ArticlePLoS computational biology2024
Learning environment-specific learning rates.
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 11 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
11 citing papers in PubMed.
- Learning and Using Different Task Rule Updating Strategies Require Extensive Practice.Cognitive science · 2026Article
- Article
- Belief updating in uncertain environments is differentially sensitive to reward and punishment learning: Evidence from ERP.iScience · 2026Article
- When models matter: Environmental demand guides the arbitration between model-based and model-free control.Cognitive, affective & behavioral neuroscience · 2026Article
- Uncertainty and reward histories have distinct effects on decisions after wins and losses.Scientific reports · 2026Article
- Cognitive flexibility versus stability via activation-based and weight-based adaptations.Communications psychology · 2026Article
- The Impact of Prior Beliefs about Volatility on Adaptive Behavior.Journal of cognition · 2026Article
- When knowledge flows backward: enhancing older employee creativity through cognitive flexibility.Frontiers in psychology · 2026Article
- Different exploration strategies along the autism spectrum: diverging effects of autism diagnosis and autism traits.Molecular autism · 2025Article
- Enhanced "learning to learn" through a hierarchical dual-learning system: the case of action video game players.BMC psychology · 2024Article
- Cognitive simulation along with neural adaptation explain effects of suggestions: a novel theoretical framework.Frontiers in psychology · 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
3 authors.
Funding
Abstract
People often have to switch back and forth between different environments that come with different problems and volatilities. While volatile environments require fast learning (i.e., high learning rates), stable environments call for lower learning rates. Previous studies have shown that people adapt their learning rates, but it remains unclear whether they can also learn about environment-specific learning rates, and instantaneously retrieve them when revisiting environments. Here, using optimality simulations and hierarchical Bayesian analyses across three experiments, we show that people can learn to use different learning rates when switching back and forth between two different environments. We even observe a signature of these environment-specific learning rates when the volatility of both environments is suddenly the same. We conclude that humans can flexibly adapt and learn to associate different learning rates to different environments, offering important insights for developing theories of meta-learning and context-specific control.
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.