ArticleNature neuroscience2025
Neural mechanisms of relational learning and fast knowledge reassembly in plastic neural networks.
Article in Nature neuroscience, 2025. 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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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.
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Who cites it
12 citing papers in PubMed.
- A novel rat spatial inference task reveals rapid deduction of transitive relationships using schemas and deliberation.Behavioral neuroscience · 2026Article
- Hierarchical learning creates invariant schema within plastic neural networks.Journal of computational neuroscience · 2026Article
- The inherent capacity of neurons to learn order relations and support abstract reasoning.Nature communications · 2026Article
- The transfer effects of executive function training on prospective memory in children with learning difficulties: a longitudinal study.BMC psychology · 2026Article
- Unraveling the geometry of visual relational reasoning.Scientific reports · 2026Article
- Human brains construct individualized global rankings from identical few-shot learning input.PLoS biology · 2026Article
- Cost-effective, open-source, automated apparatus for testing transitive inference in mice.Scientific reports · 2026Article
- A Novel Rat Spatial Inference Task Reveals Rapid Deduction of Transitive Relationships Using Schemas and Deliberation.bioRxiv : the preprint server for biology · 2025Article
- Two pathways to resolve relational inconsistencies.Scientific reports · 2025Article
- Article
- An invariant schema emerges within a neural network during hierarchical learning of visual boundaries.bioRxiv : the preprint server for biology · 2025Article
- A mathematical theory of relational generalization in transitive inference.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Humans and animals have a striking ability to learn relationships between items in experience (such as stimuli, objects and events), enabling structured generalization and rapid assimilation of new information. A fundamental type of such relational learning is order learning, which enables transitive inference (if A > B and B > C, then A > C) and list linking (A > B > C and D > E > F rapidly 'reassembled' into A > B > C > D > E > F upon learning C > D). Despite longstanding study, a neurobiologically plausible mechanism for transitive inference and rapid reassembly of order knowledge has remained elusive. Here we report that neural networks endowed with neuromodulated synaptic plasticity (allowing for self-directed learning) and identified through artificial metalearning (learning-to-learn) are able to perform both transitive inference and list linking and, further, express behavioral patterns widely observed in humans and animals. Crucially, only networks that adopt an 'active' solution, in which items from past trials are reinstated in neural activity in recoded form, are capable of list linking. These results identify fully neural mechanisms for relational learning, and highlight a method for discovering such mechanisms.
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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.