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General Knowledge. Dr. Claudia J. Stanny EXP 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

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General knowledge

General Knowledge

Dr. Claudia J. Stanny

EXP 4507

Memory & Cognition

Spring 2009


Overview
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

Claudia J. Stanny


Semantic episodic memory

Semantic Memory

General knowledge

Facts, ideas, concepts, categories

Generic information

Conceptually organized

No temporal coding

Subjective experience of retrieval: “KNOW”

Episodic Memory

Specific event knowledge

Events, episodes

May include specific information about self (Autobiographical Memory)

Temporally organized

Subjective experience of retrieval: “REMEMBER”

Semantic & Episodic Memory

Claudia J. Stanny


Types of concepts represented in semantic memory
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

Claudia J. Stanny


How are concepts represented in semantic memory
How are concepts represented in semantic memory?

  • Defining sets of features (Feature comparison model)

  • Prototypes

  • Family resemblances

  • Exemplars

  • Network models

Claudia J. Stanny


How to study these models
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

Claudia J. Stanny


Sentence verification task findings
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 category

    A canary is a bird.

    A penguin is a bird.

Claudia J. Stanny


Feature comparison model
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

Claudia J. Stanny


How well does this model work
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)

Claudia J. Stanny


Prototypes
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 ?

Claudia J. Stanny


Prototypes1
Prototypes

  • Evidence 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

Claudia J. Stanny


Family resemblance
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

Claudia J. Stanny


catch

tennis

bridge

Claudia J. Stanny


Levels of categorization rosch
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 purposes

  • Chippendale arm chair, beefsteak tomato, Siamese cat

Claudia J. Stanny


Evidence related to category levels
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 names

  • Experts develop more category levels in their domain of expertise

Claudia J. Stanny


Exemplars
Exemplars

  • Concept 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

Claudia J. Stanny


Network models
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)

Claudia J. Stanny


Collins loftus
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

Claudia J. Stanny



Testing semantic network models
Testing Semantic Network Models

  • Assume that activation of a node takes time

  • Questions 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 QuestionsCategory Questions

    A canary can sing A canary is a canary

    A canary can fly A canary is a bird

    A canary has skin A canary is an animal


Spreading activation model collins loftus
Spreading Activation Model (Collins & Loftus)


Act r model anderson
ACT-R Model (Anderson)

  • Adaptive Control of Thought

  • Declarative memory

    • Information represented in networks of interconnected nodes

  • Procedural 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



Neural network models
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

Claudia J. Stanny


Knowledge represented as a pattern of connections
Knowledge represented as a pattern of connections

Stimulus input

Response output

Claudia J. Stanny





Characteristics of distributed network models
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)

Claudia J. Stanny


Schemas scripts
Schemas & Scripts

  • Heuristics 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” during memory reconstruction

Claudia J. Stanny


How schemas and scripts are used
How schemas and scripts are used

  • Direct 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

Claudia J. Stanny


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