Abstract
An ideal observer will give equivalent weight to sources of information that are equally reliable. However, when averaging visual information, human observers tend to downweight or discount features that are relatively outlying or deviant (‘robust averaging’). Why humans adopt an integration policy that discards important decision information remains unknown. Here, observers were asked to judge the average tilt in a circular array of high-contrast gratings, relative to an orientation boundary defined by a central reference grating. Observers showed robust averaging of orientation, but the extent to which they did so was a positive predictor of their overall performance. Using computational simulations, we show that although robust averaging is suboptimal for a perfect integrator, it paradoxically enhances performance in the presence of “late” noise, i.e. which corrupts decisions during integration. In other words, robust decision strategies increase the brain’s resilience to noise arising in neural computations during decision-making.
Highlights
Decisions about the visual world often require observers to integrate information from multiple sources
Recent experiments have suggested that humans give unequal weight to sources that are deviant or unusual, a phenomenon called “robust averaging”
Our simulations show that under the assumption that information processing is limited by a source of internal uncertainty that we call “late” noise, robust averaging leads to improved performance
Summary
Decisions about the visual world often require observers to integrate information from multiple sources. Previous studies have investigated how humans average perceptual information by presenting participants with array composed of multiple visual elements and asking them to report the mean size, colour or shape of the items displayed [2,3,4,5,6]. Control analyses ruled out the possibility that the observed effect was an artefact of hardwired nonlinearities in feature space. Together, these studies suggest that humans are “robust averagers”, overweighting inliers relative to outliers rather than giving equal weight to all elements ( see [11] for a failure to replicate this finding using a 2-alternative forced choice averaging task)
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