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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
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    Cited by:

    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. 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.
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.

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