Efficient sensory encoding predicts robust averaging

Not every item in a stimulus ensemble equally contributes to the perceived ensemble average. Rather, items with feature values close to the ensemble mean (inlying items) contribute stronger compared to those items whose feature values are further away from the mean (outlying items). This nonuniform weighting process, named robust averaging, has been interpreted as evidence against an optimal integration of sensory information. Here, however, we show that robust averaging naturally emerges from an optimal integration process when sensory encoding is efficiently adapted to the ensemble statistics in the experiment. We demonstrate that such a model can accurately fit several existing datasets showing robust perceptual averaging in discriminating low-level stimulus features such as orientation. Across various feature domains, our model accurately predicts subjects' decision accuracy and nonuniform weighting profile, and both their dependency on the specific stimulus distribution in the experiments. Our results suggest that the human visual system forms efficient sensory representations on short time-scales to improve overall decision performance.