from cognitive biases to panic: modeling the mechanisms of anxiety disorders eva hudlicka...

From cognitive biases to panic:

Modeling the mechanisms of anxiety disorders

Eva HudlickaPsychometrix Associates / U.Mass - Amherst

Amherst, VAhudlicka@cs.umass.edu

psychometrixassociates.com

Workshop on “Computational Modeling of Cognition-Emotion Interactions”

CogSci 2014, Quebec City, Canada

Outline• Affective biases on cognition anxiety disorders

• Modeling Context: – Cognitive-Affective Symbolic Architecture– Search & rescue task

• Approach: Affective biases as architecture parameters

• Example

• Implications for psychotherapy 2

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Affective Biases• Emotion effects on cognition can improve

…or degrade performance

• e.g., Anxiety-induced threat bias– Adaptive: vigilance – Maladaptive : anxiety & panic

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Modeling Anxiety Effects:The Good, the Bad & the Ugly

• Anxiety effects on cognition:– Attentional narrowing– Bias toward detection of threatening stimuli– Bias toward interpretation of ambiguous stimuli as threats– Promotion of self-focus

the Good the Bad the Ugly

Anxiety disorders & panic attacks

Trait-anxiousover-protective

behavior

Adaptive vigilance

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Benefits of Modeling

• Enable construction of alternative mechanisms for observed effects

• Understand etiology of affective disorders

• Facilitate mechanism-based diagnosis (beyond DSM-5 descriptions)

• More customized / targeted treatment– Computer-based tools (serious games)– Modeling the ‘patient’ ?

Context• Symbolic cognitive-affective architecture

• Models high-level decision-making

• Models both emotion generation & emotion effects

• Emotion effects modeled in terms of parameters controlling architecture processing

• Architecture controls agent behavior… within a search & rescue team task

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Task Context- Search & rescue task in Arctic terrain- Snowcat drivers (starting in lower left) trying to reach “Lost

Party” (red, upper right)- Supply stations along routes- Emergency tasks create obstacles & trigger stress

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Task Context

Snow Cat

Supply Station

Lost Party

EmergencyTask

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MAMID Architecture: Semantics & Data Flow

CuesCues

ActionsActions

Attention

Situation Assessment

ExpectationGeneration

EmotionGeneration

Goal Manager

Action Selection

Cues: State of the world(“Emergency task within range”“Resources adequate”)

Situations: Perceived state( “Able to process task” )

Expectations: Expected state (“Task successfully completed”;“Game points gained”; “Game won”)

Goals: Desired state(“Game points = high”)

Actions: to accomplish goals (“Process Emergency Task”)

Affective state & emotions:Happiness: HighAnxiety: Low

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Modeling Emotion Effects via Parameters Controlling Cognition

Traits Extraversion Neuroticism Conscientiousness Aggressiveness

EMOTIONS /TRAITS

Emotions Anxiety Anger Sadness Joy

ARCHITECTUREPARAMETERS

COGNITIVE ARCHITECTURE

Attention

Action Selection

Situation Assessment

Goal Manager

ExpectationGeneration

EmotionGeneration

Processing

Structural

Module Parameters

Construct parameters

Architecture topology

Long-term memory

speed, capacity

Cue selection & delay….

Data flow among modules

Content & structure

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Modeling Threat BiasTRAITS / STATES

COGNITIVE ARCHITECTUREPARAMETERS

COGNITIVE ARCHITECTURE

Attention

Action Selection

Situation Assessment

Goal Manager

ExpectationGenerator

Affect Appraiser

Emotions

Higher Anxiety / Fear

Predisposes towards

ProcessingParameters

Module & Construct parms. - Cue selection - Interpretive biases

...

Preferential processing of Threatening stimuli

Threat constructsrated more highly

Process threat cues

Processthreateninginterpretations

Traits

Neuroticism

Traits

Neuroticism

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Modelling Panic Attack• High state of anxiety induces a “perfect storm” of

biases– Extreme threat bias– Extreme self bias– Reduced attention capacity

• Limited capacity precludes processing of useful cues & derivation of alternative interpretations of situations

• No goals or actions generated

• Resulting behavioral paralysis further increases anxiety

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Internal Processing During a Panic Attack

- Snowcat driver encounters an “Emergency Task” while running low on supplies

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Internal Processing During a Panic Attack

ANXIETY

Anxiety level is high

High anxiety level causes low processing capacity

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Internal Processing During a Panic Attack: Mental Constructs in Architecture Module Buffers

Attention: High threat & emotion cues only

SA: Negative situations only

Goal Manager: No goals selected

Behavior Selection: No action selected due to (a) extreme self focus; (b) no goals

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Reduced attentioncapacity

Modelling Alternative Mechanisms of Anxiety & Panic Attacks

• Multiple, interacting causal pathways… for each type of bias• Parameter values are linear combinations of weighted factors

– (Wfactor1 * factor1) + (Wfactor2 * factor2) …

High Anxiety Intensity

Higher Sensitivity to Anxiety

Lower baselineattention capacity

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Alternative Mechanisms for Increasing Attention Capacity

Increased attentioncapacity

Lower Anxiety Intensity

Lower Sensitivity to Anxiety

Increase fundamental attention capacity

Modify emotion generation to derive lower anxiety intensity:- Replace anxiety-generating belief net cluster with a cluster from ‘Happy’ agent

- Change agent’s ‘beliefs’ – e.g., cognitive therapy- Quantify contributions of specific beliefs

- Lower anxiety intensities--> Higher capacity values --> More Cues

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Alternative Mechanisms for Increasing Attention Capacity

Increased attentioncapacity

Lower Anxiety Intensity

Lower Sensitivity to Anxiety

Increase fundamental attention capacity

Reduce sensitivity to anxiety via physiological manipulations-Psychotropic medications-Exercise-MindfulnessLower sensitivity Lower anxiety Higher capacity More cues

Implications for Psychotherapy

• Identify pathway(s) contributing to anxiety– Specific (distorted?) beliefs?– Increased baseline sensitivity?

• Target specific pathways.. via customized treatment environments – Virtual reality– Serious games

• …possibly?… build model of patient within a particular context (e.g., serious gaming)

• (Dis)confirm mechanism-based diagnosis via modeling19

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Parting Thought