1. Chapter 7
Knowledge
Terms: concept, categorization, prototype, typicality effect, object concepts, rule-governed, exemplars, hierarchical organization, basic level, superordinate/global level, subordinate/specific level, semantic network, nodes, links, spreading activation, cognitive economy, category size effect

2. Some Questions to Consider
Why is it difficult to decide if a particular object belongs to a particular category, such as “chair,” by looking up its definition?
How are the properties of various objects “filed away” in the mind?
How is information about different categories stored in the brain?
Can young infants respond to the categories “cat” and “dog”?

3. Knowledge
Concept: mental representation used for a variety of cognitive functions
Categorization is the process by which things are placed into groups called categories

4. Why Categories Are Useful
Help to understand individual cases not previously encountered
“Pointers to knowledge”
Categories provide a wealth of general information about an item
Allow us to identify the special characteristics of a particular item

5. Caption: Knowing that something is in a category provides a great deal of information about it.

6. Definitional Approach to Categorization
Determine category membership based on whether the object meets the definition of the category
Does not work well
Not all members of everyday categories have the same defining features

7. Caption: Different objects, all possible “chairs.”

8. Definitional Approach to Categorization
Family resemblance
Things in a category resemble one another in a number of ways

9. The Prototype Approach
Prototype = “typical”
An abstract representation of the “typical” member of a category
Characteristic features that describe what members of that concept are like
An average of category members encountered in the past
Contains the most salient features
True of most instances of that category

10. Caption: Results of Rosch’s (1975a) experiment, in which participants judged objects on a scale of 1 (good example of a category) to 7 (poor example): (a) ratings for birds; (b) ratings for furniture.

11. The Prototype Approach
High-prototypicality: category member closely resembles category prototype
“Typical” member
For category “bird” = robin

12. The Prototype Approach
Low-prototypicality: category member does not closely resemble category prototype
For category “bird” = penguin

13. The Prototype Approach
Strong positive relationship between prototypicality and family resemblance
When items have a large amount of overlap with characteristics of other items in the category, the family resemblance of these items is high
Low overlap = low family resemblance

14. The Prototype Approach
Typicality effect: prototypical objects are processed preferentially
Highly prototypical objects judged more rapidly
Sentence verification technique

15. Caption: Results of E. E. Smith et al
Caption: Results of E.E. Smith et al.’s (1974) sentence verification experiment. Reaction times were faster for objects rated higher in prototypicality

16. The Prototype Approach
Typicality effect: prototypical objects are processed preferentially
Prototypical objects are named more rapidly

17. The Prototype Approach
Typicality effect: prototypical objects are processed preferentially
Prototypical category members are more affected by a priming stimulus
Rosch (1975b)
Hearing “green” primes a highly prototypical “green”

18. Caption: Procedure for Rosch’s (1975b) priming experiment
Caption: Procedure for Rosch’s (1975b) priming experiment. Results for the conditions when the test colors were the same are shown on the right. (a) The person’s “green” prototype matches the good green, but (b) is a poor match for the light green.

19. The Exemplar Approach
Concept is represented by multiple examples (rather than a single prototype)
Examples are actual category members (not abstract averages)
To categorize, compare the new item to stored examples

20. The Exemplar Approach
Similar to prototype view
Representing a category is not defining it
Different: representation is not abstract
Descriptions of specific examples
The more similar a specific exemplar is to a known category member, the faster it will be categorized

21. The Exemplar Approach
Explains typicality effect
Easily takes into account atypical cases
Easily deals with variable categories

22. Prototypes or Exemplars?
May use both
Exemplars may work best for small categories
Prototypes may work best for larger categories

23. Caption: Levels of categories for (a) furniture and (b) vehicles
Caption: Levels of categories for (a) furniture and (b) vehicles. Rosch provided evidence for the idea that the basic level is “psychologically privileged.”

24. Caption: Left column: category levels; middle column: examples of each level for furniture; right column: average number of common features, listed from Rosch, Mervis et al.’s (1976) experiment.

25. Evidence that Basic-Level Is Special
People almost exclusively use basic-level names in free-naming tasks
Quicker to identify basic-level category member as a member of a category
Children learn basic-level concepts sooner than other levels
Basic-level is much more common in adult discourse than names for superordinate categories
Different cultures tend to use the same basic-level categories, at least for living things

26. A Hierarchical Organization
To fully understand how people categorize objects, one must consider
Properties of objects
Learning and experience of perceivers

27. Caption: Results of Tanaka and Taylor’s (1991) “expert” experiment
Caption: Results of Tanaka and Taylor’s (1991) “expert” experiment. Experts (left pair of bars) used more specific categories to name birds, whereas nonexperts (right pair of bars) used more basic categories.

28. Semantic Networks
Concepts are arranged in networks that represent the way concepts are organized in the mind

29. Semantic Networks
Collins and Quillian (1969)
Node = category/concept
Concepts are linked
Model for how concepts and properties are associated in the mind

30. Caption: Collins and Quillian’s (1969) semantic network
Caption: Collins and Quillian’s (1969) semantic network. Specific concepts are indicated in blue. Properties of concepts are indicated at the nodes for each concept. Additional properties of a concept can be determined by moving up the network, along the lines connecting the concepts. For example, moving from “canary” up to “bird” indicates that canaries have feathers and wings and can fly.

31. Exceptions are stored at lower nodes Inheritance
Semantic Networks
Cognitive economy: shared properties are only stored at higher-level nodes
Exceptions are stored at lower nodes
Inheritance
Lower-level items share properties of higher-level items
Category size effect: verifying a concept as a member of a smaller category is faster than verifying it as a member of a larger category

32. Caption: The distance between concepts predicts how long it takes to retrieve information about concepts as measured by the sentence verification technique. Because it is necessary to travel on two links to get from canary to animal (left), but on only one to get from canary to bird (right) it should take longer to verify the statement “a canary is an animal.”

33. Semantic Networks
Spreading activation
Activation is the arousal level of a node
When a node is activated, activity spreads out along all connected links
Concepts that receive activation are primed and more easily accessed from memory

34. Semantic Networks
Lexical decision task
Participants read stimuli and are asked to say as quickly as possible whether the item is a word or not

35. Semantic Networks
Myer and Schvaneveldt (1971)
“Yes” if both strings are words; “no” if not
Some pairs were closely associated
Reaction time was faster for those pairs
Spreading activation

36. Semantic Networks
Criticism of Collins and Quillian
Cannot explain typicality effects (typical members verified as such more quickly)
Cognitive economy?
Some sentence-verification results are problematic for the model
e.g., can’t explain reversal of category size effect (“A dog is an animal” faster than “A dog is a mammal”)

37. Semantic Networks: Spreading Activation Model (extension of hierarchical model)
Collins and Loftus (1975) modifications
Shorter links to connect closely related concepts
Longer linkers for less closely related concepts
No hierarchical structure; based on person’s experience

38. Caption: Semantic network proposed by Collins and Loftus (1975)
Caption: Semantic network proposed by Collins and Loftus (1975). (Reprinted from A.M. Collins & E.F. Loftus, “A Spreading-Activation Theory of Semantic Processing.” From Psychological Review, 82, pp , Fig. 1. Copyright © 1975 with permission from the American Psychological Association.

39. Assessment of Semantic Networks
Is predictive and explanatory of some results, but not all
Generated multiple experiments
Lack of falsifiability
No rules for determining link length or how long activation will spread
Therefore, there is no experiment that would “prove it wrong”
Circular reasoning