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Sensory representations are typically endowed with intrinsic noise, leading to variability and inaccuracies in perceptual responses. The Bayesian framework accounts for an optimal strategy to deal with sensory-motor uncertainty, by combining the noisy sensory input with prior information regarding the distribution of stimulus properties. The maximum-a-posteriori (MAP) estimate selects the perceptual response from the peak (mode) of the resulting posterior distribution that ensure optimal accuracy-precision trade-off when the underlying distributions are Gaussians (minimal mean-squared error, with minimum response variability). We tested this model on human eye- movement responses toward broadband sounds, masked by various levels of background noise, and for head movements to sounds with poor spectral content. We report that the response gain (accuracy) and variability (precision) of the elevation response components changed systematically with the signal-to-noise ratio of the target sound: gains were high for high SNRs and decreased for low SNRs. In contrast, the azimuth response components maintained high gains for all conditions, as predicted by maximum-likelihood estimation. However, we found that the elevation data did not follow the MAP prediction. Instead, results were better described by an alternative decision strategy, in which the response results from taking a random sample from the posterior in each trial. We discuss two potential implementations of a simple posterior sampling scheme in the auditory system that account for the results and argue that although the observed response strategies for azimuth and elevation are sub-optimal with respect to their variability, it allows the auditory system to actively explore the environment in the absence of adequate sensory evidence.

Accuracy-Precision Trade-off in Human Sound Localisation