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CNNs reveal the computational implausibility of the expertise hypothesis
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 ca...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9923184/ https://www.ncbi.nlm.nih.gov/pubmed/36794151 http://dx.doi.org/10.1016/j.isci.2023.105976 |
Sumario: | 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. |
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