ArticleScience advances2025
Universal dimensions of visual representation.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Pseudotime uncovers restructuring dynamics and mitigators of metal catalyst deactivation.Science advances · 2026Article
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Multidimensional Feature Tuning in Category Selective Areas of Human Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Developmental tuning of functional manifold dimensionality across the human brain.bioRxiv : the preprint server for biology · 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
- Article
- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- Universal scale-free representations in human visual cortex.PLoS computational biology · 2025Article
- Convolutional architectures are cortex-aligned de novo.bioRxiv : the preprint server for biology · 2025Article
- Universality of representation in biological and artificial neural networks.bioRxiv : the preprint server for biology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Do visual neural networks learn brain-aligned representations because they share architectural constraints and task objectives with biological vision or because they share universal features of natural image processing? We characterized the universality of hundreds of thousands of representational dimensions from networks with different architectures, tasks, and training data. We found that diverse networks learn to represent natural images using a shared set of latent dimensions, despite having highly distinct designs. Next, by comparing these networks with human brain representations measured with functional magnetic resonance imaging, we found that the most brain-aligned representations in neural networks are those that are universal and independent of a network's specific characteristics. Each network can be reduced to fewer than 10 of its most universal dimensions with little impact on its representational similarity to the brain. These results suggest that the underlying similarities between artificial and biological vision are primarily governed by a core set of universal representations that are convergently learned by diverse systems.
Indexed as
Identifiers
What OpenQuestion holds
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.