Using the past to estimate sensory uncertainty

Abstract
Data availability
Article and author information
Metrics

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.

Data availability

The human behavioral raw data and computational model predictions as well as the code for computational modelling and analyses scripts are available in an OSF repository: https://osf.io/gt4jb/

The following data sets were generated

(2020) Using the past to estimate sensory uncertainty
OSF, doi:10.17605/OSF.IO/GT4JB.

https://osf.io/gt4jb/

Article and author information

Author details

Ulrik Beierholm

Psychology Department, Durham University, Durham, United Kingdom

For correspondence
ulrik.beierholm@durham.ac.uk

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-7296-7996
Tim Rohe

Institute of Psychology, Friedrich Alexander University Erlangen-Nuernberg, Erlangen, Germany

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0001-9713-3712
Ambra Ferrari

Centre for Computational Neuroscience and Cognitive Robotics, University of Birmingham, Birmingham, United Kingdom

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-1946-3884
Oliver Stegle

DKFZ, Heidelberg, Germany

Competing interests
The authors declare that no competing interests exist.
Uta Noppeney

Computational Neuroscience and Cognitive Robotics Centre, University of Birmingham, Birmingham, United Kingdom

Competing interests
The authors declare that no competing interests exist.

Funding

H2020 European Research Council (ERC-multsens,309349)

Uta Noppeney

Max Planck Society

Tim Rohe
Uta Noppeney

Deutsche Forschungsgemeinschaft (DFG RO 5587/1-1)

Tim Rohe

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Human subjects: All volunteers participated in the study after giving written informed consent. The study was approved by the human research review committee of the University of Tuebingen (approval number 432 2007 BO1) and the research review committee of the University of Birmingham (approval number ERN_15-1458AP1).

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.