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An optimization model of human motor control in a laterally constrained self-paced path control task (e. g., driving) is proposed. The model structures the task as intermittent control with Begg's relationship used to describe the growth of lateral error with distance traveled during the sampling interval. Rewards and penalties are explicitly present in the objective function to be optimized. The path geometry is used with nonlinear optimization to solve the model and show that it has the expected reactions to payoff changes. Tests of the model against existing data in the literature and directly against the data from laboratory subjects showed a very close correspondence in form and numerical values between the model's prediction and human subject's performance.