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Modeling Interaction Between Metacognition and Emotion in a Cognitive Architecture

Modeling Interaction Between Metacognition and Emotion in a Cognitive Architecture. Metacognition and Computation AAAI Spring Symposium Stanford University, CA March 21-23 2005. Eva Hudlicka Psychometrix Associates, Inc. Blacksburg, VA evahud@earthlink.net. Outline.

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Modeling Interaction Between Metacognition and Emotion in a Cognitive Architecture

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  1. Modeling Interaction Between Metacognition and Emotion in a Cognitive Architecture Metacognition and Computation AAAI Spring Symposium Stanford University, CA March 21-23 2005 Eva Hudlicka Psychometrix Associates, Inc. Blacksburg, VA evahud@earthlink.net

  2. Outline • Motivation & Objectives • Metacognition and Emotion • Emotion Modeling Methodology & MAMID Architecture • Implementing Metacognitive Functions in MAMID • Modeling Interactions Among Metacognition & Emotions • Summary & Future Work

  3. Motivation & Objectives • Understand mechanisms of metacognition - emotion interactions • Identify processes and structures necessary to implement (selected aspects of) metacognition: • Feeling of confidence (FOC) • Explore interactions among meta-cognitive functions and emotion • Anxiety-linked metacognitive strategy of emotion-focused coping • Anxiety and FOC • Develop more realistic models of human behavior • Adaptive • Maladaptive (e.g., excessive metacognition) (e.g., Wilson and Schooler 1991) • Enhance agent performance by implementing (subset of) metacognitive monitoring & control functions • Improved performance under stress through selection of appropriate coping strategies

  4. Outline • Motivation & Objectives • Metacognition and Emotion • Emotion Modeling Methodology & MAMID Architecture • Implementing Metacognitive Functions in MAMID • Modeling Interactions Among Metacognition & Emotions • Summary & Future Work

  5. Affective Factors: States & Traits • States: Transient emotional episodes (emotions, moods) • ‘Basic’ emotions (sadness, joy, fear, anger, disgust…) • Complex emotions (pride, guilt, shame…) • Modify characteristics of perceptual and cognitive processes • Speed, accuracy, capacity of attention and working memory • Specific biases (perception, memory, inferencing) • Traits: Persistent personality characteristics(temperament, personality) • Five Factor Model (extraversion, neuroticism, conscientiousness,A,O) • Influence structure / content of long-term memory • Predispose towardsparticular affective states (Watson & Clark, 94; Tellegen, 85) • High extraversion ---> positive affect, non-self focus, reward-seeking • High neuroticism ---> negative affect, self-focus, punishment-avoiding • Influence dynamic characteristics of affective states • Thresholds of emotion triggers • Ramp-up and decay rates • Maximum intensity

  6. Cognition and Emotion: Heuristics & Biases • Anxiety and Attention & WM (Williams et al., 1997; Mineka & Sutton, 1992) • Narrowing of attentional focus / reduction of WM capacity • Predisposing towards detection of threatening stimuli • Emotion and Judgment & Perception (Isen, 1993; Williams et al. 97) • Anxiety predisposes towards interpretation of ambiguous stimuli as threatening • Mood biases assessment of future outcomes / estimates of degree of control • Mood and Memory (Bower, 1981; Bower, 1986) • Mood-congruent recall • Obsessiveness and Performance (Persons and Foa, 1984; Sher et al., 1989) • Delayed decision-making • Reduced ability to recall recent activities • Reduced confidence distinguishing btw actual and imagined actions / events

  7. Metacognition and Emotion • Need to identify effects of particular affective factors (states or traits) on particular metacognitive functions and knowledge • Limited data on mutual influences among emotion and metacognition(e.g., Wells 2000; Matthews and Wells 2004) • Focus on psychopathology (e.g., excessive monitoring) • State effects on processes • Anxiety-linked emotion-focused coping (distraction, worry, avoidance) • Depression-linked self-criticism focused coping • Trait effects on structures • Neuroticism-linked predominance of negative schemas • E.g., Threat, negative self evaluations, negative future projections • Trait effects on processes • Neuroticism-linked preference for self-information • Neuroticism-linked emotion-focused coping

  8. Outline • Motivation & Objectives • Metacognition and Emotion • Emotion Modeling Methodology & MAMID Architecture • Implementing Metacognitive Functions in MAMID • Modeling Interactions Among Metacognition & Emotions • Summary & Future Work

  9. Parameters Cognitive Architecture Goals Emotions Stimuli Affect Appraiser Situations Expectations Modeling the Central Role of Emotion Cognitive Architecture Parameter Calculation

  10. Cues Attention Situation Assessment Expectation Generation Affect Appraiser Goal Manager Actions MAMID Cognitive Architecture: Modules & Mental Constructs Attended cues Current Situations Task, Self, Other Expectations Future statestask, self,other Affective state & emotions: Valence (+ | -) Anxiety, Anger, Sadness, Joy Goals Task, Self, Other Action Selection

  11. Cues Attention Situation Assessment Expectation Generator Affect Appraiser Goal Manager Action Selection Actions Cognitive Architecture: Semantics and Data Flow Cues: State of the world (“unit attacked by crowd”) Situations: Perceived state ( “unit in danger” ) Expectations: Expected state (“unit immobilized, casualties”) Goals: Desired state (“reach objective, unit safety”) Affective state & emotions: Negative valence High anxiety Actions: to accomplish goals (“unit attacks crowd”)

  12. Automatic “Universal” Abstract Elicitors Current State Modulator Valence Valence - .9 -.8 Expanded Emotion Individual Specific Elicitors Emotion Anxiety .8 Anger .6 Sad. .4 Happ. .1 Anxiety .7 Anger .4 Sad. .3 Happ. .1 Existing Valence Existing Emotion Trait Profile Affect Appraisal

  13. individual behavior influenced by ... architecture processing controlled by..... different individual profiles manifested in terms of different Cognitive Architecture Parameters Individual Differences (Emotions / Personality) Behavior Outputs Cognitive Architecture Parameter Calculation ‘prepare talk’ vs. ‘go skiing’ vs. ‘delay decision’ Cognitive Architecture Generic Modeling Methodology: Overview

  14. COGNITIVE ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE Processing Module Parameters (Attention / Working Memory) Capacity Speed Inferencing speed & biases Cue selection & delays Situation selection & delays ... Structural Architecture topology Weights on intermodule links Long term memory Content & structure of knowledge clusters (BN, rules) Cognitive Attention Speed / Capacity WM Speed / Capacity Skill level Attention Situation Assessment Expectation Generator Affect Appraiser Affective States Anxiety / Fear Anger / Frustration Sadness Joy Goal Manager Action Selection Methodology: Detail Cognitive factors/ States / Traits / Traits Extraversion Stability Conscientiousness Aggressiveness

  15. COGNITIVE ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE State / Trait Effects Modeling: Example INDIVIDUAL DIFFERENCES Threat constructs Rated more highly Processing Inferencing biases Cue selection Situation selection ... Process Threat cues Attention Situation Assessment Traits Neuroticism Process Threatening interpretations Expectation Generator Predisposes towards Affect Appraiser Preferential processing of Threatening stimuli Affective States Higher Anxiety / Fear Goal Manager Action Selection

  16. Outline • Motivation & Objectives • Metacognition and Emotion • Emotion Modeling Methodology & MAMID Architecture • Implementing Metacognitive Functions in MAMID • Modeling Interactions Among Metacognition & Emotions • Summary & Future Work

  17. Enabling MAMID to Implement Metacognition • Add structures (memory) and processes to enable MAMID to: • Monitor cognition: Trigger metacognition when necessary • Controlcognition: Direct cognitive processes to achieve metacognitive objective • Increase feeling-of-confidence • Implement a particular coping strategy • Performance outcomes may be: • Positive (improved performance, reduced stress) • Negative (metacognition interferes with performance) • Neutral (no difference)

  18. Modeling Feeling of Confidence (FOC) • Component of metacognition reflecting level of confidence in particular cognitions • Typically refers to inferred solutions to problems & memory retrieval • Controls cognitive iteration(e.g., Narens et al. 1994) • We extend FOC to include future projections • FOC that particular expectations are ‘correct’

  19. Metacognitive Knowledge / Beliefs Metacognitive Level Monitoring Processes Control Processes Attention Situation Assessment Cognitive Level Cues Expectation Generation Affect Appraiser Goal Manager Action Selection Actions

  20. Implementing FOC in MAMID • Each mental construct augmented to include an FOC attribute • Cue FOC…confidence that attended cue reflects stimulus • Situation FOC … confidence derived situation reflects accurate interpretation • Expectation FOC … expectation reflects accurate projection • Initially, FOC calculated via combination cognitive and affective factors, including: • Anxiety (reducing FOC) • Awareness of alternatives (inversely proportional to FOC) • Task difficulty (inversely proportional to FOC) • Awareness of lack of knowledge (reducing FOC)

  21. FOC Triggers Metacognition • Distinct FOC threshold for each construct type • Situation FOC threshold • Expectation FOC threshold • … • Each mental construct FOC compared with threshold • FOC (situation X) ??? FOC (situation threshold) • IF (construct FOC >= threshold) THEN (FOC = adequate) • No metacognition required • IF (construct FOC < threshold) THEN (FOC not adequate) • Metacognitive control activity triggered to increase FOC • Metacognition initiates re-derivation of construct in an attempt to increase FOC value

  22. Contents of Metacognitive Long Term Memory (mLTM) • Beliefs and knowledge about cognitions • “Worrying is helpful” • “Getting more data is always good” • Rules for selecting particular metacognitive monitoring & control strategies • “IF (anxiety = high) THEN (distract self)” == emotion-focused coping VS. • “IF (anxiety = high) THEN (understand cause)” == task-focused coping Metacognitive Knowledge / Beliefs Belief Nets Rules

  23. Differences in FOC-Triggered Metacognition • Strategy selection and outcome depend on: • Construct type (cue, situation…) • Contents of the metacognitive long-term memory (mLTM - determines strategies / triggers) • Agent’s internal context (currently activated constructs & emotional states) • Situational context (external factors) Options include… • Do nothing • Continue processing at the object level … BUT • Lower-than-desired FOC may increase anxiety • Anxiety has specific effects on attention, perception and cognition • Re-derive the construct to increase FOC - nature of process depends on: • Position of construct in the processing sequence • Amount of re-processing possible proportional to position in processing sequence (further down -- more options) • Type of re-processing possible given the current informational context • Use different cues to re-derive situation (and its FOC) • Use existing cues in a different way (different weights for different cues) • Obtain additional information (get more cues from environment / self)

  24. Alternatives for FOC Re-Derivation • Agent A: mLTM rules trigger attentional re-scanning to get more cues (allows modeling of confirmation bias) • Agent B: mLTM rules trigger repeated situation assessment, incorporating previously rejected cues • Allows exploration of alternative mechanisms: • Different metacognitive control strategies may be used for situations involving the self, a particular task, another specific individual… • Different strategies may be linked to different affective states • Low anxiety: low action-FOC triggers the re-calculation of action FOC w/ different data (e.g., taking into consideration a broader range of triggering situations and expectations, in addition to the goal). • High anxiety: low action-FOC triggers attentional re-scan for new cues

  25. Outline • Motivation & Objectives • Metacognition and Emotion • Emotion Modeling Methodology & MAMID Architecture • Implementing Metacognitive Functions in MAMID • Modeling Interactions Among Metacognition & Emotions • Summary & Future Work

  26. Modeling Emotion-Metacognition Interactions • Anxiety-linked emotion-focused coping • Supported by existing empirical data • Anxiety associated with focus on managing anxiety directly (vs. on eliminating sources of anxiety in environment) • Possible relationship between affective factors and FOC • Speculative model

  27. Anxiety-Linked Emotion-Focused Coping • Necessary structures & processes already exist: • Dynamic calculation of affective states • Ability of particular state-value pair to trigger the selection of particular goal or action • e.g. IF (anxiety = high) THEN (avoid situation) • Making a distinction between self- and task-related mental constructs allows preferential processing of one or the other type of construct Enhanced MAMID will augment coping strategy repertoire • mLTM rules link specific emotions-traits to problem-focused vs. emotion-focused coping strategies • Refinements allow choices among a broader range options • Task-focus: Improved planning, focus on removal of negative stimulus, finding help • Emotion-focus: Acceptance, venting, avoidance, worry

  28. Affective Factors and FOC: Obsessive-Compulsive Behaviors • Obsessive-compulsive behaviors include: • Excessive checking behaviors • Excessive planning and re-planning without ever taking an action – ‘paralysis by analysis’ • Possible hypotheses explaining OC behaviors: • Abnormally high situation FOC threshold prevents acceptance of any interpretation, blocking further processing • Abnormally high action FOC thresholds prevents planned action from being executed • … • Constructing a model helps elucidate mechanisms

  29. Modeling Obsessive-Compulsive Behaviors in MAMID • Data suggest that obsessiveness correlates with: • High degree of conscientiousness (trait) • High anxiety (state) (Matthews and Deary 1998) • Use conscientiousness and anxiety to calculate FOC thresholds for mental constructs • Cues, situations, expectations, goals, actions • This links affective state into the FOC-triggered metacognitive-cognitive processing feedback cycle

  30. Metacognitive Level Metacognitive Knowledge / Beliefs (FOC thresholds) increases Traits Neuroticism States increases increases Anxiety Monitoring Processes Control Processes increase Object Level (Low FOC’s) FOC and Affective Factors

  31. Modeling Maladaptive (and Adaptive) Sequences of Behaviors • Adaptive Sequence • Low FOC values for a particular mental construct trigger anxiety • Anxiety raises FOC threshold • FOC construct / threshold discrepancy triggers metacognitive processing • Which attempts to increase the construct FOC • Successful increase in FOC leads to reduction of anxiety • This then reduces the FOC threshold • Metacognitive activity intervened temporarily to correct the problem - appropriate metacognition • Maladaptive Sequence - Obsessive-Compulsive Behaviors • Regulatory feedback system is disrupted • High level of anxiety, coupled with inadequate coping strategies, prevents derivation of adequately high FOC values • This perpetuates the high level of anxiety • .. which maintains high FOC threshold • Agent is unable to arrive at a decision and remains ‘stuck’ in internal processing and re-processing of existing information - excessive metacognition

  32. Outline • Motivation & Objectives • Metacognition and Emotion • Emotion Modeling Methodology & MAMID Architecture • Implementing Metacognitive Functions in MAMID • Modeling Interactions Among Metacognition & Emotions • Summary & Future Work

  33. Summary • Described an existing cognitive-affective architecture and the design extensions to enable an explicit model of: • Selected metacognitive functions • Their interaction with several affective factors • Initial focus on: • Feeling of confidence (FOC) • Its role in triggering metacognitive processing • Metacognitive control alternatives to improve FOC • Emotion & metacognition: • Modeling anxiety-linked emotion-focused coping • Speculative model of possible interactions between the FOC and affective factors (state: anxiety & trait: neuroticism)

  34. Future Work • Implement metacognitive enhancements • Evaluate in terms of: • Realism of agent behavior • Effectiveness of elucidating causal mechanisms of emotion-metacognition interactions • Ability to generate experimental hypotheses regarding specific causal mechanisms of metacognition-emotion interactions

  35. Emotion & Rationality • Neuroscience evidence indicates that emotion and cognition function as integrated systems • Emotions appear to perform useful and necessary functions in animals • Prune decision search spaces • Rapid, undifferentiated reasoning (and action selection) • Heuristics & biases • Understanding emotions helps us to identify these functions and their mechanisms • Agents need these types of functions for effective, adaptive behavior • BUT - does that mean agents need emotions? • Goal management need not be emotional • Does ‘reward’ and ‘punishment’ in agents require emotions? • Are emotions specific to ‘wetware’ or do they represent universal processes necessary for functioning in complex, uncertain environments?

  36. Acknowledgments • Dr. Bob Witmer, US Army Research Institute • Prof. Gerald Matthews, University of Cincinnati • Prof. William Revelle, Northwestern University • Software developers: Jonathan Pfautz,Lisa Buonomano, Jim Helms, Craig Ganoe, Mark Turnbull • Ted Fichtl, The Compass Foundation

  37. Modeling Interaction Between Metacognition and Emotion in a Cognitive Architecture Metacognition and Computation AAAI Spring Symposium Stanford University, CA March 21-23 2005 Eva Hudlicka Psychometrix Associates, Inc. Blacksburg, VA evahud@earthlink.net

  38. COGNITIVE ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE State / Trait Effects Modeling Example INDIVIDUAL DIFFERENCES Reduces capacity Processing Module Parameters (Attention / Working Memory) Capacity ... Fewer cues Attention Situation Assessment Fewer situations Traits LowStability Expectation Generator Reduces Predisposes towards Affect Appraiser Affective States Higher Anxiety / Fear Goal Manager Action Selection

  39. Appraisal: Theoretical Context • Incorporates elements of recent appraisal theories (Leventhal & Scherer, Smith & Kirby) • Primary / Secondary Appraisal structure (Lazarus, Smith & Kirby) • Multiple levels and multiple stages of appraisal • Automatic and expanded appraisal • Automatic appraisal: • Low resolution - less differentiated and individualized • Uses ‘universal elicitors’ (threat, novelty, pleasantness…) • Generates valence (positive / negative) • Expanded appraisal: • Higher resolution - more differentiated and individualized • Uses more complex, idiosyncratic elicitors (individual experience with stimulus) • Generates one of four ‘basic’ emotions (fear, anger, sadness, joy)

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