Reward and Response Accuracy Trade-offs in Visuomotor Decisions Under Uncertainty

Anticipation in dynamic tasks often involves a tradeoff between the accuracy and timing of responses. For example, the earlier a tennis player begins to move left or right after a serve, the more time they will have to plan a strong countermove to the opponent’s return - but the earlier they commit, the more they risk moving into the wrong direction, potentially missing the returning ball entirely. To optimize such a tradeoff, an actor needs an insight into how (i) their own response accuracy and (ii) the expected payoffs from correct vs. incorrect choices evolve over time. In this work we experimentally measured and modeled human skill in optimizing choice behavior in a time-to-contact (TTC) estimation task presented in peripheral vision. 25 participants were asked to respond which of the targets launched horizontally from the left and right far visual periphery will arrive first to the center of the screen (i.e. had lower TTC or higher “immediacy”). To establish an explicit timing-accuracy tradeoff, the responses were rewarded in proportion to time margin left at response time, but with an incorrect response always asymmetrically incurring double the penalty of a correct response. Eccentricity-dependent visual resolution and peripheral crowding lead to complex interaction between targets position, velocity and viewing time. We propose and validate a psychophysical model for target location, velocity and TTC estimation. The model is used to derive optimal response timing distributions for different choice timing mechanisms. The model quantitatively explains TTC estimation accuracy based on contributions of position and velocity accuracies due to stimulus eccentricity and viewing time. Model-based analyses show that the participants did not employ a timing mechanism that would fully optimize their points score, given their sensory accuracy and response latency. However, insight into sensory accuracy is shown, consistent with simpler but suboptimal decision timing mechanisms.