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Knowing

Semantic memory

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Semantic Memory

Memory of the general knowledge of the world

While episodic memory is personal – events that happened to you – semantic memory is more general – information that everyone can learn about the world

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Two basic questions asked

1. What is the structure and content of semantic memory? Current perspective is that semantic memory is a

network of nodes each representing a basic concept and nodes are linked together

2. How do we access the information in semantic memory? Accessing or retrieving information from the

network involves spreading activation

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Semantic memory models

Quillen and Collins network model

Smith’s feature comparison model

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Collin and Quillian Model

A network model – interrelated concepts or nodes are organized into an interconnected network – these connections can be direct or indirect

Memory is the activation of a node which can spread to other nodes activating other memories

Two forms of connections or propositions: Category membership “is a” Property statements “has”

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Collin and Quillian Model

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Collin and Quillian Model

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Collin and Quillian Model

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Smith’s feature overlap model Showed significant problems of the Quillen and

Collins model

Used lists of characteristics instead of a network

Concepts are defined by a list of features. These features are stored in a redundant manner

The decision of whether one concept is an example of an another depends upon the level of overlap

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Smith’s feature overlap model

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Smith’s feature overlap model Feature comparison

Where features of two concepts overlap a great deal or very little, the decision is made quickly

If some features overlap and others do not, then a stage 2 comparison has to be made and the decision is slower

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Smith’s feature overlap model

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Empirical Tests of Semantic Memory Models Sentence Verification Task: Simple

sentences are presented for the subjects’ yes/no decisions.

Most early tests of semantic memory models adopted the sentence verification task.

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Challenges to Collin and Quillian Model Support for Collin and Quillian was cognitive

economy – only nonredundant facts stored in memory. Conrad (1972) found that high frequency properties were stored in a redundant fashion

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Challenges to Collin and Quillian Model Conrad (1972) found that high frequency

properties were more highly associated with the concepts and are verified faster than low frequency properties – not shown in network model

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Challenges to Collin and Quillian Model

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Challenges to Collin and Quillian Model Typicality: The degree to which items are

viewed as typical, central members of a category.

Typicality Effect: Typical members of a category can be judged more rapidly than atypical members.

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Challenges to Collin and Quillian Model

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Modified Collin and Quillian Model

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Semantic Relatedness

Semantic Relatedness Effect: Concepts that are more highly interrelated can be retrieved and judged true more rapidly than those with a lower degree of relatedness.

Resulted in a third revision of the model which required a 3-dimensional model

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Knowing

Categorization, classification, and prototypes

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Knowledge

Knowledge is the acquisition of concepts and categories – your mental representations that contain information about objects, events, etc.

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Categorization

Concepts usually involve the creation of categories

Categories – grouping things into groups based upon similar characteristics

Categories help organize information so that you do not have learn about every new thing you expereince

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Concepts and Categories

Two basic questions:

What is the nature of concepts? How do we form concepts and categories?

Three approaches to these questions, classical, prototype, and exemplar

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Classical Approach - Aristotle

Categories have defining features – semantic features that are necessary and sufficient to define the category Necessary – features have to be present for

inclusion Sufficient – if these features are present no other

features are necessary for inclusion Problem – most members of a category do

not have the same defining features

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Prototypes

A prototype of the category is developed The prototype has the semantic features that

are most typical of the members of the category

New objects compared to different prototypes of different categories, and are included in category with the most similar prototype

Members of a category that are less similar to the prototype require longer to verify their inclusion

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Prototypes (cont)

Nonmembers of a category can be seen as members if they are similar to the prototype and the differences are not known

When asked to name members of a category, those members most like the prototype are named first

Priming most effected by prototypes

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Exemplars

Identification of examples or exemplars of the category

New objects are compared to to other objects you have seen in the past – your exemplars

Advantage of the use of exemplars – it uses actual examples not just a constructed prototype – atypical members can be exemplars of a category

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Prototypes and Exemplars

Evidence supports both models of categorization

One possibility is that we use prototypes in large categories and exemplars in defining smaller categories

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Feature comparison theory of determining category membership This model focuses on the strategy used to decide

whether an exemplar (i.e. a canary) is a member of a larger category (i.e. bird)

This strategy consists of two rules: If the feature associated with the exemplar (canary has

feathers) is found to be associated with the larger category (birds have feathers), it provides positive proof the exemplar is a member of the larger category

If the feature is not associated with the category (bats have fur), they are not members of the category (a bat is not a bird)

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Support for Feature comparison model Consistent with typicality effects – typical exemplars

have extensive overlap of features; atypical exemplars have less overlap and require more time to determine their membership

Consistent with the false relatedness effect- subjects respond faster when the exemplar is unrelated to the category than when it is somewhat related

Also consistent with levels effects

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Level effects

Categories are organized in a hierarchy – one category is part of a larger category which is part of an even larger category

Superordinate category – largest and most abstract – animal

Subordinate category – smallest and least level of abstraction – a canary

Base level category – in the middle and at an intermediate level of abstraction - bird

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Base level categories

Most useful and most likely to come to mind and tend to be the most important

Children develop base categories before superordinate or subordinate categories

When asked to identify pictures, people more likely to give base level category

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Category levels

When asked for common attributes of superordinate category, people give very few (vehicle)

When asked about attributes of base level categories, many more given (car)

When asked about attributes of base level categories, not many more than those given at the base level are added (SUV)

Movement from a superordinate category to a base level category results in a great increase in information, but movement to a subordinate category adds very little information

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Base level thinking

Humans prefer to think a the base level of categorization because it provides the most useful information for predicting membership in a category

Superordinate members of a category maybe very different with few similarities – fruit

Base level share many common features – apples Subordinate categories are more informative , but

are poor discriminators – McIntosh apples share many features of other apples

Subordinate level thinking most important in areas of expertise. Choosing wine for dinner

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Knowing

Connectionism

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Importance of context

Context can act as a prime to understanding correct meaning I saw a man eating fish. Visiting relatives can be boring

Context can activate the meaning meant to be conveyed

By understanding the context of a communication, you can understand and remember the material better

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Connectionist model of semantic memory Involves a network of interconnected nodes each

node connected with specific information

The connections between nodes vary in strength – referred to as connection weights

Nodes that are more strongly connected have greater connection weights

Learning involves strengthening the connection by increasing connection weights

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A neural network

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A neural network example