the neural basis of thought and languagecs182/sp07/sections/week... · 2007-05-03 · interrupt...
TRANSCRIPT
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The Neural Basis ofThought and Language
Week 14
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Administrivia
• Final exam review session tonight
– 6-8pm Evans 75
• Final in class next Tuesday, May 8th
• Be there on time!
• Format:
– closed books, closed notes
– short answers, no blue books
• Final paper due on bSpace on Friday, May 11
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Analysis Process
Utterance
Simulation
Belief State
General Knowledge
Constructions
SemanticSpecification
“Harry walked into the café.”
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The HARRY construction
construction HARRY subcase of Ref-Expr
form selff .orth ← “Harry”
meaning: Harry
schema Harry subcase of Human
gender ← malename ← “Harry”address ← 42 Tall Elf Dr.
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The CAFE construction
construction CAFE subcase of Ref-Expr
form selff .orth ← “cafe”
meaning: Cafe
schema Cafe subcase of Building
schema Building subcase of Container
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The INTO construction
construction INTO subcase of Spatial-Relation
form selff .orth ← “into”
meaning: Trajector-Landmarkevokes Container as contevokes Source-Path-Goal as spgtrajector ↔ spg.trajectorlandmark ↔ contcont.interior ↔ spg.goalcont.exterior ↔ spg.source
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The WALKED construction
construction WALKED subcase of Motion-Verb
form selff .orth ← “walked”
meaning: Walkselfm.aspect ← simple_past
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The Spatial-Phrase construction
construction SPATIAL-PHRASEconstructional
constituentssr : Spatial-Relationlm : Ref-Expr
formsrf before lmf
meaningsrm.landmark ↔ lmm
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The Directed-Motion construction
construction DIRECTED-MOTIONconstructional
constituentsa : Ref-Expm: Motion-Verbp : Spatial-Phrase
form af before mf
mf before pf
meaningevokes Directed-Motion as dmselfm.scene ↔ dm
dm.agent ↔ am
dm.motion ↔ mm
dm.path ↔ pm
schema Directed-Motionroles
agent : Entitymotion : Motionpath : SPG
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What exactly is simulation?
• Belief update and/or X-schema execution
hungry meeting
cafe
time of day
readystart
ongoingfinish
done
iterate
WALK
at goal
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“Harry walked into the café.”
readywalk
done
walker=Harry goal=cafe
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Analysis Process
Utterance
Simulation
Belief State
General Knowledge
Constructions
SemanticSpecification
“Harry is walking to the café.”
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“Harry is walking to the café.”
readystart
ongoingfinish
done
iterateabortcancelled
interrupt resume
suspended
WALKwalker=Harry goal=cafe
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Analysis Process
Utterance
Simulation
Belief State
General Knowledge
Constructions
SemanticSpecification
“Harry has walked into the wall.”
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Perhaps a different sense of INTO?
construction INTO subcase of spatial-prep
form selff .orth ← “into”
meaningevokes Trajector-Landmark as tlevokes Container as contevokes Source-Path-Goal as spgtl.trajector ↔ spg.trajectortl.landmark ↔ contcont.interior ↔ spg.goalcont.exterior ↔ spg.source
construction INTO subcase of spatial-prep
form selff .orth ← “into”
meaningevokes Trajector-Landmark as tlevokes Impact as imevokes Source-Path-Goal as spgtl.trajector ↔ spg.trajectortl.landmark ↔ spg.goalim.obj1 ↔ tl.trajectorim.obj2 ↔ tl.landmark
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“Harry has walked into the wall.”
readystart
ongoingfinish
done
iterateabortcancelled
interrupt resume
suspended
WALKwalker=Harry goal=wall
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Map down to timeline
S
R
E
readystart
ongoingfinish
done
consequence
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Acquisition
Reorganize
Hypothesize
Production
Utterance
(Comm. Intent, Situation)
Generate
Constructions
(Utterance, Situation)
Analysis
Comprehension
Analyze
Partial
Usage-based Language Learning
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Main Learning Loop
while <utterance, situation> available and cost > stoppingCriterion
analysis = analyzeAndResolve(utterance, situation, currentGrammar);
newCxns = hypothesize(analysis);
if cost(currentGrammar + newCxns) < cost(currentGrammar)
addNewCxns(newCxns);
if (re-oganize == true) // frequency depends on learning parameter
reorganizeCxns();
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Three ways to get new constructions
• Relational mapping
– throw the ball
• Merging
– throw the block
– throwing the ball
• Composing
– throw the ball
– ball off
– you throw the ball off THROW < BALL < OFF
THROW < OBJECT
THROW < BALL
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Minimum Description Length• Choose grammar G to minimize cost(G|D):
– cost(G|D) = α • size(G) + β • complexity(D|G)
– Approximates Bayesian learning; cost(G|D) ≈ posterior probability P(G|D)
• Size of grammar = size(G) ≈ 1/prior P(G) – favor fewer/smaller constructions/roles; isomorphic mappings
• Complexity of data given grammar ≈ 1/likelihood P(D|G)– favor simpler analyses
(fewer, more likely constructions)
– based on derivation length + score of derivation
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Human Sentence Processing
Can we use any of the mechanisms we just discussed
to predict reaction time / behavior
when human subjects read sentences?
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Good and Bad News
• Bad news:
– No, not as it is.
– ECG, the analysis process and simulation process are represented at a higher computational level of abstraction than human sentence processing (lacks timing information, requirement on cognitive capacity, etc)
• Good news:
– we can construct bayesian model of human sentence processing behavior borrowing the same insights
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Bayesian Model of Sentence Processing
• Do you wait for sentence boundaries to interpret the meaning of a sentence? No!
• As words come in, we construct
– partial meaning representation
– some candidate interpretations if ambiguous
– expectation for the next words
• Model
– Probability of each interpretation given words seen
– Stochastic CFGs, N-Grams, Lexical valence probabilities
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SCFG + N-gram
Reduced RelativeMain Verb
S
NP VP
D N VBN
The cop arrested the detective
S
NP VP
NP VP
D N VBD PP
The cop arrested by
Stochastic CFG
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SCFG + N-gram
Main VerbReduced Relative
S
NP VP
D N VBN
The cop arrested the detective
S
NP VP
NP VP
D N VBD PP
The cop arrested by
N-Gram
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SCFG + N-gram
Main VerbReduced Relative
S
NP VP
D N VBN
The cop arrested the detective
S
NP VP
NP VP
D N VBD PP
The cop arrested by
Different Interpretations
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Predicting effects on reading time
• Probability predicts human disambiguation
• Increase in reading time because of...
– Limited Parallelism
• Memory limitations cause correct interpretation to be pruned
• The horse raced past the barn fell
– Attention
• Demotion of interpretation in attentional focus
– Expectation
• Unexpected words