Metacognitive Processes for Uncertainty Handling

1. Metacognitive Processes for Uncertainty Handling Connectionist Implementation of a Cognitive Model 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

2. Cognitive - Computational Architecture An Integration of Two Research Areas • Recognition - Metacognition model: Critical reflection in recognition-based decision making • SHRUTI, a connectionist model: Rapid parallel reasoning about belief and utility, with relations & dynamic variable binding Thanks to our collaborator, Dr. Lokendra Shastri, ICSI, Berkeley.

3. 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.

4. Accepted (1,0) Incomplete Evidence (0,0) Low Resolution (.5,.5) Conflicting Evidence (1,1) Assumptions Rejected (0,1) Reflection about Recognition Meta-Recognition System Recognition System Monitor Recognition Costs of delay? Costs of errors? Novel task or situation? Perceptual Encoding Monitor Uncertainty in Mental Models Activation of Domain Mental Models External Environment Regulate Uncertainty in Mental Models Shift attention, clamp assumptions -- to activate information in long-term memory & test hypotheses Action Immediate action Information collection Wait Regulate Recognition Inhibit action, allow reflective processes Permit action on current best model

5. Inner Dialogue among Three Perspectives Control Critical Dialogue Evolving mental models of alternative possible states of affairs Regulation of pragmatic tradeoffs, to achieve objectives Dynamic challenge & response First person point of view: Proponent Third person point of view: Facilitator - Judge Second person point of view: Critic

6. Control Cycle • Longer temporal scope • Comparable to “task analysis” and “strategy selection” • But more continuous, incremental, & local

7. Critiquing & Correcting Cycle • Shorter temporal scope • Comparable to simple judgments (e.g., “Feeling of knowing”)

8. 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

9. 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

10. Keeping Track of Object Roles in Rules • 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

11. 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

12. 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 assumptionthat everything is normal (i.e., average) and results in low resolution.

13. Compensation for Limitations by Reflective System • Not all relevant information in long term knowledge base may be retrieved in first cycle of attention • Critiquing & correcting cycle activates additional relevant information by shifting attention &/or clamping • 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. Balancesadvantages of thinking more versus risks of delaying action

14. What-if? Clamp: Assume we will attack. Automatic Query: Will we defeat enemy? Returning activation suggests we will defeat enemy.

15. Critiquing & Correcting Automatic Query: Will attack be unexpected? Changes center of activation. Returning activation provides no support for success of attack! Shift attention to culprit – low resolution taxon fact. Metacognitive critic recognizes uncertainty pattern corresponding to incomplete information (low activation of both + and -)

16. 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