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9 Cards in this Set

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  • Back
Hierarchical Network Model
Proposes that items are categorized by using the hierarchical relations specified in a semantic network
-Features stored at highest level - all levels below that are true
2 Assumptions
1)Takes time to move from 1 level to another
2)Takes additional time to retrieve features stored at one of the levels
Typicality Effect
Finding that the more typical members of a category are classified more quickly than the less typical category members
(eg)easier to verify that a canary is a bird than to verify an ostrich is a bird
Feature Comparison Model
Items are categorized by matching the items features to category features
-Meaning of words stored in memory as a list of features
-When concepts have too many or very few features in common a fast yes or no decision can be made
-When concepts have some, but not a lot of features in common, then an additional check of their defining features is made--> Slower Response
-Accounts for Typicality Effects & Category-Size Effect
Feature Comparison Model Limitations
1)Relies on ratings to make most of its predictions
2)Proposal that all comparisons require computations: We use the features of concepts to compute their degree of similarity
3)Argument against necessary or defining features
-Characteristic features are more salient and directly observable than defining features
Defining Feature
Feature that is necessary to be a member of that category - Possessed by all members of a category
(eg)Concept bird: Defining Feature = has wings, has feathers
Characteristic Features
Usually present in members of that category, but not necessary - Shared by some but not all category members
(eg)Concept robin: can fly, is small
Category-Size Effect
Finding that members of smaller categories are classified more quickly then members of larger categories
(eg)Verifying that a collie is a dog is quicker than verifying that collie is an animal
-Smaller Category(dog) requires fewer inferences than the larger category(animal)
-Smaller category is part of larger category and appears lower in hierarchy so its reached sooner
Spreading Activation Model
Accounts for response times by formulating assumptions about how activation spreads in a semantic network
-Concepts joined together by links that show relationships
-Length of link represents the degree of semantic relatedness between 2 concepts
-When concept is processed, activation spreads outward along paths, but loses strength over distance
-Activation of 2nd concept decreases amount of activation of 1st concept
-Semantic Priming
Spreading Activation Model - Limiations
Includes assumptions of both hierarchical net models and feature comparison models
1. Too many assumptions - can explain anything
2. Too few clear cut predictions - unless specific predictions can be tested, can't determine whether models are any good