ArticleDevelopmental cognitive neuroscience2022
Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal.
Article in Developmental cognitive neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 0 citations in OpenAlex.
- Hippocampal-cingulate dynamics in the human brain link reinforcement-learning and memory.bioRxiv : the preprint server for biology · 2026Article
- Reward-specific learning parameters change across normative adolescent development and are blunted in youth with high risk for depression.Journal of child psychology and psychiatry, and allied disciplines · 2026Article
- Set-shifting and task-switching make differential contributions to divergent thinking in adolescence.BMC psychology · 2026Article
- Prefrontal cortex development and its implications in mental illness.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Investigating hierarchical critical periods in human neurodevelopment.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Review
- Developmental differences in social information use under uncertainty: A neurocomputational approach.Developmental cognitive neuroscience · 2025Article
- Internal uncertainty impacts social information use in risky choice across adolescence.Communications psychology · 2025Article
- Latent variable sequence identification for cognitive models with neural network estimators.Behavior research methods · 2025Article
- Social inequity disrupts reward-based learning.Communications psychology · 2025Article
- Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning.PLoS computational biology · 2025Article
- Stochastic decisions support optimal foraging of volatile environments, and are disrupted by anxiety.Cognitive, affective & behavioral neuroscience · 2025Article
- More than just a phase: adolescence as a window into how the brain generates behavior.Current directions in psychological science · 2025Article
- Reversal learning is influenced by cognitive flexibility and develops throughout early adolescence.NPJ science of learning · 2025Article
- The beautiful adolescent brain: An evolutionary developmental perspective.Annals of the New York Academy of Sciences · 2025Review
- Relevance of Probabilistic Reversal Learning for Adolescent Drinking Trajectories.Addiction biology · 2025Article
- Hidden state inference requires abstract contextual representations in the ventral hippocampus.Science (New York, N.Y.) · 2024Article
- Revisiting the role of computational neuroimaging in the era of integrative neuroscience.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2024Review
- Decrease in decision noise from adolescence into adulthood mediates an increase in more sophisticated choice behaviors and performance gain.PLoS biology · 2024Article
- Mapping computational cognitive profiles of aging to dissociable brain and sociodemographic factors.npj aging · 2024Article
- Adolescent-to-adult gains in cognitive flexibility are adaptively supported by reward sensitivity, exploration, and neural variability.Current opinion in behavioral sciences · 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
5 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During adolescence, youth venture out, explore the wider world, and are challenged to learn how to navigate novel and uncertain environments. We investigated how performance changes across adolescent development in a stochastic, volatile reversal-learning task that uniquely taxes the balance of persistence and flexibility. In a sample of 291 participants aged 8-30, we found that in the mid-teen years, adolescents outperformed both younger and older participants. We developed two independent cognitive models, based on Reinforcement learning (RL) and Bayesian inference (BI). The RL parameter for learning from negative outcomes and the BI parameters specifying participants' mental models were closest to optimal in mid-teen adolescents, suggesting a central role in adolescent cognitive processing. By contrast, persistence and noise parameters improved monotonically with age. We distilled the insights of RL and BI using principal component analysis and found that three shared components interacted to form the adolescent performance peak: adult-like behavioral quality, child-like time scales, and developmentally-unique processing of positive feedback. This research highlights adolescence as a neurodevelopmental window that can create performance advantages in volatile and uncertain environments. It also shows how detailed insights can be gleaned by using cognitive models in new ways.
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.