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.
Highlights
Specialty section: This article was submitted to Developmental Psychology, a section of the journal Frontiers in Psychology
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 first tap into a process that remains important throughout the course of the human lifespan, namely the ability to learn from others, by observation, by social feedback, or from instruction
Summary
Many of our preferences (e.g., for one type of ice cream over another) are shaped by experience-driven learning mechanisms. The participant continuously updates her prediction about the value of the two flavors based on the perceived discrepancy between the expected and the experienced reward This discrepancy is expressed by the reward prediction error δ that is computed after an agent has performed action a in state s:. To illustrate how the computation of a reward prediction error works, consider a person who expects chocolate ice cream to be moderately rewarding (Q(ice cream parlor, chocolate) = 0.6) and, having selected chocolate ice cream, experiences a pleasant taste (r(s′) = 1) In this example task, the experienced reward is completely determined by the immediate reward because after tasting the ice cream, no further actions are available (Q(s′, a′) = 0).
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