ArticleNature human behaviour2022
Asymmetric reinforcement learning facilitates human inference of transitive relations.
Article in Nature human behaviour, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 44 citations in OpenAlex.
- Assessing the validity and reliability of computational phenotyping of mood.PLoS computational biology · 2026Article
- Foraging models explain human exploration in uncertain tasks.Nature communications · 2026Article
- Human brains construct individualized global rankings from identical few-shot learning input.PLoS biology · 2026Article
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- Shared enemies as prosocial tool: how to infer positive relationships from negative relationships in social networks.BMC psychology · 2026Article
- Asymmetric learning and adaptability to changes in relational structure during transitive inference.Communications psychology · 2025Article
- Transient frontopolar cortex stimulation induces prolonged disruption to counterfactual processing.PLoS biology · 2025Article
- Single-Neuron Encoding of Learnability in the Dorsal Anterior Cingulate Cortex.bioRxiv : the preprint server for biology · 2025Article
- Effects of posttransfer feedback informativeness in a transitive inference task.Memory & cognition · 2025Article
- Article
- Transitive inference as probabilistic preference learning.Psychonomic bulletin & review · 2025Review
- Humans forage for reward in reinforcement learning tasks.bioRxiv : the preprint server for biology · 2025Article
- Distorted learning from local metacognition supports transdiagnostic underconfidence.Nature communications · 2025Article
- Prefrontal cortex contribution in transitive inference task through the interplay of beta and gamma oscillations.Communications biology · 2024Article
- Altered trial-to-trial responses to reward outcomes in KCNMA1 knockout mice during probabilistic learning tasks.Behavioral and brain functions : BBF · 2024Article
- A mathematical theory of relational generalization in transitive inference.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Reinforcement learning of altruistic punishment differs between cultures and across the lifespan.PLoS computational biology · 2024Article
- A mathematical theory of relational generalization in transitive inference.bioRxiv : the preprint server for biology · 2024Article
- Emergent neural dynamics and geometry for generalization in a transitive inference task.PLoS computational biology · 2024Article
- Developmental changes in exploration resemble stochastic optimization.Nature human behaviour · 2023Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans and other animals are capable of inferring never-experienced relations (for example, A > C) from other relational observations (for example, A > B and B > C). The processes behind such transitive inference are subject to intense research. Here we demonstrate a new aspect of relational learning, building on previous evidence that transitive inference can be accomplished through simple reinforcement learning mechanisms. We show in simulations that inference of novel relations benefits from an asymmetric learning policy, where observers update only their belief about the winner (or loser) in a pair. Across four experiments (n = 145), we find substantial empirical support for such asymmetries in inferential learning. The learning policy favoured by our simulations and experiments gives rise to a compression of values that is routinely observed in psychophysics and behavioural economics. In other words, a seemingly biased learning strategy that yields well-known cognitive distortions can be beneficial for transitive inferential judgements.
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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.