IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-47749-9.html
   My bibliography  Save this article

Prediction error processing and sharpening of expected information across the face-processing hierarchy

Author

Listed:
  • Annika Garlichs

    (University Medical Center Hamburg-Eppendorf)

  • Helen Blank

    (University Medical Center Hamburg-Eppendorf)

Abstract

The perception and neural processing of sensory information are strongly influenced by prior expectations. The integration of prior and sensory information can manifest through distinct underlying mechanisms: focusing on unexpected input, denoted as prediction error (PE) processing, or amplifying anticipated information via sharpened representation. In this study, we employed computational modeling using deep neural networks combined with representational similarity analyses of fMRI data to investigate these two processes during face perception. Participants were cued to see face images, some generated by morphing two faces, leading to ambiguity in face identity. We show that expected faces were identified faster and perception of ambiguous faces was shifted towards priors. Multivariate analyses uncovered evidence for PE processing across and beyond the face-processing hierarchy from the occipital face area (OFA), via the fusiform face area, to the anterior temporal lobe, and suggest sharpened representations in the OFA. Our findings support the proposition that the brain represents faces grounded in prior expectations.

Suggested Citation

  • Annika Garlichs & Helen Blank, 2024. "Prediction error processing and sharpening of expected information across the face-processing hierarchy," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-47749-9
    DOI: 10.1038/s41467-024-47749-9
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-47749-9
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-024-47749-9?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. Wiktor Olszowy & John Aston & Catarina Rua & Guy B. Williams, 2019. "Accurate autocorrelation modeling substantially improves fMRI reliability," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    2. Fraser Aitken & Peter Kok, 2022. "Hippocampal representations switch from errors to predictions during acquisition of predictive associations," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Daniel Yon & Sam J. Gilbert & Floris P. Lange & Clare Press, 2018. "Action sharpens sensory representations of expected outcomes," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    4. Matthew A. J. Apps & Manos Tsakiris, 2013. "Predictive codes of familiarity and context during the perceptual learning of facial identities," Nature Communications, Nature, vol. 4(1), pages 1-10, December.
    5. N. Apurva Ratan Murty & Pouya Bashivan & Alex Abate & James J. DiCarlo & Nancy Kanwisher, 2021. "Computational models of category-selective brain regions enable high-throughput tests of selectivity," Nature Communications, Nature, vol. 12(1), pages 1-14, 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. Karl Friston, 2008. "Hierarchical Models in the Brain," PLOS Computational Biology, Public Library of Science, vol. 4(11), pages 1-24, November.
    8. Katharina Dobs & Leyla Isik & Dimitrios Pantazis & Nancy Kanwisher, 2019. "How face perception unfolds over time," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    9. Auréliane Pajani & Sid Kouider & Paul Roux & Vincent de Gardelle, 2017. "Unsuppressible Repetition Suppression and exemplar-specific Expectation Suppression in the Fusiform Face Area," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01659627, HAL.
    10. Christian Walther & Stefan R Schweinberger & Gyula Kovács, 2013. "Adaptor Identity Modulates Adaptation Effects in Familiar Face Identification and Their Neural Correlates," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-12, August.
    11. Shany Grossman & Guy Gaziv & Erin M. Yeagle & Michal Harel & Pierre Mégevand & David M. Groppe & Simon Khuvis & Jose L. Herrero & Michal Irani & Ashesh D. Mehta & Rafael Malach, 2019. "Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks," Nature Communications, Nature, vol. 10(1), pages 1-13, December.
    12. Wiktor Olszowy & John Aston & Catarina Rua & Guy B. Williams, 2019. "Publisher Correction: Accurate autocorrelation modeling substantially improves fMRI reliability," Nature Communications, Nature, vol. 10(1), pages 1-1, 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. Irina Higgins & Le Chang & Victoria Langston & Demis Hassabis & Christopher Summerfield & Doris Tsao & Matthew Botvinick, 2021. "Unsupervised deep learning identifies semantic disentanglement in single inferotemporal face patch neurons," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    2. Bingkai Wang & Xi Luo & Yi Zhao & Brian Caffo, 2021. "Semiparametric partial common principal component analysis for covariance matrices," Biometrics, The International Biometric Society, vol. 77(4), pages 1175-1186, December.
    3. Haider Al-Tahan & Yalda Mohsenzadeh, 2021. "Reconstructing feedback representations in the ventral visual pathway with a generative adversarial autoencoder," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-19, March.
    4. Park, Jun Young & Polzehl, Joerg & Chatterjee, Snigdhansu & Brechmann, André & Fiecas, Mark, 2020. "Semiparametric modeling of time-varying activation and connectivity in task-based fMRI data," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    5. Zachary F. Fisher & Jonathan Parsons & Kathleen M. Gates & Joseph B. Hopfinger, 2023. "Blind Subgrouping of Task-based fMRI," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 434-455, June.
    6. Valentina Krenz & Arjen Alink & Tobias Sommer & Benno Roozendaal & Lars Schwabe, 2023. "Time-dependent memory transformation in hippocampus and neocortex is semantic in nature," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    7. Micha Heilbron & Florent Meyniel, 2019. "Confidence resets reveal hierarchical adaptive learning in humans," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-24, April.
    8. Satoko Amemori & Ann M. Graybiel & Ken-ichi Amemori, 2024. "Cingulate microstimulation induces negative decision-making via reduced top-down influence on primate fronto-cingulo-striatal network," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    9. Julia Berezutskaya & Zachary V Freudenburg & Umut Güçlü & Marcel A J van Gerven & Nick F Ramsey, 2020. "Brain-optimized extraction of complex sound features that drive continuous auditory perception," PLOS Computational Biology, Public Library of Science, vol. 16(7), pages 1-34, July.
    10. John C. Boik, 2020. "Science-Driven Societal Transformation, Part I: Worldview," Sustainability, MDPI, vol. 12(17), pages 1-28, August.
    11. Manoj Kumar & Cameron T Ellis & Qihong Lu & Hejia Zhang & Mihai Capotă & Theodore L Willke & Peter J Ramadge & Nicholas B Turk-Browne & Kenneth A Norman, 2020. "BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis," PLOS Computational Biology, Public Library of Science, vol. 16(1), pages 1-12, January.
    12. 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.
    13. Rodrigo Quian Quiroga & Marta Boscaglia & Jacques Jonas & Hernan G. Rey & Xiaoqian Yan & Louis Maillard & Sophie Colnat-Coulbois & Laurent Koessler & Bruno Rossion, 2023. "Single neuron responses underlying face recognition in the human midfusiform face-selective cortex," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    14. Mateus Joffily & Giorgio Coricelli, 2013. "Emotional Valence and the Free-Energy Principle," Post-Print halshs-00834063, HAL.
    15. Katherine L. Hermann & Shridhar R. Singh & Isabelle A. Rosenthal & Dimitrios Pantazis & Bevil R. Conway, 2022. "Temporal dynamics of the neural representation of hue and luminance polarity," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    16. 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.
    17. Katherine R. Storrs & Barton L. Anderson & Roland W. Fleming, 2021. "Unsupervised learning predicts human perception and misperception of gloss," Nature Human Behaviour, Nature, vol. 5(10), pages 1402-1417, October.
    18. Agustin Lage-Castellanos & Giancarlo Valente & Elia Formisano & Federico De Martino, 2019. "Methods for computing the maximum performance of computational models of fMRI responses," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-25, March.
    19. Sreejan Kumar & Theodore R. Sumers & Takateru Yamakoshi & Ariel Goldstein & Uri Hasson & Kenneth A. Norman & Thomas L. Griffiths & Robert D. Hawkins & Samuel A. Nastase, 2024. "Shared functional specialization in transformer-based language models and the human brain," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    20. 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.

    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_s41467-024-47749-9. 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.