Peer-reviewed Article Summaries

Judgment and Decision Making in Adolescence

Dustin Albert and Laurence Steinberg Temple University

In this article, we review the most important findings to have emerged during the past 10 years in the study of judgment and decision making (JDM) in adolescence and look ahead to possible new directions in this burgeoning area of research. Three inter-related shifts in research emphasis are of particular importance and serve to organize this review. First, re- search grounded in normative models of JDM has moved beyond the study of age differences in risk perception and toward a dynamic account of the factors predicting adolescent decisions. Second, the field has seen widespread adoption of dual-process models of cognitive development that describe 2 relatively independent modes of information processing, typically contrasting an analytic (cold) system with an experiential (hot) one. Finally, there has been an increase in attention to the social, emotional, and self-regulatory factors that influence JDM. This shift in focus reflects the growing influence of findings from developmental neuroscience, which describe a pattern of structural and functional maturation that may set the stage for a heightened propensity to make risky decisions in adolescence.

Imagine, for a moment, that you are 16 years old. It is the spring of your sophomore year of high school, and you feel a newfound sense of optimism about your social prospects. Best of all, it is Friday night and you are ready to take advantage of your recently renegotiated curfew, now extended to 11 p.m. When pressed for your plans, you tell your parents that you are just going to the movies and then maybe hanging out at the coffee shop: No need to worry. In reality, you know that when your friends pick you up, you will head straight to the first big keg party to which you have ever been invited. Everyone will be there. But you will have to be careful, because these things get busted by the cops all the time, not to mention the fact that your parents will be waiting up for you when you get home. You are not really planning on drinking at the party, but if you do, you will defi- nitely need some breath mints and a believable horror movie synopsis. That should be easy enough.

We begin our review with this exercise in creative visualization not to inspire fear and suspicion in those among our readers charged with parenting a teenager, but to illustrate the multitude of factors that dynamically shape adolescents’ choices. On this one weekend evening, our hypothetical teenager will make a series of choices with potentially lasting consequences for his health, safety, criminal record, family relationships, and social status. These deci- sions are likely to be influenced not only by his ca- pacity to accurately evaluate the relative costs and benefits of alternative courses of action, but also the

social and emotional contexts in which he makes the decisionsFthe mix of excitement and anxiety he brings to the party, his in-the-moment assessment of social expectations, and his background fear of get- ting caught by police or parents, to name just a few. Stated simply, adolescent decision making is a com- plex and multiply determined phenomenon.

Fortunately, the last decade of scholarship on ado- lescent judgment and decision making (JDM) has seen remarkable progress in modeling this complexity. Building on normative models of rational decision making, the field has dramatically expanded its ex- planatory power by integrating research methods and theoretical insights from cognitive, developmental, social, and emotion perspectives, with a growing in- fluence from the neurosciences. Indeed, this move- ment toward an interdisciplinary perspective has made it increasingly difficult to define the boundaries of adolescent JDM as a topic of investigation. After all, what domain of adolescent behavior does not involve some degree of JDM? Because space limitations pre- clude an exhaustive consideration of such an expan- sively defined literature, our review is necessary selective, guided by our assessment of the most im- portant developments over the last decade within the traditional domains of interest to adolescent JDM re- searchers. Responding to public policy concerns re- garding adolescents’ relative competence to make decisions with long-term consequences for their health and well-being, the field has historically focused on identifying domains of immaturity in adolescent

r 2011 The Authors

Journal of Research on Adolescence r 2011 Society for Research on Adolescence

DOI: 10.1111/j.1532-7795.2010.00724.x

Requests for reprints should be sent to Dustin Albert, Temple University, Department of Psychology, Weiss Hall, 1701 N. 13th Street, Philadelphia, PA 19122. E-mail: [email protected]

JOURNAL OF RESEARCH ON ADOLESCENCE, 21(1), 211 – 224

decision making. Although our review reflects this tradition, we emphasize that many, if not most, ado- lescents demonstrate remarkable decision-making competence across a variety of domains. Future re- search on adolescent JDM should aspire to integrate current models focused on adolescent immaturity with the growing literature documenting the biologi- cal, psychological, and contextual factors promoting positive youth development (Lerner, 2009).

Three inter-related developments in adolescent JDM research serve to organize this review. First, developmental research grounded in normative models of rational decision making has made sig- nificant gains in identifying the factors that influence adolescents’ choices. Building on foundational work modeling the key components of rational decision making, early research in this tradition focused on identifying aspects of cognitive processing in which adolescents were deficient relative to adults, partic- ularly with regard to decisions involving risk. In response to considerable evidence that adolescents evaluate risky decisions in a manner similar to adults (Reyna & Farley, 2006), research from the past de- cade has shifted from an examination of age differ- ences in risk processing toward comprehensive modeling of the factors predicting adolescents’ de- cisions. Such models have gained considerable ex- planatory power by examining the interplay of both risk and benefit perceptions, as well as the role of experience in modifying these views.

Second, following theoretical developments in the adult JDM literature (and related trends in cognitive and social psychology), the field has seen wide- spread adoption of dual-process models of cognitive development (see Jacobs & Klaczynski, 2005, for multiple examples). These models describe two rel- atively independent modes of information process- ing, typically contrasting an analytic (deliberative, controlled, reasoned, ‘‘cold’’) system with an expe- riential (intuitive, automatic, reactive, ‘‘hot’’) system (e.g., Epstein, 1994; Gerrard, Gibbons, Houlihan, Stock, & Pomery, 2008; Jacobs & Klaczynski, 2002; Reyna & Farley, 2006). Proponents of dual-process models argue that traditional cognitive development research has been limited by its singular focus on the analytic system, leading to theories of unidirectional maturational trajectories proceeding from intuitive to reasoned processing (Klaczynsi, 2005). Given ev- idence that the use of many heuristics actually in- creases in adulthood, dual-process proponents argue that developmental models of JDM must account for the distinct maturational trajectories of analytic and experiential systems. In this view, changes in JDM over the course of adolescence do not reflect a simple

transition from experiential to analytic processing, but rather result from domain-specific shifts in the relative dominance of intuition and reason.

The influence of dual-process theories can also be felt in a third research trend, a growth in attention to the social, emotional, and self-regulatory factors that influence adolescents’ JDM. This shift in focus re- flects the growing influence of findings from devel- opmental neuroscience, which describe a pattern of structural and functional maturation that may set the stage for a heightened propensity to make risky decisions in adolescence. Social and emotional fac- tors relevant to adolescent JDM include normative changes in core motivational processes, such as sensation seeking and sensitivity to reward and punishment, as well as age-related changes in the relative influence of contextual variables (e.g., the presence or absence of peers) on risk-taking behav- ior. Together, evidence for heightened sensitivity to social and emotional factors in early-to-middle ado- lescence has offered one plausible account for the corresponding prevalence of risk taking. Comple- mentary to this focus on social and emotional factors, research has also described continued develop- ment in late adolescence of capacities supporting growth in self-regulatory competence, which is thought to contribute to a corresponding decline in risk taking. Consistent with the dual-process per- spectives described above, this research has sought to push the field beyond the study of ‘‘cold’’ cogni- tion and toward explication of the experiential factors that influence real-world, in-the-moment de- cision making.

BEYOND RISK PERCEPTION: EXPANDING THE STUDY OF RATIONAL DECISION MAKING

Before the mid-1990s, research on adolescent JDM focused largely on whether adolescents used adult- like cognitive processes when making decisions (for reviews, see Furby & Beyth-Marom, 1992; Quadrel, Fischhoff, & Davis, 1993). Much of this work stemmed from concerns about the high prevalence of risk behavior among adolescents (especially com- pared with adults) and the consequences of risk taking for adolescents’ health. As a general rule, adolescents are more likely than adults over 25 to binge drink, smoke cigarettes, have casual sex part- ners, engage in violent and other criminal behavior, and have fatal or serious automobile crashes, the majority of which are caused by reckless driving or driving under the influence of alcohol. Because many of these behaviors appear inherently irrational when individuals understand their probable long-

212 ALBERT AND STEINBERG

term consequences, it was assumed that adolescents must be less competent than adults in one or more of the elements of rational decision making.

Normative models of JDM have historically em- phasized five broad stages supporting competent de- cision making, including: (a) identifying options; (b) assessing the possible consequences of each option; (c) evaluating the desirability of each consequence; (d) estimating the probability of occurrence for each con- sequence; and (e) applying a decision algorithm to the above information to identify the option with the greatest subjective utility (Beyth-Marom, Austin, Fischhoff, Palmgren, & Jacobs-quadrel, 1993; Halpern- Felsher & Cauffman, 2001). Drawing upon these and similar models (e.g., Theory of Planned Behavior; Azjen, 1985), a great deal of research searched for the source of adolescents’ heightened propensity to make risky choices by comparing adolescent and adult performance within specific stages of the decision- making process. Specifically, much of this work ex- amined whether adolescents perceive the potential consequences of risk behavior (i.e., Stage b) and ac- curately assess the probability of those consequences occurring (i.e., Stage d) to the same degree as adults.

Contradicting popular conceptions of the typical adolescent as beset by an ‘‘invulnerability complex,’’ adolescents were shown to be no worse than adults at perceiving risk or estimating their vulnerability to it, and studies found that increasing the salience of the risks associated with making a poor or potentially dangerous decision has comparable effects on adoles- cents and adults (for a discussion of false leads in the study of adolescent risk taking, see Millstein & Halpern- Felsher, 2002; Reyna & Farley, 2006; Rivers, Reyna, & Mills, 2008; Steinberg & Cauffman, 1996). Indeed, there appear to be few, if any, age differences in individuals’ evaluations of the risks inherent in a wide range of dangerous behaviors (e.g., driving while drunk, having unprotected sex) or in their judgments about the seri- ousness of the consequences that might result from risky behavior (Beyth-Marom et al., 1993; although, see Cohn, Macfarlane, Yanez, & Imai, 1995, for an exception where adolescents judge lower risk than adults for ‘‘occasional’’ engagement in risk behavior).

Given evidence that adolescents do not differ much from adults in their capacity to rationally evaluate risk information, researchers have begun to look for other explanations of why adolescents, as a group, make riskier decisions than adults. As we will describe in later sections, this shift has led to ex- panded consideration of social, emotional, and self- regulatory factors differentiating adolescent from adult decision making. At the same time, research grounded in rational decision theory has made

considerable progress in building models describing the cognitive factors that predict adolescents’ health- risk decisions.

Before the last decade, the field’s reliance on cross- sectional self-report studies produced a puzzling set of findings regarding the relation between risk percep- tions and behaviors (for reviews, see Millstein & Hal- pern-Felsher, 2002; Reyna & Farley, 2006). Given the assumption that adolescents rationally evaluate costs and benefits to reach a decision, cognitive models typically predict that individuals who perceive lower risk will be more likely to engage in a given behavior. Although many studies have reported this expected negative correlation between risk perception and be- havior (e.g., Benthin, Slovic, & Severson, 1993; Hem- melstein, 1995), others have found the oppositeFthat adolescents engaging in risk behavior perceive higher risks than do nonengagers (e.g., Cohn et al., 1995; Gerrard, Gibbons, & Bushman, 1996). To a degree, these contradictory findings can be accounted for by differences between studies with respect to the condi- tionality of risk perception assessments (Ronis, 1992). When risk perceptions are assessed unconditionally (i.e., ‘‘How likely are you to experience negative con- sequences from smoking?’’), risk-takers accurately re- port a higher degree of personal risk than their peers who are not engaging in risk behavior. In contrast, when presented with conditional questions (i.e., ‘‘If you smoked, how likely are consequences?’’), risk- takers tend to report lower risk perceptions than their risk-abstaining counterparts.

Such findings highlight both the role of experience as a modifier of risk perceptions and the need for longitudinal studies that assess risk perceptions before individuals engage in risk behavior. Although careful longitudinal research investigating prospective pre- dictors of health-risk behavior is still much needed for a variety of domains, the last decade has seen progress in at least one area. Specifically, a number of longitudinal studies examining precursors of smok- ing initiation have provided strong evidence that ado- lescents who perceive a lower probability of harmful consequences are more likely to initiate smoking (Krosnick, Chang, Sherman, Chassin, & Presson, 2006; Rodriguez, Romer, & Audrain-Mcgovern, 2007; Song, Morrell, et al., 2009; Song, Glantz, & Halpern-Felsher, 2009). These studies provide an important reminder that, despite the inability to explain age differences in risk behavior, risk perception remains a valuable ex- planatory construct and a viable target for prevention efforts.

Recent research also has made progress in expli- cating the role of experience in modifying risk per- ceptions. As we discussed above, when adolescents

ADOLESCENT JDM 213

are asked how dangerous an activity would be if they were to engage in it, individuals experienced in the behavior consistently report lower risk perceptions than do those without experience. Although this finding can be explained in part by a presumed causal pathway from low-risk perception to subse- quent risk engagement, it is also possible that expe- rience with risk behavior causes individuals to adjust their risk judgments downward. This explanation is particularly plausible given the low frequency or long-term nature of negative consequences associ- ated with many risk behaviors (e.g., lung cancer from smoking, infectious disease from unprotected sexual activity, motor vehicle crashes resulting from drunk driving). Although adolescents may initially adopt the high-risk estimates for these behaviors provided in health education classes, direct experi- ences with the behavior in the absence of serious negative consequences could create a downward shift in risk perception. Indeed, longitudinal research indicates that failing to experience a negative outcome is associated with decreased risk percep- tions for alcohol use (Goldberg, Halpern-Felsher, & Millstein, 2002), drug use (Katz, Fromme, & D’Amico, 2000), drinking and driving (Nygaard, Waiters, Grube, & Keefe, 2003), and sexual activity (Millstein & Halpern-Felsher, 2002). These findings raise the concern that preventive efforts focused on bolstering adolescents’ perceptions of health risks could backfire by leading those adolescents with ‘‘successful’’ risk-taking experiences to radically discount the validity of the health information mes- sage, thereby increasing their propensity to engage in further risk behavior. If the Partnership for a Drug Free America claims that smoking marijuana will transform your brain into a fried egg, and you nonetheless share a joint with friends on occasion and manage to maintain your GPA, then why trust what they say about cocaine and heroin?

Finally, much of the improvement in predicting adolescents’ decision making has derived from an expanded consideration of the subjective benefits that adolescents associate with health-risk behaviors. For instance, one study found that adolescents’ per- ceptions of the benefits (e.g., social status, pleasure) of alcohol and tobacco use prospectively predicts their decisions to drink and smoke 6 months later, above and beyond age, experience with the sub- stance, and perceptions of risk (Goldberg et al., 2002; see also Halpern-Felsher, Biehl, Kropp, & Rubinstein, 2004; Meier, Slutke, Arndt, & Cadoret, 2007; Song, Morrell, et al., 2009). A recent meta-analysis of studies predicting sexual activity, alcohol and to- bacco use, and nutrition behavior in adolescents ages

10 – 18 found that perceptions of benefits are stronger predictors than risk perceptions of all four behaviors (Peters et al., 2009). By considering the benefits that adolescents’ derive from engaging in risk be- havior, the field is approaching a much richer understanding of the subjective factors that shape adolescents’ decisions (Michels, Kropp, Eyre, & Halpern-Felsher, 2005).

DUAL-PROCESS MODELS OF COGNITIVE DEVELOPMENT

Despite these improvements in modeling individual differences in adolescent health-risk behavior, many scholars have remained unsatisfied with the failure of rational decision theories to adequately describe what develops in adolescent JDM (e.g., Jacobs & Klaczynski, 2002; Reyna & Farley, 2006; Steinberg, 2003). As we noted above, most research on adoles- cent decision making conducted before the mid- 1990s sought to identify gains in decision processing skills, largely based on the assumption that compe- tence developed along a unidirectional, linear tra- jectory progressing from childhood intuition to mature, deliberative thinking in adulthood (Jacobs & Klaczynski, 2002). Investigators concerned with ad- olescent JDM have raised two strong challenges to this theoretical framework. First, the claim that de- cision processing develops over the course of child- hood and adolescence toward an end state of rational, deliberate decision making is inconsistent with a wealth of evidence from the adult JDM liter- ature that adult decision making is riddled with cognitive biases and heuristics (see Kahneman, Slovic, & Tversky, 1982). Indeed, as we will describe below, developmental studies suggest that certain heuristics and biases become more prevalent over the course of childhood and adolescence (Reyna & Farley, 2006). In essence, if the road of normative de- velopment leads to logically rigorous decision making, most adults fail to reach the destination. Second, given that few gains in logical reasoning or information processing are apparent after mid-adolescence (Kuhn, 2009), the development of rational competence cannot explain the many age differences observed between adolescents and adults in real-world decision making, particularly adolescents’ heightened risk-taking be- havior (Gerrard et al., 2008; Reyna & Farley, 2006; Steinberg, 2008). We discuss evidence for each of these critiques in turn and describe a class of dual process models that have been advanced to account for the seemingly paradoxical nature of adolescent cognitive development.

214 ALBERT AND STEINBERG

The Development of Heuristic Processing

A large body of research on adult JDM has demon- strated that although adults behave in accord with logical processing expectations on many JDM tasks, they also commonly show evidence of biases in judgment and reliance on heuristic ‘‘shortcuts’’ (Kahneman et al., 1982). Rather than interpret this ‘‘heuristics and biases’’ literature as evidence that adults are inherently irrational, some have suggested that rational (or ‘‘adaptive’’) decision making is sup- ported by two separate, parallel modes of cogni- tionFa conscious, analytic system responsible for logical, computational processing, and a pre-con- scious, experiential system that supports quick, intu- itive, heuristic processing, and is based in implicit memory (e.g., Epstein, 1994; Klaczynski, 2001; Stano- vich, 1999). In response to mounting evidence in support of this and similar dual-process models of adult cognition, developmental JDM researchers have argued that theories of cognitive development focused on the maturation of logical competence have ne- glected a central aspect of cognition (Jacobs & Klaczynski, 2002). From this perspective, both the analytic and the experiential system mature over the course of development and together support the par- adoxical mix of logic and bias observed in adult JDM.

Research guided by dual-process models of cogni- tive development has demonstrated age-related pro- gressions in the use of different classes of heuristics that at first glance appear quite paradoxical. On the one hand, studies comparing younger and older ad- olescents on a variety of standard JDM tasks demon- strate growth in ‘‘normative’’ (i.e., logically coherent, heuristic-resistant) reasoning from early to middle adolescence (for a review, see Klaczynsi, 2005). For instance, middle adolescents show improvements in statistical reasoning, conditional reasoning, and co- variation judgments and show less evidence of out- come bias and use of the ‘‘sunk cost’’ fallacy than early adolescents (Klaczynski, 2001; Klaczynski & Cottrell, 2004). Klaczynsi (2005) has argued that such im- provements in normative reasoning are related to the maturation in adolescence of metacognitive skills; as adolescents develop the capacity and motivation to monitor and direct their thinking, they are more likely to resist the pull of certain heuristics and engage an- alytic processing systems.

On the other hand, evidence suggests that some biases and heuristics are engaged more frequently with age across childhood and adolescence (for re- views, see Jacobs & Klaczynski, 2002; Reyna & Far- ley, 2006). In particular, studies utilizing a variety of JDM tasks involving social content have demon-

strated age-related increases in the tendency to in- correctly apply stereotype information to reasoning problems, resulting in more transitivity errors (e.g., Markovits & Dumas, 1999), conjunction fallacies (Davidson, 1995), and use of the representativeness heuristic (Jacobs & Potenza, 1991). Research on the development of the representativeness heuristic, the tendency to rely on salient features of a scenario ra- ther than base rate information to inform likelihood judgments, nicely illustrates this phenomenon. For problems involving social content (e.g., Is the perky, outgoing girl a cheerleader or a member of the band?), adolescents show an increased propensity to favor stereotype-based information (e.g., cheerlead- ers are perky) over base rates (e.g., more girls are band members than cheerleaders; Jacobs & Klaczynski, 2002). In contrast, adolescents show de- velopmental gains in the use of base-rate information on parallel problems that do not involve social con- tent. In sum, adolescents are capable of engaging both analytical and heuristic processing systems when making judgments and decisions, but in con- texts that activate their increasingly rich and salient social schemas, heuristic processing appears to gain influence over the course of adolescent development.

Reasoned and Reactive Pathways to Risk Behavior

The last decade has also seen the advancement of a growing number of dual-process models to describe the developmental mechanisms underlying the heightened incidence of risk-taking behavior in ad- olescence, relative to adulthood (e.g., Gerrard et al., 2008; Rivers et al., 2008; Steinberg, 2008). Expanding upon the rational decision models described in the first section of this review, these theories typically argue that age differences in risk behavior cannot be explained by the development of analytic compe- tence alone, but rather result from developmental changes in the balance between two modes of pro- cessingFone that is deliberate and reasoned and one that is intuitive and reactive. Although the models share a similar conceptual framework, the distinctions between them are important enough to warrant a brief review of each.

The ‘‘prototype-willingness’’ model draws di- rectly on the dual-process distinction between ana- lytic and experiential cognition to argue that two different modes of processing influence risk-relevant decision making (Gerrard et al., 2008). The first modeFa ‘‘reasoned pathway’’Fis based on con- scious, deliberate evaluation of the costs and benefits of choice alternatives and results in behavioral in- tentions, consistent with rational decision models

ADOLESCENT JDM 215

like the Theory of Planned Behavior (Azjen, 1985). Given that most adolescents claim that they do not intend to engage in risky behavior (Gerrard, Gibbons, & Gano, 2003), and yet many clearly do take risks when presented with the opportunity, this rea- soned pathway through behavioral intentions leaves important variance in adolescent risk taking unex- plained. Prototype-willingness theory contends that adolescents engage in unintended risk behavior via an alternative mode of processing, a ‘‘social reaction pathway’’ that is grounded in experiential processing, guided by social prototypes (i.e., schematic images of typical risk-takers), and reflected in individual differ- ences in behavioral willingness to take risks if presented with the opportunity. An impressive body of evidence suggests that, in contrast to the poor predictive value of behavioral intentions for adolescent risk behavior, behavioral willingness prospectively predicts adoles- cents’ engagement in behaviors as diverse as smoking, drinking, substance use, unprotected sex, reckless and intoxicated driving, and even tanning (for a review, see Gerrard et al., 2008). Recent evidence suggests that, as adolescents approach adulthood, the power of be- havioral intentions to predict behavior grows, ulti- mately surpassing behavioral willingness (Pomery, Gibbons, Reis-bergan, & Gerrard, 2009). In essence, as adolescents’ gain further experience navigating their social world, they begin to better understand their own behavioral tendencies and more accurately pre- dict (and presumably control) their future decision making.

Similar to the dual-process model of cognition proposed by Jacobs and Klaczynski (2002), the ‘‘fuzzy-trace’’ model of adolescent risk taking draws upon evidence of developmental increases in heu- ristic processing to argue that cognitive maturation entails not only growth in reasoning capacity, but also the increasing application of intuition to JDM (Reyna & Farley, 2006; Rivers et al., 2008). This de- velopmental pattern is exemplified by age differ- ences in framing effects on risk taking. Whereas adults show a differential tendency to gamble when a choice is framed in terms of a loss versus a gain (i.e., preferring the risky option to avoid a loss, but preferring the ‘‘sure thing’’ to guarantee a small gain), young children do not differentiate between gain/loss frames; framing effects become progres- sively more common with age (Reyna & Ellis, 1994). Fuzzy trace theory interprets these findings as evi- dence for developmental maturation of ‘‘gist-based’’ processingFthe adaptive tendency to rely on sim- ple, categorical intuitions, derived from experience, to guide JDM. In the problems described above, adult preferences are more likely to be guided by the

simple intuition that ‘‘it is better to win some money than to win none.’’ In contrast, children and ado- lescents make choices that reflect logically ‘‘norma- tive’’ evaluation of loss and gain probabilities. Applied to real-world risk taking, this model sug- gests that adolescents lack the experience with neg- ative consequences to support mature, categorical avoidance of risky choices and instead show an over- reliance on conscious evaluation of the costs and benefits of risky behavior, which often favor the risky choice. Thus, in contrast to the prototype-willingness model, which argues that adolescents’ over-reliance on experiential processing increases their tendency to engage in risky behavior, fuzzy trace theory claims that adolescents’ heightened propensity toward taking risks derives from their under-reliance on intuition.

Finally, recent work from our lab (and others) has drawn on findings from developmental neuroscience to argue that the differential maturational trajectories of two core neurobiological systems creates a window of vulnerability for increased risk taking in adoles- cence (e.g., Casey, Getz, & Galvan, 2008; Chambers, Taylor, & Potenza, 2003; Dahl, 2004; Steinberg, 2008). We describe and review evidence for these models in the following section, which discusses important gains in our understanding of social, emotional, and self- regulatory influences on adolescent JDM.

EMOTION, CONTEXT, AND SELF- REGULATION

Let us return for a moment to the hypothetical teenager who introduced this review. Headed to his first big keg party, he was not exactly intending to drink, but he knew he would have to be very careful not to get caught if he did. Based on the research described in the first section of this review, we know that a variety of cognitive factors will influence his decision of whether or not to drink at the party, most notably his perceptions of the risks and benefits in- volved. However, a dual-process perspective sug- gests that decision making is influenced by not only cognitive inputs, but also feelings: the excitement of being with friends, the thrill of crossing parental or legal boundaries, and the fear of getting caught are all plausible affective contributions to our teenager’s in-the-moment decision of whether or not to drink.

Research with adult populations has identified several pathways by which affect contributes to the decision-making process (for reviews, see Loewen- stein, Weber, Hsee, & Welch, 2001; Winkielman, Knutson, Paulus, & Trujillo, 2007). First, the antici- pated emotional outcomes of behavioral alternatives

216 ALBERT AND STEINBERG

contribute to cognitive assessments of their expected value (Loewenstein et al., 2001). The teenager at the keg party might imagine that joining his friends in drinking beer will lessen his social anxiety and in- crease his positive emotion, whereas abstaining will make him feel excluded and increase his anxiety. These anticipated emotional consequences contrib- ute to his global evaluation of the desirability of the risky choice.

Second, direct emotional responses to qualities of the choice alternativesFthat is, anticipatory emo- tionsFinfluence their evaluation, and motivate ap- proach or avoidance behavior (Loewenstein et al., 2001). Research grounded in inferential models of the influence of emotion on cognition suggests that individuals adaptively consult their feelings as a source of information when making a judgment about a given target (e.g., ‘‘the affect heuristic’’; Slovic, Peters, Finucane, & MacGregor, 2005). Re- turning to the keg party, if our teenager had a prior negative experience drinking beer, he may respond with a degree of disgust to the smell of spilled beer around the keg, and this aversive emotion might influence his behavior either indirectly (by contrib- uting to a negative evaluation of the desirability of drinking) or directly (through heightened avoidance motivation).

A third class of affective inputs has variously been referred to as incidental emotion or background mood, and includes emotions elicited by factors not related to the decision itself (Loewenstein et al., 2001). Dating back to Zajonc’s seminal affective priming studies (Zajonc, 1980), research on the in- terplay of emotion and cognition has demonstrated the influence of pre-existing or experimentally elic- ited affective states on perception, memory, judg- ment, and behavior (Winkielman et al., 2007). For instance, individuals surveyed on a sunny day rate their life satisfaction as higher than those contacted on a rainy day (Schwarz & Clore, 1983), and exper- imental elicitation of positive or negative emotion is associated with corresponding shifts toward opti- mistic or pessimistic judgments about risk (Johnson & Tversky, 1983). Indeed, recent experimental work suggests that emotionsFwhether consciously expe- rienced or notFmay modulate an individual’s sen- sitivity to unrelated incentive stimuli, biasing the individual toward approach- or avoidance-related behavior (Winkielman, Berridge, & Wilbarger, 2005). Returning to the keg party one last time, our hypo- thetical teenager is likely bombarded with socio- emotional stimuli, perhaps in the form of a crowd of friends’ smiling faces. If these smiling faces elicit a positive emotional response from our teenager, his

elevated mood may sensitize him to respond appe- titively to the incentive value of the cup of beer he is subsequently offered. In effect, his immersion in a happy crowd might sensitize him to perceive the beer as more appealing.

Given that these emotion effects have all been demonstrated in studies of adult samples, it is im- portant to examine their relevance for understanding the development of adolescent decision making. However, recent research suggests that two broad patterns in adolescent neurobehavioral development may combine to confer unique adolescent suscepti- bility to socioemotional influences on JDM (e.g., Casey et al., 2008; Chambers et al., 2003; Dahl, 2004; Stein- berg, 2008). First, around the time of puberty, devel- opmental changes in the dynamics of dopamine (e.g., Laviola, Pascucci, & Pieretti, 2001) and oxytocin (e.g., Chibbar, Toma, Mitchell, & Miller, 1990) neurotrans- mission are thought to alter the sensitivity of a net- work of brain regions we refer to as the incentive processing system. Increased sensitivity in this network, which includes regions involved in reward (e.g., ventral striatum) and social information (e.g., medial prefrontal cortex) processing, may contribute to nor- mative increases in sensation seeking and sensitivity to socioemotional stimuli in early adolescence (Spear, 2009).

In contrast, prolonged structural refinements over the course of adolescence and early adulthood in regions associated with cognitive control (e.g., dorso- lateral prefrontal and posterior parietal cortex) are thought to support older adolescents’ emerging ca- pacity to regulate their behavior (Casey et al., 2008). Although the details of these structural refinements and their relation to behavioral improvements re- main a matter of debate, there is now extensive evi- dence demonstrating late adolescent changes in gray and white matter density, as well as gains in the coherence of white matter connections within and between cortical and subcortical regions (see Paus, 2009, for a review of the evidence). At the functional level, adolescents show progressive gains in the efficient (i.e., focal) recruitment of prefrontal and parietal circuits thought to support mature cognitive control, including the ability to suppress impulsive responding (Durston et al., 2006). In sum, to the de- gree that adolescents are primed to seek out and respond to rewards, and at the same time possess immature self-regulatory skills, the influence of so- cioemotional stimuli is likely to loom large for their decision making.

Because of space limitations, we will forgo a de- tailed review of the neuroscientific evidence in sup- port of this model (see Casey et al., 2008; Chambers

ADOLESCENT JDM 217

et al., 2003; Dahl, 2004; Steinberg, 2008), and instead focus on related developments in social, emotional, and self-regulatory behavior in adolescence. How- ever, we note that the application of neuroscience to the study of adolescent JDM has been one of the most dramatic (and fruitful) developments in the field’s last decade. Not only has neuroscience pro- vided new evidence to inspire and constrain our hypotheses about adolescent behavioral develop- ment, it has also hastened the emergence of JDM as a nexus for research integrating cognitive, social, and affective perspectives on adolescent development.

Developmental Trends in Reward Motivation, Affective Learning, and Sensitivity to Peer Influence

It has long been known that self-reported sensa- tion seeking (i.e., the motivation to seek out novel, varied, and highly stimulating experiences) declines between adolescence and adulthood (e.g., Zuckerman, Eysenck, & Eysenck, 1978). Although Zuckerman (1969) originated the hypothesis that sensation seeking increases from childhood to early adolescence before beginning its decline, only recently have studies been conducted with samples broad enough in age range to confirm this curvilinear developmental trend (e.g., Romer & Hennessy, 2007; Steinberg et al., 2008). Im- portantly, one study found that the growth in sensa- tion seeking observed in the transition from childhood to adolescence was more closely related to pubertal status than age (Martin et al., 2002), suggesting that observed age trends reflect normative biological de- velopment (rather than, for instance, changing peer norms). This interpretation is further supported by observations of adolescent peaks in reward seeking in studies of rodents and nonhuman primates (Spear, 2009). Relating to our earlier discussion of the influ- ence of anticipated emotion, sensation seeking may increase adolescents’ propensity to make risky deci- sions by imbuing risky options with strong reward value. Indeed, research has consistently found a pos- itive relation between sensation seeking and engage- ment in a variety of risk behaviors (e.g., Arnett, 1992; Roberti, 2004; Zuckerman, 1994; Zuckerman & Kuhl- man, 2000), with at least one recent study reporting that this relation is mediated by the degree to which adolescents expect that risk taking will produce posi- tive feelings (i.e., their anticipated affect; Romer & Hennessy, 2007).

Studies utilizing affective learning paradigms further suggest that adolescents are not necessarily hypersensitive to all emotional stimuli, but rather respond more strongly to reward than punishment

feedback. According to the somatic marker hypoth- esis, motivational behavior is guided by subtle affective learning from prior experience with rein- forcement and/or punishment outcomes; such learning is thought to result in anticipatory feelings that bias the individual toward approach or avoidance in future encounters with the stimulus (Damasio, 1994). Research utilizing age-appropriate variants of the Iowa Gambling Task (IGT) has shown developmental gains over the course of childhood and adolescence in the capacity to adjust behavior in response to conflicting reward and punishment feedback (e.g., avoiding choices that result in small immediate gains but large long-term losses; Crone & Van der Molen, 2004; Crone, Vendel, & Van Der Molen, 2003). Furthermore, these gains appear re- lated to gradual maturation of the capacity to learn from punishment outcomes (Crone, Bunge, Laten- stein, & Van Der Molen, 2005), reflected in stronger anticipatory autonomic arousal when confronted with a previously punished choice (Crone & Van der Molen, 2007). Finally, a recent study that assessed a large sample of 10 – 30-year-olds on a variant of the IGT that produces separate measures of reward and punishment sensitivity found that, whereas sensi- tivity to punishment matures in a linear trajectory across development, reward sensitivity evinces a peak in adolescence before declining into adulthood, similar to the pattern observed for sensation seeking (Cauffman et al., 2010). Findings from this same program of work, discussed in a later section, also indicate that adolescents may be especially drawn to immediate rewards (Steinberg, Graham, et al., 2009). In sum, evidence is beginning to accumulate sug- gesting that adolescents are not only overly sensitive to rewards, but also relatively deficient in anticipat- ing and learning from punishment.

Given this evidence for heightened sensation seeking and reward sensitivity, it is perhaps not that surprising that adolescents also demonstrate an ex- aggerated susceptibility to peer influences on risk- relevant decision making (Albert & Steinberg, in press-a). What could be more fun and rewarding than joining one’s friends in exploring uncharted territory? Research has long indicated that adoles- cents are more likely than adults to take risks in the context of peer groups (rather than alone), and one of the best-documented predictors of adolescents’ risky behavior is the behavior of their peers, a finding that has been attributed to a combination of social learning processes, opportunity effects, and the ten- dency for risk-takers to seek out similar friends (Prinstein & Dodge, 2008). One recent study suggests that the simple presence of peers differentially biases

218 ALBERT AND STEINBERG

adolescents toward increased risk-taking behavior (Gardner & Steinberg, 2005). In this study, adoles- cents (mean age 5 14), youths (mean age 5 19), and adults (mean age 5 37) were tested on a computer driving task that mimicked the real-life decision of whether to run a series of yellow lights and risk being hit by an unseen car. Peer context was ma- nipulated by randomly assigning each group of three participants to play the game either individually (alone in the room) or with two same-aged peers in the room. When tested alone, the three age groups engaged in a comparable amount of risk taking. In contrast, adolescents took twice as many risks when tested with their peers in the room (relative to the alone condition), whereas the college-aged group was approximately 50% riskier, and adults showed no differences in risky driving related to context.

Taken together, these developmental patterns are beginning to provide a plausible account of why adolescents sometimes demonstrate poor JDM in the real world, despite a mature capacity to understand and reason about the costs and benefits of their choices. Consistent with recent functional neuro- imaging work demonstrating adolescent peaks in sensitivity to reward (e.g., Galvan et al., 2006) and social (e.g., Blakemore, 2008) stimuli, the behavioral research described above suggests that adoles- centsFmore so than adultsFare motivated to seek out novel and exciting experiences, more capable of learning from the positive than the negative conse- quences of those experiences, and more likely to take risks when in the presence of their peers. It is thus no great surprise that adolescents tend to make riskier decisions that adults, especially in peer contexts. This brings us to our final question: What develop- mental mechanisms might account for the normative decline in risky decision making observed across the transition from adolescence to adulthood?

The Development of Self-Regulatory Competence

In contrast to the relatively sudden changes in social, emotional, and reward processing that occur around the time of puberty, cognitive capacities supporting mature self-regulation appear to develop in a gradual, linear pattern over the course of adolescence, fre- quently extending into early adulthood (Steinberg, 2008). A growing body of evidence from cognitive neuroscience suggests that these improvements in cognitive control are supported by structural and functional maturation of a phylogenetically recent brain system that includes the lateral PFC, parietal association cortices, and parts of the anterior cingulate cortex, as well as enhanced connectivity between

this system and subcortical areas (for a review, see Casey et al., 2008).

Whereas adolescents demonstrate adult-like competence in logical reasoning and information processing by about age 15 or 16, developmental improvements in higher-order executive functions known to simultaneously recruit multiple sub- regions of the PFC are evident across the course of adolescence and into early adulthood. For instance, improved performance is evident in late adolescence on tasks assessing response inhibition (e.g., Luna et al., 2001), strategic problem solving (e.g., Luciana, Collins, Olson, & Schissel, 2009), and flexible rule use (e.g., Crone, Somsen, Zanolie, & Van Der Molen, 2006). Furthermore, these age-related gains in exec- utive function are reflected in a growing body of developmental neuroimaging research, which has generally demonstrated adolescent improvements in the efficient recruitment of task-relevant brain regions (e.g., dorsolateral PFC) supporting mature cognitive control (e.g., Durston et al., 2006).

Most importantly for the purposes of this discus- sion, improved coordination of cognitive and affec- tive processes is also evident in late adolescence and early adulthood (Steinberg, 2008). Broadly speaking, as adolescents mature, they appear increasingly ca- pable of regulating the social and emotional influ- ences that previously biased their JDM toward risky behavior. For instance, self-report and behavioral evidence indicate a pattern of linear growth in im- pulse control extending through adolescence and into the twenties (e.g., Galvan, Hare, Voss, Glover, & Casey, 2007; Leshem & Glicksohn, 2007; Steinberg et al., 2008). Moreover, research utilizing temporal discounting paradigms demonstrates steady de- clines across adolescence in the tendency to choose small immediate rewards, rather than larger delayed rewards, reflecting the capacity or willingness to delay gratification in support of long-term goals (e.g., Olson, Hooper, Collins, & Luciana, 2007; Stein- berg, Graham, et al., 2009). This pattern of gradual, prolonged maturation of self-regulatory capacity is further supported by evidence of late adolescent gains in future orientation (e.g., Steinberg, Graham, et al., 2009), planning (e.g., Albert & Steinberg, in press-b; Luciana et al., 2009), and metacognition (e.g., Klaczynsi, 2005). Finally, recent self-report evi- dence showing continued gains through at least age 18 in the ability to resist peer influence suggests that this emerging capacity for self-regulation extends to adolescents’ social worlds (Steinberg & Monahan, 2007); moreover, recent neuroimaging research in- dicates that individual differences in resistance to peer pressure are correlated with relatively more

ADOLESCENT JDM 219

mature patterns of white matter organization (Paus et al., 2008). In sum, one compelling explanation for why adults tend to make more adaptive decisions than adolescents is that they have a more mature capacity to resist the pull of social and emotional influences and remain focused on long-term goals (Steinberg, Cauffman, Woolard, Graham, & Banich, 2009).

CONCLUDING COMMENTS AND FUTURE DIRECTIONS

As exemplified by the research and theory reviewed in this article, the last decade has witnessed a kind of redefinition of what it means to study JDM in ado- lescence. Moving beyond a relatively narrow focus on age differences in the rational processing of de- cision elements, the field has begun to grapple with the dynamic quality of adolescents’ subjective deci- sion-making experienceFtheir beliefs and values, intentions and intuitions, emotions and self-aware- ness, all developing in the midst of a changing social world. We conclude our review by suggesting four lines of research that are needed to bolster this in- tegrative progress and ultimately to inform better interventions and policies charged with supporting adolescents’ well-being.

First, although research examining the cognitive predictors of adolescent health-risk behavior made important strides in the last decadeFnotably, by moving beyond a narrow focus on perceptions of risk and vulnerability, toward a broader view of the concerns that adolescents’ themselves find sa- lientFfurther longitudinal research is crucial for disentangling the reciprocal influence of perception and experience. We are certainly not the first to suggest this (see Millstein & Halpern-Felsher, 2002), but given the implications for the effectiveness of health-risk messages, the importance of under- standing this perception – experience relation cannot be overstated. If adolescents develop inflated per- ceptions of risk based on the efforts of well-meaning health educators, but these perceptions are inher- ently unstable and subject to radical discounting in response to unpunished experience (i.e., the most probable outcome), then we are doing them a dis- service by not honestly discussing the realistic costs and benefits of risk behavior. In addition to longi- tudinal studies better capable of modeling percep- tion – experience interactions, experimental studies are needed that can examine the degree to which adolescents adjust their ‘‘instructed’’ risk percep- tions in response to direct or vicarious experience with unpunished risk behavior.

Second, much of the credit for the field’s increas- ingly integrative focus is due to the theoretical ex- pansion provided by the dual-process models described in the second section of this review. Rec- ognizing that adolescent JDM is notably inconsistent with their capacities and reported intentions, these theories proposed that something else must be at work. Taken together, this something else looks ‘‘hot,’’ reactive, intuitive, experiential, not necessarily con- scious, and often based on social stereotypes or pro- totypes. By opening the door to these domains of thinking and feeling, the field has greatly enhanced its explanatory power. We caution, however, that these dual-process explanations are themselves likely to represent heuristics of a sort; multiple separable pro- cesses contribute to adolescent JDM, and while con- ceptually useful, dual-process theories must remain flexible enough to avoid false dualities and rather at- tempt to model this complexity. This cautionary note aside, several of these theories converge in identifying particularly influential ‘‘hot’’ contributions to decision making. Research on social heuristics and social pro- totypes highlight the increasing importance of ‘‘social meanings’’ for guiding behavior in adolescence (Sun- stein, 2008). Attention should continue to be given to the pathways by which these social meanings influ- ence adolescent JDM and, in particular, the environ- mental factors (e.g., media, peers, parents, school) that shape the social meaning of risk behavior. Moreover, evidence that adolescents are slow to develop ‘‘gist- based’’ avoidance of risk, despite exposure to count- less risk-avoidant messages, again raises interesting questions regarding the most effective way to present health-promoting information. If adolescents are differentially sensitive to the reward potential of their decisionsFas suggested by developmental trends in sensation seeking and reward learning, as well as the influence of perceived benefits in predicting risk be- haviorFprevention research might gain more traction by working to strengthen adolescents’ intuitive ap- preciation of the benefits of health-promoting behav- iors (and challenge their intuitions about the benefits of risk taking).

Third, the field’s enthusiasm (including our own) for the emerging work on the social neuroscience of adolescent JDM must be tempered by two observa- tions. First, during the past decade research on the neural underpinnings of JDM in adolescence has far outpaced research on the very behaviors that the neuroscience is intended to inform. Indeed, as we note elsewhere (Steinberg, 2010), some of the best behavioral research on adolescents’ reward seeking and self-reg- ulation conducted in the past 10 years comes from functional imaging research on these phenomena.

220 ALBERT AND STEINBERG

Neuroimaging studies of JDM, while useful, need to be complemented by experimental and nonexperi- mental research on adolescent JDM in the real world. Much of the literature on novelty seeking and self- regulation outside the scanner is now very old.

Finally, as illustrated by the Gardner and Steinberg (2005) study of peer influences on risk taking cited previously, the social context in which adolescent JDM is assessed may have a profound influence on the conclusions one draws regarding age differences in decision making; studies of individuals making deci- sions on their own likely minimize differences between adolescents and adults. More research that takes the social context of JDM into account would be especially informative. Along similar lines, it is likely that age differences in JDM are accentuated under conditions of emotional arousal, just as they are when individuals are socially aroused. For example, a recent study that used a relatively simple experimental manipulation to increase the affective arousal of a decision-making task demonstrated substantially larger age differences in risk taking than were seen on an otherwise-identical low-arousal task (Figner, Mackinlay, Wilkening, & Weber, 2009). The social psychology literature is replete with examples of research on ways in which affective and social factors moderate JDM, but this work has, by and large, been adevelopmental, involving samples of college undergraduates. Developmentally informed work in this vein is sorely needed.

As we have detailed in this review, research on JDM during adolescence took several new directions during the past decade, moving away from studies that focus purely on rational processing and toward research that adds psychosocial factors into the mix and that attempts to link behavioral research with emergent models of adolescent brain development. By bridging work on biological, cognitive, emotional, and social development in adolescence, we will gain a deeper and richer understanding of the processes that influence JDM at keg parties and in the other real-world contexts in which adolescents spend time.

REFERENCES

Albert, D., & Steinberg, L. (in press-a). Peer influences on

adolescent risk behavior. In M. T. Bardo, D. H. Fishbein, &

R. Milich (Eds.), Inhibitory control and drug abuse preven-

tion: From research to translation. New York, NY: Springer. Albert, D., & Steinberg, L. (in press-b). Age differences in

strategic planning as indexed by the Tower of London.

Child Development. Arnett, J. J. (1992). Reckless behavior in adolescence: A

developmental perspective. Developmental Review, 12,

339 – 373.

Azjen, I. (1985). From intentions to actions. In J. Kuhl & J. Beckman (Eds.), Action control from cognition to behavior (pp. 11 – 39). New York, NY: Springer-Verlag.

Benthin, A., Slovic, P., & Severson, H. (1993). A psycho- metric study of adolescent risk perception. Journal of Adolescence, 16, 153 – 168.

Beyth-Marom, R., Austin, L., Fischhoff, B., Palmgren, C., & Jacobs-Quadrel, M. (1993). Perceived consequences of risky behaviors: Adults and adolescents. Developmental Psychology, 29, 549 – 563.

Blakemore, S. (2008). The social brain in adolescence. Na- ture Reviews Neuroscience, 9, 267 – 277.

Casey, B. J., Getz, S., & Galvan, A. (2008). The adolescent brain. Developmental Review, 28, 62 – 77.

Cauffman, E., Shulman, E., Steinberg, L., Claus, E., Banich, M., Graham, S., et al. (2010). Age differences in affec- tive decision making as indexed by performance on the Iowa Gambling Task. Developmental Psychology, 46, 193 – 207.

Chambers, R. A., Taylor, J. R., & Potenza, M. N. (2003). Developmental neurocircuitry of motivation in adoles- cence: A critical period of addiction vulnerability. The American Journal of Psychiatry, 160, 1041 – 1052.

Chibbar, R., Toma, J. G., Mitchell, B. F., & Miller, F. D. (1990). Regulation of neural oxytocin gene-expression by gonadal-steroids in pubertal rats. Molecular Endocrinol- ogy, 4, 2030 – 2038.

Cohn, L. D., Macfarlane, S., Yanez, C., & Imai, W. K. (1995). Risk perception: Differences between adolescents and adults. Health Psychology, 14, 217 – 222.

Crone, E. A., Bunge, S. A., Latenstein, H., & Van der Molen, M. W. (2005). Characterization of children’s decision- making: Sensitivity to punishment frequency, not task complexity. Child Neuropsychology, 11, 245 – 263.

Crone, E. A., Somsen, R. J. M., Zanolie, K., & Van der Molen, M. W. (2006). A heart rate analysis of develop- mental change in feedback processing and rule shifting from childhood to early adulthood. Journal of Experi- mental Child Psychology, 95, 99 – 116.

Crone, E. A., & Van der Molen, M. W. (2004). Develop- mental changes in real-life decision-making: Perfor- mance on a gambling task previously shown to depend on the ventromedial prefrontal cortex. Developmental Neuropsychology, 25, 251 – 279.

Crone, E. A., & Van der Molen, M. W. (2007). Development of decision-making in school-aged children and ado- lescents: Evidence from heart rate and skin conductance analysis. Child Development, 78, 1288 – 1301.

Crone, E. A., Vendel, I., & Van der Molen, M. W. (2003). Decision-making in disinhibited adolescents and adults: Insensitivity to future consequences or driven by im- mediate reward? Personality and Individual Differences, 35, 1625 – 1641.

Dahl, R. (2004). Adolescent brain development: A period of vulnerabilities and opportunities. Annals of the New York Academy of Sciences, 1021, 1 – 22.

Damasio, A. R. (1994). Descartes’ error: Emotion, reason, and the human brain. New York, NY: Avon Books.

ADOLESCENT JDM 221

Davidson, D. (1995). The representativeness heuristic and

the conjunction fallacy effect in children’s decision

making. Merrill-Palmer Quarterly, 41, 328 – 346. Durston, S., Davidson, M., Tottenham, N., Galvan, A.,

Spicer, J., Fossella, J., et al. (2006). A shift from diffuse to

focal activity with development. Developmental Science, 9,

1 – 20. Epstein, S. (1994). Integration of the cognitive and

psychodynamic unconscious. American Psychologist, 49,

709 – 724. Figner, B., Mackinlay, R. J., Wilkening, F., & Weber, E. U.

(2009). Affective and deliberative processes in risky

choice: Age differences in risk taking in the Columbia

Card Task. Journal of Experimental Psychology: Learning,

Memory, and Cognition, 35, 709 – 730. Furby, L., & Beyth-Marom, R. (1992). Risk taking adoles-

cence: A decision-making perspective. Developmental

Review, 12, 1 – 44. Galvan, A., Hare, T., Parra, C., Penn, J., Voss, H., Glover, G.,

et al. (2006). Earlier development of the accumbens

relative to the orbitofrontal cortex might underlie

risk-taking behavior in adolescents. Journal of Neu-

roscience, 26, 6885 – 6892. Galvan, A., Hare, T., Voss, H., Glover, G., & Casey, B. J.

(2007). Risk taking and the adolescent brain: Who is at

risk? Developmental Science, 10, F8 – F14. Gardner, M., & Steinberg, L. (2005). Peer influence on risk

taking, risk preference, and risky decision making in

adolescence and adulthood: An experimental study.

Developmental Psychology, 41, 625 – 635. Gerrard, M., Gibbons, F. X., & Bushman, B. J. (1996). Re-

lation between perceived vulnerability to HIV and pre-

cautionary sexual behavior. Psychological Bulletin, 119,

390 – 409. Gerrard, M., Gibbons, F. X., & Gano, M. L. (2003). Ado-

lescents’ risk perceptions and behavioral willingness:

Implications for intervention. In D. Romer (Ed.), Reduc-

ing adolescent risk: Toward an integrated approach (pp. 75 –

81). Thousand Oaks, CA: Sage. Gerrard, M., Gibbons, F. X., Houlihan, A. E., Stock, M. L., &

Pomery, E. A. (2008). A dual-process approach to health

risk decision making: The prototype willingness model.

Developmental Review, 28, 29 – 61. Goldberg, J. H., Halpern-Felsher, B. L., & Millstein, S. G.

(2002). Beyond invulnerability: The importance of ben-

efits in adolescents’ decisions to drink alcohol. Health

Psychology, 21, 477 – 484. Halpern-Felsher, B., & Cauffman, E. (2001). Costs and

benefits of a decision: Decision making competence in

adolescents and adults. Journal of Applied Developmental

Psychology, 22, 257 – 273. Halpern-Felsher, B. L., Biehl, M., Kropp, R. Y., & Rubin-

stein, M. L. (2004). Perceived risks and benefits of

smoking: Differences among adolescents with different

smoking experiences and intentions. Preventive Medicine,

39, 559 – 567.

Hemmelstein, N. (1995). Adolescent marijuana use and

perception of risk. Journal of Alcohol and Drug Education,

41, 1 – 15. Jacobs, J. E., & Klaczynski, P. A. (2002). The development of

judgment and decision-making during childhood and

adolescence. Current Directions in Psychological Science,

11, 145 – 149. Jacobs, J. E., & Klaczynski, P. A. (Eds.). (2005). The devel-

opment of judgment and decision making in children and

adolescents. Mahwah, NJ: Erlbaum. Jacobs, J. E., & Potenza, M. (1991). The use of judgment

heuristics to make social and object decisions: A devel-

opmental perspective. Child Development, 62, 166 – 178. Johnson, E. J., & Tversky, A. (1983). Affect, generalization,

and the perception of risk. Journal of Personality and Social

Psychology, 45, 20 – 31. Kahneman, D., Slovic, P., & Tversky, A. (Eds.). (1982).

Judgment under uncertainty: Heuristics and biases. Cam-

bridge, UK: Cambridge University Press. Katz, E. C., Fromme, K., & D’Amico, E. J. (2000). Effects of

outcome expectancies and personality on young adults’

illicit drug use, heavy drinking, and risky sexual be-

havior. Cognitive Therapy and Research, 24, 1 – 22. Klaczynski, P. A. (2001). Analytic and heuristic processing

influences on adolescent reasoning and decision-mak-

ing. Child Development, 72, 844 – 861. Klaczynsi, P. A. (2005). Metacognition and cognitive vari-

ability: A dual-process model of decision making and its

development. In J. Jacobs & P. Klaczynski (Eds.), The

development of judgment and decision making in children and

adolescents (pp. 39 – 76). Mahwah, NJ: Erlbaum. Klaczynski, P. A., & Cottrell, J. M. (2004). A dual-process

approach to cognitive development: The case of chil-

dren’s understanding of sunk cost decisions. Thinking

and Reasoning, 10, 147 – 174. Krosnick, J., Chang, L., Sherman, S., Chassin, L., & Presson,

C. (2006). The effects of beliefs about the health conse-

quences of cigarette smoking on smoking onset. Journal

of Communication, 56, S18 – S37. Kuhn, D. (2009). Adolescent thinking. In R. M. Lerner &

L. Steinberg (Eds.), Handbook of adolescent psychology,

Volume 1: Individual bases of adolescent development (3rd

ed., pp. 152 – 186). Hoboken, NJ: John Wiley & Sons. Laviola, G., Pascucci, T., & Pieretti, S. (2001). Striatal do-

pamine sensitization to damphetamine in periadoles-

cent but not in adult rats. Pharmacology, Biochemistry and

Behavior, 68, 115 – 124. Lerner, R. M. (2009). The positive youth development

perspective: Theoretical and empirical bases of a

strengths-based approach to adolescent development. In

S. J. Lopez & C. R. Snyder (Eds.), Oxford handbook of

positive psychology (2nd ed., pp. 149 – 163). New York,

NY: Oxford University Press. Leshem, R., & Glicksohn, J. (2007). The construct of imp-

ulsivity revisited. Personality and Individual Differences,

43, 681 – 691.

222 ALBERT AND STEINBERG

Loewenstein, G. F., Weber, E. U., Hsee, C. K., & Welch, N.

(2001). Risk as feelings. Psychological Bulletin, 127, 267 – 286.

Luciana, M., Collins, P. F., Olson, E. A., & Schissel, A. M.

(2009). Tower of London performance in healthy ado-

lescents: The development of planning skills and asso-

ciations with self-reported inattention and impulsivity.

Developmental Neuropsychology, 34, 1 – 15. Luna, B., Thulborn, K., Munoz, d., Merriam, E., Garver, K.,

Minshew, N., et al. (2001). Maturation of widely dis-

tributed brain function subserves cognitive develop-

ment. NeuroImage, 13, 786 – 793. Markovits, H., & Dumas, C. (1999). Developmental pat-

terns in the understanding of social and physical tran-

sitivity. Journal of Experimental Child Psychology, 73, 95 – 114.

Martin, C. A., Kelly, T. H., Rayens, M., Brogli, B. R., Bren-

zel, A., Smith, W. J., et al. (2002). Sensation seeking,

puberty and nicotine, alcohol and marijuana use in ad-

olescence. Journal of the American Academy of Child and Adolescent Psychiatry, 41, 1495 – 1502.

Meier, M. H., Slutke, W. S., Arndt, S., & Cadoret, R. J.

(2007). Positive alcohol expectancies partially mediate

the relation between delinquent behavior and alcohol

use: Generalizability across age, sex, and race in a cohort

of 85,000 Iowa schoolchildren. Psychology of Addictive Behaviors, 21, 25 – 34.

Michels, T. M., Kropp, R. Y., Eyre, S. L., & Halpern-Felsher,

B. L. (2005). Initiating sexual experiences: How do

young adolescents make decisions regarding early sex-

ual activity? Journal of Research on Adolescence, 15, 583 – 607.

Millstein, S. G., & Halpern-Felsher, B. L. (2002). Perceptions

of risk and vulnerability. Journal of Adolescent Health, 31, 10 – 27.

Nygaard, P., Waiters, E. D., Grube, J. W., & Keefe, D. (2003).

Why do they do it? A qualitative study of adolescent

drinking and driving. Substance Use and Misuse, 38, 835 – 863.

Olson, E. A., Hooper, C. J., Collins, P., & Luciana, M. (2007).

Adolescents’ performance on delay and probability

discounting tasks: Contributions of age, intelligence,

executive functioning, and self-reported externalizing

behavior. Personality and Individual Differences, 43, 1886 – 1897.

Paus, T. (2009). Brain development. In R. M. Lerner & L.

Steinberg (Eds.), Handbook of adolescent psychology, Vol- ume 1: Individual bases of adolescent development (3rd ed., pp. 95 – 115). Hoboken, NJ: John Wiley & Sons.

Paus, T., Toro, R., Leonard, G., Lerner, J., Lerner, R., Perron,

M., et al. (2008). Morphological properties of the action-

observation network in adolescents with low and high

resistance to peer influence. Social Neuroscience, 3, 303 – 316.

Peters, L. W. H., Wiefferink, C. H., Hoekstra, F., Buijs, G. J.,

ten Dam, G. T. M., & Paulussen, T. G. W. M. (2009). A

review of similarities between domain-specific deter-

minants of four health behaviors among adolescents.

Health Education Research, 24, 198 – 223. Pomery, E. A., Gibbons, F. X., Reis-Bergan, M., & Gerrard,

M. (2009). From willingness to intention: Experience

moderates the shift from reactive to reasoned behavior.

Personality and Social Psychology Bulletin, 35, 894 – 908. Prinstein, M. J., & Dodge, K. A. (Eds.). (2008). Under-

standing peer influence in children and adolescents. New York, NY: Guilford.

Quadrel, M. J., Fischhoff, B., & Davis, W. (1993). Adolescent

(in)vulnerability. American Psychologist, 48, 102 – 116. Reyna, V. F., & Ellis, S. C. (1994). Fuzzy-Trace Theory and

framing effects in children’s risky decision making.

Psychological Science, 5, 275 – 279. Reyna, V. F., & Farley, F. (2006). Risk and rationality in

adolescent decision making: Implications for theory,

practice, and public policy. Psychological Science in the Public Interest, 7, 1 – 44.

Rivers, S. E., Reyna, V. F., & Mills, B. (2008). Risk taking

under the influence: A fuzzy-trace theory of emotion in

adolescence. Developmental Review, 28, 107 – 144. Roberti, J. W. (2004). A review of behavioral and biological

correlates of sensation seeking. Journal of Research in Personality, 38, 256 – 279.

Rodriguez, D., Romer, D., & Audrain-McGovern, J. (2007).

Beliefs about the risks of smoking mediate the relation-

ship between exposure to smoking and smoking. Psy- chosomatic Medicine, 69, 106 – 113.

Romer, D., & Hennessy, M. (2007). A biosocial-affect model

of adolescent sensation seeking: The role of affect eval-

uation and peer-group influence in adolescent drug use.

Prevention Science, 8, 89 – 101. Ronis, D. L. (1992). Conditional health threats: Health be-

liefs, decisions, and behaviors among adults. Health Psychology, 11, 127 – 134.

Schwarz, N., & Clore, G. L. (1983). Mood, misattribution,

and judgments of well-being: Informative and directive

functions of affective states. Journal of Personality and Social Psychology, 45, 513 – 523.

Slovic, P., Peters, E., Finucane, M., & MacGregor, D. G.

(2005). Affect, risk, and decision making. Health Psy- chology, 24S, S35 – S40.

Song, A. V., Glantz, S. A., & Halpern-Felsher, B. L., (2009).

Perceptions of second-hand smoke risks predict future

adolescent smoking initiation. Journal of Adolescent Health, 45, 618 – 625.

Song, A. V., Morrell, H. E. R., Cornell, J. L., Ramos, M. E.,

Biehl, M., Kropp, R. Y., & Halpern-Felsher, B. L. (2009).

Perceptions of smoking-related risks and benefits as

predictors of adolescent smoking initiation. American Journal of Public Health, 99, 487 – 492.

Spear, L. (2009). The behavioral neuroscience of adolescence. New York, NY: Norton.

Stanovich, K. E. (1999). Who is rational? Studies of individual differences in reasoning. Mahway, NJ: Erlbaum.

Stanovich, K. E., Toplak, M. E., & West, R. F. (2008). The

development of rational thought: A taxonomy of heu-

ADOLESCENT JDM 223

ristics and biases. Advances in Child Development and Behavior, 36, 251 – 285.

Steinberg, L. (2003). Is decision making the right frame- work for research on adolescent risk taking? In D. Ro- mer (Ed.), Reducing adolescent risk: Toward an integrated approach (pp. 18 – 24). Thousand Oaks, CA: Sage.

Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78 – 106.

Steinberg, L. (2010). Commentary: A behavioral scientist looks at the science of adolescent brain development. Brain and Cognition, 72, 160 – 164.

Steinberg, L., Albert, D., Cauffman, E., Banich, M., Graham, S., & Woolard, J. (2008). Age differences in sensation seeking and impulsivity as indexed by be- havior and self-report: Evidence for a dual systems model. Developmental Psychology, 44, 1764 – 1777.

Steinberg, L., & Cauffman, E. (1996). Maturity of judgment in adolescence: Psychosocial factors in adolescent decision making. Law and Human Behavior, 20, 249 – 272.

Steinberg, L., Cauffman, E., Woolard, J., Graham, S., & Banich, M. (2009). Are adolescents less mature than adults? Minors’ access to abortion, the juvenile death penalty, and the alleged APA ‘‘flip-flop’’. American Psy- chologist, 64, 583 – 594.

Steinberg, L., Graham, S., O’Brien, L., Woolard, J., Cauff- man, E., & Banich, M. (2009). Age differences in future orientation and delay discounting. Child Development, 80, 28 – 44.

Steinberg, L., & Monahan, K. (2007). Age differences in resistance to peer influence. Developmental Psychology, 43, 1531 – 1543.

Sunstein, C. R. (2008). Adolescent risk-taking and social

meaning: A commentary. Developmental Review, 28, 145 –

152. Winkielman, P., Berridge, K. C., & Wilbarger, J. (2005).

Unconscious affective reactions to masked happy versus

angry faces influence consumption behavior and judg-

ments of value. Personality and Social Psychology Bulletin,

1, 121 – 135. Winkielman, P., Knutson, B., Paulus, M., & Trujillo, J. L.

(2007). Affective influence on judgments and decisions:

Moving toward core mechanisms. Review of General

Psychology, 11, 179 – 192. Zajonc, R. B. (1980). Feeling and thinking: Preferences need

no inferences. American Psychologist, 35, 151 – 175. Zuckerman, M. (1969). Theoretical formulations: I. In

P. Zubeck (Ed.), Sensory deprivation: Fifteen years of

research (pp. 407 – 432). New York: Appleton-Century-

Crofts. Zuckerman, M. (1994). Behavioral expressions and biosocial

bases of sensation seeking. New York, NY: Cambridge

University Press. Zuckerman, M., Eysenck, S., & Eysenck, H. J. (1978).

Sensation seeking in England and America: Cross-cul-

tural, age, and sex comparisons. Journal of Consulting and

Clinical Psychology, 46, 139 – 149. Zuckerman, M., & Kuhlman, D. M. (2000). Personality and

risk taking: Common biosocial factors. Journal of Per-

sonality, 68, 999 – 1029.

224 ALBERT AND STEINBERG

This document is a scanned copy of a printed document. No warranty is given about the accuracy of the copy.

Users should refer to the original published version of the material.