the neural basis of thought and languagecs182/sp07/sections/... · 2007-05-03 · interrupt resume...
TRANSCRIPT
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The Neural Basis ofThought and Language
Final
Review Session
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Administrivia
• 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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Resources
• Textbook!
• Class slides
• Section slides
• Joe Makin's class notes from last year
– on notes page
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The Second Half
Cognition and Language
Computation
S tructured Connectionism
Computational Neurobiology
Biology
Midterm Final
abst
ract
ion
Motor Control Metaphor Grammar
Bailey Model
KARMA ECG
SHRUTI
ECG Learning
Bayes Nets
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Overview
• Bailey Model
– feature structures
– Bayesian model merging
– recruitment learning
• KARMA
– X-schema, frames
– aspect
– event-structure metaphor
– inference
• Grammar Learning
– parsing
– construction grammar
– learning algorithm
• SHRUTI
• FrameNet
• Bayesian Model of Human Sentence Processing
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Important topics
• Regier's model of spatial relation learning
• Bailey's model of verb learning
• KARMA model of metaphor
• Binding and inference
– SHRUTI, short signatures
• Grammars and learning
• ECG
– Learning ECG
• Bayes nets
• Model merging, MDL
• Petri nets
• Language
– Metaphor
– Aspect
– Grammars
– Schemas
– Frames
– SemSpec
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Q & A
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Bayes Nets
• Bayes' Rule / Product Rule
– P(A|B) = P(A,B) / P(B)
– P(A,B) = P(A|B) P(B)
– P(B|A) = P(A|B) P(B) / P(A)
– All the same!
• Variables have distributions
• Variables depend on other variables
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Regier's model
• Learn spatial relation terms
– e.g. in, on, above
• Neural network + hand-designed “vision” parts
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Bailey's model
• Verb learning
• Learn parameters matched to words
– word senses
– can connect to simulator
• Model merging!
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6palmextendslide
accelpostureelbow jntschema
8palmextendslide
accelpostureelbow jntschema
[6]palm 0.9extend 0.9slide 0.9
accelpostureelbow jntschema
data #1
data #2
data #3
data #4
[2]index 0.9fixed 0.9depress 0.9
accelpostureelbow jntschema
[6 - 8]palm 0.9extend 0.9slide 0.9
accelpostureelbow jntschema
palm 0.7
grasp 0.3
extend 0.9slide 0.9
postureelbow jntschema
2indexfixeddepress
accelpostureelbow jntschema
2graspextendslide
accelpostureelbow jntschema
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Computational Details
• complexity of model + ability to explain data
• maximum a posteriori (MAP) hypothesis
)|( argmax DmPm
rule Bayes'by )()|( argmax mPmDPm
=
how likely is the data given this model?
penalize complex models – those with too many word senses
wants the best model given data
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Model merging
• Start with a simple model
• Merge to refine it
– “Greedy” merges: reduce cost without thought for future
• Cost metric
– prefer simple representation
– prefer to explain data well
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Metaphor
• There are LOTS of metaphors we use
– Power is size
– Knowing is seeing
– Event structure is motion
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Event Structure Metaphor
• States are Locations • Changes are Movements • Causes are Forces• Causation is Forced Movement• Actions are Self-propelled Movements• Purposes are Destinations• Means are Paths• Difficulties are Impediments to Motion• External Events are Large, Moving Objects• Long-term, Purposeful Activities are Journeys
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Ego Moving versus Time Moving
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Results
30.8%69.2%Object Moving
73.3%26.7%Ego Moving
Meeting is Friday
Meeting is Monday
PRIME
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KARMA simulator
• Invented by Carson Daly
• Allows metaphor understanding
– Event structure metaphor
• Source domain is Petri net
– Target domain is Bayes net
– Metaphor maps connect
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KARMA
• DBN to represent target domain knowledge
• Metaphor maps link target and source domain
• X-schema to represent source domain knowledge
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Temporal synchrony and SHRUTI
• Binding problem
– bind properties to objects
– don't mix them up!
• Reflexive reasoning
– understand implied information
– not conscious of this
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SHRUTI
• entity, type, and predicate focal clusters
• An “entity” is a phase in the rhythmic activity.
• Bindings are synchronous firings of role and entity cells
• Rules are interconnection patterns mediated by coincidence detector circuits that allow selective propagation of activity
• An episode of reflexive processing is a transient propagation of rhythmic activity
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• asserting that walk(Harry, café)
• Harry fires in phase with agent role
• cafe fires in phase with goal role
+ - ? agt goal
+e +v ?e ?v
+ ?
walk
cafe
Harry
type
entity
predicate
“Harry walked to the café.”
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• asserting that walk(Harry, café)
• Harry fires in phase with agent role
• cafe fires in phase with goal role
+ - ? agt goal
+e +v ?e ?v
+ ?
walk
cafe
Harry
type
entity
predicate
“Harry walked to the café.”
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Activation Trace for walk(Harry, café)
+: Harry
+: walk
+e: cafe
walk-agt
walk-goal
1 2 3 4
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Alternative: short signatures
+ - ? agt goal
+e +v ?e ?v
+ ?
walk
cafe
Harry
type
entity
predicate
= 010
= 110
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Language
• Grammar
– Syntax
• Tense
• Aspect
• Semantics
• Metaphor
• Simulation
• Unification
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Computer-science style grammar
• Regular grammar
– X -> a b c Y
• Context-free grammar
– X -> a Y b Z W
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Analysis Process
Utterance
Simulation
Belief State
General Knowledge
Constructions
SemanticSpecification
“Harry walked into the café.”
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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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Unification
• Basic idea: capture agreement and semantic features in feature structures
• Enforce constraints on these features using unification rules
Patnumber : SGperson : 3rd agreement
Inumber : SGperson : 1stagreement
Went
agreementShop
agreementnumber : person : 1st
VP → Verb NP VP.agreement ↔ Verb.agreement
S → NP VPNP.agreement ↔ VP.agreement
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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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further questions?
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How do you learn…
the meanings of spatial relations,
the meanings of verbs,
the metaphors, and
the constructions?
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How do you learn…
the meanings of spatial relations,
the meanings of verbs,
the metaphors, and
the constructions?
That’s the Regier model.
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How do you learn…
the meanings of spatial relations,
the meanings of verbs,
the metaphors, and
the constructions?
That's Bailey's model
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How do you learn…
the meanings of spatial relations,
the meanings of verbs,
the metaphors, and
the constructions?
conflation hypothesis(primary metaphors)
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How do you learn…
the meanings of spatial relations,
the meanings of verbs,
the metaphors, and
the constructions?
construction learning
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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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further questions?