Module 7

Question 1

Category

Answer 1

Group of objects that belong together because they are similar in some way; help predict behavior and make decisions, especially in novel situations

Question 2

Knowledge Organization

Answer 2

Information is stored interconnectedly, and people in cultures have similar structures

Question 3

Exemplar

Answer 3

An item in the category; treated as similar to each other

Question 4

Concept

Answer 4

Mental representation of a category

Question 5

Classical Theory of categorization

Answer 5

Categories are defined by a list of necessary and sufficient features (defining features); objects in a category have defined membership based on whether they possess these features

Question 6

Defining features

Answer 6

Set of necessary and sufficient features for category membership; difficult to create or name, but that doesn’t mean they don’t exist

Question 7

Necessary

Answer 7

Set of features are required for membership

Question 8

Sufficient

Answer 8

Set of features are the only features required for membership

Question 9

Typicality effects

Answer 9

Some items are more typical examples of a concept, and can be rated as such. Rejects idea of defining features.
Evidence: respond faster to more typical exemplars, generate typical more than atypical, typical items produce semantic priming effects (help processing of following category members)

Question 10

Prototype theory

Answer 10

Categories have fuzzy boundaries, and are described by characteristic features. The prototype is the set of features that objects are matched to to determine membership. More matching features = more typical

Question 11

Characteristic features

Answer 11

Set of features that are likely, but are not necessary. Used to match objects to a category in prototype theory

Question 12

Prototype

Answer 12

Theoretical/mental representation of a ‘perfect’ example/member of a category

Question 13

Central tendency

Answer 13

Strongest characteristics (the prototype is made of the central tendency)

Question 14

Family resemblance

Answer 14

All objects of a category must have some features in common (at least 1 feature in common with at least 1 other member)

Question 15

Exemplar theory

Answer 15

Similar to prototype theory, but matching to a specific example of a category member rather than a feature list. Addresses atypical members (objects can be members of multiple groups).

Question 16

Difference in prototype and exemplar theory

Answer 16

Main storage system/process being used. Prototype uses a feature list, exemplar uses specific objects that are most typical of a category

Question 17

Forming categories

Answer 17

We learn about members of a category and that can shift our prototype; categories begin forming in childhood and can form quickly and naturally

Question 18

Problems with prototype and exemplar theory

Answer 18

Rely on similarity; potentially infinite number of similarities between any objects and no relevance marker.

Question 19

Issue with typicality ratings

Answer 19

People can generate ratings for fixed categories (odd numbers); perhaps comes from wanting to please experimenter; if these results are flawed, we possibly don’t need prototype or exemplar theory to try and explain themE

Question 20

Explanation based theory

Answer 20

We have implicit, unconscious ideas about categories and concepts. Follows psychological essentialism. Explains typicality ratings.
Issue: we assume essences apply to all natural categories but that is false (leads to stereotypes)

Question 21

Psychological essentialism

Answer 21

There is a cause of a category (essence), we just can’t define it. Every object has an essence of membership. Essences (almost) always need a verbal label

Question 22

Child experiment of essentialism

Answer 22

Told children stories of a transspecies cat and a toaster turning into a coffee maker. Kids will ascribe an essence to the natural animal (a cat is always a cat) and not to an object (the toaster can become the coffee maker). Supports Explanation based theory

Question 23

Hierarchy

Answer 23

Organizing objects by their level in comparison to each other; superordinate -> basic -> subordinate; expertise can shift where you place your baseline of ‘basic’, but won’t change the structure

Question 24

Basic level

Answer 24

informative and distinctive (ex: dog)

Question 25

Superordinate level

Answer 25

Broad: less informative, more distinctive (ex: mammal)

Question 26

Subordinate level

Answer 26

Specific: more informative, less distinctive (ex: poodle)

Question 27

Semantic network model

Answer 27

All info is stored in nodes which are connected with pathways. Info will travel through spreading activation (one node will activate connected nodes)

Question 28

Collin’s and Quillian’s hierarchical model

Answer 28

First made for computers, adapted for humans. Knowledge is stored in the nodes, and nodes are arranged hierarchically connected by property and ISA pathways. System holds property inheritance

Question 29

Property pathways

Answer 29

describe characteristics of the info node

Question 30

ISA pathways

Answer 30

“is a”, explains the hierarchy (ex: a tree is a plant)

Question 31

Property inheritance

Answer 31

lower concepts inherit the properties from higher concepts; saves cognitive resources by not saving characteristics multiple times

Question 32

Evidence of the Collin’s Quillian model

Answer 32

Presented sentences with different associations (a bird is a bird) and timed reaction of participants confirming true or false. Farther associations took longer. Supported that spreading activation had to travel through the hierarchy to reach farther points and that took time
Issue: sometimes objects don’t follow this pattern, so maybe only typical objects follow it

Question 33

Collins and Loftus semantic relatedness model

Answer 33

Nodes and pathways but not arranged in a hierarchy; organized by semantic similarity (determined by pathway length). Organization occurs based on experience
Support: longer activation time for atypical items
Issue: anything can be explained with experience; no restrictions

Question 34

Neural Network models

Answer 34

Connectionism/parallel distributed processing. Nodes do not hold information, but have potential (inactive, excitatory, neutral). Pathways are weighted numerically. Only input and output layers are observable. Can only handle basic level things. Information is stored as a pattern of activity across the system. Learning occurs through back propagation where behavior is corrected until a pattern is settled on