The normalization model predicts responses during object-based attention in the human visual cortex

  1. Narges Doostani
  2. Gholam-Ali Hossein-Zadeh
  3. Maryam Vaziri-Pashkam  Is a corresponding author
  1. Institute for Research in Fundamental Sciences, Islamic Republic of Iran
  2. University of Tehran, Islamic Republic of Iran
  3. National Institute of Mental Health, United States

Abstract

Divisive normalization of the neural responses by the activity of the neighboring neurons has been proposed as a fundamental operation in the nervous system based on its success in predicting neural responses recorded in primate electrophysiology studies. Nevertheless, experimental evidence for the existence of this operation in the human brain is still scant. Here, using functional MRI, we explored the role of normalization across the visual hierarchy in the human visual cortex. Using stimuli form the two categories of human bodies and houses, we presented objects in isolation or in clutter and asked participants to attend or ignore the stimuli. Focusing on the primary visual area V1, the object-selective regions LO and pFs, the body-selective region EBA, and the scene-selective region PPA, we first modeled single-voxel responses using a weighted sum, a weighted average, and a normalization model and demonstrated that although the weighted sum and weighted average models also made acceptable predictions in some conditions, the response to multiple stimuli could generally be better described by a model that takes normalization into account. We then explored the observed effects of attention on cortical responses and demonstrated that these effects were predicted by the normalization model, but not by the weighted sum or the weighted average models. Our results thus provide evidence that the normalization model can predict responses to objects across shifts of visual attention, suggesting the role of normalization as a fundamental operation in the human brain.

Data availability

fMRI data have been deposited in OSF under DOI 10.17605/OSF.IO/8CH9Q.

The following data sets were generated

Article and author information

Author details

  1. Narges Doostani

    School of Cognitive Sciences, Institute for Research in Fundamental Sciences, Tehran, Islamic Republic of Iran
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5775-6595
  2. Gholam-Ali Hossein-Zadeh

    School of Electrical and Computer Engineering, University of Tehran, Tehran, Islamic Republic of Iran
    Competing interests
    The authors declare that no competing interests exist.
  3. Maryam Vaziri-Pashkam

    Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, United States
    For correspondence
    maryam.vaziri-pashkam@nih.gov
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1830-2501

Funding

National Institutes of Health (ZIA-MH002035)

  • Maryam Vaziri-Pashkam

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 participants gave written consent prior to their participation in the experiment. Imaging was performed according to safety guidelines approved by the ethics committee of the Institute for Research in Fundamental Sciences with the reference number 98/60.1/2184.

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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  1. Narges Doostani
  2. Gholam-Ali Hossein-Zadeh
  3. Maryam Vaziri-Pashkam
(2023)
The normalization model predicts responses during object-based attention in the human visual cortex
eLife 12:e75726.
https://doi.org/10.7554/eLife.75726

Share this article

https://doi.org/10.7554/eLife.75726

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