Concepts and Categorization

Categorization and Concepts Basic cognitive function is to categorize

Use experience to aid in future behavior and decision-making Cognitive economy

Concepts Mental representation of a category serving multiple

functions We can use associations to organize the environment

and our behavior Distill our experience (knowledge) by utilizing

functional relations

Functions of Concepts Classification

Determine category membership Understanding, making predictions, inference

Once classified one can then understand its relevant parts, know how to interact with it, infer other properties

Explanation and Reasoning For example, of others’ behavior

Learning New entities compared to and understood in terms of old and provide

feedback for modification Communication

Shared concepts and categorization allow for easier expression of ideas to others

Categories Categories

Collection of objects, attributes, or actions, etc. List of concepts Hierarchy

Set of entities or examples picked out by the concept How is experience distilled? How are functional relations established?

Category learning How is knowledge represented in a category?

Structure Schema

General knowledge structure that integrates objects, attributes, and actions into a cohesive representation Script Sequence

How do we use categorical knowledge?

Classification Determining the category membership of

various things (objects, properties, abstractions etc.)

Allows for treating otherwise discriminable entities as similar Similarity as the organizing principle for

categories and categorization

Structure of Categories Classical View Natural categories were structured in terms of

necessary and sufficient features If some entity has the set of necessary and

sufficient features, it belongs to that category, otherwise it does not

Rigid category boundaries

Classical view Problems

Duck-billed platypus and brown dwarf There simply do not seem to be defining features

for many categories

Perhaps features are not available to consciousness? Uncertain as to whether the necessary feature is present? Unlikely as folks are in disagreement as to what

would constitute category membership (even with themselves at different times)

Even when certain, some examples are obviously better than others

Bye-bye classical view

Probabilistic View Certain features may be necessary, and so weighted

heavily in categorization Probabilistic features, which are usually present but

not always, will also influence categorization E.g. Flies, for birds

How might we classify and represent structured knowledge?

Features/Typicality Theories

Prototype Exemplar

Features and typicality Some instances may have more features than others The more frequently a category member’s properties appear

within a category the more typical a member it is Robins vs. Penguins

Arrange objects based on some attribution. Comparison to average member (central tendency) Based on experience with category which may be different for different

folks

Great DaneChihuahua

Labrador

“Dogs”

Prototype Categorization instead may

reflect typicality judgments based on comparison to an ideal Concepts as abstractions

People abstract common elements of a formed category and use a common representation to stand for that category

How is the category updated? Family Resemblance

Overlap of common attributes Classification is made based on

overlap between prototype and exemplar

Prototype The prototype view can explain both typicality effects and the

fact that prototypes that had not been previously presented are correctly classified (even more accurately)

Problems with prototype explanation Doesn’t take into account category size or variability in examples Context

What may be more typical in one setting may not be elsewhere Correlations among attributes

E.g. smaller birds more likely to sing Implies linear separability among categories

Categorization is perfect by adding up and weighing the evidence from features present

If this is not the case for separating categories, one would be hard pressed to come up with worthwhile prototypes

Exemplar theory Exemplar theory

Sort of a bottom up approach to categorization Each instance is compared to others from past experience Category arises by the lumping together of similar exemplars

Similarity based retrieval Since the exemplar approach retains more information about

the category itself it gets around some of the problems faced by the prototype theory (e.g. context effects), but also how a prototype could be recognized at test when wasn’t presented previously

Has similarity to previous examples and activates those stored representations

Exemplar/Prototype theory Hybrid view

Perhaps a little of both* It may be that concepts

rarely consist of only prototype or exemplar representation Once rule is learned

categorize according to it. When exceptions arise, use an exemplar approach

E.g. grammatical rules

MC’s thought for the day: metacategorizationHow do we classify the empirical evidence as supporting (belonging to) one theory or another?

Between Category structure Up to this point the discussion has focused on

classifying items within one category or another i.e. how a particular category is represented Within category structure

But how are categories themselves organized? Between category structure

Types of Categories Examples

Abstract vs. Concrete Love vs. Mammal

Hierarchical vs. Non Mammal vs. woman

Different processes required? Hard to determine difference in kind

Hierarchical Membership assumes a hierarchy such that

classification in a subordinate category means an exemplar belongs to the superordinate category Poodle Animal

Basic level The default category classification

How will an item be typically classified? Poodle as dog rather than animal

The basic level is found at a middle level of abstraction (e.g. between type of dog and more abstract categories like Living)

Typically learned first, the natural level at which objects are named and the level at which exemplars are likely to share the most features

With expertise, the basic level may move to a subordinate level Child: Dog vs. Cat Adult: Poodle vs. Irish setter Expert: Minature vs. Toy

Structure of Categories Rosch

Hierarchal structure of concepts

Vehicles

CAR TRUCK BOAT

Sedan Sports SUV Garbage Row Yacht

-Corvette

-Mustang

Structure of Categories Vertical = Level of abstraction Horizontal = variability within category

Vehicles

CAR TRUCK BOAT

Sports SUV Garbage Row YachtSedan

Vertical StructureSuperordinateVehicles

CAR TRUCK BOAT

Sports SUV Garbage Row YachtSedan

Basic

Subordinate

Superordinate = defines category

Basic = overlap of common features

Subordinate = examplars

Properties of Hierarchy Each level gives a similar degree of information Converging operations for Basic Level

Common attributes Shape similarity Ease of labeling Similar verification time

SuperordinateVehicles

CAR TRUCK BOAT

Sports SUV Garbage Row YachtSedan

Basic

Subordinate

Non-hierarchical No clear structure

How would you classify yourself? No clear hierarchy, no basic level

E.g. socially relevant categories to which a member may belong to several

The various applicable categories can be seen as competing for classification rights Those used more frequently and recently will be more

likely applied for classifying a new instance E.g. gender, race

What processes are involved in categorization?

Does judgment of similarity in and of itself explain categorization?

Variable People’s judgments of similarity

change depending on the situation Medin Goldstone & Gentner (1993)

Depending on which pair of objects shown would change what determined a judgment of similarity

Similarity What constraints if any are placed on determinations

of similarity? What constraints does similarity place on what counts as a feature? Rocks and squirrels

Both exist, are bounded, can be run over etc.

Can similarity alone explain classification? Perhaps serves as guideline rather than definitive

delineator Abandoned if additional info suggests it is misleading Gelman & Markman (1986)

Classification by theory Organization of concepts is knowledge-based as

opposed to similarity-based Apply theory to the data

Concepts develop and change with experience/evidence E.g. various mental disorders

Theory and Similarity Theories will affect similarity judgments Similarity constrains theory Psychological essentialism

The way people approach the world Essences of things (e.g. what makes male or female)

Models of Categorization Generalized Context Model Exemplar-Based Random Walk

See Nosofsky link on class webpage ALCOVE Combinations of exemplar and rule-based processing Decision-bound approaches Rational model

Anderson

Categorization and memory What memory system or systems are used during

category learning? Essentially theories of category learning virtually all

assumed a single category learning system E.g. exemplar theory

When a novel stimulus is encountered, its similarity is computed to the memory representation of every previously seen exemplar from each potentially relevant category, and a response is chosen on the basis of these similarity computations

Category learning uses many, or perhaps all of the major memory systems that have been hypothesized by memory researchers.

Working memory Heavily used in reasoning and problem solving Could be the primary mediating memory system in tasks

where the categories are learned quickly. Two possibilities:

The categories contain few enough exemplars that the process of explicitly memorizing their category labels does not exceed the span of working memory Though possible, probably unlikely, however if comparisons are made

to a single ideal or prototype perhaps Working memory could be used if the category structures were

simple enough that they could be discovered quickly via a logical reasoning process. In other words if the means of categorization can be reduced to one or

two dimensions (e.g. some rule)

Working memory Evidence

Single rule-based categorization is interfered with in divided attention tasks where more complex category learning is not

Rule-based category learning is possibly mediated by a conscious process of hypothesis generation and testing. If the feedback indicates response was incorrect, then must decide

whether to try the same rule again, or whether to switch to a new rule If the latter decision is made then a new rule must be selected and

attention must be switched from the old rule to the new. Such operations require attention and working memory.

Episodic and semantic memory Memory for personally experienced events and general world knowledge No empirical evidence from category learning suggests separate

contributions of episodic and semantic memory systems These declarative memory systems are used during explicit memorization,

so category structures that encourage memorization are especially likely to be learned via these systems.

Two conditions: First, memorization is an especially effective strategy if each category

contains a small number of perceptually distinct exemplars. Second, other simpler strategies are ineffective

Indirect evidence from successful exemplar-based models that assume use of stored representations from prior learning

Some direct evidence from amnesiacs that suffer in category learning

Non-declarative memory Procedural knowledge

Memories of skills that are learned through practice Little awareness of details Is slow and incremental and it requires immediate and consistent feedback

Like declarative memory systems, would not be utilized for simple rule-based categorization

Example of radiologists and tumors Many exemplars in the set of X-rays, but identification takes practice and

process is not well-defined by practitioners Evidence

Information integration (more complex multi-dimensional categorization) tasks affected similarly as serial reaction time tasks Changing the way in which one responds (key press) leads to poorer performance

that is not seen in simple rule-based categorization tasks As with procedural tasks, complex category learning can be hindered without

appropriately timed feedback

Perceptual learning The specific and relatively permanent

modification of perception and behavior following sensory experience

No behavioral evidence implicating the perceptual representation system, jury out on neuropsych evidence

Use of categories in reasoning Ad hoc categories

Spontaneously constructed for the purposes of some goal Constructed differently from other categories?

Show similar results e.g. typicality effects, however, more of a comparison to an ideal rather than prototype

Gist: goals can affect category structure Conceptual combination

Construction of new concepts by combining the previous representations Recall structural alignment

Typicality may not be predictable from previous concepts Properties of new concepts may not be present in old.

Use of Categories Classification

Process of assigning objects to categories Treat (use) different “things” as the same

Explanation Bringing knowledge to bear in novel situation By classifying a novel event into an existing

category, an explanation is provided.

Use of Categories Prediction

Understanding of an event guides reactions and behaviors Allows us to expect certain outcomes or properties

Reasoning Categories are the basis for inferences

Allow categorical knowledge to stand for an event Allows for “filling-in” of ambiguous information

Allow for conceptual combinations Paper Bee Wooden Spoon

Other stuff Just because two instances might be lumped

together under one category, does not mean we experience them similarly Ferrari vs a Tempo

Some would say we experience events, not categories Recall ‘situated action’