1. Semantic Memory

2. General Knowledge Structure of Semantic Memory Schemas & Scripts
Background
Feature Comparison Model
Prototype Approach
Exemplar Approach
Network Models
Schemas & Scripts
Recall of Scripts
Schemas & Memory Selection
Schemas & Boundary Extension
Schemas & Memory Abstraction
Schemas & Memory Inferences
Schemas & Integration in Memory
Conclusions

3. Remember the question I asked you the first day of class
Answer this question as quickly as you can:
How many hands did Aristotle have?
What mental processes were involved in answering that question?

4. Processes involved in answering this question:
We’ve talked about attention, pattern recognition, short- term (working memory), and long-term memory.
You have to process the visual information to recognize the letters & words as letters & words
You have to recognize the words and access their meaning (these are separate processes)
You have to understand the meaning of the question.
You also have to make inferences: i.e. You don’t actually have stored in your semantic memory that Aristotle had 2 hands, do you?
So how do you arrive at this decision and respond ‘Two’?

5. “When Lisa was on her way back from the store with the balloon, she fell and the balloon floated away.”

6. “When Lisa was on her way back from the store with the balloon, she fell and the balloon floated away.”
Think about all of the information that you assume when you are reading this sentence. Now think about all of the inferences you make. An inference refers to the logical interpretations and conclusions that were never part of the original stimulus material.
For example, consider the balloon. How large was it? What was it made out of? Was it filled with anything?
What about Lisa. What was she doing? How old is she? Etc.
Reread the sentence and write down as many additional inferences as you can think of that people are likely to make after reading the sentence.

7. Semantic Memory General Conceptual Knowledge
Lexical Knowledge (e.g., “apple” and )
Organized - (e.g., ‘pencil’ related to ‘pen’; think of ‘apple’ ----> ‘banana’
Categories and Concepts
Category - a class of objects that belong together (e.g., variety of objects: ‘fruits’ or ‘apple’)
Concept - mental representation of a category

8. Natural concepts vs. Artifacts
Concepts allow us to make inferences when we encounter new instances (e.g. read ‘chair)
Natural concepts vs. Artifacts
Questions
Organization and Structure?
Storage?
Inferences?
Cognitive Economy?
Relatedness and Similarity?

9. Classic Feature Comparison Models
Concepts = list of features or attributes (e.g., Smith, Shoben, and Rips 1974)
Classic Definition of Concepts
Defining vs. Characteristic Features
Decision Process - 2 Stages
Stage 1 = compare all features (global comparison)
Stage 2 = compare only the defining features
Typicality Effects

10. Feature Comparison Model

11. The Sentence Verification Technique
For each of the items below, answer as quickly as possible either true or false.
A poodle is a dog.
A squirrel is an animal.
A flower is a rock.
A carrot is a vegetable.
A mango is a fruit.
A petunia is a tree.
A robin is a bird.
A rutabaga is a vegetable.

12. The Sentence Verification Technique
How does the Feature Comparison Model account for differences in response times?
A poodle is a dog.
A squirrel is an animal.
A flower is a rock.
A carrot is a vegetable.
A mango is a fruit.
A petunia is a tree.
A robin is a bird.
A rutabaga is a vegetable.

13. Comparison & Decision in Feature Comparison Model

14. The Prototype Approach

15. Category Exemplar Generation Task

16. Number your paper from 1 to 15.
Consider the category “Vehicle.” Rank the following examples as to how good (typical) of an example it is of the category. Rank them from 1 (most typical) to 15 (least typical):
Sled, truck, bus, cart, tractor, car, train, motorcycle, raft, wheelchair, boat, bicycle, airplane, tank, tractor

17. Prototype Ratings for Words in Three Categories

18. Prototype Approach Classical View vs. Protoype Eleanor Rosch
Category organized around a Prototype – An ideal member
Membership established by comparing possible members to the prototype
Graded membership – members vary in how representative of the category they are

19. What is a bachelor?
According to the classic definition of conceptual representation of category membership:
bachelor = unmarried, male.
But which of these individuals is really a bachelor?
My 6 month old son Tim
An elderly Catholic Priest
My 32-year old cousin, John, who works at a bank in Chicago

20. Characteristics of Prototypes
Prototypes are supplied as examples of a category.
Prototypes serve as reference points.
Prototypes are judged more quickly after priming.
Prototypes can substitute for a category name in a sentence.
Prototypes share common attributes in a family resemblance category.
No one attribute shared by all members
In / out phenomenon

21. Prototypes Supplied as Examples of a Category – (e. g
Prototypes Supplied as Examples of a Category – (e.g. Mervis, Catlin, & Rosch (1976))
Group 1: generated examples for 8 different categories
Birds? … robin, sparrow …
Fruits?
Sports?
Etc.
Group 2: provided prototype ratings (low to high) for each example
e.g., sparrow high
penguin low
Strong correlation between frequency and rating
Typicality Effect

22. Prototypes serve as Reference Points

23. Prototypes Judged More Quickly than Nonprototypes, After Semantic Priming

24. Lexical Decision Task
For the following items: Decide whether each item is a word (‘yes’) or not a word (‘no’). Respond by pressing the ‘yes’ button or the ‘no’ button:
apple
table
tadjld
mountain
pudor

25. Lexical Decision Task
You must decide whether each item is a word (‘yes’) or not a word (‘no’). The DV is the response time & the IV is the type of stimulus (e.g. word vs. nonword)
apple
table
tadjld
mountain
pudor

26. What Is a Priming Effect?
Lexical Decision Task
doctor
doctor
Y/N
400 ms
hospital
doctor
Y/N
450 ms
automobile
Y/N
450 ms

27. Category Priming Effect
Robin
Penguin
550 ms.
670 ms.
Prime:
Bird
Bird
Bird
Bird
Robin
Penguin
? ms.
? ms.
RTs?:
480 ms.
660 ms.

28. Prototypes can substitute for a Category Name

29. Prototypes Share Attributes in a Family Resemblance Category

30. Prototype Ratings for Words in Three Categories

31. Group 1: Prototype Ratings
e.g., vehicles: car, truck, tractor, sled
vegetable: carrots, beets, eggplant
clothing: shirt, sweater, vest
Group 2: List attributes possessed by each item:
e.g., car: wheels, steering wheel, doors, etc.
Score: What proportion of an item’s attributes were shared by other category member’s
Strong correlation between score and prototype rating.

32. Levels of Categorization

33. What are these objects?

34. Levels of Categorization 1
Superordinate Level
furniture, animal, tool
Basic Level
chair, dog, screwdriver
Subordinate Level
desk chair, German Shepherd, flathead screwdriver

35. Characteristics of the Basic Level
Basic-level names are used to identify objects
Members of basic-level categories have more attributes in common
Basic-level names produce the priming effect
Experts use subordinate categories differently

36. Carrot
Vegetable
Same / Different
Same / Different
Priming Effect
No Priming Effect

38. Conclusions about Prototype Approach
Prototype approach accounts for our ability to form concepts about groups or categories that are loosely structured
What constitutes a prototype for a given category can shift over time (e.g. a prototypical piece of clothing)
Prototypes are idealizations or abstractions from many examples of a category
What about specific information about a particular member? Need another mechanism perhaps.

39. Exemplar Approach
First learn information about specific examples of a category.
Each example stored in memory is called an exemplar
The conceptual representation of a category is simply the collection of stored exemplars for that category
Decision Process = Potential ‘new’ member compared to the collection of exemplars.
We classify new stimulus by deciding how closely it resembles all of the stored examples of a category.

40. Conclusions about the Exemplar Approach
Exemplar approach emphasizes there is no need for abstraction.
Abstraction (or forming a prototype) requires throwing away specific information about individual cases
Problem with the exemplar approach: Semantic memory storage requirements may be enormous
May be more suitable for smaller categories

41. Comparing Prototype & Exemplar Approaches
Both approaches suggest category membership is determined by comparing a new item against a stored representation of the category.
If the similarity of new item is strong enough, you conclude the item belongs in the category.
Prototype approach – Stored representation is a summary (or abstraction), an idealized, best member
Exemplar approach – Stored representation is the collection of stored exemplars (or examples) of the category.

42. Stored Representation
Prototype Approach
Exemplar Approach
Typical or idealized
representation
Stored representation =
Specific members / instances

43. In the Prototype Approach the conceptual representation of the category is a prototype or summary representation of all members of that category.

44. In the Exemplar Approach the conceptual representation of the category is the collection of stored exemplars for that category.

45. Combining Both Approaches
Exemplar approach may be most relevant when a category is small.
Prototypes may be more relevant as category size increases
Individual differences (e.g. as a function of expertise)
Possible co-existence of prototypes and exemplars
Strategic differences
Importance of category learning

46. Network Models Semantic networks Collins & Loftus
(concepts and connections ----> nodes and links)
Collins & Loftus
Node = concept
Link = relation or connection
Spreading activation
Sentence verification ----> intersections
Explaining ‘Typicality Effect’
Anderson’s ACT* Theory

47. Example of a network structure

48. Portion of Semantic Net

49. Activation Spread
Does a robin breathe?

50. Hierarchical Network Structure

51. Levels Effect

52. Anderson ACT = Adaptive Control of Thought
Declarative vs. Procedural Knowledge
Propositional Networks
Proposition - the smallest unit of knowledge with a truth value
Proposition = node + link
Working Memory - active part of Long Term Memory

53. Susan gave a white cat to…
Susan gave a white cat to Maria, who is the president of the club.
1. Susan gave a cat to Maria.
2. The cat was white.
3. Maria is the president of the club.

54. Propositional Network for Susan Gave

55. Partial Representation of a Cat in Memory

56. Schemas Larger cognitive units Packages of interrelated units
Used to interpret, encode, understand, and remember new instances
Provide expectations about what should occur (top - down)
Default values / parts - filled in when schema activated
Sometimes - errors

57. “When Lisa was on her way back from the store with the balloon, she fell and the balloon floated away.”

60. Scripts
Simple, well- structured sequence of events associated with a highly familiar activity
Schema vs. script
Recall of scripts
Different from conceptual categories (Barsalow & Sewell, )
Script Identification - early vs. late (Trafimow and Wyer, 1993)
Appreciating the similarity of scripts

61. Trafimow & Wyer (1993) 4 different scripts
Photocopying a piece of paper
Cashing a check
Making tea
Taking the subway
Irrelevant details added (e.g., taking candy out of pocket)
Script - identification information presented first or last
Filler
Recall: of script - related events
23% vs. 10%
(script identified first) (script identified last)

62. Demo 7.5: Nature of Scripts

63. Picture of Room

64. Schemas and Memory Selection
Remember best info consistent with schema or inconsistent
Brewer & Treyons (1981)
Rojahn & Pettigrew (1992)
Incidental vs. Intentional learning

65. Schemas and Boundary Extension

66. Schemas and Memory Abstraction
Verbatim vs. Gist
Constructive Approach
Bransford & Franks (1971)
Holmes & Colleagues (1998)
Pragmatic Approach
Murphy & Shapiro (1994)
Attention Allocation / Control
C & P compatible

67. Demo 7.7: Contructive Memory

68. Constructive Memory: part 2

69. Murphy & Shapiro (1994)

70. Schemas and Inferences in Memory
Bartlett (1932)
Ebb vs. Bartlett
Interaction of prior knowledge and experience and formation of new memories
“War of the Ghosts” story
Initial vs. Delayed Recall
Bransford, et al (1972)
Implications - e.g., advertising

71. Schemas and Integration in Memory
Final process in memory formation
Result of selection, abstraction, and inference
Important!!
Integration and Delayed Recall
Integration and Limited Memory Capacity