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Human information processing and task selection procedures in a dynamic multitask supervisory control environment are discussed. The results of a joint experimental and analytic program were assimilated into a normative dynamic-decision model for predicting human task-selection performance. To this end a general multitask experimental paradigm has been developed, wherein tasks of different value, time requirement, and deadline compete for a human's attention. Via this framework, the effects of various task related variables on human-decision processes have been studied empirically. Conceptually the normative dynamic-decision model (DDM) is an outgrowth of the well-known optimal control modeling technology as applied to multitask situations. Thus the analytic framework of the DDM is rooted in modern control, estimation, and semiMarkov decision-process theories. In order to validate the model via comparison with experimental results, several time history and scalar measures of performance similarity are proposed. Excellent model-data agreement is obtained for all the experimental conditions studied.