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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Nursing Research (NINR)
Type
Research Project (R01)
Project #
1R01NR014368-01
Application #
8413274
Study Section
Special Emphasis Panel (ZRG1-BBBP-R (51))
Program Officer
Hardy, Lynda R
Project Start
2012-09-27
Project End
2015-06-30
Budget Start
2012-09-27
Budget End
2013-06-30
Support Year
1
Fiscal Year
2012
Total Cost
$523,442
Indirect Cost
$178,294
Name
Cornell University
Department
Other Health Professions
Type
Other Domestic Higher Education
DUNS #
872612445
City
Ithaca
State
NY
Country
United States
Zip Code
14850
Reyna, Valerie F; Wilhelms, Evan A (2017) The Gist of Delay of Gratification: Understanding and Predicting Problem Behaviors. J Behav Decis Mak 30:610-625
Brust-Renck, Priscila G; Reyna, Valerie F; Wilhelms, Evan A et al. (2017) Active engagement in a web-based tutorial to prevent obesity grounded in Fuzzy-Trace Theory predicts higher knowledge and gist comprehension. Behav Res Methods 49:1386-1398
Brainerd, C J; Nakamura, K; Reyna, V F et al. (2017) Overdistribution illusions: Categorical judgments produce them, confidence ratings reduce them. J Exp Psychol Gen 146:20-40
Romer, Daniel; Reyna, Valerie F; Satterthwaite, Theodore D (2017) Beyond stereotypes of adolescent risk taking: Placing the adolescent brain in developmental context. Dev Cogn Neurosci 27:19-34
Poldrack, Russell A; Monahan, John; Imrey, Peter B et al. (2017) Predicting Violent Behavior: What Can Neuroscience Add? Trends Cogn Sci :
Blalock, Susan J; DeVellis, Robert F; Chewning, Betty et al. (2016) Gist and verbatim communication concerning medication risks/benefits. Patient Educ Couns 99:988-94
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
Romer, Adrienne L; Reyna, Valerie F; Pardo, Seth T (2016) Are rash impulsive and reward sensitive traits distinguishable? A test in young adults. Pers Individ Dif 99:308-312
Cedillos-Whynott, Elizabeth M; Wolfe, Christopher R; Widmer, Colin L et al. (2016) The effectiveness of argumentation in tutorial dialogues with an Intelligent Tutoring System for genetic risk of breast cancer. Behav Res Methods 48:857-68
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

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