Tutorials - CogSci 2007 - 29th Annual Meeting of the Cognitive Science Society

We have an exciting program of tutorials lined up for CogSci 2007! All tutorial presenters and attendees are required to register for their tutorial through the on-line conference registration system (available beginning in April, 2007), but there is no additional fee for the tutorial. Tutorial attendance is FREE of charge!

Space is limited, so admittance into the tutorials will be on a first come, first served basis. Rooms will be assigned at a later date or on the day of the tutorials.

The tutorial program will be held on Wednesday, August 1, 2007. The full-day tutorial sessions will run from 8:30AM to 5:00PM, with a break for lunch at noon. The half-day tutorial will run from 1:30 to 5:00.

Tutorial 4 (Half-day): ACT-R
Presenters: Niels Taatgen and Hedderik van Rijn

Abstract:
Computational cognitive neuroscience involves the fabrication, analysis, and evaluation of computational models that attempt to bridge the gap between brain function and overt behavior. The Leabra modeling framework provides an integrated collection of conceptual tools for the construction of such models. Leabra incorporates important biological features of neural systems, such as membrane potential dynamics, rapid shunting lateral inhibition, and biologically realistic mechanisms for synaptic plasticity, while incorporating computationally efficient approximations of aggregate network behavior, allowing model simulations to scale up to tasks of psychological relevance. Thus, Leabra spans a middle ground between biophysically detailed neural simulations and cognitive models, including abstract connectionist models, that are grounded in psychological theory. Leabra has been implemented in the PDP++ simulator: an open source software package that includes support for a variety of connectionist frameworks in addition to Leabra. PDP++ provides a graphical point-and-click interface for constructing, executing, and analyzing computational models, but it may also be easily extended through the incorporation of additional C++ code. This tutorial will provide an overview of the Leabra framework, as well as hands-on experience with Leabra models of perception, attention, learning, memory, and cognitive control.

David C. Noelle is an Assistant Professor at the University of California, Merced, with appointments in Computer Science and Cognitive Science. Until recently, he was Assistant Professor of Computer Science and Psychology at Vanderbilt University. He
received his Ph.D. in Cognitive Science and Computer Science from the University of California, San Diego, and did postdoctoral work at the Center for the Neural Basis of Cognition, a joint project of Carnegie Mellon University and the University of Pittsburgh. His research primarily involves the design, analysis, and evaluation of computational cognitive neuroscience models of rule learning and rule use, with a focus on the role of prefrontal cortex in the learning and production of rule-guided behavior. Along with an international team of collaborators, he is currently investigating the development of hierarchical control representations in frontal cortex, employing computational models using the Leabra framework. One of these fellow scientists is Randy O'Reilly, the principal architect of Leabra, with whom Noelle has been collaborating for almost ten years. Indeed, Noelle has taught computational cognitive neuroscience modeling techniques using PDP++ for nearly a decade.

Abstract:
The cognitive revolution that occurred in the 1960’s was based on classical computational logic, and the connectionist/neural network movements of the 1970’s were based on classical dynamical systems. These classical assumptions remain at the heart of both cognitive architecture and neural network theories, and they are so commonly and widely applied that we take them for granted and presume them to be obviously true. What are these critical but hidden assumptions upon which all traditional theories rely? Quantum information processing theory provides a fundamentally different approach to logic, reasoning, probabilistic inference, and dynamical systems. For example, quantum logic does not follow the distributive axiom of Boolean logic; quantum probabilities do not obey the disjunctive axiom of Kolmogorov probability; quantum reasoning does not obey the principle of monotonic reasoning. Nevertheless Mother Nature seems to rely quite heavily on quantum computing principles in many domains of science. This tutorial will provide an exposition of the basic assumptions of classic versus quantum information processing theories. These basic assumptions will be examined, side by side, in a parallel and elementary manner. For example, classical systems assume that measurement merely observes a pre existing property of a system; in contrast, quantum systems assume that measurement actively creates the existence of a property in a system. The logic and mathematical foundation of classic and quantum theory will be laid out in a simple and elementary manner that uncovers the mysteries of both theories. Classic theory will emerge to be seen as a possibly overly restrictive case of the more general quantum theory. The fundamental implications of these contrasting assumptions will be examined closely with concrete examples and applications to cognition. New research programs in cognition based on quantum information processing theory will be reviewed.

Dr. Busemeyer is a professor of Psychological and Brain Science and Cognitive Science at Indiana University. He is also the chief editor of the Journal of Mathematical Psychology. He recently ended his position as manager of the Cognition and Decision Program at the Air Force Office of Scientific Research. His research interests include decision making and dynamic modeling. Dr. Zheng Wang is an assistant professor of Communication at Ohio State University. Her main research interests are concerned with multi media influences on communication.

You can download the following related papers:
Busemeyer, J. R. , Matthew, M., Wang, Z. (2006) A Quantum Game Theory Explanation of Disjunction Effects. Proceedings of the Cognitive Science Society.
http://mypage.iu.edu/~jbusemey/quant_games.pdf

Busemeyer, J. R., Wang, Z., & Townsend, J. T. (2006) Quantum dynamics of human decision making. Journal of Mathematical Psychology, 50, 220-241.
http://mypage.iu.edu/~jbusemey/QD.pdf

Abstract:
The tutorial will provide participants an understanding of the details of Soar so that they can create simple Soar programs. This will be a full-day hands-on tutorial that starts with an overview of Soar, its history, goals, and previous research done with it. The rest of the morning will emphasize understanding the syntax and structure of the architecture (the memories and processes), and the emphasis in the afternoon on agent development. In the morning, participants will learn to run, modify, and debug small demonstration programs that illustrate the various parts of Soar's structure, including it new reinforcement learning component. They will also be introduced to Soar‚s editing, debugging, and runtime tools. In the afternoon, we will work on simple agents that interact with a dynamic simulated environment. The students will build their own complete agents that navigate and compete in a simple maze world.

John E. Laird is a Professor of Electrical Engineering and Computer Science at the University of. He received his Ph.D. in Computer Science from Carnegie Mellon University in 1983. His research interests spring from a desire to understand the nature of the architecture underlying artificial and natural intelligence. He is one of the original developers of the Soar architecture and leads its continued development and evolution. Over the last ten years he has been developing autonomous agents for military simulations and interactive computer games. His current research includes extending Soar through the addition of reinforcement, semantic, and episodic learning, as well as emotions, spatial reasoning and imagery, and clustering. He is a Fellow of AAAI and ACM

Abstract:
This tutorial serves as a general introduction to the ACT-R theory. It therefore assumes no knowledge not already present in the typical cognitive science audience: some basic experimental psychology and some understanding of what a formal theory entails. The tutorial will not attempt to teach ACT-R modeling, because the time span is too limited for that. Instead we hope to wet the appetite of the participants enough for the 7 day tutorial that is available online, and that can be followed by attending the yearly ACT-R summer school.
The general strategy in the tutorial is to introduce the various elements of the theory on the basis of example models and research paradigms, largely following Taatgen, Lebiere and Anderson (2006). The abstract provides some more details on the topics. Apart from a general introduction of approximately half an hour, we will devote about half an hour to each of the five research paradigms. We will not focus too much on the more syntactic aspects of the theory, but will provide enough details to give participants a feeling for what is going on in the models.

You can download the following related papers:
Anderson, J. R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y. (2004) An integrated theory of Mind. Psychological Review, 111, 1036-1060. Available online: http://act-r.psy.cmu.edu/papers/403/IntegratedTheory.pdf

Taatgen, N.A., Lebiere, C. & Anderson, J.R. (2006). Modeling paradigms in ACT-R. In R. Sun (ed.), Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation (pp. 29-52). Cambridge University Press. Available online: http://www.ai.rug.nl/~niels/publications/taatgenLebiereAnderson.pdf