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?
REPRESENTATION OF
CONCEPTUAL KNOWLEDGE
What kind of information is behind
concepts and schemas?
• 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)
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
Number
water
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
small
big
boy
girl
tiny
Prob.
.48
.12
.08
.05
.03
Time (sec)
1.4
1.7
1.9
2.0
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
r e a c t io n tim e ( m s )
NETWORK MODELS OF
SEMANTIC KNOWLEDGE
Collins & Quillian 1969
1500
1300
1100
900
a canary
can fly
a canary
can sing
a canary
has skin
a canary is
an animal
a canary is
a canary
a canary
is a bird
category
property
0
1
2
number of levels crossed
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
Then decide about:
decision time
John owns a car
TRUE
1280
Bob plays golf
FALSE
1340
Fred plays golf
DON’T KNOW ____
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
SEMANTIC PRIMING
• Automatic and attentional factors
(Neely, 1977)
Prime
BIRD
BIRD
XXXX
Target
robin
arm
robin
(lexical decision)
related
unrelated
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
Primed
656 ms
Unprimed 736
Priming
80
client
672
704
719
734
47
30