ReviewDevelopmental cognitive neuroscience2018
Computational neuroscience across the lifespan: Promises and pitfalls.
Review in Developmental cognitive neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 16 papers.
What it found
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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.
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Who cites it
16 citing papers in PubMed.
- Interpretation of individual differences in computational neuroscience using a latent input approach.Developmental cognitive neuroscience · 2025Review
- Enhancing experimental design through Bayes factor design analysis: insights from multi-armed bandit tasks.Wellcome open research · 2024Article
- Understanding mental health through computers: An introduction to computational psychiatry.Frontiers in psychiatry · 2023Review
- Article
- A methodological perspective on learning in the developing brain.NPJ science of learning · 2022Review
- Reinforcement learning and Bayesian inference provide complementary models for the unique advantage of adolescents in stochastic reversal.Developmental cognitive neuroscience · 2022Article
- The computational challenge of social learning.Trends in cognitive sciences · 2021Review
- What do Reinforcement Learning Models Measure? Interpreting Model Parameters in Cognition and Neuroscience.Current opinion in behavioral sciences · 2021Article
- Beyond dichotomies in reinforcement learning.Nature reviews. Neuroscience · 2020Review
- Neural tracking of subjective value under riskand ambiguity in adolescence.Cognitive, affective & behavioral neuroscience · 2019Article
- Reinforcement learning across development: What insights can we draw from a decade of research?Developmental cognitive neuroscience · 2019Review
- Adolescent Health Risk Behaviors: Convergent, Discriminant and Predictive Validity of Self-Report and Cognitive Measures.Journal of youth and adolescence · 2019Article
- The computational basis of following advice in adolescents.Journal of experimental child psychology · 2019Article
- Developmental perspectives on risky and impulsive choice.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019Review
- Social Influence in Adolescent Decision-Making: A Formal Framework.Frontiers in psychology · 2019Article
- Developmental Changes in Learning: Computational Mechanisms and Social Influences.Frontiers in psychology · 2017Review
Corrections and comments
- Erratum issuedErratum.2020
Authors and funding
5 authors.
Funding
Abstract
In recent years, the application of computational modeling in studies on age-related changes in decision making and learning has gained in popularity. One advantage of computational models is that they provide access to latent variables that cannot be directly observed from behavior. In combination with experimental manipulations, these latent variables can help to test hypotheses about age-related changes in behavioral and neurobiological measures at a level of specificity that is not achievable with descriptive analysis approaches alone. This level of specificity can in turn be beneficial to establish the identity of the corresponding behavioral and neurobiological mechanisms. In this paper, we will illustrate applications of computational methods using examples of lifespan research on risk taking, strategy selection and reinforcement learning. We will elaborate on problems that can occur when computational neuroscience methods are applied to data of different age groups. Finally, we will discuss potential targets for future applications and outline general shortcomings of computational neuroscience methods for research on human lifespan 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.