ArticlePLoS computational biology2016
The Computational Development of Reinforcement Learning during Adolescence.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 1 of them a synthesis that pooled it.
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Who cites it
68 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Decomposing the effects of context valence and feedback information on speed and accuracy during reinforcement learning: a meta-analytical approach using diffusion decision modeling.Cognitive, affective & behavioral neuroscience · 2019Pooled it
- Levodopa does not affect expression of reinforcement learning in older adults.Scientific reports · 2019Trial
- Mesocorticolimbic connectivity and motivational sensitivity: sex-specific effects of puberty in early adolescence.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
- Foraging models explain human exploration in uncertain tasks.Nature communications · 2026Article
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- 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
- Synaptic pruning, myelination and the emergence of psychiatric disorders in late adolescence.bioRxiv : the preprint server for biology · 2026Article
- AI-Discovered Cognitive Models Reveal Novel Insights into Human and Animal Learning.bioRxiv : the preprint server for biology · 2026Article
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- Functional connectivity between the nucleus accumbens and amygdala underlies avoidance learning during adolescence: Implications for developmental psychopathology.Development and psychopathology · 2025Article
- Nucleus accumbens dopamine release reflects Bayesian inference during instrumental learning.PLoS computational biology · 2025Article
- Characterizing age-related change in learning the value of cognitive effort.Journal of experimental psychology. General · 2025Article
- Reversal learning is influenced by cognitive flexibility and develops throughout early adolescence.NPJ science of learning · 2025Article
- Interpretation of individual differences in computational neuroscience using a latent input approach.Developmental cognitive neuroscience · 2025Review
- The preference for surprise in reinforcement learning underlies the differences in developmental changes in risk preference between autistic and neurotypical youth.Molecular autism · 2025Article
- Electrical brain activations in preadolescents during a probabilistic reward-learning task reflect cognitive processes and behavior strategies.Frontiers in human neuroscience · 2025Article
- Decrease in decision noise from adolescence into adulthood mediates an increase in more sophisticated choice behaviors and performance gain.PLoS biology · 2024Article
- Compulsive avoidance in youths and adults with OCD: an aversive pavlovian-to-instrumental transfer study.Translational psychiatry · 2024Article
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8 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
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Abstract
Adolescence is a period of life characterised by changes in learning and decision-making. Learning and decision-making do not rely on a unitary system, but instead require the coordination of different cognitive processes that can be mathematically formalised as dissociable computational modules. Here, we aimed to trace the developmental time-course of the computational modules responsible for learning from reward or punishment, and learning from counterfactual feedback. Adolescents and adults carried out a novel reinforcement learning paradigm in which participants learned the association between cues and probabilistic outcomes, where the outcomes differed in valence (reward versus punishment) and feedback was either partial or complete (either the outcome of the chosen option only, or the outcomes of both the chosen and unchosen option, were displayed). Computational strategies changed during development: whereas adolescents' behaviour was better explained by a basic reinforcement learning algorithm, adults' behaviour integrated increasingly complex computational features, namely a counterfactual learning module (enabling enhanced performance in the presence of complete feedback) and a value contextualisation module (enabling symmetrical reward and punishment learning). Unlike adults, adolescent performance did not benefit from counterfactual (complete) feedback. In addition, while adults learned symmetrically from both reward and punishment, adolescents learned from reward but were less likely to learn from punishment. This tendency to rely on rewards and not to consider alternative consequences of actions might contribute to our understanding of decision-making in adolescence.
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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.