Decision-making in a sampling-based neural representation

Abstract

Event Abstract Back to Event Decision-making in a sampling-based neural representation Ralf M. Haefner1*, Pietro Berkes1 and József Fiser1 1 University Brandeis, Department of Psychology, United States According to the sampling hypothesis, the activity of sensory cortex can be interpreted as drawing samples from the probability distribution over features that it implicitly represents. Perceptual inference is performed by assuming that the samples are drawn from an internal model that the brain has built of the external world (Fiser et al 2010). We explore the implications of this hypothesis in the context of a perceptual decision-making task and present three findings: (1) Because the simple generative model for typical experimental stimuli does not match the rich internal model of the brain, the psychophysical performance is below what is theoretically possible based on the sensory neurons' responses. This can explain why previous studies have found that surprisingly few sensory neurons are required to match the performance of the animal, and why traditional decoding models need to invoke ad-hoc “decision noise” (Shadlen et al 1996) when pooling the responses of all relevant sensory neurons. (2) We show that in the sampling framework typical 2AFC tasks induce higher correlations between neuron pairs supporting the same choice, than between those contributing to different choices - as has previously been observed empirically (Cohen & Newsome 2008). (3) We demonstrate that, given the limited number of samples in a trial and a reward structure that is strongly concentrated on particular parts of the sampling space, expected reward is maximized by sampling from a probability distribution other than the veridical posterior (for a related, but parametric, idea see Lacoste-Julien et al 2011). Based on these findings we propose that the brain actively adapts the posterior distribution to account for (1) and (3), and that this adaptation is closely related to the cognitive concept of attention. Using this interpretation of attention, we replicate existing neurophysiological findings and make new predictions. Keywords: computational neuroscience, Decision Theory, Visual Perception Conference: Neural Coding, Decision-Making & Integration in Time, Rauischholzhausen, Germany, 26 Apr - 29 Apr, 2012. Presentation Type: Poster Presentation Topic: Neural Coding, Decision-Making & Integration in Time Citation: Haefner RM, Berkes P and Fiser J (2012). Decision-making in a sampling-based neural representation. Front. Neurosci. Conference Abstract: Neural Coding, Decision-Making & Integration in Time. doi: 10.3389/conf.fnins.2012.86.00004 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 12 Jan 2012; Published Online: 16 Jan 2012. * Correspondence: Dr. Ralf M Haefner, University Brandeis, Department of Psychology, Boston, United States, ralf@brandeis.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Ralf M Haefner Pietro Berkes József Fiser Google Ralf M Haefner Pietro Berkes József Fiser Google Scholar Ralf M Haefner Pietro Berkes József Fiser PubMed Ralf M Haefner Pietro Berkes József Fiser Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.