Event Abstract

Decision-making in a sampling-based neural representation

  • 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

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

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