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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.

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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

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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

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