Abstract

Perceptual decision-making relies on the gradual accumulation of noisy sensory evidence. It is often assumed that such decisions are degraded by adding noise to a stimulus, or to the neural systems involved in the decision making process itself. But it has been suggested that adding an optimal amount of noise can, under appropriate conditions, enhance the quality of subthreshold signals in nonlinear systems, a phenomenon known as stochastic resonance. Here we asked whether perceptual decisions made by human observers obey these stochastic resonance principles, by adding noise directly to the visual cortex using transcranial random noise stimulation (tRNS) while participants judged the direction of coherent motion in random-dot kinematograms presented at the fovea. We found that adding tRNS bilaterally to visual cortex enhanced decision-making when stimuli were just below perceptual threshold, but not when they were well below or above threshold. We modelled the data under a drift diffusion framework, and showed that bilateral tRNS selectively increased the drift rate parameter, which indexes the rate of evidence accumulation. Our study is the first to provide causal evidence that perceptual decision-making is susceptible to a stochastic resonance effect induced by tRNS, and to show that this effect arises from selective enhancement of the rate of evidence accumulation for sub-threshold sensory events.

Highlights

  • Noise is an intrinsic property of all biological systems [1]

  • We found that adding an optimal level of neural noise to the visual cortex bilaterally enhanced decision-making, for below-threshold stimuli, consistent with a stochastic resonance effect

  • In addition to measuring the influence of central noise on perceptual decisions, we investigated which aspects of the decision process itself are sensitive to stochastic resonance using drift diffusion modelling (DDM; see Fig 2 [29, 30])

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Summary

Introduction

Noise is an intrinsic property of all biological systems [1]. Typically, noise is viewed as being detrimental for neuronal computations and the behaviors they regulate [1, 2], including decision-making [3]. A key limiting factor in decision-making arises from noisy representations of sensory evidence in the brain [4, 5]. On this view, noisy sensory information representations are not optimal, and this leads to errors in decisions. Small amounts of noise added to a nonlinear system can increase stimulus quality by increasing the signal-to-noise ratio (SNR)[6]. This phenomenon is known as stochastic resonance (Fig 1), and its expression has been demonstrated in different sensory modalities [7,8,9]. Stochastic resonance occurs when an optimal amount of noise is added to a sub-threshold signal, which makes the signal cross a threshold and enhances detection performance (Fig 1) [10,11,12,13]

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