Semantic Memory_2014(1)-3.ppt
- 2. I. Introduction to Semantic Memory 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 Semantic Memory 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 Semantic Memory 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 of the 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 of the Semantic Network
- 11. III. Smith’s Feature Comparison Model 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 Comparison Model 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 Comparison Model 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 Comparison Model 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 Comparison Model 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 Comparison Model 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
- 18. III. Smith’s Feature Comparison Model 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 is a BIRD…
- 20. III. Smith’s Feature Comparison Model 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
- 22. III. Smith’s Feature Comparison Model 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
- 24. IV. Direct Comparisons of 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 Comparisons of 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 Comparisons of 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 Comparisons of 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 Comparisons of 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 Comparisons of Models – Central Themes C. Typicality Effects 4. Typical members can be judged more rapidly than atypical members – typicality effect
- 30. IV. Direct Comparisons of 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 Comparisons of 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 Comparisons of 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 Comparisons of 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 Comparisons of Models – Central Themes D. Semantic Relatedness 4. Modern semantic network models • Redundant information • Distance represents how similar concepts are Learning and Motivation,
- 36. I. Types of Categories A. Natural Categories 1. Occur naturally in the world 2. Essentially define themselves 3. Naturally occurring concepts are labeled after discovery
- 37. I. Types of Categories A. Natural Categories 1. Occur naturally in the world 2. Essentially define themselves 3. Naturally occurring concepts are labeled after discovery
- 38. I. Types of Categories 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 of Categories 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 of Categories 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 of Categories D. Ad Hoc Categories 1. Formed for a purpose 2. Can be influenced by context
- 42. I. Types of Categories D. Ad Hoc Categories 1. Formed for a purpose 2. Can be influenced by context
- 43. I. Types of Categories 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 of Categories 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 of Categories F. Levels of Categorization 1. Categories are both horizontally and vertically organized • Animals – plants • Animals – Birds Raptors
- 46. I. Types of Categories 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 Hawk Canary Sparrow Golden Bald Lions Tigers
- 48. II. Approaches to Concept Representation A. Classic approaches B. Prototype theories C. Exemplar theories
- 49. II. Approaches to Concept 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 to Concept 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 to Concept 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 Concepts and 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 Concepts and 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 Concepts and 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 Concepts and 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 ways our 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