A machine learning approach to identify stochastic resonance in human perceptual thresholds.

Journal: Journal of neuroscience methods
Published Date: Mar 12, 2022

Abstract

BACKGROUND: Stochastic resonance (SR) is achieved when a faint signal is improved with the addition of the appropriate amount of white noise. Perceptual thresholds are expected to follow a characteristic performance improvement curve as a function of the white noise level added (i.e., thresholds are reduced with an optimal amount of added white noise, beyond which excessive white noise is no longer beneficial). Since SR exhibition in perceptual thresholds is defined by a shape rather than a statistical difference, the presence of SR is typically identified qualitatively. The current state-of-the-art is for blinded human judges to categorize the presence of SR by visually examining data. While categorizations are made with subject data intermixed within a balanced, simulated dataset, which accounts for false positives, this method is still subjective and prone to human error.

Authors

  • Jamie Voros
    Bioastronautics Laboratory, Smead Aerospace Engineering Sciences, University of Colorado-Boulder, 3775 Discovery Dr, Boulder, CO 80303, USA.
  • Rachel Rise
    Bioastronautics Laboratory, Smead Aerospace Engineering Sciences, University of Colorado-Boulder, 3775 Discovery Dr, Boulder, CO 80303, USA.
  • Sage Sherman
    Bioastronautics Laboratory, Smead Aerospace Engineering Sciences, University of Colorado-Boulder, 3775 Discovery Dr, Boulder, CO 80303, USA.
  • Abigail Durell
    Bioastronautics Laboratory, Smead Aerospace Engineering Sciences, University of Colorado-Boulder, 3775 Discovery Dr, Boulder, CO 80303, USA.
  • Allison P Anderson
    Bioastronautics Laboratory, Smead Aerospace Engineering Sciences, University of Colorado-Boulder, 3775 Discovery Dr, Boulder, CO 80303, USA.
  • Torin K Clark
    Bioastronautics Laboratory, Smead Aerospace Engineering Sciences, University of Colorado-Boulder, 3775 Discovery Dr, Boulder, CO 80303, USA. Electronic address: torin.clark@colorado.edu.

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