Gain control of saccadic eye movements is probabilistic

Significance Decades of research have shown that, when measured in laboratory conditions, saccadic eye movements are not only variable, due to noise in sensory-motor pathways, but also inaccurate, displaying systematic biases toward smaller movement amplitudes (hypometria) or toward the mean location of the targets (central tendency). Here, we show that such biases are explained by a probabilistic strategy to find the optimal balance of bias and variance that minimizes the expected costs of saccadic errors. Our findings indicate that the oculomotor system possesses a probabilistic representation of its own sensorimotor uncertainty and uses that representation to adjust the parameters of each saccade. Saccades are rapid eye movements that orient the visual axis toward objects of interest to allow their processing by the central, high-acuity retina. Our ability to collect visual information efficiently relies on saccadic accuracy, which is limited by a combination of uncertainty in the location of the target and motor noise. It has been observed that saccades have a systematic tendency to fall short of their intended targets, and it has been suggested that this bias originates from a cost function that overly penalizes hypermetric errors. Here, we tested this hypothesis by systematically manipulating the positional uncertainty of saccadic targets. We found that increasing uncertainty produced not only a larger spread of the saccadic endpoints but also more hypometric errors and a systematic bias toward the average of target locations in a given block, revealing that prior knowledge was integrated into saccadic planning. Moreover, by examining how variability and bias covaried across conditions, we estimated the asymmetry of the cost function and found that it was related to individual differences in the additional time needed to program secondary saccades for correcting hypermetric errors, relative to hypometric ones. Taken together, these findings reveal that the saccadic system uses a probabilistic-Bayesian control strategy to compensate for uncertainty in a statistically principled way and to minimize the expected cost of saccadic errors.