Knowing

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. 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