ArticleNature communications2022
A self-supervised domain-general learning framework for human ventral stream representation.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 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
62 citing papers in PubMed.
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Category selectivity observed in the human brain is distinct from category selectivity observed in artificial neural networks.bioRxiv : the preprint server for biology · 2026Article
- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- Article
- Rethinking category-selectivity in human visual cortex.Cognitive neuroscience · 2026Review
- Combined evidence from artificial neural networks and human brain-lesion models reveals that language modulates vision in human perception.Nature human behaviour · 2026Article
- Infants have rich visual categories in ventrotemporal cortex at 2 months of age.Nature neuroscience · 2026Article
- Achieving more human brain-like vision via human EEG representational alignment.Communications biology · 2026Article
- A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision.Nature communications · 2026Article
- Computational constraints underlying shape and texture functional domain organization in macaque V4.Cerebral cortex (New York, N.Y. : 1991) · 2026Article
- Article
- Drawings of THINGS: A large-scale drawing dataset of 1854 object concepts.Behavior research methods · 2026Article
- Application of hierarchical self-supervised contrastive learning in domain adaptation matching of multimodal remote sensing image.Scientific reports · 2026Article
- Teaching CORnet human fMRI representations for enhanced model-brain alignment.Cognitive neurodynamics · 2025Article
- Article
- Article
- End-to-end topographic networks as models of cortical map formation and human visual behaviour.Nature human behaviour · 2025Article
- A feedforward mechanism for human-like contour integration.PLoS computational biology · 2025Article
- Convolutional architectures are cortex-aligned de novo.bioRxiv : the preprint server for biology · 2025Article
2 more citing papers are in PubMed but not listed here.
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
Anterior regions of the ventral visual stream encode substantial information about object categories. Are top-down category-level forces critical for arriving at this representation, or can this representation be formed purely through domain-general learning of natural image structure? Here we present a fully self-supervised model which learns to represent individual images, rather than categories, such that views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find that category information implicitly emerges in the local similarity structure of this feature space. Further, these models learn hierarchical features which capture the structure of brain responses across the human ventral visual stream, on par with category-supervised models. These results provide computational support for a domain-general framework guiding the formation of visual representation, where the proximate goal is not explicitly about category information, but is instead to learn unique, compressed descriptions of the visual world.
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.