1 Chair of Lifespan Developmental Neuroscience, Department of Psychology, Technische Universität Dresden, Dresden, Germany.
2 Department of Neurology, Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
3 Department of Psychology, Concordia University, Montreal, QC, Canada.
4 PERFORM Centre, Concordia University, Montreal, QC, Canada.

Developmental Changes in Learning: Computational Mechanisms and Social Influences.

Front Psychol. 2017;8:2048

Authors: Bolenz F, Reiter AMF, Eppinger B

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.

PMID: 29250006 [PubMed]