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Semantic Memory_2014(1)-3.ppt
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- 2. I. Introduction to SemanticMemory A. What is semantic memory? 1. Permanent memory store of our world knowledge 2. Different from episodic memory – no representation of when or where we learned the information 3. Examples • What is the capitol of Colorado? • How many legs does a horse have? • What color is a canary?
- 3. I. Introduction to SemanticMemory B. Key questions: 1. How is information stored? 2. What is the nature of the representation? 3. How is information learned? 4. How is information retrieved?
- 4. I. Introduction to SemanticMemory C. New terminology 1. Concepts • Mental representations • Often “the fundamental unit of thought” • “An idea that includes all that is characteristically associated with it” (Medin, 1989) 2. Proposition • The relationship between concepts • E.G. – A canary is yellow – A canary is a bird – A bird has wings – A bird is an animal
- 5. II. The Collins& Quillian Hierarchical Model A. Key Properties 1. Network: An interrelated set of concepts / body of knowledge. 2. Node: A point or location in the network representing a single concept. 3. Pathways: associations between concepts (propositions) that are directional.
- 6. II. The Collins& Quillian Model A. Key Properties 3. Pathways: associations between concepts (propositions) that are directional. • “ISA” pathways denote category membership – “Canary is a bird” • Property pathways describe properties of concepts “Bird has feathers”
- 7. Sample Portion ofthe Semantic Network
- 8. II. The Collins& Quillian Model B. Spreading Activation 1. The mental activity of accessing and retrieving information from the network. 2. Takes passive concepts (those not currently in working memory) and activates them (puts them in working memory). 3. Activation then spreads to related nodes (e.g., activation to the doctor node would also spread to the nurse node).
- 9. II. The Collins& Quillian Model C. Key Features of the Model 1. Hierarchical – concepts are arranged in a hierarchy 2. This allows for cognitive economy – removes any redundancy 3. Takes time for activation to spread across inactive nodes 4. Activation spreads quickly across recently activated nodes
- 10. Sample Portion ofthe Semantic Network
- 11. III. Smith’s Feature ComparisonModel A. General Structure 1. Information about concepts are represented as feature lists 2. These lists include both defining features and characteristic features • Defining features are essential to the meaning of a concept • Characteristic features are common, but not essential to the meaning of a concept
- 12. III. Smith’s Feature ComparisonModel 2. These lists include both defining features and characteristic features • Defining features are essential to the meaning of a concept • Characteristic features are common, but not essential to the meaning of a concept Robin Physical object Living Animate Feathered Red breasted
- 13. III. Smith’s Feature ComparisonModel 2. These lists include both defining features and characteristic features • Defining features are essential to the meaning of a concept • Characteristic features are common, but not essential to the meaning of a concept Robin Eats worms Roosts in trees
- 14. III. Smith’s Feature ComparisonModel 2. These lists include both defining features and characteristic features • Defining features are essential to the meaning of a concept • Characteristic features are common, but not essential to the meaning of a concept Leos Born between July 23 and August 22
- 15. III. Smith’s Feature ComparisonModel 2. These lists include both defining features and characteristic features • Defining features are essential to the meaning of a concept • Characteristic features are common, but not essential to the meaning of a concept Leos Loyal Self-assured Charming Generous Opinionated Overbearing Proud
- 16. III. Smith’s Feature ComparisonModel A. General Structure 1. Information about concepts are represented as feature lists 2. These lists include both defining features and characteristic features 3. Features are stored starting with most defining followed by most characteristic
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- 18. III. Smith’s Feature ComparisonModel B. Feature Comparison 1. Models are often tested using sentence verification tasks – e.g. a ROBIN is a BIRD 2. The model begins with Stage 1 – global feature comparison • ‘Fast yes’ responses occur when there is a large number of shared features • ‘Fast no’ responses occur when there are few shared features
- 19. A ROBIN isa BIRD…
- 20. III. Smith’s Feature ComparisonModel B. Feature Comparison 2. The model begins with Stage 1 – global feature comparison • ‘Fast yes’ responses occur when there is a large number of shared features • ‘Fast no’ responses occur when there are few shared features • Intermediate comparisons (some shared, some not shared) move to a Stage 2 Comparison of Defining Features
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- 22. III. Smith’s Feature ComparisonModel B. Feature Comparison 2. The model begins with Stage 1 – global feature comparison • Intermediate comparisons (some shared, some not shared) move to a Stage 2 Comparison of Defining Features • When the defining features match, a ‘slow yes’ response is given • When there is a feature mismatch, a ‘slow no’ response is given
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- 24. IV. Direct Comparisonsof Models – Central Themes A. The Principle of Cognitive Economy 1. A primary assumption of the Hierarchical Model is cognitive economy – only non-redundant facts are stored 2. The members of a category inherit the properties of the category itself – the principle of inheritance
- 25. IV. Direct Comparisonsof Models – Central Themes B. Property Statements 1. Problem with the Feature Comparison model, it is unable to account for property statements 2. Can handle “Robin has wings” but… 3. The model assumed a feature list that corresponded to the properties – i.e. “THINGS WITH WINGS” 4. Hierarchical models handle the same as any other statement
- 26. IV. Direct Comparisonsof Models – Central Themes C. Typicality Effects 1. Typicality refers to the degree to which items are viewed as typical, central members of a category 2. Battig & Montague (1969) category membership norms 3. Less frequent category members have lower feature overlap than common members • Robin is a bird – common • Chicken is a bird – uncommon
- 27. IV. Direct Comparisonsof Models – Central Themes C. Typicality Effects 3. Less frequent category members have lower feature overlap than common members • Robin is a bird – common • Chicken is a bird – uncommon 4. Typical members can be judged more rapidly than atypical members
- 28. IV. Direct Comparisonsof Models – Central Themes C. Typicality Effects 3. Less frequent category members have lower feature overlap than common members • Robin is a bird – common • Chicken is a bird – uncommon 4. Typical members can be judged more rapidly than atypical members
- 29. IV. Direct Comparisonsof Models – Central Themes C. Typicality Effects 4. Typical members can be judged more rapidly than atypical members – typicality effect
- 30. IV. Direct Comparisonsof Models – Central Themes D. Semantic Relatedness 1. A direct prediction of feature comparison relates to how similar concepts are 2. Rips (1975) – demonstration
- 31. IV. Direct Comparisonsof Models – Central Themes D. Semantic Relatedness 1. A direct prediction of feature comparison relates to how similar concepts are 2. Multi-dimensional Scaling Solutions
- 32. IV. Direct Comparisonsof Models – Central Themes D. Semantic Relatedness 1. A direct prediction of feature comparison relates to how similar concepts are 2. Multi-dimensional Scaling Solutions
- 33. IV. Direct Comparisonsof Models – Central Themes D. Semantic Relatedness 1. A direct prediction of feature comparison relates to how similar concepts are 2. Multi-dimensional Scaling Solutions 3. Modern semantic network models
- 34. IV. Direct Comparisonsof Models – Central Themes D. Semantic Relatedness 4. Modern semantic network models • Redundant information • Distance represents how similar concepts are Learning and Motivation,
- 35.
- 36. I. Types ofCategories A. Natural Categories 1. Occur naturally in the world 2. Essentially define themselves 3. Naturally occurring concepts are labeled after discovery
- 37. I. Types ofCategories A. Natural Categories 1. Occur naturally in the world 2. Essentially define themselves 3. Naturally occurring concepts are labeled after discovery
- 38. I. Types ofCategories B. Artifact Categories 1. Objects or conventions designed by humans to serve particular functions 2. Category membership is primarily determined by function / intended use
- 39. I. Types ofCategories B. Artifact Categories 1. Objects or conventions designed by humans to serve particular functions 2. Category membership is primarily determined by function / intended use 3. Should herding dogs be an artifact category?
- 40. I. Types ofCategories C. Nominal Categories 1. Linguistic conventions that involve the arbitrary assignment of a label to things that fit a particular set of conditions 2. Often defined as a matter of convenience
- 41. I. Types ofCategories D. Ad Hoc Categories 1. Formed for a purpose 2. Can be influenced by context
- 42. I. Types ofCategories D. Ad Hoc Categories 1. Formed for a purpose 2. Can be influenced by context
- 43. I. Types ofCategories D. Ad Hoc Categories 1. Formed for a purpose 2. Can be influenced by context feet dust bird atom hands animal cell children
- 44. I. Types ofCategories D. Ad Hoc Categories 1. Formed for a purpose 2. Can be influenced by context 3. “Small things” 4. Other examples: Things you take on vacation feet dust bird atom hands animal cell children
- 45. I. Types ofCategories F. Levels of Categorization 1. Categories are both horizontally and vertically organized • Animals – plants • Animals – Birds Raptors
- 46. I. Types ofCategories F. Levels of Categorization 1. Categories are both horizontally and vertically organized 2. Categories are also hierarchical with super ordinate and subordinate categories
- 47. Animals Birds Cats Raptors Songbirds Eagle HawkCanary Sparrow Golden Bald Lions Tigers
- 48. II. Approaches toConcept Representation A. Classic approaches B. Prototype theories C. Exemplar theories
- 49. II. Approaches toConcept Representation A. Classic approaches 1. Similar to Feature theories 2. Classification is based on certain features or characteristics 3. Features are both necessary and sufficient for categorization 4. Problem – not everything can be categorized this way: Games • Typicality effects • Fuzzy boundaries
- 50. II. Approaches toConcept Representation B. Prototype theories 1. As we learn, we “abstract out” a prototype 2. A prototype is the most typical, or representative idea of a category
- 51. II. Approaches toConcept Representation C. Exemplar theories 1. Category membership is conducted by comparison to stored examples (exemplars) 2. More typical members will be similar to many exemplars 3. Categorization is entirely based on comparison to stored examples
- 52. III. Applications of Conceptsand Categories A. Stereotyping 1. We automatically categorize people based on visual features (sex, age, race, weight) 2. Social-category level beliefs have the power to shape impressions of individuals 3. Some are automatic, others are based on either acquired information or assumptions about category membership (e.g. either someone discloses they are gay or assumed to be gay).
- 53. III. Applications of Conceptsand Categories B. Perceptions of minorities 1. Ethnic minorities 2. Sexual minorities • Merritt et al (2013) gay actors perceived as less masculine 3. Knowledge may influence our behavior in these cases
- 54. III. Applications of Conceptsand Categories C. Stereotype threat 1. Stereotype threat is the self threat experienced by members of a negatively stereotyped groups that they will be judged or behave in ways that confirm the stereotype 2. Often this precipitates the undesired behavior 3. Our knowledge of our own group membership can influence our behavior
- 55. III. Applications of Conceptsand Categories D. How might categories affect behavior? 1. Social categories may be formed via prototypes or exemplars 2. Examples • Gender • Masculinity and femininity • Sexual orientation • Occupations
- 56. IV. Other waysour knowledge influences behavior A. Schemas and scripts 1. Guide our actions 2. Can they lead us into trouble • Sexual schemas • Sexual scripts B. Selective perception & Selective memory