graham center knowledge test the largest land animal is: –hippopotamus –elephant –whale...
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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