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Further simulations of multiple task performance have been conducted with computational models that are based on the Executive Process Interactive Control (EPIC) architecture for human information processing. These models account well for patterns of reaction times and psychological refractory period phenomena (delays of overt responses after short stimulus onset asynchronies) in a variety of laboratory paradigms and realistic situations. This supports the claim of the present theoretical framework that multiple task performance relies on adaptive executive control, which enables substantial amounts of temporal overlap among stimulus identification, response selection, and movement production processes for concurrent tasks. Such overlap is achieved through optimized task scheduling by flexible executive processes that satisfy prevailing instructions about task priorities and allocate limited capacity perceptual motor resources efficiently.

A new theoretical framework, executive-process interactive control (EPIC), is introduced for characterizing human performance of concurrent perceptual-motor and cognitive tasks. On the basis of EPIC, computational models may be formulated to simulate multiple-task performance under a variety of circumstances. These models account well for reaction-time data from representative situations such as the psychological refractory-period procedure. EPIC's goodness of fit supports several key conclusions: (a) At a cognitive level, people can apply distinct sets of production rules simultaneously for executing the procedures of multiple tasks; (b) people's capacity to process information at "peripheral" perceptual-motor levels is limited; (c) to cope with such limits and to satisfy task priorities, flexible scheduling strategies are used; and (d) these strategies are mediated by executive cognitive processes that coordinate concurrent tasks adaptively.

A stochastic optimized-submovement model is proposed for Pitts' law, the classic logarithmic tradeoff between the duration and spatial precision of rapid aimed movements. According to the model, an aimed movement toward a specified target region involves a primary submovement and an optional secondary corrective submovement. The submovements are assumed to be programmed such that they minimize average total movement time while maintaining a high frequency of target hits. The programming process achieves this minimization by optimally adjusting the average magnitudes and durations of noisy neuromotor force pulses used to generate the submovements. Numerous results from the literature on human motor performance may be explained in these terms. Two new experiments on rapid wrist rotations yield additional support for the stochastic optimizedsubmovement model. Experiment 1 revealed that the mean durations of primary submovements and of secondary submovements, not just average total movement times, conform to a square-root approximation of Pitts' law derived from the model. Also, the spatial endpoints of primary submovements have standard deviations that increase linearly with average primary-submovement velocity, and the average primary-submovement velocity influences the relative frequencies of secondary submovements, as predicted by the model. During Experiment 2, these results were replicated and extended under conditions in which subjects made movements without concurrent visual feedback. This replication suggests that submovement optimization may be a pervasive property of movement production. The present conceptual framework provides insights into principles of motor performance, and it links the study of physical action to research on sensation, perception, and cognition, where psychologists have been concerned for some time about the degree to which mental processes incorporate rational and normative rules. An enduring issue in the study of the human mind concerns of mathematical probability theory and statistical decision thethe rationality and optimality of the mental processes that guide ory (e.g., see Edwards, 1961; Edwards, Lindman, & Savage