1. Natural Categories Hierarchical organization of categories –Superordinate (e.g., furniture) –Basic-level (e.g., chair) –Subordinate (e.g., armchair) Rosch et al. (1976) –Basic level category is most important Categories very different from eachother We learn basic-level categories first Used in everyday language (see Table 8.2 – basic level names are simple, shorter)

2. Goal-derived categories Members of a category serve a function –Birthday presents Members organized around a principle –Principle – to make someone happy on their birthday Unlike natural categories, –Typicality is NOT determined by family resemblance (Barsalou, 1985)

3. Models of categorization Prototype model: abstract representations that match any member of a category (e.g., prototypical dog) Feature frequency model: place objects into categories based on how many shared attributes (features) Exemplar model: store specific members of a category in memory; compare new items to all members of a category

4. More models of categorization Prototype model has good economy of storage  conceptual advantage to this model Reed (1972) + Reed & Friedman (1973) Used fake (schematic, line drawings) faces (p. 241) Taught Ss categories of faces Then, Ss classify new faces into one of the two categories

5. results Majority of classifications were based on the prototype model –In other words, people use prototypes to classify new information and place it into existing categories

6. Why categories? Categories organize material –Make material easier to find –Make material faster to find –Help you learn new material faster Semantic = meaning –Semantic organization

7. Collins and Quillian (1969) model Aka, semantic network model Aka, hiearchical network model “network” – individual memories are connected to each other (networking) in such a way that allows for efficient mental functioning

8. example (Fig. 9.5) (p. 261) animals Birds canaries

9. animals birds canaries Have wings feathers beak yellow sing breathe eat reproduce “node” “properties” “is a subset” “IS-A” link

10. Sentence verification task Given a sentence –E.g., All canaries are birds. Yes/no rapid response –Main measure is RT To be able to say “yes” –People find a “path” in the network that connects the two concepts in the sentence appropriately

11. experiment All canaries are birds. Vs. All canaries are animals. C&Q Model predicts : animals slower than birds because animals takes one extra link to check (to verify) the sentence

12. Semantic distance effect Results: the more “links” that need to be traversed to verify that two concepts are related, the longer the RT (look on p. 263 graph) Supports the C&Q Model (semantic network)