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Abstract: Dynamic decision tasks include important activities such as stock trading, air traffic control, and managing continuous production processes. In these tasks, decision makers may use outcome feedback to learn to improve their performance on-line as they participate in the task. We have developed a computational formulation to model this learning. Our formulation assumes that decision makers acquire two types of knowledge: (1) How their actions affect outcomes; (2) Which actions to take to achieve desired outcomes. Our formulation further assumes that fundamental aspects of the acquisition of these two types of knowledge can be captured by two parallel distributed processing (neural network) models placed in series. To test our formulation, we instantiate it to learn the Sugar Production Factory (Stanley, Mathews, Buss, & Kotler-Cope, Quart. J. Exp. Psychol., 1989) and then apply its predictions to a human subjects experiment. Our formulation provides a good account of human decision makers performance during training and two tests of subsequent ability to generalize: (1) answering questions about which actions to take to achieve goals that were not encountered in training; and (2) a new round of performance in the task using one of these new goals. Our formulation provides a less complete account of decision makers ability after training to predict how prespecified actions affect the factory s performance. Overall, our formulation represents an important step toward a process theory of how decision makers learn on-line from outcome feedback in dynamic decision tasks.

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