IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-51243-7.html
   My bibliography  Save this article

Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields

Author

Listed:
  • Eline R. Kupers

    (Stanford University)

  • Insub Kim

    (Stanford University)

  • Kalanit Grill-Spector

    (Stanford University
    Stanford University)

Abstract

When multiple visual stimuli are presented simultaneously in the receptive field, the neural response is suppressed compared to presenting the same stimuli sequentially. The prevailing hypothesis suggests that this suppression is due to competition among multiple stimuli for limited resources within receptive fields, governed by task demands. However, it is unknown how stimulus-driven computations may give rise to simultaneous suppression. Using fMRI, we find simultaneous suppression in single voxels, which varies with both stimulus size and timing, and progressively increases up the visual hierarchy. Using population receptive field (pRF) models, we find that compressive spatiotemporal summation rather than compressive spatial summation predicts simultaneous suppression, and that increased simultaneous suppression is linked to larger pRF sizes and stronger compressive nonlinearities. These results necessitate a rethinking of simultaneous suppression as the outcome of stimulus-driven compressive spatiotemporal computations within pRFs, and open new opportunities to study visual processing capacity across space and time.

Suggested Citation

  • Eline R. Kupers & Insub Kim & Kalanit Grill-Spector, 2024. "Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51243-7
    DOI: 10.1038/s41467-024-51243-7
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-51243-7
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-51243-7?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. Dawn Finzi & Jesse Gomez & Marisa Nordt & Alex A. Rezai & Sonia Poltoratski & Kalanit Grill-Spector, 2021. "Differential spatial computations in ventral and lateral face-selective regions are scaffolded by structural connections," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    2. Sonia Poltoratski & Kendrick Kay & Dawn Finzi & Kalanit Grill-Spector, 2021. "Holistic face recognition is an emergent phenomenon of spatial processing in face-selective regions," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. Steven J. Luck & Edward K. Vogel, 1997. "The capacity of visual working memory for features and conjunctions," Nature, Nature, vol. 390(6657), pages 279-281, November.
    4. Evi Hendrikx & Jacob M. Paul & Martijn Ackooij & Nathan Stoep & Ben M. Harvey, 2022. "Visual timing-tuned responses in human association cortices and response dynamics in early visual cortex," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    Full references (including those not matched with items on IDEAS)

    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. Marisa Nordt & Jesse Gomez & Vaidehi S. Natu & Alex A. Rezai & Dawn Finzi & Holly Kular & Kalanit Grill-Spector, 2023. "Longitudinal development of category representations in ventral temporal cortex predicts word and face recognition," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Igor S. Utochkin & Vladislav A. Khvostov & Yulia M. Stakina, 2017. "Ensemble-Based Segmentation in the Perception of Multiple Feature Conjunctions," HSE Working papers WP BRP 78/PSY/2017, National Research University Higher School of Economics.
    3. Jastrzębski, Jan & Ciechanowska, Iwona & Chuderski, Adam, 2018. "The strong link between fluid intelligence and working memory cannot be explained away by strategy use," Intelligence, Elsevier, vol. 66(C), pages 44-53.
    4. Aki Kondo & Jun Saiki, 2012. "Feature-Specific Encoding Flexibility in Visual Working Memory," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-8, December.
    5. Hongwei Tan & Sebastiaan van Dijken, 2023. "Dynamic machine vision with retinomorphic photomemristor-reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    6. Robert W. Faff & Sebastian Kernbach, 2021. "A visualisation approach for pitching research," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(4), pages 5177-5197, December.
    7. Yuri A. Markov & Natalia A. Tiurina & Igor S. Utochkin, 2018. "Different features are stored independently in visual working memory but mediated by object-based representations," HSE Working papers WP BRP 101/PSY/2018, National Research University Higher School of Economics.
    8. Tullo, Domenico & Faubert, Jocelyn & Bertone, Armando, 2018. "The characterization of attention resource capacity and its relationship with fluid reasoning intelligence: A multiple object tracking study," Intelligence, Elsevier, vol. 69(C), pages 158-168.
    9. Jifan Zhou & Jun Yin & Tong Chen & Xiaowei Ding & Zaifeng Gao & Mowei Shen, 2011. "Visual Working Memory Capacity Does Not Modulate the Feature-Based Information Filtering in Visual Working Memory," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-10, September.
    10. Nathaniel J. S. Ashby & Stephan Dickert & Andreas Glockner, 2012. "Focusing on what you own: Biased information uptake due to ownership," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 7(3), pages 254-267, May.
    11. Lior Fink & Daniele Papismedov, 2023. "On the Same Page? What Users Benefit from a Desktop View on Mobile Devices," Information Systems Research, INFORMS, vol. 34(2), pages 423-441, June.
    12. Li, Qian & Huang, Zhuowei (Joy) & Christianson, Kiel, 2016. "Visual attention toward tourism photographs with text: An eye-tracking study," Tourism Management, Elsevier, vol. 54(C), pages 243-258.
    13. Yuri A. Markov & Igor S. Utochkin, 2017. "The Effect of Object Distinctiveness on Object-Location Binding in Visual Working Memory," HSE Working papers WP BRP 79/PSY/2017, National Research University Higher School of Economics.
    14. Carlo Baldassi & Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2020. "A Behavioral Characterization of the Drift Diffusion Model and Its Multialternative Extension for Choice Under Time Pressure," Management Science, INFORMS, vol. 66(11), pages 5075-5093, November.
    15. S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    16. J David Timm & Frank Papenmeier, 2019. "Reorganization of spatial configurations in visual working memory: A matter of set size?," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-16, November.
    17. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci, 2020. "Multinomial logit processes and preference discovery: outside and inside the black box," Working Papers 663, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    18. Ziqi Gao & Chenran Jiang & Jiawen Zhang & Xiaosen Jiang & Lanqing Li & Peilin Zhao & Huanming Yang & Yong Huang & Jia Li, 2023. "Hierarchical graph learning for protein–protein interaction," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    19. Clark, Cameron M. & Lawlor-Savage, Linette & Goghari, Vina M., 2017. "Comparing brain activations associated with working memory and fluid intelligence," Intelligence, Elsevier, vol. 63(C), pages 66-77.
    20. Ociepka, Michał & Kałamała, Patrycja & Chuderski, Adam, 2022. "High individual alpha frequency brains run fast, but it does not make them smart," Intelligence, Elsevier, vol. 92(C).

    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:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-51243-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    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.