Bayesian integration during sensorimotor estimation in elite athletes.

Abstract

An experiment was designed to determine the effects of sensory uncertainty on sensorimotor estimation in elite athletes compared to non-athletes. Nineteen elite athletes and 16 non-athletes were required to estimate when and where a cursor arrived at a target location. The cursor position was displayed through its entire trajectory in the certain condition while only briefly in the uncertain condition. Accuracy and variability in time and spatial domains were calculated. A Bayesian analysis using subsets of subjects' total spatial variance was also performed. The results indicated that athletes and non-athletes used estimation strategies consistent with Bayesian integration. The results also showed a decrease in variability for spatial performance for both groups during the uncertain condition compared to the certain condition, especially when the cursor location was further away from the prior mean. This decrease in variability was significantly greater for non-athletes. By concentrating performance around the end-point mean location, an increase in spatial error occurred. More spatial and timing errors were observed in non-athletes than athletes, indicating athletes were more certain about likelihood information or their interpretation of likelihood information than non-athletes. These results suggest that athletic experience may facilitate the use of probabilistic information for optimal sensorimotor estimations.