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Course Description

Complex Adaptive Systems Analyses of Individual and Group Behavior

Robert Goldstone

Indiana University

This course will explore the nature of adaptive systems in general, but will focus on learning in humans. We will initially discuss properties of adaptation, considering both the adaptation of parts within a system to the rest of the system, and the adaptation of whole systems to their environment and tasks. Then, we will consider human adaptation in the form of learning. Both perceptual and conceptual learning will be covered, as well as interactions between these two kinds of learning. Finally, we will discuss adaptation at the level of groups, and some of the emergent collective phenomena that occur when people interact. Some central questions throughout the course will be: What mechanisms can a system use to self-organize its own internal representations, How do elements within a system all mutually and simultaneously affect each other, and what is the best theory of human conceptual knowledge? Throughout the course, neural network and agent-based computational models of behavior will be presented and applied to empirical results in psychology. Students will get the opportunity to actively explore computational simulations of several adaptive systems.

Lecture 1. Complex Adaptive Systems PowerPoint Presentation

Characteristics of adaptive systems

Emergent descriptions

Interactions between simple agents

Dynamics of adaptive systems

Adaptation of agents to each other

Case studies

Simulated Annealing

Competitive Specialization

Cellular Automata

Apparent motion perception

Required readings:

Flake, G. W. (1998). The computational beauty of nature. Cambridge, MA: MIT Press. (Chapter 15 - Cellular Automata)

Optional readings:

Ball, P. (1999). The self-made tapestry. Oxford, England: Oxford University Press. (Chapter 4 - bodies).

Dawson, P. (1991). The how and why and what went where in apparent motion: Modeling solutions to the motion correspondence problem. Psychological Review, 98, 569-603.

Lecture 2. Perceptual Learning I: Empirical Findings PowerPoint Presentation

Mechanisms of perceptual learning

Imprinting

Selective attention

Dimension differentiation

Unitization

Neurophysiological bases of perceptual learning

Important empirical phenomena of perceptual learning

Hyperacuity

Hard-to-easy transfer

Specificity and transfer of perceptual skill

Optimal training regimes for perceptual learning

Required readings:

Goldstone, R. L. (1998). Perceptual Learning. Annual Review of Psychology, 49, 585-612.

Optional readings:

Gauthier, I., & Tarr, M. J. (2002). Unraveling mechanisms for expert object recognition: Bridging brain activity and behavior. Journal of Experimental Psychology: Human Perception & Performance, 28, 431-446.

Goldstone, R. L. (2000). Unitization during category learning. Journal of Experimental Psychology: Human Perception and Performance, 26, 86-112.

Goldstone, R. L., & Stevyers, M. (2001). The sensitization and differentiation of dimensions during category learning. Journal of Experimental Psychology: General, 130, 116-139.

Lecture 3. Perceptual Learning II: Computational Perspective

Interactions between perceptual and conceptual learning

Development of perceptual features

Neural network models of perceptual plasticity

Required readings:

Goldstone, R. L. (in press). Learning to perceive while perceiving to learn. In R. Kimchi, M. Behrmann, & C. Olson (Eds.) Perceptual organization in vision: Behavioral and neural perspectives.

Palmeri, T. J., Wong, A. C-N., & Gauthier, I. (2004). Computational approaches to the development of perceptual expertise. Trends in Cognitive Sciences, 8, 378-386.

Optional readings:

Schyns, P. G., Goldstone, R. L, & Thibaut, J. (1998). Development of features in object concepts. Behavioral and Brain Sciences, 21, 1-54.

Lecture 4. Conceptual Learning PowerPoint Presentation

Theories of conceptual representation

External grounding of concepts

Conceptual web accounts of meaning

Neural network models of conceptual alignment

Required readings:

Goldstone, R. L., & Rogosky, B. J. (2002). Using relations within conceptual systems to translate across conceptual systems. Cognition, 84, 295-320.

Medin, D. L., & Rips, L. J. (2005). Concepts and categories: Memory, Meaning, and Metaphysics. In K. J. Holyoak & R. G. Morrison (Eds.) The Cambridge Handbook of Thinking and Reasoning. Cambridge Universitty Press: Cambridge, England. (pp. 37-72).

Optional readings:

Feng, Y., Goldstone, R. L., & Menkov, V. (2005). A Graph Matching Algorithm and its Application to Conceptual System Translation. International Journal on Artificial Intelligence Tools, 14, 77-100.

Goldstone, R. L., & Barsalou, L. (1998). Reuniting perception and conception. Cognition, 65, 231-262.

Goldstone, R. L., & Kersten, A. (2003). Concepts and Categorization. In A. F. Healy & R. W. Proctor (Eds.) Comprehensive handbook of psychology, Volume 4: Experimental psychology. (pp. 599-621). New Jersey: Wiley.

Lecture 5. A Complex Adaptive Systems Perspective on Collective Behavior PowerPoint Presentation

An introduction to agent-based models in the social sciences

Foraging for resources

Collective path formation

Dissemination of innovations in social networks

Required readings:

Macy, M. W., & Willer, R. (20002). From factors to actors: Computational sociology and agent-based modelling. Annual Review of Sociology, 28, 143-166.

Optional readings:

Axtell, R. L., Epstein, J. M., Dean, J. S., Gumerman, G. J., Swedlund, A. C., Harburger, J., Chakravarty, S., Hammond, R., Parker, R., Parker, M. (2002). Proceedings of the National Academy of Science, 99, 7275-7279.

Ball, P. (1999). The self-made tapestry. Oxford, England: Oxford University Press. (Chapter 9 - Communities).

Goldstone, R. L., Jones, A., & Roberts, M. E. (in press). Group path formation. IEEE Transactions on System, Man, and Cybernetics, Part A.

Kennedy, J., & Eberhart, R. C. (2001).  Swarm intelligence.  San Francisco, CA: Morgan Kaufmann. (Chapter 6).

Small Group Sessions

Participants will get hands-on experience with several simulations of complex adaptive systems, and neural networks for concept learning and perceptual learning. Student will explore the NetLogo environment for developing Complex Adaptive System simulations. Some of the systems that we will explore include:

1. Conway's Game of Life, replication and bridging explanations

2. The Chaos Game and bridging explanations

3. Apparent motion

4. Resource covering and competitive learning

Assignments

There will be assignments associated with the small group laboratories. Workshop participants wishing to receive credit will be expected to turn in individually-prepared write-ups of these assignments

Robert Goldstone

Robert Goldstone has been a professor in the psychology department and cognitive science program at Indiana University since 1991, the same year he received a Ph.D. in psychology from University of Michigan. His research interests include concept learning and representation, perceptual learning, collective behavior, and computational modeling of human cognition. He was awarded two American Psychological Association (APA) Young Investigator awards in 1995 for articles appearing in Journal of Experimental Psychology, the 1996 Chase Memorial Award for Outstanding Young Researcher in Cognitive Science, the 2000 APA Distinguished Scientific Award for Early Career Contribution to Psychology in the area of Cognition and Human Learning, and a 2004 Troland research award from the National Academy of Sciences. He is the current editor of Cognitive Science.