General Knowledge

Dr. Claudia J. StannyEXP 4507

Memory & Cognition

Spring 2009

Overview• How is knowledge represented in semantic

memory?• Models of the structure of semantic memory• Feature comparison model• Prototypes and family resemblances• Exemplars• Network models

• Schemas and scripts• Influence on memory storage and retrieval

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Semantic & Episodic MemorySemantic Memory

General knowledgeFacts, ideas, concepts,

categoriesGeneric informationConceptually organizedNo temporal coding

Subjective experience of retrieval: “KNOW”

Episodic MemorySpecific event knowledgeEvents, episodesMay include specific

information about self (Autobiographical Memory)

Temporally organized

Subjective experience of retrieval: “REMEMBER”

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Types of concepts represented in semantic memory

Logical categories and concepts• Clear definitions• Clear category membership

Natural categories and concepts• Things that occur naturally in the environment• Tend to be thought about in terms of essential

elements or features, but specific examples do not always have these features

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How are concepts represented in semantic memory?

Defining sets of features (Feature comparison model)

Prototypes

Family resemblances

Exemplars

Network models

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How to study these models?

Sentence verification tasks• Present a sentence• Measure RT to respond that the sentence is true

or false• Use patterns in the RT to make decisions about

organization and retrieval of semantic information

Example of trials in a sentence verification task

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Sentence Verification Task Findings

Some sentences take longer to verify than others (semantic difference effect)

Typicality Effect: RT is faster for sentences about typical examples of a concept or categoryA canary is a bird.

A penguin is a bird.

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Feature Comparison Model

Concepts are defined in terms of features

Defining features (necessary / required features)• Features must be present for meaning of a

concept or category membership

Characteristic features (features that are descriptive but not required)• Anything with all of the necessary features is

automatically included in the concept

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How well does this model work?Logical categories • Easily described with a list of defining features• Membership is clear and unambiguous• All members of the concept are equally good as

examples of the concept

Natural categories• Not all members have all the “defining” features• Features are correlated with one another• Members vary in how well they fit the concept

(typicality effects, graded category membership)

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Prototypes

Abstract, idealized representations of a concept

The prototype stored need not correspond to any specific example

Features of the prototype are highly typical of the concept

What might the prototype be for dog ?What might the prototype be for animal ?

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PrototypesEvidence for prototypes• Typicality effects (graded structure of categories)• Ease of access as an example of a category:

Name a type of fruit

• Prototypes benefit more from semantic priming than non-prototypes

Problem: prototypes do not address how we represent our knowledge of the variability of members of a category

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Family Resemblance

Category membership is not determined by a common set of defining features

Games

Instead, category members share an overlapping set of common traits that create a family resemblance for the category

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catch

tennis

bridge

Levels of Categorization (Rosch)Superordinate level categories• General categories

furniture, food, animals

Basic level categories• Specific enough to identify objects clearly

chair, tomato, cat

Subordinate level categories• More specific, more detail than needed for some

purposesChippendale arm chair, beefsteak tomato, Siamese cat

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Evidence related to category levels

Basic level categories are our “default” category levels • We use basic level category names to identify and

talk about objects• We access basic category names faster than other

levels of category names• Memory for category information migrates

toward basic level names (errors in recall will be basic level substitutions)

Bigger priming effects for basic level namesExperts develop more category levels in their domain

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ExemplarsConcept is represented by the set of specific

representations for members of the category we have previously encountered and classified

Variability of category members is represented directly (in a set of examples)

Typical members and prototypes are created from existing representations

No economy in storage: all examples are stored

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Network Models

Characteristics are derived from the pattern of associations or linkages among concepts stored in semantic memory

Collins & Loftus Model (knowledge)

Anderson’s ACT-R Theory (knowledge and procedures)

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Collins & Loftus

Knowledge is stored in a network of connected nodes and links

Retrieval and sentence verification task entail activation of relevant information in the network

Spreading activation moved from node to node though links, RT depends on number of links

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Based on Collins & Quillian (1969) Semantic Network Model

Testing Semantic Network ModelsAssume that activation of a node takes timeQuestions that require activating nodes at greater

distances in the network will require more time than questions that activate nodes close together in the network

Property Questions Category QuestionsA canary can sing A canary is a canaryA canary can fly A canary is a birdA canary has skin A canary is an animal

Spreading Activation Model (Collins & Loftus)

ACT-R Model (Anderson)Adaptive Control of ThoughtDeclarative memory• Information represented in networks of

interconnected nodesProcedural memory• Knowledge represented as production rules• Goal → Required Conditions → Actions• Model for acquisition of skilled behavior

motor programs as production rules• Application to skilled cognition

problem solving algorithms as production rules

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Neural Network Models

Parallel Distributed Processing (PDP) approach

Connectionistic, neural network model• Networks of neuron-like units or nodes• Highly interconnected – multiple connections

between units• System learns by adjusting connection weights

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Knowledge represented as a pattern of connections

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Stimulus input

Response output

Knowledge represented as a pattern of connections

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apple

Knowledge represented as a pattern of connections

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pear

Knowledge represented as a pattern of connections

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cat

Characteristics of distributed network models

Network knowledge is built up by encoding specific experiences (exemplars)

Spontaneous generation of categories emerges from patterns of connectivity & shared units

Fill in missing information in new examples (default assignment)

Protection from damage to part of the network • Graceful degradation (partial retrievals)

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Schemas & ScriptsHeuristics or organizational structures• Categorical information (schemas)• Event information (scripts)

Facilitates comprehension

Organizes information in memory

Provides retrieval cues to facilitate recollection

Potential explanation for errors in recollection• Use of schematic information to “fill in blanks”

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How schemas and scripts are usedDirect attention to relevant details during

encoding

Fill in partial recollections with details from relevant schema or script

Schemas represent the gist or general meaning of an experience or event

Use schemas to make inferences about ambiguous information presented in a story

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