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Area of Science:

  • Auditory perception
  • Computational neuroscience
  • Human motor control

Background:

  • Sensory systems exhibit intrinsic noise, leading to perceptual inaccuracies.
  • The Bayesian framework offers an optimal strategy for dealing with sensory uncertainty by integrating noisy input with prior information.
  • Maximum-a-posteriori (MAP) estimation is a Bayesian approach for optimal accuracy-precision trade-off under Gaussian distributions.

Purpose of the Study:

  • To test the Bayesian MAP model against human eye and head movement responses to auditory stimuli.
  • To investigate how signal-to-noise ratio (SNR) affects accuracy and precision in auditory-driven movements.
  • To explore alternative decision strategies beyond MAP estimation in auditory perception.

Main Methods:

  • Human participants performed eye movements toward broadband sounds under varying noise levels.
  • Head movements were recorded for sounds with poor spectral content.
  • Response gain (accuracy) and variability (precision) were analyzed in relation to SNR and stimulus properties.

Main Results:

  • Elevation response gain decreased with decreasing SNR, deviating from MAP predictions.
  • Azimuth response components maintained high gains, consistent with maximum-likelihood estimation.
  • Elevation data were better explained by a posterior sampling strategy rather than MAP estimation.

Conclusions:

  • Human auditory-driven movements do not consistently follow the optimal MAP Bayesian strategy.
  • A posterior sampling strategy better describes elevation responses, suggesting a trade-off between accuracy and environmental exploration.
  • While sub-optimal in variability, posterior sampling may facilitate active exploration in uncertain auditory environments.