EXAM 2: Concepts and Categories Flashcards

You may prefer our related Brainscape-certified flashcards:
1
Q

Concepts

A

The mental representation of a category
Can be concrete (clear rules for category membership) or abstract (vague rules that don’t apply to all members)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Category

A

A set of things that are grouped together

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Concepts: What is Instance/Exemplar

A

A member of a particular concept

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

Concepts: What is a feature?

A

Specific attribute that members share

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What are the 3 levels of categorization?

A

Superordinate, Basic, Subordinate

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

What are some examples of superordinate levels of categorization?

A

Clothes, Furniture

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

What are some examples of Basic levels of categorizations

A

Coat, Chair

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What are some subordinate levels of categorization

A

Pea coat, Folding chair

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

How do we form concepts?

A

(1) We encounter instances
(2) Get feedback (from person or environment)
(3) Store info in long-term memory (the info that is stored differs depending on the theory

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Recognition by components: What is the structure of a concept?

A

Geons!

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Recognition by components: Ho do we classify new instances?

A

Assess the spatial relationship of the geons, search memory for a match

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Recognition by components: What info is stored in LTM?

A

The specific arrangement of the Geons

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Recognition by components: Limitations

A

Works well for objects that can be defined by their shape, but fails with many other concepts

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Classical View: What is the structure of this concept?

A

List of necessary and sufficient features

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Classical View: How do we classify new instances?

A

See if instance matches the list for a specific concept

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Classical View: What info is stored in LTM?

A

The list of features (once you know them, you’ll never categorize them incorrectly) (works really well for well-defined, concrete categories)

17
Q

Problems with classical view

A

Some categories aren’t so easy to define. Between-category boundaries can be “fuzzy”, assumes all exemplars are equally good instances

18
Q

Problems with classical view: Typicality effect

A

Some instances are more typical

19
Q

Problem with classical view: Family resemblance

A

The degree of overlap between members of a category

20
Q

Problems with classical view: Classical view cannot account for typicality effect because

A
  • Assumes all exemplars are equally good instances
  • The feature list is all you need
21
Q

Prototype View: What is the structure of a concept?

A
  • Idealized, “average” example that includes typical features
  • One prototype for each concept/category
22
Q

Prototype View: How do we classify new instances?

A
  • Compare new instance to all prototypes closest match wins
23
Q

Prototype View: What info is stored in LTM?

A
  • The individual’s ONE prototypical example of the concept
  • Prototype will MORPH over time with each new instance
24
Q

Problems with prototype view

A

(1). How do we form these prototypes? Not well understood yet
(2). Not good at capturing knowledge at boundaries (EX: baby puppy looks like a guiniea pig but we know its a dog… why?)
(3). Context effects: These can influence typicality ratings (if you are always comparing to your prototype, you should always give the same rating, but people dont.

25
Q

Exemplar View: What is the structure of a concept?

A

All instances of that concept

26
Q

Exemplar View: How do we classify new instances?

A

Compare new isntance to every instance for all concepts - If new instance is most similar to instances of one concept, gets categorized as that concept

27
Q

Exemplar View: What info is stored in LTM?

A

All instances

28
Q

Strengths of exemplar view

A

(1) No need for concrete list of features
(2) Can account for the typicality effect (more stored instances of some cateogory members)
(3) Can account for context effects (context may heighten specific instances)

29
Q

Problems with exemplar view

A

Comparing to every instance in LTM seems pretty inefficient

30
Q
A