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 to the estimate compared to those items whose feature values are further away from the mean (outlying items). This non-uniform weighting process, named robust averaging, has been taken as evidence that human observers are not optimally integrating sensory information. Here, we argue that robust averaging naturally emerges from an optimal integration process where the sensory en- coding is efficiently adapted to the overall ensemble statistics in the experiment. We demonstrate that such a model can accurately fit several existing datasets showing robust averaging behavior of low-level stimulus features such as orientation. Across various feature domains, our model accurately predicts subjects’ estimation accuracy and non-uniform weighting profile, and both their dependency on the specific stimulus distribution in the experiments. Our results suggest that the human visual system can form efficient sensory representations on short time-scales.