br Conclusions Previous research has

Conclusions Previous research has primarily studied risk taking and prosociality in isolation. However, examining interactions between these characteristic adolescent behaviors, in conjunction with increased social sensitivity and neurobiological development, may update our understanding of adolescence as more a time of opportunity and less a period of heightened risk and vulnerability. Drawing on developmental, cognitive, and social neuroscience research, we suggest that another facet of adolescent risk taking is the additional STF-62247 of prosocial and social status goals. Despite emerging evidence that social and neural processes undergoing significant development may also confer positive decision making (e.g., Schriber and Guyer, 2016), little is known about how interactions between risk taking and prosociality influence adolescent outcomes.
Author contributions
Acknowledgments The authors would like to thank the members of the Developmental Social Neuroscience Laboratory at the University of North Carolina for their valuable insight in the development of this manuscript. This work was supported by a grant from the National Institutes of Health (R01DA039923 to E.H. Telzer).
Introduction Individuals can recruit a variety of evaluative strategies to make everyday decisions. Reinforcement learning theory distinguishes two such strategies: model-based and model-free learning (Daw et al., 2005, 2011; Glascher et al., 2010). Model-based learning requires the construction of a cognitive model of potential actions and their consequences, which can be consulted to determine the best way to pursue a current goal. Such learning supports flexible behavior in novel situations and can readily take into account changes in the environment. By contrast, model-free learning simply estimates the value of reflexively repeating an action based on whether it previously led to good or bad outcomes, without representing the specific outcomes themselves. While model-free learning is computationally efficient, it cannot rapidly adjust to changes in the value of an outcome or changes in contingency between an action and outcome. Many decisions or actions can be evaluated in a model-based or a model-free manner. Effective behavioral control often involves striking a context-dependent balance between these deliberative versus automatic strategies. Recent research suggests Single-strand assimilation while model-free learning is consistently employed across developmental stages, recruitment of model-based learning tends to increase with age (Decker et al., 2016). Across diverse decision-making contexts or tasks, younger individuals exhibit patterns of behavior that reflect greater reliance on a model-free strategy, whereas older individuals rely more on model-based learning (Decker et al., 2016; Klossek et al., 2008; Piaget, 1954; Zelazo et al., 1996). The developmental timepoint at which one typically shifts toward employing a model-based strategy may depend on both the intrinsic complexity of the task at hand, as well as the maturity of the myriad cognitive processes required for the formation and recruitment of a mental model of that task. To make goal-directed decisions, individuals must be able to anticipate likely events, consider the consequences of their potential actions, and evaluate the most efficient means to obtain a desired outcome. The ability to recognize which events tend to follow each other in sequence or covary with high probability is often referred to as statistical learning (Turk-Browne et al., 2005). Simple forms of statistical learning are present in infants and children (Amso and Davidow, 2012; Fiser and Aslin, 2002), demonstrating that individuals can build cognitive models of environmental statistics from early on in development. However, in other tasks, statistical learning performance has been observed to improve with age (Schlichting et al., 2016), suggesting that learning of more complex sequential structures may emerge later in development. More accurate representations of the statistical structure of a task may facilitate model-based choice. However, whether increased recruitment of model-based learning with age might reflect developmental improvements in statistical learning remains an open question.