This article reviews the behavioral literature on the control of goal-directed aiming and presents a multiple-process model of limb control. The model builds on recent variants of Woodworth's (1899) two-component model of speed-accuracy relations in voluntary movement and incorporates ideas about dynamic online limb control based on prior expectations about the efferent and afferent consequences of a planned movement. The model considers the relationship between movement speed and accuracy, and how performers adjust their trial-to-trial aiming behavior to find a safe, but fast, zone for movement execution. The model also outlines how the energy and safety costs associated with different movement outcomes contribute to movement planning processes and the control of aiming trajectories. Our theoretical position highlights the importance of advance knowledge about the sensory information that will be available for online control and the need to develop a robust internal representation of expected sensory consequences. We outline how early practice contributes to optimizing strategic planning to avoid worst-case outcomes associated with inherent neural-motor variability. Our model considers the role of both motor development and motor learning in refining feed-forward and online control. The model reconciles procedural and representational accounts of the specificity-of-learning phenomenon. Finally, we examine the breakdown of perceptual-motor precision in several special populations (i.e., Down syndrome, Williams syndrome, autism spectrum disorder, normal aging) within the framework of a multiple-process approach to goal-directed aiming.

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We examined the planning and control of goal-directed aiming movements in young adults with autism. Participants performed rapid manual aiming movements to one of two targets. We manipulated the difficulty of the planning and control process by varying both target size and amplitude of the movements. Consistent with previous research, participants with autism took longer to prepare and execute movements, particularly when the index of difficulty was high. Although there were no group differences for accuracy, participants with autism exhibited more temporal and spatial variability over the initial phase of the movement even though mean peak accelerations and velocities were lower than for control participants. Our results suggest that although persons with autism have difficulty specifying muscular force, they compensate for this initial variability during limb deceleration. Perhaps persons with autism have learned to keep initial impulses low to minimize the spatial variability that needs to be corrected for during the online control phase of the movement.

Two experiments were conducted in which participants (N = 12, Experiment 1; N = 12, Experiment 2) performed rapid aiming movements with and without visual feedback under blocked, random, and alternating feedback schedules. Prior knowledge of whether vision would be available had a significant impact on the strategies that participants adopted. When they knew that vision would be available, less time was spent preparing movements before movement initiation. Participants also reached peak deceleration sooner but spent more time after peak deceleration adjusting limb trajectories. Consistent with those findings, analysis of spatial variability at different points in the trajectory indicated that variability increased up to peak deceleration but then decreased from peak deceleration to the end of the movement.

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