Neural signatures of vigilance decrements predict behavioural errors before they occur

  1. Hamid Karimi-Rouzbahani  Is a corresponding author
  2. Alexandra Woolgar
  3. Anina N Rich
  1. Macquarie University, Australia
  2. University of Cambridge, United Kingdom

Abstract

There are many monitoring environments, such as railway control, in which lapses of attention can have tragic consequences. Problematically, sustained monitoring for rare targets is difficult, with more misses and longer reaction times over time. What changes in the brain underpin these 'vigilance decrements'? We designed a multiple-object monitoring (MOM) paradigm to examine how the neural representation of information varied with target frequency and time performing the task. Behavioural performance decreased over time for the rare target (monitoring) condition, but not for a frequent target (active) condition. This was mirrored in neural decoding using Magnetoencephalography: coding of critical information declined more during monitoring versus active conditions along the experiment. We developed new analyses that can predict behavioural errors from the neural data more than a second before they occurred. This facilitates pre-empting behavioural errors due to lapses in attention and provides new insight into the neural correlates of vigilance decrements.

Data availability

We have shared the Magnetoencephalography data (i.e. time series) as well as behavioral data in Matlab '.mat' format on the Open Science Framework website at https://osf.io/5aw8v/ with the DOI: 10.17605/OSF.IO/5AW8V. We have also uploaded a video of the "Multiple-Object-Monitoring" paradigm, developed for this study, for easier understanding of the task at the same address. The mentioned address is dedicated to this project and we will regularly update the contents to make them easier to follow for other researchers.

The following data sets were generated

Article and author information

Author details

  1. Hamid Karimi-Rouzbahani

    Perception in Action Research Centre, Department of Cognitive Science, Macquarie University, Sydney, Australia
    For correspondence
    hamid.karimi-rouzbahani@mq.edu.au
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2694-3595
  2. Alexandra Woolgar

    Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Anina N Rich

    Perception in Action Research Centre, Department of Cognitive Science, Macquarie University, Sydney, Australia
    Competing interests
    The authors declare that no competing interests exist.

Funding

Australian Research Council (DP170101780)

  • Anina N Rich

Australian Research Council (FT170100105)

  • Alexandra Woolgar

MRC Cognition and Brain Sciences Unit (SUAG/052/G101400)

  • Alexandra Woolgar

The Royal Society (NIF\R1\192608)

  • Hamid Karimi-Rouzbahani

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

Ethics

Human subjects: The Human Research Ethics Committee of Macquarie University approved the experimental protocols and the participants gave informed consent before participating in the experiment. The approval identifier is 52020297914411.

Reviewing Editor

  1. Peter Kok, University College London, United Kingdom

Publication history

  1. Received: July 3, 2020
  2. Accepted: April 2, 2021
  3. Accepted Manuscript published: April 8, 2021 (version 1)
  4. Version of Record published: April 21, 2021 (version 2)

Copyright

© 2021, Karimi-Rouzbahani 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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  1. Hamid Karimi-Rouzbahani
  2. Alexandra Woolgar
  3. Anina N Rich
(2021)
Neural signatures of vigilance decrements predict behavioural errors before they occur
eLife 10:e60563.
https://doi.org/10.7554/eLife.60563

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