Stimulus-independent fluctuations in the responses of sensory neurons are traditionally considered as mere noise, and thus a source of perceptual ambiguity. In contrast, sampling-based models of perceptual inference suggest that the magnitude of this intrinsic variability acts as a signal: it conveys information about the uncertainty in low-level perceptual estimates. In both cases, to improve accuracy, downstream areas need to average sensory responses over time, as in classical models of evidence accumulation. However, due to the different roles that upstream sensory variability plays under the “noise” and “signal” hypotheses, the uncertainty about this average behaves in fundamentally different ways in them: it is respectively related to the standard error or the standard deviation of responses. In order to compare these hypotheses, we used a modified orientation estimation paradigm in which, on every trial, subjects simultaneously reported their best estimate of one of several briefly viewed, static line segments and their confidence about this estimate. We varied the difficulty of trials by changing the number of line segments, their contrast level, and the presentation time of the display. In general, we found that subjects’ confidence predicted their accuracy even when controlling for these experimentally manipulated stimulus parameters. This indicated that subjects had a well-calibrated trial-by-trial subjective measure of their uncertainty and did not only rely on extrinsic stimulus parameters to gauge the difficulty of a trial. Critically, while both models could account for changes in estimation performance with stimulus parameters, only the “signal” model predicted correctly the experimentally observed changes in confidence reports, and in the strength of correlation between confidence reports and actual accuracy. These results offer a new psychophysical window onto the role of sensory variability in perception and indicate that it conveys useful information about uncertainty.

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