Quantitative Explorations of Category Learning
نویسندگان
چکیده
We are striving to create cognitive models that produce veridical human behavior for complex tasks in real time. This paper details the application of an existing model of human category learning, Symbolic Concept Acquisition, to model category learning within a complex, real-time, perceptual-motor task. The chief modeling challenge was quantitatively matching human data within the constraints imposed by theory and its implementation in an integrative cognitive architecture. We describe how we improved aggregate fits to human learning results by considering the significant differences in the learning trajectories of individual subjects.
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