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Using the past to estimate sensory uncertainty

  1. Ulrik Beierholm  Is a corresponding author
  2. Tim Rohe
  3. Ambra Ferrari
  4. Oliver Stegle
  5. Uta Noppeney
  1. Durham University, United Kingdom
  2. Friedrich Alexander University Erlangen-Nuernberg, Germany
  3. University of Birmingham, United Kingdom
  4. DKFZ, Germany
Research Article
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Cite this article as: eLife 2020;9:e54172 doi: 10.7554/eLife.54172


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

Article and author information

Author details

  1. Ulrik Beierholm

    Psychology Department, Durham University, Durham, United Kingdom
    For correspondence
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7296-7996
  2. Tim Rohe

    Institute of Psychology, Friedrich Alexander University Erlangen-Nuernberg, Erlangen, Germany
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9713-3712
  3. 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.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1946-3884
  4. Oliver Stegle

    DKFZ, Heidelberg, Germany
    Competing interests
    The authors declare that no competing interests exist.
  5. Uta Noppeney

    Computational Neuroscience and Cognitive Robotics Centre, University of Birmingham, Birmingham, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.


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.


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).

Reviewing Editor

  1. Tobias Reichenbach, Imperial College London, United Kingdom

Publication history

  1. Received: December 4, 2019
  2. Accepted: December 13, 2020
  3. Accepted Manuscript published: December 15, 2020 (version 1)
  4. Version of Record published: January 13, 2021 (version 2)


© 2020, Beierholm et al.

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.


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