Computational models of category-selective brain regions enable high-throughput tests of selectivity
<jats:title>Abstract</jats:title><jats:p>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 precis...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/138200 |
| _version_ | 1811076632006361088 |
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| author | Ratan Murty, N Apurva Bashivan, Pouya Abate, Alex DiCarlo, James J Kanwisher, Nancy |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Ratan Murty, N Apurva Bashivan, Pouya Abate, Alex DiCarlo, James J Kanwisher, Nancy |
| author_sort | Ratan Murty, N Apurva |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>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.</jats:p> |
| first_indexed | 2024-09-23T10:25:11Z |
| format | Article |
| id | mit-1721.1/138200 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:25:11Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1382002023-04-11T20:23:58Z Computational models of category-selective brain regions enable high-throughput tests of selectivity Ratan Murty, N Apurva Bashivan, Pouya Abate, Alex DiCarlo, James J Kanwisher, Nancy Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences McGovern Institute for Brain Research at MIT Center for Brains, Minds, and Machines <jats:title>Abstract</jats:title><jats:p>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.</jats:p> 2021-11-22T19:36:16Z 2021-11-22T19:36:16Z 2021-12 2021-11-22T19:33:55Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/138200 Ratan Murty, N Apurva, Bashivan, Pouya, Abate, Alex, DiCarlo, James J and Kanwisher, Nancy. 2021. "Computational models of category-selective brain regions enable high-throughput tests of selectivity." Nature Communications, 12 (1). en 10.1038/s41467-021-25409-6 Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Ratan Murty, N Apurva Bashivan, Pouya Abate, Alex DiCarlo, James J Kanwisher, Nancy Computational models of category-selective brain regions enable high-throughput tests of selectivity |
| title | Computational models of category-selective brain regions enable high-throughput tests of selectivity |
| title_full | Computational models of category-selective brain regions enable high-throughput tests of selectivity |
| title_fullStr | Computational models of category-selective brain regions enable high-throughput tests of selectivity |
| title_full_unstemmed | Computational models of category-selective brain regions enable high-throughput tests of selectivity |
| title_short | Computational models of category-selective brain regions enable high-throughput tests of selectivity |
| title_sort | computational models of category selective brain regions enable high throughput tests of selectivity |
| url | https://hdl.handle.net/1721.1/138200 |
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