1. PSY 324 Topic 8: Knowledge Dr. Ellen Campana Arizona State University
Memory and Cognition
PSY 324
Topic 8: Knowledge
Dr. Ellen Campana
Arizona State University

2. Concepts and Categories

3. Why do we have concepts?
Last time … memories constructed through the process of inference
“War of the ghosts”
Estimating high school grades
Flashbulb memories (and other episodic memories)
Inference happens all the time, not just in memory
New store opens, scripts help us know how to buy
Strange hungry (but healthy) cat at the door… food?

4. Concepts
Concepts are mental representations that make it possible to do inference, understand language, do reasoning, and remember
Make up semantic memories from last section
Used for construction and other inferences, so also part of episodic memory
Can be used for categorization
Entities placed into groups called categories

5. Categories
Knowing what category an entity is a member of gives you a lot of information about it
Without concepts all knowledge about each entity would come from experience with that entity
Each restaurant, each person, each cat, each class, etc.
Broken pencil replaced – learn to use it for writing all over
Helps explain things that would otherwise be odd
Pittsburgh Steelers fan

6. CAT Categories Difficult to train Likes to rub up against
people and objects
Has a tail
Likes milk, fish
Catches mice
CAT
A feline: related to
lions and tigers
Sleeps a lot, but more active at night

7. Categories
TEAPOT

8. Models of Categorization
Definitional Approach (Aristotle)
Membership by definition, like a checklist
Family Resemblances (Wittgenstein)
Membership by similarity
Prototype Approach (Rosch)
Membership by similarity to “average” of category
Exemplar Approach
Membership by similarity to examples of members
More specific version of family resemblances

9. Definitional Approach
In geometry a square can be defined as “a plane figure having four equal sides”
Features of a square: planar figure, four sides, sides are equal
If something has all of these, it is a square
If something is a square it must have all of these
Definitional approach uses definitions like this for everything
Bachelor: Male, unmarried, adult, human
What about the pope?

10. Definitional Approach
Assumes
Sharp category boundary (in or out)
Equality of members
Representation of category is list of necessary and sufficient features
If an entity meets the conditions it is a member
If an entity is a member we know it meets the conditions
In practice it is difficult to find such features
Is a bookend furniture?

11. Family Resemblance No one feature that all have in common….
… Yet all are in some way similar to the other category members
Variation within categories OK

12. Family Resemblance Assumes Think about it long and it gets confusing….
No strict “definition” of what’s in/out based on individual features
Membership based on similarity
Some members can be “better” examples than others
Think about it long and it gets confusing….
Similarity, but similarity to what?
Next two approaches are more detailed versions

13. Prototype Approach What’s a prototype? An “average” of all members
The guy in the center is closest to the prototype (highest prototypicality) but he isn’t the prototype
Prototype isn’t here

14. Prototype Approach Features: Prototype Glasses (yes/no)
Hair (dark/light)
Nose (big/small)
Ears (big/small)
Mustache (yes/no)
Prototype
2/3 glasses, 7/9 light hair, 7/9 big nose, 7/9 big ears, 5/9 mustache
Center guy has the highest prototypicality

15. Support for Prototypes
Rosch (1975)- prototypicality rating
Participants got category names (bird) and lists of 50 members (robin, canary, ostrich, penguin, sparrow…)
Provided rating on how well the item represented the category
Results: Much agreement on ratings between participants

16. Rosch (1975) Bat Penguin Owl Sparrow CATEGORY: BIRDS Telephone Mirror
Poor
Very Good
CATEGORY: BIRDS
Telephone
Mirror
China Closet
Chair,
Sofa
Very Good
Poor
CATEGORY: FURNITURE

17. Rosch and Mervis (1975)
Participants wrote down as many characteristics / attributes as they could think of for each item
Bicycle: two wheels, you ride them, handlebars, pedals, don’t use fuel….
Dog: have four legs , bark, have fur…
When attributes overlap with many other members’ attributes family resemblance is high
Results: Items with high prototypicality ratings also have high family resemblance
Chairs and sofas, birds, etc.

18. Other Studies about Prototypes
Smith and Coworkers (1974)- Typicality Effect
Used sentence verification
T/F: an apple is a fruit
T/F: a pomegranate is a fruit
Results: sentences about highly prototypical objects are judged more quickly (typicality effect)
Mervis and Coworkers (1976)
When people name objects in a category, the most prototypical objects tend to come first

19. Other Studies about Prototypes
Rosch (1975b) – repetition priming
Prototypical members of a category are affected by a priming stimulus more than nonprototypical ones
Task: Ignore words, just say whether the two color circles match or not
610ms
“Same”
Hear
“Green”
780ms
“Same”
“Different”

20. Other Studies about Prototypes
Rosch (1975b) – repetition priming
Take-away message: people are faster to respond “same” when the colors are more highly prototypical
Word “green” may be linked to the prototype
610ms
“Same”
Hear
“Green”
780ms
“Same”
“Different”

21. Exemplar Approach
Exemplars are specific examples – provide another account for the effects of we have seen
Examples of category members are saved in memory (typical as well as atypical)
Potential members compared to all exemplars
Those with high family resemblance are like more of the exemplars
Remember: Family resemblance correlates w/ prototypicality
May be more useful for smaller categories (US presidents, very tall mountains)

22. Exemplar Approach Features: Why does center guy fit? Glasses (yes/no)
Hair (dark/light)
Nose (big/small)
Ears (big/small)
Mustache (yes/no)
Why does center guy fit?
Glasses like 2/3, light hair like 7/9, big nose like 7/9, big ears like 7/9, mustache like 5/9
Center guy has high family resemblance

23. Models of Categorization
Four Approaches
Definitional Approach (Aristotle)
Family Resemblances (Wittgenstein)
Prototype Approach (Rosch)
Exemplar Approach
Current consensus – people use both the prototype approach and the exemplar approach (perhaps for different types of categories)

24. Levels of Categories

25. Levels of Categories Categories have hierarchical organization
Is one level more important or “privileged”?
Furniture
Superordinate
Level
Chair
Table
Basic Level
Kitchen
Dining
room
Kitchen
Dining
room
Subordinate
Level

26. Levels of Categories LEVEL EXAMPLE Superordinate Furniture Basic Table
Subordinate
Kitchen Table
Common
Features 3
Lose a lot of
information 9
Gain just
a little info
10.3

27. Levels of Categories Rosch, Mervis and Coworkers (1976)
Rating of common features for each category
BASIC level seems to be “special” (go higher and you lose a lot of information, go lower and it doesn’t help much)
When given a picture (Levis/Jeans/Clothes) people label it with the basic level category
Rosch, Simpson and Coworkers (1976)
Category label followed by picture – is it a member?
People were faster for basic level categories
Coley and Coworkers (1997)
Students described trees with the word “trees” rather than “oak tree” or “plant”

28. Experience and Categories
When people become experts at a category, they tend to use more specific categories
Tanaka & Taylor (1991) – bird experts and novices
Experts: “robin, sparrow, jay, cardinal”
Nonexperts: “bird”
Members of Itza culture use “oak tree” , not “tree”
Partly because they live in close contact with the natural environment
Basic levels may not be so “special” for everyone (depends on experience)

29. Semantic Networks

30. Semantic Networks
Semantic networks describe a theory for how concepts are organized in the mind
Goal: to develop a computer model of memory
Model we’ll discuss is Collins & Quillian (1969)
Network of nodes connected by links
Nodes = individual categories or concepts
Links = relationships between categories or concepts
Nodes are also associated with properties

31. Semantic Network Collins & Quillian (1969)
Breathes
Has skin
Animal
Can move around
Eats
Has fins
Has wings
Can swim
Can fly
Has gills
Bird
Fish
Has feathers
Is pink
Has long thin legs
Is edible
Is tall
Can’t fly
Canary
Ostrich
Shark
Salmon
Can sing
Can bite
Is dangerous
Swims upstream
to lay eggs
Is yellow

32. Semantic Network Collins & Quillian (1969)
Information about the properties of an individual category is retrieved by accessing a node, then following links until we find the desired property (or it’s opposite)
Properties are associated with the highest-level category that they apply to in general
saves space in memory (Cognitive economy)
Exceptions added at lower nodes to deal with unusual cases
Example: Properties of a canary

33. Properties of a Canary Animal List of all properties Can sing
Breathes
Animal
Has skin
List of all properties
Can move around
Can sing
Is yellow
Eats
Has wings
Has wings
Can fly
Has feathers
Bird
Can fly
Breathes
Has skin
Can move around
Eats
Has feathers
Canary
Can sing
Is yellow

34. Properties of an Ostrich
Breathes
Animal
Has skin
List of all properties
Can move around
Can’t fly
Has long thin legs
Is tall
Eats
Has wings
Bird
Has wings
Can fly
Has feathers
Can fly
Has feathers
Breathes
Has skin
Can move around
Eats
Can’t fly
Ostrich
Has long thin legs
Is tall

35. Semantic Network Collins & Quillian (1969)
Network is a functional model, not a physiological one
It is designed to address how concepts are organized in the mind, not the brain
Nodes DO NOT correspond to specific brain areas
Links DO NOT correspond to connections between neurons or networks of neurons
But how well does the model fit with data on how the mind organizes information?

36. Properties of a Canary Levels Animal away from “canary”
Breathes
Levels
away from
“canary”
Animal
Has skin
List of all properties
Can move around
Can sing
Is yellow
Eats
Has wings
Has wings
Can fly
Has feathers
Bird 1
Can fly
Breathes
Has skin
Can move around
Eats
Has feathers 2
Canary
Can sing
Is yellow

37. Collins and Quillian (1969)
Experiment: timed true/false judgments
Compared properties with different distances from the original concept
Measured reaction time to simple statements

38. Collins and Quillian (1969)
A canary has skin
A canary can fly
A canary can sing
Reaction time
(higher is slower)
A canary is an animal
A canary is a bird
A canary is a canary 1 2
Levels away from “canary”

39. Collins and Quillian (1969)
Experiment: timed true/false judgments
Compared properties with different distances from the original concept
Measured reaction time to simple statements
Results: As properties increased in distance from the concept node, reaction times increased
Provides support for counter-intuitive claims related to cognitive economy
Provides support for the model in general

40. Spreading Activation
We have been talking about retrieval as traveling through the semantic network, but the model actually claims that retrieval happens through the process of spreading activation
Whenever a node becomes “active”, some activity travels through links to closely associated nodes
Spreading activation leads to further predictions in the model
Priming of associated concepts

41. Spreading Activation Network as a person searches from canary to bird
Animal
Network as a person searches from canary to bird
Activation spreads from bird to all linked nodes
Nodes that get activation are primed (faster to recognize)
Bird
Canary
Ostrich
Robin

42. Priming Priming leads to faster recognition of concepts
One type is repetition priming, which you know
Myer and Schvaneveldt (1971) looked at priming of associates using a lexical decision method
Presented pairs of words, participants decided whether or not both words in the pair were real words
YES: Chair/Money or Bread/Wheat
NO: Fundt/Glurb or Bleem/Dress
Critical comparison: pairs of real words, close associates vs. distantly related concepts

43. Myer & Schvaneveldt (1971) Reaction Time Words Associated
Words Not Associated

44. Problems with C & Q model
Didn’t account for typicality effect
A canary is a bird verified faster than an ostrich is a bird
Model predicts equal reaction times for both
Evidence against cognitive economy idea
People store some properties at concept node
Evidence against hierarchical structure
A pig is an animal verified faster than a pig is a mammal
Model predicts the opposite because mammal is supposed to be between pig and animal

45. Collins and Lofus Version
Abandon hierarchy structure in favor of a structure based on individual experience
Allowed multiple links between concepts
Pig to animal AND pig to mammal to animal
Link distances could affect spread of activation
Shorter links for closely related concepts
Longer links for more distantly related concepts
This accounts for typicality effect

46. Collins and Loftus Version
Street
Vehicle
Car
Truck
Bus
Ambulance
Too powerful!
Fire Engine

47. A Theory that Explains Too Much?
Collins & Loftus’s model rejected for being too powerful (Johnson-Laird & Coworkers, 1984)
Too difficult to falsify
Model can “explain” any pattern of data by adjusting link length
No definite methods for determining length
Can vary from person to person
No constraints on how long activation hangs around, or how much information is needed to trigger a node

48. Elements of Good Psychological Theories
Explanatory power – the theory can tell us what caused a specific behavior
Predictive power – the theory can make predictions about future experiments
Falsifiability – the theory can be shown to be wrong through specific experimental outcomes
Generation of experiments – the theory stimulates a lot of research to test the theory, improve it, use methods suggested by it, and/or study new questions that it raises

49. The Connectionist Approach

50. Connectionist Networks
Research in semantic networks dropped by 80’s but came back with rise of connectionism
Book series: Parallel Distributed Processing… (McClelland & Rumelhart, 1986)
Connectionist models contain structures like nodes and links, but they operate much differently from semantic networks
Inspired by the biology of the nervous system, specifically neurons in the brain

51. Connectionist Networks
Units – “neuron-like”, connect to form circuits, can be activated, can inhibit/excite other units
Input units – activated by stimulation from environment (like receptors)
Hidden units – get input from input units, connect to output units
Output units – get input only from hidden units
Concept = pattern of activation across units
distributed coding (unlike semantic nets!)

52. Connectionist Networks
Processing is achieved by weights at each connection between neurons
Positive weights = excitation in neural system
Negative weights = inhibition in neural system
Can vary in connection strength, too
Connectionist approach also called Parallel Distributed Processing (PDP) approach
Representation is distributed across neurons
Processing happens in parallel

53. A Connectionist Network
Output
Units +5 +3
+10 +4
Hidden
Units
Input
Units

54. Supervised Learning Weights modified through supervised learning
Like a child, making mistakes and being corrected
In the beginning all weights are random
This is a computer program (not a person)

55. Steps in Supevised Learning
Step 1: input presented, activation propagates through the layers to the output layer

56. Output
Units
+10 +6 +5 +2
Hidden
Units
Input
Units -1 -1 +1 +1
CANARY

57. Steps in Supevised Learning
Step 1: input presented, activation propagates through the layers to the output layer
Step 2: output compared to correct output, difference is an error signal

58. -5 -3 +5 +2 CANARY +5 +3 +10 +4 Correct Pattern Error Signal Output
Units
+10 +6 +5 +2
Hidden
Units
Input
Units -1 -1 +1 +1
CANARY

59. Steps in Supevised Learning
Step 1: input presented, activation propagates through the layers to the output layer
Step 2: output compared to correct output, difference is an error signal
Step 3: error signal is used to adjust weights using a process called back propagation
Connections that contributed most error are adjusted the most

60. -5 -3 +5 +2 CANARY +5 +3 +10 +4 Correct Pattern Error Signal +10 +6 +5
Weights
are all
Adjusted -1 -1 +1 +1
CANARY

61. Steps in Supevised Learning
Step 1: input presented, activation propagates through the layers to the output layer
Step 2: output compared to correct output, difference is an error signal
Step 3: error signal is used to adjust weights using a process called back propagation
Connections that contributed most error are adjusted the most
Repeat the whole thing many times
Stop when error signal is 0

62. CANARY +5 +3 +10 +4 Correct Pattern Error Signal Output Units +5 +3
Output
Units +5 +3
+10 +4
Hidden
Units
Input
Units -1 -1 +1 +1
CANARY

63. Supervised Learning
Supervised learning may seem straightforward, but the trick is training the network to represent many concepts at the same time
It can do it if learning all concepts at the same time
Making only small changes to the weights helps
Hidden units free to respond in any pattern
Over time ( trials), hidden unit patterns differ for different concepts
Similar concepts have similar hidden unit activation

64. Advantages of Connectionist Models
Goals of the approach
Slow learning process creates network that can handle many different inputs
Representation is distributed (as in the brain)
Graceful degradation: Damage and/or incomplete information does not completely disrupt a trained network
Learning can be generalized: because similar concepts have similar patterns, network can make predictions about concepts it has never seen
Computer models have been developed that match some aspects of human performance and deficits related to brain damage

65. Connectionism Today Opinions are divided
Some like the connection with biology, while some think the connection is tenuous at best
Recent trend in cognitive science to return to ideas about semantic networks and combine them with connectionist approaches
Those who are really interested in connectionist models and what they can express have begun to call their work connection science (not cognitive science or cognitive psychology)

66. Categories in the Brain
Warrington and Shallice (1984) – patients with damage to the inferior temporal lobe (IT)
Visual agnosia – patients can see, but not name
Double dissociation for living / nonliving things
Hills and Caramazza (1991) – IT damage as well
Patient could not name nonliving things or some living things (fruits & veggies), could name other living things (animals)
Chao and Coworkers (1999) – fMRI shows specialized areas for categories… with overlap

67. Categories in the Brain
It looks like there are areas in the brain that are specialized for different categories
Distributed coding with overlap
Categories with similar features, similar activation
Category-specific neurons respond to individual categories like “house” or “snake”
Result from single-cell recording
Specific example may cause a specialized pattern of activation across many category-specific neurons
Example: faces from chapter 2

68. Reminder: Midterm 2 is next week!
The End
Reminder: Midterm 2 is next week!