ArticleJournal of vision2021
A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities.
Article in Journal of vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Behavioral differences between humans and machines arise early in visual processing.Journal of vision · 2026Article
- Digital dementia and testing of cognitive intervention for degenerating neural networks.NPJ systems biology and applications · 2025Article
- Potential role of developmental experience in the emergence of the parvo-magno distinction.Communications biology · 2025Article
- Biological age prediction using a DNN model based on pathways of steroidogenesis.Science advances · 2025Article
- Towards realistic simulation of disease progression in the visual cortex with CNNs.Scientific reports · 2025Article
- Computational complexity as a potential limitation on brain-behaviour mapping.The European journal of neuroscience · 2025Review
- Brain-machine convergent evolution: Why finding parallels between brain and artificial systems is informative.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Deep social neuroscience: the promise and peril of using artificial neural networks to study the social brain.Social cognitive and affective neuroscience · 2024Review
- Sparsity in an artificial neural network predicts beauty: Towards a model of processing-based aesthetics.PLoS computational biology · 2023Article
- The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks.Journal of vision · 2023Article
- The neuroconnectionist research programme.Nature reviews. Neuroscience · 2023Review
- Simulation of neuroplasticity in a CNN-basedFrontiers in computational neuroscience · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep neural networks (DNNs) have revolutionized computer science and are now widely used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as neuroscientific models of the human visual system; the debate centers on to what extent certain shortcomings of DNNs are real failures and to what extent they are redeemable. Here, we argue that the main problem is that we often do not understand which human functions need to be modeled and, thus, what counts as a falsification. Hence, not only is there a problem on the DNN side, but there is also one on the brain side (i.e., with the explanandum-the thing to be explained). For example, should DNNs reproduce illusions? We posit that we can make better use of DNNs by adopting an approach of comparative biology by focusing on the differences, rather than the similarities, between DNNs and humans to improve our understanding of visual information processing in general.
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Registered trials
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.