Humans employ different strategies when making decisions. Previous research has reported reduced reliance on model-based strategies with aging, but it remains unclear whether this is due to cognitive or motivational factors. Moreover, it is not clear how aging affects the metacontrol of decision making, that is the dynamic adaptation of decision-making strategies to varying situational demands. In this cross-sectional study, we tested younger and older adults in a sequential decision-making task that dissociates model-free and model-based strategies. In contrast to previous research, model-based strategies led to higher payoffs. Moreover, we manipulated the costs and benefits of model-based strategies by varying reward magnitude and the stability of the task structure. Compared to younger adults, older adults showed reduced model-based decision making and less adaptation of decision-making strategies. Our findings suggest that aging affects the metacontrol of decision-making strategies and that reduced model-based strategies in older adults are due to limited cognitive abilities.

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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During adolescence, the ability to engage in more complex decision-making strategies increases (Raab & Hartley, 2019). However, the successful use of a given decision-making strategy does not only depend on the mere ability to engage in it-it also depends on how flexible individuals are in adjusting their reliance on decision-making strategies to changes in internal and external demands. In this study, we ask how the ability for metacontrol of decision making (i.e. the dynamic adaptation of decision-making strategies; Eppinger et al., 2021;Ruel, Devine, et al., 2021) develops from adolescence into young adulthood and whether framing effects differentially affect the flexible usage of decision-making strategies in adolescents as compared to young adults.To study metacontrol, we draw on previous work that dissociates two major decision-making strategies: modelbased and model-free decision making (Daw et al., 2011;Dayan & Niv, 2008). Model-based decision making represents a deliberative, prospective strategy that evaluates different choice options by means of forward planning based on knowledge about the structure of the environment (a cognitive model). In contrast, model-free decision making represents a more reflexive, retrospective strategy that relies on previously experienced actionreward contingencies. Previous developmental research shows that the reliance on model-based decision making (but not model-free decision making) becomes more pronounced from childhood to adulthood