Robust Averaging Protects Decisions from Noise in Neural Computations

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 brains resilience to noise arising in neural computations during decision-making.\n\nAuthor SummaryHumans often make decisions by averaging information from multiple sources. When all the sources are equally reliable, they should all have equivalent impact (or weight) on the decisions of an \"ideal\" observer, i.e. one with perfect memory. However, recent experiments have suggested that humans give unequal weight to sources that are deviant or unusual, a phenomenon called \"robust averaging\". Here, we use computer simulations to try to understand why humans do this. 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 actually leads to improved performance. Using behavioural testing, we replicate the finding of robust averaging in a cohort of healthy humans, and show that those participants that engage in robust averaging perform better on the task. This study thus provides new information about the limitations on human decision-making.