IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v392y1998i6676d10.1038_33402.html
   My bibliography  Save this article

A cortical representation of the local visual environment

Author

Listed:
  • Russell Epstein

    (Massachusetts Institute of Technology)

  • Nancy Kanwisher

    (Massachusetts Institute of Technology)

Abstract

Medial temporal brain regions such as the hippocampal formation and parahippocampal cortex have been generally implicated in navigation1,2,3,4,5,6 and visual memory7,8,9. However, the specific function of each of these regions is not yet clear. Here we present evidence that a particular area within human parahippocampal cortex is involved in a critical component of navigation: perceiving the local visual environment. This region, which we name the ‘parahippocampal place area’ (PPA), responds selectively and automatically in functional magnetic resonance imaging (fMRI) to passively viewed scenes, but only weakly to single objects and not at all to faces. The critical factor for this activation appears to be the presence in the stimulus of information about the layout of local space. The response in the PPA to scenes with spatial layout but no discrete objects (empty rooms) is as strong as the response to complex meaningful scenes containing multiple objects (the same rooms furnished) and over twice as strong as the response to arrays of multiple objects without three-dimensional spatial context (the furniture from these rooms on a blank background). This response is reduced if the surfaces in the scene are rearranged so that they no longer define a coherent space. We propose that the PPA represents places by encoding the geometry of the local environment.

Suggested Citation

  • Russell Epstein & Nancy Kanwisher, 1998. "A cortical representation of the local visual environment," Nature, Nature, vol. 392(6676), pages 598-601, April.
  • Handle: RePEc:nat:nature:v:392:y:1998:i:6676:d:10.1038_33402
    DOI: 10.1038/33402
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/33402
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1038/33402?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. 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.
    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. Ying Wang & Xue Zhang & Chunhui Wang & Weifen Huang & Qian Xu & Dong Liu & Wen Zhou & Shanguang Chen & Yi Jiang, 2022. "Modulation of biological motion perception in humans by gravity," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. 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.
    6. Joel Z Leibo & Qianli Liao & Fabio Anselmi & Tomaso Poggio, 2015. "The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-29, October.
    7. 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.
    8. István Czigler & Helene Intraub & Gábor Stefanics, 2013. "Prediction Beyond the Borders: ERP Indices of Boundary Extension-Related Error," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-1, September.
    9. Jeongho Park & Edward Soucy & Jennifer Segawa & Ross Mair & Talia Konkle, 2024. "Immersive scene representation in human visual cortex with ultra-wide-angle neuroimaging," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    10. Mengna Yao & Bincheng Wen & Mingpo Yang & Jiebin Guo & Haozhou Jiang & Chao Feng & Yilei Cao & Huiguang He & Le Chang, 2023. "High-dimensional topographic organization of visual features in the primate temporal lobe," Nature Communications, Nature, vol. 14(1), pages 1-23, December.
    11. 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.
    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. Benjamin Lahner & Kshitij Dwivedi & Polina Iamshchinina & Monika Graumann & Alex Lascelles & Gemma Roig & Alessandro Thomas Gifford & Bowen Pan & SouYoung Jin & N. Apurva Ratan Murty & Kendrick Kay & , 2024. "Modeling short visual events through the BOLD moments video fMRI dataset and metadata," Nature Communications, Nature, vol. 15(1), pages 1-26, December.
    15. 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.
    16. 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.
    17. Stephen Ramanoël & Louise Kauffmann & Emilie Cousin & Michel Dojat & Carole Peyrin, 2015. "Age-Related Differences in Spatial Frequency Processing during Scene Categorization," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-24, August.
    18. Krisztina Nagy & Mark W Greenlee & Gyula Kovács, 2011. "Sensory Competition in the Face Processing Areas of the Human Brain," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-12, September.
    19. 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.
    20. Kai J Miller & Gerwin Schalk & Dora Hermes & Jeffrey G Ojemann & Rajesh P N Rao, 2016. "Spontaneous Decoding of the Timing and Content of Human Object Perception from Cortical Surface Recordings Reveals Complementary Information in the Event-Related Potential and Broadband Spectral Chang," PLOS Computational Biology, Public Library of Science, vol. 12(1), pages 1-20, January.
    21. 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.
    22. Ping‐Shou Zhong & Jun Li & Piotr Kokoszka, 2021. "Multivariate analysis of variance and change points estimation for high‐dimensional longitudinal data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 375-405, June.

    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:nature:v:392:y:1998:i:6676:d:10.1038_33402. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.