing many taxonomic facts does not necessarily

preclude one from knowing just as many or more ecological facts, but salient pieces of

knowledge have to compete for limited access to working memory. In a task like this, where

participants are free to list as many features as they want, the total number of listed taxonomic

and ecological features inevitably reflects some mix of knowledge amount and salience (thus

justifying our label for this measure, the “amount of salient knowledge”). In our studies, we

view this combination of knowledge amount and salience as an advantage of the measure

rather than a confound. For our purposes, “amount of salient knowledge” serves as a

comprehensive descriptor of the knowledge available to a participant when they are presented

with the animal name (in this study, in isolation; in the subsequent studies, in the context of

an inductive problem).

Besides having the theoretical reasons to describe possible differences in baseline

availability of taxonomic and ecological knowledge, this study also serves a practical purpose

in the context of this project: to characterize the knowledge that a given population

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(undergraduate students at Northeastern University) has about a specific set of animals that

will be used as stimuli in subsequent studies.

Method

Participants. Twenty nine Northeastern university undergraduates (15 females),

participated in exchange for course credit. Additional data from one subject were discarded,

after she mentioned to the experimenter that since she was not familiar with some of the

animals, she googled their attributes during the experiment using her smart phone.

Materials. The experiment was run using Superlab 4.0.4 software running on a

MacBook laptop. Forty-two animals, all relatively good members of their respective

taxonomic and ecological categories (based on typicality ratings described in Appendix B),

were selected as stimuli. However, feature listing data for one animal were lost due to a

software error, leaving 41 animals. A full list of stimuli can be found in the Appendix B.

Procedure. Participants were tested individually. The instructions were as follows:

This is a very simple experiment to find out what characteristics and attributes come

to people's mind when they think of different animals. You will be presented with 42

animal names, one at a time. Below the animal name, there will be a space where you

can type. Please type in anything you can think of that is generally true of that animal.

There are no right or wrong kinds of attributes; if there's something you know or

believe about the animal and it came to your mind, type it in. However, try not to just

free associate - for example, if an animal just happens to remind you of your spring

break last year, DON'T write down "spring break". For every animal, please type as

many characteristics and attributes as you can.

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The experiment was self-paced; on each trial a participant saw one animal name at

a time, in singular form, presented at the top of the screen, with a space to type below. There

was no limitation on how much text participants could type in. The order of animals was

randomized for each participant. Participants could move on to the next animal by clicking on

the “Next” button at the bottom of the screen; there was no option to go back and revise

previous responses.

Data processing & Coding. Each response consisted of a list of participant-generated

features. Most participants typed features as a list, with one feature per line. For the small

number of participants who wrote descriptions of animals as complete sentences, the

responses were converted into feature lists by the experimenter (e.g. “it’s a big green reptile”

was converted into “big”, “green”, and “reptile”).

In order to systematically quantify participants’ responses, we developed a coding

system characterizing each feature. Based on theoretical principles discussed above, we

divided features into two broad classes. In general, taxonomic features referred to category

membership, perceptual features, or non-interactive aspects of behaviors and physiology. In

contrast, ecological features involved references to animals’ diet, habitat, behavioral,

physiological, or other unspecified interactions with other entities in their environment7.

Coding categories were not mutually exclusive; a given response could receive multiple

                                                                                                               7 Although this may be an atypical way to code feature content, it was important to account for animal features referring to interactions or potential bases for interactions, since interactions are a likely basis for inferences about these animals. Our objective was to apply the same codes to features (this study) and inferences (Studies 3 & 4), in order to be able to examine the relationship between features and inferences in a coherent manner. The coding system was originally developed for quantifying open-ended inferences from one animal to another animal or class of animals, and with minor modifications adapted to feature coding. The detailed description of the inference coding system can be found in the Study 3.

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codes8.  Four or five trained coders coded each response independently, and later met to

discuss and resolve cases on which their original code assignment did not match. Agreement

was achieved on 99% of the cases. The coding scheme is summarized in Table 2.

Table 2. Coding scheme for animal features

Feature Type   Definition   Example  Taxonomic Features      Category membership   Reference to a biological class

or category  [beaver] mammal  

Perceptual feature   Reference to superficial surface appearance  

[butterfly] colorful  

Behavior (non-interactive)   Reference to behaviors that do not imply interaction with other entities  

[duck] poops a lot  

Physiology (non-interactive)   Reference to specific internal organs or systems  

[alligator] cold-blooded  

     Ecological Features      Diet   Reference to animal’s diet or

role in a food chain  [whale] eats plankton [seal] eaten by whales  

Habitat   Reference to animal’s habitat   [deer] forest animal  Behavior (interactive)   Reference to behaviors

involving interaction with other entities  

[bee] pollinates flowers, stings people  

Physiology (interactive)   Reference to physiological interactions (often involving exchange of bodily liquids)  

[rattlesnake] can inject poison through its fangs  

General Interaction   General reference to interaction with other entities  

[koala] likes trees [bee] contact with honey  

     General similarity   Reference to similarity without

specifying details  [deer] horse-like  

     Other (uncodable)   No applicable code, or the

response is too vague to be assigned a specific code  

[clownfish] Nemo [bear] camping  

                                                                                                               8 Multiple-code assignments did not represent ambiguous responses, but rather responses in which participants invoked different kinds of reasons to support a particular inference “because it is also a bird, and it lives in the same area”, or listed a complex characteristic (e.g. “walks on four feet” refers both to the behavior of “walking” and to a structural attribute of having “four feet”).  

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To examine taxonomic and ecological knowledge about animals, the initial coding

categories were collapsed into two broad classes: taxonomic (combining references to

category, perceptual characteristics, non-interactive aspects of behavior and physiology) and

ecological (combining references to animals’ diet, habitat, interactive aspects of behaviors and

physiology, and general interaction). Features coded as general similarity were not included

into either of the categories since such features cannot be unambiguously classified as either

referring to taxonomic or ecological attributes. The precise make up of these categories can be

found in Table 2. If a given feature was coded as any of the component feature types, it was

scored as taxonomic or ecological. Again, these categories were not mutually exclusive.

Table 3. Mean number of features listed per animal (averaged over subjects), by feature type.

Feature Type   Mean(St.Dev.)   Range  Taxonomic   3.10(0.67)   1.58-4.82  

Category membership   0.32(0.20)   0.03-0.68  

Perceptual   1.49(0.59)   0.42-3.24  Behavior (non-interactive)   1.32(0.59)   0.17-2.72  Physiology (non-interactive)   0.13(0.19)   0-0.79  Ecological   1.04(0.41)   0.06-2  

Diet   0.20(0.16)   0-0.58  

Habitat   0.52(0.23)   0.03-1.03  Behavior (interactive)   0.20(0.19)   0-1.10  Physiology (interactive)   0.03(0.11)   0-0.65  General interaction   0.08(0.09)   0-0.48  General Similarity   0.14(0.11)   0-0.51  

Other   0.17(0.11)   0-0.58  

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Results

Data were scored and analyzed by item. To examine the amount of salient taxonomic

and ecological knowledge, we calculated average number of each type of feature generated

for each animal (first, separately for each participant, then averaged across participants). To

examine knowledge salience more directly, via relative order in which taxonomic and

ecological features tend to come to mind, we computed two indices. First, for each animal we

calculated the mean serial position in the participant-generated feature lists at which a

taxonomic feature first appeared, and the mean serial position at which an ecological feature

first appeared on the list. Second, for each animal we calculated the proportion of subjects

whose first feature on the list was taxonomic, and proportion of subjects whose first feature

was ecological9.

On average, 4.2 features were listed for each animal. If taxonomic knowledge is more

abundant and salient than ecological, we expect to see participants listing more taxonomic

than ecological features. This was supported by the data: the mean number of taxonomic

features listed per animal, 3.1 (range across animals, averaged over subjects 1.6-4.8) was

higher than the mean number of ecological features, 1.0 (range 0.1-2.0), paired samples

t(40)=17.53, p<.001, Cohen’s d=2.74). This pattern, of mean number of taxonomic features

exceeding ecological, held true for every animals in the dataset. This result suggests that

within the set of knowledge that becomes available upon the presentation of animal name,

taxonomic information is likely to dominate ecological.

                                                                                                               

9  These  indices  are  not  redundant  because,  in  principle,  the  proportions  of  taxonomic  and  ecological  first  features  do  not  have  to  sum  to  1,  since  one  feature  could  receive  more  than  one  code  (although  this  did  not  happen  frequently),  and  some  codes  (other,  general  similarity)  were  not  included  into  either  category.  

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As mentioned in the introduction to this study, the mean number of listed

taxonomic and ecological features reflects both amount and salience of participants’

knowledge about animals (which serves our larger purposes well). The next analyses,

however, tap more directly into knowledge salience, via examining the relative order in which

taxonomic and ecological features come to mind.

The first index of the relative order was calculated as the mean serial position of the

first taxonomic and ecological features in the participant-generated lists. The analysis of this

measure had to be qualified by the fact that oftentimes, participants listed no ecological

features for animals at all, making calculation of a serial position impossible (for a given

animal, on average, 11 participants out of 29 (38%) did not list any ecological features (with

animals ranging between having 5 to 27 participants listing no ecological features), compared

to on average, less than 1 (.78, or 2.7%) participant listing no taxonomic features per animal

(ranging from 0 to 4 participants, across animals)). Excluding the cases when participants

listed no ecological or no taxonomic features for a given animal, on average taxonomic

features appeared reliably earlier in the list than ecological (taxonomic 1.18 (range 1-1.69),

ecological 2.48 (range 1-4.43), paired samples t(41)=12.04, p<.001, Cohen’s d=1.88),

suggesting that taxonomic knowledge comes to mind more easily than ecological.

This claim was supported by the analysis of first-listed features (the second index of

the relative order in which taxonomic and ecological features come to mind). The first listed

feature was more likely to be taxonomic than ecological: .83 of the first features were

taxonomic, while only .18 of the first features were ecological (paired samples t(40)=18.59,

p<.001, Cohen’s d=2.9). Moreover, this result could not be accounted by a random sampling

from a pool of equally salient taxonomic and ecological features in memory, with the former

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being simply more abundant than the latter. The proportion of taxonomic first features was

larger than expected based on the overall proportion of generated taxonomic features (one

sample test against .74510 t(40)=5.47, p<.001, Cohen’s d=.85); likewise, the proportion of

ecological first features was lower than expected based on the overall proportion of generated

ecological features (one sample test against .2511 t(40)=-3.12, p=.003, Cohen’s d=.49),

suggesting that taxonomic knowledge is not only more abundant, but also more salient than

ecological knowledge (more likely to come to mind early).

The last analysis examined the relationship between the mean number of taxonomic

and ecological features listed per animal in order to addresses two potential concerns with the

experimental procedure and stimuli set. First, participants might have felt constrained to list a

more or less constant number of features per animal, which would force them to choose

among taxonomic and ecological features to fill the spots. This would interfere with our

intention to use the mean number of listed taxonomic and ecological features as the measure

reflecting both natural amount and salience of participants’ knowledge about animals. If

participants were choosing between taxonomic and ecological features to fill the limited

number of slots on the list, we would observe a negative correlation between the number of

taxonomic and ecological features listed per animal. The second concern has to do with the

stimuli selection. If some animals in the stimuli set were more familiar to participants than

others and elicited more features in general, we would expect to see a positive correlation

between the number of taxonomic and ecological features. Luckily, the mean number of

                                                                                                               10  Calculated  as  proportion  of  mean  number  of  taxonomic  features  (3.11)  out  of  mean  number  of  features  listed  per  animal  (4.18)  

11  Calculated  as  proportion  of  mean  number  of  ecological  features  (1.05)  out  of  mean  number  of  features  listed  per  animal  (4.18)  

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taxonomic and ecological features listed per animal showed no relationship (r=.122,

p=.448), suggesting that there were no task demands imposing restrictions on feature list

length, and the animals in the stimuli set were roughly equally familiar to the participants.

Discussion

The main goal of this study was to describe the salient knowledge about animals.

Examination of the participant-generated lists of animal features yielded two main findings.

First, participants possess both taxonomic and ecological knowledge about animals: a sizeable

proportion, about a quarter of all listed features, were ecological, the rest were taxonomic.

Second, taxonomic and ecological knowledge are not equal. For the set of 41 animals from a

variety of biological categories and habitats, taxonomic knowledge appears to be more

abundant and salient than ecological knowledge: in a free feature listing task people listed

more taxonomic than ecological attributes for animals, and they tend to list taxonomic

attributes before ecological attributes. These results support the proposal about taxonomic

knowledge dominating ecological knowledge.

These findings have implications that go beyond description of relative amount and

salience of taxonomic and ecological knowledge. In the context of testing our model of

property effects, once we translate the general proposal about the moderating effects of

property on retrieval of knowledge about premises to retrieval of taxonomic and ecological

knowledge, the expectations about what knowledge will be more active at any given time will

have to be qualified by the baseline differences in availability of these two knowledge types.

Summary

The goal of Studies 1 & 2 was to catalog knowledge recruited by the inferences in

terms of its sources (property, premise category) and type (taxonomic, ecological). Study 1

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demonstrated that property can have a relatively taxonomic or ecological content, likely

based on the statistical beliefs about property distribution across relevant systems of

categories (taxonomic or ecological). Study 2 showed that participants possess both

taxonomic and ecological knowledge about animals, with the former being more abundant

and salient. Now that we have outlined what knowledge serves as an input to inference-

generation processes, we can move on to characterizing these processes.

 

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Chapter IV. Study 3. Property Effects and Knowledge Recruitment in

Induction

In this chapter, we take a step from documenting the ingredients towards describing

the recipe, and examine how information about the premise categories and properties is

combined to produce an inductive inference. Study 3 focuses on the relationship between the

knowledge about premises and the generated inferences, and addresses the question of

whether (and how) this relationship is moderated by the property.

Background

On the proposed account of property effects in inference generation, there are three

retrieval strategies that can yield property effects: retrieving property knowledge only,

retrieving both property and premise category knowledge independently, and retrieving

property and premise category knowledge interactively. The nature of the relationship

between the input (available knowledge) and the output (generated inferences) can be

informative in determining which strategy is used.

As mentioned in Chapter II, knowledge recruitment (what ended up being used to

construct an inference) can be distinguished from knowledge retrieval (what knowledge

stored in long-term memory was activated to detectable levels). Although retrieval (process)

constrains recruitment (outcome), recruitment does not tell a full story about retrieval. Some

knowledge might have been retrieved but not recruited. This issue will be addressed by Study

4. For now we examine the relationship between stored prior knowledge and its recruitment

by inferences as a first rough constraint on the possible retrieval process that might have

generated such a relationship.

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The goals of this study are to examine how knowledge from different sources is

combined in the course of inference generation, and how taxonomic and contextual

(ecological) knowledge about premise categories and properties interact in the course of

inference generation. In addition, this study documents property effects in a new version of

the open-ended hypothesis generation paradigm, in which the premise of inductive questions

contains a single category (e.g. “gene X is found in ducks”). There is an important difference

between this version of the task and the paired-premise paradigm (“gene X is found in ducks

and otters”) that Coley, Vasilyeva & Muratore, 2008 and Coley & Vasilyeva, 2010 used to

document property effects in inference generation. In a paired-premise paradigm, each

premise category can be viewed as a context for the other, which makes it suboptimal as a

first attempt to examine moderating effects of property on knowledge retrieval: we would

need to take into account not only influence of property, but also (possibly interactive)

knowledge retrieval about 2 premise categories. Another new feature of this study is a within-

subject manipulation of the property. Coley, Vasilyeva & Muratore, 2008 and Coley &

Vasilyeva, 2010 used a between-subject design. It remains an open question whether

participants will still vary the generated hypotheses based on the property when they do not

have the opportunity to enter a task set of generating one type of inferences repeatedly.

In this study, participants are asked to generate inferences about 42 animals in an

open-ended inference generation task with single premise categories and a within-subject

manipulation of the property. The generated inferences were examined in two ways that

address two questions. First, we examine whether people generate different inferences about

different properties. Second, to examine whether property affects recruitment of knowledge

about premise categories, we use the feature-listing data collected in Study 2 (the measure of

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available salient knowledge about a set of animals) to predict inferences about the same

animals presented in the context of different properties. This analysis will tell us whether

knowledge about animals predicts inferences about them, whether this relationship between

knowledge and inferences varies with the property, and finally, whether the relationship

between knowledge and inference is based on facilitation on congruent inferences

(taxonomic-taxonomic, or ecological-ecological), or on inhibition of incongruent inferences

(taxonomic-ecological and vice versa), or both.

Relations Among Property, Knowledge, and Inference

When a person is presented with an inductive problem “Parasite Z9 is found in ducks.

What else is likely to have parasite Z9? Why?”, potentially relevant knowledge may include

taxonomic and ecological knowledge about the premise category ducks, and taxonomic and

ecological knowledge about the property has parasite Z9 (beliefs about how parasites are

distributed across taxonomic and ecological systems of classes). Starting from that, the

participant has options of making a taxonomic inference, an ecological inference, both or

neither.

Property only. If participants recruit property knowledge, we expect inferences to

follow the distributional beliefs about the properties, resulting in property effects, i.e.

property-congruent inferences. In the scenario where the property knowledge is the only

knowledge recruited, we expect to see no relationship between prior knowledge about animals

and inferences. However, the absence of such a relationship would not necessarily imply the

“property only” strategy (we come back to this point in the discussion for this chapter). A

more reliable indication of a “property only” strategy would be frequent “generic” inferences,

i.e. inferences without reference to specific features of animals, e.g. “whatever lives in the

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same area with ducks” or “anything else from the same family” (as opposed to “other pond

animals”, “other birds”).

Independent recruitment of property and premise knowledge. In contrast, if

participants recruit both property and premise category knowledge, we expect to see not only

a significant effect of property on likelihood of producing a given inference type, but also a

significant predictive relationship between knowledge and inferences. As shown by Coley &

Vasilyeva (2010) in the paired premise paradigm (inductive questions of the format “Ducks

and otters have disease X, what else?”), beliefs about taxonomic and ecological relatedness of

premise species can predict inferences about them. Specifically, beliefs about taxonomic

relatedness can facilitate taxonomic and inhibit ecological inferences, and beliefs about

ecological relatedness can facilitate ecological and inhibit taxonomic inferences. If in our

single-premise paradigm inferences draw upon knowledge about premise animals, we can

predict a similar pattern. First, we expect category knowledge and inferences of a matching

type to be related via congruent facilitation, where amounts of salient knowledge of a given

type are associated with higher likelihood of congruent inferences (e.g. having a larger

amount of ecological knowledge about ducks relative to toads should be associated with

larger likelihood of making an ecological inference about ducks than toads). When a category

representation is accessed as a part of processing the premise information, the salient

knowledge about that category becomes available, and that available knowledge is likely to be

translated into the inference. Thus, to the extent that ecological knowledge is available, we

can expect it to translate into ecological inferences. Second, we expect category knowledge

and inferences of mismatching types to be related via incongruent inhibition: taxonomic

knowledge could inhibit ecological inferences and/or ecological knowledge could inhibit

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taxonomic inferences. Such inhibitory effects could arise from direct suppression between

different knowledge structures (taxonomic and ecological), or from competition among

taxonomic and ecological pieces of knowledge for the working memory space. (Of course,

knowledge could have both facilitatory and inhibitory effects). If the retrieval of property and

premise category knowledge proceeds independently, there should be no interaction between

knowledge-inference relationship and property.

Interactive recruitment of property and premise knowledge. If recruitment of

category knowledge varies depending on the context provided by the property knowledge, the

property and premise category knowledge are recruited interactively. Of course, we may also

consider the reverse case of interactive recruitment: recruitment of property knowledge

affected by category knowledge, or both knowledge sources affecting each other mutually.

For this project, we selected taxonomic and ecological properties as biasing and not open to

interpretation as possible, and focused on examining unidirectional effects of property on

retrieval of category knowledge, saving detailed investigation other cases for later studies.

If recruitment of premise category knowledge is affected by the property, we expect

the relation of premise category knowledge to inferences to vary depending on the property.

Knowledge recruitment by inferences in the context of a neutral property (property X,

substance in the bloodstream) can be viewed as an unbiased baseline of knowledge

recruitment. This relationship can be positive (congruent facilitation, when knowledge and

inference type match) or negative (incongruent inhibition, when knowledge and inference

types mismatch). When the property is informative, it could strengthen the influence of

congruent knowledge (relative to neutral property), by guiding selective recruitment of that

knowledge to inform inferences. This can be manifested by a stronger positive relationship

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(for matching inference type) or a stronger negative relationship (for mismatching

inference type). For example, if the property is ecological, it may increase the positive

predictive power of eco-knowledge for eco-inferences, whereas a taxonomic property may

increase negative predictive power of taxonomic knowledge for eco-inferences (relative to a

neutral property).

Likewise, an informative property may weaken the effects of incongruent knowledge

on inferences, and thus reduce its predictive power – because in the light of the property,

incongruent knowledge is likely to appear less relevant. For example, if a property is clearly

taxonomic, it may indicate to the participant that ecological features of the animal are

irrelevant to projecting this property, thus minimizing recruitment of ecological knowledge

and its effect on inferences. This can be manifested by weakening a positive relationship (in

case of congruent facilitation between knowledge and matching inference type), or by

weakening a negative relationship (in case of incongruent inhibition between knowledge and

mismatching inference type).

Figure 3 summarizes the questions and predictions about the possible relationship

between knowledge about animals and inferences about them, alongside with possible ways a

property can moderate this relationship.

Finally, we can expect the answers to all these questions to depend on the specific type

of knowledge in question. As shown by Study 2, taxonomic knowledge is more abundant and

salient than ecological (see also Shafto, Coley, & Vitkin (2007), for a review on differences

between taxonomic and ecological knowledge). Thus, we may expect disproportionally strong

effects of taxonomic knowledge on both taxonomic and ecological inferences, compared to

corresponding effects of ecological knowledge.

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Figure 3. Summary of questions and predictions about the relationship between knowledge about animals and inferences about them, as well as moderating effect of property on this relationship. (Only the ecological inferences are shown). See text for explanation. Note: within this diagram, “knowledge” refers to knowledge about premise categories.

Method

Participants

One hundred Northeastern University undergraduates (65 females) participated in

exchange for course credit.

Materials

The experiment was run using Superlab 4.0.4 software running on a MacBook laptop.

The stimuli were open-ended inductive questions of the format “GENE X5 is found in

DUCKS. What else is likely to have gene X5? Why?” We used the same set of 42 animals as

in Study 2. All animals were relatively good members of their respective taxonomic and

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ecological categories (see Appendix B). The stimuli were constructed by combining each

of the forty-two animals, with each of the six properties that were selected based on

participants’ distribution estimates, as described below. Since properties repeated across

animals, each time a property appeared it was accompanied by a unique alphanumeric code

(X5, A3), to encourage participants to treat questions as independent.

Selection of properties. Based on participants’ distribution estimates collected in the

Study 1, we selected the following six properties: has gene X and has X-cells (taxonomically

biasing properties), has flu X and has parasite X (ecologically biasing properties), and has

property X and has substance X in the bloodstream (neutral, or non-biasing properties). To

confirm that the properties were indeed biasing or neutral as expected, we conducted two

additional one-way ANOVAs, one on same biological family estimates, the other one on same

habitat estimates, comparing mean estimates for selected taxonomic (average of gene and

cells), ecological (average of flu and parasite) and neutral (average of property X and

substance). Animals in the same biological family were estimated to share taxonomic

properties (69%) more than neutral properties (56%), which were shared more than ecological

properties (32%) (F=36.98, p<.0001, η2partial =.60, all pairwise Tukey/Kramer comparisons

p<.05). In contrast, animals living in the same area were expected to share ecological

properties (58%) more than neutral properties (47%) which were shared more than taxonomic

properties (32%) (F=17.96, p<.0001, η2partial =.42, all pairwise Tukey/Kramer comparisons

p<.05).

In addition, for every one of the six properties, we counted the number of participants

whose percentage estimates for the animals in the same biological family were higher than for

the animals in the same habitat, indicating that the property was viewed as distributed more

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along taxonomic lines relative to ecological lines, as well as the number of participants for

whom the direction of difference was reversed, indicating ecological distribution of the

property. For cell, 23 out of 26 participants gave higher estimates in the “biological family”

question than in the “habitat” question, which significantly deviated from chance indicating

preference for taxonomic distribution (χ2=15.39, p<.0001). Gene was also viewed as

taxonomically distributed by the majority of participants, 21 out of 26 (1 participant assigned

equal percentages12) (χ2=11.56, p=.0007). In contrast, for flu and parasite, the majority of

participants assigned higher ratings in “habitat” question than “biological family” question

(flu: 23 out of 26, χ2=15.39, p<.0001; parasite: 19 out of 26, and 1 participant assigned equal

percentages (χ2=6.76, p=.0093); in other words, most participants believed that animals in the

same habitat were more likely to be homogeneous in terms of a novel flu or parasite than

animals in the same biological family. Finally, for substance and property X, participants did

not consistently assign higher estimates in one question or the other: for both properties, two

participants assigned equal percentages; for Substance, 15 out 26 (χ2=1.5, p=.22), and for

property X, 14 out of 26 (χ2=.67, p=.41) gave higher estimates in “biological family” than

“habitat” question, suggesting that across our participants there was no systematic preference

of taxonomic over ecological distribution for these properties.

Thus, gene and cells were biased taxonomically, both in relative (taxonomic >

ecological estimates) and, as shown in Study 1, absolute (compared to 50%) sense. Flu and

parasite were biased ecologically, both in relative (ecological > taxonomic estimates) and

                                                                                                               12  Participants  assigning  equal  percentages  in  response  to  both  questions  were  not  included  in  the  analyses.    

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absolute (compared to 50%) sense. Property and substance proved to be neutral, or non-

biasing in terms of distributional beliefs about them.

Design

The main independent variable, property, had six levels, nested in three property types

(two ecological properties: flu, parasite; two taxonomic properties: gene, cell; two neutral

properties: substance, property) and was manipulated within subjects. Henceforth, we discuss

the property manipulation in terms of the three property types: ecological, taxonomic, and

neutral. Each participant was presented with 42 questions, one about each animal, seven

questions per property. Six blocks of seven animals rotated through the property in a Latin

square design, creating six versions of the experiment. Thus, within a version each subject

was presented with all animals and all properties, and across versions, each animal was

presented with each property. The order of questions was randomized for each participant.

The dependent variables were the frequencies of taxonomic and ecological inferences, based

on coded open-ended inferences and justifications generated by participants.

Procedure

Participants were tested individually. The general instructions read as follows:

In this experiment, you will read a number of statements about different kinds of animals. Your task will be to come up with other animals or general kinds of organisms that these statements could apply to.

For example, in one question, you may learn that one kind of animals has a certain gene. In a different question, you may learn that another kind of animal has a certain parasite, or a substance in its bloodstream, or cells, or a flu, or just some unspecified property. ALL you know about the gene, or cells, or the parasite, or the substance in blood, or the flu, or the property, is that the listed animal has it. You will be asked to

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list other animals or kinds of organisms you think might also have it, as well as reasons for your answers.

There are no right or wrong answers; we are only interested in what you think. Please consider each question independently. That is, the information provided for an animal in one question should not affect your judgments about a different question.

You will be asked 42 questions total. You will see one question at a time, and below the question, there will be a space where you can type your response. After listing the answers, remember to give a one- or two-sentence reason for each of them, so we know what you were thinking while responding to the question.

During the experiment, participants saw one question at a time, with a space to type in

their responses. The questions were phrased as follows: “GENE T5 is found in DUCKS. What

else is likely to have gene T5? Why?” Neither the amount of text they could type nor time to

spend on a given question were limited. Participants could move through questions by

clicking on the “Next” button on the screen; they could not go back and change their previous

responses.

Results

Data processing & Coding

Each response consisted of a list of participant-generated conclusion categories and an

explanation for why a property true of the premise category was likely to be shared by those

categories. Inferences were coded in two ways. First, based on the type of relation between

the premise and conclusion, inferences were coded as taxonomic and/or ecological. Second,

based on the absence or presence of references to specific attributes of the premise animal,

inferences were coded as generic or non-generic.

Taxonomic vs. ecological inferences. In order to systematically quantify participants’

responses, we used a coding system to characterize the basis of each inference. Responses

were coded based on the justification generated by the participant specifying relevant relation

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between experimenter-generated premise category and participant-generated conclusion

(not based on the animals that participant mentioned). Responses were initially coded into

fine-grained categories described in Table 4. Coding categories were not mutually exclusive;

a given response could receive multiple codes13. Four or five trained coders coded each

response independently, and later met to discuss and resolve cases on which their original

code assignment did not match. Consensus (defined as agreement between N-1 coders) was

reached on over 99% of the codes. During the coding, for any given inference the coders had

access to the premise animal, but not to the property condition.

Table 4. Coding scheme for characterizing basis of inference.

Basis of Inference   Definition   Example  

Taxonomic Inferences      

Category membership   P and C belong to the same class or category  

[Bee] Other insects such as mosquitoes and ladybugs  

Perceptual similarity   P and C are similar with respect to some aspect of superficial surface appearance  

[Panda] Zebras – black and white.  

Behavioral similarity   P and C are similar with respect to some aspect of behavior  

[Butterfly] Moths because both fly and stick to walls.  

Physiological similarity   P and C are similar with respect to specific organs or systems  

[Monkey] Apes. They (…) have similar immune systems.  

Ecological Inferences      

Similar diet   P and C are similar with respect to diet or eating the same kind of thing  

[Panda] Sheep might also have Flu D6 because they both consume mostly plants.  

Similar habitat   P and C share similar or the same habitat without specification that the property is transmitted via

[Camel] Rattlesnakes, lizards, because they all live in the desert  

                                                                                                               13  Multiple-­‐code  assignment  did  not  represent  ambiguous  responses,  but  rather  responses  in  which  participants  invoked  different  kinds  of  reasons  to  support  a  particular  inference.    

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habitat  

Predatory interaction   P and C interact with respect to predation, that is, P eats or is eaten by C  

[Ant] An anteater because it would eat the ant with the parasite

[Bear] berries, small animals – food sources for bear  

Habitat interaction   P and C share or pass a property by coming into contact through the same habitat  

[Beaver] Fish. If the beaver were to leave the substance in the water it was working in the fish could contract the substance.  

Behavioral interaction   P and C interact via some aspect of behavior  

[Dragonfly] Big animals that dragonflies land on  

Physiological Interaction   P and C interact va some aspect of physiology (usually involving exchange of bodily liquids)  

[Bee] Humans. Bee’s venom transfers cells to the human.

[Kangaroo] Kangaroo milk that is fed to the young because it passes through the mothers body  

General Interaction   P and C interact without further specification of the nature of the interaction  

[Rattlesnake] I think flu G5 would also be found in humans, because humans sometimes have contact with rattlesnakes, and the flu may be able to pass between species.

[Ants] Every organism that comes into contact with the ants... because ants are everywhere.  

General similarity   P and C are alike or have similarities without further specifying the nature of the similarity  

[Hyena] Panther because they are similar.  

Other (uncodable)   No explicit justification for the inference / no applicable code for the justification / the response is too vague to be assigned a specific code / participant does not know the animal  

[Shark] Stingrays.

[Monkey] People? Scientists say so..

[Scorpion] Beetles and spiders. They both come to mind when I think of scorpions  

Note: P = (given) premise category; C = (participant generated) conclusion categories

To examine the effects of property on the two broad classes of inferences discussed

above, we collapsed the initial coding categories into those that reflect reasoning based on

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common category membership, appearance, or other shared intrinsic features (henceforth,

we will refer to these as taxonomic inferences), those that reflect reasoning based on extrinsic

similarities, shared contextual features like similar diet or habitat, and on some causal

mechanism including co-occurrence in space and time or direct contact through behavior or

predation (ecological inferences). The precise makeup of these broad categories can be found

in Table 4. If a given response was coded as any of the component inference types, it was

scored as a taxonomic or ecological inference. Again, these categories were not mutually

exclusive. For example, a response based on category membership and shared habitat

interaction would be counted both as a taxonomic inference and an ecological inference. For

example, a projection from duck to "geese, because they are also birds" would be classified as

taxonomic; to "geese, because they also live in water" would be classified as ecological; to  

"geese, because they are also aquatic birds" would be classified as both ecological and

taxonomic; and finally, an projection to “geese” with no further justification would get coded

as “other” (because it would not be possible to tell if the participant was relying on taxonomic

relations, ecological relations, or free-associating). The two general categories, taxonomic and

ecological inferences, accounted for 88% of all responses.

For each animal, relative frequency of taxonomic inferences was calculated as

percentage of subjects making a taxonomic inference about that animal, separately for each

property type condition. The three property types were taxonomic (collapsing over gene and

cell), ecological (collapsing over flu and parasite), and neutral (collapsing over property X

and substance in the bloodstream). Relative frequency of ecological inferences was computed

in the same way. All the analyses on proportions reported below were conducted on arcsine-

transformed data, while the reported means are non-transformed and presented as percentages.

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Since items (animals) were held constant across Studies 3 (inference-generation) and 2

(feature listing) and participants varied, all analyses were done by-item, unless noted

otherwise.

Table 5. Mean percentage of participants making each type of inference, per animal, by property type. Standard deviations are shown in brackets.

  Overall   Ecological

Property  Neutral Property  

Taxonomic Property  

Taxonomic Inference   59 (10)    

47 (13)  

59 (13)  

70 (12)    

Category   41 (15)  

35 (18)  

39 (20)  

49 (17)  

Perceptual similarity   16 (8)  

11 (9)  

17 (13)  

21 (13)  

Behavioral similarity   13 (9)  

10 (9)  

16 (13)  

14 (12)  

Physiological similarity   6 (5)  

4 (5)  

7 (9)  

7 (8)  

Ecological Inference   48 (12)    

64 (14)  

47 (17)  

33 (14)    

Similar diet   8 (5)  

9 (9)  

10 (10)  

4 (6)  

Similar habitat   29 (13)  

36 (17)  

27 (17)  

24 (16)  

Predatory interaction   13 (4)  

20 (12)  

14 (11)  

5 (5)  

Habitat interaction   1 (1)  

3 (4)  

0 (2)  

0 (1)  

Behavioral interaction   2 (2)  

3 (5)  

1 (3)  

1 (2)  

Physiological interaction 0 (0)

0 (1)

0 (1)

0 (1)

General interaction 1 (1)

3 (4)

0 (1)

0 (1)

General similarity 9 (5)

8 (7)

9 (9)

11 (9)

Other 4 (2)

3 (4)

3 (5)

5 (5)

Note: relative frequencies of composite taxonomic and ecological inferences do not necessarily equal the sum of frequencies of their components, because one inference could receive multiple scores.

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Generic inferences. Inferences were coded as generic if they did not contain any

references to the specific attributes of the premise category, i.e. inferences that do not show

signs of recruiting knowledge about specific animal, such as “anything that eats them”,

“whatever lives in the same place”, “anything from the same biological family”. If by looking

at an inference alone it was impossible to tell what the premise animal was, it got coded as

generic. All inferences (taxonomic, ecological, based on general similarity or “other”) were

coded for generic format.

For each animal, relative frequency of generic inferences was calculated as percentage

of subjects making a generic inference about that animal – overall and separately for each

property type condition.

Relative Frequency of Inferences

Undergraduates generated inferences based on both taxonomic and ecological

relations, although the former were more frequent. Overall, 59% of inferences were

taxonomic (e.g., an inference from a bee to “other insects such as mosquitoes and ladybugs”),

and 48% of inferences were ecological (e.g., an inference from an ant to “an anteater because

it would eat the ant with the parasite”) (paired samples t(41)=3.47, p=.001, Cohen’s d= .54).

Generic inferences were infrequent, and accounted for only 3.8% of all inferences.

Generic inferences were more frequent for ecological inferences (6.3% of which were

generic) than for taxonomic inferences (only 1.8% generic; paired samples t(41)=6.508,

p<.001, Cohen’s d=1.00).

Effects of Property on Inferences

Property affected the relative frequency with which participants generated taxonomic

and ecological inferences (see Figure 4). A one-way repeated measures ANOVA on relative

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frequency of taxonomic inferences showed a significant effect of property type (F(2,

82)=64.64, p<.001, η2partial=.61): taxonomic inferences were most frequent when the property

was taxonomic, followed by neutral and ecological properties (all planned pairwise

comparisons p’s<.001). Property also affected relative frequency of ecological inferences

(one-way repeated measures ANOVA F(2, 82)=95.05, p<.001, η2partial =.70): ecological

inferences were most frequent when the property was ecological, followed by neutral and

taxonomic properties (all planned pairwise comparisons p’s<.001). Moreover, planned

comparisons across inference type indicated that for ecological properties, ecological

inferences were more frequent than taxonomic (paired samples t(41)=4.41, p<.001, Cohen’s

d=.68), whereas for taxonomic properties the difference was reversed (paired samples

t(41)=10.47, p<.001, Cohen’s d=1.62). When the property was neutral, taxonomic inferences

were more likely than ecological (paired samples t(41)=3.00, p=.005, Cohen’s d=.46),

although the gap between inference type frequencies was smaller than when the property was

taxonomic.

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Figure 4. Property effects on relative frequency (percentage of participants, per animal) of ecological and taxonomic inferences. Error bars represent one standard error of the mean14.

We did not have specific expectations about the effect of property on the frequency of

generic inferences, but we found that it did vary with the property (repeated measures

ANOVA F(2,82)=3.221, p=.045, η2partial =.073). Generic inferences about ecological

properties were more frequent than taxonomic properties (5.3% vs. 2.7%, paired samples

t(41)=2.315, p=.026, Cohen’s d=.36). Generic inferences about neutral properties fell

inbetween and did not differ from the other properties (4.5%, paired samples vs. ecological

t(41)=.944, p=.351; vs. taxonomic t(41)=1.62, p=.113).

Relationship Between Premise Category Knowledge and Inferences

In this analysis we used data from the feature-listing task (Study 2) to predict

inferences collected in Study 3, in order to determine whether there is a relationship between

category knowledge and inferences. The mean number of participant-generated taxonomic

and ecological features of forty-one animals collected in Study 2 served as a measure of

                                                                                                               14  The  bars  that  appear  not  to  differ  across  inference  type,  i.e.,  ecological  inferences  about  ecological  properties  and  taxonomic  inferences  about  neutral  properties  indeed  did  not  differ  (paired  samples  t(41)=1.24,  p=.223,  Cohen’s  d=.19).  Likewise  ecological  inferences  about  neutral  properties  were  as  frequent  as  taxonomic  inferences  about  an  ecological  property  (paired  samples  t(41)=.01,  p=.992,  Cohen’s  d=.001).  

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available salient knowledge, the predictor variables. The relative frequencies of participant-

generated taxonomic and ecological inferences about same forty-one animals (calculated

either collapsing across property or separately for ecological, neutral and taxonomic

properties) served as predicted values. The first set of analyses addressed the question of

whether knowledge predicts inferences.

Taxonomic inferences. In two simple regressions using mean number of taxonomic

and ecological features listed per animal as predictors, and relative frequency of taxonomic

inferences (collapsed across property), taxonomic inferences were not significantly predicted

by either taxonomic (R2=.010, β=.102, p=.526) or ecological (R2=.054, β= -.233, p=.142)

knowledge.

Ecological inferences. Ecological inferences showed a marginally significant

tendency to decrease (incongruent inhibition) with increasing amounts of taxonomic

knowledge about an animal (R2=.079, β= -.281, p=.075). At the same time, ecological

inferences were reliably predicted by ecological knowledge: the mean number of ecological

features listed for an animal positively predicted the relative frequency of ecological

inferences about it (congruent facilitation, R2=.116, β=.340, p=.030).

So far, the low frequency of generic inferences and the presence of a predictive

relationship between premise category knowledge and ecological inferences (albeit for

ecological inferences only) suggest that inferences recruited knowledge not only about

property, but also about premise animals. Next, we examine possible interactions between

knowledge from these two sources.

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Interactions Between Property and Premise Category Knowledge

To examine the relationship between knowledge and inferences in more detail, we

conducted 12 simple linear regressions. In one triplet of regressions, ecological premise

category knowledge served as a predictor of ecological inferences, separately for ecological,

neutral and taxonomic property. The other triplets covered the three remaining combinations

between knowledge type and inference type, broken down by the property.

Ecological inferences. The standardized regression coefficients are shown in Figure

5. For ecological inferences (panel a.) the predictive power of knowledge appears to vary with

the property. Ecological knowledge was overall a positive, albeit non-significant, predictor of

ecological inferences when participants were reasoning about a neutral (R2=.057, β=.239,

p=.132) or taxonomic (R2=.034, β=.242, p=.128) property, but its contribution was

strengthened and became significant in the presence of an ecological property (R2=.124,

β=.352, p=.024).

When we examined the contribution of taxonomic knowledge to ecological inferences,

overall larger amounts of taxonomic knowledge were associated with lower frequency of

ecological inferences (all β’s are negative), and this relationship again varied with property.

Taxonomic knowledge inhibited eco-inferences marginally when the property was neutral

(R2=.076, β= -.276, p=.08), and reliably so when it was intensified by a taxonomic property

(R2=.12, β= -.346, p=.027). Relative to taxonomic and neutral properties, ecological property

largely neutralized the inhibitory effect of taxonomic knowledge on eco-inference (R2=.007,

β= -.083, p=.605).

Taxonomic inferences. Consistent with the overall regressions presented above,

taxonomic inferences were not significantly predicted by knowledge (all p’s >.121). However,

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as shown in panel b. of Figure 5, the sign and ordering of predictors follow the predicted

pattern of strengthening effects of congruent knowledge and weakening effects of incongruent

knowledge (illustrated in Figure 3) with two exceptions – the regression coefficient for

taxonomic knowledge predicting taxonomic inferences in the context of an ecological

property is a very small negative (rather than positive) value, and the predictive value of

taxonomic knowledge for taxonomic inferences in the presence of taxonomic property drops

relative to neutral property.

To further test for moderating effects of property on knowledge-inference relationship,

we constructed 95% confidence intervals around the slopes for all 12 regressions. Although in

the analysis reported above some of the β’s within each property triplet came out significant,

and some did not, suggesting that property moderates the knowledge-inference relationship, in

this analysis the confidence intervals of all three slopes within each property triplet

overlapped, failing to support the claim about moderating effects of property on recruitment

of category knowledge.

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Figure 5. Ecological and taxonomic knowledge about animals (measured as a mean number of ecological and taxonomic features) as predictors of relative frequency of taxonomic and ecological inferences about these animals, separately for ecological, neutral and taxonomic properties. *p<.05, +p<.1, °p<.15

a. Ecological inferences predicted by the mean number of ecological and taxonomic features.

b. Taxonomic inferences predicted by the mean number of ecological and taxonomic features.

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Discussion

This study examined the relationship among property, premise category knowledge

and inferences as a first approach to explore how property exerts its effect on inferences.

Within the larger context of this project, the goal of this study is to examine recruitment of

knowledge by inferences in order to provide constraints on the underlying retrieval processes.

The question of main interest was how knowledge from different sources is recruited and

combined in inferences. We examined three possible scenarios: only property knowledge is

recruited; both property and premise knowledge are recruited independently; both property

and premise knowledge are recruited interactively. We tested these scenarios by examining

how taxonomic and ecological types of knowledge about premise categories and properties

interact in inferences about biological world.

To address these questions, we examined how the knowledge that participants have

about a set of animals (feature lists collected in the Study 2) is recruited by the inferences

another group of participants generated about this very same set of animals, and how property

might moderate this relationship. The inferences were collected in a novel open-ended

inference generation paradigm with single-category premises and property manipulated within

subjects. In this task, participants were forming hypotheses about other species of animals that

might have a property based on the evidence provided by the premise animal category and the

property.

Taxonomic vs. Ecological Inferences

Taxonomic inferences were more frequent than ecological inferences. This held for

overall percentages of inferences as well as for inferences about neutral properties. This fits

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with the findings of Study 2 demonstrating that taxonomic knowledge about animals is

more abundant and salient.

Despite the dominance of taxonomic inferences, ecological reasoning was also quite

common. In part, this might be due to the open-ended format of the task. Coley & Vasilyeva

(2010) reported an increase in ecological reasoning in open-ended inference generation

relative to typical pattern reported in argument-evaluation studies of induction, and speculated

that this might be due to generic inferences. In this study we had the opportunity to quantify

this claim. Indeed, it appears that ecological inferences benefit from the possibility to

formulate generic inferences – more so than taxonomic inferences (6.3% of ecological

inferences were generic, compared to 1.8% of taxonomic inferences). Moreover, generic

inferences were more frequent when participants were reasoning about ecological than

taxonomic properties. It appears that, when participants intended to make an ecological

inference (perhaps, based on the property they were reasoning about) but lacked specific

ecological knowledge about the animal, they were able to make up for it with generic

inferences.

Property Effects on Inferences

Overall, property had a profound effect on the inferences participants generated:

reasoning about genes and cells biased participants towards taxonomic inferences, while flu

and parasite promoted ecological (interaction and spatial contiguity-based) inferences. This

replication of property effects is particularly impressive in this novel experimental setup with

property varied within subjects. Even when participants did not have a chance to enter a task

set of producing the same type of inference about the same property repeatedly, they varied

their inferences based on property from one question to another. This suggests that the

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variability in inferences was driven by the specific attributes of each inductive question that

participants were presented with. This brings us to the main question of the study: what

knowledge about the premise did participants recruit to make inferences?

Recruitment of Property Knowledge

The bare minimum that participants need to do in order to generate property-sensitive

inferences is to access their knowledge about properties. As discussed above, one possibility

that this is the only source of knowledge participants recruit. The “property knowledge only”

strategy predicts a high percentage of generic inferences and absence of predictive

relationship between animal knowledge and inferences. This strategy was ruled out by the low

frequency of generic inferences, and the presence of the relationship between knowledge

about animals and ecological inferences.

Recruitment of Premise Category Knowledge

When participants generated ecological inferences, they used both taxonomic and

ecological knowledge about premise animals. Ecological inferences were facilitated by

congruent knowledge about premise categories, and inhibited by incongruent knowledge. In

other words, higher amounts of salient ecological knowledge about animals facilitated

ecological inferences about them, and higher amounts of taxonomic knowledge about animals

inhibited ecological inferences.

The overall pattern of congruent facilitation and incongruent inhibition held for

taxonomic inferences as well, although it was weaker and did not reach significance. Even

though evidence of relationship between knowledge and inferences was present for ecological

inferences but absent for taxonomic inferences, we would like to argue that (1) premise

category knowledge did get recruited by taxonomic inferences, but our main measure of

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recruitment failed to detect it, and (2) the positive finding about knowledge recruitment

by ecological (albeit not by taxonomic) inferences still provides a useful constraint on the

general underlying retrieval process.

First, even in the absence of statistical support of category knowledge recruitment by

taxonomic inferences, we have reasons to believe such knowledge was nevertheless recruited.

If participants were not using their knowledge of animals at all, their taxonomic inferences

would be predominantly generic, i.e. driven exclusively by property information. However,

only 1.8% of taxonomic inferences were generic, providing evidence against this possibility.

Moreover, closer examination of taxonomic inferences showed that a majority of them were

categorical, i.e. based on common membership in a taxonomic category. Prior literature on

conceptual representation shows that objects tend to be good members of only one taxonomic

category (within a given level of a taxonomic hierarchy) (Ross & Murphy, 1999). Moreover,

we took special effort to select animal stimuli that would be typical representatives of only

one taxonomic category. If taxonomic inferences were predominantly drawing upon category

membership (and less so on other types of taxonomic knowledge), and if number of

taxonomic categories does not vary much, it is not surprising that the variability in total

number of taxonomic features was not a good predictor of taxonomic inferences – even

though it appears to have been recruited.

Finally, we have reasons to believe that the analyses we conducted might have failed

to detect knowledge recruitment in some cases because the measure of knowledge we used

was not an optimal predictor of inferences. Although amount of salient knowledge about

animals does capture the knowledge that becomes available to a person as they process the

premise of inductive argument, variability in this measure may not necessarily result in

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variability in inferences. Strictly speaking, insensitivity to amount of salient knowledge

(measured as number of features) does not mean that the knowledge is not used at all – it

could be always recruited to the same stable extent, regardless of how much of it is available

per animal. This could explain one puzzling finding: the weak or nearly absent predictive

value of prior knowledge about premise animals in the context of reasoning about a neutral

property. Neutral properties did not provide participants with much information that could be

used to guide inferences (“property X” is not helpful, and “substance X in the bloodstream” is

highly ambiguous – is it internally-produced or picked up from the environment?). If

participants did not reliably use their knowledge about animals to reason about such

properties, what could have they possibly used? One possibility is that they relied on some

other knowledge type, not accounted by taxonomic and ecological categories, which seems

unlikely given that most animal features spontaneously generated by participants were

successfully accounted for by taxonomic and ecological coding categories (on average, less

than one (.17) feature per animal was coded as neither). It seems likely that using the “mean

number of taxonomic and ecological features” as predictors of inferences might have missed

category knowledge recruitment in this case. Alongside with other suggestive evidence, the

lack of relationship between knowledge and inferences for uninformative properties points to

the same idea – that the category knowledge had been recruited, even though the regression

analyses failed to detect them.

In addition, we find the logical argument against “property only” retrieval strategy

more compelling than the argument in support of this strategy. Essentially, this strategy makes

two predictions: (a) absence of a relation between knowledge and inferences, and (b) a large

amount of generic inferences. Our results provide evidence for (a) but not (b). Evidence for

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(a) supports the “property only” strategy via affirming the consequent fallacy15. But

evidence against (b) denies the “property only” strategy by a valid logic of modus tollens.

Thus, based on the two pieces of evidence about taxonomic inferences - absence of

relationship between knowledge and inferences (a) and paucity of generic inferences (not-b) -

we prefer to draw the overall conclusion that the “property only” strategy lacks support, and

the category knowledge is in fact recruited.

Finally, of course it is also possible that participants could selectively switch retrieval

strategy from question to question, sometimes retrieving property knowledge only (and

making taxonomic inferences showing no signs of category knowledge recruitment),

sometimes retrieving both property and premise category knowledge (and making ecological

inferences with clear signs of category knowledge recruitment). Although we cannot

completely discard this possibility, we do not find it very plausible.

In sum, we argue that, despite the absence of a predictive relationship between

premise category knowledge and taxonomic inferences, people recruited both knowledge of

the property and the premise category to generate inferences.

The second point that we would like to argue for, despite the absence of predictive

relationship between knowledge an taxonomic inferences, is that the positive evidence for

recruitment of premise category knowledge by ecological inferences still supports general

conclusions about retrieval. If we observe that some knowledge is recruited, this implies that

it must have been retrieved. The route from retrieval to recruitment, however, is less direct: a

piece of knowledge might have been retrieved, but it does not necessarily imply that we are

                                                                                                               15  Not  to  question  the  entirety  of  experimental  method  here,  which  by  and  large  accepts  the  logic  of  affirming  the  consequent.  

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going to see signs of its recruitment. For example, the knowledge could have been judged

irrelevant later in the processing and got discarded, not making it to the final product of an

inference (i.e. was not recruited). Or it might have made its way to the inference, but our

measure of recruitment could have failed to detect it. Thus it is not surprising that the

presence or absence of evidence for knowledge recruitment can vary with the outcome - in

our case, between ecological and taxonomic inferences. What matters in this case is the

“existence proof” of a relationship between category knowledge and inferences. Since we see

such a relationship of taxonomic and ecological category knowledge with ecological

inferences, we can claim that such knowledge was retrieved, whether or not we observe this

relationship for taxonomic inferences.

Recruitment of Property and Premise Category Knowledge: Independence or

Interaction?

The question of whether property moderates recruitment of knowledge about premise

categories remains somewhat open. There was a tendency of property to strengthen effects of

matching knowledge type on ecological inferences, and weaken effects of mismatching

knowledge type on ecological inferences. Ecological knowledge reached significance as a

positive predictor or ecological inferences only when the property was ecological as well, and

taxonomic knowledge only served as a significant negative predictor for ecological

knowledge when the property was taxonomic as well, reinforcing the inhibitory potential of

the taxonomic knowledge on ecological inferences. However, the statistical support for these

claims was not solid (the analysis of slope confidence intervals failed to show significant

differences in category knowledge--inference relationship based on property). In light of this

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evidence any claims about interactive recruitment of property and premise category

knowledge will need to be interpreted with caution.

In sum, we see that inferences recruit prior knowledge both about properties and

premise animal categories, and we have tentative evidence that recruitment of category

knowledge might be moderated by the property: when a property matches categorical

knowledge in type, such knowledge has slightly higher chances to affect ecological

inferences. Such evidence of interactive recruitment of premise category and property

information by ecological inferences is consistent with the proposal about interactive retrieval

as a locus of property effects in inference generation (one of the three scenarios we outlined at

the outset of this discussion). Thus, with this study we take the first step towards specifying

the mechanism of knowledge retrieval in induction.

Study 3 raises some specific questions that examination of retrieval can address. For

example, in this study we see no relations between the amount of taxonomic knowledge and

the likelihood of inferences in the presence of an ecological property, and (to a lesser extent)

no relation between ecological knowledge and inferences in the presence of a taxonomic

property. Did property completely suppress retrieval of this knowledge, or is this knowledge

initially retrieved but is later judged to be irrelevant and is discarded? For example, when

participants are asked about ducks having a certain parasite, do they not retrieve the facts that

ducks are birds and they have wings and beaks and feathers (taxonomic information) from

their long-term memory at all, or do they retrieve this knowledge but discard it after they

judge it irrelevant for reasoning about a parasite? These and other questions about retrieval

will be examined more directly in the Study 4.

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In sum, the amount of salient knowledge measured as the mean number of listed

features may have its limitations, and future studies may want to use a different measure of

knowledge to examine knowledge recruitment – perhaps based on a more labor-intensive

matching of specific features between generated feature lists and inferences. However, in this

study this measure served its purpose reasonably well, providing some suggestive evidence

that property and category knowledge interact in the process of inference generation. This

study examined knowledge recruitment by inferences, but left some important questions

about knowledge retrieval open. The following Study 4 addresses these questions by mapping

out activation of premise category knowledge in the course of inference generation “in real

time”.  

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Chapter V. Study 4. Property Effects over the Time Course of Premise

Category Knowledge Retrieval in Induction.

Study 3 examined the relationship between premise category knowledge and inductive

inferences, and found some, albeit not very convincing, signs of interactive recruitment:

property may moderate the contribution of animal knowledge to inferences, by promoting

recruitment of property-congruent knowledge and suppressing recruitment of property-

incongruent knowledge (although this effect was limited to knowledge recruitment by

ecological inferences). Study 4 is an attempt to localize this property-premise interaction in

retrieval. Specifically, the goal is to describe the contextual effect of property on retrieval of

property-congruent and –incongruent category knowledge by examining the time course of

knowledge activation during inference generation.

Study 4

In order to examine the time-course of knowledge retrieval, this experiment borrows

the cross-modal priming paradigm from Swinney’s (1979) study of the mechanism and time

course of context effects in ambiguity resolution during sentence comprehension. Since we

are using a similar experimental technique, we will describe this study in some detail here.

The main question was about retrieval of different meanings of ambiguous words: when such

words are encountered in a context strongly biasing towards one of the meanings, are the

inappropriate meanings retrieved at all? In Swinney (1979) participants listened to sentences

containing an ambiguous word. The first part of the sentence (context) either helped to

disambiguate the word or was uninformative (e.g., if the ambiguous word were “port”, the

preceding context would either suggest the “wine” meaning (“the waiter poured the port into

the glass”), or the “nautical” meaning (“the ship arrived at the port in the afternoon”), or be

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neutral (“nobody knew where the port was”)). At the end of the ambiguous word (at

varying ISIs, interstimulus intervals) participants were visually presented with a lexical

decision task (i.e. shown a letter sequence and asked to decide whether it was a word or a

non-word). On critical (i.e. word) trials, the visually presented word could be contextually

related (“wine”, if the context suggested the “wine” meaning), contextually inappropriate

(“ship”), or unrelated (“book”). The decision time was taken as a measure of the activation of

each meaning of the ambiguous word during ambiguity resolution. Swinney found that

immediately after the ambiguous word was presented, recognition of visual targets related to

both appropriate and inappropriate readings of the ambiguity was facilitated relative to

baseline neutral context, even when the ambiguous word was preceded by a strong biasing

context. However, as soon as 700msec later, only the relevant meaning reaction time showed

advantage relative to inappropriate and unrelated word, that did not differ from each other.

This suggested that all meanings of an ambiguous word are initially activated, and context

aids selection of the relevant meaning, as opposed to guiding selective activation of the

relevant meaning from the beginning.

There are multiple parallels between the questions addressed by Swinney (1979) study

and knowledge retrieval in course of induction. Multiple meanings of an ambiguous word are

akin to multiple sets of knowledge about a premise, which provide potential bases for

projection in induction. In sentence comprehension, the biasing context is the sentence or

phrase containing the ambiguity, that can resolve ambiguity by indicating the correct

interpretation compatible with the rest of the sentence. In induction, the biasing context is the

property that can reduce number of relevant attributes of the premise and, consequently, the

number of plausible projection hypotheses.

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However,  the  analogy  between  lexical  access  and  selective  induction  has  its  

limit:  ambiguity  resolution  in  lexical  access  typically  requires  commitment  to  a  single  

meaning,  whereas  in  induction,  one  can  legitimately  make  a  mixed  inference,  referring  

to  multiple  kinds  of  knowledge.  An  projection  to  an  ecologically  related  species  -­‐  a  

predator  or  animal  sharing  a  habitat  -­‐  is,  strictly  speaking,  not  incompatible  with  

projecting  a  gene,  it  is  simply  less  likely.  Although  such  probabilistic  nature  of  property  

information  adds  noise  to  selective  knowledge  retrieval,  it  does  not  invalidate  the  use  of  

Swinney’s  approach  to  studying  effects  of  biasing  properties  on  induction.

To examine whether the property provides a context that biases retrieval of knowledge

about premises, and if yes, how this bias acts over time, this experiment employed an adapted

version of Swinney’s task. Participants were auditorily presented with a property and an

animal premise in an open ended inductive question, and were asked to generate possible

conclusions. The property varied between taxonomically biasing (gene, cells), ecologically

biasing (flu, parasite), or non-biasing (substance, property X). In addition, upon hearing the

property and animal, participants were presented with a lexical decision task involving targets

related to salient taxonomic or ecological knowledge about the premise animal. For example,

a participant might hear a property, gene, followed by the animal, duck, and, after a varying

time interval, see on the screen a taxonomic target bird, or an ecological target pond, or an

unrelated target sofa, or a non-word soach. Their task was to decide whether the letter

sequence is a word or a non-word. The time to respond to the related targets is taken as a

measure of activation of taxonomic or ecological knowledge about the premise animal. If

corresponding knowledge about duck is activated, we expect decisions about related targets

(bird and pond) to be faster than about unrelated targets (sofa). If property moderates

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knowledge retrieval, we would expect decisions about ecologically related targets (pond)

to be faster in the presence of an ecological property (congruent facilitation) and/or slower in

the presence of a taxonomic property (incongruent inhibition) relative to a neutral, or

unbiasing property context. Similarly, we would expect decisions about taxonomically related

targets (bird) to be faster in the presence of a taxonomic and/or slower in the presence of an

ecological property compared to neutral. By varying the property and time interval between

the premise and the lexical decision task, we examine if, how and when properties influence

knowledge retrieval.

We use this method addresses three questions: Does property moderate knowledge

retrieval? If property moderates knowledge retrieval, is the mechanism based on facilitating

property-congruent knowledge, inhibiting property-incongruent knowledge, or both? And

finally, does inhibition of property-incongruent knowledge (if any) proceed monotonously, or

do we see knowledge initially retrieved (activated) but subsequently discarded (inhibited)?

Property Effects on Knowledge Retrieval: How do You Know Them When You See

Them?

In Study 3, ecological properties increased the predictive power of ecological

knowledge and decreased the predictive power of taxonomic knowledge about premise

categories, relative to neutral properties (for ecological inferences). If these property effects in

knowledge recruitment reflect property effects in knowledge retrieval, we can expect a

biasing property to facilitate retrieval of congruent knowledge, and inhibit retrieval of

incongruent knowledge. Because of likely differences in the strength of connection of

taxonomic and ecological knowledge to a target animal, we cannot make blanket predictions

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about which kind of knowledge will be more active at any given moment16. However,

predictions can be made about increases or decreases in activation of property-congruent and

property-incongruent knowledge compared to the baseline, the neutral property context.

In the absence of a biasing context (i.e. in the context of a non-biasing property), we

expect related knowledge to rise in activation after the animal name is presented17, while

unrelated knowledge should maintain a relatively low level of activation. This is illustrated in

the Figure 6, panel a. By saying that knowledge has been retrieved, we mean that its

activation has risen to a detectable level above its baseline18. For example, after a person is

presented with the animal “duck”, related knowledge – “bird”, “pond” is more activated than

before presentation of “duck”, and more activated than unrelated and unprimed knowledge

(“sofa”). Moreover, there can be differences between the level of activation of different

related pieces of knowledge. The panel a. of Figure 6 shows the animal label “duck” to trigger

higher activation of the taxonomically related target (“bird”) than of an ecologically related

target (“pond”) - based on our finding of higher baseline salience of taxonomic relative to

ecological knowledge (feature-listing data, Study 2).

Next, we can add a biasing property to the picture. As shown in Figure 6, panel b. with

a biasing taxonomic property we can expect congruent facilitation of taxonomic knowledge

retrieval, and incongruent inhibition of ecological knowledge retrieval. A taxonomically

                                                                                                               16  I.e.,  although  we  do  not  expect  systematic  differences  in  baseline  activation  between  “bird”,  “pond”,  and  “sofa”,  we  do  expect  that  “duck”  might  prime  “bird”  more  strongly  than  it  primes  “pond”.    

17  We  only  consider  a  relatively  short  time  window  after  the  animal  name  is  presented;  later  decay  of  knowledge  activation  is  not  discussed  here  

18  For  the  ease  of  display,  all  the  knowledge  types  are  shown  having  equal  baseline  level  of  activation,  although  we  do  not  make  such  an  assumption  about  the  state  of  natural  knowledge.  For  this  experiment,  however,  we  took  effort  to  select  stimuli  that  do  not  vary  dramatically  in  baseline  activation  level  (see  the  Method  section,  stimuli  norming).    

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biasing property should facilitate the retrieval of congruent taxonomic knowledge and

inhibit the retrieval of incongruent ecological knowledge. Conversely, an ecologically biasing

property should facilitate the retrieval of congruent ecological knowledge and inhibit the

retrieval of incongruent taxonomic knowledge (not shown in the Figure 6). Such property-

biased retrieval would be consistent with the pattern observed in the Study 3 (most

pronounced for ecological inferences): property-congruent knowledge had stronger cognitive

effects on inferences, while property-incongruent knowledge had weaker cognitive effects on

inferences, relative to the same knowledge in the context of a neutral property.

If knowledge retrieval is moderated by property, we expect to see faster and more

accurate verification of targets when they are congruent with a biasing property than when the

property is neutral. We also expect to see slower and less accurate verification of targets when

they are property-incongruent than when the property is neutral.

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Figure 6. Hypothesized curves of knowledge retrieval in unbiased and biased contexts. TAX/ECO/UNRtarg stand for taxonomic, ecological, and unrelated targets, respectively. NEU/TAXprop stand for neutral and taxonomic property, respectively.

a. knowledge activation in the context of a neutral property. Retrieval: activation of related knowledge relative to unrelated (r).

b. Knowledge activation in the context of a biasing taxonomic property. Congruent facilitation: activation advantage in the presence of a congruent property relative to a neutral property (f). Incongruent inhibition: activation decrease in the presence of an incongruent property relative to a neutral property (i).

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Non-Monotonicity in Knowledge Retrieval.

Another question is how inhibition of property-incongruent knowledge unfolds over

time. In Swinney’s (1979) study of resolution of lexical ambiguity during sentence

comprehension, contextually inappropriate meaning of a word was initially activated, and

shortly afterwards suppressed. Thus, inhibition proceeded non-monotonically: first no signs

of inhibition, followed by a quick discarding of inappropriate meaning. If property serves as a

biased context for the animal name, it raises a question of whether property-incongruent

knowledge is inhibited non-monotonically.

Is it there? In the course of retrieval, does knowledge activation change

monotonically in the presence of an incongruent biasing property? If retrieval of knowledge

about animal premises in the context of a biasing property is not radically different from

retrieval of word meanings in the context of a disambiguating sentence, we may expect to see

initial activation followed by inhibition19. Based on the results of Study 3, we have two

candidate cases for such activation trajectory: taxonomic knowledge in the context of an

ecological property, and ecological knowledge in the context of a taxonomic property. In both

cases the knowledge appeared to be non-recruited by inferences (mean number of listed

features, taxonomic in the first case and ecological in the second case, did not predict

inferences). This raises the question of whether unrecruited knowledge is simply not retrieved

in the context of an incongruent property, or is it retrieved and later discarded?

                                                                                                               19  Strictly  speaking,  non-­‐monotonic  retrieval  could  also  refer  to  delayed  facilitation  (detectable  changes  in  speed  of  activation  driven  by  congruent  context).  This  question  lies  beyond  our  interest  here,  and,  as  a  practical  point,  change  in  speed  of  rising  (delayed  facilitation)  would  be  harder  to  detect  than  a  reversal  from  rise  in  activation  to  decline  (delayed  inhibition).  Thus  this  discussion  is  limited  to  delayed  inhibition  of  context-­‐incongruent  knowledge.    

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Figure 7 shows an activation trajectory of ecological knowledge initially retrieved

and then inhibited. For a given knowledge type retrieved in the context of an incongruent

property, sampling across time after the presentation of an animal, we can detect retrieval

non-monotonicity if we observe a detectable activation advantage at earlier times that

significantly declines or completely goes away at later times. The activation advantage has to

be detectable, otherwise we cannot claim that knowledge has been retrieved at all. The

advantage should be measured, ideally, relative to the baseline activation of that same

knowledge (activation of “pond” without being presented with “duck” before). But for the

sake of this study we take activation of unrelated targets (“sofa”) to represent common

baseline activation of all targets (“sofa”, “bird”, “pond” in the absence of “duck” prime)20. In

order to attribute this effect to the presence of a biasing incongruent property, we should also

see knowledge activation suppressed in the presence of a biasing incongruent property

relative to neutral property context.

Thus, the pattern of interest can be described as “non-monotonic retrieval of

knowledge inhibited by an incongruent property.” The signature of such a pattern would be

lower activation in an incongruent property context relative to a neutral property context (the

“inhibition” component of the pattern), accompanied by a rise-then-drop activation trajectory

in an incongruent property context relative to unrelated targets (the “non-monotonic”

component of the pattern). We need to demonstrate both parts of the pattern, because just

observing no inhibition at early times in retrieval followed by pronounced inhibition by itself

does not prove non-monotonicity: activation of knowledge takes time, and of course very

                                                                                                               

20  See  the  description  of  the  norming  of  target  words  in  the  method  section  below  for  the  justification  of  this  assumption  

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early in retrieval we expect to see no difference between activation of related and

unrelated targets. We need to show inhibition accompanied by clear signs of initial retrieval –

hence, the two-component signature pattern of non-monotonic inhibition.

Figure 7. Knowledge activation in the context of a biasing taxonomic property. Incongruent inhibition: significant activation decrease in the presence of an incongruent property relative to a neutral property (i). Non-monotonic retrieval of property-incongruent knowledge: initial detectable activation advantage relative to unrelated knowledge (r) which significantly declines or completely goes away shortly afterwards (r1>r2)

Where (when) to look for it? In order to know when to look for such possible brief

activation followed by inhibition, we attempted to derive time course estimates from existing

sentence-comprehension and priming studies. The first estimate of the duration of such

“initial” process can be taken from Swinney (1979). When participants were asked to make

lexical decisions about the words related to one of the two meanings of an ambiguous word

presented in a biasing context, when the target for lexical decision was presented immediately

at the offset of the ambiguous word (0msec ISI measured from the offset of the previous

stimulus), the decisions about targets related to both meanings of ambiguous words were

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equally facilitated relative to a baseline unrelated target. In the delayed condition (3

syllables after the offset, or 750-1000msec, according to Swinney (1979)), only the target

related to the context-appropriate meaning of the word was facilitated, suggesting that the

decision process selecting the meaning was completed by that time. The time period of 750-

1000msec is, of course, a generous estimate providing only the maximum duration of the

period when both context-appropriate and context-inappropriate meanings are “immediately

and momentarily accessed” (Swinney, 1979, p. 657).

A rather different study of Neely (1977) provides a similar estimate of the duration of

initial “automatic” retrieval period. Neely tested the attention theory of Posner & Snyder

(1975; as cited in Neely, 1977) postulating two distinct components of attention, a fast

automatic inhibitionless spreading activation process and a slow limited-capacity conscious-

attention mechanism. In the critical condition of Neely’s (1977) study, participants were

instructed to expect primes to be followed by targets which came from a specified category

other than the category represented by the prime itself. E.g., participants were told that the

prime “body” would be followed by furniture items such as “door”. ISI was varied between

250, 450 and 750 msec. The facilitation of the “body” prime on decisions about “door” target

was first evident only at the longest ISI of 750msec. Crucially, when participants were

presented with “unexpected” targets naturally related to the prime (“heart” following the

“body” prime), there was a short period of facilitation (250msec), which changed to inhibition

by 750 msec (with no facilitation or inhibition at the intermediate ISI of 400msec21). This

suggests a more stringent estimate: before 250-400msec, information access proceeds

relatively automatically and is not subject to additional contextual or goal-based constraints,                                                                                                                21  Reaction  times  in  all  ISI  conditions  were  around  700-­‐800msec.  

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and by 400msec, other selection or access mechanisms may begin to dominate

processing. However, such an estimate should be taken with caution: the upper boundary on

the time window of the initial retrieval process may be determined either by termination of

initial retrieval processes, or by initiation of next-step processes. The timing when such next-

step processes are initiated in Neely’s task (shifting attention to a prime category to a different

prespecified category) may differ from timing when selection processes in property-sensitive

induction may be initiated. But in the absence of studies of time-course of information

activation in induction22, we will take this estimate as the best available.

Thus, in order to detect temporary activation of property-incongruent knowledge

followed by inhibition, the best place to look is a window of 250-1000msec following the

animal name. This estimate determined our choice of SOA levels for this experiment, as

described in the method section.

The goal of the study was to examine whether property moderates knowledge

retrieval, and if it does – does it act by facilitating property-congruent knowledge, inhibiting

property-incongruent knowledge, or both? And finally, how does retrieval of property-

incongruent knowledge unfold overtime: is it steadily suppressed by the property, or is it

initially retrieved and then inhibited?

                                                                                                               22  McElree,  Murphy  &  Ochoa  (2006)  examined  the  dynamics  of  context-­‐dependent  feature  retrieval  using  an  SAT  methodology  which  could  provide  a  better  and  more  appropriate  estimate.  Unfortunately  for  the  purposes  of  the  current  study,  McElree,  Murhpy  &  Ochoa  (2006)  focused  on  emergent  properties  of  combined  concepts  (soft  being  an  emergent  property  of  boiled  celery).  Since  deriving  such  properties  that  are  not  true  of  the  original  concepts  (celery  is  not  soft)  is  likely  to  require  additional  steps  in  reasoning,  it  is  not  surprising  they  obtained  much  longer  estimate  of  the  non-­‐context-­‐sensitive  time  window  -­‐  as  long  as  2000msec  –  which  is  unlikely  to  apply  to  the  proposed  study  

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Method

Participants. One hundred eleven Northeastern undergraduates, all native speakers of

English, participated in the main experiment in exchange for a course credit (45 in SOA400,

32 in SOA900, 34 in SOA1650 condition). In addition, 29 participants, all native speakers of

English, were recruited for two norming studies, also in exchange for a course credit.

Materials.

Open-ended induction task. The stimuli set was composed of 36 animal premises,

each belonging to one salient taxonomic category (mammal, bird, reptile, fish, insect) and one

salient habitat-based ecological category (forest, desert, pond, ocean, savannah). Of the 36

animals, 30 were a subset of already normed and tested animal premises used in Studies 2 and

3, and six animals were new. In addition, 36 new animal names were used as fillers. Each of

the animals was presented in the context of an inductive problem about one of the six

properties from Study 3: flu, parasite, property X, substance, gene, and cell, presented with

unique alphanumeric codes (X5, Z9).

All the animal names and properties along with unique alphanumeric codes were

recorded in the voice of a female native speaker of English. The quality of recordings and

familiarity with the spoken animal labels were checked by asking a separate sample of 11

native English speakers (6 females) to listen to the recordings of animal names, write down

the names of the animals they heard23 and rate each animal on a 5-point scale (1= I don’t

know this animal, 2 = I’ve heard this animal name before but I don’t really know anything

about it, 3 = I know a little bit about this animal, 4 = I am moderately familiar with this

                                                                                                               23  The  accuracy  on  transcribing  animal  names  from  the  audio  recording  was  100%,  discounting  (extremely  abundant  and  diverse)  spelling  errors.    

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animal, 5 = I know a lot about this animal). The mean familiarity rating was 4.2

(SD=.54), suggesting that the tested population of Northeastern undergraduates was familiar

with the animals we selected.

Lexical decision task. Seventy-two letter strings were used as targets for the lexical

decision task. Half of these were words, and half were pronounceable non words. The word

targets were either taxonomically related, ecologically related, or unrelated to the animals

used as premise categories in the induction task. The source of target words was previously

collected open-ended responses to inductive problems referring to the animal premise

categories (Study 3) and feature listings (Study 2). The unrelated targets were nouns from a

non-biological domain, similar to the related targets in their consonant-vowel structure. The

full list of lexical decision targets is shown in Appendix C.

The strength of association of the taxonomic, ecological, and unrelated targets to the

corresponding animals, as well as lack of direct associations between properties and target

words, were verified with a separate group of 18 native speakers of English. The details of the

association norming are presented in the Appendix D. The ecological, taxonomic, and

unrelated targets did not differ in mean length (F(2,33)=.253, p=.780), frequency

(F(2,32)24=.093, p=.910), or number of orthographic neighbors (F(2,33)=.311, p=.735) (based

on the CELEX database, Medler & Binder, 2005). They also did not differ in non-primed

verification times (M=631, 640, 657msec, respectively, F(2,33)=.416, p=.663) or accuracy

(M=.96, .95, .97, respectively, F(2,33)=.5, p=.611) (based on the English Lexicon Project

database, http://elexicon.wustl.edu). These comparisons suggest that the baseline activation

levels of ecological, taxonomic and neutral targets in the absence of the animal name prime                                                                                                                24  Frequency  data  for  one  of  the  targets  were  not  available  

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do not differ. This justifies using the unrelated items (“sofa”) as a general activation

baseline, as opposed to getting a separate baseline measure of activation of related items

(“pond”, “bird”) in the context of our task but not primed by an animal name (“duck”). Thus,

the forthcoming analyses of accuracy and response times will compare related items to

unrelated items (baseline), and the resulting differences (if any) will be taken to reflect

retrieval of related knowledge. The pronounceable non-word fillers were equated with the

word targets on mean length (t(70)=.771, p=.440) and number of orthographic neighbors

(t(70)=-.126, p=.900) (CELEX database, Medler & Binder, 2005).

Design. The main independent variable was property type (taxonomically biasing,

represented by gene and cell; ecologically biasing, represented by flu and parasite; and non-

biasing represented by substance and property). The second independent variable was the

target word type (taxonomic vs. ecological vs. unrelated). Each non-filler animal was yoked

to one target word type (taxonomic, ecological, or unrelated)25.

Finally, we varied stimulus onset asynchrony (SOA), the time between the onset of the

auditorily-presented animal name and the appearance of the visually-presented target word

(400, 900 or 1650msec)26. Mean duration of animal name was 640msec (SD=128.5; range

412-958msec), meaning that on average, at SOA=400 the target appeared 240msec before the

end of the animal name, at SOA=900, the target word appeared 260msec after the animal

                                                                                                               25  Although originally a fully crossed set of stimuli was developed, in which each of the non-filler animals was matched with all 3 target words – taxonomic, ecological, and unrelated – we only report data from a subset of experimental conditions, in which each animal was yoked to one target word type.

26 We are using SOA rather than ISI in order to minimize noise from different durations of animal labels. Since lexical access begins before the complete word is presented, and in this experiment lexical access is facilitated because participants know to expect to hear animal names rather than random words, by 400msec into the recording of the animal name participants can unambiguously determine which animal is being presented to them, and the duration of “tail” of the word carries little extra information.    

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name, and at SOA=1650 the target appeared 1010msec after the end of the animal name,

thus covering the time-window of interest estimated based on Swinney (1979) and Neely

(1977). The property and target type were manipulated within subjects, and SOA between

subjects.

There were 72 trials total, of which 36 were experimental trials (six properties x three

target types with two animals per cell) and 36 were fillers (six properties with six non-word

targets per cell). The order of trials was randomized for every subject. Lexical decision

accuracy was measured as proportion of correct responses; reaction time was measured from

the moment the target word was presented until the response key was pressed.

Procedure. Participants were tested individually, on a MacBook laptop running

Superlab 4.0.4 software and set up with headphones and a microphone. The experiment

consisted of an open-ended induction task with intervening lexical decision task. Both tasks

were computerized.

Participants were instructed that they would be listening to utterances that would

introduce a property, followed by an animal that possesses that property, and their task is was

say out loud (at a cued moment) the first other thing that comes to their mind that is likely to

share that property, alongside with a short explanation. Participants were also informed that at

“random” moments a sequence of characters would appear on the screen, and their task was to

identify it as a word or a non-word as quickly as possible without sacrificing accuracy, using

the response buttons. Before starting the actual task, participants went through a practice

session that gradually introduced them to the format of the open-ended induction question, the

auditory format of the question, and allowed them to practice doing two tasks at the same time

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– generating inferences and doing the lexical decision task. The full text of the

instructions is shown in the Appendix E.

Each trial began with a 2000msec pause; then a participant heard the property to be

projected (e.g., flu M3), followed by a pause of 1000msec, followed by the name of the

premise animal (e.g., bear), followed by a pause and a signal to start speaking. At varying

SOA’s (400, 900, or 1650msec from the onset of the animal name), a target for lexical

decision appeared on the screen and stayed there until the participant responded or for

3500msec, whichever came first. No accuracy feedback was provided. After a 2000msec

pause following the participant’s response or the end of lexical decision target presentation, a

short beep signaled that the participant could start saying their inference. Participants had 15

seconds to say their response, after which the experiment automatically moved on to the next

trial. If participants finished saying their inference sooner than in 15 seconds, they could

move on to the next trial by pressing a space bar.

Participants were instructed to keep their eyes at the center of the screen and their

index and middle fingers of the right hand on two response buttons throughout the experiment

(Yes (“o”) and No (“p”)). During the experiment, participants were given 4 breaks. Duration

of the breaks was not limited, participants could restart the experiment at any time by pressing

a space bar.

Results

Open-ended induction task. The inferences produced by participants in this task

were likely contaminated by the cues from the lexical decision task, and thus do not reflect

natural reasoning. Therefore, the data from this task only served as a check that participants

did indeed engage in inference generation, as instructed, and that the task format – auditory

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presentation of the premise and vocalization of the inference – did not disrupt their ability

to reason.

Open-ended responses from a randomly selected subset of 70 subjects were

transcribed and coded by 4-5 trained coders using the same coding scheme as used in the

Experiment 1. All the coders were naïve to the hypothesis; while coding, the property that the

participant reasoned about on a given trial was concealed from the coders (unless the

participant mentioned it in their response).

Inferences from five subjects were discarded since they failed to provide justifications

for their inferences on more than half of the trials, making their responses uncodable. Using

the data from the remaining subjects, for each of the 36 non-filler animals we calculated the

proportion of subjects making taxonomic and ecological inferences for that animal, collapsing

across SOA conditions, split by property type (taxonomic, ecological, neutral). All the

analyses were conducted on arcsine transformed data; the reported statistics are based on

arcsine-transformed data, but the reported means are raw. The analyses were conducted both

by subjects (reported in the main text) and by items (indicated only if different from by

subjects).

Overall, participants generated more taxonomic (.64) than ecological (.43) inferences

(paired samples t(64)=7.16, p<.001, Cohen’s d=.89). The results confirmed that property did

affect inferences in the expected way. Taxonomic inferences increased in relative frequency

from ecological to neutral to taxonomic property (eco .53, neu .66, tax .74; repeated measures

ANOVA F(2,128)=18.741, p<.001, η2partial=.227; all pairwise comparisons p’s ≤.001).

Ecological inferences increased in frequency from taxonomic to neutral to ecological property

(tax .31, neu .40, eco .56; repeated measures ANOVA F(2,128)=21.881, p<.001, η2partial=.255;

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all pairwise comparisons p’s ≤.004). This indicates that, despite a somewhat challenging

nature of the task – listening to open-ended inductive questions, making word/non-word

decisions about a letter string on the screen, and saying out loud the inferences – participants

were able to perform the task, and their performance overall matched what participants did in

a more standard paradigm (Study 3).

Next, we examined the effect of the target type on inferences. (Recall that a

taxonomically related, ecologically related, or unrelated word was visually presented in the

lexical decision task after the animal name was played to the participant.) The main function

of the target type manipulation in this study was to “probe” for activation of taxonomic and

ecological knowledge about the premise animals at different times in the course of inductive

inference, not to affect the outcome inferences. However, it is hard to have a “probe” that

would not have any consequences on the reasoning processes it is supposed to measure. We

expected that target type could have an effect on inferences, mostly for pragmatic rather than

theoretical reasons: participants might interpret the target words as (probabilistic) cues from

the experimenter to base their inferences on, and/or the target words could remind participants

of taxonomic or ecological features of the premise animals that they might not have thought

about otherwise, guiding their inferences. Thus, a main effect of target type on inferences

would not be of much theoretical interest. However, any interactions of target type with other

variables (property, SOA), meaning that our “probe” had differential effects on inferences

depending on levels of other variables, would require adjustments in interpreting the effects of

these variables (property, SOA) on accuracy and decision times about these targets that serve

as DVs in subsequent analyses.

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As expected, target type had a main effect27 on participants’ taxonomic inferences

(F(2,128)=13.834, p<.001, η2partial=.178) and ecological inferences (F(2,128)=20.158, p<.001,

η2partial=.24). Taxonomic inferences were more frequent when the intervening lexical decision

target was taxonomic (.70) than when it was ecological (.58, paired samples t(64)=5.15,

p<.001, Cohen’s d=.64); taxonomic inferences in the presence of an unrelated target fell in

between (.62) and were significantly less frequent than taxonomic (t(64)=3.018, p=.004,

Cohen’s d=.37) and significantly more frequent than ecological inferences (t(64)=2.18,

p=.033, Cohen’s d=.27).28 Ecological inferences were more frequent when the intervening

lexical decision target was ecological (.51) than when it was taxonomic (.35, independent

samples t(64)=6.19, p<.001, Cohen’s d=.77); in the presence of an unrelated target the

inferences fell in between (.43) and was significantly different from both (related samples test

vs. tax t(64)=3.252, p=.002, Cohen’s d=.40); vs. eco t(64)=3.209, p=.002, Cohen’s d=.40)29.

The target type did not interact with property for taxonomic (F(4,256)=.497, p=.738)

or ecological (F(4,256)=.505, p=.732) inferences, as indicated by repeated measures 3 target

type (taxonomic, ecological, unrelated) x 3 property (taxonomic, ecological, neutral)

ANOVAs.

Nor did target type interact with SOA for taxonomic inferences, as indicated by a

mixed 3 target type (taxonomic, ecological, unrelated; within subjects) x 3 SOA (400, 900,

1650; between subjects) ANOVA on relative frequency of taxonomic inferences

                                                                                                               27  Marginally significant in a by-item analysis, (F(2,33)=3.171, p=.055, η2

partial=.161)  28 In a by item analysis, frequency of taxonomic inferences in the presence of unrelated targets did not differ from either taxonomic (independent samples test t(22)=1.553, p=.135) or ecological targets (independent samples test t(22)=1.073, p=.295). 29 In a by item analysis, frequency of ecological inferences in the presence of unrelated targets did not differ from either taxonomic (independent samples test t(22)=1.776, p=.09) or ecological targets (independent samples test t(22)=1.236, p=.229).  

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(F(2,124)=1.576, p=.185). However, there was a significant interaction between the target

type and SOA for ecological inferences, as indicated by an analogous mixed ANOVA on

relative frequency of ecological inferences (F(4,124)=3.746, p=.007, η2partial=.108).

Figure 8. Relative frequency of ecological inferences as a function of target type and SOA. Error bars represent one standard error of the mean. Solid lines indicate significant difference at p<.05, dotted lines indicate significant difference at p<.1.

As shown in Figure 8, the overall pattern of target effect on ecological inferences held

across SOAs: relative to trials where an animal premise was accompanied by an unrelated

target (e.g., premise category “duck” followed by a lexical decision target “sofa”), ecological

inferences were more frequent on trials where an animal premise was followed by an

ecological target (e.g. “duck” followed by “pond”) less frequent on trials where an animal

premise was followed by a taxonomic target (e.g. “duck” followed by “bird”). However, this

pattern was more expressed at shorter SOA of 400msec (all pairwise comparisons between

target types p<.02) than longer SOAs of 900msec and 1650msec, as reflected by the

significant interaction between target type and SOA.

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At an SOA of 400msec the target appears roughly in the middle of the auditory

presentation of the animal name. In other words, targets at this SOA are more likely to

overlap with knowledge retrieval about the animal, than targets with longer SOAs. The fact

that we see the most pronounced effect of target on inferences at this point suggests that we

might be observing context effects in lexical access. In light of this interaction, we can

speculate that the target provides additional retrieval context for the animal name (albeit

delayed relative to the beginning of the animal name), and this target-context affects category

knowledge retrieval by priming target-related information about the animal. On the one hand,

this (speculative) result is promising: if context in the form of a target word can affect

retrieval of category knowledge, we expect that so will the context in the form of a property.

On the other hand, this result might mean bad news: if the effects of property-context are

weaker than the effects of target-context (in other words, if our supposedly neutral “probe”

turned out to be more invasive with respect to knowledge retrieval than our main

manipulation), we have fewer chances to detect the effects of property on retrieval, if they

exist – especially at the shorter SOA of 400msec when the effect of the target is at its

strongest.

There were no significant main effects of SOA on either taxonomic (F(2,62)=.013,

p=.987) or ecological inferences (F(2,62)=1.75, p=.182), and there were no significant three-

way interactions between property, SOA, and target type (repeated measures ANOVA on

taxonomic inferences F(8,248)=1.063, p=.39; on ecological inferences F(8,248)=.716,

p=.677).

Lexical decision: accuracy. Overall accuracy was high, 97% correct on word and

96% correct on non-word targets. All the analyses were performed on arcsine-transformed

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proportions; the reported statistics are based on arcsine-transformed data; the reported

means are raw. All  the  analyses  on  accuracy  and  RT  in  the  lexical  decision  task  were  

conducted  both  by  subjects  (reported  in  the  main  text)  and  by  items  (indicated  only  if  

different  from  by  subjects).

Property and target effects on lexical decision accuracy. Does property moderate

retrieval of knowledge about premise animals? The question of main interest was whether

property type interacts with target type, by promoting accuracy of property-congruent targets

and interfering with it for property-incongruent targets. Specifically, we expected accuracy of

decisions about ecologically-related targets to be higher when the property is ecological,

lower when the property is taxonomic, and at the intermediate level when the property is

neutral. Similarly, we expected accuracy of decisions about taxonomically-related targets to

be higher when the property is taxonomic, lower when it is ecological, and in-between for the

neutral property.

The effects of property and target on accuracy were analyzed in a series of 3 property

(taxonomic, ecological, neutral) x 3 target (taxonomic, ecological, unrelated) repeated

measures ANOVAs on lexical decision accuracy, performed separately for each SOA

condition. Results are depicted in Figure 9. Contrary to our prediction, property did not

interact with target type at any SOA (SOA400: F(4,176)=1.456, p=.217; SOA900:

F(4,124)=.660, p=.621; SOA1650: F(4,132)=1.145, p=.338). Thus, if we take accuracy of

verifying targets related taxonomically and ecologically to the premise animal as the measure

of activation of taxonomic and ecological knowledge about that animal, then based on

accuracy results, property does not appear to moderate activation of premise category

knowledge.

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Figure 9. Mean accuracy on the lexical decision task as a function of SOA, property and target. Error bars represent one standard error of the mean. Solid lines indicate significant difference at p<.05, dotted lines indicate significant difference at p<.1.

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In order to examine whether premise animal name primed the two related targets –

taxonomic and ecological – more than the unrelated target, we examined the effect of target

type on accuracy. If hearing “duck” primes “pond” (ecological target) and “bird” (taxonomic

target) more than “sofa” (unrelated target), we would expect higher accuracy on ecological

and taxonomic targets than on unrelated targets. Based on the ANOVA analyses described

above, target type had a main effect on accuracy at SOA400 (F(2,88)=4.092, p=.02,

η2partial=.085) and at SOA900 (F(2,62)=3.325, p=.042, η2

partial=.097), but not at SOA1650

(F(2,66)=.376, p=.688)30. As shown in Figure 10, at SOAs 400 and 900, the pattern matched

our predictions: accuracy on related targets was overall higher than on unrelated, although not

all pairwise comparisons reached significance: at SOA400, accuracy on taxonomic but not

ecological items was higher than accuracy on unrelated targets (paired samples test, tax vs.

unrelated t(44)=3.026, p=.004, Cohen’s d=.45; eco vs. unrelated t(44)=1.337, p=.188); at

SOA 900, accuracy on ecological but not taxonomic items was higher than accuracy on

unrelated targets (paired samples test, eco vs. unrelated t(31)=2.996, p=.005, Cohen’s d=.53;

tax vs. unrelated t(31)=1.486, p=.147). At SOA1650, all pairwise comparisons were non-

significant (all p’s>.56). This suggests that at earlier SOAs, the animal name (“duck”)

sufficiently primed taxonomic (“bird”) and ecological (“pond”) targets to secure accuracy

advantage for them relative to unrelated targets (“sofa”), however, by 1650msec after the

beginning of the animal name, this effect is gone.

                                                                                                               30  Despite  the  differences  in  significance  of  target  type  effect  on  inferences  between  SOA  levels,  the  interaction  between  target  type  and  SOA  was  not  significant  in  a  mixed  3  target  type  (within  subjects)  x  3  SOA  (between  subjects)  ANOVA,  F(4,  216)=1.223,  p=.302).  

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Figure 10. Effect of target type on lexical decision accuracy, split by SOA. Error bars represent one standard error of the mean. Solid lines indicate significant difference at p<.05, dotted lines indicate significant difference at p<.1.

We did not predict any main effects of property on the lexical decision accuracy at any

SOA (unless reasoning about some properties turned out to be overall more taxing than about

others). Overall the results were consistent with this prediction (SOA900: F(2,62)=1.130,

p=.33; SOA1650: F(2,66)=.122, p=.886), except at SOA400, where property had a marginally

significant effect on accuracy (F(2,88)=3.082, p=.051, η2partial=.065). At SOA400, trials

involving a taxonomic property yielded a higher accuracy (.987) than trials with an ecological

(.970, paired samples t(44)=2.408, p=.02, Cohen’s d=.36) or neutral property (.972, paired

samples t(44)=2.462, p=.018, Cohen’s d=.37) which did not differ from each other (paired

samples t(44)=.404, p=.688).

We did not have predictions about main effect of SOA on accuracy, but there was a

marginal effect of SOA(one-way ANOVA F(2,108)=2.713, p=.071, η2partial=.048), caused by

a trend of decreasing accuracy at SOA of 1650msec (.96) relative to SOA of 400 (.98) and

900msec (.98) (independent samples 400 vs. 1650 t(77)=2.013, p=.048, Cohen’s d=.46; 900

vs. 1650 t(64)=2.021, p=.047, Cohen’s d=.51; 400 vs. 900 t(75)=.155, p=.877).

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Changes in activation of premise category knowledge over time. The next

question we planned to address was whether the retrieval of property-incongruent knowledge

is subject to non-monotonic inhibition. In other words, for a given knowledge type retrieved

in the context of an incongruent property, do we see a detectable activation advantage at

earlier times that significantly declines or completely goes away at later times? (Advantage

measured relative to baseline (unrelated targets), as shown in Figure 7.)

Admittedly, it makes little sense to ask “how does incongruent property affect

knowledge retrieval?” in the absence of evidence that it does affects it at all, as indicated by

non-significant target x property interactions reported above. Nevertheless we examined the

results for the signs of non-monotonic retrieval.

In order to claim that non-monotonic inhibition of property-incongruent knowledge

takes place, property-incongruent premise category knowledge should show early signs of

retrieval that disappear later in processing. To examine whether such pattern is present, we

compare the accuracy of related but property-incongruent targets to the accuracy of unrelated

targets across at each SOA (schematically represented by r1 and r2, retrieval index at time1 and

time2 in Figures 11 and 12, panel a. In order to attribute the later decline in knowledge

activation to inhibition from an incongruent biasing property, such decline must bring this

knowledge below the activation level of the same knowledge type in the presence of a neutral

property (as represented by i, inhibition in Figures 11 and 12, panel a.)

The first case of interest was retrieval of taxonomic knowledge in the context of an

incongruent (ecological) property. The first part of the predicted pattern was examined by a

series of planned comparisons of taxonomic vs. unrelated target accuracy which showed that

the former was more accurate at SOA400 (.994 vs .944, t(44)=3.317, p=.002, Cohen’s d=.49)

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, but not at SOA900 (.984 vs .977, t(31)=.442, p=.662) or 1650 (.963 vs .949, t(33)=.643,

p=.524) (Figure 11, panel b.). This suggests that property-incongruent taxonomic knowledge

showed early signs of retrieval that disappeared later in processing. However, this alone does

not constitute evidence for non-monotonic inhibition: we need to be able to attribute the later

decline in activation to inhibition from the incongruent property. The pairwise comparisons

of taxonomic target accuracy in neutral vs. incongruent biasing (ecological) property context

across SOA conditions were non-significant (SOA400  t(44)=1,  p=.323;  SOA900  t(31)=.570,  

p=.572;  SOA1650  t(33)=1.421,  p=.165) yielding no evidence for incongruent inhibition. In

sum, we do not have sufficient evidence to claim that an ecological property non-

monotonically inhibits taxonomic knowledge about premise animals.

Figure 11. Mean accuracy of verifying taxonomic targets across SOA in the context of a biasing incongruent (ecological) property – contrasted with first, unrelated targets in an ecological property context, and second, taxonomic targets in a neutral property context. a. Predicted pattern for incongruent  inhibition  of  taxonomic  category  knowledge  (non-­‐monotonic  retrieval)

b. Results

The second case of interest was retrieval of ecological knowledge in the context of an

incongruent (taxonomic) property. Planned comparisons of ecological vs. unrelated target

accuracy showed that the former was more accurate at SOA900 (.992 vs .945, t(31)=2.396,

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p=.023, Cohen’s d=.42) , but not at SOA400 (.989 vs .983, t(44)=.216, p=.83) or 1650

(.956 vs .956, t(33)=0, p=1) (Figure 12, panel b). This again demonstrates signs of retrieval

(detectable activation advantage) for ecological knowledge that is most evident at 900msec

SOA and then reduced. However, again there were no differences between ecological target

accuracy in neutral vs. incongruent biasing (taxonomic) property context across SOA

conditions (SOA400  t(44)=1.431,  p=.16;  SOA900  t(31)=.571,  p=.572;  SOA1650  

t(33)=1.44,  p=.16), suggesting that the late decline in activation in the first part of the pattern

cannot be attributed to property-incongruent inhibition. Again, the evidence does not support

the prediction that a taxonomic property might non-monotonically exhibit ecological

knowledge about premise animals.

Figure 12. Mean accuracy of verifying ecological targets across SOA in the context of a biasing incongruent (taxonomic) property – contrasted with first, unrelated targets in a taxonomic property context, and second, ecological targets in a neutral property context. a. Predicted pattern for incongruent  inhibition  of  ecological  category  knowledge  (non-­‐monotonic  retrieval)

b. Results

Accuracy on filler (non-word) items. Accuracy of negative decisions about fillers

(nonwords) was not affected by the property (F(2,216)=1.313, p=.271) or by SOA

(F(2,108)=.544, p=.582), and the two variables did not interact (F(4,216)=1.304, p=.269).

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Lexical decision: reaction time. All analyses were conducted on raw RT data, as

well as with outliers eliminated by using Tukey’s 1.5-interquartile range method, Tukey’s 3-

interquartile range method, as well as by trimming 5% of the mean and trimming 10% of the

mean. Removing outliers did not affect the results. The analyses reported here were

performed on raw data.

Property and target effects on lexical decision RT. Just like for the accuracy analyses,

the first question is whether property moderates retrieval of knowledge about premise animals

by promoting speed of property-congruent targets and interfering with it for property-

incongruent targets. Such moderation would manifest itself as a significant interaction

between property type and target type. Specifically, we expected decisions about ecologically-

related targets to be faster when the property is ecological, slower when the property is

taxonomic, and at the intermediate level when the property is neutral. Similarly, we expected

decisions about taxonomically-related targets to be faster when the property is taxonomic,

slower when it is ecological, and at the intermediate level when the property is neutral.

The effects of property and target on RT were analyzed in a series of three property

(taxonomic, ecological, neutral) x three target (taxonomic, ecological, unrelated) repeated

measures ANOVAs, performed separately for each SOA condition. Mean RTs are shown as a

function of property, target and SOA in Figure 13. Contrary to our prediction, but consistent

with the accuracy analyses, property did not interact with target type at any SOA (SOA400:

F(4,176)=1.467, p=.214; SOA900 F(4,120)=1.056, p=.382); SOA1650: F(4,128)=.712,

p=.585).

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Figure 13. Mean RT on the lexical decision task as a function of SOA, property and target. Error bars represent one standard error of the mean.

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Although the interaction terms were not significant, we conducted planned

comparisons of simple effects of property for each target type (comparing taxonomic,

ecological, and neutral properties to each other), separately for each SOA condition. None of

them were significant (all p’s≥.162). Thus, if we take speed of verifying targets related

taxonomically and ecologically to the premise animal as the measure of activation of

taxonomic and ecological knowledge about that animal, then based on accuracy results,

property does not appear to moderate activation of premise category knowledge.

Next, we examined whether animal names primed the two related targets – taxonomic

and ecological – more than the unrelated target. If hearing “duck” primes “pond” (ecological

target) and “bird” (taxonomic target) more than “sofa” (unrelated target), we would expect

faster decisions about ecological and taxonomic targets than on unrelated targets. In

agreement with the accuracy analyses, RT was affected by the target type at SOA400

(F(2,88)=4.257, p=.017, η2partial=.088) and 900 (F(2,60)=3.41, p=.04, η2

partial=.102)31, but not

SOA1650 condition (F(2,64)=1.478, p=.236).

As shown in Figure 14, at SOAs 400 and 900, the pattern matched our predictions:

accuracy on related targets was overall higher than on unrelated, although not all pairwise

comparisons reached significance: at SOA400 decisions about ecological targets (1153msec)

were reliably faster than about unrelated targets (1233msec, related samples t(44)=-3.544,

p=.001, Cohen’s d=.53), but decisions about taxonomic targets (1210msec) did not differ

from unrelated targets (related samples t(44)=-.740, p=.463). Ecological targets were

marginally faster than taxonomic (1210msec, related samples t(22)=-1.855, p=.07, Cohen’s

d=.28). At SOA900, decisions about ecological (1183msec) but not taxonomic targets                                                                                                                31  Not  significant  by  item,  p=.507  

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(1207msec) were faster relative to unrelated targets (1244msec, paired samples eco vs.

unrelated t(31)=-2.604, p=.014, Cohen’s d=.46; tax vs. unrelated t(31)=-1.566, p=.128).

Decisions about ecological and taxonomic targets did not differ (paired samples test vs. eco

t(21)=-1.247, p=.222;). At SOA1650, all pairwise comparisons were non-significant (all

p’s>.229). Consistent with accuracy results, at shorter SOAs the animal names primed related

targets, but by SOA of 1650msec the effect was gone. Although the RT results only show

significant priming for ecological but not taxonomic targets, the fact that taxonomic targets

did show a trend towards priming in RT, and a significant priming in accuracy, suggests to us

that the taxonomic targets were most likely primed, although more weakly than ecological

targets.

Figure 14. Effect of target type on lexical decision RT, split by SOA. Error bars represent one standard error of the mean. Solid lines indicate significant difference at p<.05, dotted lines indicate significant difference at p<.1.

We did not expect property to have a main effect on the lexical decision RT at any

SOA, and indeed it did not (SOA400: F(2,88)=.375, p=.689; SOA900: F(2,60)=1.197,

p=.309; SOA1650: F(2,64)=.59, p=.557). SOA was not predicted to have a main effect on

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RT, and although there was a trend of increasing RT for longer SOA, it was not

significant (F(2,108)=.652, p=.523).

Changes in activation of premise category knowledge over time. The next question

on the list was whether property-incongruent knowledge is inhibited non-monotonically. As

shown in Figure 15 panel b., ecological properties did not lead to changes in reaction time for

taxonomic targets relative to neutral targets (F(1,107)=1.092, p=.298) at any SOA

(interaction F(2,107)=.433, p=.650). Participants were not any faster to make decisions about

taxonomically related targets than about unrelated targets. Thus, based on the reaction time,

we have no evidence that taxonomic knowledge was retrieved in the context of an ecological

property. There was also no evidence for incongruent inhibition of taxonomic knowledge by

an ecological property (paired samples comparisons of taxonomic target RT for neutral vs.

ecological property, all p’s≥.155). In sum, there is no evidence that an ecological property

non-monotonically inhibits retrieval of taxonomic knowledge about premise animals.

Figure 15. Time to verify taxonomic targets across SOA conditions in the context of a biasing incongruent (ecological) property – contrasted with first, unrelated targets in an ecological property context, and second, taxonomic targets in a neutral property context. a. Predicted pattern for incongruent  inhibition  of  taxonomic  category  knowledge  (non-­‐monotonic  retrieval)

b. Results

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Another potential place for non-monotonic inhibition is in retrieval of ecological

knowledge in the context of a taxonomic property. Planned comparisons showed that

decisions about ecological targets were faster than about unrelated targets at SOA900 (paired

t(30)=2.72, p=.011) but not at SOA400 (paired t(44)=1.675, p=.101) or SOA1650 (paired

t(33)=.256, p=.800) (see Figure 16, panel b.). This demonstrates an early activation advantage

for ecological knowledge that disappears with time. However, ecological target RT did not

differ for neutral vs. taxonomic property (all p’s≥.302) suggesting no non-monotonic

inhibition of ecological knowledge retrieval given a taxonomic property.

Figure 16. Time to verify ecological targets across SOA conditions in the context of a biasing incongruent (taxonomic) property – contrasted with first, unrelated targets in a taxonomic property context, and second, ecological targets in a neutral property context.

a. Predicted pattern for incongruent  inhibition  of  ecological  category  knowledge  (non-­‐monotonic  retrieval)

b. Results

Decision times on filler (non-word) items. Overall, it took participants longer to

correctly respond “No” to non-words (mean RT 1305msec) than to correctly respond “Yes” to

words (mean RT 1227msec; paired samples t(110)=6.213, p<.001, Cohen’s d=.59). To our

surprise and dismay, the speed of lexical decisions about filler items (non-words) was

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consistently affected by the property (SOA400: F(2,88)=6.033, p=.004, η2partial=.121;

SOA900: F(2,62)=4.404, p=.016, η2partial=.124; SOA1650: F(2,66)=4.964, p=.01,

η2partial=.131). The order of the RT means at all SOAs was neutral (shortest RT) to taxonomic

to ecological property (longest RT), with ecological being significantly longer than neutral in

all SOA conditions (SOA400: paired samples t(44)=2.914, p=.006, Cohen’s d=.43; SOA900:

paired samples t(31)=2.865, p=.007, Cohen’s d=.51; SOA1650: paired t(33)=2.732, p=.01,

Cohen’s d=.47). In addition, the difference between taxonomic and neutral property

conditions was significant at SOA400 (paired samples t(44)=2.877, p=.006, Cohen’s d=.43)

and 1650 (paired samples t(33)=2.228, p=.033, Cohen’s d=.38). However, with Bonferroni

adjustment of significance for 9 non-planned comparisons, only two of the pairwise

comparisons were borderline significant (critical p=.006)

Discussion

The goal of this study was to examine the time course of knowledge retrieval while

participants were engaged in an open-ended induction task. Participants’ active knowledge

was probed across three time points by using lexical targets related to the premise animal via

taxonomy, ecology, or unrelated.

Based on the results of Study 3 we expected to find effects of property on knowledge

retrieval: specifically, facilitation of property-congruent knowledge and inhibition of

property-incongruent knowledge. However, property did not significantly affect accuracy or

RT of word target verification in the lexical decision task and did not interact with target type.

In other words, we did not obtain any evidence of property facilitating or inhibiting

knowledge retrieval.

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Even in the absence of evidence of property moderating knowledge retrieval, we

attempted to examine targeted cases for non-monotonic retrieval of property-incongruent

knowledge (e.g., looking for cases when taxonomic knowledge might have been retrieved

originally, even though the property was ecological, and was subsequently inhibited). Not

surprisingly, although we did see some tenuous evidence of non-monotonic retrieval, it was

not accompanied by evidence of property inhibiting incongruent knowledge, which made

these findings as a whole uninformative with respect to the mechanism & time-course of

property effects on knowledge retrieval.

The effects of property that we did observe in this study were not predicted: property

affected accuracy and speed of decisions about filler (non-word) items. Ecological properties

(and to some extent taxonomic properties) slowed down correct decisions about non-words,

relative to neutral properties; accuracy of decisions dropped from neutral to taxonomic

properties as well (although at SOA1650 only). This may reflect overall cognitive load;

perhaps when participants are reasoning about informative properties, they have more

information to process and integrate with the animal information, and are thus doing “more

work” than when they are reasoning about neutral or uninformative properties. However, it’s

not clear why this would be restricted to filler items only.

The lack of interpretable results in Study 4 was not likely caused by faulty stimuli. As

indicated by target effects on accuracy and RT – consistent, if not always significant, pattern

of faster and more accurate verification of related vs. unrelated target words at SOA400 and

900 - animal names did prime related targets at 400 and 900msec SOA relative to unrelated

targets, which suggests that at earlier SOAs, the animal name (“duck”) likely triggered

retrieval of taxonomic and ecological knowledge about the animal. This shows that there were

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no major problems with the stimuli, and the task itself was sensitive enough to detect

priming effects.

One interesting finding was that taxonomically related targets had a significant

accuracy advantage at an earlier SOA400, whereas ecologically related targets had a

significant advantage at a later SOA900. This might reflect the higher availability of

taxonomic knowledge that, as suggested by the feature listing results of Study 2, is likely to

come to mind earlier than ecological. This adds to the list of differences between taxonomic

and ecological knowledge: not only the first one is more abundant and likely to come to mind

overall, but this advantage is visible from the very first moments of knowledge retrieval.

Surprisingly, by SOA1650 the related items did not show any accuracy advantage

relative to the unrelated items. This was accompanied by two patterns: first, a significant drop

in overall accuracy by SOA1650 relative to shorter SOAs, and second, a non-significant trend

for increasing RT from shorter to longer SOAs. Perhaps by 1650msec after the beginning of

the animal name, the inference generation process enters the stage of hypothesis formulation,

and other factors override priming effects from animal name to related targets. This, however,

still leaves earlier SOA conditions tapping into the retrieval stage.

One unanticipated finding suggested that knowledge retrieval in this experiment was

open to context effects. The effect of intervening targets on participant-generated ecological

inferences was most pronounced at the shortest SOA of 400, the time most likely to overlap

with knowledge retrieval. This suggests that when participants were presented with an

inductive question “Gene X5 is found in ducks. What else is likely to have gene X5? Why”

(in the form of an auditory input “Gene X5.. duck”), their retrieval of knowledge about ducks

was significantly affected by a seemingly random word popping up on the screen (“bird” or

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“pond”), but it was not systematically influenced by the property (“Gene”). This might be

attributed to modality (auditory for property, visual for target) or order differences in

presentation (property preceding, but target following the animal name). These possibilities

will need to be addressed in further experiments. Nevertheless, taken together, these results

show that it was possible to influence knowledge retrieval by context in this study – and yet,

property did not influence knowledge retrieval.

Another explanation of the lack of property effects in this experiment might have to do

with the inexplicably long RTs observed in this study. In Swinney’s (1979) study, reaction

times in a similar task ranged from 702 to 974msec whereas in this study they ranged from

1143-1317msec. Overall increase in reaction times is likely to come with increased

variability, masking the effects. This may explain our failure to find other meaningful patterns

in the data as well. Several features of this study may have invited longer reaction times: first,

overall cognitive load – after all, generating hypotheses is likely to be a more demanding task

than passive comprehension of sentences. This possibility can be addressed by measuring

duration of other well-calibrated tasks, and if we see a consistent increase in all RTs,

increasing a number of subjects in order to detect the effect. Second, longer reaction times

could have been due to a lack of motivation to respond fast. This can be addressed in future

experiments by introducing a monetary incentive for short RTs, and/or changing the

instructions so that lexical decision is introduced as a primary rather than secondary task for

the participant. Third, participants might have been slow due to overall fatigue: the

experiment started with an extended practice session followed by 72 open-ended induction

questions; indeed some participants remarked that the experiment lasted too long. This can be

addressed by cutting down the number of trials (indeed, we addressed this possibility in a

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follow-up test not reported here; it did not change the results). Finally, another possibility

has to do with the procedural detail of this study: participants were cued to begin speaking

with a beep, which only came following a delay after the lexical target was presented. Perhaps

knowing that they would not be able to do anything else until the beep, participants delayed

their responses in the lexical decision task to fill in the gap (however, a follow-up test not

reported here ruled out this explanation). In sum, there may be several factors that contributed

to longer reaction times and decreased our chances to detect property effects. However, longer

reaction times cannot fully account for the absence of such effects on word targets; property

did affect reaction times on non-word (filler) items, even though the mean reaction time on

non-word trials was even longer than on words.

In sum, we cannot completely rule out the possibility that the lack of property effects

was caused by some procedural flaws of the study. However, because we did see property

effects on some aspects of the lexical decision task, we know that the method is in principle

capable of detecting property effects. And because the premise category did differentially

prime related vs. unrelated targets, we know that the method is capable of detecting

differential priming. Therefore, it is likely that we failed to see property effects on retrieval

because property does not moderate retrieval of knowledge. This would mean that we need to

revise our assumption about the locus of property effects in induction. It is this latter

possibility that we are going to explore a bit further here.

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Chapter VI. Revising the Model of Induction: Explaining Evidence as the

Engine of Property Effects in Induction (Study 5)

Study 3 provided some, albeit tenuous, evidence, for interactive recruitment of

property and animal knowledge by inferences: ecological properties strengthened the potential

of ecological knowledge to promote ecological inferences, and taxonomic properties

strengthened the potential of taxonomic knowledge to inhibit ecological inferences.

Study 4 examined the moderating effects of property on knowledge retrieval in real

time, and found none. If this finding reflects the actual absence of property effects on

retrieval, rather than an experimental failure, this would require revision of the proposed

model of induction. We are going to discuss one such possible revision (which, in the end, is

not conditional on the success or failure of Study 4, as we argue below).

On the proposed account (see Figure 2), if property effects on inference do not take

place in retrieval, they must take place during hypothesis generation. As discussed earlier,

little is known about how hypotheses are generated. This fact lays ground for the new, revised

working model of induction shown in Figure 17.

Figure 17. Model of induction 2.0.

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Upon closer examination, this version of the model turned out to be somewhat

unsatisfactory in terms of explaining the mechanism of property effects in induction. With

minor further elaboration, however, we arrived to a revised model of induction (version 2.1),

which is described next.

Admittedly, hypothesis generation is inherently a creative process, which makes it less

appealing as an object of systematic study. Previously, we made the simplifying assumption

that the hypothesis generation is a simple, direct transformation of currently active knowledge

about premise category (ducks are birds) into a hypothesis by the “other things like that”

function (other birds will have it). The revised model of induction is driven by a second

attempt to de-black-box the hypothesis generation component of inference generation. Our

patch to model 2.0 is to upgrade the hypothesis generation component by including

explanation of presented evidence in the hypothesis generation component.

What is an explanation, and what does it have to do with induction?

The topic of explanation has been in the focus of philosophers’ attention for centuries,

and recently it has started attracting increasing attention from cognitive psychologists. It is

beyond our goals here to review the philosophical debates about what constitutes an

explanation; instead, we focus on one proposal that appears promising in terms of advancing

our model of property effects in inductive inference generation.

On the subsumption account proposed by Williams & Lombrozo (2010), explaining an

observation involves identifying a larger pattern of which the observation is a part of.

“Subsumption (...) theories suggest the defining property of an explanation is that it shows

how the observation being explained is an instance of (subsumed by) a general pattern or

regularity” (Williams, Lombrozo, & Rehder, 2010, p.1); “Explanations tend to relate a

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particular property or event being explained to broader principles or regularities”

(Lombrozo, in press, p. 20). In this sense, explanation identifies a relevant subset of

knowledge about the observation that can serve as a basis for generalizing to other cases.

There are multiple ways to explain an observation. Aristotle identified four modes of

explanation, or four ways to answer a “why” question: by reference to a formal, efficient,

final, or material cause. A formal explanation refers to the “form or properties that make

something what it is”. For example, a formal explanation of why a tiger has stripes would be

“because it is a tiger, and tigers have stripes”. Perhaps the most common mode of explanation,

an efficient, or causal explanation refers to the proximal mechanisms of change. For example,

one can explain why tigers have stripes by describing the biological mechanisms of gene

expression and protein synthesis resulting in the striped pattern. Final, or teleological

explanations refer to ends, goals or functions. A teleological explanation of tigers’ stripes

would refer to the functions they help to achieve, e.g. how they help this animal to

camouflage. Finally, a material explanation would refer to pigments in the fur (Lombrozo,

2006; Falcon, 2011).

Despite a long history of discussion of explanation and its types in philosophy, it is a

relatively recent topic in cognitive psychology. One relevant line of research focuses on

people’s use of different types of explanation. This work has documented that people

spontaneously engage in formal, causal and teleological explanations, and can selectively

deploy them depending on the specific attributes of the problem. For example, adults accept

teleological explanations selectively, for artifacts, biological parts (chairs are for sitting, feet

are for walking) but not for biological entities or phenomena (lions are not for anything)

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(Kelemen, 1999), and they have systematic intuitions about appropriateness of formal

explanations (Prasada & Dillingham, 2006).

Another line of research focuses on the role that presence or absence of explanation

(rather than its type) plays in inductive inference. Evidence suggests that a readily available

explanation can undermine reliance on similarity between premises and conclusion. In

Rehder’s (2006) study of induction with artificial categories, featural overlap between

premises and conclusions drove induction ratings, except in conditions where participants

received an explanation of causal connections between features, which eliminated the

contribution of similarity to induction judgments. In a more realistic setting, Shafto & Coley

(2003) showed that while novices’ inferences about diseases shared by marine creatures are

strongly predicted by similarity between species, expert fishermen’s reasoning, in contrast,

shows less reliance on similarity and more on specific explanations about disease

transmission in marine ecosystems (see also Lopez, Atran, Coley, Medin, & Smith, 1997;

Proffitt, Coley, & Medin, 2000). Sloman (1994) used a somewhat different approach to show

that explanations have an effect on inductive inferences over and above similarity. His study

demonstrated that when both premise and conclusion could be explained by reference to the

same principle, the perceived strength of an inductive argument was higher than when

premise and conclusion statements required different explanations, even when the similarity

between premise and conclusion was held constant.

Explanation can also override the effects of premise diversity on argument strength.

Typically, more taxonomically diverse premises provide stronger basis for generalizing a

property to the entire superordinate class (Osherson et al, 1990). However, as shown by

Medin et al. (2003), an argument with diverse premises (skunks & stink bugs have property

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X; therefore, all animals have property X) can be judged weaker than an argument with

less diverse premises (skunks & deer have property X; therefore, all animals have property X)

– if a more diverse argument invites a ready explanation that overrides diversity (in case of

skunks and stink bugs, property X may have something to do with smell).

In sum, the two lines of research briefly reviewed above suggest that people can

engage in different types of explanations, and the presence vs. absence of explanation affects

induction. Connecting these two lines of work, we pose new questions: does the type of

explanation inform inductive inference? Can different properties trigger different types of

explanation? And can different types of inferences stem from different types of explanations?

We start addressing these questions by revising our model of inference generation.

Hypothesis generation as a locus of property effects

We propose to make the contribution of explanation to induction official by including

it into the model of inference generation. There are several reasons for this proposal. First,

property effects are ubiquitous, and do not seem to be localized in knowledge retrieval.

Second, based on the evidence briefly reviewed above, explanation is involved in inference.

Finally, based on the subsumption account, explanation is a promising candidate for

accounting for property effects; by identifying different relevant patterns or regularities that

an observation is a part of, explanations can support selective generalization that is

characteristic of property effects.

We propose to unpack the black box of hypothesis generation and claim that it

involves explaining the available evidence (premise category + property)32, i.e. identifying the

                                                                                                               32  Explanation  is  presented  as  a  part  of  hypothesis  generation,  because  it  involves  going  beyond  the  given  information  (in  contrast  to  knowledge  retrieval).    

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larger pattern that this evidence exemplifies, and then formulating a guess about other

entities that might share the property by virtue of belonging to the same pattern or regularity.

This opens new territory for property effects: besides (or instead of) moderating retrieval of

knowledge, property can also influence hypothesis generation in a systematic way. On this

account of property effects, different properties can trigger different types of explanation. If

explaining consists of identifying an observation as a part of a larger pattern or regularity, and

different properties can trigger different explanations, then different properties can determine

whether a premise of an inductive argument is viewed as a part of one regularity or another.

As the outcome, such different explanations would in lead to different generalization

hypotheses.

This version of the model can explain the interactive nature of inferences in the

absence of evidence for interactive retrieval of knowledge. If a property systematically

triggers one or the other type of explanation (e.g. formal, in terms of taxonomic classes, or

causal, in terms of ecological interactions), it can strengthen recruitment of different subset of

knowledge by inferences, even if the knowledge about an animal premise and property had

been retrieved independently (see Figure 18).

Figure 18. Model of induction, v 2.1 “Hypothesis generation as a locus of property effects”

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Although the revision of the model was invited by the failure of Study 4, the

proposal about the contribution of explanation to inference generation is not conditional on

the outcome of prior experiments. Conceptually, the idea that the property can promote

different types of explanations is not incompatible with the proposal that property can

moderate knowledge recruitment. However for now, as we are taking the first step towards

exploring the role of explanation in induction, we acknowledge but side-step possible

interactions between property-moderated knowledge retrieval and explanation.

Study 5: Property Effects on Spontaneous Explanations

The proposal about explanations makes a number of predictions. For example, if

participants are generating explanations, and if these explanations are responsible for property

effects on inferences, we should observe different types of explanations for inductive

questions involving different properties. To test the proposal that property affects inferences

by triggering different types of explanation for the provided evidence, we re-examined the

inferences collected in the Study 3. During original data processing, the coders noted that

participants often spontaneously came up with explanations for the provided evidence. For

example, participants might elaborate on the property: “this is a bird flu”; “perhaps B6-cells

defend deer from particular viruses that they are exposed to”; “maybe substance B7 allows

owls to store more glucose in their body which gives them more energy to move their large

body through the air”. In other cases, participants came up with explanations of why the

premise animal might have ended up with the property: “ vultures may get flu E5 from the

dead and decaying animals they feed off of”, “…this gene could have been developed as a

result of the environment that the birds live in”, or even “[the gene will be found in] fish since

pelicans eat them, the pelicans might develop that gene from the fish”.

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To examine the plausibility of the explanation hypothesis, we conducted a follow-

up analysis, in which we examined the spontaneously generated explanations from Study 3 to

determine whether different properties trigger different types of explanations. We expected

taxonomic properties to trigger predominantly formal explanations, referring to classes of

objects (that would eventually translate into category-based, or taxonomic inferences) and

ecological properties to trigger predominantly causal explanations, referring to interactions

between animals and/or their environment (that would eventually translate into causal

inferences based on ecological interactions). We had no specific predictions about teleological

explanations; given Kelemen’s (1999) findings, if adult participants treat cells, genes, flu

viruses, substances occurring in the bloodstream as biological “parts”, they might attribute

purpose to them; if they treat them as self-standing biological phenomena, we would not

expect to see teleological explanations about them. Preliminary examination of inferences

suggested that material explanations were absent or nearly absent, so this explanation type

was not considered in this analysis.

Coding

Three trained coders independently re-coded all the inferences collected in Study 3 for

the presence of formal, causal and teleological explanations. The coding was done in two

steps. In the first round, the coders identified all responses that contained explanations of the

evidence (regardless of whether the participant actually made a codable projection to another

entity in their response). Explanations of how the animal got the property (“from eating its

prey”), elaborations on the type of property (“it’s a bird flu”), general statements about

property type and distribution (“viruses are transmitted via contact”) were counted as

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explanations. Twelve percent of inferences (467 out of 3920 codable responses) contained

such spontaneous explanations.

In the second round of coding, the selected explanations were coded into four

categories: formal, causal, teleological, or other type of explanation. Explanations that

referred to kind membership (“mammal gene”, “forest parasite”) were coded as formal;

explanations describing a “story” of interactions between animals and other entities, or in

general a sequence of events that resulted in the premise category having the property

(“vultures got it from eating dead animals”) were coded as casual; explanations referring to

goals, functions or purposes of properties (“these cells are to protect them from the cold”)

were coded as teleological; idiosyncratic or vague explanations that could not be assigned to

any of the three categories were coded as “other”. Multiple codes could apply to a single

response if it contained more than one type of explanation (but not when the response was too

vague to be unambiguously assigned to one category – in which case it was coded as “other”).

Table 6 summarizes the coding scheme and illustrates the coding categories with examples.

Throughout coding, the coders did not have access to the property information (unless

the participant mentioned the property in the response).

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Table 6. The explanation coding categories - formal, causal, teleological (with examples of premise explanations that participants spontaneously generated during the open-ended induction task).

Explanation type

Property Formal Causal Teleological

Ecological: Flu

Seagulls and birds. they have the bird flu.

Vultures may get flu E5 from the dead and decaying animals they feed off of.

Herbivores allows them to digest ?....

Ecological: Parasite

Anything else that lives in the three places in china that pandas still live... environmental.

Bugs since the bugs could have been on the trees that the beavers were taking wood from to make their shelter. The bugs could have given the parasite to the beavers.

Emus might also have parasite Q1, as both are hunted by the same type of animal. Parasite Q1 could help keep them both safe.

Neutral: Property X

Eagles or any bird because property b7 can be a property only prevalent in birds .

PROPERTY H7 could be in the water in which the turtles swim thus giving them the property.

Property R5 is probably a substance that allows camels to be better adapted to their environment. Therefore, I would guess that scorpions would also have this property R5, because they are also adapted to live in hot, dry climates.

Neutral: Substance in the bloodstream

I have no way of knowing what this substance is. It may be found in all animals or only in owls.

Certain trees might have [substance] K8 because beavers consume tree barks and so it would enter their bloodstream.

Those animals which ingest rubbish as Z1 can help to digest bacteria in the rubbish.\\\ Maybe substance B7 allows owls to store more glucose in their body which gives them more energy to move their large body through the air, so I would guess that other large birds, such as a eagle, would also have substance B7.

Taxonomic: Cell

Mammals, Cougars, Cheetah, Panthers - All the listed mammals are in the cat family, V*-Cell would be a common cell found in the list of mammals.

Anything that the lion has eaten most likely had R8-CELLS and passed it on to the lions.\\\ Roadrunner --> people who run because they are likely to have the same cells as a result of sharing the same activity.

I would guess that moose would also have B6- cells. Perhaps B6- cells defend deer from particular viruses that they are exposed to.

Taxonomic: Gene

Other species of fish because maybe this gene is prevalent in all types of fish.

Other tropical birds, because this gene could have been developed as a result of the environment that the birds live in.\\\ fish since pelicans eat them, the pelicans might develop that gene from the fish.

Birds. (…) this could be a gene related to their bone structure that allows them to fly.

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Results

Scoring. The initial dataset contained inferences made by 100 subjects about 42

animals. Within this dataset, each subject made an inference about each animal paired with a

particular property, and animals rotated through the properties between subjects. After the

data from 5 participants were discarded as uncodable, it left 95 responses per animal, with a

varying number of subjects per property condition (30 to 34 responses per animal per property

type (taxonomic, ecological, neutral)). For each animal, we calculated the percentage of

subjects who generated each type of explanation out of total number of participants who

reasoned about that animal, separately for each property type (e.g. the proportion of subjects

generating formal explanations out of 30-34 subjects who reasoned about ducks having an

ecological property (flu or parasite)). This yielded 12 percentages, or relative frequency

scores, per animal (3 property types x 4 explanation types), that were arcsine-transformed for

the analyses33.

Analyses. First, we analyzed the percentage of subjects giving uncodable (“other”)

explanations as a function of the property. Uncodable explanations were, on average,

generated by 1.9% of participants per animal, and their frequency did not differ as a function

of the property (one-way repeated measures ANOVA F(2, 82)=1.21, p=.303)). Thus, they

were excluded from the following analysis.

The main question, whether different properties trigger different types of explanations,

was addressed by a 3 property type (ecological, neutral, taxonomic) x 3 explanation type

(formal, causal, teleological) repeated measures ANOVA on relative frequency of

explanations. The overall likelihood of providing an explanation did not differ with the                                                                                                                33  All  the  reported  statistics  are  based  on  the  arcsine-­‐transformed  data,  all  the  reported  means  are  raw  

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property (F(2, 82)=.044, p=.957). But the explanations of different types differed in

frequency (F(2,76)=18.836, p<.001, η2partial=.315): causal explanations were more frequent

(5.3%) than formal (2.8%, repeated samples t(41)=4.599, p<.001, Cohen’s d=.71) and

teleological (2.4%, repeated samples t(41)=5.017, p<.001, Cohen’s d=.77), that did not differ

from each other (repeated samples t(41)=1.267, p=.212, Cohen’s d=.2).

Of most theoretical interest was the significant interaction between the property and

explanation type, shown in Figure 19 (F(4,164)=34.442, p<.001, η2partial=.457). When the

property was ecological, out of the total of 13.4% of inferences containing explanations, the

majority of explanations (8.9%) were causal, followed by formal explanations (2.5%)34, with

only a few teleological explanations (0.4%), all significantly different from each other (causal

vs. formal t(41)=6.719, p<.001, Cohen’s d=1.04; causal vs. teleological t(41)=10.984, p<.001,

Cohen’s d=1.69; formal vs. teleological t(41)=4.524, p<.001, Cohen’s d=.7). When the

property was neutral, within a total of 11.6% of inferences containing explanations, causal

explanations (5.3%) still dominated (paired samples causal vs. formal t(41)=5.257, p<.001,

Cohen’s d=.81; causal vs. teleological t(41)=3.005, p=.005, Cohen’s d=.46), while formal

(1.6%) and teleological (3.0%) were equally frequent (t(41)=1.432, p=.16, Cohen’s d=.22).

When the property was taxonomic, within a total of 11.7% of inferences containing

explanations, two most frequent types of explanation were formal (3.7%) and teleological

(3.8%) (not different from each other, paired samples t(41)=.039, p=.969, Cohen’s d=.01) that

                                                                                                               34  Most  formal  explanations  about  ecological  properties  were  provided  in  the  context  of  flu  (“it’s  a  bird  flu”,  3.6%),  rather  than  parasite  (“it’s  a  bird  parasite”,  1.4%).  Perhaps,  the  recent  upsurge  of  avian  and  swine  flu  references  the  media  made  the  species-­‐specificity  of  viruses  particularly  salient  to  our  participants.  For  the  future  studies  of  property  effects,  we  would  recommend  replacing  flu  with  another  ecological  property,  such  as  infection.  

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were both more frequent than causal explanations (1.8%, paired samples vs. formal

t(41)=3.423, p=.001, Cohen’s d=.53; vs. teleological t(41)=3.812, p<.001, Cohen’s d=.59).

Figure 19. Percentage of participants spontaneously producing formal, causal and teleological explanations as a part of generating open-ended inferences about ecological, neutral and taxonomic properties.

Pairwise comparisons across property type confirmed that frequency of a given

explanation type varied with the property. Causal explanations were more frequent for

ecological than neutral than taxonomic properties (paired samples tests, ecological property

vs. neutral t(41)=3.532, p=.001, Cohen’s d=.54, eco vs. tax t(41)=9.659, p<.001, Cohen’s

d=1.49, tax vs. neutral t(41)=6, p<.001, Cohen’s d=.93 (all significant after Bonferroni

correction for 9 unplanned comparisons, critical significance level .006)). Frequency of

formal explanations was higher for taxonomic than neutral properties (t(41)=3.848, p<.001,

Cohen’s d=.59), but it did not differ between taxonomic and ecological (t(41)=2.315, p=.026,

Cohen’s d=.36, n.s. after Bonferroni correction) or ecological and neutral (t(41)=.983,

p=.332, Cohen’s d=.15). Finally, frequency of teleological explanations was significantly

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higher for taxonomic properties that ecological (t(41)=7.327, p<.001, Cohen’s d=1.13), as

well as for neutral relative to ecological (t(41)=3.575, p=.001, Cohen’s d=.55), but did not

differ for taxonomic and neutral properties (t(41)=2.027, p=.049, Cohen’s d=.13, n.s. after

Bonferroni correction).

The revised proposal claims that the property affects inferences via explanation. The

analyses reported above demonstrate the link between property and explanation type. To

examine the second link, between explanation and inferences, we calculated percentage of

taxonomic and ecological inferences out of the subset of inferences containing explanations,

separately for each explanation type. Most inferences containing formal, teleological, or

uncodable explanations were taxonomic (formal explanations: taxonomic (86.7%) vs.

ecological (17.8%) inferences, paired samples t(41)=9.969, p<.001, Cohen’s d=1.54;

teleological explanations: taxonomic (63.8%) vs. ecological (31.5%) inferences, paired

samples t(41)=4.381, p<.001, Cohen’s d=.68 ; uncodable explanations: taxonomic (62.3%)

vs. ecological (24.8%) inferences, paired samples t(41)=4.295, p<.001, Cohen’s d=.66). In

contrast, the majority of the inferences containing causal explanations were ecological

(taxonomic (30.3%) vs. ecological (88.5%) inferences, paired samples t(41)=-8.608, p<.001,

Cohen’s d=1.33). Such systematic relationship between explanation type and inferences,

taken together with the evidence for the relationship between property and explanation type,

is consistent with the proposal that explanations mediate the effect of property on inferences.

Discussion

This chapter examined the explanation hypothesis by testing the proposal that different

properties trigger different types of explanations. We predicted taxonomic properties to

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trigger predominantly formal explanations (referring to classes of objects), and ecological

properties to trigger predominantly causal explanations (referring to interactions between

animals and/or their environment). We had no specific predictions about teleological

explanations (referring to goals and functions of properties).

To test this proposal, we examined the premise explanations that participants

spontaneously generated in 12% of their inferences. Since we were interested in how

explanations of the given evidence can affect generated inferences, we focused on

explanations of the information provided to the participants, not on the justifications for

inferences. For example, when given the premise “parasite X is found in ducks”, a participant

might come up with an explanation “ducks must have gotten the parasite from the food” or

“that’s a bird parasite” or even “this parasite helps keep them safe from predators”, and then

generate an inference projecting the parasite to other animals. We were interested in whether

the property affects the type of explanation of the given evidence.

To address this question, we examined the explanations participants spontaneously

generated in Study 3. As predicted, different properties triggered different types of

explanations. When participants were reasoning about ecological properties, the majority of

explanations they provided were causal, referring to a mechanism of property transmission

that could have endowed the animal with the property (e.g., “Owls eat mice and could

contract the flu from the mice that it eats”). In contrast, when participants were reasoning

about taxonomic properties, they were less likely to use causal explanations, preferring formal

(“this cell could be specific to jaguars”) or teleological (“T5 is something to keep them

warm”) explanations. For neutral properties, causal explanations dominated, although they

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were not as frequent than for ecological properties. In contrast, teleological explanations

were more frequent for neutral than ecological properties.

Our investigation of teleological explanations in this study was exploratory – we did

not have a priori predictions about their distribution across properties. Kelemen (1999)

demonstrated that adults accept teleological explanations for biological parts and organs

(eyes), but do not extend teleological explanations to biological entities or phenomena (lions,

rivers). The results of this study – abundance of teleological explanations about taxonomic

and neutral but not ecological properties - extend Kelemen’s finding of selective deployment

of teleological explanations based on entity type to selective deployment of teleological (as

well as causal and formal) explanations based on the projected property.

Finally, besides demonstrating the relationship between property and explanation type,

we also obtained evidence for a systematic relationship between explanation type and

inferences. Ecological inferences were associated with causal explanations, whereas

taxonomic inferences were associated with formal, teleological or uncodable explanations.

This finding is consistent with the idea that explanations serve as a mediator between

properties and inferences. Although of course, the analyses demonstrating the property-

explanation and explanation-inferences relationships were correlational, and more work is

needed to make stronger claims to determine the role of explanations in property-inference

relationship. One important question for future study is whether explanations play a causal

role in property effects on inferences. The current data do not readily lend themselves to

addressing this question directly. Nevertheless, the informal comparison of effect sizes

indicates that the mean effect size for meaningful explanations on inferences (d=1.18) is

larger than the mean effect size for properties on inferences (d=0.92). This suggests that

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property-driven explanations are likely to affect inferences directly, rather than being a

mere correlate of properties. We come back to better ways of testing the questions about the

role of explanation in inference at the end of this chapter.

Admittedly, examining open-ended responses for the presence of explanations is not

the most straightforward way to test the proposal about property triggering different types of

explanations. We can identify at least a few objections to this analysis – that are, nevertheless,

not fatal for interpreting its results. First of all, the overall frequency of spontaneously

explanations was lower (12% of inferences) that one might expect if explanation were central

to inference generation. However, the absence of an explanation in the final hypothesis does

not mean that participants did not explain the evidence prior to verbalizing the hypothesis -

after all, participants were not explicitly asked to explain the premise in this task.

Another objection might be raised about the reliability of the pattern based on just

12% of the data. However, 12% of all inferences amounted to 467 explanations, which is not

such a small dataset. Thus we claim that the observed interaction between property and

explanation type is not likely to be a fluke of an extremely small sample.

We also argue that the observed interaction between property and explanation type is

not likely to be a side effect of liberal coding. Of course, open-ended responses are inherently

vague, and since participants were not particularly invested in conveying their message to the

experimenter accurately, their answers were often telegraphic and/or ambiguous. Thus, just

like in coding inferences, we took a rather conservative stance in coding ambiguous

explanations. For example, when a participant commented that a property “is an adaptation”,

it was hard to tell whether they relied on correct understanding of evolutionary theory and

meant that the current form of the trait is a product of natural selection (a causal explanation),

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or whether they were driven by a common misconception about evolution as progress and

claimed that animal developed that trait in order to improve its performance in the

environment (a teleological explanation). The best way to deal with such ambiguous

responses was to assign them to the category of uncodable explanations. Since the frequency

of uncodable explanations did not vary with the property, the worst effect of response

ambiguity was loss of data, but it was not a likely confound for the observed results.

Finally, the observed interaction was not likely to pragmatic and linguistic factors

affecting likelihood of verbalizing explanations. People may view formal explanations as

self–evident and feel like they do not need to state them explicitly, or have no ready ways of

verbalizing them. For example, “other species of fish because maybe this gene is prevalent in

all types of fish” (involving a formal explanation referring to a “fish gene”) may seem more

redundant than “bugs since the bugs could have been on the trees that the beavers were taking

wood from to make their shelter. The bugs could have given the parasite to the beavers.”

(involving a causal explanation “bugs gave the parasite to beavers”). These factors could

account for overall difference in frequency of explanations, causal being more frequent than

formal and teleological, but not for the interaction between the property and explanation type.

The results of this study provide initial support for the proposal that different

properties invite participants to come up with different types of explanations for the evidence

they are provided with. In Study 3, we saw that inferences vary with property; Study 4

suggested that knowledge retrieval does not differ with property; and this follow-up analysis

shows that explanation type does vary with the property. So far, explanations appear to be the

most promising candidate for a mechanism connecting properties and their effect on

inferences. In sum, these results show that different properties can trigger different

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explanations, which can potentially lead to different inferences. Although the idea of

explanations being involved in induction is not novel, the claim about property affecting the

type of explanation is an original proposal, which stands in contrast to prior (speculative)

accounts of property effects based on selective attention to different aspects of similarity

between premises and conclusions (Heit & Rubinstein, 1994; Heit, 1998).

Future directions: better ways to test the property-explanation-inductive

hypothesis link. To examine the link between property and different explanation types in

more detail, and to avoid the limitations of coding spontaneous explanations from an open-

ended inference-generation task, we plan on using a closed-end task, in which participants are

given initial evidence (“ducks have B5-cells”) and are asked to select an explanation from

several provided options: “because they are birds” (formal), “because their parents passed

these cells on to them” (causal), “because these cells serve some functions useful for ducks”

(teleological). If different properties are associated with different explanatory modes, we

would expect property to moderate choice of best explanation for the evidence: causal

explanations for ecological properties, formal and teleological for taxonomic properties.

Next, we will examine the link between explanation and inference generation more

directly. The main question is whether explaining the evidence is the cause of property-

sensitivity of final hypotheses. To address this question, we propose three experiments. In the

first two experiments, the main idea is to manipulate presence or absence of explanation, i.e.

prevent participants from explaining evidence in some conditions and see whether this

reduces property effects in inferences. One way to prevent participants from explaining is to

introduce time pressure after knowledge retrieval is completed. For example, participants can

be given unlimited time to study a display of multiple animals and properties (within the

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limits of working memory), presumably completing retrieval of knowledge about them.

Then, after a button press, a connector between an animal and property would appear, and

participants will be forced to make a decision about a projection target (does it share the

property?) within a limited time period. It is of course possible that participants might attempt

to pre-generate explanations, but with as few as three properties and four animals, they would

need to generate 12 explanations – instead of waiting till they know the specific premise

combination, and generating just one explanation. Knowing that often participants are trying

to get through class-credit experiments with as little investment of effort as possible, we have

some faith that this manipulation might successfully impair explanation generation.

Another way to prevent participants from explaining could be by manipulating overall

framing of questions as an intentional “set up”: they can be told that someone was trying to

confuse the biologists on purpose, and introduced a certain gene/parasite/substance in ducks

that they studied”, and invited to make an inference. To the extent that these manipulations

impair generation of explanations, and if explanations play a causal role in property effects in

induction, the revised model predicts that they should lead to reduction of property effects in

the inferences.

The third experiment would examine the link between the type of explanation and

final hypotheses. Instead of relying on participants generating explanation, we can control the

type of explanation by providing participants with the explanation of the evidence – “ducks

got this property from the food they consumed” – and see whether this manipulation is a.

sufficient to drive differences in generated hypotheses, and b. strong enough to override the

initial intuitions about properties (in cases where provided explanation does not match the

typical explanation induced by that property type, e.g. teleological explanation for an

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ecological property (“this parasite helps to keep away the predators”), or a causal

explanation for a taxonomic property (“this gene developed as a result of a mutation caused

by extreme heat in tropics”).

These studies that will examine the role of explanation in inductive inference,

alongside with modified versions of the Study 4 examining property effects on knowledge

retrieval (see the discussion of Study 4 for improvement suggestions) outline our plan for

further investigation of property effects in inference generation.

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Chapter VII. General discussion

Overview

The goal of this project was to examine the mechanism of property effects in inductive

inference generation. Property effects - changes in argument strength or generated

conclusions based on the projected property – are ubiquitous in induction, but the theoretical

accounts of induction capable of addressing the underlying psychological mechanism are

lacking.

Model 1.0: Property Effects via Knowledge Retrieval

To address this gap, we proposed a simple model of inductive inference, which

identifies two components within inference generation: retrieval of knowledge about the

premise, and generation of an inductive hypothesis based on that information (and general

knowledge about the world). We decided to tackle the problem of specifying the mechanism

of property effects by targeting the knowledge retrieval component rather than the hypothesis

generation component because more work had been done on context-dependent retrieval of

information than on the complex and inherently creative process of hypothesis generation.

As a first approach to localizing property effects within theinference generation

process, we proposed that they arise during knowledge retrieval. We outlined three retrieval

strategies that could yield property effects. In the first, “minimalist” strategy, only the

knowledge about property is retrieved. In the second strategy, knowledge about property and

knowledge about premise category are retrieved autonomously and provide independent

contributions to the inferences formulated at the. In the third strategy, both property and

premise category knowledge are retrieved, but this time interactively; property serves as a

context which affects retrieval of knowledge about premise category, and such context-

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dependent retrieval of information from semantic memory gives rise to the observed

property effects in inferences. To illustrate the latter strategy with an example, when a person

is presented with the premise “flu X is found in ducks”, and is asked to generate an inductive

hypothesis about what else might have the same flu, the epidemiological nature of the

property (flu) could influence which knowledge about the premise category (ducks) is

retrieved from the memory (such as that ducks are eaten by foxes, and could thereby transmit

flu to them). This strategy is consistent with the non-verbalized or vaguely verbalized

assumption in the field that “the property being considered influences which features [of the

categories] are important for induction” (Heit & Rubinstein, 1994, p. 411) and the property

effects have something to do with selective sampling of premise category features from

memory (Heit, 1998; Heit & Rubinstein, 1994).

We tested this proposal about retrieval-localized property effects on the example of

folkbiological reasoning, or reasoning about natural world. This knowledge domain provides

a good stage for investigating property effects in inductive reasoning, since it is structured by

two relatively well defined knowledge structures that can serve as bases for inferences –

taxonomic, based on hierarchy of biological classes, and ecological, based on shared habitat

and interactions between species. In addition, property effects have already been documented

in this knowledge domain, in a variety of experimental paradigms – from evaluation of

inductive arguments, to open-ended inference generation tasks that we used in this project.

We split the objective of examining property effects in folkbiological induction into

two tasks and addressed them in a series of four studies. First, we characterized the types and

sources of knowledge brought about by the premise of an inductive argument (description of

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the “ingredients”; Studies 1 & 2). Second, we tested our proposal the retrieval-based

mechanism of property effects in Studies 3 & 4 (evaluating our proposal about the “recipe”).

The Ingredients

Study 1 demonstrated that knowledge about properties can contain systematically

biased beliefs about their distribution. Some properties are believed to be homogeneous

within taxonomic categories (e.g. fish tend to share similar genes), other properties are

believed to be homogeneous within ecological properties (e.g. jungle animals tend to share

infectious diseases). Such distributional beliefs can be the origin of the taxonomic or

ecological bias that properties have been shown to exert on inferences. Although the idea of

distributional beliefs about properties is not new (see Goodman’s discussion of

overhypotheses (1955)), the demonstration that people actually do possess such beliefs, and

that they can differ from property to property, is novel.

Study 2 examined taxonomic and ecological knowledge about premise categories.

Based on the features that participants listed for a set of animals, participants possess both

taxonomic and ecological knowledge about species, but taxonomic knowledge proved to be

more abundant and salient. Taken together, Studies 1 & 2 described what an inductive

premise like “flu X is found in ducks” brings to the table: abundant and salient taxonomic

knowledge about ducks (they are birds, they have beaks and feathers and webbed feet, they

quack, walk funny, and can fly), less abundant and less salient ecological knowledge about

ducks (they live in ponds, eat plants and insects, they are eaten by foxes and coyotes), and the

beliefs about the property distribution (the property of having a certain flu is likely to be

homogeneous within animals that share a habitat and/or interact ecologically).

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The Recipe

The next two studies examined how this knowledge about premise category and

property is combined and translated into inferences. Study 3 replicated property effects in a

novel experimental setup with property varied within subjects. The fact that the same

participant could vary their inferences from question to question based on the property they

were reasoning about suggested that the property effects are indeed driven by processing of

specific attributes of each inductive question. To characterize how premise information is

translated into inferences, this study examined the recruitment of premise category

knowledge, or the relation between the available taxonomic and ecological knowledge about

animals and the inferences generated about them. It showed that salient ecological knowledge

about animals facilitates ecological inferences about them, and the salient taxonomic

knowledge inhibits ecological inferences. Most importantly, both facilitation and inhibition

were moderated by the property; ecological properties strengthened the facilitatory effect of

ecological knowledge on ecological inferences, while taxonomic properties strengthened the

inhibitory effect of taxonomic knowledge on ecological inferences. Although no such

property-moderated relationship to knowledge was observed for taxonomic inferences, we

took the results of this study to suggest overall that premise category knowledge is recruited

to generate an inference, and that such recruitment is moderated by the nature of the property.

This interactive recruitment of premise category knowledge & property knowledge provided

the first constraint on the process that might have generated property effects, suggesting the

interactive retrieval of property and category knowledge strategy (rather than retrieval of

property knowledge only, or independent retrieval of property and category knowledge).

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Study 4 subjected the interactive retrieval strategy proposal to a further test, by

probing directly into the retrieval of category knowledge unfolding over time. This study

employed a combination of two tasks: the open-ended induction task insured participants

engaged in inference generation, while a lexical decision task served as a priming probe for

activation of taxonomic and ecological knowledge about premise animals. Even though the

method proved capable of detecting activation of information about the premise animals (as

reflected by accuracy of decisions about related vs. unrelated targets), the results provided no

evidence of property-moderated retrieval of category knowledge. This was surprising, given

the results of Study 3 which suggested that some interactive processing of property and

premise must have taken place, in order to yield the observed interactive recruitment of

knowledge by ecological inferences.

Implications for Model 1.0

In sum, by the end of Study 4, we had data from Study 3 saying that property and

category knowledge were combined interactively, and the data from Study 4 saying the

interaction was not likely taking place in retrieval. Based on the proposed retrieval-based

model of property effects, if both property and premise category knowledge are retrieved

independently, and the retrieved knowledge is directly translated into inferences, the resulting

inferences should reflect independent contributions of property and premise category. Yet, the

outcome inferences showed clear signs of interaction between property and category

knowledge: the effects of knowledge on inferences were strengthened in the presence of a

congruent property and weakened in the presence of an incongruent property. Such result was

not compatible with the Model 1.0 and required a model revision.

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Model 2.1: Property Effects via Explaining the Evidence

The revised model had to be able to account for the pattern in which premise category

and property knowledge are combined interactively, but not during retrieval. Looking for such

interaction elsewhere within our model of induction, we introduced explanation of the

evidence to the model (within the hypothesis generation component) and nominated it as a

source of property effects on inferences. By explanation we mean identifying a larger pattern

or regularity that the observation is a part of (Williams & Lombrozo, 2010). The revised

proposal claims that first, knowledge about both property and premise category is retrieved,

and, since we have no evidence for interactive retrieval, we assume they are retrieved

independently. Next, knowledge retrieval is followed by hypothesis generation which

involves first, explaining the evidence provided by the premise (the combination of premise

category and the property) by identifying a larger regularity that it belongs to, and second,

formulating a guess about other entities that might share the property by virtue of belonging to

the same regularity. Within this account, different properties can affect inferences by

triggering different types of explanations – formal, based on category membership, or causal,

based on a sequence of enabling events. Different explanations, i.e., identifying an

observation as a part of different types of regularities, can in turn lead to the generation of

different hypotheses, and ultimately different inferences.

The revised proposal can accommodate the interactive relationship between premise

and property observed in outcome inferences (Study 3), and absence of evidence for such

interaction during retrieval (Study 4). The knowledge about premise category and property

may be retrieved via two independent processes, but a subsequent explanation of the evidence

can introduce the interactive relationship between knowledge and outcome inferences. The

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variability in inference types associated with the property – formal explanations, in terms

of taxonomic classes, prevalent for taxonomic properties, and causal explanations, in terms of

ecological interactions, prevalent for ecological properties – can be driving the interactive

effects in the inferences.

To subject this proposal to a first test, we conducted Study 5 in which we explored the

explanations many participants spontaneously generated for the premises while making

inferences. The revised proposal predicted that the type of explanation should vary with the

property. The data supported this prediction: for ecological properties, causal explanations

dominated; for taxonomic properties, the majority of explanations were formal or teleological

(attributing ends or purposes to properties). Neutral properties showed a mixed pattern of

explanation types. Besides the link between property and explanation, this study also

demonstrated a systematic relationship between explanation and inferences: ecological

inferences were associated with causal explanations, while taxonomic inferences were

associated with and formal and teleological explanations. This evidence provides initial

support to our proposal localizing property effects within the hypothesis generation

component. Specifically, it shows that participants systematically vary in the kinds of

spontaneous explanations they provide for the evidence provided by the premise, depending

on the property, and these varying explanations are likely to translate in property-sensitive

inferences.

Conclusions and Implications

In sum, across the studies within this project, we have some evidence that – contrary

to our original hypothesis - property effects do not take place in retrieval. This questions the

existing, but not tested, assumption in the field about the mechanism of property effects based

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on context-dependent retrieval of information from semantic memory (Heit & Rubinstein,

1994).

We also have some promising evidence that property effects may take place during

hypothesis generation. This potentially clarifies the connection between two lines of research

- on explanations and on induction. Most researchers agree that these are related, but very

little supporting empirical work exists, although it is increasingly acknowledged that presence

of available explanation can reduce reliance on overall similarity and override effects of

similarity and diversity of evidence on induction (see Lombrozo, in press, and Lombrozo,

2006, for a review). In this project we demonstrated that explanations do not just “mess up”

existing regularities in induction, but may in fact be a part of the mechanism of one such

established regularity – property effects in induction.

The revised model can also account for other phenomena in induction. For example, it

can explain why idiosyncratic properties discussed in the introduction, such as “has a gum

stuck on the bottom”, “fell on the floor this morning”, are deemed unprojectable. If an

explanation suggests that an observation is not a part of a larger regularity, such property

would not be generalized from one observation to other entities. The explanation component

of the model can also account for a finding from an unpublished study from our laboratory:

participants often refuse to generalize a property from a premise postulating two animals not

related either taxonomically or ecologically to share a property (e.g. “flu X is found in

peacocks and geckos”) – even though they are perfectly willing to make inferences if that

paired premise is split in two separate inductive questions (“flu X is found in peacocks”, “flu

Y is found in geckos”). On the proposed account, the absence of an obvious relationship

between peacocks and geckos suggests that a shared flu is more likely a coincidence rather

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than a part of a larger regularity – preventing participants from generalizing it to other

entities.

The explanation-based proposal of property effects also fits well with the Relevance

Theory of Induction (Medin, Coley, Storms, & Hayes, 2003). This theory, as reviewed in the

introduction, claims that people assume that the premise is relevant to the conclusion and seek

to identify a relation between the premise and conclusion that would justify such an

assumption. We can extend this proposal to open-ended inference generation. When

participants are presented with a premise only, they can still ask, “why am I given this

particular information, this property plus this category?” (instead of “why is this premise

given with this particular conclusion?”). With this question, participants seek to identify a

relevant connection between the property and the category within the premise – in other

words, they attempt to explain the premise evidence. This goes in line with Medin et al.’s

(2003) proposal about the way people reason about uninformative properties, that people seek

to interpret them and extract some meaning for the property from the salient features of

premise categories. For example, when presented with the premise “penguins have Property

Y”, “people might expect that property Y is an adaptation to an Antarctic environment or

linked to swimming and waddling rather than flying” (p. 522). Indeed, as reported in Chapter

VI, for neutral, or uninformative properties, explicit spontaneous explanations were just as

frequent as for informative properties – consistent with Medin et al. (2003). In addition, our

results extend their proposal and demonstrate that in reasoning about neutral properties,

people use a variety of explanation types - formal, causal, or teleological – without a marked

preference for any single type, in contrast to reasoning about informative properties, that

apparently bias people towards one type of explanation or another.

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Future Directions

This project by no means has exhausted the list of questions about property effects in

induction, and in fact, it raised some interesting new questions. As the first next step, we plan

on addressing some questions that the Study 4 left open. Although we did see priming effects

from animal names to related words presented as targets in the lexical decision task reflected

in accuracy of decisions (e.g. decisions about related words “bird” and “pond” following

“duck” were more accurate than decisions about an unrelated word “sofa”), the timing of

decisions was not affected. Thus, although we were able to extract useful (if tentative)

conclusions from that study – that property may not moderate retrieval of premise category

knowledge – we have not characterized retrieval process very well. Definitely, more work is

needed to describe how knowledge is retrieved in the context of inductive inference.

Introducing explanation into the account of property effects appears to be a step in the

right direction, but it also raises many new questions. In a sense, the question is shifted from

how inferences are generated to how explanations are generated. Lombrozo (in press)

concludes her review of explanation by saying that the question of how explanations are

generated remains largely unaddressed, and “explanation generation confronts many of the

most difficult questions in cognitive science concerning contents and structure of general

beliefs, and how these are retrieved in particular contexts” (p. 32). Thus, we need to explore

how explanations are generated, what factors affect the type of generated explanation (besides

the property), and what factors affect whether a person seeks explanation for the evidence

before generating a hypothesis or not.

One interesting avenue of research could examine the relationship between property

effects on inferences and individual’s need for cognition (Cohen, Stotland, & Wolfe,1955).

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Individuals with higher need to find meaningful structure in information may tend to

explain the evidence provided by the premises more, and if explanations drive property

effects, this should result in a more pronounced pattern of property-driven differences in

inferences. Unless higher need for cognition is correlated with higher context-sensitivity of

retrieval (which can be tested separately), correlation between need for cognition and

inferences’ sensitivity to property differences would provide further support to our revised

proposal about the explanation-driven mechanism of property effects in induction.

To sum up, this project makes a step towards specifying the mechanism of property

effects in induction in two ways. First, it suggests that property effects do not work via

property-based retrieval of knowledge about premise categories from memory. Second, it

introduces property-driven explanations as a possible source of property effects. Of course,

these proposals are not mutually exclusive, and our main suggestion for the further research

would be not to abandon studying knowledge retrieval in induction, but to expand research on

the mechanism of property effects to include explanations.

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Appendix A. General Instructions Used in Study 1.

In this experiment, you will be asked to imagine that one animal species has a certain

property, and asked to estimate one of two things – 1. What percentage of other animals in the

same biological family is likely to have that property, or 2. What percentage of other animals

living in the same area is likely to have that property.

Here are a couple of examples:

Imagine that one animal species has slow metabolism.

- What percentage of animals in the same biological family is likely to have slow

metabolism?

Enter a number between 0 & 100 _____

or

Imagine that one animal species has thick fur.

- What percentage of animals living in the same area is likely to have thick fur?

Enter a number between 0 & 100 _____

Animal species are kinds of animals. Examples of animal species are monkey, deer,

trout, parrot, bluejay, scorpion, etc. When you are answering the questions, don’t try to guess

what exact animal species it refers to. Instead, read each question as referring to some

unspecified animal species. By biological families we mean more general classes of animals

that include many different species. Examples of biological families are mammal, bird, fish,

insect, reptile, etc. An area where an animal lives can refer to different habitats and/or

geographical locations, e.g. desert, forest, ocean, pond, savannah, Australia, Africa, etc.

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Appendix B. Selection of Animal Stimuli for Studies 2 and 3: Animal Typicality

Ratings

Fifty-seven Northeastern University undergraduate students completed typicality

rating task in exchange for course credit. Typicality rating packets contained total of 97

animal names from a variety of biological classes and habitats. Each animal name was

accompanied by twelve 9-point typicality rating scales, asking to rate each animal on “how

good of an example the animal is of the category, 1 being not a good example at all and 9

being the best possible example”. Participants rated each animal on their goodness of

membership in the following thirteen categories: bird, fish, insect, mammal, reptile

(taxonomic categories) and Arctic animal, desert animal, forest animal, pond animal, ocean

animal, rainforest animal, savannah animal (ecological categories). The rating scales appeared

in fixed random order, same for all animals and across participants. Participants worked

through the rating packets at their own pace.

Based on these ratings, we selected 42 animals aiming for a diverse set that would

represent a variety of taxonomic and ecological categories, with all animals relatively good

members of their dominant categories (above the scale midpoint of 5). The selected animals

and their respective typicality ratings are shown in Table A1. Some cells remained naturally

uncovered, since there exist few desert fish or ocean insects that would be familiar to our

participants.

Mean taxonomic (7.69) and ecological (6.75) typicality ratings for the selected animal

set were significantly above the scale midpoint of 5 (ecological typicality t(41)=10.734,

p<.001; taxonomic typicality t(41)=20.480, p<.001), suggesting that animals were relatively

good members of both taxonomic and ecological categories.

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Despite the effort to equate mean typicality in taxonomic and ecological

categories across the animals, this was not possible: the mean ecological typicality was lower

than the mean taxonomic typicality (paired samples t(41)=4.600, p<.001, Cohen’s d=.71).

This tendency of mean typicality in animal’s dominant taxonomic category to exceed mean

typicality in animal’s dominant ecological category held across the sample of 97 animals:

mean ecological typicality 6.3, mean taxonomic typicality 7.1, paired samples t(96)=3.980,

p<.001). This is consistent with well-established finding of contextual categories having more

graded internal structure (including a range of good to moderately-good members) than

taxonomic categories with their all-or-none membership (items are either good members or

non-members) (Ross & Murphy, 1999). Thus, our selection of stimuli is constrained by the

properties of natural categories.

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Table A1. The final set of animals and their typicality rating in corresponding ecological and taxonomic categories (ecological typicality / taxonomic typicality).

Eco / Tax category Bird Fish Insect Mammal Reptile

Desert Animal roadrunner (5.67/5.33)

scorpion (7.36/5.52)

camel (7.59/7.24)

rattlesnake (6.53/8.31)

vulture (6.72/8.43)

Forest Animal owl (6.82/7.29)

ant (6.43/8.81)

monkey (8.07/8.44)

toad (5.57/5.09)

toucan (7.12/7.65)

bee (5.28/8.08)

bear (7.20/8.12)

iguana (6.00/7.89)

butterfly (6.63/7.91)

jaguar (5.29/7.53)

deer (8.22/8.10)

fox (7.90/8.27)

rabbit (7.37/8.10)

moose (7.57/9.00)

koala (5.14/7.86)

panda (4.71/8.86)

Ocean Animal gull (5.35/7.82)

tuna (8.11/8.41)

whale (7.75/7.63)

pelican (4.73/8.09)

shark (8.29/6.46)

seal (7.59/6.76)

salmon (5.86/9.00)

Pond Animal duck (8.11/7.93)

dragonfly (5.81/7.78)

otter (5.01/7.16)

turtle (7.47/7.19)

goose (6.18/7.71)

beaver (5.93/7.61)

Savannah Animal

ostrich (6.81/7.5)

lion (7.82/8.22)

zebra (7.81/7.79)

hyena (7.29/7.41)

giraffe (7.12/7.35)

gazelle (7.12/7.76)

elephant (7.80/8.30)

kangaroo (6.57/8.00)

Note: Forest category includes both forest and rainforest animals, the ecological typicality rating for forest animals is based on the highest of the two categories.

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Appendix C. Stimuli Used in Study 4.

Table C1. The animals and target words used in the lexical decision task

Animal Target Taxonomic Ecological Unrelated Non-word filler Monkey primate Whale mammal Bee insect Tuna fish Scorpion bug Vulture bird Otter warm Coyote hair Fox fur Duck wing Flamingo feather Iguana reptile Bear forest Camel desert Shark ocean Dragonfly pond Anaconda jungle Beaver river Owl tree Giraffe safari Ant leaf Lion savannah Turtle lake Pelican coast Zebra plug Rabbit hotel Seal chapel Parrot dime Deer apron Hyena sofa Frog canvas Ostrich paper Butterfly wallet Salmon seducer Rattlesnake gum Goose desk Eagle knurdge Grasshopper deg Piranha sount Spider hork Robin lumf Trout beeg Lizard bipe

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Koala gimits Moose scrame Ferret dascara Dolphin tir Hedgehog flunt Woodpecker lerd Seahorse malk Mosquito vank Salamander gly Penguin blord Eel bonch Gecko strolf Jaguar wrebe Squirrel chift Walrus thake Skunk jeech Elephant wook Toucan shround Tarantula moft Crab trosh Toad nonk Bluejay gruck Snail jume Weasel roop Panda lought Llama fice Gorilla scronth Kangaroo fruick Crocodile tich

Note: The table shows a subset of the full set of stimuli, in which each of the non-filler animals was matched with 3 target words – taxonomic, ecological, and unrelated. A small number of participants were tested with other versions of the experiment covering the full stimuli set, however, since these results are not reported here, we only present the relevant subset.

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Appendix D. Study 4: Association Norming.

The goal of this norming was to determine the strength of association between the

animal names and target words selected for lexical decision task, as well as to verify lack of

direct association between properties used in the open-ended induction task and lexical

decision target words.

Method

Participants

Eighteen native speakers of English (12 females) participated in exchange for course

credit.

Materials

Six properties (gene, cell, property, substance, flu, parasite), thirty-six animal names

and corresponding taxonomic, ecological and unrelated targets served as stimuli (see

Appendix C, Table C1). The full set of targets, including those not shown in the table but

described in the note to the Table C1 (Appendix C), were used in this norming, yielding a

total of 108 lexical decision targets).

Design

There were three versions of the experiment, varied between participants. Each

participant was presented with 108 trials rating association of 36 animals to each of the three

targets, and 108 trials rating association of six properties to a subset (60) of the same target

words. The order of questions was randomized for each participant.

Procedure

Participants were tested in the lab individually. The experiment was run using

Qualtrics survey website. On each trial, participants were presented with a word (animal name

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or property) on top of the screen, an arrow pointing down, and one lexical decision target

below, and a 9-point rating scale with five labeled points, shown in Table D1. The task was to

rate how much the top word brought to mind the bottom word. The general instructions read:

In this experiment, you will read pairs of words. Please rate how much the first word

brings to mind the second word, using the provided scale.

The relationship between the two words is not necessarily symmetrical. For example,

"dollar" may evoke "green" more than the reverse. In this task, please rate how much

the first word brings to mind the second, not vice versa.

Throughout the experiment, some words may repeat. Please treat each rating as

independent: that is, your rating for one pair of words should not depend on the rating

you gave to another pair.

Table D1. Association norming rating scale.

1 1.5 2 2.5 3 3.5 4 4.5 5 does not bring to mind at

all

weakly brings to

mind

moderately brings to mind

strongly brings to

mind

immediately brings to mind

Results

For the analyses, the scale was converted from a 1-5 to a 1-9 scale. Animals were

rated as bringing to mind the selected ecological targets and to taxonomic targets (mean

ratings of 6.35 and 6.91, both significantly above the scale midpoint of 5: ecological

t(11)=3.31, p=.006, taxonomic t(11)=4.72, p=.002)), but not the unrelated targets (mean rating

1.28, significantly below the scale midpoint t(11)=-67.40, p<.001).

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Animals’ association to unrelated targets was rated as significantly weaker than to

ecological (t(22)=12.46, p<.001) or taxonomic (t(22)=6.46, p<.001) targets, while the latter

did not differ from each other (t(22)=.94, p=.34),

All six properties (gene, cell, flu, parasite, property, substance) were rated as not

bringing to mind the target words, all means significantly below the scale midpoint of 5 (all

p’s<.001, range 1.75-2.33).

Some properties varied in the extent to which they brought to mind ecological,

taxonomic, and unrelated targets. Gene, cell, and parasite brought to mind ecological and

taxonomic targets more than unrelated targets (independent samples pairwise comparisons, all

p’s<.016), probably because these are biological properties, and they are likely to be

associated to other biological terms (bird, jungle) more so than to non-biological unrelated

items (sofa, wallet). For cell, association to taxonomic targets (2.86) was stronger than to

ecological (2.08, t(20)=2.25, p=.036). However, the main point is that overall, properties were

rated as relatively unrelated to target words.

Discussion

The association norming validated the selected target words for the lexical decision

task: the animals’ names did bring to mind ecological and taxonomic targets equally, and did

not bring to mind unrelated targets. Hearing the property alone did not bring the lexical

decision targets to mind.

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Appendix E. Full text of instructions for the Study 4.

General Instructions

In this experiment, you will be presented with a number of statements about different

kinds of animals. Your task will be to come up with other animals or general kinds of

organisms that these statements could apply to.

For example, in one question, you may learn that one kind of animal has a certain

gene. In a different question, you may learn that another kind of animal has a certain parasite,

or a substance in its bloodstream, or cells, or a flu, or just some unspecified property. ALL

you know about the gene, or cells, or the parasite, or the substance in blood, or the flu, or the

property, is that the listed animal has it. You will be asked to list other animals or kinds of

organisms you think might also have it, as well as reasons for your answers.

There are no right or wrong answers; we are only interested in what you think.

Please consider each question independently. That is, the information provided for an

animal in one question should not affect your judgments about a different question.

First, you will do a practice session that will familiarize you with the types of

questions and how they will be presented, and then you will begin the experiment.

If you have any questions at any point, feel free to ask the experimenter.

Press space bar to begin the practice session.

Practice session, Step I

You will now be presented with several questions. These questions are very similar to

the ones you will be answering during the experiment.

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In this part of the practice session, you will see one question at a time, and below

the question, there will be a space where you can type your response. After listing the

answers, remember to give a short reason for each of them, so we know what you were

thinking while responding to the question. You can move on to the next question by clicking

on the "Next" button.

Press space bar to continue.

FLU H7 is found in RACCOONS.

What else is likely to have flu H7? Why?

[6 practice trials]

Practice session, Step II

Thank you for answering the practice questions. Now you have an idea of the

questions we'll be asking during the experiment.

However, instead of reading the questions and typing up your responses, YOU WILL

HEAR THE QUESTIONS IN THE HEADPHONES, AND YOU WILL SAY YOUR

RESPONSE OUT LOUD.

Also, to save time, every question will be presented in a shortened form. For example,

you may hear:

"FLU H7 - RACCOON"

This will stand for "FLU H7 is found in RACCOONS. What else is likely to have flu

H7? Why?"

After the question, following a short pause, you will hear a beep - this is a signal for

you to begin saying your response out loud. Please start speaking right after the beep, not

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before, and not waiting till much after. Don't worry about how you say it, we are only

interested in the content of what you say.

You will have 15 seconds to say your response. At the end of 15 seconds, you will

hear another signal, and the program will automatically proceed to another question.

If you finish your response sooner than in 15 seconds, you may press SPACE BAR

ONCE to proceed to the next question.

If you have any questions at this point, please ask the experimenter.

Press space bar to practice listening to questions and saying your responses out loud.

Practice session, Step III

Now you should have a fairly good idea of how the experiment will proceed.

However, there is one more thing. In parallel with listening to questions and saying

your responses, you will also be doing a secondary task.

Alongside with the questions, at certain times a string of letters will appear on the

screen. Your task is to decide if the string is an English word (for example, "sock") or if it's a

non-word (for example, "glym"). If it's a word, press YES. If it's a non-word, press NO.

Please try to respond by pressing the key as fast as possible after the string appears on

the screen, but without sacrificing accuracy. You will not receive feedback on the correctness

of your responses.

Now you will practice listening to questions, saying your responses out loud, and

making decisions whether a string of letters is a word or not. Doing all this at once may be

confusing at first, so try to get a hang of it during the practice.

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Now please place your index finger on YES key, and your middle finger on NO

key. Keep looking at the middle of the screen all the time. Press a space bar to proceed to the

practice.

Instructions reminder

You will now proceed to the main part of the experiment. You will be doing two tasks

simultaneously. First, you will be listening to the questions and after the cue signal, saying

your response. While listening to the questions, please pay attention both to the property and

the animal. Second, you will be looking for strings of letters appearing on the screen, and

deciding if they are words or non-words as quickly as possible.

If at this point you are not sure about what you will be doing in this experiment, please

find the experimenter and let them know. Otherwise, proceed to the main part of the

experiment.

There will be 72 questions total. Over the course of the experiment, you will be given

4 breaks. While the screen says "You may now take a break", you can rest for as long as you

need. But between the breaks please keep going through the questions without stopping. The

estimated time for completion of this experiment is under 40 min.

Remember that if you finish your response sooner than in 15 seconds, you may press

SPACE BAR ONCE to proceed to the next question.

Please keep looking at the center of the screen at all times. Also, keep your index and

middle fingers on the YES and NO keys at all times.

Press space bar to begin the experiment.