ArticleNature communications2021
Computational models of category-selective brain regions enable high-throughput tests of selectivity.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
- Higher visual areas act like domain-general filters with strong selectivity and functional specialization.Nature communications · 2026Article
- Category selectivity observed in the human brain is distinct from category selectivity observed in artificial neural networks.bioRxiv : the preprint server for biology · 2026Article
- From localization to distribution: revisiting the functional organization of the extrastriate body area.Cerebral cortex (New York, N.Y. : 1991) · 2026Review
- Rethinking category-selectivity in human visual cortex.Cognitive neuroscience · 2026Review
- Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks.Nature communications · 2026Article
- Deep Sound Synthesis Matched to Brain Activity Recapitulates Preferential Responses to Speech and Music.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- The Emergence of Topography and Hemispheric Lateralization in High-Level Vision.Quarterly journal of experimental psychology (2006) · 2026Review
- Individual differences in artificial neural networks capture individual differences in human behavior.bioRxiv : the preprint server for biology · 2026Article
- Article
- Rethinking the centrality of brain areas in understanding functional organization.Nature neuroscience · 2026Review
- A 2D Gabor-wavelet baseline model out-performs a 3D surface model in scene-responsive cortex.PLoS computational biology · 2026Article
- Investigating action topography in visual cortex and deep artificial neural networks.Nature communications · 2025Article
- Human-like individual differences emerge from random weight initializations in neural networks.bioRxiv : the preprint server for biology · 2025Article
- Article
- In silico discovery of representational relationships across visual cortex.Nature human behaviour · 2025Article
- Cross-sectional and longitudinal changes in category selectivity in visual cortex following pediatric cortical resection.Communications biology · 2025Article
- Deep neural networks explain spiking activity in auditory cortex.PLoS computational biology · 2025Article
- A neural computational framework for face processing in the human temporal lobe.Current biology : CB · 2025Article
- TopoNets: High performing vision and language models with brain-like topography.... International Conference on Learning Representations · 2025Article
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5 authors.
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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.
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