The undershoot bias: learning to act optimally under uncertainty

Abstract - Learning in stochastic environments is increasingly viewed as an important psychological ability. To extend these results from a perceptual to a motor domain, we tested whether participants could learn to solve a stochastic minimal-time task using exploratory learning. The task involved moving a cursor on a computer screen to a target. We systematically varied the degree of random error in movement in three different conditions; each condition had a distinct time-optimal solution. We found that participants approximated the optimal solutions with practice. The results show that adults are sensitive to the stochastic structure of a task and naturally adjust the magnitude of an undershoot bias to the particular movement error of a task.