A New Perceptual Bias Reveals Suboptimal Population Decoding of Sensory Responses

Tom Putzeys,Robbe Goris,Johan Wagemans,Felix Wichmann,Matthias Bethge,Konrad P Kording

doi:10.1371/journal.pcbi.1002453

Abstract

Several studies have reported optimal population decoding of sensory responses in two-alternative visual discrimination tasks. Such decoding involves integrating noisy neural responses into a more reliable representation of the likelihood that the stimuli under consideration evoked the observed responses. Importantly, an ideal observer must be able to evaluate likelihood with high precision and only consider the likelihood of the two relevant stimuli involved in the discrimination task. We report a new perceptual bias suggesting that observers read out the likelihood representation with remarkably low precision when discriminating grating spatial frequencies. Using spectrally filtered noise, we induced an asymmetry in the likelihood function of spatial frequency. This manipulation mainly affects the likelihood of spatial frequencies that are irrelevant to the task at hand. Nevertheless, we find a significant shift in perceived grating frequency, indicating that observers evaluate likelihoods of a broad range of irrelevant frequencies and discard prior knowledge of stimulus alternatives when performing two-alternative discrimination.

Highlights

Perceptual decisions in a wide range of visual tasks rely on information encoded in neural responses in primary visual cortex (V1)
Results are not unambiguous: some studies suggest that the visual system uses a flexible precision pooling scheme in which the contribution of sensory responses to perceptual decisions depends on their reliability and relevance to the task at hand [2,3,4,5,6,7]
An attractive view on human information processing proposes that inference problems are dealt with in a statistically optimal fashion

Summary

Introduction

Perceptual decisions in a wide range of visual tasks rely on information encoded in neural responses in primary visual cortex (V1) This information may not be readily available to higher levels of the visual system because it is distributed across entire populations of neurons. Results are not unambiguous: some studies suggest that the visual system uses a flexible precision pooling scheme in which the contribution of sensory responses to perceptual decisions depends on their reliability and relevance to the task at hand [2,3,4,5,6,7]. Other studies [8,9,10,11] do not report such optimal decoding but rather suggest a crude, unselective pooling scheme in which the decision pool includes sensitive as well as many insensitive neurons. It is not clear why results differ across studies, which illustrates that neural decision making is, as yet, not fully understood

Methods

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Discussion

Conclusion