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

N. Apurva Ratan Murty (), Pouya Bashivan, Alex Abate, James J. DiCarlo and Nancy Kanwisher
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N. Apurva Ratan Murty: Massachusetts Institute of Technology
Pouya Bashivan: McGill University
Alex Abate: Massachusetts Institute of Technology
James J. DiCarlo: Massachusetts Institute of Technology
Nancy Kanwisher: Massachusetts Institute of Technology

Nature Communications, 2021, vol. 12, issue 1, 1-14

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-25409-6

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