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C T I
Metacognitive Processes for Uncertainty Handling
Marvin S. Cohen, Ph.D.
Bryan B. Thompson
Cognitive Technologies, Inc.
4200 Lorcom Lane
Arlington, VA 22207
21 March 2005 Army Research Institute: DASW01-97-C-0038
Office of Naval Research: N00014-00-M0070
National Science Foundation: DMI-9861411
Connectionist Implementation of a Cognitive Model
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Cognitive - Computational Architecture
An Integration of Two Research Areas
• Recognition - Metacognition model: Critical reflection in recognition-based decision making
Thanks to our collaborator, Dr. Lokendra Shastri, ICSI, Berkeley.
• SHRUTI, a connectionist model: Rapid parallel reasoning about belief and utility, with relations & dynamic variable binding
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The Recognition / Metacognition Model
Naturalistic Decision Making
• Empirical investigation of how experienced, effective decision makers make decisions
• And – how they differ from less experienced, less effective decision makers
• Proficient Decision Makers combine pattern recognition with strategies for facilitating recognition, verifying & improving its results, when familiar patterns don’t fit.
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Meta-Recognition System
Recognition System
Reflection about Recognition
ExternalEnvironment
Perceptual Encoding
Activation ofDomainMental Models
ActionImmediate actionInformation collectionWait
Localize targetLocalize target
Approach targetApproach target
Current IntentCurrent IntentMeansMeans
MotiveMotive
OpportunityOpportunity
What condition, events,or objectives may
motivate hostility? Whatprevious activities may
reflect hostility?
Gunboat is lesscapable than Libyan
air assets.
Another US cruiseris further below Lineof Death and closer
to the gunboat.
The enemy's plan: Whattargets they will attack,
with what assets.
Gunboat cannotdetect own ship at
present range.
Ghaddafi hasresponded to US
challenge bythreatening US
ships.
Libyan gunboatintends to attack
own ship.
Time = t0
Libyan gunboatturned toward own
ship.
Time = t1
Gunboat increasesspeed.
Maintain SecurityMaintain SecurityTime= t0
Gunboat is inharm's way.
Time = t1
Has ignoredwarning.
Monitor RecognitionCosts of delay? Costs of errors?Novel task or situation?
Regulate RecognitionInhibit action, allow reflective processesPermit action on current best model
Monitor Uncertainty in Mental Models
Regulate Uncertainty in Mental Models
Shift attention, clamp assumptions-- to activate information in long-term memory & test hypotheses
IncompleteEvidence(0,0)
ConflictingEvidence(1,1)
Accepted (1,0)
Rejected (0,1)
Assumptions
LowResolution(.5,.5)
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Control
Critical Dialogue
Inner Dialogue among Three Perspectives
First person point of view: Proponent
Second person point of view: Critic
Third person point of view: Facilitator - Judge
Evolving mental models of alternative
possible states of affairs
Dynamic challenge &
response
Regulation of pragmatic
tradeoffs, to achieve
objectives
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Control Cycle
• Longer temporal scope– Comparable to “task analysis” and “strategy selection”
– But more continuous, incremental, & local
Yes
No
Low
Nominal
High
Reflect
Low
Nominal
High
Cost of time?
Low
Nominal
High
Cost of error?
Yes
No
Task Novelty?Evaluate task
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Critiquing & Correcting Cycle
• Shorter temporal scope– Comparable to simple judgments (e.g., “Feeling of
knowing”)
Accept (1,0)
Reject (0,1)
No change
Yes
No
Clamp
P1
Pi
Pn
Unstable?
Incomplete
Conflict
Low Resolution
Accept / Reject
Shift attention
P1
Pi
Pn
Uncertainty Type?Attend
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Advantages of R-M Strategy
• Proponent: Utilizes experience & intuition in novel & uncertain situations
– No need to convert to “analytical” mode of thinking– A small number of concrete, visualizable scenarios, not an
unrealizable statistical aggregation (e.g., 70% hostile, 30% friendly)
• Critiquing & correcting cycle: Improves on experience & intuition
– Stimulates retrieval & use of implicit knowledge– Helps understand solution’s strengths and weaknesses – by
reflective annotation of mental models
• Control cycle: Can stop process any time and go with the best solution so far
– Adapt to available time vs potential benefits
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The Recognitional Model: SHRUTI
Rapid, parallel, and relational inference
• Processing time is independent of size of long-term knowledge base
• Space linear in size of long term knowledge base
• Supports complex relational reasoning involving multiple objects and n-ary predicates
– Has representational and inferential capability of predicate calculus, within processing limitations
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Keeping Track of Object Roles in Rules
DEFEATS + - ? victor vanguished
ATTACKS + - ? event attacker attacked
+ ?
UNEXPECTED + - ? event time place participant OCCURS + - ? event time place
• Represents same object throughout a chain of inferences by assigning a different phase of neural activation to each object & using temporal pattern matching
• Neurally plausible
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Limitations on Reflexive Reasoning • Limits number of different objects that can be
distinguished in a particular incident of reasoning (based on temporal resolution for assigning objects to phases)
• Limits length of reasoning/retrieval chain (based on increasing error with length of propagation)
• Limits number of instances of same predicate that can be used in an incident of reasoning
• These limitations provide context in which metacognitive skills may improve both performance and learning
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+ ?+ ?
+ ? + ?
PREPARING-FOR-ATTACK + - ? time place agentDEFEATS + - ?
ATTACKS + - ?
+ ?
UNEXPECTED + - ? OCCURS + - ?
CONCEALMENT + - ? time place perpetrator victim
EPISODIC FACT
INFERENTIAL HORIZON
Enemy is preparingfor attack.
EPISODIC FACT
Concealment notgood at this place
TAXON FACT
General likelihoodthis kind of enemywill be surprised byattack at this type oftime and place
DYNAMIC FACTDYNAMIC FACT
Time and place ofattack
Attacking andattacked forces
Limits of Working Memory
Taxon Fact = Prior probability
of being surprised
Specific information is
not attended to
Predicates at edge of network have impact equal to average of activation in previous situations.This is in effect the assumption that everything is normal (i.e., average) and results in low resolution.
C T I
Compensation for Limitations by Reflective System
• Not all relevant information in long term knowledge base may be retrieved in first cycle of attention
• New information propagates effects through network Results of successive attention shifts are integrated through priming
• Control cycle monitors changes in uncertainty, and (possibly) estimate of time available & costs of errors. Balances advantages of thinking more versus risks of delaying action
• Critiquing & correcting cycle activates additional relevant information by shifting attention &/or clamping
C T I
+ ?+ ?
+ ? + ?
PREPARING-FOR-ATTACK + - ? time place agentDEFEATS + - ?
ATTACKS + - ?
+ ?
UNEXPECTED + - ? OCCURS + - ?
CONCEALMENT + - ? time place perpetrator victim
EPISODIC FACT
INFERENTIALHORIZON
Enemy is preparingfor attack.
EPISODIC FACT
Concealment notgood at this place
TAXON FACT
General likelihoodthis kind of enemywill be surprised byattack at this type oftime and place
DYNAMICFACT
DYNAMICFACT
Time and place ofattack
Attacking andattacked forces
Clamp: Assume we will attack.
What-if?
Automatic Query: Will we defeat enemy?
Returning activation
suggests we will defeat
enemy.
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+ ?+ ?
+ ? + ?
PREPARING-FOR-ATTACK + - ? time place agentDEFEATS + - ?
ATTACKS + - ?
+ ?
UNEXPECTED + - ? OCCURS + - ?
CONCEALMENT + - ? time place perpetrator victim
EPISODIC FACT
INFERENTIALHORIZON
Enemy is preparingfor attack.
EPISODIC FACT
Concealment notgood at this place
TAXON FACT
General likelihoodthis kind of enemywill be surprised byattack at this type oftime and place
DYNAMICFACT
DYNAMICFACT
Time and place ofattack
Attacking andattacked forces
Metacognitive critic recognizes uncertainty pattern corresponding to incomplete
information (low activation of both + and -)
Critiquing & Correcting
Shift attention to culprit –
low resolution taxon fact.
Automatic Query: Will attack be unexpected?
Returning activation
provides no support for success of
attack!
Changes center of
activation.
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Effects of Metacognition on Learning
• Decision makers acquire domain-specific metacognitive skills
– Which predicates to scrutinize when trouble arises
– Basic associative learning of weights on links between relations, e.g., by backpropagation
• Decision makers acquire general metacognitive skills
– Strategies for shifting attention based on conflict, gaps, resolution, & instability (assumptions).
• Result 1: More effective working memory
• Result 2: Speeded compilation of domain knowledge– Extends reach of backpropagation learning for domain