CNNs reveal the computational implausibility of the expertise hypothesis
Name
1-s2.0-S2589004223000536-main.pdf
Description
Published version
Size
1.2 MB
Format
Adobe PDF
Checksum (MD5)
b5f8e71893899f040f7e82a0edea4fdc
Author(s)
Kanwisher, Nancy
Gupta, Pranjul
Dobs, Katharina
Date Issued
February 2023
Journal
iScience
Publisher
Elsevier BV
Citation
Kanwisher, Nancy, Gupta, Pranjul and Dobs, Katharina. 2023. "CNNs reveal the computational implausibility of the expertise hypothesis." iScience, 26 (2).
Version
Final published version
Abstract
Face perception has long served as a classic example of domain specificity of mind and brain. But an alternative "expertise" hypothesis holds that putatively face-specific mechanisms are actually domain-general, and can be recruited for the perception of other objects of expertise (e.g., cars for car experts). Here, we demonstrate the computational implausibility of this hypothesis: Neural network models optimized for generic object categorization provide a better foundation for expert fine-grained discrimination than do models optimized for face recognition.
MIT Department
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.isci.2023.105976
