Trajectories of Risk Learning and Real-World Risky Behaviors During Adolescence


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Virginia Tech


Adolescence is a transition period during which individuals have increasing autonomy in decision-making for themselves (Casey, Jones, and Hare, 2008), often choosing among options about which they have little knowledge and experience. This process of individuation and independence is reflected as real-world risk taking behaviors (Silveri et al., 2004), including higher motor accidents, unwanted pregnancies, sexually transmitted diseases, drug addictions, and death (Casey et al., 2008). The extent to which adolescents continue to display increased behaviors with negative consequences during this period of life depends critically on their ability to explore and learn potential consequences of actions within novel environments. This learning is not limited to the value of the outcome associated with making choices, but extends to the levels of risk taken in making those choices. While the existing adolescence literature has focused on neural substrates of risk preferences, how adolescents behaviorally and neurally learn about risks remain unknown. Success or failure to learn the potential variability of these consequences, or the risks involved, in ambiguous decisions is hypothesized to be a crucial process to allow the individuals to make decisions based on their risk preferences. The alternative in which adolescents fail to learn about the risks involved in their decisions leaves the adolescent in a state of continued exploration of the ambiguity, reflected as continued risk-taking behavior. This dissertation comprises 2 papers. The first paper is a perspective paper outlining a paradigm that risk taking behavior observed during adolescents may be a product of each adolescent's abilities to learn about risk. The second paper builds on the hypothesis of the perspective paper by first examining neural correlates of risk learning and quantifying individual risk learning abilities and then examining longitudinal risk learning developmental trajectories in relation to real-world risk-trajectories in adolescent individuals.



Reinforcement Learning, Adolescence, fMRI, longitudinal, computational modeling