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Metacognition and Computation

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1 Metacognition and Computation
Modeling Interaction Between Metacognition and Emotion in a Cognitive Architecture Eva Hudlicka Psychometrix Associates, Inc. Blacksburg, VA Metacognition and Computation AAAI Spring Symposium Stanford University, CA March

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

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

11 Cognitive Architecture: Semantics and Data Flow
Cues: State of the world (“unit attacked by crowd”) Cues Actions Expectation Generator Affect Appraiser Attention Action Selection Situation Assessment Goal Manager 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 Affect Appraisal “Universal” Automatic Valence Abstract Current State
Elicitors Automatic Valence - .9 Current State Modulator Anxiety .8 Anger Sad Happ Expanded Emotion Individual Specific Trait Profile Existing Valence Existing Emotion -.8 Anxiety .7 Anger Sad Happ

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

14 COGNITIVE ARCHITECTURE
Methodology: Detail Cognitive factors / States / Traits / COGNITIVE ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE Attention Action Selection Situation Assessment Goal Manager Expectation Generator Affect Appraiser Cognitive Attention Speed / Capacity WM Skill level 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) Traits Extraversion Stability Conscientiousness Aggressiveness Affective States Anxiety / Fear Anger / Frustration Sadness Joy

15 State / Trait Effects Modeling: Example
INDIVIDUAL DIFFERENCES COGNITIVE ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE Processing Inferencing biases Cue selection Situation selection ... Threat constructs Rated more highly 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 Control cognition: 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 Level Metacognitive Knowledge / Beliefs Monitoring
Processes Control Processes Cues Actions Attention Situation Assessment Expectation Generation Affect Appraiser Action Selection Goal Manager Cognitive Level

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)
Knowledge / Beliefs Belief Nets Rules 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

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

31 Modeling Maladaptive (and Adaptive) Sequences of Behaviors
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 Metacognition and Computation
Modeling Interaction Between Metacognition and Emotion in a Cognitive Architecture Eva Hudlicka Psychometrix Associates, Inc. Blacksburg, VA Metacognition and Computation AAAI Spring Symposium Stanford University, CA March

38 State / Trait Effects Modeling Example
INDIVIDUAL DIFFERENCES COGNITIVE ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE Reduces capacity Processing Module Parameters (Attention / Working Memory) Capacity ... Fewer cues Attention Situation Assessment Fewer situations Traits Low Stability 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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