How do humans choose one arm or the other to reach single targets in front of the body? Current theories of reward-driven decisionmaking predict that choice results from a comparison of "action values," which are the expected rewards for possible actions in a given state. In addition, current theories of motor control predict that in planning arm movements, humans minimize an expected motor cost that balances motor effort and endpoint accuracy. Here, we test the hypotheses that arm choice is determined by comparison of action values comprising expected effort and expected task success for each arm, as well as a handedness bias. Right-handed subjects, in either a large or small target condition, were first instructed to use each hand in turn to shoot through an array of targets and then to choose either hand to shoot through the same targets. Effort was estimated via inverse kinematics and dynamics. A mixed-effects logistic-regression analysis showed that, as predicted, both expected effort and expected success predicted choice, as did arm use in the preceding trial. Finally, individual parameter estimation showed that the handedness bias correlated with mean difference between right- and left-arm success, leading to overall lower use of the left arm. We discuss our results in light of arm nonuse in individuals' poststroke.

Whether the CNS minimizes variability or effort in planning arm movements can be tested by measuring the preferred movement duration and end-point variability. Here, we conducted an experiment in which subjects performed arm-reaching movements without visual feedback in fast, medium, slow, and preferred duration conditions. Results show that (i) total endpoint variance was smallest in the medium duration condition, and (ii) subjects preferred to carry out movements that were slower than this medium duration condition. A parsimonious explanation for the overall pattern of end-point errors across fast, medium, preferred, and slow movement durations is that movements are planned to minimize effort as well as endpoint error due to both signal-dependent and constant noise

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