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Robust averaging protects decisions from noise in neural computations

Li, V., Herce Castañón, S., Solomon, J. A., Vandormael, H. and Summerfield, C. (2017). Robust averaging protects decisions from noise in neural computations. PLoS Computational Biology, 13(8), e1005723. doi: 10.1371/journal.pcbi.1005723

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.

Publication Type: Article
Additional Information: © 2017 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Subjects: R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
R Medicine > RE Ophthalmology
Departments: School of Health Sciences > Optometry & Visual Science
URI: http://openaccess.city.ac.uk/id/eprint/18057
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