Optimal computation regarding risk attitudes in motor decision-making under risk

The process of motor planning involves considering the movement variability and value of the motor outcome. Previous studies have shown that individuals tend to exhibit a consistent risk-seeking bias when performing a temporal aiming task, which involves aiming to respond as closely as possible to a reference time without any delay. However, it is unclear whether this bias is caused by a subjective attempt to take risks or by overestimating motor accuracy. Here, we examined changes in participants’ aiming points after they were instructed regarding subjective risk-attitudes. The results from four different task settings in Experiments 1, 2a, 2b, and 2c, consistently showed a good correspondence between participants’ objective and subjective risk-attitudes. However, in a free-choice situation where participants were instructed to maximize their scores, a robust risk-seeking bias was identified. Computational models suggested that the risk-seeking bias in the free-choice situation was linked to the Maximax strategy. Furthermore, Experiment 3 showed that the participants’ strategy selection in the free-choice situation was similar to the behavior when they were instructed to perform the Maximax strategy. Overall, our findings suggest that in motor planning under risk, humans process near-optimal computation regarding risk attitudes, but their strategic preferences can lead to risk-seeking biases.