ArticleNature communications2024
A large-scale examination of inductive biases shaping high-level visual representation in brains and machines.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers.
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Who cites it
66 citing papers in PubMed.
- Systematic image perturbations reveal persistent gaps between human and machine vision.iScience · 2026Article
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- A single computational objective can produce specialization of streams in visual cortex.Nature communications · 2026Article
- Triple-N dataset: large-scale fMRI-guided dense recordings of nonhuman primate neural responses to natural scenes.Nature neuroscience · 2026Article
- Integrating neuroscience across species and scales.Nature neuroscience · 2026Article
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Can neural networks model the human perception of geometric shapes?PLoS computational biology · 2026Article
- Ecological Visual Processing in the Mouse.Annual review of neuroscience · 2026Review
- Conceptual priorities shape individual gaze patterns during naturalistic visual attention.Proceedings of the National Academy of Sciences of the United States of America · 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
- Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.PLoS computational biology · 2026Article
- Epistemic frontiers and the distinction between causality, information, and predictability in pattern recognition.Scientific reports · 2026Article
- Article
- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- Exposure to naturalistic occlusion promotes generalized, human-like robustness in deep neural networks.bioRxiv : the preprint server for biology · 2026Article
- Spurious alignment between large language models and brains can emerge from non-robust methods and overlooked confounds.Nature communications · 2026Article
- Deep learning in fetal, infant, and toddler neuroimaging research.Developmental cognitive neuroscience · 2026Review
- Neuronal tuning aligns dynamically with object and texture manifolds across the visual hierarchy.Nature neuroscience · 2026Article
- Local lateral connectivity is sufficient for replicating cortex-like topographical organization in deep neural networks.Nature communications · 2026Article
6 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The rapid release of high-performing computer vision models offers new potential to study the impact of different inductive biases on the emergent brain alignment of learned representations. Here, we perform controlled comparisons among a curated set of 224 diverse models to test the impact of specific model properties on visual brain predictivity - a process requiring over 1.8 billion regressions and 50.3 thousand representational similarity analyses. We find that models with qualitatively different architectures (e.g. CNNs versus Transformers) and task objectives (e.g. purely visual contrastive learning versus vision- language alignment) achieve near equivalent brain predictivity, when other factors are held constant. Instead, variation across visual training diets yields the largest, most consistent effect on brain predictivity. Many models achieve similarly high brain predictivity, despite clear variation in their underlying representations - suggesting that standard methods used to link models to brains may be too flexible. Broadly, these findings challenge common assumptions about the factors underlying emergent brain alignment, and outline how we can leverage controlled model comparison to probe the common computational principles underlying biological and artificial visual systems.
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