1. Chapter 9
Knowledge

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

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
Cognitive economy?
Some sentence-verification results are problematic for the model

37. Semantic Networks
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

40. The Connectionist Approach
How do neurons that can only either signal or not signal (on-off) represent knowledge?
Model on digital circuitry
1 bit can only be on or off
Group them together and get many more patterns (1 byte: )
Our neurons group together to form a network to represent concepts
A network of nodes and links but operates very differently from semantic networks

41. The Connectionist Approach
“Neuron-like units”
Input units: activated by stimulation from environment
Hidden units: receive input from input units
Output units: receive input from hidden units

42. Caption: A connectionist network showing input units, hidden units, and output units. Incoming stimuli activate the input units, and signals travel through network, activating the hidden and output units. Note that this is an extremely simplified version of a connectionist network. Networks used in research on connectionism contain many more units and more complex connections between units.

43. The Connectionist Approach
Parallel distributed processing
Knowledge represented in the distributed activity of many units
Weights determine at each connection how strongly an incoming signal will activate the next unit

44. The Connectionist Approach
How learning occurs
Network responds to stimulus
Provided with correct response
Modifies responding to match correct response

45. The Connectionist Approach
Error signal
Difference between actual activity of each output unit and the correct activity

46. The Connectionist Approach
Back-propagation: error signal transmitted back through the circuit
Indicates how weights should be changed to allow the output signal to match the correct signal
The process repeats until the error signal is zero

47. Caption: Learning in a connectionist network
Caption: Learning in a connectionist network. Bars represent activity in the eight representation units. Notice how the pattern of activation changes as learning progresses.

48. The Connectionist Approach
Slow learning process that creates a network capable of handling a wide range of inputs
Learning can be generalized

49. The Connectionist Approach
Graceful degradation: disruption of performance occurs gradually as parts of the system are damaged

50. Categories in the Brain
Different areas of the brain may be specialized to process information about different categories
Double dissociation for categories “living things” and “nonliving things”

51. Categories in the Brain
Categories are represented by distributed activity
More similar patterns of brain activity for categories with similar features
Category-specific neurons

52. Caption: (a) Testing procedure for determining if monkeys can categorize cats and dogs. (b) Activity recorded from a neuron in the monkey’s IT cortex during the testing sequence. This neuron responds more to a 60 percent dog stimulus than to a 60 percent cat stimulus during presentation of the sample. (c) Activity recorded from a neuron in the monkey’s PF cortex during the testing sequence. This neuron responds better to the dog stimulus during the delay and test periods.