The International Conference on Cognitive Modeling brings together researchers who develop computational models to explain and predict cognitive data. The core theme of the 2004 conference was "Integrating Computational Models," encompassing an integration of diverse data through models of coherent phenomena; integration across modeling approaches; and integration of teaching and modeling. This text presents the proceedings of that conference. The International Conference on Cognitive Modeling 2004 sought to grow the discipline of computational cognitive modeling by providing a sophisticated modeling audience for cutting-edge researchers, in addition to offering a forum for integrating insights across alternative modeling approaches in both basic research and applied settings, and a venue for planning the future growth of the discipline. The meeting included a careful peer-review process of 6-page paper submissions; poster-abstracts to include late-breaking work in the area; prizes for best papers; a doctoral consortium; and competitive modeling symposia that compare and contrast different approaches to the same phenomena.

The International Conference on Cognitive Modeling brings together researchers who develop computational models that explain and predict cognitive data. The 2004 conference encompassed an integration of diverse data through models of coherent phenomena;

This book explores the intersection between individual cognitive modeling and modeling of multi-agent interaction.

This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience.

The field of cognitive modeling has progressed beyond modeling cognition in the context of simple laboratory tasks and begun to attack the problem of modeling it in more complex, realistic environments, such as those studied by researchers in the field of human factors. The problems that the cognitive modeling community is tackling focus on modeling certain problems of communication and control that arise when integrating with the external environment factors such as implicit and explicit knowledge, emotion, cognition, and the cognitive system. These problems must be solved in order to produce integrated cognitive models of moderately complex tasks. Architectures of cognition in these tasks focus on the control of a central system, which includes control of the central processor itself, initiation of functional processes, such as visual search and memory retrieval, and harvesting the results of these functional processes. Because the control of the central system is conceptually different from the internal control required by individual functional processes, a complete architecture of cognition must incorporate two types of theories of control: Type 1 theories of the structure, functionality, and operation of the controller, and type 2 theories of the internal control of functional processes, including how and what they communicate to the controller. This book presents the current state of the art for both types of theories, as well as contrasts among current approaches to human-performance models. It will be an important resource for professional and student researchers in cognitive science, cognitive-engineering, and human-factors. Contributors: Kevin A. Gluck, Jerry T. Ball, Michael A. Krusmark, Richard W. Pew, Chris R. Sims, Vladislav D. Veksler, John R. Anderson, Ron Sun, Nicholas L. Cassimatis, Randy J. Brou, Andrew D. Egerton, Stephanie M. Doane, Christopher W. Myers, Hansjörg Neth, Jeremy M Wolfe, Marc Pomplun, Ronald A. Rensink, Hansjörg Neth, Chris R. Sims, Peter M. Todd, Lael J. Schooler, Wai-Tat Fu, Michael C. Mozer, Sachiko Kinoshita, Michael Shettel, Alex Kirlik, Vladislav D. Veksler, Michael J. Schoelles, Jerome R. Busemeyer, Eric Dimperio, Ryan K. Jessup, Jonathan Gratch, Stacy Marsella, Glenn Gunzelmann, Kevin A. Gluck, Scott Price, Hans P. A. Van Dongen, David F. Dinges, Frank E. Ritter, Andrew L. Reifers, Laura Cousino Klein, Michael J. Schoelles, Eva Hudlicka, Hansjörg Neth, Christopher W. Myers, Dana Ballard, Nathan Sprague, Laurence T. Maloney, Julia Trommershäuser, Michael S. Landy, A. Hornof, Michael J. Schoelles, David Kieras, Dario D. Salvucci, Niels Taatgen, Erik M. Altmann, Richard A. Carlson, Andrew Howes, Richard L. Lewis, Alonso Vera, Richard P. Cooper, and Michael D. Byrne

This book presents the theory of threaded cognition, a theory that aims to explain the multitasking mind. The theory states that multitasking behavior can be expressed as cognitive threads-independent streams of thought that weave through the mind's processing resources to produce multitasking behavior, and sometimes experience conflicts to produce multitasking interference. Grounded in the ACT-R cognitive architecture, threaded cognition incorporates computational representations and mechanisms used to simulate and predict multitasking behavior and performance.