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Title: Computational Modelling of Mental Imagery in Chess: A Sensitivity Analysis
Authors: Waters, A J
Gobet, F
Keywords: chess;computer modelling;expertise;mental imagery;learning;recall task;intersection positions;CHREST;sensitivity analysis;unified theories of cognition;UTC
Issue Date: 2008
Publisher: Erlbaum
Citation: Gobet, F., & Waters, A. J. (in press). Computational modelling of mental imagery in chess: A sensitivity analysis. Proceedings of the 30th Annual Meeting of the Cognitive Science Society. Mahwah, NJ: Erlbaum.
Abstract: An important aim of cognitive science is to build computational models that account for a large number of phenomena but have few free parameters, and to obtain more veridical values for the models’ parameters by successive approximations. A good example of this approach is the CHREST model (Gobet & Simon, 2000), which has simulated numerous phenomena on chess expertise and in other domains. In this paper, we are interested in the parameter the model uses for shifting chess pieces in its mind’s eye (125 ms per piece), a parameter that had been estimated based on relatively sparse experimental evidence. Recently, Waters and Gobet (2008) tested the validity of this parameter in a memory experiment that required players to recall briefly presented positions in which the pieces were placed on the intersections between squares. Position types ranged from game positions to positions where both the piece distribution and location were randomised. CHREST, which assumed that pieces must be centred back to the middle of the squares in the mind’s eye before chunks can be recognized, simulated the data fairly well using the default parameter for shifting pieces. The sensitivity analysis presented in the current paper shows that the fit was nearly optimal for all groups of players except the grandmaster group for which, counterintuitively, a slower shifting time gave a better fit. The implications for theory development are discussed.
Appears in Collections:Psychology
Dept of Life Sciences Research Papers

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