IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-49073-8.html
   My bibliography  Save this article

Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses

Author

Listed:
  • Samy A. Abdel-Ghaffar

    (UC Berkeley
    Google LLC)

  • Alexander G. Huth

    (UT Austin)

  • Mark D. Lescroart

    (Department of Psychology University of Nevada Reno)

  • Dustin Stansbury

    (UC Berkeley)

  • Jack L. Gallant

    (UC Berkeley
    UC Berkeley
    UC Berkeley)

  • Sonia J. Bishop

    (UC Berkeley
    UC Berkeley
    Trinity College Dublin
    Trinity College Dublin)

Abstract

In everyday life, people need to respond appropriately to many types of emotional stimuli. Here, we investigate whether human occipital-temporal cortex (OTC) shows co-representation of the semantic category and affective content of visual stimuli. We also explore whether OTC transformation of semantic and affective features extracts information of value for guiding behavior. Participants viewed 1620 emotional natural images while functional magnetic resonance imaging data were acquired. Using voxel-wise modeling we show widespread tuning to semantic and affective image features across OTC. The top three principal components underlying OTC voxel-wise responses to image features encoded stimulus animacy, stimulus arousal and interactions of animacy with stimulus valence and arousal. At low to moderate dimensionality, OTC tuning patterns predicted behavioral responses linked to each image better than regressors directly based on image features. This is consistent with OTC representing stimulus semantic category and affective content in a manner suited to guiding behavior.

Suggested Citation

  • Samy A. Abdel-Ghaffar & Alexander G. Huth & Mark D. Lescroart & Dustin Stansbury & Jack L. Gallant & Sonia J. Bishop, 2024. "Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49073-8
    DOI: 10.1038/s41467-024-49073-8
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-49073-8
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-49073-8?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. Kendrick N. Kay & Thomas Naselaris & Ryan J. Prenger & Jack L. Gallant, 2008. "Identifying natural images from human brain activity," Nature, Nature, vol. 452(7185), pages 352-355, March.
    2. Russell Epstein & Nancy Kanwisher, 1998. "A cortical representation of the local visual environment," Nature, Nature, vol. 392(6676), pages 598-601, April.
    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. Johannes Haushofer & Margaret S Livingstone & Nancy Kanwisher, 2008. "Multivariate Patterns in Object-Selective Cortex Dissociate Perceptual and Physical Shape Similarity," PLOS Biology, Public Library of Science, vol. 6(7), pages 1-9, July.
    2. 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.
    3. Vasiliki Bougou & Michaël Vanhoyland & Alexander Bertrand & Wim Paesschen & Hans Op De Beeck & Peter Janssen & Tom Theys, 2024. "Neuronal tuning and population representations of shape and category in human visual cortex," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Isabella C. Wagner & Luise P. Graichen & Boryana Todorova & Andre Lüttig & David B. Omer & Matthias Stangl & Claus Lamm, 2023. "Entorhinal grid-like codes and time-locked network dynamics track others navigating through space," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    5. Zvi N. Roth & Kendrick Kay & Elisha P. Merriam, 2022. "Natural scene sampling reveals reliable coarse-scale orientation tuning in human V1," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Hamed Nili & Cai Wingfield & Alexander Walther & Li Su & William Marslen-Wilson & Nikolaus Kriegeskorte, 2014. "A Toolbox for Representational Similarity Analysis," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-11, April.
    7. Hamed Nili & Alexander Walther & Arjen Alink & Nikolaus Kriegeskorte, 2020. "Inferring exemplar discriminability in brain representations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.
    8. Jacob M. Paul & Martijn Ackooij & Tuomas C. Cate & Ben M. Harvey, 2022. "Numerosity tuning in human association cortices and local image contrast representations in early visual cortex," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    9. Kay H Brodersen & Thomas M Schofield & Alexander P Leff & Cheng Soon Ong & Ekaterina I Lomakina & Joachim M Buhmann & Klaas E Stephan, 2011. "Generative Embedding for Model-Based Classification of fMRI Data," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-19, June.
    10. Ming Bo Cai & Nicolas W Schuck & Jonathan W Pillow & Yael Niv, 2019. "Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-30, May.
    11. Ghislain St-Yves & Emily J. Allen & Yihan Wu & Kendrick Kay & Thomas Naselaris, 2023. "Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    12. Marcelo G Mattar & Michael W Cole & Sharon L Thompson-Schill & Danielle S Bassett, 2015. "A Functional Cartography of Cognitive Systems," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-26, December.
    13. Guohua Shen & Tomoyasu Horikawa & Kei Majima & Yukiyasu Kamitani, 2019. "Deep image reconstruction from human brain activity," PLOS Computational Biology, Public Library of Science, vol. 15(1), pages 1-23, January.
    14. Batrancea Larissa, 2021. "Research Insights From Cognitive Neuroscience For Everyday Economists," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 2, pages 35-41, April.
    15. Zhou, Lixing & Takane, Yoshio & Hwang, Heungsun, 2016. "Dynamic GSCANO (Generalized Structured Canonical Correlation Analysis) with applications to the analysis of effective connectivity in functional neuroimaging data," Computational Statistics & Data Analysis, Elsevier, vol. 101(C), pages 93-109.
    16. Raheel Zafar & Sarat C Dass & Aamir Saeed Malik, 2017. "Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-23, May.
    17. Tarana Nigam & Caspar M. Schwiedrzik, 2024. "Predictions enable top-down pattern separation in the macaque face-processing hierarchy," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    18. Michael F Bonner & Russell A Epstein, 2018. "Computational mechanisms underlying cortical responses to the affordance properties of visual scenes," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-31, April.
    19. Yujia Zhai & Binbin Fan & Jingyao Yu & Ruoyu Gong & Jie Yin, 2024. "Effects of Spatial Type and Scale of Small Urban Open Spaces on Perceived Restoration: An Online Survey-Based Experiment," Land, MDPI, vol. 13(9), pages 1-17, August.
    20. Jörn Diedrichsen & Nikolaus Kriegeskorte, 2017. "Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-33, April.

    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_s41467-024-49073-8. 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.