Risky behavior contributes to substantial morbidity and mortality during adolescence, and unhealthy patterns of behavior that debut during this period have consequences that play out over a lifetime. For example, in a representative sample of 5,547 12- to 19-year-olds, none met criteria for cardiovascular health. Sensitivity to rewards (e.g., tasty foods) contributes to obesity and other unhealthy behaviors, and recent reviews of research on risky decision making in adolescence have focused on this topic. Although there is evidence that adolescents are more sensitive than adults are to rewards, not all studies find this pattern: Some studies find less sensitivity to rewards among adolescents, which cannot be explained simply by reward stages (anticipation vs. receipt). Other studies were not designed to isolate reward sensitivity, and so confound it with known developmental differences in risk attitudes, memory for outcomes, or feedback-induced strategies. Also, definitions of reward sensitivity vary across fields, and research on adolescent decision making does not distinguish among 4 different hypotheses tested here. These hypotheses make starkly different predictions about adolescent risk taking and effects of incentives on their behavior. Moreover, we examine the interplay between such factors as sensitivity to reward and risk, on the one hand, with emotions and drive states on the other hand. We test surprising, but theoretically motivated predictions, for example: (1) Drive states will induce reverse framing (taking greater risks for greater rewards and accepting larger sure losses) among adolescents even for objectively low rewards. (2) Inducing gist processing will have a protective effect on ris taking for rewards, reducing vulnerability to drive states. (3) Although most theories anticipate that adolescents will be more vulnerable to strong emotion than adults, and less able to accurately forecast their risky decisions, there is theoretical justification for the prediction tht adolescents will approach such risks more coldly than adults. Adolescents and adults will provide reward ratings and make decisions involving these same rewards (in counterbalanced order) using consequential and motivating incentive-compatible procedures. We examine common currency and domain-specific effects for candy bars and money, and use neuroimaging to test hypotheses about neural circuitry of risk taking. The Principal Investigator and other investigators are highly proficient data analysts and mathematical modelers. Analyses will include standard ANOVA for factorial designs (Table 1) with either decision choices or reward ratings as dependent variables. Using multiple regressions, measures of individual differences (principal components analysis will be used to reduce the number of predictors;see Reyna, Estrada et al., 2011), plus laboratory decisions and reward ratings, will be used to predict real-life risk taking on the Adolescent Risk Questionnaire. Therefore, we both manipulate levels of reward and measure sensitivity to reward as an individual difference, as well as manipulate challenges to cognitive control (e.g., drive states) and measure cognitive control, including inhibition (Behavioral Inhibition Scale and go/no-go task).

Public Health Relevance

Risky behavior contributes to substantial morbidity and mortality during adolescence, and the unhealthy patterns of behavior that debut during this period have consequences that play out over a lifetime. Bringing together economists, psychologists, and neuroscientists, we systematically examine the interplay between cognition versus emotional and motivational states, such as hunger, fear, and tempting rewards, as adolescents and young adults make consequential risky decisions.

National Institute of Health (NIH)
National Institute of Nursing Research (NINR)
Research Project (R01)
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Special Emphasis Panel (ZRG1-BBBP-R (51))
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Hardy, Lynda R
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Cornell University
Other Health Professions
Other Domestic Higher Education
United States
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Blalock, Susan J; Reyna, Valerie F (2016) Using fuzzy-trace theory to understand and improve health judgments, decisions, and behaviors: A literature review. Health Psychol 35:781-92
Reyna, Valerie F; Corbin, Jonathan C; Weldon, Rebecca B et al. (2016) How Fuzzy-Trace Theory Predicts True and False Memories for Words, Sentences, and Narratives. J Appl Res Mem Cogn 5:1-9
Chick, Christina F; Reyna, Valerie F; Corbin, Jonathan C (2016) Framing effects are robust to linguistic disambiguation: A critical test of contemporary theory. J Exp Psychol Learn Mem Cogn 42:238-56
Reyna, Valerie F; Weldon, Rebecca B; McCormick, Michael (2015) Educating Intuition: Reducing Risky Decisions Using Fuzzy-Trace Theory. Curr Dir Psychol Sci 24:392-398
Corbin, Jonathan C; Reyna, Valerie F; Weldon, Rebecca B et al. (2015) How Reasoning, Judgment, and Decision Making are Colored by Gist-based Intuition: A Fuzzy-Trace Theory Approach. J Appl Res Mem Cogn 4:344-355
Reyna, Valerie F; Nelson, Wendy L; Han, Paul K et al. (2015) Decision making and cancer. Am Psychol 70:105-18
Brainerd, C J; Reyna, Valerie F (2015) Fuzzy-Trace Theory and Lifespan Cognitive Development. Dev Rev 38:89-121
Wolfe, Christopher R; Reyna, Valerie F; Widmer, Colin L et al. (2015) Efficacy of a web-based intelligent tutoring system for communicating genetic risk of breast cancer: a fuzzy-trace theory approach. Med Decis Making 35:46-59
Reyna, Valerie F; Wilhelms, Evan A; McCormick, Michael J et al. (2015) Development of Risky Decision Making: Fuzzy-Trace Theory and Neurobiological Perspectives. Child Dev Perspect 9:122-127
Wilhelms, Evan A; Reyna, Valerie F; Brust-Renck, Priscila et al. (2015) Gist Representations and Communication of Risks about HIV-AIDS: A Fuzzy-Trace Theory Approach. Curr HIV Res 13:399-407

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