Humans are good at performing visual tasks, but experimental measurements have revealed substantial biases in the perception of basic visual attributes. An appealing hypothesis is that these biases arise through a process of statistical inference, in which information from noisy measurements is fused with a probabilistic model of the environment. However, such inference is optimal only if the observer's internal model matches the environment. We found this to be the case. We measured performance in an orientation-estimation task and found that orientation judgments were more accurate at cardinal (horizontal and vertical) orientations. Judgments made under conditions of uncertainty were strongly biased toward cardinal orientations. We estimated observers' internal models for orientation and found that they matched the local orientation distribution measured in photographs. In addition, we determined how a neural population could embed probabilistic information responsible for such biases.
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We wish to thank D. Ganguli for helpful discussions on image statistics and population coding, T. Saarela for the monitor calibration and N. Solomon for assisting with the experiments. This work was funded by US National Institutes of Health grants EY019451 (A.R.G.) and EY16165 (M.S.L.), and the Howard Hughes Medical Institute (E.P.S.).
The authors declare no competing financial interests.
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Girshick, A., Landy, M. & Simoncelli, E. Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat Neurosci 14, 926–932 (2011). https://doi.org/10.1038/nn.2831
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