1. Graham Center KNOWLEDGE TEST The largest land animal is: –Hippopotamus –Elephant –Whale –Horse At Yorktown, Cornwallis was defeated by General: –Ulysses S. Grant –George Washington –Douglas McArthur –Andrew Jackson Who was Gen. George C. Marshall? You discover a bat with feathers, that lays eggs. Is it a bird?

2. REPRESENTATION OF CONCEPTUAL KNOWLEDGE defining features and how they’re combined (CLASSIC) Specific examples (instances) of the concept or category (EXEMPLAR) Typical, characteristic features and their correlations (PROTOTYPE) “Metaknowledge” about families and hierarchies of concepts, “theories” about concepts (SCHEMA) What kind of information is behind concepts and schemas?

3. CLASSIFYING PHYSICS PROBLEMS BY EXPERTS AND NOVICES (Chi, 1981) Novices group by superficial aspects “these deal with blocks on an inclined plane” “inclined plane problems, coefficient of friction” “blocks on inclined planes with angles” Experts group by underlying principles “conservation of Energy problems” “work-energy Theorem. Straigthforward (!).” “..Can be done from energy considerations.”

4. LEARNING CONCEPTS BY TESTING HYPOTHESES (Bruner, 1956) positive instance negative instance What’s the concept rule? An active, deliberate form of learning More likely to be used if: –concepts are well-defined, rule-based –explicit concept-learning instructions –learners are familiar with the “domain” of rules and objects And more likely to succeed if: –concepts are simple and affirmative –learners are practiced at the task –and can select instances of testing –working memory load is minimized

5. IMPLICIT LEARNING OF CONCEPTS AND RULES (Reber, 1976) “Artificial grammar” of letter sequence rules –e.g. B -> (F or Z); Z -> (B or L); L -> B “grammatical” strings of letters studied –BFZBZLBFLLBLBZB students classify new strings as “grammatical or not” –e.g., BZF versus LFB and demonstrate “implicit learning” –classify new strings better than chance –can’t verbalize sequence rules –explicit learning instructions often no better than implicit

6. ORGANIZATION AND RETRIEVAL OF SEMANTIC KNOWLEDGE The associationist approach to semantic memory –Aristotle’s Laws of Association –Use of “free association” in clinical and experimental psychology –The Behaviorist approach Associative responses as “meaning” of concepts Publication of “associative norms” Associative fluency as “meaningfulness” Speed of responses as “associative strength”

7. Associative Norms (e.g., Minnesota Norms; Jenkins, 1952) THIRSTY ResponseNumber water348 drink296 dry121 hungry 99 beer 16 cold 9 wet 8 whisky 8 glass 6 hot 6 tired 6. 25-67 1 avid, bar, content, cool, crave, drank, drown, liquid, shy, stimulus, wish, well..

8. Associative speed and probability LITTLE ResponseProb.Time (sec) small.481.4 big.121.7 boy.081.9 girl.052.0 tiny.032.2 The less likely the response (defined by norms), The slower the response Marbe’s Law (Thumb & Marb, 1903)

9. Sentence Verification Task A rabbit has fur A shark is a plant A robin is a bird A flower has petals A rock is a fish A trout is a plant A collie has legs A table is an object A chicken has skin A pear is a plant First set involve a single “proposition” or level; the second set, two

10. NETWORK MODELS OF SEMANTIC KNOWLEDGE Collins & Quillian 1969

11. Then decide about: decision time John owns a car TRUE 1280 Bob plays golf FALSE 1340 Fred plays golf DON’T KNOW ____ DECISIONS ABOUT IGNORANCE Glucksberg, 1980 How do we know we don’t know? Students study sentences: John owns a car Bob Doesn’t play golf Fred owns a bike etc

12. TYPICALITY AND SEMANTIC DECISIONS Rosch, 1975 Natural categories have graded structure –some members more typical than others Typical members are those whose features are common in category –APPLE: round, edible, sweet,... “Typical” or central exemplars have “favored status” in various tasks: –faster to be judged as members of that category robin is a bird vs. chicken isa bird –given first as associates BIRD-?: robin, sparrow,.. ostrich.. –better “primed” by category name lexical decision to ROBIN speeded by first seeing BIRD

13. SEMANTIC PRIMING Automatic and attentional factors (Neely, 1977) PrimeTarget (lexical decision) BIRDrobinrelated BIRDarmunrelated XXXXrobinneutral (no prime) Stimulus Onset Asynchrony 250-2000 ms NO SHIFT condition: 80% BIRD - (bird e.g.) SHIFT condition: 80%BODY - (bldg e.g.)

14. Neely (1977) Priming and Expectancy No shift expected Shift expected (Y-axis is RT for Neutral – RT for primed)

15. MEANING OF SENTENCES AS PROPOSITIONAL NETWORKS “Susan gave a white cat to Maria, who is president of the club.”

16. THE “REALITY” OF PROPOSITIONAL STRUCTURES Priming follows propositional, not physical, distance (McKoon & Ratcliff, 1980) The businessman gestured to a waiter. The waiter brought coffee. The coffee stained the napkins. The businessman flourished the documents. The documents explained the contract. The contract satisfied the client. waiter coffeenapkins Businessman documents contract client Primed 656 ms 672 704 Unprimed 736 719 734 Priming 80 47 30