COMPUTATIONAL MODELING OF INTEGRATED COGNITION AND EMOTION

Bob Marinier University of Michigan

Introduction

Existing research in cognitive science tends to ignore emotion research

Existing research in emotion tends to ignore cognitive science

Goal is to develop a computational theory of the control of immediate behavior in which emotion has a clear functional role

Claim: Cognitive and emotion theories are actually very complementary

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Newell’s Abstract Functional Operations(NAFO) for immediate behaviorPerceive Obtain raw perception

Encode Create domain-independent representation

Attend Choose stimulus to process

Comprehend

Generate structures that relate stimulus to tasks and can be used to inform behavior

Task Perform task maintenance

Intend Choose an action, create prediction

Decode Decompose action into motor commands

Motor Execute motor commands

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NAFO is incomplete

Perceive What information is generated?

Encode What information is generated?

Attend What information is required?

Comprehend

What information is required and generated?

Task What information is required?

Intend What information is required?

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Appraisal theories

Idea: Humans evaluate a situation with respect to their goals along a number of innate dimensions E.g., Novelty, Goal Relevance, Causality,

Conduciveness Appraisals trigger emotional responses

Mapping between appraisal values and emotions is fixed

Problem: Existing process models of appraisal are weak

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Appraisals to emotions6

Integration of cognition and emotion

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NAFO: process without data Appraisal theories: Data without process Claim: Appraisals are the data generated

and used by NAFO

Integration of NAFO and appraisal

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Extended theory

Implemented in Soar, a cognitive architecture Provides independently-motivated

constraints Allows for integration with other cognitive

mechanisms

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Integration with Soar10

Long-Term Memories

Body

Episodic

Perception Action

Procedural Semantic

Short-Term Memory

De

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P

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raisa

l D

ete

ctor

Soar 9

Chunking EpisodicLearning

ReinforcementLearning

SemanticLearning

Extended theory

Distinction between emotion, mood, and feeling Emotion: Result of appraisals

Is about the current situation Mood: “Average” of recent emotions

Provides historical context Feeling: Emotion “+” Mood

What agent actually perceives

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Cognition

Emotion

Mood

Feeling

Com

bina

tio

n Fu

nction

Pull

Decay

Active Appraisa

ls

Perceived Feeling

Emotion, mood, and feeling12

Extended theory: Learning

Feeling should drive reinforcement learning Feelings give an indication of how well things are

going well Use feeling intensity and valence as intrinsic

reward signal What is being learned?

Choices related to NAFO What to Attend to When to Intend (vs. Ignore) What to Intend When to create which subtasks, and when to return to

supertask

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Learning task14

Start

Goal

Optimal Subtasks

Results: With and without mood

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Results: With and without mood

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Future work17

Near future Explore learning further Explore new capabilities

Giving up Interruptability

Richer domain Continuous time, space

Eventually Multiple agents: social interaction Physiology Interaction between other cognitive mechanisms

and emotion