IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0058594.html
   My bibliography  Save this article

Good Exemplars of Natural Scene Categories Elicit Clearer Patterns than Bad Exemplars but Not Greater BOLD Activity

Author

Listed:
  • Ana Torralbo
  • Dirk B Walther
  • Barry Chai
  • Eamon Caddigan
  • Li Fei-Fei
  • Diane M Beck

Abstract

Within the range of images that we might categorize as a “beach”, for example, some will be more representative of that category than others. Here we first confirmed that humans could categorize “good” exemplars better than “bad” exemplars of six scene categories and then explored whether brain regions previously implicated in natural scene categorization showed a similar sensitivity to how well an image exemplifies a category. In a behavioral experiment participants were more accurate and faster at categorizing good than bad exemplars of natural scenes. In an fMRI experiment participants passively viewed blocks of good or bad exemplars from the same six categories. A multi-voxel pattern classifier trained to discriminate among category blocks showed higher decoding accuracy for good than bad exemplars in the PPA, RSC and V1. This difference in decoding accuracy cannot be explained by differences in overall BOLD signal, as average BOLD activity was either equivalent or higher for bad than good scenes in these areas. These results provide further evidence that V1, RSC and the PPA not only contain information relevant for natural scene categorization, but their activity patterns mirror the fundamentally graded nature of human categories. Analysis of the image statistics of our good and bad exemplars shows that variability in low-level features and image structure is higher among bad than good exemplars. A simulation of our neuroimaging experiment suggests that such a difference in variance could account for the observed differences in decoding accuracy. These results are consistent with both low-level models of scene categorization and models that build categories around a prototype.

Suggested Citation

  • Ana Torralbo & Dirk B Walther & Barry Chai & Eamon Caddigan & Li Fei-Fei & Diane M Beck, 2013. "Good Exemplars of Natural Scene Categories Elicit Clearer Patterns than Bad Exemplars but Not Greater BOLD Activity," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-12, March.
  • Handle: RePEc:plo:pone00:0058594
    DOI: 10.1371/journal.pone.0058594
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0058594
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0058594&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0058594?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. 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. 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. 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.
    3. 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.
    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. Heleen A Slagter & Tom Johnstone & Iseult A M Beets & Richard J Davidson, 2010. "Neural Competition for Conscious Representation across Time: An fMRI Study," PLOS ONE, Public Library of Science, vol. 5(5), pages 1-10, May.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Julien Barra & Laetitia Laou & Jean-Baptiste Poline & Denis Lebihan & Alain Berthoz, 2012. "Does an Oblique/Slanted Perspective during Virtual Navigation Engage Both Egocentric and Allocentric Brain Strategies?," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-15, November.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    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:plo:pone00:0058594. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.