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Computational models of category-selective brain regions enable high-throughput tests of selectivity

Author

Listed:
  • N. Apurva Ratan Murty

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Pouya Bashivan

    (McGill University)

  • Alex Abate

    (Massachusetts Institute of Technology)

  • James J. DiCarlo

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

  • Nancy Kanwisher

    (Massachusetts Institute of Technology
    Massachusetts Institute of Technology
    Massachusetts Institute of Technology)

Abstract

Cortical regions apparently selective to faces, places, and bodies have provided important evidence for domain-specific theories of human cognition, development, and evolution. But claims of category selectivity are not quantitatively precise and remain vulnerable to empirical refutation. Here we develop artificial neural network-based encoding models that accurately predict the response to novel images in the fusiform face area, parahippocampal place area, and extrastriate body area, outperforming descriptive models and experts. We use these models to subject claims of category selectivity to strong tests, by screening for and synthesizing images predicted to produce high responses. We find that these high-response-predicted images are all unambiguous members of the hypothesized preferred category for each region. These results provide accurate, image-computable encoding models of each category-selective region, strengthen evidence for domain specificity in the brain, and point the way for future research characterizing the functional organization of the brain with unprecedented computational precision.

Suggested Citation

  • 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.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25409-6
    DOI: 10.1038/s41467-021-25409-6
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    Cited by:

    1. 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.
    2. 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.
    3. Tianye Wang & Tai Sing Lee & Haoxuan Yao & Jiayi Hong & Yang Li & Hongfei Jiang & Ian Max Andolina & Shiming Tang, 2024. "Large-scale calcium imaging reveals a systematic V4 map for encoding natural scenes," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. 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.

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