Using the past to estimate sensory uncertainty.

Ulrik Beierholm,Ambra Ferrari,Uta Noppeney,Tim Rohe,Oliver Stegle

doi:10.7554/elife.54172

Abstract

To form a more reliable percept of the environment, the brain needs to estimate its own sensory uncertainty. Current theories of perceptual inference assume that the brain computes sensory uncertainty instantaneously and independently for each stimulus. We evaluated this assumption in four psychophysical experiments, in which human observers localized auditory signals that were presented synchronously with spatially disparate visual signals. Critically, the visual noise changed dynamically over time continuously or with intermittent jumps. Our results show that observers integrate audiovisual inputs weighted by sensory uncertainty estimates that combine information from past and current signals consistent with an optimal Bayesian learner that can be approximated by exponential discounting. Our results challenge leading models of perceptual inference where sensory uncertainty estimates depend only on the current stimulus. They demonstrate that the brain capitalizes on the temporal dynamics of the external world and estimates sensory uncertainty by combining past experiences with new incoming sensory signals.

Highlights

Perception has been described as a process of statistical inference based on noisy sensory inputs (Knill and Pouget, 2004; Knill and Richards, 1996)
In a spatial localization task, we presented participants with audiovisual signals in a series of four experiments, in which the physical visual noise changed dynamically over time either continuously or discontinuously (Figure 1)
The results from our four experiments challenge classical models of perceptual inference where a perceptual interpretation is obtained using a likelihood that depends solely on the current sensory inputs (Ernst and Banks, 2002). These models implicitly assume that sensory uncertainty is instantaneously and independently accessed from the sensory signals on each trial based on initial calibration of the nervous system (Jacobs and Fine, 1999)

Summary

Introduction

Perception has been described as a process of statistical inference based on noisy sensory inputs (Knill and Pouget, 2004; Knill and Richards, 1996). Key to this perceptual inference is the estimation and/or representation of sensory uncertainty (as measured by variance, i.e. the inverse of reliability/precision). It influences whether observers infer that signals come from a common cause and should be integrated or else be processed independently (Aller and Noppeney, 2019; Kording et al, 2007; Rohe et al, 2019; Rohe and Noppeney, 2015b; Rohe and Noppeney, 2015a; Rohe and Noppeney, 2016; Wozny et al, 2010; Acerbi et al, 2018). Accumulating evidence suggests that human observers are close to optimal in many perceptual tasks (though see Acerbi et al, 2014; Drugowitsch et al, 2016; Shen and Ma, 2016; Meijer et al, 2019) and weight signals approximately according to their

Methods

Results

Conclusion