In search of (a few good graduate students and post-docs)

Like the Marines I am in search of a few good people. Unlike the Marines, I don't want to send you off to combat, rather I am searching for would-be explorers--people who want to come and work with me on the next generation of ideas that may transform the ways in which we think about ourselves and our world. Pointers to my current mission are given by the following phrases: embodied cognition, milliseconds matter, and interactive behavior.

Embodied cognition entails the interaction of low-level cognitive, perceptual, and motor operations. It is how we think and act. Curiously, until very recently, it has been ignored by mainstream Cognitive Science and Cognitive Psychology. My work with Bonnie John on Project Ernestine (see Gray, John, & Atwood, 1992: Gray, John, & Atwood, 1993) is some of the earliest work in this "new" area. Note that unlike some people, I believe that the push towards embodied cognition was clearly foreshadowed by mainstream cognitive researchers in the 80's and earlier (most notably by the work of Allen Newell and his students, for an example see, Card, Moran, and Newell, 1983).

This trend towards embodied cognition is great stuff. This is where milliseconds matter. This is the level that Bonnie and I were working at when we saved the Phone Company all of those big bucks. It is a level that is little-explored for its implications for human-computer interaction (HCI) or its implications for cognitive theory generally. My current research is delving into an issue that lies at the heart of views of embodied cognition; namely, resource allocation. We find that the cognitive control system is not biased to favor perceptual-motor over cognitive costs. Rather, at the 1/3 to 3 sec level of embodiment, the allocation of cognitive, perceptual, and motor resources is based on cost-benefit tradeoffs measured in time (see Gray, Sims, Fu, & Schoelles, 2006).

In much of my research, we proceed by building simulated task environments (Gray 2002). Simulated task environments may be as complex as a flight simulator or may be as simple as a VCR (Gray, 2000). We build such environments because:

  • In field research there is too much complexity to allow for definite conclusions; whereas, in laboratory research there is too little complexity to allow for interesting conclusions (Brehmer & Dörner, 1993)
  • Simulated task environment allow us to get at just the right level of complexity to push the embodied cognition agenda.

    An important component of my research are our attempts to understand the ETA triad; namely, that interactive behavior emerges from the constraints and opportunities provided by the interaction of embodied cognition (E) with tasks (T) and the artifact (A) (interfaces or devices) designed to accomplish the task.

      

    We are currently finding that subtle changes in the design of an interface lead people to go from a memoryless strategy, in which information is acquired, "just-in-time", by an eye movement, to a strategy that relies on working memory. What makes all of this very interesting is the fact that to save 2 sec NOW, people adopt a memory-based strategy that results in more errors and more work (extra trials or error recovery time) later on. Neat stuff. We are pursuing the question of whether cognition optimizes locally rather than globally (Fu & Gray, 2006, Gray & Fu, 2001, Gray, et al., 2006, Neth, Sims, & Gray, 2006).

     Oh yes, and another thing. As a research strategy, we are committed to writing ACT-R models that interact with the same interfaces as do our research participants. For recent examples of this see the Gray, Schoelles, and Fu paper (2000) and the Schoelles and Gray one (2000). We feel that this keeps us honest. If we tell you that our models do the same thing as our people, it will be because that our models use the same interfaces as our people.

     So I am looking for self-motivated people who want to come and work with me. I enjoy talking about what I do, so do not hesitate to send me email or call me up. My contact information in on my home page.

     

    Papers mentioned above

    Brehmer, B., & Dörner, D. (1993). Experiments with computer-simulated microworlds: Escaping both the narrow straits of the laboratory and the deep blue sea of the field study. Computers in Human Behavior, 9(2-3), 171-184.

    Byrne, M. D., & Anderson, J. R. (1998). Perception and action. In J. R. Anderson & C. Lebiére (Eds.), The atomic components of thought (pp. 167-200). Hillsdale, NJ: Erlbaum.

    Card, S. K., Moran, T. P., & Newell, A. (1983). The psychology of human-computer interaction. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Fu, W.-T., & Gray, W. D. (2006). Suboptimal tradeoffs in information seeking. Cognitive Psychology, 52(3), 195-242.

    Gray, W. D. (2000). The nature and processing of errors in interactive behavior. Cognitive Science, 24(2), 205-248.

    Gray, W. D. (2002). Simulated task environments: The role of high-fidelity simulations, scaled worlds, synthetic environments, and microworlds in basic and applied cognitive research. Cognitive Science Quarterly, 2(2), 205-227.

    Gray, W. D., John, B. E., & Atwood, M. E. (1992). The précis of Project Ernestine or an overview of a validation of GOMS. Proceedings of the ACM CHI'92 Conference on Human Factors in Computing Systems (pp. 307-312). New York: ACM Press.

    Gray, W. D., John, B. E., & Atwood, M. E. (1993). Project Ernestine: Validating a GOMS analysis for predicting and explaining real-world performance. Human-Computer Interaction, 8(3), 237-309.

    Gray, W. D., Schoelles, M. J., & Fu, W.-t. (2000). Modeling a continuous dynamic task. In N. Taatgen & J. Aasman (Eds.), Proceedings of the Third International Conference on Cognitive Modeling (pp. 158-168). Veenendal, The Netherlands: Universal Press..

    Gray, W. D., Sims, C. R., Fu, W.-T., & Schoelles, M. J. (2006). The soft constraints hypothesis: A rational analysis approach to resource allocation for interactive behavior. Psychological Review, 113(3), 461-482.

    Neth, H., Sims, C. R., & Gray, W. D. (2006). Melioration Dominates Maximization: Stable Suboptimal Performance Despite Global Feedback, Proceedings of the 28th Annual Meeting of the Cognitive Science Society (pp. 627-632).

    Schoelles, M. J., & Gray, W. D. (2000). Argus Prime: Modeling emergent microstrategies in a complex simulated task environment. In N. Taatgen & J. Aasman (Eds.), Proceedings of the Third International Conference on Cognitive Modeling (pp. 260-270). Veenendal, NL: Universal Press.

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