ArticleeLife2023
Statistical inference on representational geometries.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Dog brain representations of human facial expressions: Encoding happiness and differentiating specific negative expressions.iScience · 2026Article
- Compressive learning scaffolds higher-order network structure to enhance human knowledge acquisition.Nature communications · 2026Article
- Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training.Nature communications · 2026Article
- Auditory perceptual maps in humans and mice share common structures and predict perceptual decisions in discrimination learning.Communications psychology · 2026Article
- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- A population vector model of visual working memory for real-world scenes.Journal of experimental psychology. General · 2026Article
- Distinct perceptual and conceptual representations of natural actions along the lateral and dorsal visual streams.Communications biology · 2026Article
- The retrieval of previously learned motor memories is facilitated by the reinstatement of default mode network manifold structures.PLoS biology · 2026Article
- Entorhinal Cortex Signals Dimensions of Past Experience That Can Be Generalized in a Novel Environment.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Neural and computational mechanisms underlying one-shot perceptual learning in humans.Nature communications · 2026Article
- A Population Vector Model of Visual Working Memory for Real-World Scenes.bioRxiv : the preprint server for biology · 2026Article
- Article
- Transfer of motor learning is associated with patterns of activity in the default mode network.PLoS biology · 2025Article
- Relational Integration Demands Are Tracked by Temporally Delayed Neural Representations in Alpha and Beta Rhythms Within Higher-Order Cortical Networks.Human brain mapping · 2025Article
- Unraveling other-race face perception with GAN-based image reconstruction.Behavior research methods · 2025Article
- Perceptual and semantic maps in individual humans share structural features that predict creative abilities.Communications psychology · 2025Article
- The Scope and Limits of Fine-Grained Image and Category Information in the Ventral Visual Pathway.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Universality of representation in biological and artificial neural networks.bioRxiv : the preprint server for biology · 2024Article
- The topology and geometry of neural representations.Proceedings of the National Academy of Sciences of the United States of America · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neuroscience has recently made much progress, expanding the complexity of both neural activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by resampling of calcium-imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox (rsatoolbox.readthedocs.io).
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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.