Adaptive Risk-Return Control in Motor Planning

Bayesian decision-making theory presumes that humans can maximize the expected gains by trading off risk-returns in a predefined gain function. Recent findings from spatial reaching and coincident timing tasks have challenged this theory by revealing that humans exhibited risk-seeking or risk-aversive rather than risk-neutral tendency (i.e., failed to achieve Bayesian optimality) in asymmetric gain functions (the gain/loss asymmetric to the target time/position). The debate on why these participants’ performances were sub-optimal remains unsettled. In the current paper, we argue that the abrupt change (i.e., gain volatility, a.k.a., risk magnitude) around the optimal point in the gain function, rather than its asymmetry, is a significant factor of this phenomenon, and that sub-optimality is resolved with an “adaptive risk control” where individual participants voluntarily adjust risk-return trade-off through a controllable task variable. We propose that the relationship between risk sensitivity and risk magnitude determines optimal motor planning.