Exam 3 (Concepts & Semantic Memory)

Question 1

concept

Answer 1

mental representation of a class or category

Question 2

exemplar

Answer 2

particular instance

Question 3

prototype

Answer 3

average or typical example

Question 4

semantic memory

Answer 4

lexical, conceptual, and encyclopedic knowledge of the world

Question 5

What do concepts allow us to do? (4)

Answer 5

1. allow classification
2. enable generalization - ex. exotic bird has eyes
3. support reasoning and predictions
4. support communication - ex. find “chair”

Question 6

What are the two approaches to concept formation?

Answer 6

1. Theory of Subtractive Abstraction

2. Probabilistic View

Question 7

Theory of Subtractive Abstraction

Answer 7

ignore insignificant differences, find what they have in common

Question 8

Classical View

Answer 8

research on artificial concepts (e.g. things that are blue and round)

Question 9

What are problems with the theory of subtractive abstraction?

Answer 9

1. properties relative (e.g. tall)
2. we have concepts of things we’ve never experienced (e.g. aliens)
3. typicality effects - easier to classify something (ex. sparrow -> bat)
4. any truly common properties?

Question 10

Probabilistic view

Answer 10

concepts organized around properties that are typical of members

Question 11

Theory of Resemblance

Answer 11

all members often have no properties in common - they share a “family resemblance”
e.g. games: card games, board games, video games

Question 12

What are the models of semantic memory? (2)

Answer 12

1. Prototype approach

2. Exemplar Approach

Question 13

Prototype Approach

Answer 13

based on resemblance to a prototype

e.g. dog = average dog

Question 14

The prototype approach allows for the ___ ___ & fuzzy ___

Answer 14

internal structure, boundaries

Question 15

What are the problems with prototype approach?

Answer 15

1. category size
2. variability
3. correlations of attributes - e.g. small dog, high-pitched bark
4. memories for individual examples

Question 16

Exemplar approach

Answer 16

based on resemblance to a collection of examples

e. g. dog = thing that resembles 1 or more dogs
- German shepard more typical than chihuahua

Question 17

Natural concepts are based on __ not defining features

Answer 17

resemblance

Question 18

Graded structure

Answer 18

some members are “better” (more representative)

e.g. furniture

Question 19

Levels of categorization of resemblance and natural concepts?

Answer 19

1. basic-level (e.g. pen)
2. subordinate (BIC fine-point pen)
3. superordinate (writing instrument

Question 20

fuzzy boundaries

Answer 20

membership ill-defined or uncertain

Question 21

What evidence supports resemblance models?

Answer 21

1. typicality
2. semantic priming
3. induction
4. pseudo-memory
5. network models

Question 22

Typicality effect

Answer 22

• T/F judgments (does a tiger have stripes)

- typical exemplars (fruit = apple)

Question 23

semantic priming effect

Answer 23

just by saying “bird” or “feathers”, can reduce RT to sentence verification test

Question 24

Induction

Answer 24

people often predict that all instances will have a property if a typical instance does
- e.g. bird infected, will infect all species

Question 25

Network Models

Answer 25

concepts formed by a network of interconnections

Question 26

interconnections

Answer 26

spread of activation effects including semantic priming

Question 27

Parallel Distributed Processing (PDP)

Answer 27

info stored as network of interconnected nodes

- connections vary in weight (e.g. red more connected with cherries than firetrucks)

Question 28

3 properties of PDP

Answer 28

1. graceful degradation
2. retrieval from incomplete information
3. default assignment

Question 29

Graceful degradation

Answer 29

errors or missing info do not lead to complete failures

e.g. tip-of-tongue

Question 30

retrieval from incomplete info

Answer 30

if a set of features is sufficient to uniquely characterize an “item” it will activate that node

Question 31

default assignment

Answer 31

educated guessing