Computational Modeling of Mood and Feeling from Emotion

1. Bob Marinier John Laird University of Michigan Electrical Engineering and Computer Science August 2, 2007 Computational Modeling of Mood and Feeling from Emotion

2. 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

3. 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

4. Appraisals to emotions

5. 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

6. 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

7. 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

8. Appraisal frame representation

9. Interaction betweenemotion, mood, and feeling Cognition Perceived Feeling Active Appraisals Feeling Emotion Combination Function Pull (10% per cycle) Mood Decay (1% per cycle)

10. 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

11. 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

12. 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

13. 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

14. 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)

15. 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)

16. Combination function • Good • Limited range • Non-linear • Centered at zero • Problems • Doesn’t work with negative values (not symmetrical)

17. 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

18. 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

19. Example

20. Feeling intensity • Often useful to compress feeling frame into a single “intensity” value

21. 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

22. Intensity function • Realization principle: Surprise factor • OP = Outcome Probability • DE = Discrepancy from Expectation

23. Intensity function • No dominant appraisal • Just average the rest of the appraisals together

24. Intensity function • Normalize ranges to same size • Treat values as magnitudes

25. Example

26. 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