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A combined manual and decision task may be viewed as an arrangement of subtasks, one or more of which involves uncertainty due to some input random process I(X). Very little is known about the relationship between the subtask performance times and I(X), particularly in an operational environment. An experiment was run involving a task containing a 4-alternative choice decision subtask. The other subtasks involved motions that have been found to be typical of many operational environments. The probabilistic conditions that were varied over the 4-alternative choice element consisted of six discrete probability functions for each of two distinct structures: independent (random sequences) and Markov dependent. The subtask performance time distributions were found to fit gamma's, but tended to independent approximate normal variates as subjects progressed toward fully learned states. For fully learned subjects the only subtask affected by the uncertainty was the decision subtask, and these effects did not vary significantly between the independent and Markov structures. Subjects employed a motion strategy at the decision subtask that was a function of the most probable stimulus. The results provide factors pertinent to systems design and evaluation in general.