ArticleProceedings. Biological sciences2023
Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms.
Article in Proceedings. Biological sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- How AI can advance psychological science.British journal of psychology (London, England : 1953) · 2026Article
- Experience Alone Can Generate Human Face Specialization: Evidence From Deep Learning Models.Open mind : discoveries in cognitive science · 2026Article
- Text-related functionality and dynamics of visual human pre-frontal activations revealed through neural network convergence.Communications biology · 2025Article
- Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms.Journal of vision · 2025Article
- Configural processing as an optimized strategy for robust object recognition in neural networks.Communications biology · 2025Article
- The face inversion effect through the lens of deep neural networks.Proceedings. Biological sciences · 2024Article
- Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Deep learning models challenge the prevailing assumption that face-like effects for objects of expertise support domain-general mechanisms.Proceedings. Biological sciences · 2023Article
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Authors and funding
3 authors at 1 institution in 1 country.
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Abstract
The question of whether task performance is best achieved by domain-specific, or domain-general processing mechanisms is fundemental for both artificial and biological systems. This question has generated a fierce debate in the study of expert object recognition. Because humans are experts in face recognition, face-like neural and cognitive effects for objects of expertise were considered support for domain-general mechanisms. However, effects of domain, experience and level of categorization, are confounded in human studies, which may lead to erroneous inferences. To overcome these limitations, we trained deep learning algorithms on different domains (objects, faces, birds) and levels of categorization (basic, sub-ordinate, individual), matched for amount of experience. Like humans, the models generated a larger inversion effect for faces than for objects. Importantly, a face-like inversion effect was found for individual-based categorization of non-faces (birds) but only in a network specialized for that domain. Thus, contrary to prevalent assumptions, face-like effects for objects of expertise do not support domain-general mechanisms but may originate from domain-specific mechanisms. More generally, we show how deep learning algorithms can be used to dissociate factors that are inherently confounded in the natural environment of biological organisms to test hypotheses about their isolated contributions to cognition and behaviour.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.