IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/2006812.html
   My bibliography  Save this article

Attention promotes the neural encoding of prediction errors

Author

Listed:
  • Cooper A Smout
  • Matthew F Tang
  • Marta I Garrido
  • Jason B Mattingley

Abstract

The encoding of sensory information in the human brain is thought to be optimised by two principal processes: ‘prediction’ uses stored information to guide the interpretation of forthcoming sensory events, and ‘attention’ prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Because prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models propose that attention increases the activity of prediction (or ‘representation’) neurons and would therefore suggest that attention and prediction synergistically modulate selectivity for ‘feature information’ in the brain. Here, we applied forward encoding models to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, as well as support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.Author summary: The human brain is theorised to operate like a sophisticated hypothesis tester, using past experience to generate a model of the external world, testing predictions of this model against incoming sensory evidence, and generating a ‘prediction error’ signal that updates the model when predictions and sensory evidence do not match. In addition to predicting the content of sensory signals, an optimal system should also predict the reliability (or ‘precision’) of those signals to minimise the influence of unreliable sensory information. It has been proposed that attention optimises this process by boosting prediction error signals, which are coded as the difference (or ‘mismatch’) between predicted and observed stimulus features. Accordingly, this theory predicts that attention should increase the selectivity for mismatch information in the neural response to surprising stimuli. We tested this hypothesis in human participants by training a decoding algorithm to identify ‘mismatch information’ in the brain, recorded by electroencephalography (EEG), following the presentation of surprising stimuli that were either attended or ignored. We found that attention did indeed increase the selectivity for mismatch information in the neural response, supporting the notion that attention and prediction are intricately related processes.

Suggested Citation

  • Cooper A Smout & Matthew F Tang & Marta I Garrido & Jason B Mattingley, 2019. "Attention promotes the neural encoding of prediction errors," PLOS Biology, Public Library of Science, vol. 17(2), pages 1-22, February.
  • Handle: RePEc:plo:pbio00:2006812
    DOI: 10.1371/journal.pbio.2006812
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2006812
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.2006812&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.2006812?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Falk Lieder & Klaas E Stephan & Jean Daunizeau & Marta I Garrido & Karl J Friston, 2013. "A Neurocomputational Model of the Mismatch Negativity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-14, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Matthew F. Tang & Ehsan Kheradpezhouh & Conrad C. Y. Lee & J. Edwin Dickinson & Jason B. Mattingley & Ehsan Arabzadeh, 2023. "Expectation violations enhance neuronal encoding of sensory information in mouse primary visual cortex," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boris Vladimirskiy & Robert Urbanczik & Walter Senn, 2015. "Hierarchical Novelty-Familiarity Representation in the Visual System by Modular Predictive Coding," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-19, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pbio00:2006812. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.