1. Knowing
Semantic memory

2. Semantic Memory Memory of the general knowledge of the world
While episodic memory is personal – events that happened to you – semantic memory is more general – information that everyone can learn about the world

3. Two basic questions asked
1. What is the structure and content of semantic memory?
Current perspective is that semantic memory is a network of nodes each representing a basic concept and nodes are linked together
2. How do we access the information in semantic memory?
Accessing or retrieving information from the network involves spreading activation

4. Semantic memory models
Quillen and Collins network model
Smith’s feature comparison model

5. Collin and Quillian Model
A network model – interrelated concepts or nodes are organized into an interconnected network – these connections can be direct or indirect
Memory is the activation of a node which can spread to other nodes activating other memories
Two forms of connections or propositions:
Category membership “is a”
Property statements “has”

6. Collin and Quillian Model

7. Collin and Quillian Model

8. Collin and Quillian Model

9. Smith’s feature overlap model
Showed significant problems of the Quillen and Collins model
Used lists of characteristics instead of a network
Concepts are defined by a list of features. These features are stored in a redundant manner
The decision of whether one concept is an example of an another depends upon the level of overlap

10. Smith’s feature overlap model

11. Smith’s feature overlap model
Feature comparison
Where features of two concepts overlap a great deal or very little, the decision is made quickly
If some features overlap and others do not, then a stage 2 comparison has to be made and the decision is slower

12. Smith’s feature overlap model

13. Empirical Tests of Semantic Memory Models
Sentence Verification Task: Simple sentences are presented for the subjects’ yes/no decisions.
Most early tests of semantic memory models adopted the sentence verification task.

14. Challenges to Collin and Quillian Model
Support for Collin and Quillian was cognitive economy – only nonredundant facts stored in memory. Conrad (1972) found that high frequency properties were stored in a redundant fashion

15. Challenges to Collin and Quillian Model
Conrad (1972) found that high frequency properties were more highly associated with the concepts and are verified faster than low frequency properties – not shown in network model

16. Challenges to Collin and Quillian Model

17. Challenges to Collin and Quillian Model
Typicality: The degree to which items are viewed as typical, central members of a category.
Typicality Effect: Typical members of a category can be judged more rapidly than atypical members.

18. Challenges to Collin and Quillian Model

19. Modified Collin and Quillian Model

20. Semantic Relatedness
Semantic Relatedness Effect: Concepts that are more highly interrelated can be retrieved and judged true more rapidly than those with a lower degree of relatedness.
Resulted in a third revision of the model which required a 3-dimensional model

21. Categorization, classification, and prototypes
Knowing
Categorization, classification, and prototypes

22. Knowledge
Knowledge is the acquisition of concepts and categories – your mental representations that contain information about objects, events, etc.

23. Categorization Concepts usually involve the creation of categories
Categories – grouping things into groups based upon similar characteristics
Categories help organize information so that you do not have learn about every new thing you expereince

24. Concepts and Categories
Two basic questions:
What is the nature of concepts?
How do we form concepts and categories?
Three approaches to these questions, classical, prototype, and exemplar

25. Classical Approach - Aristotle
Categories have defining features – semantic features that are necessary and sufficient to define the category
Necessary – features have to be present for inclusion
Sufficient – if these features are present no other features are necessary for inclusion
Problem – most members of a category do not have the same defining features

26. Prototypes A prototype of the category is developed
The prototype has the semantic features that are most typical of the members of the category
New objects compared to different prototypes of different categories, and are included in category with the most similar prototype
Members of a category that are less similar to the prototype require longer to verify their inclusion

27. Prototypes (cont)
Nonmembers of a category can be seen as members if they are similar to the prototype and the differences are not known
When asked to name members of a category, those members most like the prototype are named first
Priming most effected by prototypes

28. Exemplars Identification of examples or exemplars of the category
New objects are compared to to other objects you have seen in the past – your exemplars
Advantage of the use of exemplars – it uses actual examples not just a constructed prototype – atypical members can be exemplars of a category

29. Prototypes and Exemplars
Evidence supports both models of categorization
One possibility is that we use prototypes in large categories and exemplars in defining smaller categories

30. Feature comparison theory of determining category membership
This model focuses on the strategy used to decide whether an exemplar (i.e. a canary) is a member of a larger category (i.e. bird)
This strategy consists of two rules:
If the feature associated with the exemplar (canary has feathers) is found to be associated with the larger category (birds have feathers), it provides positive proof the exemplar is a member of the larger category
If the feature is not associated with the category (bats have fur), they are not members of the category (a bat is not a bird)

31. Support for Feature comparison model
Consistent with typicality effects – typical exemplars have extensive overlap of features; atypical exemplars have less overlap and require more time to determine their membership
Consistent with the false relatedness effect- subjects respond faster when the exemplar is unrelated to the category than when it is somewhat related
Also consistent with levels effects

32. Level effects
Categories are organized in a hierarchy – one category is part of a larger category which is part of an even larger category
Superordinate category – largest and most abstract – animal
Subordinate category – smallest and least level of abstraction – a canary
Base level category – in the middle and at an intermediate level of abstraction - bird

33. Base level categories
Most useful and most likely to come to mind and tend to be the most important
Children develop base categories before superordinate or subordinate categories
When asked to identify pictures, people more likely to give base level category

34. Category levels
When asked for common attributes of superordinate category, people give very few (vehicle)
When asked about attributes of base level categories, many more given (car)
When asked about attributes of base level categories, not many more than those given at the base level are added (SUV)
Movement from a superordinate category to a base level category results in a great increase in information, but movement to a subordinate category adds very little information

35. Base level thinking
Humans prefer to think a the base level of categorization because it provides the most useful information for predicting membership in a category
Superordinate members of a category maybe very different with few similarities – fruit
Base level share many common features – apples
Subordinate categories are more informative , but are poor discriminators – McIntosh apples share many features of other apples
Subordinate level thinking most important in areas of expertise. Choosing wine for dinner

36. Knowing
Connectionism

37. Importance of context
Context can act as a prime to understanding correct meaning
I saw a man eating fish.
Visiting relatives can be boring
Context can activate the meaning meant to be conveyed
By understanding the context of a communication, you can understand and remember the material better

38. Connectionist model of semantic memory
Involves a network of interconnected nodes each node connected with specific information
The connections between nodes vary in strength – referred to as connection weights
Nodes that are more strongly connected have greater connection weights
Learning involves strengthening the connection by increasing connection weights

39. A neural network

40. A neural network example