Bob Marinier John Laird University of Michigan Electrical Engineering and Computer Science August 2, 2007 Computational Modeling of Mood and Feeling from Emotion
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
Appraisal theories • Idea: Humans evaluate a situation with respect to their goals along a number of innate dimensions • Novelty, Goal Relevance, Causality, Conduciveness • Appraisals trigger emotional responses • Mapping between appraisal values and emotions is fixed
Appraisals to emotions
Relationship betweenemotion, 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
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
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
Appraisal frame representation
Interaction betweenemotion, mood, and feeling Cognition Perceived Feeling Active Appraisals Feeling Emotion Combination Function Pull (10% per cycle) Mood Decay (1% per cycle)
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
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
Criteria for combiningemotion and mood • Limited range: Avoid going out of scale as much as possible • Averaging 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
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
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)
Combination function • Good • Limited range • Non-linear • Problems • Not centered at zero: C(0,0) = 0.069 • Doesn’t work with negative values (not symmetrical)
Combination function • Good • Limited range • Non-linear • Centered at zero • Problems • Doesn’t work with negative values (not symmetrical)
Combination function • Good • Limited range • Non-linear • Symmetrical • Problems • Distinguishability of inputs • C(-0.1, 0.9) = 0.899979 • C(-0.5, 0.9) = 0.898164
Combination function • Good • Distinguishability of inputs • C(-0.1, 0.9) = 0.854532 • C(-0.5, 0.9) = 0.585615 • Limited range • Non-linear • Symmetrical
Example
Feeling intensity • Often useful to compress feeling frame into a single “intensity” value
Feeling intensity criteria • Limited range: Should map onto [0,1] • No dominant appraisal: No single value should drown out all the others • Can’t just multiply values, because if any are 0, then intensity is 0 • Realization principle: Expected events should be less intense than unexpected events
Intensity function • Realization principle: Surprise factor • OP = Outcome Probability • DE = Discrepancy from Expectation
Intensity function • No dominant appraisal • Just average the rest of the appraisals together
Intensity function • Normalize ranges to same size • Treat values as magnitudes
Example
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
