Computational Models
of Emotions
Eva Hudlicka
Psychometrix
Associates
Blacksburg, VA, USA
Emotion plays a critical role in adaptive behavior.
Neuroscience and psychology research has demonstrated a close connection
between cognition and emotion. Cognition plays a key role in emotion generation
via the processes involved in cognitive appraisal. Emotions in turn exert a broad range of
effects on attention, perception and the cognitive processes involved in decision-making,
problem-solving and learning.
Computational models of emotion and emotion-cognition interactions
provide an important tool for understanding the mechanisms of these
processes. The resulting models also have
a broad range of applications in human-computer interaction.
This course discusses
the motivation and alternatives for developing computational models of emotion
and emotion-cognition interactions, within the context of cognitive-affective
architectures. The primary focus will be on symbolic models of the core
affective processes: cognitive appraisal and emotion effects on cognition. The
course will provide background information about emotion research in psychology
and neuroscience, that provide the empirical and theoretical basis for model
development. An analytical framework will be presented to help organize the
variety of existing models. Approaches
to model development, and examples of existing models and architectures, will
then be presented, within the context of this framework. The course will
conclude with a discussion of validation approaches, and the challenges and
future trends in emotion modeling.
The afternoon
seminar sessions will provide opportunities for discussion of the material
covered in the morning lectures, and the broader relevance of emotion models
for affective human-computer interaction and affective agents. Participants will also be able to explore the
use of belief nets in modeling cognitive appraisal and emotion effects on
cognition.
Course
outline
Day 1: Overview of Emotion Research in Psychology and Neuroscience [download presentation]
o
Definitions
o
Multi-modal
nature of emotions
o
Taxonomy
of affective states & traits
o
Roles of
emotion: interpersonal & intrapsychic
o
Core
processes in emotion: cognitive appraisal and emotion effects on cognition
o
Neuroscience
theories & data
Required readings
Ellsworth,
P.C. and Scherer, K. R. (2003). Appraisal Processes in Emotion.
In R. J. Davidson, K.R. Scherer & H.H. Goldsmith (Eds.), Handbook of Affective Sciences.NY: Oxford University Press.
Ellsworth,
P.C. and Scherer, K. R. (2003). Introduction: Cognitive components of emotion.
In R. J. Davidson, K.R. Scherer & H.H. Goldsmith (Eds.), Handbook of Affective Sciences.NY: Oxford University Press.
Frijda, N. H. (1993). Moods, Emotions Episodes, and
Emotions. In M. Lewis & J.M. Haviland (Eds.), Handbook of Emotions. New York: The
Guilford Press.
Optional readings
Mineka, S., Rafael, E. & Yovel, I. (2003). Cognitive Biases in Emotional Disorders:
Information Processing and Social-Cognitive Perspectives. In
R. J. Davidson, K.R. Scherer & H.H. Goldsmith (Eds.), Handbook of Affective Sciences. NY:
Oxford University Press.
Phelps, E. A.
(2006). Emotion and
Cognition: Insights from Studies
of the Human Amygdala. Annual Review of Psychology, 57:27–53.
Rolls, E. T. (2002). A
Theory of Emotion, Its Functions, an Its Adaptive Value.
In R. Trappl, P. Petta
& S. Payr (Eds.), Emotions in Humans and Artifacts. Cambridge, MA: The MIT Press.
Scherer,
K. (2003). Introduction: Cognitive Components of Emotion.
In R. J. Davidson, K.R. Scherer, H.H. Goldsmith (Eds.), Handbook of Affective Sciences. NY:
Oxford University Press.
Day 2: Modeling Methods & Data Requirements [download presentation]
o
Computational
models of cognition: research & applied objectives
o
Categories
of modeling approaches & model resolution levels
o
Representational
and reasoning formalisms: symbolic & connectionist
o
Cognitive
architectures, agent architectures & user models
o
Data
& knowledge requirements
Required readings
Cooper, R.P. (2002). Modelling High-Level Cognitive Processes. Mahwah, NJ: Lawrence Erlbaum
Associates. (Chapter 1)
Hudlicka, E. (forthcoming).Cognitive Models and Architectures. In Organizational Simulation and Modeling. S. Van Hemel, G. Zacharias, J.McMillan (eds.). Washington, DC: National Academies Press.
Optional readings
Pew, R. &
Mavor, A. (1998). Modeling Human and Organizational Behavior. DC: National Academies
Press. (Chapter 3) (Although oriented
toward military modeling, this chapter provides a comprehensive overview of
cognitive architectures developed in the US up to 1998).
Ritter, F.E., Shadbolt, N.R., Elliman, D., Young, R., Gobet, F.
and Baxter, G.D. (1999). Techniques for modelling
human performance in synthetic
environments: A supplementary review. Technical Report # 62. University of
Nottingham, UK: Department of Psychology.
Day 3: Modeling emotion I: Appraisal and Emotion Effects [download presentation]
o
Models of
cognitive appraisal
o
Models of
effects of emotions on attention, perception & cognition
o
Theories
& methods (representational & reasoning formalisms)
o
Multiple
levels of resolution: black-box vs. process models
o
Data
& knowledge requirements
Required readings
Gratch, J. & Marsella, S..
(2004). A Domain-independent Framework
for Modeling Emotion. Journal of
Cognitive Systems Research, 5(4),
269-306.
Hudlicka, E. (in press). Reasons for Emotions. In W. Gray (Ed.), Advances in Cognitive Models and Cognitive Architectures. NY: Oxford University Press.
Optional readings
Busemeyer, J., Dimperio, E., & Jessup, R.K. (in press). Integrating Emotional Processes into Decision Making Models. In W. Gray (Ed.), Advances in Cognitive Models and Cognitive Architectures. NY: Oxford University Press.
Fellous, J-M. (2004). From Human Emotions to Robot Emotions. In Proceedings of the AAAI Spring Symposium: Architecture for Modeling Emotion: Cross-Disciplinary Foundations. AAAI Technical Report SS-04-02. Menlo Park, CA: AAAI Press.
Roseman, I.J.
& Smith, C.A. (2001). Appraisal Theory: Overview, Assumptions, Varieties,
Controversies. In K.R. Scherer, A. Schorr, & T. Johnstone (Eds.), Appraisal Processes in Emotion: Theory,
Methods, Research. NY: Oxford University Press.
Day 4: Modeling emotion II: Cognitive - Affective Architectures & Framework for Model Requirements and Analysis [download presentation]
o
Research
& applied objectives for cognitive-affective architectures & models
o
Framework
for model requirements & analysis
o
Model
development guidelines
o
Examples
of cognitive-affective architectures
o
Emergence
of generic architectures
Required readings
Canamero, L. (2002).
Designing Emotions for Activity Selection in Autonomous Agents. In R. Trappl, P. Petta & S. Payr (Eds.), Emotions
in Humans and Artifacts. Cambridge, MA: The MIT Press.
Ortony, A., Norman,
D., & Revelle, W. (2005). Affect and Proto-Affect
in Effective Functioning. In J-M. Fellous & M. A.
Arbib (Eds.), Who Need Emotions? NY: Oxford University
Press.
Optional readings
Breazeal, C. &
Brooks, R. (2005). Robot Emotion: A Functional Perspective. In J-M. Fellous and M. A. Arbib
(Eds.), Who Need Emotions? NY: Oxford University Press.
Fellous, J-M. & Arbib, J. (2005). Toward Basic Principles for Emotional Processing: What the Fearful Brain Tells the Robot. In J-M. Fellous and M. A. Arbib (Eds.), Who Needs Emotions? NY: Oxford University Press.
Hudlicka, E. (2006). Framework for Modeling Emotion Within
Cognitive Architectures. Technical Report 06-08. Blacksburg, VA: Psychometrix Associates.
Sloman, A., Chrisley, R., and Scheutz, M. (2005). The Architectural Basis of Affective States and Processes. In J-M. Fellous and M. A. Arbib (Eds.), Who Need Emotions? NY: Oxford University Press.
Day 5: Model validation,
Challenges, Future Developments
o
Approaches
to validation of cognitive architecture: shared data sets and benchmark
problems
o
Extending
‘cognitive’ validation methods to models of emotion
o
Pragmatic
considerations in model development:
o
Data
& theory requirements
o
Brittleness
& model knowledge-base development
o
Facilitating
model development (standards, shared components & ontologies)
Required readings
Campbell, G. E. a. B., A.E. (2005). HBR Validation: Integrating Lessons Learned From Multiple Academic Disciplines, Applied Communities, and the AMBR Project. In R. W. P. K.A.Gluck (Ed.), Modeling Human Behavior with Integrated Cognitive Architectures: Comparison, Evaluation, and Validation (pp. 365-395). Mahwah, NJ: Lawrence Erlbaum Associates.
Gratch, J., Marsella, S. (2004). Evaluating a Computational Model of Emotion. Journal of Autonomous Agents and Multiagent Systems, Special Issue on the best of AAMAS 2004.
Prendinger, H., Ishizuka, M. (2005). Human Physiology as a Basis for Designing and Evaluating Affective Communication with Life-Like Characters. IEICE Transactions on Information and Systems, E88-D(11), 2453-2460.
Optional readings
Tenney, Y. J., Diller, D.E., Deutsch, S., Godfrey, K. (2005). The AMBR Experiments: Methodology and Human Benchmark Results. In R. W. P. K.A. Gluck (Ed.), Modeling Human Behavior with Integrated Cognitive Architectures: Comparison, Evaluation, and Validation (pp. 13-44). Mahwah, NJ: Lawrence Erlbaum Associates.
Richard W. Pew, R. W., Gluck, K.A., Deutsch, S. . (2005). Accomplishments, Challenges, and Future Directions for Human Behavior Representation. In R. W. P. K.A. Gluck (Ed.), Modeling human behavior with integrated cognitive architectures: Comparison, evaluation, and validation. (pp. 397-414). Mahwah, NJ: Lawrence Erlbaum Associates.
Kieras, D. E. (2003). Model-based evaluation. In J. Jacko & A. Sears (Eds.), Handbook for human-computer interaction (pp. 1139-1151). Mahwah, NJ: Lawrence Erlbaum.
Assignments
Participants taking the course for credit will have the option of
either writing a short paper elaborating a selected topic discussed in class,
or describing a high-level design of an emotion model for a particular
application. The paper or design description should be between 5 – 8 pages.
Eva
Hudlicka
Psychometrix Associates, USA
Eva
Hudlicka is a Principal Scientist and President of Psychometrix
Associates, in Blacksburg, VA, US. Her primary research focus is the
development of computational models of emotion; both the cognitive processes
involved in appraisal, and the effects of emotions on cognition. Her prior
research includes affect-adaptive user interfaces, UI design, decision-support
system design, and knowledge elicitation. Dr. Hudlicka received her BSc in Biochemistry from Virginia Tech (1977), her MSc from The Ohio State University in Computer Science
(1979), and her PhD in Computer Science from the University of
Massachusetts-Amherst (1986). Prior to founding Psychometrix
Associates in 1995, she was a Senior Scientist at Bolt Beranek
& Newman in Cambridge, MA.