Organization of Semantic Memory

1. Important features: Nodes: location in network representing a concept Pathways or links: connections between nodes along which activation can spread Spreading activation: associative retrieval of info moving along pathways Propositions: relational fact statements represented in network (“sports car is a status symbol”) RT: function of distance traveled in network Problem: cognitive economy, listing all properties or features of concept can get unweildly Organization of Semantic Memory Typical empirical testing paradigm: propositional verification task – rt to car has four wheels vs. car is a status symbol.

2. Collins & Quillian Spreading activation model: hierarchical structure • Reduced cognitive economy problem, but introduces “typicality” problem RT’s following hierarchical structure, longer for more distance superordinates.

3. Semantic feature comparison model: two stage process – 1. General feature overlap; 2. (if necessary) comparison of defining features • Characteristic vs. defining features • Generally solves atypical category member problem

4. Two stage feature comparison process: (1) General similarity (2) Defining features (if necessary). Encode comparison Low overall similarity (rock is bird) High overall similarity (robin is bird) Compare all features Intermediate similarity (ostrich is bird) Compare just defining features Mismatch Match “Yes” “No”

5. Modified semantic network model (Collins & Loftus). – removes hierarchical assumption. Distance and therefore rts represent semantic relatedness

6. PDP or connectionist model of semantic memoryImportant addition: weights between connections signifying strength. Some weights can be negative. Often negative connections occur for same category items, so types of cars might inhibit each other (chevy vs. ford vs. toyota, etc.) .2 .7 .8

7. Semantic Priming: Lexical Decision task • Word or non-word RT measure • FORK = word; DXMZ = non-word • SIGN – FORK • DXMZ – FORK • SPOON – FORK (sig reduction in rt): why? Spreading activation between related concepts. • Implicit or unconscious priming effects

8. Automatic and controlled priming • Automatic priming should be reflexive, immediate • Controlled priming should take time • Category shift studies • LDT, but subjects told that when certain category word appears (such as body), expect the next item (if it is a word) to be the member of a different category (building). Occasionally, this was false and a category was followed by same category items (body followed by heart).

9. Semantically-driven recall: Frederick Barlett and Schemas

10. Schema • Framework for organizing, encoding, interpreting, storing and recalling information • Schema often infers relevant information even if not experienced (What color was the car? Answer: Red, of course!) • Script or frame: specific type of schema used for understanding ordered events (such as a wedding, going to a restaurant, or a typical day at the office). Script has slots – expected roles or events that are often assumed in recall even if they did not actually happen. “Now Mr. Jones, on the day in question, did you actually have a cup of coffee before you sat down at your desk?” Ans: “Of, course. I always do.” • From memory or from script?

11. Theories of Categorization • How are categories stored and represented in the head? • Classical view: categories separated by defining features • Problem: people rarely use or even know defining features of category • Problem: typicality effects Probabilistic view: categories defined by common features or most frequently occurring features of category members. Degree of ‘family resemblance’ determines typicality of category members Exemplar view: category defined by a particular or few typical members. Argued to retain more information about correlated attributes compared to probabilistic view. For example: average bird (probabilistic view) may sing, but exemplar shows that large birds –no sing; smaller/medium birds –sing