Bob MarinierJohn Laird

University of MichiganElectrical Engineering and Computer Science

August 2, 2007

Introduction Want to build computational models of emotion, mood,

and feeling Need specific algorithms and data structures for representing

and manipulating these Existing computational models propose “reasonable”

solutions But little attempt to define “reasonable”

We present a more comprehensive theory of the integration of emotion, mood, and feeling

We present explicit criteria for evaluating models of integration Human data would be best, but isn’t available We propose functional, “simple” criteria

We apply these criteria by building on existing models and suggesting actual functions

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Appraisal theories

Idea: Humans evaluate a situation with respect to their goals along a number of innate dimensionsNovelty, Goal Relevance, Causality,

Conduciveness

Appraisals trigger emotional responsesMapping between appraisal values and

emotions is fixed

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Appraisals to emotionsScherer 2001 Joy Fear Anger

Suddenness High/medium High High

Unpredictability High High High

Intrinsic pleasantness Low

Goal/need relevance High High High

Cause: agent Other/nature Other

Cause: motive Chance/intentional Intentional

Outcome probability Very high High Very high

Discrepancy from expectation High High

Conduciveness Very high Low Low

Control High

Power Very low High

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Relationship betweenemotion, mood, and feeling Emotion: Result of appraisals

Is about the current situation

Mood: “Average” of recent emotionsProvides historical context

Feeling: Emotion “+” MoodWhat agent actually perceives

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Representation ofemotion, mood, and feeling Use a frame that contains the current

value of each appraisal dimension (Gratch & Marsella 2004)Since appraisal-to-emotion mapping is fixed,

this frame can represent the emotion

For simplicity, use appraisal frames to represent emotion, mood, and feeling

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Appraisal frame representation Values are represented on one of two

scales[0,1] : Dimension has endpoints that

correspond to low and high intensity○ E.g., Suddenness

[-1,1] : Dimension has endpoints that correspond to high intensity, with midpoint of low intensity○ E.g., Conduciveness

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Appraisal frame representation

Scherer 2001 Range

Suddenness [0,1]

Unpredictability [0,1]

Intrinsic pleasantness [-1,1]

Goal/need relevance [0,1]

Cause: agent (self) [0,1]

Cause: agent (other) [0,1]

Cause: agent (nature) [0,1]

Cause: motive (intentional) [0,1]

Cause: motive (negligence) [0,1]

Cause: motive (chance) [0,1]

Outcome probability [0,1]

Discrepancy from expectation [0,1]

Conduciveness [-1,1]

Control [-1,1]

Power [-1,1]

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Cognition

Emotion

Mood

Feeling

Com

bina

tion

Func

tion

Pull (10% per cycle)

Decay (1% per cycle)

Active Appraisals

Perceived Feeling

Interaction betweenemotion, mood, and feeling

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Criteria for combiningemotion and mood Neal Reilly (1996, 2006) developed the

basis for many of these criteria Assumption: Dimension Independence

Can compute combination of each dimension in the frame separately

vfeeling = C(vmood, vemotion)Output must fall in [0,1] or [-1,1] range

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Criteria for combiningemotion and mood Distinguishability of inputs

Don’t want a large range of inputs to map to a small range of outputs○ The agent wouldn’t be able to distinguish

between the inputs, and thus couldn’t form diverse responses

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Criteria for combiningemotion and mood Limited range: Avoid going out of scale

as much as possibleAveraging doesn’t make sense

○ Example: If mood is one of mild conduciveness, and emotion is of strong conduciveness, feeling should be of stronger, not weaker conduciveness

Output should be between the input with the maximum magnitude and the sum of the inputs

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Criteria for combiningemotion and mood Non-linear

Consider these examples○ C(0.5, 0.5) = ?○ C(0.8, 0.9) = ?○ C(0.5, 0.9) = ?

If sum the inputs, then first two are not distinguishable

If max the inputs, then the last two are not distinguishable

Relationship may be logarithmic

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Criteria for combiningemotion and mood Symmetry

Emotion and mood contribute equally to feeling○ We have no reason to assume the function is

symmetrical, but it seems like a reasonable place to start

Symmetry around 0○ C(x, 0) = C(0, x) = x

C(0, 0) = 0

Symmetry of opposite values○ C(x, -x) = 0

Symmetry of all values○ C(x, y) = C(y, x)

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Combination function

GoodLimited rangeNon-linear

ProblemsNot centered at zero: C(0,0) = 0.069Doesn’t work with negative values (not symmetrical)

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Combination function

GoodLimited rangeNon-linearCentered at zero

ProblemsDoesn’t work with negative values (not symmetrical)

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Combination function

GoodLimited rangeNon-linearSymmetrical

ProblemsDistinguishability of inputs

○ C(-0.1, 0.9) = 0.899979○ C(-0.5, 0.9) = 0.898164

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Combination function

GoodDistinguishability of inputs

○ C(-0.1, 0.9) = 0.854532○ C(-0.5, 0.9) = 0.585615

Limited rangeNon-linearSymmetrical

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Example

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Emotion Mood FeelingSuddenness [0,1] 0 .235 .235Unpredictability [0,1] .250 .400 .419Intrinsic-pleasantness [-1,1] 0 -.235 -.235Goal-relevance [0,1] .750 .222 .750Causal-agent (self) [0,1] 0 0 0Causal-agent (other) [0,1] 0 0 0Causal-agent (nature) [0,1] 1 .660 1Causal-motive (intentional) [0,1] 0 0 0Causal-motive (chance) [0,1] 1 .660 1Causal-motive (negligence) [0,1] 0 0 0Outcome-probability [0,1] .750 .516 .759Discrepancy [0,1] .250 .326 .362Conduciveness [-1,1] .500 -.269 .290Control [-1,1] .500 -.141 .402Power [-1,1] .500 -.141 .402Label ela-joy anx-wor ela-joy

Feeling intensity

Often useful to compress feeling frame into a single “intensity” value

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Feeling intensity criteria

Limited range: Should map onto [0,1] No dominant appraisal: No single value

should drown out all the othersCan’t just multiply values, because if any are

0, then intensity is 0

Realization principle: Expected events should be less intense than unexpected events

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Intensity function

Realization principle: Surprise factor OP = Outcome Probability DE = Discrepancy from Expectation

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OP=low OP=high

DE=low SF=high SF=low

DE=high SF=low SF=high

Intensity function

No dominant appraisalJust average the rest of the appraisals together

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Intensity function

Normalize ranges to same size Treat values as magnitudes

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Example

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Emotion Mood FeelingSuddenness [0,1] 0 .235 .235Unpredictability [0,1] .250 .400 .419Intrinsic-pleasantness [-1,1] 0 -.235 -.235Goal-relevance [0,1] .750 .222 .750Causal-agent (self) [0,1] 0 0 0Causal-agent (other) [0,1] 0 0 0Causal-agent (nature) [0,1] 1 .660 1Causal-motive (intentional) [0,1] 0 0 0Causal-motive (chance) [0,1] 1 .660 1Causal-motive (negligence) [0,1] 0 0 0Outcome-probability [0,1] .750 .516 .759Discrepancy [0,1] .250 .326 .362Conduciveness [-1,1] .500 -.269 .290Control [-1,1] .500 -.141 .402Power [-1,1] .500 -.141 .402Label ela-joy anx-wor ela-joyIntensity .127

Conclusion Contributions

Proposed concrete distinction between emotion, mood and feeling

Proposed common representation for these, including value ranges

Listed criteria for models of mood-emotion combinations Listed criteria for models of feeling intensity Proposed functions that fulfill those criteria

Future work Discover more criteria and alternative functions Demonstrate that usage of these functions confers a

functional advantage Human data

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