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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
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
Outline
  • Motivation & Objectives
  • Metacognition and Emotion
  • Emotion Modeling Methodology & MAMID Architecture
  • Implementing Metacognitive Functions in MAMID
  • Modeling Interactions Among Metacognition & Emotions
  • Summary & Future Work
motivation objectives
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
outline1
Outline
  • Motivation & Objectives
  • Metacognition and Emotion
  • Emotion Modeling Methodology & MAMID Architecture
  • Implementing Metacognitive Functions in MAMID
  • Modeling Interactions Among Metacognition & Emotions
  • Summary & Future Work
affective factors states traits
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
cognition and emotion heuristics biases
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
metacognition and emotion
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
outline2
Outline
  • Motivation & Objectives
  • Metacognition and Emotion
  • Emotion Modeling Methodology & MAMID Architecture
  • Implementing Metacognitive Functions in MAMID
  • Modeling Interactions Among Metacognition & Emotions
  • Summary & Future Work
modeling the central role of emotion

Parameters

Cognitive Architecture

Goals

Emotions

Stimuli

Affect Appraiser

Situations

Expectations

Modeling the Central Role of Emotion

Cognitive Architecture

Parameter Calculation

mamid cognitive architecture modules mental constructs

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

cognitive architecture semantics and data flow

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”)

affect appraisal

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
generic modeling methodology overview

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
methodology detail

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

state trait effects modeling example

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

outline3
Outline
  • Motivation & Objectives
  • Metacognition and Emotion
  • Emotion Modeling Methodology & MAMID Architecture
  • Implementing Metacognitive Functions in MAMID
  • Modeling Interactions Among Metacognition & Emotions
  • Summary & Future Work
enabling mamid to implement metacognition
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)
modeling feeling of confidence foc
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’
slide19

Metacognitive

Knowledge / Beliefs

Metacognitive Level

Monitoring

Processes

Control

Processes

Attention

Situation

Assessment

Cognitive Level

Cues

Expectation

Generation

Affect Appraiser

Goal Manager

Action

Selection

Actions

implementing foc in mamid
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)
foc triggers metacognition
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
contents of metacognitive long term memory mltm
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

differences in foc triggered metacognition
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)
alternatives for foc re derivation
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
outline4
Outline
  • Motivation & Objectives
  • Metacognition and Emotion
  • Emotion Modeling Methodology & MAMID Architecture
  • Implementing Metacognitive Functions in MAMID
  • Modeling Interactions Among Metacognition & Emotions
  • Summary & Future Work
modeling emotion metacognition interactions
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
anxiety linked emotion focused coping
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
affective factors and foc obsessive compulsive behaviors
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
modeling obsessive compulsive behaviors in mamid
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
foc and affective factors

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
modeling maladaptive and adaptive sequences of behaviors
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
outline5
Outline
  • Motivation & Objectives
  • Metacognition and Emotion
  • Emotion Modeling Methodology & MAMID Architecture
  • Implementing Metacognitive Functions in MAMID
  • Modeling Interactions Among Metacognition & Emotions
  • Summary & Future Work
summary
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)
future work
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
emotion rationality
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?
acknowledgments
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
modeling interaction between metacognition and emotion in a cognitive architecture1
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

state trait effects modeling example1

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

appraisal theoretical context
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)