Modeling Memories of Large-scale Space Using a Bimodal Cognitive Architecture

We present an extension to biSoar, a bimodal version of the cognitive architecture Soar, by adding a bimodal version of chunking, Soar’s basic learning mechanism. We show how this new biSoar is a useful tool in modeling cognitive phenomena involving spatial or diagrammatic elements by applying it to the modeling of problem solving involving large-scale space, such as way-finding. We suggest how such models can help in identifying variables to control for in human subject experiments.

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