Neurobiological and memory models of risky decision making in adolescents versus young adults.

Abstract

Predictions of fuzzy-trace theory and neurobiological approaches are examined regarding risk taking in a classic decision-making task--the framing task--as well as in the context of real-life risk taking. We report the 1st study of framing effects in adolescents versus adults, varying risk and reward, and relate choices to individual differences, sexual behavior, and behavioral intentions. As predicted by fuzzy-trace theory, adolescents modulated risk taking according to risk and reward. Adults showed standard framing, reflecting greater emphasis on gist-based (qualitative) reasoning, but adolescents displayed reverse framing when potential gains for risk taking were high, reflecting greater emphasis on verbatim-based (quantitative) reasoning. Reverse framing signals a different way of thinking compared with standard framing (reverse framing also differs from simply choosing the risky option). Measures of verbatim- and gist-based reasoning about risk, sensation seeking, behavioral activation, and inhibition were used to extract dimensions of risk proneness: Sensation seeking increased and then decreased, whereas inhibition increased from early adolescence to young adulthood, predicted by neurobiological theories. Two additional dimensions, verbatim- and gist-based reasoning about risk, loaded separately and predicted unique variance in risk taking. Importantly, framing responses predicted real-life risk taking. Reasoning was the most consistent predictor of real-life risk taking: (a) Intentions to have sex, sexual behavior, and number of partners decreased when gist-based reasoning was triggered by retrieval cues in questions about perceived risk, whereas (b) intentions to have sex and number of partners increased when verbatim-based reasoning was triggered by different retrieval cues in questions about perceived risk.

Adolescents’ risk-taking behavior is driven by tolerance to ambiguity

“…We note that other studies, focused on technical risk, have found mixed evidence on age and risk-taking behavior. Though some studies confirm our findings that adolescents, at least those over 14 years of age, are not more technically risk-seeking than adults (33,34), other important and well-regarded studies find that there are significant differences in risk-taking that depend on, among other things, the magnitude of the potential win (26,27), providing compelling evidence that technical risk attitudes are distinct in adolescence. Our unique separation of ambiguity and risk at a within-subjects level allows us to contribute a unique perspective on the basic processes underlying the differences and similarities between adolescents and adults.…”

“…In the first, we refit the structural model but included each individual's psychological attraction to desired objects [known as behavioral approach scores (BAS); higher for less sensitive to rewarding stimuli] and each individual's psychological propensity to avoid things that are unpleasant [behavior inhibition scores (BIS); higher for less sensitive to nonrewarding stimuli] (25) as covariates with the risk aversion parameter, the ambiguity aversion parameter, and the noise parameter. Previous results in the literature suggest that sensation-seeking and behavioral inhibition develop over the lifespan and correlate with experimental as well as self-reported real-life risk-taking practices (14,26,27). Our approach allowed us to ask whether there was an independent effect of age group on risk and/or ambiguity attitudes even when each individual's BIS/BAS scores were controlled for in the fitting process.…”

The case for a dual-process theory of transitive reasoning

“…We are currently testing it using an extensive-training task both with adults and children; using an incremented learning paradigm. On Reyna et al's (2011) findings, we should expect that during test phases the reporting of premises plus the reporting of inference would both be drawn from the same global gist representation. But during the training phases, as there is no need to consult the entire transitive array, there should be a greater tendency for participants to rely on verbatim representations instead of the global gist.…”

“…The fact that both these studies produced findings that supported experimental (H1) hypotheses rather than simply having to accept null (H0) hypotheses, adds to our view that extensive-training invokes LTM far more than it invokes WM. However, we concede that, ideally, we need additional studies based on individual differences plus studies based on tracking the strength of relationship between transitivity and WM as training progressed, in order to be more definitive on this issue (e.g., see Reyna, Estrada, DeMarinis, Myers, Stanisz & Mills, 2011 for use of such triangulation regarding data analyses).…”

Peer effects in risky choices among adolescents

“…Psychologists suggest that "risk taking [in adolescence] is the product of competition between the socio-emotional and cognitive-control networks, and adolescence is a period in which the former abruptly becomes more assertive (i.e., at puberty) while the latter gains strength only gradually, over a longer period of time" (Steinberg, 2007). Hence, increased susceptibility to peer influences may arise from the heightened role of socio-emotional networks in the adolescent brain (see Galvan et al, 2006Galvan et al, , 2007Steinberg, 2008;Van Leijenhorst et al, 2010;Reyna et al, 2011). Card and Giuliano (2013) highlights social interaction effects in risky behaviours such as sexual initiation, smoking, marijuana use, and truancy (for similar results, see also Arnett (1992); Brown et al (1986); McPhee (1996)).…”