Graham CenterKNOWLEDGE 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?

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?

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.”

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

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 – BFZBZ LBF LLBL BZB

• 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

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”

Associative Norms(e.g., Minnesota Norms; Jenkins, 1952)

THIRSTY

Response Numberwater 348

drink 296

dry 121

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..

Associative speed and probability

LITTLE

Response Prob. Time (sec)

small .48 1.4

big .12 1.7

boy .08 1.9

girl .05 2.0

tiny .03 2.2

The less likely the response (defined by norms),

The slower the response

Marbe’s Law (Thumb & Marb, 1903)

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

NETWORK MODELS OFSEMANTIC KNOWLEDGE

Collins & Quillian 1969

a canarycan sing

900

1100

1300

1500

rea

ctio

n t

ime

(m

s)

0 1 2number of levels crossed

category

property

a canarycan fly

a canary isa canary

a canaryhas skin

a canary isan animal

a canaryis a bird

Then decide about: decision time

John owns a car TRUE 1280 Bob plays golf FALSE 1340 Fred plays golf DON’T KNOW ____

DECISIONS ABOUT IGNORANCEGlucksberg, 1980

How do we know we don’t know?

Students study sentences:

John owns a carBob Doesn’t play golfFred owns a bike

etc

TYPICALITY AND SEMANTIC DECISIONSRosch, 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

SEMANTIC PRIMING

• Automatic and attentional factors (Neely, 1977)

Prime Target (lexical decision)

BIRD robin related

BIRD arm unrelated

XXXX robin neutral (no prime)

Stimulus Onset Asynchrony 250-2000 ms

NO SHIFT condition: 80% BIRD - (bird e.g.)

SHIFT condition: 80% BODY - (bldg e.g.)

Neely (1977)Priming and Expectancy

No shift expected Shift expected

(Y-axis is RT for Neutral – RT for primed)

MEANING OF SENTENCES AS PROPOSITIONAL NETWORKS

“Susan gave a white cat to Maria, who is president of the club.”

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 coffee napkins

Businessman

documents contract client

Primed 656 ms 672 704

Unprimed 736 719 734

Priming 80 47 30