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Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
Depth of Feelings: Modeling Emotions in User Models and Agent Architectures
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Depth of Feelings: Modeling Emotions in User Models and Agent Architectures

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Overview of alternative approaches to modeling emotions in user models and cognitive-affective agent architectures.

Overview of alternative approaches to modeling emotions in user models and cognitive-affective agent architectures.

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  • 1. Depth of Feelings: Alternatives for Modeling Affect in User Models & Cognitive Architectures Eva Hudlicka Psychometrix Associates Blacksburg, US [email_address] psychometrixassociates.com TSD 2006 Masarykova Universita, Brno, Czech Republic September 15, 2006
  • 2. “ Diseases of the Mind”* Are emotions….. *Immanuel Kant
  • 3. “ reason is, and ought only to be the slave of the passions” <ul><li>Hume, 1739 </li></ul>Or are emotions essential for adaptive intelligent behavior…
  • 4. Emotions in “Human” Interaction “ too little…”
  • 5. Emotions in Human Interaction “ too much..”
  • 6. Or Is There a Middle Ground?
  • 7. Outline <ul><li>Motivation & Objectives </li></ul><ul><li>Emotions – Background Info </li></ul><ul><li>Computational Models of Emotion </li></ul><ul><li>Framework for Model Analysis </li></ul><ul><li>Summary & Conclusions </li></ul>
  • 8. Emotions in HCI: State-of-the-art KISMET - Cynthia Breazeal, MIT Media Lab
  • 9. Emotions in HCI: State-of-the-art Agent Max - Becker-Asano et al.
  • 10. Requirements for Affective HCI Affective User Model / Cognitive-Affective Architecture Emotion Sensing & Recognition “ Emotion” Expression OR? GRETA, Fiorella de Rosis, U. Bari
  • 11. Why Include Emotions in User Models & Agent Architectures? <ul><li>Emotion is a critical component of social interaction & individual motivation </li></ul><ul><li>Affective user models are more realistic, enabling: </li></ul><ul><ul><li>Socially-appropriate dialogue (speech tone, content, turn taking) </li></ul></ul><ul><ul><li>More effective dialogue (persuasion, empathy) </li></ul></ul><ul><ul><ul><li>Infer implied meaning & motivation </li></ul></ul></ul><ul><ul><ul><li>Predict affective reaction to system’s utterances </li></ul></ul></ul><ul><li>Affective agent architectures enable: </li></ul><ul><ul><li>Socially-appropriate responses and behavior </li></ul></ul><ul><ul><li>May improve agent autonomy in complex, uncertain environments </li></ul></ul><ul><li>Affect-adaptive user interfaces and responses </li></ul>
  • 12. Outline <ul><li>Motivation & Objectives </li></ul><ul><li>Emotions – Background Info </li></ul><ul><li>Computational Models of Emotion </li></ul><ul><li>Framework for Model Analysis </li></ul><ul><li>Summary & Conclusions </li></ul>
  • 13. Definition(s) of Emotions <ul><li>(See: roles & characteristics of emotions…) </li></ul><ul><li>Evaluative judgments of world, others, self …in light of agent’s goals and beliefs </li></ul>
  • 14. Roles of Emotions Intrapsychic Interpersonal WHAT? * Social coordination * Rapid communication of behavioral intent; HOW? Express emotions via: -Facial expression -Speech (content & properties) -Gesture, Posture -Specific actions WHAT? * Motivation * Homeostasis * Adaptive behavior <ul><li>HOW? </li></ul><ul><li>- Emotion generation (appraisal) </li></ul><ul><li>Emotion effects (processing biases) </li></ul><ul><li>Global interrupt system </li></ul><ul><li>Goal management </li></ul><ul><li>Prepare for coordinated actions </li></ul>
  • 15. How Do We Recognize an Emotion if We See One? <ul><li>Manifested across multiple , interacting modalities: </li></ul><ul><ul><li>Somatic / Physiological (neuroendocrine - e.g., heart rate, GSR) </li></ul></ul><ul><ul><li>Cognitive / Interpretive (“Nothing is good or bad but thinking makes it so…”) </li></ul></ul><ul><ul><li>Behavioral / Motivational (action oriented, expressive, ‘visible’) </li></ul></ul><ul><ul><li>Experiential / Subjective (“that special feeling…”, consciousness) </li></ul></ul><ul><li>Much terminological confusion can be attributed to a lack of consideration of these multiple modalities of emotions </li></ul><ul><ul><li>e.g., Is emotion a feeling or a thought? - It’s both </li></ul></ul>
  • 16. Simple Fear “Signature”: Large, Approaching Object Increased heart-rate; Attacked? Crushed? Flee? Freeze? Feeling of fear Cognitive Subjective
  • 17. A Taxonomy of Affective Factors Traits Affective Factors NOT ALL TRAITS are affective! Attitudes, Preferences… Affective States Emotions Moods Negative Positive Traits States “ Big 5” … Basic Anger Joy Fear … Complex Shame Guilt Pride …
  • 18. Core Processes of Emotions Effects of Emotions (on cognition & behavior) Generation of Emotions (via cognitive appraisal) Cognitive-Affective Architecture Stimuli Situations Expectations Goals Cognitive Appraisal Emotions
  • 19. Emotion Generation via Appraisal Stimuli Appraisal Dimensions Recalled Perceived Imagined Appraisal Process Emotions Existing emotions, moods, traits Goals (desires, values, standards) Beliefs, Expectations
  • 20. Emotion Generation via Appraisal Stimuli Appraisal Dimensions Recalled Perceived Imagined Appraisal Process Emotions Existing emotions, moods, traits Goals (desires, values, standards) Beliefs, Expectations <ul><ul><li>Domain-Independent Appraisal Dimensions </li></ul></ul><ul><ul><li>Novelty </li></ul></ul><ul><ul><li>Valence </li></ul></ul><ul><ul><li>Goal / Need relevance </li></ul></ul><ul><ul><li>Goal congruence </li></ul></ul><ul><ul><li>Agency </li></ul></ul><ul><ul><li>Coping potential </li></ul></ul><ul><ul><li>Social and self norms and values </li></ul></ul>
  • 21. Emotion Effects on Cognition <ul><li>Emotion and cognition function as closely-coupled information processing systems </li></ul><ul><li>Emotions influence fundamental processes mediating high-level cognition: </li></ul><ul><ul><li>Attention speed and capacity </li></ul></ul><ul><ul><li>Working memory speed and capacity </li></ul></ul><ul><ul><li>Long-term memory recall and encoding </li></ul></ul><ul><li>Influences on processing and contents </li></ul><ul><ul><li>Transient biases influence processing </li></ul></ul><ul><ul><li>Long-term biases result in differences in long-term memory content & structure </li></ul></ul>
  • 22. Examples of Affective Biases <ul><li>Anxiety </li></ul><ul><ul><li>Narrows attentional focus </li></ul></ul><ul><ul><li>Reduces working memory capacity </li></ul></ul><ul><ul><li>Biases towards detection of threatening stimuli </li></ul></ul><ul><ul><li>Biases towards interpretation of ambiguous stimuli as threatening </li></ul></ul><ul><ul><li>Promotes self-focus </li></ul></ul><ul><li>Positive emotions </li></ul><ul><ul><li>Increase estimates of degree of control </li></ul></ul><ul><ul><li>Overestimate of likelihood of positive events </li></ul></ul><ul><ul><li>Promote creative problem-solving </li></ul></ul><ul><ul><li>Promotes ‘big picture’ thinking - focus on ‘the forest’ </li></ul></ul><ul><li>Biases can be adaptive or maladaptive, depending on context </li></ul>
  • 23. “ Thank God! Those blasted crickets have finally stopped!”
  • 24. Outline <ul><li>Motivation & Objectives </li></ul><ul><li>Emotions – Background Info </li></ul><ul><li>Computational Models of Emotion </li></ul><ul><li>Framework for Model Analysis </li></ul><ul><li>Summary & Conclusions </li></ul>
  • 25. Considerations Guiding Model Requirements <ul><li>Why and when to model emotions? </li></ul><ul><li>Which emotions? </li></ul><ul><li>Which aspects of emotions? </li></ul><ul><li>Which affective processes? </li></ul><ul><li>Which theory? </li></ul><ul><li>What level of resolution? (Are the data available?) </li></ul><ul><li>Which architecture? </li></ul><ul><li>… Knowledge and data requirements </li></ul><ul><li>…… Representational and reasoning requirements </li></ul><ul><li>……… ..Representational and reasoning formalisms and methods </li></ul>
  • 26. Why and When to Model Emotions? <ul><li>Research </li></ul><ul><ul><li>Understand how emotions work in biological agents </li></ul></ul><ul><li>Applied </li></ul><ul><ul><li>More effective and ‘fun’ human-computer interaction </li></ul></ul><ul><ul><ul><li>Decision-support </li></ul></ul></ul><ul><ul><ul><li>Training & tutoring </li></ul></ul></ul><ul><ul><ul><li>Recommender systems </li></ul></ul></ul><ul><ul><ul><li>Entertainment </li></ul></ul></ul><ul><ul><li>More robust agent behavior </li></ul></ul>
  • 27. Why and When to Model Emotions? <ul><li>Research </li></ul><ul><ul><li>Understand how emotions work in biological agents </li></ul></ul><ul><li>Applied </li></ul><ul><ul><li>More effective and ‘fun’ human-computer interaction </li></ul></ul><ul><ul><ul><li>Decision-support </li></ul></ul></ul><ul><ul><ul><li>Training & tutoring </li></ul></ul></ul><ul><ul><ul><li>Recommender systems </li></ul></ul></ul><ul><ul><ul><li>Entertainment </li></ul></ul></ul><ul><ul><li>More robust agent behavior </li></ul></ul>(Breazeal, 2003) (de Rosis, 2003)
  • 28. Which Emotions and Affective Factors to Model? <ul><li>Model objectives & application influence selection: </li></ul><ul><ul><li>User models for decision-support systems in stressful settings: fear, anxiety, frustration, surprise, boredom – probably not pride, shame, guilt </li></ul></ul><ul><ul><li>Synthetic agents for children: happiness, sadness, fear, anger …also pride, shame </li></ul></ul><ul><ul><li>User models & agents in training and tutoring: happiness, fear/anxiety, frustration, surprise, boredom </li></ul></ul>
  • 29. A Taxonomy of Affective Factors States Affective States Emotions Moods Basic Complex Negative Positive Anger Joy Fear Shame Guilt Pride Traits Traits Affective Factors “ Big 5” …
  • 30. But Exactly Which Aspects of Emotions Should We Model? <ul><li>Recall the multiple modalities of emotions: </li></ul><ul><ul><li>Somatic / Physiological (infrequent) </li></ul></ul><ul><ul><li>Cognitive / Interpretive (most frequent) </li></ul></ul><ul><ul><li>Behavioral / Motivational (most frequent) </li></ul></ul><ul><ul><li>Experiential / Subjective (infeasible?) </li></ul></ul>Cognitive Subjective
  • 31. Emotion Roles Emotion Generation Emotion Effects on Cognition & Behavior Which Processes to Model? <ul><li>Social </li></ul><ul><li>Communication - Coordination </li></ul><ul><li>… . </li></ul>Intrapsychic: - Goal management - Behavior preparation -…… implement
  • 32. Computational Tasks for Appraisal Models Stimuli <ul><li>Emotion attributes: </li></ul><ul><li>Complexity of emotion construct </li></ul><ul><li>* type </li></ul><ul><li>* intensity </li></ul><ul><li>* cause … </li></ul><ul><li>* direction </li></ul><ul><li>* … </li></ul><ul><li>Types of stimuli: </li></ul><ul><li>Internal / External </li></ul><ul><li>Real / Imagined </li></ul><ul><li>Past / Present / Future </li></ul><ul><li>Domain specific / Abstract appraisal dimensions </li></ul><ul><li>Complexity of stimulus structure </li></ul><ul><li>Mental constructs required (e.g., goals, expectations) </li></ul><ul><li>Stimuli-to-emotion mappings </li></ul><ul><li>Intensity calculation </li></ul><ul><li>Nature of mapping process: </li></ul><ul><li>* Stages & functions </li></ul><ul><li>* Degree of variability </li></ul><ul><li>Integrating multiple emotions </li></ul><ul><li>Emotion dynamics over time </li></ul>Emotions
  • 33. Most Influential Appraisal Theories in Computational Models <ul><li>Ortony, Clore and Collins (OCC) (1988) </li></ul><ul><li>Leventhal and Scherer --> Scherer (1984 …) </li></ul><ul><li>Arnold  Lazarus  Smith and Kirby (1960 …) </li></ul>
  • 34. Example #1: OCC Appraisal Model
  • 35. Valenced Reactions Event-based emotions Attribution emotions Attraction emotions Event Related Appraised wrt goals “ Does this promote world peace?” Acts-by-Agents Related Appraised wrt standards “ Was it appropriate for John to rob the bank?” Object Related Appraised wrt attitudes “ Is this appealing to me?”
  • 36. Valenced Reactions Event-based emotions happy for, pity, gloating.. joy,distress hope, fear gratitude, anger desirability (pleased / displeased) desirability for other (deserving, liking) likelihood praiseworthiness (approve / disapprove) appealingness (like / dislike) degree of autonomy, expectation deviation familiarity Attribution emotions Attraction emotions Fortunes-of-self emotions Fortunes-of-others emotions Prospect-based emotions Well-being emotions pride, shame, reproach love,hate
  • 37. Valenced Reactions Event-based emotions Attribution emotions Attraction emotions Fortunes-of-self emotions Fortunes-of-others emotions happy for, pity, gloating.. distress Prospect-based emotions Well-being emotions anger reproach love,hate Desirability = low fear Praiseworthiness = low degree of autonomy = high expectation deviation = high
  • 38. Example #1: OCC Appraisal Model <ul><li>Developed to provide a “computationally tractable model of emotion” </li></ul><ul><li>Taxonomy of triggering conditions and emotion types </li></ul><ul><li>Specification of variables affecting intensity </li></ul><ul><ul><li>“ Global” (physiological state…) </li></ul></ul><ul><ul><li>“ Local” (appraisal dimensions…) </li></ul></ul><ul><li>Many implementations (Elliot, Reilly, Bartneck, Andre, Gratch…) </li></ul>
  • 39. Example #2: Scherer‘s “Component Process Model”
  • 40. Coping potential Norms Relevance Appraisal variables Novelty Valence Goal relevance Certainty Urgency Goal congruence Agency Stimuli Implications Coping Norms Emotion
  • 41. STIMULI Novelty Valence Goal relevance Outcome probability Urgency Goal congruence Agency Coping potential Norms high high v. high low other low low high FEAR
  • 42. Example #2: Scherer‘s “Component Process Model” <ul><li>Emphasis on domain-independent appraisal dimensions (emotion components) </li></ul><ul><ul><li>Emotions defined as patterns of appraisal variable values </li></ul></ul><ul><ul><li>Variables evaluated in a fixed sequence </li></ul></ul><ul><li>Appraisal as a dynamic, evolving process </li></ul><ul><li>… across multiple modalities </li></ul><ul><li>… at multiple levels of complexity </li></ul><ul><ul><li>Conceptual </li></ul></ul><ul><ul><li>Schematic </li></ul></ul><ul><ul><li>Perceptual-motor </li></ul></ul><ul><li>Implementations: </li></ul><ul><ul><li>Black-box implementations </li></ul></ul><ul><ul><li>Appraisal dimensions adopted in cognitive-affective architectures </li></ul></ul>
  • 43. Results of the Appraisal Process: Emotion ‘Specification’ fear .90 probability, importance of affected goals 2 minutes (exp. decay) { aggressive dog | owner} “ aggressive dog approaching” negative { dog | negligent owner | self } low { safety of self | safety of dog | delay } Other appraisal variables….: Type: Descriptive detail: Intensity: Variables affecting intensity: Cause: Direction: Coping potential: Duration: Valence: Goals affected:
  • 44. Representation & Reasoning Alternatives <ul><li>Vector spaces (Scherer) </li></ul><ul><li>Connectionist (Velasquez) </li></ul><ul><li>Symbolic </li></ul><ul><ul><li>Rules (Marinier, Jones, Henninger, Hudlicka…) </li></ul></ul><ul><ul><li>Belief nets (de Rosis, Hudlicka, …) </li></ul></ul><ul><li>Complex symbolic structures (Elliot, Reilly, Gratch & Marsella) </li></ul><ul><ul><li>Appraisal frames, causal plan structures </li></ul></ul><ul><li>Spreading activation over networks of processes (Breazeal) </li></ul><ul><li>Decision-theoretic </li></ul><ul><ul><li>Decision trees </li></ul></ul><ul><ul><li>Decision theoretic formulations (Gratch & Marsella, Lisetti & Gmytrasiewicz) </li></ul></ul><ul><li>Blackboards and ‘specialists’ (Gratch & Marsella) </li></ul><ul><li>Finite state machines (Kopecek) </li></ul><ul><li>Markov models (El Nasr) </li></ul><ul><li>Theorem proving (Zippora) </li></ul><ul><li>Dynamical systems </li></ul>
  • 45. Bayesian Belief Networks (MAMID, Hudlicka)
  • 46. Complex Causal Interpretation (EMA, Gratch & Marsella)
  • 47. Emotion Effects on Cognition Cognitive-Affective Architecture Stimuli Situations Expectations Goals Affect Appraiser Emotions
  • 48. Computational Tasks for Modeling Emotion Effects Emotion(s) <ul><li>Cognition Attention, perception, memory, </li></ul><ul><li>learning, problem-solving, decision-making…) </li></ul><ul><li>Behavior Verbal, non-verbal, action selection </li></ul><ul><li>… & other affective factors: </li></ul><ul><li>Affective States </li></ul><ul><li>Moods </li></ul><ul><li>Traits </li></ul><ul><li>Processes and structures affected </li></ul><ul><li>Variability in effects (by intensity, by individual…) </li></ul><ul><li>Integration of multiple emotions (in cognition, in behavior) </li></ul>Effect(s)
  • 49. Influential Theories <ul><li>Fewer theories exist than for appraisal </li></ul><ul><li>Specific mechanisms of emotion effects not as well developed </li></ul><ul><li>Existing theories: </li></ul><ul><ul><li>Spreading activation & priming (Bower, 1984) </li></ul></ul><ul><ul><ul><li>… memory effects & biases </li></ul></ul></ul><ul><ul><li>Distinct modes of processing associated with different emotions (Oatley & Johnson-Laird, 1987) </li></ul></ul><ul><ul><li>Emotions as patterns of parameters modulating processing </li></ul></ul><ul><ul><ul><li>(Fellous, Hudlicka, Matthews, Ortony et al., …) </li></ul></ul></ul>
  • 50. Emotions As Distinct Modes of Processing <ul><li>Parameter-controlled ‘global’ effects across multiple processes </li></ul><ul><ul><li>Neuromodulation theories (Fellous, 2004) </li></ul></ul><ul><li>Effects on low-level fundamental processes: attention & working memory </li></ul><ul><ul><li>Speed & capacity & content bias (e.g., threat, self) </li></ul></ul><ul><li>Effects on long-term memory </li></ul><ul><ul><li>Encoding and retrieval: speed & elaboration & bias (threat, self) </li></ul></ul><ul><li>Effects on higher-level processes </li></ul><ul><ul><li>Problem-solving, decision-making, planning.. </li></ul></ul><ul><ul><li>Affect appraiser processes (e.g., assessments of coping potential) </li></ul></ul><ul><li>Can ‘higher-level’ effects be explained (& implemented ) in terms of effects on the fundamental processes? </li></ul>
  • 51. Emotions As Parameters (MAMID, Hudlicka) Traits Extraversion Stability Conscientiousness Aggressiveness STATES / TRAITS Processing Structural Module Parameters Construct parameters Architecture topology Long-term memory speed, capacity Cue selection & delay …. Data flow among modules Content & structure Affective States Anxiety Anger Sadness Joy ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE Attention Action Selection Situation Assessment Goal Manager Expectation Generator Affect Appraiser
  • 52. Modeling Threat Bias Processing Parameters Construct parms. - Cue selection - Interpretive biases ... Process Threat cues Process Threatening interpretations Traits Low Stability TRAITS / STATES COGNITIVE ARCHITECTURE PARAMETERS COGNITIVE ARCHITECTURE Attention Action Selection Situation Assessment Goal Manager Expectation Generator Affect Appraiser Emotions Higher Anxiety / Fear Predisposes towards Preferential processing of Threatening stimuli Threat constructs Rated more highly
  • 53. Modeling Affect-Induced Differences in Behavior <ul><li>MAMID architecture modeled behavior of peacekeeper unit leaders </li></ul><ul><li>Units encountered a series of ‘surprise’ events en route </li></ul><ul><ul><li>Hostile crowds </li></ul></ul><ul><ul><li>Ambushes </li></ul></ul><ul><ul><li>Destroyed bridges </li></ul></ul><ul><li>Different leaders defined by distinct personality profiles: </li></ul><ul><ul><li>“ Normal” leader </li></ul></ul><ul><ul><li>“ High anxious” leader </li></ul></ul><ul><ul><li>“ High aggressive” leader </li></ul></ul><ul><li>Parameter-controlled ‘micro effects’ resulted in observable differences in behavior & distinct ‘mission outcomes’ </li></ul>
  • 54. Distinct Individual Profiles & Behavior “ Normal” “Anxious” Attention Perception / Situation Assessment Expectation Generation Affect Appraisal Goal Selection Action Selection Hostile large crowd Hostile large crowd Objective near Unit capability high Limited # of high-threat & self cues Movement blocked Danger to unit low Danger to unit and self high Perceptual threat & self bias Anxiety: Normal Anxiety: High Rapid-onset of high anxiety Danger from crowd unlikely Danger to unit and self high Career success threatened Threat and self oriented expectations Non-lethal crowd control Reduce anxiety Defend unit Threat and self focus goals Stop Stop; Lethal crowd control Non-lethal crowd control Report info Request help Request info Anxiety regulating behavior
  • 55. Representation & Reasoning Alternatives <ul><li>Symbolic - specific emotions linked to particular effects & behavior </li></ul><ul><ul><li>Rules </li></ul></ul><ul><ul><li>Belief nets </li></ul></ul><ul><li>Connectionist (Araujo) </li></ul><ul><ul><li>Parameters bias processing within a network </li></ul></ul><ul><li>Decision-theoretic (Busemeyer) </li></ul><ul><ul><li>Decision field theory </li></ul></ul>
  • 56. What Level of Resolution? “Black box” vs. “Process” models <ul><li>May be all that is required for a particular application </li></ul><ul><li>Easier to build (…initially) </li></ul>Black box models - simulate input-output mappings ??? INPUT OUTPUT Don’t know and don’t care Stimulus Emotion Emotion Effects
  • 57. Process Models - emulate internal processing <ul><li>Implement hypothesized mechanisms mediating the I-O mapping </li></ul><ul><li>Necessary if aiming to understand emotion processes </li></ul><ul><li>More difficult initially, but more robust and general </li></ul>Cognitive-Affective Architecture INPUT OUTPUT Process #1 Process #2 Process #3 Memory A Memory B Would like to know and do care
  • 58. What Type of an Architecture? <ul><li>Which architectural components are necessary? </li></ul><ul><ul><li>Attention, situation assessment, expectation generation, affect appraiser, planner..? </li></ul></ul><ul><ul><li>Data and control paths among the modules? </li></ul></ul><ul><li>What fundamental processing paradigm? </li></ul><ul><ul><li>Sequential see-think-do (see-think/ feel- do?) </li></ul></ul><ul><ul><li>vs. parallel distributed processing </li></ul></ul><ul><li>Where does emotion reside within the architecture? </li></ul><ul><ul><li>Emotion as dedicated modules? </li></ul></ul><ul><ul><li>vs. emotions as modulating parameters? </li></ul></ul><ul><ul><li>vs. emotions as emergent properties of a complex, multi-level architecture? </li></ul></ul>
  • 59. Components of a Cognitive-Affective Architecture : See-Think-Feel-Do <ul><li>“ See” </li></ul><ul><ul><li>Attention </li></ul></ul><ul><ul><li>Sensing and Perception </li></ul></ul><ul><li>“ Think” </li></ul><ul><ul><li>Situation Assessment </li></ul></ul><ul><ul><ul><li>Causes </li></ul></ul></ul><ul><ul><ul><li>Current assessments </li></ul></ul></ul><ul><ul><ul><li>Future predictions (expectations) </li></ul></ul></ul><ul><ul><li>Goal management </li></ul></ul><ul><ul><ul><li>Goals, drives, desires, norms, value </li></ul></ul></ul><ul><ul><li>Problem solving, Planning, Learning </li></ul></ul><ul><ul><li>Memory (declarative, procedural, episodic) (sensory, working , long-term) </li></ul></ul><ul><li>“ Feel” </li></ul><ul><ul><li>Affect appraiser </li></ul></ul><ul><ul><li>Emotion effects </li></ul></ul><ul><li>“ Do” </li></ul><ul><ul><li>Effectors </li></ul></ul><ul><ul><li>Performance monitoring </li></ul></ul>
  • 60. Questions Regarding Representational & Reasoning Requirements <ul><li>What must represented explicitly? </li></ul><ul><ul><li>Time (present, past, future) </li></ul></ul><ul><ul><ul><li>Hope needs expectations, regret needs past </li></ul></ul></ul><ul><ul><li>Mental constructs </li></ul></ul><ul><ul><ul><li>situations, expectations, goals </li></ul></ul></ul><ul><ul><li>Memories </li></ul></ul><ul><ul><ul><li>what type – declarative, episodic, procedural </li></ul></ul></ul><ul><ul><li>Explicit representation of the self </li></ul></ul><ul><ul><ul><li>need for complex emotions, social interaction, coping </li></ul></ul></ul><ul><li>What types of reasoning are necessary? </li></ul><ul><ul><li>What-if </li></ul></ul><ul><ul><ul><li>… to generate expectations which influence emotions </li></ul></ul></ul><ul><ul><li>Causal explanation </li></ul></ul><ul><ul><ul><li>..important for attribution </li></ul></ul></ul>
  • 61. Examples of Cognitive-Affective Architectures <ul><li>Emotion-augmented cognitive architectures </li></ul><ul><ul><li>Recognition-primed decision-making (Hudlicka) </li></ul></ul><ul><ul><li>Belief-Desire-Intention architectures (de Rosis…) </li></ul></ul><ul><ul><li>Soar ( Marinier, Jones, Henninger ) </li></ul></ul><ul><ul><li>ACT ( Ritter ) </li></ul></ul><ul><li>Generic - the ‘triune’ architectures </li></ul><ul><ul><li>Sloman et al., (Cog_Aff) or Sim_Agent (implementation) </li></ul></ul><ul><ul><li>Leventhal & Scherer (design) </li></ul></ul><ul><ul><li>Ortony, Normal and Revelle (ONR) (design) </li></ul></ul>
  • 62. MAMID Cognitive-Affective Architecture Action Selection Cues: State of the world ( “growling dog”, “approaching”) Situations: Perceived state ( “aggressive dog” ) Expectations: Expected state (“dog will attack”, “bite wound”) Goals: Desired state (“protect self”) Actions: to accomplish goals (“climb tree”) Affective state & emotions: Negative valence High anxiety Low happiness Cues Actions Attention Situation Assessment Expectation Generator Affect Appraiser Goal Manager
  • 63. “ The Triune” Generic Architectures (Sloman; Leventhal & Scherer; Ortony et al.; Arbib & Fellous..) Reactive Routine Reflective hardwired, fixed Well-learned behavior Awareness Compare alternatives - detect deviations Simple ‘what if’ Symbolic processing Approach / Avoid Simple drives Complex mental models Self representations & self-awareness Explicit predictions, causality… Meta-cognition Proto-affect Good/bad Primitive Emotions Good/bad Now/later Full fledged emotions -Basic -Complex flexible
  • 64. Outline <ul><li>Motivation & Objectives </li></ul><ul><li>Emotions – Background Info </li></ul><ul><li>Developing Affective User Models </li></ul><ul><li>Framework for model analysis </li></ul><ul><li>Summary & Conclusions </li></ul>
  • 65. Framework for Development, Analysis and Comparison of Models <ul><li>Which modeling objectives? </li></ul><ul><li>Which emotions? </li></ul><ul><li>Which aspects of emotions? (modality, functions, roles) </li></ul><ul><li>Which processes modeled? (appraisal, effects) </li></ul><ul><li>Which theory is basis of model? </li></ul><ul><li>What degree of model resolution </li></ul><ul><li>Which architecture? (modules, processes, data & control flow) </li></ul><ul><li>Which representational & reasoning formalisms used? </li></ul><ul><li>What validation method used? </li></ul>
  • 66. Outline <ul><li>Motivation & Objectives </li></ul><ul><li>Emotions – Background Info </li></ul><ul><li>Developing Affective User Models </li></ul><ul><li>Framework for model analysis </li></ul><ul><li>Summary & Conclusions </li></ul>
  • 67. Summary <ul><li>Need for including (some) emotions in (some) user models </li></ul><ul><li>Background info from emotion research in psychology and neuroscience </li></ul><ul><li>Guidelines for development of affective user models & cognitive-affective architectures </li></ul><ul><ul><li>Requirements for modeling core components of emotions: </li></ul></ul><ul><ul><ul><li>Cognitive appraisal </li></ul></ul></ul><ul><ul><ul><li>Emotion effects and emotion-cognition interactions </li></ul></ul></ul><ul><li>Framework for analysis of computational models of emotion </li></ul>
  • 68. Successes & State of the Art (1) <ul><li>Research </li></ul><ul><ul><li>Terminological clarifications </li></ul></ul><ul><ul><li>Increasing interaction among experimentalists & modelers and theorists </li></ul></ul><ul><ul><li>Construction of process models of appraisal theories </li></ul></ul><ul><ul><li>Beginnings of process models of emotion effects </li></ul></ul><ul><ul><li>Convergence on architecture structure </li></ul></ul><ul><ul><li>Beginnings of principled analyses of modeling requirements </li></ul></ul>
  • 69. Successes & State of the Art (2) <ul><li>Research </li></ul><ul><ul><li>Terminological clarifications </li></ul></ul><ul><ul><li>Increasing interaction among experimentalists & modelers and theorists </li></ul></ul><ul><ul><li>Construction of process models of appraisal theories </li></ul></ul><ul><ul><li>Beginnings of process models of emotion effects </li></ul></ul><ul><ul><li>Convergence on architecture structure </li></ul></ul><ul><ul><li>Beginnings of principled analyses of modeling requirements </li></ul></ul><ul><li>Applications </li></ul><ul><ul><li>Many ‘shallow’ models enhancing HCI and agents </li></ul></ul><ul><ul><li>Beginnings of ‘deep’ models driving synthetic agent & robot behavior </li></ul></ul><ul><ul><li>Emotion sensing & recognition </li></ul></ul><ul><ul><li>Emotion “expression” </li></ul></ul>Gratch & Marsella De Rosis Breazeal
  • 70. Challenges <ul><li>Theories to guide model building </li></ul><ul><ul><li>Appraisal, mechanisms of emotion effects, meta-cognition & emotion </li></ul></ul><ul><ul><li>Emotion dynamics </li></ul></ul><ul><ul><ul><li>Multiple emotions & non-linear effects </li></ul></ul></ul><ul><ul><ul><li>Interaction among multiple modalities </li></ul></ul></ul><ul><li>Data </li></ul><ul><ul><li>Emotion experiments are difficult </li></ul></ul><ul><ul><li>Model data requirements frequently exceed data availability </li></ul></ul><ul><li>Model development </li></ul><ul><ul><li>Standards, shared data & ontologies, plug & play modules, guidelines </li></ul></ul><ul><ul><li>Can we build LTM’s or must they “evolve” through agent-environment interactions (Matthews, 2004) </li></ul></ul><ul><li>Validation </li></ul><ul><ul><li>Verification vs. validation </li></ul></ul><ul><ul><li>Developing validation criteria & benchmark problems </li></ul></ul>
  • 71. Parting Thought <ul><li>“ Anyone can model emotions. That is easy. </li></ul><ul><li>But to model emotions </li></ul><ul><li>- in the right context, </li></ul><ul><li>- to the right degree, </li></ul><ul><li>- at the right time, </li></ul><ul><li>- for the right reason, and </li></ul><ul><li>- in the right way, </li></ul><ul><li>this is not easy.” </li></ul><ul><li>Paraphrasing “On anger”, Aristotle, Nichomachean Ethics </li></ul>
  • 72. Depth of Feelings: Alternatives for Modeling Affect in User Models & Cognitive Architectures Eva Hudlicka Psychometrix Associates Blacksburg, US [email_address] psychometrixassociates.com TSD 2006 Masarykova Universita, Brno, Czech Republic September 15, 2006

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