ReviewFrontiers in psychology2017
Developmental Changes in Learning: Computational Mechanisms and Social Influences.
Review in Frontiers in psychology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 54 citations in OpenAlex.
- Relevance of Probabilistic Reversal Learning for Adolescent Drinking Trajectories.Addiction biology · 2025Article
- Decrease in decision noise from adolescence into adulthood mediates an increase in more sophisticated choice behaviors and performance gain.PLoS biology · 2024Article
- Article
- Goal-directed learning in adolescence: neurocognitive development and contextual influences.Nature reviews. Neuroscience · 2024Review
- Self-reported childhood family adversity is linked to an attenuated gain of trust during adolescence.Nature communications · 2023Article
- Confirmatory reinforcement learning changes with age during adolescence.Developmental science · 2023Article
- Cortical Grey Matter Mediates Increases in Model-Based Control and Learning from Positive Feedback from Adolescence to Adulthood.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Article
- A methodological perspective on learning in the developing brain.NPJ science of learning · 2022Review
- Impaired learning to dissociate advantageous and disadvantageous risky choices in adolescents.Scientific reports · 2022Article
- Neurocomputational models of altruistic decision-making and social motives: Advances, pitfalls, and future directions.Wiley interdisciplinary reviews. Cognitive science · 2021Review
- What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience.Current opinion in behavioral sciences · 2021Article
- Preference uncertainty accounts for developmental effects on susceptibility to peer influence in adolescence.Nature communications · 2021Article
- Uncertainty-driven regulation of learning and exploration in adolescents: A computational account.PLoS computational biology · 2020Article
- The rational use of causal inference to guide reinforcement learning strengthens with age.NPJ science of learning · 2020Article
- Reinforcement learning across development: What insights can we draw from a decade of research?Developmental cognitive neuroscience · 2019Review
- Social Influence in Adolescent Decision-Making: A Formal Framework.Frontiers in psychology · 2019Article
- Neural and behavioural differences in multisensory statistical and reinforcement learning across development and task variants.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.
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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.