ArticlePLoS computational biology2024
Emergent neural dynamics and geometry for generalization in a transitive inference task.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 11 citations in OpenAlex.
- Learning to infer transitively on a mental line in premotor cortex.Science advances · 2026Article
- A novel rat spatial inference task reveals rapid deduction of transitive relationships using schemas and deliberation.Behavioral neuroscience · 2026Article
- Emergent representations of graphical structure in mechanistic neural models of causal judgment.bioRxiv : the preprint server for biology · 2026Article
- Unraveling the geometry of visual relational reasoning.Scientific reports · 2026Article
- Dynamics of oscillatory power support the integration of perceptual and mnemonic information within the premotor cortex.Nature communications · 2026Article
- Human brains construct individualized global rankings from identical few-shot learning input.PLoS biology · 2026Article
- A Novel Rat Spatial Inference Task Reveals Rapid Deduction of Transitive Relationships Using Schemas and Deliberation.bioRxiv : the preprint server for biology · 2025Article
- Article
- Cognitive processes are disentangled at cortex-wide scales.bioRxiv : the preprint server for biology · 2025Article
- Neural mechanisms of relational learning and fast knowledge reassembly in plastic neural networks.Nature neuroscience · 2025Article
- Prefrontal cortex contribution in transitive inference task through the interplay of beta and gamma oscillations.Communications biology · 2024Article
- A mathematical theory of relational generalization in transitive inference.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Neural dynamics of robust legged robots.Frontiers in robotics and AI · 2024Article
- An active neural mechanism for relational learning and fast knowledge reassembly.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Relational cognition-the ability to infer relationships that generalize to novel combinations of objects-is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought to be essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) expressed different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.
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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.