affective computing intro class

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Affective Computing Intro Class"

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Stacy Marsella & Jon Gratch!!!Institute for Creative Technologies!University of Southern California!

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Background"

Computational Models of Human Behavior"!Cognitive and Emotional processes"

!Social Interaction & Theory of Mind!

!Nonverbal behavior (Facial Expressions, gesture, posture, gaze…)!Applications!

!Virtual Humans, Social Simulation, Interactive Drama"Architectures!

!Tools: PsychSim/Thespian, SmartBody, NVBG!

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Outline"

!  Emotion and it’s impact on behavior"

!  Why model emotion computationally?"–  Applications!

!  How we model it"–  EMA ! !

!  Can the model predict human behavior in a game"

Why study emotion?"

�Reason is, and ought to be, only the slave of the passions�

David Hume, 1711-1776

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""

Marsella & Gratch, Journal of Cognitive Systems Research 2009

What is emotion?"

!  Functions associated with individual autonomy"–  Rapid, continually adjusting assessment of significant events!–  Interruption of behaviors and changing of goals!–  Action preparation: energizes body, changes physical orientation!

!  Functions associated with social interaction"–  Signaling: broadcast information about mental state!–  Coordination: orient and coordinate group response!

!  Immersion and contagion"

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Why Study Emotion? Emotion influences cognitive processes"

!  Emotions change perception & decision-making"–  Angry people blame others/outgroups

(Keltner et al 93; Mackie et al 00) !–  Quicker to perceive threats (DeSteno et

al 2000/2004)!

–  Underestimate risk (Lerner & Keltner 2000/2001)!

!  Emotions impact memory"–  Mood Congruent Recall & Learning

(Bower, 91)!

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Why Study Emotion? Emotion influences human behavior"

!  Emotions change behavior"–  Anger primes aggressive responses

(Keltner & Haidt 1999)!–  Characteristic displays (Spoor&Kelly 04,

Parkinson 01, Darwin, Ekman)!

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Why Study Emotion? Emotion influences social behavior"

!  Emotions impact social interaction"–  Distress elicits helping (Eisenberg et al 89)!

–  Anger elicits fear (Dimberg&Ohman96) !

–  Negotiators concede more to angry partner (van Kleef et al. 2007)!

–  Emotion communicates information to other social actors (Darwin; Parkinson01)!

!  Emotions impact social systems"–  Improves group utility (Darwin; Frank, 1988)!

Why model emotion? Claim: emotion research has important benefits:"

!  Model human behavior"–  Inform theory development (Marsella et al. 2010)!

!  Inform general theories of intelligence"–  Provide insights to expanding general theories !–  Simon,67; Minsky, 85; Scheutz, & Sloman, 2001!

!  Applications"

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Applications: Better predictive models of human behavior"!  More accurate simulations of

human behavior "!  Use: emergency response

preparation and disaster planning"

!  How:"–  Model Emotion’s impact on decision-

making!–  Model Emotional contagion!

Applications: Enhance human computer interaction"

!  Recognize and respond to human�s emotions"–  Intelligent Tutoring Systems!

!  (Lisetti & Schiano, 2000; Conati & MacLaren , 2004)!

–  Security (Ekman & Friesen)!

!  How:"–  Model Player, User, Student !–  Recognize emotional

behavior!!  Facial Expression Recognition

(Movellen, Cohn)!!

!  )!

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Applications: Enhance human computer interaction II"

!

!  Elicit emotional responses to engage user"

!  Design-A-Plant’s Herman the Bug"

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Applications: Virtual Humans"

Intelligent agent that supports social interactions with human users in virtual reality!

!

Characters with a brain !–  Reason about environment!–  Understand and express emotion!–  Communicate through speech & gesture!–  Play the role of teachers, peers, adversaries!

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Support range of applications""Social-skills training"

Interview skills (TacQ/IEWTPT)!Meeting and negotiation skills (ELECT BiLAT)!Command and personal issues ( FITE, VOLT)!

"Clinical training"Medical or psychiatric assessment (Virtual Patient; People factory)!Medical triage (Sick Call)!

"Persuasive interfaces"Health seeking / social support (Sim Coach, TOPPS-VW)!Medical adherence / outpatient procedures (Bickmore)!Tutoring (Lester; Graesser; !Advertising / Marketing / Customer support (CodeBaby)!Informal science education (Twins)!

"Assessment and Experimentation"Explore factors that promote rapport / self-disclosure (Rapport Agent)!Personality assessment (Lester)!

"Entertainment"Gunslinger!!!!

Health Interventions •  Social Skills Training (Marsella et al) •  Autism (Tartaro & Cassell)

Diagnosis & Assessment •  ADHD Assessment (Rizzo et. al)

Education and Training •  Intelligent tutoring systems (Lester et al; Graesser) •  Health care provider training (Lok et al)

Increased interest in applications"

Entertainment •  Façade (Mateas and Stern) •  Thespian (Si et al.) !

Informal Science Education •  Museum Guides (Boston Science Museum, Cooper-Hewett)

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Health Interventions •  Bullying (Aylett et al) •  Coping (Marsella et al) •  Autism (Tartaro & Cassell) •  AIDS Prevention (Miller et al.)

Virtual Patients to train health care providers •  Kron et al., Lok et al.; Kenny et al

Social Skills Training (& Entertainment) w/ Virtual Humans"

Cross-cultural trainer for Military •  Language (Johnson et al) •  Negotiation (Kim et al)

Medical CyberWorld�s Robin

Gunslinger

Entertainment •  Gunslinger

Common Theme: Central Role of Emotion •  User immersed in emotional scenario

•  Informing a patient they have a fatal disease •  Confronting a gunfighter

•  Virtual human must convey emotion •  Patient finding out she has a fatal disease •  Bartender having a gun pointed at him

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Cognition

Understanding

Behavior Perception

Speech Prosody

Language

Dialogue

Generation

Planning

Annotated Text

Intent

Annotated Text

Emotion

Nonverbal

Gesture

Language Nonverbal

Speech

Gesture

Intent

Rea

ctiv

ity

Research &Technology of

Virtual Humans"

Perception •  Morency NLP •  DeVault, Hovy, Traum Emotion/Cognition •  Gratch, Marsella, Pynadath Behavior Generation •  Lee & Marsella Animation •  Chiu, Marsella & Shapiro

EMA: Computational Model of Emotion"

!  How do we computationally model emotion?!–  Both the causes and cognitive/behavioral

consequences of emotion?!

!  How do we evaluate/validate these models?!–  Can it predict human behavior?!–  And model users?!

!  How do we use the model in virtual humans?!

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Theoretical Perspective:�Appraisal Theory

Magda Arnold

•  Emphasizes cognitive antecedents of emotion – Emotion arises from an evolving subjective

interpretation of person’s relation to their environment – Well-suited to computational realization

•  Emotion arises from inference over representations

(Arnold, Lazarus, Frijda, Scherer, Ortony et al.)

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(act on world) (act on self)

Emotion

Coping Strategy

Action Tendencies “Affect” Physiological

Response

Problem-Focused Emotion-Focused

Environment Goals/Beliefs/ Intentions

Theoretical Framework: Appraisal Theory " " "(Arnold, Lazarus, Frijda, Scherer, Ortony et al.)"

Desirability

Expectedness

Controllability

Causal Attribution

Appraisal

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Emotion

Coping Strategy

Action Tendencies “Affect” Physiological

Response



Theoretical Framework: Appraisal Theory " " " " (Arnold, Lazarus, Frijda, Scherer, Ortony et al.)"

Desirability

Expectedness

Controllability

Causal Attribution

Resignation

Distancing

Wishful Thinking

Take action

Seek support

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Appraisal Theory as Architectural Specification"

To derive a computational model need to model:"•  Representation of Person-Environment relation!•  Derivation of appraisal values.!•  Map from appraisals to emotions.!•  Behavioral, cognitive consequents of emotion!

–  Coping!

•  EMA: EMotion & Adaptation!–  (Gratch & Marsella 2005; Marsella & Gratch 2009)!

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Computational Appraisal Models"

Appraisal Theories

AR Elliott

EM Neal Reilly

FLAME El Nasr

EMILE Gratch

CBI Marsella

EMA Gratch/Marsella

Fatima Dias

PEACTIDM Marinier

ALMA Gebhard

WASABI Becker-Asano

ACRES Swagerman

WILL Moffat Frijda!

OCC!

Lazarus!

Scherer!

ParleE Bui

THESPIAN Si et al.

TABASCO Staller&Petta

ActAffAct Rank

EMA: EMotion & Adaptation!(Gratch & Marsella 2005; Marsella & Gratch 2009)!

Planning Perception Dialogue Action

Causal Interpretation Working memory of plans, beliefs, desires, intentions

Cognitive Operations (inference)

Past Events

Future Plans Present

EMA: Person-Environment Relation

Future Act Cause: self Intend: yes Probability: 50%

Goal Utility: 50 Probability: 50% Intend-that: True

Goal Utility: 50 Probability: 100% Belief: False Past Act

Cause: Other Intend: yes Prob: 100%

Facilitates Inhibits

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EMA: Appraisal Derivation Model"

•  Appraisal as evaluation of the causal interpretation"•  Define appraisal variables in terms of features!•  Fast, parallel pattern matching!

•  Appraisal Variables"•  Desirability: Does proposition facilitate/inhibit agent’s utility!•  Likelihood: Likelihood of state.!•  Expectedness: Could truth value of a state be predicted.!•  Controllability: Can the outcome be altered by agent!•  Changeability: Can outcome be altered by other causal agent!•  Causal Attribution: what agent is blameworthy/praiseworthy!

•  Scope"•  Blueprint for a social agent!

EMA: Coping Model"

•  Sequential, deliberate, mediated by focus of attention"•  Problem-focused � Take Action, Make Plans"•  Emotion-focused � Alters Attention, Beliefs & Desires:"

•  Attention!•  Avoidance � Take action that alters attention!

•  Beliefs!•  Wishful Thinking � Change belief / likelihood!•  Shift blame � Change causal attribution!

•  Desires!•  Distancing � Lower utility of desired, threatened state!•  Find silver lining � Change utilities!

•  Intentions!•  Resignation � Drop intention to achieve a desired state!

•  Key: Finding ecological niche"•  Concretize Coping Theory: Framework for modeling emotion’s impact!

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➧ Past Present Future ➨

Goal Utility: 50 Probability: 100% Belief: False

Past Act Cause: Other Intend: yes Prob: 100%

Challenge Desirability: 50 Likelihood: 50% Causal Attribution: self Control: Moderate Emotion: Hope(25)

Threat Desirability: -50 Likelihood: 100% Causal Attribution: Other Control: moderate Emotion: Anger(50)

Facilitates

Inhibits

Appraisal

Threat Desirability: -100 Likelihood: 50% Causal Attribution: Other Control: Low Emotion: Sadness(50)

➧ Past Present Future ➨

Goal Utility: 50 Probability: 100% Belief: False

Past Act Cause: Other Intend: yes Prob: 100%

Threat Desirability: -50 Likelihood: 100% Causal Attribution: Other Control: moderate Emotion: Anger(50)

Inhibits

Coping

Challenge Desirability: 50 Likelihood: 50% Causal Attribution: self Control: Moderate Emotion: Hope(25)



Facilitates

Resignation (abandon goal)

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Surprise Fear Anger

Empathy Humor/Relief

Emotional Dynamics

Compassion

Relevance

Implication

Coping Potential

Normative Significance

Causal Interpretation (“Working Memory”) Environment

Control Signals

Appraisal Frames

Affective State

Appraisal

Coping

Inference Action

Appraisal

Scherer’s Sequential Checking Model EMA

Inference

Dynamics in perceived world relationship

Environment

Dynamics in the world

Action

Dynamics through action

Marsella & Gratch JCSR 2009

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Bird Flies In Personal Health Desire: Self (100)

Satisfied: True Probability: 80%

Surprise

Perspective: Self Unexpectedness: High

Controllability: Low Blame/Credit: unresolved

Appraisal

Virtual Actor’s Appraisal of Bird Flying in Window

Seek Info

Coping: Seek More Information

Cop

ing

Bird Attack Responsibility: Bird

Bird Flies In Personal Health Desire: Self (100) Satisfied: False Probability: 60%

Fear: 48

Further Inference: The Bird will attack

Perspective: Self Desirability: Undesirable

Controllability: Low Blame/Credit: Bird

Appraisal

Threatens

Move Away

Avoid Threat C

opin

g

Coping: Avoid

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Bird Attacking Responsibility: Bird

Bird Flies In Personal Health Desire: Self (100) Satisfied: False Probability: 50%

Anger: 48

Re-Appraisal & Further Planning

Perspective: Self Desirability: Undesirable Controllability: Medium

Blame/Credit: Bird

Appraisal

Threatens

Attack Threat

Whack Bird

Coping: Plan

Cop

ing

Threatens

Bird at safer dx

Marsella & Gratch JCSR 09

Control Signals

Causal Interpretation (Goals, Beliefs, Causal Relations, Plans, Intentions) Environment

Appraisal Frames

Affective State

Appraisal

Coping

Plan Inference

Belief Formation

Explanation

Action

Dialogue

Emotion as Meta-control system

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37!

Validity & Application"

•  Does the model predict human emotional reactions and their impact on behavior?"

"!!

Theoretical Framework: Computational Appraisal Theory"


Emotion

Coping Strategy

Action Tendencies �Affect� Physiological

Response



Desirability

Expectedness

Controlability

Causal Attribution

Appraisal

!  Autonomy: What are emotion�s cognitive consequences?"!  Multiagents: What are emotion�s social consequences?"

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Cognitive & Behavioral Consequences: Coping"

!  Human tendency to alter mental beliefs and commitments in response to emotion "

!  Often characterized as irrational"!  But argued to serve important adaptive functions""Simon 1967, Frank 1988, Damasio 1994, Mele 2001"

!  Questions:"–  Can EMA predict such �distortions�!–  How do they differ from �rational� models!!

Danger!

Danger!

Play%Game%Lose%

Win%

p=.8

p=.2 Utility= 20

Intention(play) EU=4%

!  Rational models decouple preferences and beliefs"–  Desires shouldn�t change beliefs (and vice versa)!

!  e.g., Just wanting something shouldn�t make it true!–  Preferences fixed over time!

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Wishful Thinking

Play%Game%Lose%

Win%

p=.8

p=.2 Utility= 20

Intention(play) Sad%

Hope%Joy%

Distancing

Fear%

Utility= 10 p=.6

p=.4

Resignation

•  Coping serves to �confound� beliefs and desires"!  Emotion-biases on decision making (Loewenstein & Lerner, 2003)!!  Cognitive dissonance (Festinger57)!!  Motivated inference (Kunda87)!

–  Few attempts to model computationally (Marsella&Gratch; Dias)!

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Coping Study (Marsella, Gratch, Wang, Stankovic, ACII09)"

Can EMA predict human behavior in experimental games" !!

! ! ! ! ! Does success/failure during game alter!! ! ! ! ! !Desire to win?!! ! ! ! ! !Intention to play?!! ! ! ! ! !Perceived likelihood of winning?!

!! ! ! ! ! !!

""Model Driven Experimentation Paradigm"–  Use EMA to generate task specific predictions!–  Evaluate predictions through human subject experiments!

"

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Build Model"Simulate some games"!  �Winning� vs. �Losing�!!  Developed and validated

in 2 pilot studies!

Note EMA predictions"!  Automatically derives

emotion and coping tendencies!

!  Appraisals and coping tendencies constitute a set of predictions that can be tested against data!

EMA Predictions"

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EMA�s Coping Predictions (subset)"

!  Beliefs (wishful thinking)"–  H3: Losing interacts with utility to predict probability bias!

!  Players that want to win will perceive higher win probability in lose condition!

Emotion will �irrationally� change"!  Desires (distancing)"

–  H1: Subjective utility of winning will drop as player loses!!  H1a: Magnitude of subjective utility predicts intensity of effect!

!  Intentions (resignation)"–  H2: Intention to play will drop as player loses!

!  H1a: Magnitude of subjective utility predicts intensity of effect!!

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Con

fede

rate

Hid

den

Cam

era

Par

ticip

ant

Prior Expectations LOSING LOST GAME

Assess emotions and coping

Time 0 Time 1 Time 2

Prior Expectations WINNING WON GAME Winning condition

Losing condition

Human subjects study 100 participants (2 conditions)*

Instruments

– Measured Emotions and Appraisals –  Self-Report: based on Ellsworth and colleagues� appraisal

questionnaire (e.g., Treynor, Ellsworth, & Gonzalez, in preparation; also see Smith & Ellsworth, 1985)

– Measured coping –  Self-Report: Computer Coping Questionnaire

(based on SCPQ; Perrez & Reicherts, 1992)

–  Behaviorally: Response time

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0 5

10 15 20 25 30

0 10 20 30 40 50 60 70 80 90 100 Probability

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100 Probability

0 5

10 15 20 25 30

0 10 20 30 40 50 60 70 80 90 100 Probability

0

20

40

60

80

100

0 10 20 30 40 50 60 70 80 90 100 Probability

JOY FEAR

HOPE SADNESS

Emotion

Subjects find experience to be emotional

!  Emotions change predictably throughout game

!  Appraisals of utility and probability predict emotion intensity

Hi win motivation

Lo win motivation Winning Losing Winning Losing

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Coping"

!  Emotion distorts desires and intentions

!  H1 and H2 supported: losers distance and resign (sour grapes)

winners

losers

�Rational� behavior

Self-reported desire to win Self-reported willingness to play

losers

!  But also �coping� with winning observed !  EMA implementation makes no prediction

winners

!  Rationality assumption: desire should be independent of probability

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Individual differences: Impact depends on initial preferences"

!  H1a and H2a also supported: strength of an individual�s desire predicts strength of coping response !  In Lose condition: Subject�s with initial high desire to win distanced

and resigned more

!  In Win condition: Subject�s with initial low initial desire to win became more engaged (EMA makes no prediction)

50!

Discussion"

!  Computational appraisal models (e.g., EMA) can improve predictive power of models"

!  Overall, results suggest that coping moves subjects toward more positive emotional states"–  Distancing and resignation of losing, high motivated subjects reduces

negative emotionality of threat to high utility goal!–  Increased engagement of winning, low motivated subjects enhances

positive emotions from previously unattainable goal!

!  Suggests appraisal theory as a tool for understanding and influencing user’s desires and intentions"

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Behavioral Consequences: social influence"

!  If an agent expressed its emotions, would you care?"

!  Fact: People influenced by partner�s expressions"–  Guilt/Anger fosters cooperation in Prisoner�s Dilemma (Frank)!–  Anger promotes concessions in Integrative Bargaining (van Kleef)!–  Smiling yields cooperation in Trust Games (Krunhumber)!

!  Question: is there value in communicating emotion"–  Give agents different goals!

!  Cooperative agent values joint reward!!  Self-interested agent values individual reward!

–  Appraise events in the game (according to EMA)!!  Cooperative agent �feels� guilty if it exploits human!!  Self-interested agent �feels� satisfaction!

–  Display resulting emotions!!  Are people influenced by displays of agent emotion?"

!"

Effects of Agent Emotions on People in experimental games"

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!  Two studies""Can emotion promote cooperation?"

–  Cooperative vs. unemotional agent!

"Does impact depend on agent goals?"–  Cooperative vs. Individualistic emotions!!Create patterns of displays consistent with!!cooperative or individualistic goal orientation!!!

!  Play iterated prisoner�s dilemma for chance to win $100"!  Participants believe they are playing against computer"!  Agent behavior identical (tit-for-tat) except for face expressions"!  Predict greater cooperation with cooperative agent""

Studies"

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!  Greater cooperation with emotional agent!

!  More cooperation with cooperative emotions!

!  Same emotion (e.g., smile) has different impact depending on context in which it is produced!

!  Mechanism still unclear!–  Is emotion just information?!–  Is it inducing emotional

reactions!–  Some evidence for the later in

more recent studies!

9.16!

6.80!

0.00!1.00!2.00!3.00!4.00!5.00!6.00!7.00!8.00!9.00!

10.00!

Cooperative! Individualistic!

Aver

age

Num

ber o

f Co

oper

atio

n"

Overall cooperation rate

Cooperation rate by round

Results"

Summary: should emotions influence machine behavior? "!  Social science theory can inform social agent design"

–  In this talk we focused on emotion!–  Use appraisal theory as architectural specification for agent design!

!  Value for modeling and predicting human behavior"–  EMA enhanced predictive power of classical decision models!!Predicted changes in desires and intentions in competitive game!

!  Value for shaping human computer interaction"–  Virtual human emotion alters human behavior in experimental games!–  Not to mention considerable research into recognizing and reacting to

human emotional cues (Picard; Graesser; Movellan & Bartlett; Pantic; Cohn;…)!

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Broader picture"

!  AI born from interest in human behavior"–  The cognitive revolution in psychology, linguistics, !!neuroscience, anthropology and philosophy!

!  Emotion important in early discourse "–  Simon, H. A. (1967). "Motivational and emotional controls of cognition."

Psychological Review 74: 29-39."

"but fell from favor "–  as AI turned to formal models of individual problem solving!

Simon & Newell

!  AAMAS a crucible for a social cognitive revolution"–  Social cognitive theory clearly can benefit man-machine interaction!–  Can it inform general theories of artificial intelligence?!

affective computing intro class

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