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Abstract: A formulation of learning in dynamic decision-making tasks is developed, building on the application of control theory to the study of human performance in dynamic decision making and a connectionist approach to motor control. The formulation is implemented as a connectionist model and compared with human subjects in learning a simulated dynamic decision-making task. When the model is pretrained with the prior knowledge that subjects are hypothesized to bring to the task, the model's performance is broadly similar to that of subjects. Furthermore, individual runs of the model show variability in learning much like individual subjects. Finally, the effects of various manipulations of the task representation on model performance are used to generate predictions for future empirical work. In this way, the model provides a platform for developing hypotheses on how to facilitate learning in dynamic decision-making tasks.
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