Introduction

Social scientists and casual observers of human development have long noted that the transitional period between childhood and adulthood is a time of heightened risk-taking. Indeed, despite the relative absence of illness and disease during this period, rates of morbidity and mortality increase substantially in adolescence, largely due to risk taking. The question of why adolescents seem predisposed toward recklessness is age-old; however, work in the field of developmental psychology, and more recently, developmental neuroscience, has provided new insights into the phenomenon.

For many years psychologists had attempted to explain adolescent recklessness as a consequence of cognitive deficiencies in young people's thinking, including irrationality, poor information processing, and ignorance about risk. As we have noted in previous publications (e.g., Steinberg, 2008), these accounts have been largely undermined by available evidence. Generally speaking, by age 15 or so, adolescents perform as well as adults on tasks measuring logical reasoning, information processing, and risk perception.

1. The emergence of dual systems models

About a decade ago, the budding field of developmental cognitive neuroscience began to provide insight into how patterns of brain development might explain aspects of adolescent decision-making (see, e.g. Dahl, 2004). In 2008, our lab at Temple University (Steinberg, 2008, Steinberg et al., 2008) and Casey's lab at Cornell (Casey et al., 2008) simultaneously proposed similar variations of a “dual systems” account of adolescent decision-making. This perspective attributes adolescents’ vulnerability to risky, often reckless, behavior in part to the divergent developmental courses of two brain systems: one (localized in the striatum, as well as the medial and orbital prefrontal cortices) that increases motivation to pursue rewards and one (encompassing the lateral prefrontal, lateral parietal, and anterior cingulate cortices) that restrains imprudent impulses (see e.g., Casey et al., 2008, Duckworth and Steinberg, 2015, Evans and Stanovich, 2013, Luna and Wright, 2016, Metcalfe and Mischel, 1999, Steinberg, 2008). Specifically, it proposes that risk-taking behaviors peak during adolescence because activation of an early-maturing incentive-processing system (the “socioemotional system”) amplifies adolescents’ affinity for exciting, novel, and risky activities, while a countervailing, but slower to mature, “cognitive control” system is not yet far enough along in its development to consistently restrain potentially hazardous impulses.

Several variations on this dual systems model have been proposed. The version that guides our work (Steinberg, 2008) is very similar to that proposed by Casey et al. (2008). Both conceive of a slowly developing cognitive control system, which continues to mature through late adolescence. However, whereas we propose that the socioemotional system follows an inverted-U shaped developmental course, such that responsiveness to reward increases in early adolescence and declines in early adulthood, Casey et al. have portrayed the socioemotional system as increasing in arousability until mid-adolescence, at which point it reaches a plateau, remaining at this level into adulthood. Furthermore, our version of the dual systems model posits that the decline in socioemotional arousability occurs independently of the development of the control system, whereas Casey et al.’s model proposes that the strengthening of the cognitive control system causes the socioemotional system to become less arousable. More recently, Luna and Wright (2016) have proposed another variation on the dual systems model (the “driven dual systems” model), which, like our model, hypothesizes an inverted-U shaped trajectory of socioemotional arousability, but, unlike our model, hypothesizes a trajectory of cognitive control that plateaus in mid-adolescence rather than continuing to increase into the 20s, as suggested by us and by Casey et al. In a similar vein, Luciana and Collins (2012) endorse a model that emphasizes the role of a hyperactive socioemotional system (“subcortical limbic-striatal systems” in their terminology) undermining the regulatory ability of the cognitive control system (the “prefrontal executive system”) resulting in greater risk-taking during adolescence. Like Luna and Wright, Luciana and Collins argue that the development of cognitive control is complete by mid-adolescence, as evidenced by adolescents’ adult-like performance on non-affective measures of cognitive capacity. Fig. 1 illustrates the similarities and differences between these versions of the dual systems model.

Fig. 1. Alternative theoretical models of the development of the socioemotional (reward processing) and cognitive control systems from about age 10 to age 25.

Another perspective, Ernst's (2014) triadic model, expands on the dual systems concept by hypothesizing that a third brain system—one responsible for emotional intensity and avoidance, anchored in the amygdala—is also important for understanding the developmental differences in “motivated behavior.” With respect to the type of reward-seeking risky behavior that the dual systems models seek to explain, Ernst (2014) speculates that this emotion/avoidance system may serve to boost impulsive decisions in adolescence by amplifying the perceived cost of delay. She also proposes that this system may become hypoactive—dampening avoidance impulses—in the face of a potential reward that activates the socioemotional system. While this model is intuitively appealing, there is not much evidence to date indicating that the emotion/avoidance system and its developmental trajectory help to explain heightened levels of risk taking in adolescence. Also, the role of the amygdala in decision-making is not yet clear (see e.g., Somerville et al., 2014). Therefore, our review does not address this third hypothesized system.

2. The current article

In this article, we review evidence from both the behavioral and neuroimaging literatures that has emerged since the dual systems model was originally articulated in 2008. In particular, we consider the degree to which extant research findings support, extend, modify, and challenge the theory. We focus our discussion on three main propositions of the model: (1) that reward sensitivity peaks in adolescence; (2) that cognitive control increases linearly during this period; and (3) that heightened risk-taking during adolescence is the product of heightened reward-seeking and relatively weaker cognitive control.

We begin by addressing a recent criticism of the basic premise that middle adolescence is an especially intensified period of risky behavior. We then examine evidence regarding the trajectory of sensation seeking across development, the reward processing circuitry that might underlie developmental changes in sensation-seeking behavior, and the extent to which heightened sensation seeking and reward sensitivity are related to pubertal development. Next, we survey evidence on the developmental trajectory of the ability to control impulsive behavior through self-regulatory processes, and on the maturation of the brain's cognitive control network, which is proposed to undergird this ability. Finally, we consider evidence concerning the interaction of the two proposed systems during risky decision making, identify several unresolved issues, and offer some recommendations for how they might be addressed in future research.

In examining how recent evidence informs the dual systems model, we are cognizant of critiques of this viewpoint, including contentions that the model inadequately accounts for studies that do not find adolescents to be particularly sensitive to reward (Pfeifer and Allen, 2012; but see Strang et al., 2013 for a response to this critique), that cognitive control does not unequivocally improve during adolescence (Crone and Dahl, 2012), and that adolescence may not actually be a peak period of vulnerability to risk-taking (e.g., Defoe et al., 2014, Willoughby et al., 2013; but see Ernst, 2014 for a response to Willoughby et al.). We briefly address these critiques here.

We do not disagree with a fourth critique of the dual systems model—that it is insufficiently nuanced (Pfeifer and Allen, 2012)—because this is almost certainly correct. However, we believe that even an admittedly simplified model can serve as a useful heuristic and, more important, can help to motivate research needed to flesh out the details of an initially simplistic account (for a full discussion see Strang et al., 2013). Moreover, given the influence this perspective continues to have on legal policy and practice, public health, and popular discourse about adolescence (Steinberg, 2014), it is important to ask whether this simplified account is helpful or misguided.

It may be useful at this juncture to clarify our terminology. To begin, the term “adolescence” warrants discussion. Largely as a matter of convenience, scholars generally agree that adolescence begins when pubertal development becomes evident, around age 10 (somewhat later among males). The end of adolescence—the attainment of adult status—is not easily pegged to any single biological or social event, however. In research, adulthood is often defined as beginning at either age 18 or 21, the two ages most often tied to legal majority in the developed world. However, given that 18- to 21-year-olds in industrialized societies are rarely regarded outside the legal system as fully mature adults, and typically have not attained many of the traditional markers of adult status (e.g., financial independence, completion of formal education, stable romantic relationships, full-time employment, parenthood), we prefer to refer to this age range as “late adolescence.” For purposes of this paper, our focus is mainly on the second decade of life, from about ages 10 to 21, which we subdivide into early adolescence (10–13), middle adolescence (14–17), and late adolescence (18–21).

Another source of confusion in discussions of the dual systems perspective concerns levels of analysis, since the perspective refers to overt behaviors (such as risk taking), the psychological states hypothesized to motivate them (such as sensation seeking), and the neural processes believed to undergird these states (such as reward sensitivity). In an earlier paper (Smith et al., 2013), we suggested that “reward sensitivity” and “cognitive control” be used to refer to the neurobiological constructs that are measured in studies of brain structure or function (see Fig. 2). These neurobiological phenomena have psychological manifestations (in our terminology, “sensation seeking” and “self-regulation”) that are measured by assessing psychological states or traits through the subjective reports of individuals or their evaluators.

Fig. 2. Constructs implicated in the dual systems model of adolescent risk-taking arranged by level of analysis.

For heuristic purposes, we use “sensation seeking” as an overarching label for a number of interrelated constructs that refer to the inclination to pursue “varied, novel, complex, and intense sensations and experiences and the willingness to take physical, social, legal, and financial risks for the sake of such experiences” (Zuckerman, 1994, p. 26). Recruitment of brain regions and systems implicated in reward-processing (e.g., ventral striatum, orbitofrontal cortex) has been linked to measures of sensation seeking in humans and other animals (Abler et al., 2006, Leyton et al., 2002, Lind et al., 2005). In a similar vein, we use the label “self-regulation” to refer to a group of interrelated but distinguishable constructs that refer to the capacity to deliberately modulate one's thoughts, feelings, or actions in the pursuit of planned goals; among these constructs are impulse control, response inhibition, emotion regulation, and attentional control. Aspects of self-regulation have been linked to the functioning of brain regions and systems that subserve cognitive control (e.g., lateral prefrontal, lateral parietal, and anterior cingulate cortices) (Luna et al., 2010, Mennigen et al., 2014).

Variations in sensation seeking and self-regulation, in turn, are associated with variations in behaviors, including risk taking, which can be measured through objective reports or observations. In our model, risk taking is a subset of many aspects of decision making that share some, but not all, characteristics in common. Furthermore, as Fig. 2 indicates, all decision making takes place within a broader context that encourages and enables some acts but discourages and prohibits others. As we discuss, the fact that adolescents’ risk taking is influenced by the broader context in which it occurs makes it difficult to move seamlessly between laboratory studies and the real world.

3. Are adolescents particularly prone to risk taking?

Allusions to adolescence as a time of rash behavior and poor decision making predate the articulation of the dual systems model by centuries. And yet, empirical evidence of a mid-adolescent peak in risk taking (at least in humans) is not unequivocal. As pointed out in a recent review of epidemiological data, the peak age for risk taking varies across different behaviors, and very often it is late adolescents, not middle adolescents, who exhibit the highest levels of recklessness (see Willoughby et al., 2013). For example, one of the most dangerous forms of substance use—binge drinking—is most common during the early 20s (Chassin et al., 2002, Willoughby et al., 2013).

Although some argue that these data pose a problem for the dual systems model, we disagree. The model does not posit that middle adolescents necessarily demonstrate the highest levels of all forms of risk taking in the real world. Rather, it asserts that risk-taking propensity is highest in mid-adolescence, but that the expression of this propensity is expected to vary depending on the context (as noted in Fig. 2). Our position is that late adolescents are less biologically predisposed to risk taking than middle adolescents (consistent with the dual systems model), but that they exhibit higher levels of many forms of real-world risk-taking due to greater opportunity. Compared to younger individuals, people in their early 20s typically experience less supervision from adults, have more financial resources, and are afforded greater legal access to many forms of risk taking (e.g., driving, alcohol, and gambling). Thus, we contend that maturational factors predispose middle adolescents to greater risk taking, but that social and legal factors constrain their opportunities to realize this predisposition. Simply put, it is far easier for the average 21-year-old to take risks with alcohol, cars, and gambling than it is for the average 15-year-old. If 15-year-olds were permitted to drive, purchase alcohol, and enter casinos legally, our prediction is that they would likely crash, binge drink, and gamble more than people in their early 20s.

3.1. Risk taking in the laboratory

In an effort to investigate age differences in risk-taking propensity, unconfounded by age differences in opportunity, researchers have tested adolescent and adult participants using artificial tasks—typically gambling games and driving simulations—that give them the option to take risks in the safety of a laboratory setting. While such tasks are often lacking in ecological validity, they do have the advantage of controlling for contextual differences between adolescents and other age groups, as well as for age differences in behavior preferences. These studies yield inconsistent results, with some finding greater risk taking in adolescence than in adulthood (e.g., Burnett et al., 2010, Mitchell et al., 2008, Van Leijenhorst et al., 2008, Van Leijenhorst et al., 2010a), others finding no age effects (e.g., Bjork et al., 2007, Eshel et al., 2007, de Water et al., 2014), and still others finding that adolescents engage in less risk taking compared to children (Paulsen et al., 2011). These inconsistent findings suggest that if there is an increased risk taking propensity in adolescence it may only manifest under certain conditions (see Defoe et al., 2014 for a recent meta-analysis).

Recently, researchers have used laboratory tasks and manipulations that better approximate certain aspects of real-life risky decision-making. These studies have helped to delineate the conditions under which adolescents may be more predisposed than other age groups to take risks. For example, noting that during most real-world risk taking the actual chances of a positive or negative outcome are unknown, researchers recently tested whether age differences in risk taking depend on whether the probabilities of a successful outcome are known or unknown (Tymula et al., 2012, Tymula et al., 2013). Tymula and colleagues (2012) had adolescents and adults complete a risk-taking task with two different conditions: a “known risk” condition and an “ambiguous risk” condition. In the “known risk” condition, participants chose between a sure bet (100% chance of receiving $5) and a “risky” bet with known reward probabilities (e.g., a 50% chance of winning $50, versus $0 if they lost). In the “ambiguous risk” condition, participants again chose between a sure and risky option, but this time the likelihood of winning or losing on the risky option was unknown. Compared to adults, adolescents made fewer risky decisions when the probabilities of loss were known (i.e., adolescents were less risk tolerant). However, when the probabilities were unknown, adolescents made significantly more risky decisions than adults. Thus, under conditions that are more representative of real-life risk-taking (where risk probabilities are typically unknown), adolescents evince a greater risk-taking propensity than adults.

Another way in which real-life risk taking differs from risk taking in the laboratory is with respect to emotional arousal. Contexts in which risk taking occurs outside the lab are often thrilling or frightening; in the lab, both the nature of the risk taking (the stakes and considerations involved) and the surrounding environment are typically less exciting. Scholars have argued that differences in arousal give rise to fundamentally different ways of processing information (e.g., Luna and Wright, 2016, Metcalfe and Mischel, 1999). The dual systems model holds that, to the extent that decision-making occurs under conditions that arouse the socioemotional system (e.g., conditions that are relatively more thrilling), differences between adolescent and adult decision-making and, hence, risk taking will be more pronounced. This pattern was observed in one study that experimentally manipulated the degree to which a card game risk-taking task was affectively arousing (Figner et al., 2009). Consistent with the dual systems account, adolescents evinced greater risk taking and poorer use of risk-relevant information than adults, but only in the more arousing version of the task.

A third difference between most laboratory risk-taking tasks and real-life risk taking is that, in the laboratory, adolescents are asked to make decisions when they are alone, whereas the majority of risky behaviors during adolescence occur in groups (Albert et al., 2013). To mimic this context in the lab, researchers have employed experimental manipulations in which adolescents complete risk-taking tasks in the presence of peers (real or illusory). Some studies have asked participants to bring same-aged peers to the lab (Chein et al., 2011, Gardner and Steinberg, 2005, Kretsch and Harden, 2014), while others have deceived participants into believing that they are being observed remotely by a peer (Smith et al., 2014a). Not only does the “presence” of peers increase the ecological validity of the risk-taking task (because adolescent risk taking often occurs in groups), but it also appears to elevate emotional arousal, which further increases the comparability to real-world risk-taking contexts.

Studies that have manipulated the social context have found that adolescents are more induced by peer presence to take risks than are adults (Chein et al., 2011, Gardner and Steinberg, 2005, Smith et al., 2014a). These findings, which are largely consistent with other studies of peer effects on adolescent driving [e.g., Segalowitz et al., 2012; see Lambert et al. (2014) for a review], suggest that adolescents are particularly vulnerable to the effects of peer presence on risk-taking behaviors. Moreover, neuroimaging data suggest that the effect of peer presence on risk taking is due to increased affective arousal, as evidenced by greater activation of brain regions within the socioemotional system (Chein et al., 2011). A recent extension of this line of work in our lab using a rodent model found that adolescent mice, but not adult mice, consume more alcohol in the presence of same-aged conspecifics than when alone (Logue et al., 2014).

Overall, then, there is evidence for increased risk taking in adolescence compared to adulthood, though developmental differences may only be evident under certain conditions, such as emotional arousal, ambiguous risk, and the presence of others. The tendency for adolescents to engage in more risky behaviors in highly-arousing contexts together with increased engagement of their socioemotional system during peer observation point to the importance of reward processing in decision making during this period of life, a topic to which we now turn.

4. The development of sensation seeking and reward sensitivity

Increased adolescent risk taking in contexts that are emotionally arousing is consistent with one of the central tenets of the dual systems model—that activation and reactivity of the socioemotional system reaches its peak during mid- to late adolescence. A growing literature interrogates this aspect of the model by examining the psychological and neurological evidence for heightened responsiveness of the socioemotional system during adolescence, including in situations that do not involve risky decision-making. This is important because the dual systems model proposes that the socioemotional system is more responsive generally in adolescence than at other ages, not only in the context of risk taking.

Moreover, the model hypothesizes that the developmental course of the socioemotional system is, unlike the development of the cognitive control system, closely tied to pubertal development (for review see Smith et al., 2013). Around age 12 (for boys) or 11 (for girls), pubertal hormones inundate the brain, triggering a series of changes in neural structure and function (Euling et al., 2008, Schulz et al., 2009), especially in dopamine-rich limbic regions associated with reward processing (Blakemore et al., 2010, Sinclair et al., 2014). It is thought that these hormone-related changes sensitize the adolescent brain to reward (Forbes and Dahl, 2010, Peper and Dahl, 2013), as appears to be the case in animal studies (Alexander et al., 1994, Clark et al., 1996, Miele et al., 1988). More specifically, the reward system is particularly sensitive to the sudden surge of hormones at the start of puberty, heightening sensitivity to affective stimuli. Although pubertal hormones do not decline into adulthood, we posit that a decrease in reward sensitivity ensues during later adolescence and into young adulthood as the reward system becomes desensitized to the effects of these hormones (Smith et al., 2013). While admittedly limited, recent evidence integrating measures of puberty into psychological, behavioral, and neuroscience studies supports this claim as well.

4.1. Sensation seeking

One psychological manifestation of socioemotional reactivity is sensation seeking. As anticipated by the dual systems model, measures of sensation seeking are often found to be predictive of self-reported risk taking (e.g., Kong et al., 2013, MacPherson et al., 2010). True sensation-seeking behavior is difficult to elicit in laboratory environments (at least, among human subjects); consequently, the vast majority of studies examining age-related changes in sensation seeking rely on self-report. As would be expected within the dual systems account, longitudinal and cross-sectional studies generally find evidence of a peak in self-reported sensation seeking around mid-adolescence and a decrease into adulthood (Harden and Tucker-Drob, 2011, Peach and Gaultney, 2013, Quinn and Harden, 2013, Romer and Hennessy, 2007, Shulman et al., 2014a, Shulman et al., 2014b, Steinberg and Chein, 2015, Steinberg et al., 2008). This overall pattern is further corroborated by a number of longitudinal studies following individuals from childhood into adolescence, which find that sensation seeking increases across this time period (Collado et al., 2014, Lynne-Landsman et al., 2011, MacPherson et al., 2010). For example, using the Brief Sensation-seeking Scale (Hoyle et al., 2002), Collado and colleagues (2014) found a linear increase in sensation seeking in individuals aged 9–13. Fewer longitudinal studies of sensation seeking have followed individuals from adolescence into adulthood. However, two recent studies using a large, longitudinal data set (the National Longitudinal Study of Youth 1979 Child and Young Adult Survey) have helped to address this gap and clarify the developmental pattern of sensation seeking across adolescence. Harden and Tucker-Drob (2011) found that self-reported sensation seeking increased from age 10 to mid-adolescence, and then decreased thereafter into early adulthood. Analyzing the same data set, Shulman et al., 2014a, Shulman et al., 2014b found that females demonstrated an earlier peak in sensation seeking (age 16–17) than males (age 18–19), and a steeper decline thereafter. Overall, these studies suggest that, as the dual systems model would predict, sensation seeking follows an inverted-U pattern over time, consistent with the proposed pattern of change in the socioemotional system.

The hypothesis that pubertal development drives developmental change in the socioemotional system in adolescence is derived in part from older studies linking higher levels of sensation seeking to more advanced pubertal status (Martin et al., 2002, Resnick et al., 1993). Newer studies have replicated this result (Castellanos-Ryan et al., 2013, Gunn and Smith, 2010, Quevedo et al., 2009, Urošević et al., 2014) and have found evidence that the correlation between self-reported pubertal development and sensation seeking may be stronger for boys than for girls (Castellanos-Ryan et al., 2013, Steinberg et al., 2008). Also, as would be expected based on the link between puberty and sensation seeking, recent studies have found that more advanced pubertal status in adolescents is associated with greater involvement in behaviors that are closely related to sensation seeking, such as substance use (Castellanos-Ryan et al., 2013, de Water et al., 2013, Gunn and Smith, 2010), law-breaking (Collado et al., 2014, Kretschmer et al., 2014), and risk taking in laboratory contexts (Collado et al., 2014, Kretsch and Harden, 2014, Steinberg et al., 2008; but see van Duijvenvoorde et al., 2014 who did not find a correlation between pubertal status and performance on a gambling task).

4.2. Behavioral manifestations of reward sensitivity

Compared to self-report studies of sensation seeking, there are markedly fewer behavioral studies examining the development of reward sensitivity, and these have heterogeneous methodologies and findings, which makes it difficult to draw firm conclusions about age differences. One large-scale study utilized the Iowa Gambling Task (IGT; Cauffman et al., 2010) to explore age-related changes in reward sensitivity. In the standard version of the IGT, participants are presented with four decks of cards, two that will win them money over repeated play (advantageous decks) and two that will lose them money over repeated play (disadvantageous decks); participants are permitted to choose freely from the four decks (e.g., Smith et al., 2011b). However, Cauffman et al. (2010) modified the task such that the computer pseudorandomly selected a deck on each trial and the participant was asked to decide whether to play or pass. This modification allowed the researchers to disentangle affinity for the advantageous decks—a measure of reward sensitivity—from avoidance of disadvantageous decks. The results indicated that mid-adolescents aged 14–17 and older adolescents aged 18–21 learned to play from advantageous decks faster than either younger adolescents (ages 10–13) or adults (ages 22–25), a finding that was recently replicated in an international sample of more than 5000 individuals (Steinberg and Chein, 2015). This outcome suggests that ages 14–21 are a period of heightened sensitivity to reward. Using the same data set, Steinberg (2010) also found that self-reported sensation seeking, but not impulsivity, was associated with overall rate of plays from rewarding decks at the end of the task.

Another way researchers have examined developmental differences in reward sensitivity is by substituting neutral stimuli (e.g., letters) with rewarding ones (e.g., happy faces) in traditional behavioral tasks (e.g., measures of impulse control), and then observing the extent to which the presence of rewarding stimuli impacts performance. Two such studies have examined age differences (comparing children, adolescents, and adults) in performance on an “Emotional Go/No-Go” task. In all Go/No-Go tasks, participants are presented with a rapid sequence of target and non-target stimuli, both of which are typically emotionally neutral. Participants are instructed to press a button when a target stimulus is presented (a “go” trial) and to withhold the button press (do nothing) when a non-target stimulus is presented (a “no-go” trial). Non-target events occur relatively infrequently, making it challenging for participants to restrain the impulse to press the button on no-go trials. [As with most measures of self-regulation, performance improves linearly with age on traditional Go/No-Go tasks (see Casey et al., 2002 for a review).]

In the Emotional Go/No-Go task employed by Somerville et al. (2011), the stimuli were photographs of either happy or calm faces. They found that for adolescents (ages 13–17) more than for children (ages 6–12) or adults (ages 18–29), withholding a button press was more difficult when the no-go stimulus was a happy face—a rewarding stimulus—than when it was a calm face. In fact, only adolescents showed impaired impulse control in the happy, relative to the calm, no-go condition. The researchers proposed that adolescents’ greater emotional response to the (rewarding) happy face made it harder for them to restrain the impulse to “approach” it (i.e., press the “go” button). If so, these results support the proposition that adolescents are particularly sensitive to reward. However, another study (Tottenham et al., 2011) using a similar, but not identical, Emotional Go/No-Go task did not find this pattern (i.e., they found no emotion by condition by age group interaction for erroneous button presses). Though there were methodological differences between these two studies, the failure to find the effect in one of the two underscores the need for further research in this vein. It also highlights the benefits of being able to incorporate neuroimaging methods. Engagement of the socioemotional system may not always be robust enough (especially in laboratory settings) to consistently bias behavior. Neuroimaging enables researchers to detect age differences in the engagement of this system, even absent behavioral consequences.

4.3. Neuroimaging of reward sensitivity

In recent years, many neuroimaging studies have asked whether adolescents are particularly sensitive to reward. Beyond identifying differences between adolescents and other age groups, these studies help address questions about the neural mechanisms underlying adolescents’ heightened reward-seeking. For example, among those who agree that adolescents are more inclined than adults to seek out rewards, there has been disagreement over whether this results from the fact that rewards are experienced as exceedingly pleasurable during adolescence (and are therefore more enticing) or because they are experienced as less so (and are therefore less satisfying). Indeed, one early notion, now largely discredited, posited that adolescents suffer from a “reward deficiency syndrome” which impels them to seek out exciting experiences because mundane ones are not sufficiently rewarding, much like addicts who seek out drugs because quotidian experiences no longer excite them (for a discussion, see Spear, 2002).

To date, most of the developmental neuroscience literature has focused on developmental differences in the striatum, and more specifically in the ventral portion of the striatum, which is considered one of the main regions involved in the calculation of reward (Knutson et al., 2001, Luciana and Collins, 2012). In our dual systems account, increases in risk taking and other reward-seeking behaviors are thought to be a consequence of increased engagement of the striatum during decision-making, thus biasing adolescents toward more rewarding choices. Heightened sensitivity to rewarding outcomes of prior decisions may contribute to adolescent risk-taking as well. There is evidence, for example, that the volume of the nucleus accumbens (part of the ventral striatum and presumed to be the central structure in the reward system) increases during the first part of adolescence and then shrinks thereafter (Luciana and Collins, 2012).

As discussed by Steinberg (2008), the neuroscience literature includes both studies that support and challenge the dual systems account of heightened striatal engagement during adolescence (e.g., Bjork et al., 2004, Galvan et al., 2006). Since that 2008 publication, several reviews have discussed methodological differences across these studies that may have contributed to the inconsistent findings (see Galvan, 2010, Richards et al., 2013). Indeed, the developmental neuroimaging literature on reward processing has grown substantially over the last several years, and we believe there are patterns to be noted, and conclusions to be drawn, that help explain what appear to be contradictory findings.

In its current state, the literature provides considerable evidence that when developmental differences in striatal activation are present during reward processing (both during the anticipation and the receipt of reward) adolescents engage the striatum to a greater extent than both children and adults (Barkley-Levenson and Galvan, 2014, Christakou et al., 2011, Galvan and McGlennen, 2013, Galvan et al., 2006, Geier et al., 2010, Hoogendam et al., 2013, Jarcho et al., 2012, Padmanabhan et al., 2011, Silverman et al., 2015, Van Leijenhorst et al., 2010b). For example, a recent longitudinal study found that, across mid-adolescence (roughly ages 15–18), ventral striatal activation in response to “risk taking” on the balloon analogue task (which also reflects reward-seeking) declines intra-individually over time, and that striatal activation during the task is correlated with self-reported risk taking outside the laboratory (Qu et al., 2015). However, a handful of studies find the opposite pattern—dampened striatal response during adolescence relative to adulthood (Bjork et al., 2004, Bjork et al., 2010, Hoogendam et al., 2013, Lamm et al., 2014)—and others fail to demonstrate any age differences (Krain et al., 2006, Teslovich et al., 2014, Van Leijenhorst et al., 2006).

In trying to explain this inconsistency, it is important to note that disparate findings emerge only for contrasts that focus on the anticipation of a reward. Studies focusing on striatal engagement during the receipt of a reward consistently find that adolescents engage the striatum to a greater extent than adults (Galvan and McGlennen, 2013, Hoogendam et al., 2013, Van Leijenhorst et al., 2010b), suggesting that adolescents are—as the dual systems model claims—more sensitive to rewarding outcomes.

The fact that striatal engagement is relatively higher among adolescents than among children or adults during receipt of rewards but not necessarily during reward anticipation potentially challenges our conception of adolescent risk taking as being driven by the prospect of a reward. However, nuances in task design, modeling of the anticipatory event in imaging analyses, and the relationship between striatal engagement and behavioral reward sensitivity may account for these seemingly inconsistent results, for several reasons. First, the time points at which events are modeled, and the specific trial periods that are included within the model, can dramatically affect the observed neural response (e.g., Geier et al., 2010). One factor that seems to differentiate studies that do and don’t report increased adolescent engagement of the striatum during reward anticipation is the degree to which anticipatory cues reliably predict the delivery of the reward. Studies using a task design in which the reward cue signals not only the opportunity for reward, but also an increased likelihood of earning that reward, tend to find increased adolescent activity in the striatum during anticipation (e.g., Barkley-Levenson and Galvan, 2014, Van Leijenhorst et al., 2010b). Meanwhile, studies using tasks for which the anticipatory cue signals the possibility to earn a reward, but is equivocal with respect to the likelihood of succeeding in obtaining the reward (as in typical implementations of the Monetary Incentive Delay task), do not yield a consistent pattern of developmental differences (e.g., Bjork et al., 2007, Teslovich et al., 2014).

Second, developmental findings regarding striatal outputs during reward anticipation are more consistent in studies where there is also concomitant behavioral evidence that the adolescents are relatively more sensitive to the rewards being presented (e.g., faster reaction times on rewarded trials, more reward-related errors, etc.). While most reward processing tasks used in neuroimaging studies do not include a behavioral measure or control for behavioral differences in reward sensitivity across development, the handful of studies that do (Barkley-Levenson and Galvan, 2014, Christakou et al., 2011, Geier et al., 2010, Padmanabhan et al., 2011, Somerville et al., 2011) all report both greater recruitment of the striatum during anticipation of reward and higher reward sensitivity among adolescents compared to adults, reflected in the behavioral outcomes. Unfortunately, the majority of reward tasks used in the developmental literature lack a useful behavioral index of reward sensitivity—an issue that may also account for variability in striatal findings across development.

Our lab recently explored how socioemotional arousal influences adolescents’ neural responses to reward by testing whether the presence of peers increased striatal activation during a reward-processing task in which no risk was involved (Smith et al., 2015). In this study, we examined the effects of peer observation on adolescents’ and adults’ neural response to reward using a modified version of the High/Low Card Guessing Task (Delgado et al., 2003, May et al., 2004). During the receipt of reward, adolescents who completed the task in the presence of their peers recruited the striatum to a greater degree than when they completed the task alone. Furthermore, only when peers were present did adolescents evince greater striatal activation than adults. These findings provide corroborating evidence that, during adolescence, social context is an important modulator of reward processing, even when this processing is uncoupled from risk taking. Consistent with this claim, we have shown that, in the presence of peers, adolescents evince a stronger preference for immediate (as opposed to delayed) rewards on a Delay Discounting task that does not involve risk taking (O’Brien et al., 2011, Weigard et al., 2014).

Recent neuroimaging studies also support the idea that, in addition to having profound effects on brain structure [a topic not covered in the present article; see Blakemore et al. (2010) and Smith et al. (2013) for reviews], pubertal development plays a role in developmental change in the sensitivity of the striatum to reward (Braams et al., 2015, Forbes et al., 2010, Op de Macks et al., 2011). For example, a landmark, longitudinal neuroimaging study of children, adolescents and young adults (N = 299, ages 8–27) found, as previous studies have, that activation of the nucleus accumbens in response to monetary reward (relative to loss) was higher in mid-adolescence than at other ages (Braams et al., 2015). Moreover, activation of this region was related both to greater self-reported pubertal stage and higher levels of salivary testosterone (Braams et al., 2015). This finding provides strong support for the claim that the heightened responsiveness of the socioemotional system during adolescence is, at least in part, a result of pubertal development.

Thus far, we have discussed reward sensitivity specifically with respect to striatal activation. However, there also have been advances in how we understand developmental changes in the functioning of other regions hypothesized to participate in reward processing, including the dorsal portion of the striatum (Benningfield et al., 2014, Hoogendam et al., 2013, Lamm et al., 2014), mPFC (Christakou et al., 2011), OFC (Galvan et al., 2006, Galvan and McGlennen, 2013, Hoogendam et al., 2013, Van Leijenhorst et al., 2010b), and the anterior insular cortex (AIC) (Galvan and McGlennen, 2013, Van Leijenhorst et al., 2010b).

In a recent paper, we posited that continuing maturation of connectivity between the striatum and the AIC, which appears to act as connective hub that influences the engagement of both the control and reward processing networks, may account for inconsistent recruitment of the striatum in adolescent reward processing (Smith et al., 2014b). Because reward processing entails the coordinated action of a network of regions, developmental studies examining the reward system as a whole, rather than focusing on activation of specific regions considered in isolation, will likely yield greater insight into changes in reward processing during adolescence, including the reasons for the inconsistent recruitment of the striatum in adolescent reward processing (Smith et al., 2014b).

One study has already demonstrated the potential value of such a network-based approach. Using resting state data, Cho and colleagues (2012) examined functional connectivity between the striatum, thalamus, and AIC as adolescents and adults completed a reward processing task. They found that during anticipation of reward (i.e., during cue presentation) adolescents and adults did not differ in the functional connectivity between these regions. Further, they observed that activity in the AIC and thalamus preceded VS activation in both adolescents and adults. These results suggest that the bottom-up processing of rewards (as demonstrated by communication between these three regions) is adequately developed by adolescence. Therefore, it may be that developmental differences between adolescents and adults in reward sensitivity are not due to immature connectivity, but rather to differences in top-down influences on the subjective valuation of reward. More studies considering the socioemotional system as a coordinated network are needed to inform our understanding of how the development of this system relates to age-differences in reward processing.

In summary, despite occasional inconsistencies in the literature, self-reported sensation seeking, behavioral measures of reward sensitivity, and neuroimaging studies of reward processing support the contention that reward sensitivity reaches its apex during adolescence (e.g., Barkley-Levenson and Galvan, 2014, Christakou et al., 2011, Collado et al., 2014, Galvan and McGlennen, 2013, MacPherson et al., 2010, Shulman et al., 2014a, Shulman et al., 2014b, Somerville et al., 2011, Van Leijenhorst et al., 2010b). The bulk of developmental research on this topic provides evidence for a mid-adolescent peak in reward sensitivity, and although the neuroimaging literature does not allow for a precise estimation of age of peak striatal response, the weight of the evidence indicates that adolescents engage the striatum (and other components of the reward network) to a greater extent than adults, particularly during receipt of reward and when differences in reward sensitivity are reflected in decision-making behavior. Also consistent with the dual systems account, studies that have incorporated measures of puberty typically find that sensation seeking and reward sensitivity are higher among those (particularly boys) who are more pubertally advanced.

5. The development of self-regulation and cognitive control

5.1. Self-reported impulsivity

A second major claim of the dual systems model is that cognitive control increases linearly across adolescence and does not reach full maturity until several years after the peak period of reward sensitivity. In the developmental literature, impulse control (or its inverse, impulsivity) is the psychological variable most often used to assess self-regulation (or its absence). Impulsiveness—acting in an unplanned and reactive, or less thought out, fashion—is often considered a quintessential adolescent characteristic that predisposes adolescents to engage in reckless behaviors (Romer, 2010). To date, studies examining age differences in self-reported impulsivity—both cross-sectional (Leshem and Glicksohn, 2007, Steinberg et al., 2008) and longitudinal (Harden and Tucker-Drob, 2011)—find that impulsivity decreases with age across the second decade of life.

Importantly, the protracted maturation of impulse control is believed to continue into young adulthood, where even 18–19 year olds report higher impulsivity (i.e., less impulse control) than individuals in their early twenties (Vaidya et al., 2010). Although adults sometimes engage in impulsive acts, by the early-to-mid 20s the frequency of impulsive behavior appears to stabilize at levels much lower than those exhibited by adolescents (Steinberg et al., 2008, Quinn and Harden, 2013). For example, using a three-item impulsivity scale, Quinn and Harden (2013) found a linear decrease in self-reported impulsivity between the ages of 15 and 21, but no further age differences among individuals between 21 and 25.

5.2. Behavioral measures of self-regulation

Self-regulation is commonly assessed in behavioral tasks that require response inhibition, a form of cognitive control that involves overcoming automatic or inappropriate responses in favor of goal-relevant information processing and actions (Casey et al., 2002). The most widely used measures of response inhibition (e.g., Go/NoGo, antisaccade, and Stroop paradigms) are typically configured to assess “reactive inhibition,” which refers to the outright restraint of motor and perceptual impulses in response to an external stimulus (e.g., canceling a prepotent response upon seeing a signal, or maintaining attention in the presence of distractions). A wealth of behavioral evidence on reactive inhibitory control demonstrates that self-regulation improves from childhood to adulthood (Bezdjian et al., 2014, Bunge et al., 2002, Casey et al., 1997, Casey et al., 2002, Durston et al., 2002, Marsh et al., 2006, Paulsen et al., 2015, Rubia et al., 2006, Rubia et al., 2013, Smith et al., 2011a, Tamm et al., 2002, Velanova et al., 2009, Veroude et al., 2013).

Within this literature, adolescents and adults consistently demonstrate better inhibitory control compared to children; however, differences between adolescents and adults are not consistently found unless the behavioral paradigm is particularly challenging. For example, studies that use the traditional Stroop color-word task find no differences in cognitive control between adolescents and adults (e.g., Andrews-Hanna et al., 2011), whereas studies that use an emotional version of the Stroop to assess the effect of emotional interference in cognitive control report improvements in self-regulation from adolescence to adulthood (e.g., Veroude et al., 2013). Thus, while adolescents’ ability to inhibit impulses appears to be comparable to adults’ in relatively simple tasks, the sort of self-regulatory skills necessary to appropriately respond to more cognitively demanding situations continue to improve from adolescence to adulthood. This developmental pattern is also observed in measures of proactive (as opposed to reactive) inhibitory control, which involves advance planning and monitoring in anticipation of the need to stop a response or to not engage in a future action (e.g., slowing down responses to go-stimuli in anticipation of a no-go signal approaching, therefore allowing more time to appropriately cancel a response when the no-go signal appears; Vink et al., 2014). These findings suggest that while basic response inhibition mechanisms may be mature by adolescence, self-regulatory mechanisms underlying challenging reactive response inhibition tasks and proactive response inhibition (e.g., planning) may still be developing into the early 20s.

The proposition that the prolonged development of self-regulation is more evident under challenging conditions has been demonstrated using the Tower of London task. In this task, which requires strategic planning, participants must rearrange objects on pegs (either real or depicted on a computer monitor) to produce a specific pattern in the fewest possible moves (De Luca et al., 2003, Steinberg et al., 2008). Researchers manipulate the difficulty of trials by increasing the number of moves required to complete the rearrangement successfully. The amount of time a participant spends deliberating before making his or her first move (latency to first move) is used as a measure of impulse control (because making an initial move too rashly can extend the number of moves needed to solve the problem). A study from our lab found no differences between children, adolescents, and adults in latency to first move or in the number of moves taken to complete the trial on easy trials (i.e., those that can be solved in 3 moves) (Albert and Steinberg, 2011). However, on difficult trials (i.e., those that required 5 or more moves to be solved), performance improved with age from childhood to adulthood, and this trend coincided with greater deliberation time prior to the initial move. These findings suggest that when difficult tasks are used, such as those that require strategic planning, improvement in self-regulation continues throughout adolescence and into the early 20s, consistent with the dual systems model.

Importantly, the ongoing development of self-regulation into early adulthood is also in line with the idea that the development of self-regulation is independent of pubertal development (Smith et al., 2013). In the most comprehensive test of the relationship between self-regulation, and pubertal status Steinberg and colleagues (2008) found that self-reported and behavioral self-regulation was correlated with age but not pubertal status. Instead, pubertal status was more closely tied to sensation seeking. While this is the only study we know of that simultaneously examines the relationship between age, pubertal status, self-regulation, and sensation seeking, thus far the findings support the notion that the development of the socioemotional system is dependent on pubertal status while self-regulation seems to develop independently.

Other findings suggest that adolescents’ ability to exert adult-like self-control also may vary depending on whether rewards are offered for better performance (Luna et al., 2001). In several studies that have rewarded participants for better performance on an antisaccade task, researchers have found that incentives boost adolescents’ performance to adult levels (Geier et al., 2010, Jazbec et al., 2006, Padmanabhan et al., 2011). For example, using an antisaccade task where some trials were rewarded and some were not, Geier and colleagues (2010) found that adolescents performed better on rewarded trials, compared to non-rewarded trials, though their overall task performance did not differ from that of adults. At first blush, it may appear that these results are incompatible with the dual systems model, since its basic claim is that heightened awareness of the availability of rewards should undermine cognitive control in adolescents, not strengthen it. It is important to note, though, that this proposition of the dual systems model is posited specifically with respect to situations in which reward-seeking impulses conflict with self-regulatory efforts, as do most instances of risk taking. In contexts where increased sensitivity to the opportunity for reward serves to motivate faster and more attentive responding, without disturbing relevant cognitive processes, adolescents’ relatively heightened sensitivity to reward may be helpful rather than harmful. For that matter, even in certain risk-taking scenarios—in particular, those in which increased risk taking results in more optimal performance, such as in certain gambling tasks for which risky choices have a higher expected value—greater reward sensitivity can offer an advantage.

Overall, the self-report and behavioral literatures on self-regulation suggest that this capacity improves with age across childhood, adolescence, and into adulthood. Furthermore, it may be that adolescents’ ability to self-regulate is more dependent than adults’ on contextual factors, such as task difficulty, the prospect of a reward for better self-control, and the manner in which rewards are presented. Although adolescents may exhibit adult-like self-regulation under ideal circumstances by around age 15, this capacity is still tenuous, and maturation of self-regulation may be best indexed by the consistency with which individuals demonstrate self-control across different contextual circumstances.

As we have noted previously (Strang et al., 2013), it is imprudent to conclude that heightened reward sensitivity is inherently disadvantageous, or that impulsivity is always problematic. In situations in which greater attentiveness to reward or more impetuous behavior is desirable, adolescents may enjoy a distinct advantage over adults. Indeed, one of the tenets of the dual systems model is that adolescence evolved as a period during which individuals are more likely to engage in sensation seeking and less likely to restrain urges to pursue immediate rewards because this combination may confer a reproductive advantage during a period of heightened fertility (Steinberg, 2014).

5.3. Neuroimaging of cognitive control

In recent years, developmental neuroimaging has helped elucidate the neural mechanisms underlying age-related improvements in cognitive control. Continuing maturation of response inhibition is often examined in terms of development of the prefrontal cortex, and particularly the lateral prefrontal cortex (lPFC). In line with the dual systems framework, we postulate that developmental improvements in cognitive control are supported by the concurrent maturation of these underlying neural regions and by enhancements in top-down connectivity between frontal cognitive control regions and other cortical and subcortical areas associated with motor processing, affective processing, and the execution of selected actions.

Irrespective of age, individuals who perform better on response inhibition tasks (i.e., Go/No-Go, Flanker, Stroop, Stop Signal, antisaccade) exhibit greater activation of the lPFC compared to those who perform poorly (Durston et al., 2006, Rubia et al., 2006, Rubia et al., 2013, Velanova et al., 2009). Across adolescence, performance on response inhibition tasks improves with age—a pattern that appears to be explained by continuing maturation of the lPFC, with most studies finding either a linear increase in lPFC recruitment with age (Adleman et al., 2002, Bunge et al., 2002, Durston et al., 2006, Marsh et al., 2006, Paulsen et al., 2015, Spielberg et al., 2015, Tamm et al., 2002, Velanova et al., 2009, Vink et al., 2014) or significantly increased engagement of the lPFC from adolescence to adulthood (Rubia et al., 2000, Rubia et al., 2006, Rubia et al., 2013, Veroude et al., 2013). Furthermore, several studies have demonstrated a direct relationship between age-related increases in lPFC engagement and successful cognitive control (Adleman et al., 2002, Andrews-Hanna et al., 2011, Bunge et al., 2002, Casey et al., 1997, Durston et al., 2006, Rubia et al., 2006, Rubia et al., 2007, Rubia et al., 2013, Velanova et al., 2009).

Whereas the behavioral and neuroimaging literatures generally indicate a relationship between increases in cognitive control and engagement of the lPFC from adolescence into adulthood, the relationship between age, behavior, and neural engagement from childhood to adolescence is not as consistent (Alahyane et al., 2014, Booth et al., 2003, Braet et al., 2009, Casey et al., 1997, Durston et al., 2002). In fact, some studies find that children utilize more frontal regions than adults—in terms of overall volume and/or magnitude of activity—in order to successfully withhold a prepotent action. These findings have led researchers to posit that increases in self-regulation from childhood to adolescence and into adulthood may be due to a developmental progression from diffuse to focal activation (Durston et al., 2002). In this account, during childhood and early adolescence, the brain is inefficient and needs to “work harder,” recruiting neurons across a larger frontal area in order to successfully inhibit a response (though see Poldrack, 2014 for a critique of the explanatory value of the term “efficiency” in this context). As the brain undergoes continued reorganization across adolescence, necessary neural connections are strengthened and unnecessary ones are pruned, creating a more efficient brain and leading to more focal recruitment of regions within the lPFC during successful inhibition.

Cognitive control encompasses the integration of several (often simultaneous) processes that support planning behavior in accord with one's intentions (Miller, 2000). The effective integration of these processes relies not only on the functional recruitment of implicated brain regions, but also on the strength of connectivity among them (Hwang et al., 2010, van Belle et al., 2014). For example, a study by Hwang and colleagues (2010) examined developmental changes in connectivity underlying inhibitory control and found that connectivity between the prefrontal cortex and other cortical areas increased from childhood into adolescence, with some connections continuing to strengthen from adolescence to adulthood. The increases they observed in the number and strength of frontal connections to both cortical and subcortical regions during the transition from adolescence into adulthood suggest that developmental improvements in cognitive control may be supported by age related enhancements in the top-down regulation of task-engaged regions. Results such as these underscore the potential benefit to the field of fMRI studies moving beyond simplistic models of regional activation toward more elaborate models that consider connectivity among regions throughout development, as well as the strength and efficiency of those connections, which likely support age-related increases in the acquisition and execution of complex cognitive control skills (see, e.g., Satterthwaite et al., 2013). This is particularly true because, as noted earlier, there is reason to believe that continuing changes in connectivity account for the observation that some aspects of cognitive control continue to strengthen into early adulthood, instead of plateauing in adolescence.

6. Is risk taking during adolescence related to heightened reward sensitivity and immature cognitive control?

As reviewed above, research largely supports the dual systems model's characterization of adolescence as a time of heightened socioemotional reactivity (relative to earlier and later periods) and still maturing cognitive control. Moreover, there is considerable evidence consistent with the proposition that the developmental trajectories of reward sensitivity and cognitive control (and, by extension, sensation seeking and self-regulation) differ, with the former following an inverted U-shaped pattern and the latter evincing protracted, linear improvement that extends into the third decade of life.

How well does the literature support the claim that developmental change in these two systems explains heightened risk taking during adolescence? The model posits that it is the confluence of the developmental patterns of the socioemotional and cognitive control systems—relatively high responsiveness to reward combined with relatively weak self-regulation—that renders adolescents particularly vulnerable to risk taking. If the two systems contribute to risk taking in an additive manner, we should find independent correlations between the functional state of each system and risk-taking propensity. Indeed, there is evidence for this pattern in the literature.

In order to serve as a test of the dual systems model in predicting risk taking, a behavioral study must include measures of both socioemotional reactivity and cognitive control. Unfortunately, constructs reflecting the functional status of the socioemotional and cognitive control systems, like sensation seeking and impulsivity, tend to be highly correlated (e.g., Shulman and Cauffman, 2014, Steinberg et al., 2008), despite being theoretically and empirically separable (Duckworth and Kern, 2011, Duckworth and Steinberg, 2015). Thus, for studies that examine the relationship between only one of these constructs and risk taking, the correlation will be contaminated by the contribution of the unmeasured construct. Some of this overlap between sensation seeking and self-regulation may be artifactual—a result of the difficulty of developing measures that cleanly assess one construct and not the other. (For example, items like “I often get myself into trouble” could reflect either sensation seeking or impulsivity.) But some of the observed association between these constructs may be attributable to an ongoing dynamic interplay between the socioemotional and cognitive control systems; for example, when the socioemotional system generates an impulse to pursue an intrinsically rewarding experience and the cognitive control system counters with a signal meant to restrain the impulse. Although a large number of studies have examined risky behavior in relation to measures of either sensation seeking or impulse control, very few have examined the concurrent contributions to risk taking of psychological manifestations of socioemotional activation and cognitive control. Even fewer have examined this question in a sample spanning childhood, adolescence, and adulthood, which would be necessary to fully test whether variation in the functional status of these two systems explains age-related patterns in risk taking.

In the few studies that have simultaneously assessed constructs reflective of the socioemotional and cognitive control systems (e.g., sensation seeking and impulse control) along with measures of risk taking, the anticipated correlations are found. Both higher levels of sensation seeking and lower levels of impulse control explain variation in risk taking, over and above the effects of one another (Cyders et al., 2009, Donohew et al., 2000, Quinn and Harden, 2013). For example, one study of college students found that sensation seeking uniquely predicted increases in the frequency of alcohol use, over and above several measures of impulsivity (Cyders et al., 2009). Another found that both sensation seeking and “impulsive decision making” were independently associated with greater odds of ninth-graders engaging in sex, non-coital sexual behavior, alcohol use, and marijuana use (Donohew et al., 2000). Moreover, these associations were comparable in magnitude, except that impulsive decision-making was more strongly associated with having sex and sensation seeking was more strongly associated with marijuana use. Similarly, unpublished data from our lab—based on a sample of 283 10–30-year olds and using a scale that surveyed involvement in a wide range of risk-taking behaviors (see Shulman and Cauffman, 2014)—suggests that impulse control and sensation seeking contribute equally (betas = −.21 and .21, respectively) to self-reported engagement in risky behaviors (controlling for age, sex, and each other). An obvious shortcoming of these studies is that they rely exclusively on self-report. However, if common method variance alone were driving the findings, one would not expect to see independent relations between risk taking and either sensation seeking or impulsivity once the other predictor was controlled. Another limitation of these studies is that it is not yet clear how well self-report measures reflect the functional status of the socioemotional and cognitive control systems.

Neuroimaging studies have the advantage over behavioral studies of being able to measure activation within distinguishable regions thought to correspond to the socioemotional and cognitive control systems (although heightened activity in these regions during a laboratory task does not constitute a direct measure of the functional status of these systems). A few studies have examined engagement of regions associated with the socioemotional and/or cognitive control systems during adolescent decision making (e.g., Cascio et al., 2015, Kahn et al., 2015, Paulsen et al., 2011, van Duijvenvoorde et al., 2015b). However, only Chein et al. (2011) found increased engagement of structures within the socioemotional system and decreased activation of structures within the cognitive control system simultaneously within a risk-taking task (a driving simulation). One additional study (Paulsen et al., 2011) found age effects in both the PFC and striatum during risk taking. During risky (i.e., varying expected values) compared to sure (i.e., guaranteed reward) decisions, several PFC regions showed increased activation with age, consistent with Chein et al. (2011). On the other hand, striatal activation was inconsistent, making these results difficult to interpret.

Another recent study examined the extent to which age differences in impatience during a temporal discounting task—in which participants choose between a smaller immediate reward and a larger delayed reward—are explained by variations in self-reported “present hedonism” (i.e., reward sensitivity) and “future orientation” (i.e., impulse control) and engagement of neural regions and networks during decision making (van den Bos et al., 2015). Though the decision making task did not involve risk, the study is nonetheless relevant to the dual systems model because it was designed to probe the degree to which adolescents’ tendency to discount the future is due to greater reward sensitivity or weaker self control. The results indicated that choices to delay gratification in the decision task were associated with greater self-reported future orientation and increased engagement of frontoparietal control circuitry, but not with variation in self reported hedonism. Also, improvements in frontostriatal connectivity mediated the link between age and willingness to wait for a larger reward. The authors interpreted these results as showing that weak cognitive control, rather than heightened reward sensitivity, explains adolescents’ tendency to discount the future. However, limitations of the methodology (e.g., limited range on the present hedonism scale, lumping immediate rewards together with rewards to be received in two weeks for analysis of the discounting data, and the unemotional context of the laboratory) may have biased the study against finding linkages between reward sensitivity and impatience (see Steinberg and Chein, 2015).

Whereas other studies have not demonstrated simultaneously heightened socioemotional activation and dampened cognitive control within the same task, a few recent ones have observed heightened striatal activation when adolescents receive a reward following a decision (Braams et al., 2014, Braams et al., 2015). In one further relevant study (Cascio et al., 2015), researchers recruited recently licensed drivers (∼age 16) to complete a response inhibition task and, one week later, a driving simulation in the presence of a peer confederate. The peer either encouraged risky driving or safe driving. In the latter condition (encouragement of safe driving), greater engagement of cognitive control circuitry (i.e., IFG and BG) during the response inhibition task (indicative of better cognitive control) predicted safer driving behavior in the simulated driving task. Participants who exhibited higher cognitive control also showed no increase in risky driving in the condition in which the peer encouraged risk taking, suggesting that individuals who evince greater engagement of cognitive control circuitry may be more resistant socioemotional arousal. These findings indicate that poor cognitive control, as expected, also plays a role in risk-taking behavior. However, because the study did not compare age groups, it cannot address whether maturation of cognitive control helps to account for developmental patterns in risk taking.

In another recent study, van Duijvenvoorde and colleagues (2015b) had children, adolescents, and adults complete a risk-taking task (Columbia Card Task). While overall risk-taking tendency did not differ by age, adolescents showed greater activation of control circuitry (including the dmPFC) as the riskiness of the decision increased. This effect was not seen in children or adults. The authors suggest that heightened recruitment of control circuitry was necessary due to the heightened emotional response to risk during this age. Although there is good reason to believe that the functional status of both the socioemotional and cognitive control systems during adolescence contribute to heightened risk taking during this stage of development, the dual systems model still awaits a comprehensive study that confirms (or disconfirms) the purported joint effects of the developmental trajectories of the socioemotional and cognitive control systems on risk-taking behavior.

7. Unresolved questions and future directions

There are many unresolved issues in the literature that await further research attention. Here we highlight just a few of them. First, because of differences in opportunity to engage in risky behavior outside the laboratory environment, the effects of maturation of the socioemotional system and cognitive control system on real-world risk taking are likely to be modest and difficult to detect. Undoubtedly, contextual constraints on the behavior of adolescents relative to adults overwhelm any putative effect on actual risk taking. To take an obvious example, even if 15-year-olds are higher in sensation seeking and lower in self-regulation than people in their 20s, these differences will not be reflected in age differences in reckless driving in a country where 15-year-olds are not permitted to drive. Thus, tests of the dual systems model will require the continued development of laboratory tasks that are ecologically valid but that afford individuals of different ages equal opportunity to take risks. Future efforts to test the dual systems model's claims also would benefit from collaboration between behavioral researchers and neuroscientists to develop measures that more precisely reflect the functioning of the neural systems underlying the socioemotional and cognitive control systems.

Second, still unresolved is the question of why the socioemotional system declines in responsiveness between adolescence and adulthood. Luciana and Collins (2012) have speculated that experience with rewards and learning lead to lower background levels of dopamine, a proposition that has not yet been tested. Casey et al.’s (2008) model implies that decreases in risk taking after the maturation of the socioemotional system, which in their view is complete by mid-adolescence, are ultimately attributable to the continued strengthening of the cognitive control system. Given the evidence of reduced reward responsiveness in the key node of the socioemotional system in adulthood (relative to adolescence), it would seem that their version of the model suggests that strengthening of the cognitive control system prospectively dampens the reactivity of the socioemotional system. One study from our lab has tested this hypothesis: Shulman et al. (2014b) examined the effects of self-reported impulse control (a reflection of the cognitive control system) and sensation seeking (a reflection of the socioemotional system) on one another over time in a large sample of youth, ages 10–25, who were assessed biennially as part of the NLSY79 Children and Young Adults Study. The analysis failed to find evidence that increases in impulse control prospectively predict decreases in sensation seeking; in general, these two traits developed independently. Recently, a neuroimaging study using intrinsic connectivity found that increases in dlPFC-subcortical (thalamus and striatum when not controlling for age2) connectivity across adolescence were associated with increases in cognitive control but not with decreases in reward sensitivity (van Duijvenvoorde et al., 2015a). Instead, decreases in reward sensitivity were related to age-related decreases in connectivity within the socioemotional system (vmPFC, OFC, and striatum). Together these findings lend support to the notion that these traits develop independently. However, further investigation of this question is warranted; in particular, longitudinal studies employing more closely spaced measures and more sensitive assessments of the functional states of the socioemotional and cognitive control systems are needed.

A third issue concerns the operationalization of mature cognitive control. It is clear that in some respects, key nodes of the cognitive control system have reached adult levels of structural and functional maturation by mid-adolescence, a point made recently by Luna et al. (2014). Yet there are other signs that aspects of cognitive control are immature, unreliable, or easily disrupted during mid-adolescence relative to adulthood, which challenges Luna and Wright's (2016) notion that cognitive control is mature by mid-adolescence. Part of this inconsistency stems, we believe, from heterogeneous operationalizations of “mature” cognitive control. It is now eminently clear that whether activation of this system is weaker or stronger is not a useful way of conceptualizing the maturity of the cognitive control system, because on some tasks adolescents show greater or wider activation than adults, whereas on others the reverse is true. As we have argued (Strang et al., 2013), a more sensible index of maturation of cognitive control would focus on structural and functional connectivity within the cognitive control network and between this system and other brain regions (DeWitt et al., 2014, Jacobus et al., 2013). In order to pursue this idea further, research is needed that correlates psychological and behavioral measures of self-regulation with indices of structural and functional connectivity that involve the cognitive control system. Recent connectivity analyses have demonstrated that increases in control behaviors across adolescence are associated with an increase in connectivity between striatal and prefrontal regions (van den Bos et al., 2015, van Duijvenvoorde et al., 2015a, Vink et al., 2014). However, some studies have also found that same-aged individuals with more well-developed connections between cortical and subcortical areas engage in relatively more risk taking (Berns et al., 2009, DeWitt et al., 2014). This apparent inconsistency between studies of development and studies of individual differences among same-aged adolescents warrants further study.

Finally, it will almost certainly be necessary to consider the development of, and interactions among, brain systems that are not included in the dual systems model in order to account for the full array of evidence on the development of risky behavior across the period from preadolescence into adulthood. Such expansion of the model is consistent with the triadic model (Ernst, 2014) and with our recent efforts to consider how development in other pathways, including those linking the AIC to the cognitive control and reward systems (Smith et al., 2014b), may impact risk-taking behavior. According to our recent work, the transition between adolescence and adulthood may involve a shift in the ways in which the VS, PFC, and AIC are functionally connected, with relatively the stronger connections between the insula and striatum characteristic of adolescence giving way to stronger connections between the PFC and insula in adulthood.

8. Concluding comment

The dual systems model attributes elevated levels of risk taking in adolescence to the heightened arousal of the socioemotional system before the cognitive control system fully attains functional maturity. Moreover, the decrease in risky behavior between adolescence and adulthood is attributed to the continued strengthening of the cognitive control system and the attenuation of arousal within the socioemotional system. Whether and in what respects the contributions of these changes in the cognitive control and socioemotional systems are independent, interactive, or reciprocal—or a combination of all three—are important questions for future research.

It is important to note, however, that the ways in which these systems work together in motivating increases in risky behavior between childhood and adolescence are not necessarily the same as the ways in which they combine to create a decline in risky behavior between adolescence and adulthood. It is entirely possible, for example, that the increase in recklessness seen in early adolescence is due mainly to increases in reward sensitivity whereas the decrease in recklessness seen in young adulthood is driven mainly by improvements in cognitive control. It is also possible (albeit unlikely) that the initial increase and later decline in risk taking seen during the transition between childhood and adulthood is entirely explained by the rise and fall in socioemotional reactivity and not related to changes in cognitive control. While advances in neuroscience have permitted researchers to distinguish between these systems in studies of brain structure and function, these systems likely engage in ongoing interactions with one another, and it is therefore unwise to think about them as if they are independent entities.

As we have asserted throughout this review, the weight of the evidence amassed to date is consistent with the dual systems perspective. Although there are occasional exceptions to the general trends, self-report, behavioral, and neuroimaging studies generally support the model, finding that psychological and neural manifestations of reward sensitivity increase between childhood and adolescence, peak sometime during the late teen years, and decline thereafter, whereas psychological and neural reflections of better cognitive control increase gradually and linearly throughout adolescence and into the early 20s, and that the combination of amplified reward sensitivity and still-developing cognitive control makes middle and late adolescence a time of heightened predisposition to risky and reckless behavior. Whether this inclination translates into real-world risk-taking, however, is contingent on the context in which adolescent development occurs.

In our view, the published research that has appeared since the introduction of this viewpoint has strengthened, rather than called into question, the model's utility. Of course, there have been studies yielding results inconsistent with one or more aspects of the dual systems model. This is to be expected given the large number of relevant studies and wide variety of methodologies employed. Importantly, studies that have failed to support the dual systems model have not provided consistent evidence for an alternative developmental model. They do, however, serve as a reminder that there may be conditions under which the general finding of heightened reward sensitivity in adolescence or age-related increases in cognitive control may not apply. This highlights the fact that, as we have pointed out, the dual systems perspective is at times overly simplistic. As a heuristic device, however, the model provides a far better account of adolescent risk taking than prior models that have attributed this period of transient recklessness to adolescents’ cognitive deficiencies. It also continues to be generative, and has informed ongoing research in multiple fields, research that will almost certainly support continued refinement of the model (refinements already partially reflected in the multiple variations of the perspective advanced by different research groups).

Importantly, the dual systems model does not suggest that adolescents are universally risky or incompetent decision makers. On the contrary, the model recognizes that basic reasoning capacity is almost fully mature by mid-adolescence. Indeed, under conditions that minimize arousal of the socioemotional system and allow for deliberative, calculated decision making, adolescents tend to make decisions and judgments that are quite similar to those of adults (e.g., Chein et al., 2011, Figner et al., 2009, Van Leijenhorst et al., 2008). Instead, what the dual systems model suggests is that when decision making occurs under conditions that excite, or activate, the socioemotional system (e.g., when decisions are made in the presence of friends, under emotionally arousing circumstances, or when there is a potential to obtain an immediate reward) adolescents are more prone than other age groups to pursue exciting, novel, and risky courses of action. Far from being a biologically deterministic model, the dual systems perspective explicitly emphasizes the context in which decision making takes place.

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Adolescents' Heightened Risk-Taking: Examining the Dual Systems Model

Abstract: This review examines the dual systems model of adolescent risk-taking, which posits that heightened risk behavior during this developmental period arises from the interaction of two distinct neurobiological systems: a heightened reward system and an underdeveloped cognitive control system. We review recent behavioral and neuroimaging evidence supporting the model's core propositions: (1) reward sensitivity peaks during adolescence, (2) cognitive control exhibits linear development throughout adolescence and into adulthood, and (3) heightened risk-taking in adolescence results from the interplay of these two systems. We discuss criticisms and unresolved issues within the model, highlighting areas for future research.

1. The Emergence of Dual Systems Models

Traditional psychological accounts of adolescent risk-taking, which attributed recklessness to cognitive deficiencies, have been largely refuted. Adolescents, by age 15, demonstrate adult-like performance on tasks measuring logical reasoning, information processing, and risk perception (Steinberg, 2008).

The advent of developmental cognitive neuroscience introduced a new perspective—the "dual systems" model—which attributes adolescent vulnerability to risk-taking to the divergent developmental trajectories of two brain systems (Dahl, 2004). Our lab (Steinberg, 2008; Steinberg et al., 2008) and Casey's lab (Casey et al., 2008) proposed similar models, positing an early-maturing "socioemotional system" (striatum, medial and orbital prefrontal cortices) responsible for reward-seeking and a slower-maturing "cognitive control system" (lateral prefrontal, lateral parietal, and anterior cingulate cortices) responsible for inhibiting impulsive behaviors. The model proposes that risk-taking peaks during adolescence due to heightened activation of the socioemotional system, while the cognitive control system is not yet sufficiently developed to consistently regulate impulsive actions.

Variations of the dual systems model exist. While we (Steinberg, 2008) and Casey et al. (2008) concur on the slow development of the cognitive control system, we differ in our characterization of the socioemotional system's trajectory. We propose an inverted-U shaped trajectory, with reward responsiveness peaking in adolescence and declining in adulthood, while Casey et al. suggest a plateauing of arousability in mid-adolescence. Furthermore, we posit that this decline occurs independently of cognitive control development, contrasting with Casey et al.'s view that strengthened cognitive control dampens socioemotional arousability. Luna and Wright (2016), in their "driven dual systems" model, concur with our inverted-U shaped trajectory for the socioemotional system but propose a mid-adolescent plateau for cognitive control, unlike our model and Casey et al.'s. Luciana and Collins (2012) similarly emphasize a hyperactive socioemotional system ("subcortical limbic-striatal systems") undermining the regulatory ability of the cognitive control system ("prefrontal executive system").

Ernst's (2014) triadic model expands upon the dual systems concept by introducing a third system—an emotion/avoidance system anchored in the amygdala. This system, responsible for emotional intensity and avoidance, is hypothesized to influence motivated behavior, potentially amplifying impulsivity in adolescents by increasing the perceived cost of delaying gratification. However, empirical support for this third system's role in adolescent risk-taking remains limited.

2. The Current Article

This review examines evidence from behavioral and neuroimaging studies published since 2008, assessing their support for, extension of, modification of, or challenges to the dual systems model. We focus on three key propositions: (1) reward sensitivity peaks in adolescence, (2) cognitive control exhibits linear development during this period, and (3) heightened risk-taking during adolescence results from the interplay of these systems.

We address criticisms of the model, such as the lack of consistent evidence for heightened adolescent reward sensitivity (Pfeifer & Allen, 2012) and unequivocal cognitive control improvements during adolescence (Crone & Dahl, 2012). We acknowledge the model's simplicity (Pfeifer & Allen, 2012), arguing that even a simplified model can serve as a valuable heuristic, stimulating research to elaborate upon its initial framework (Strang et al., 2013).

We define "adolescence" as beginning with pubertal onset (around age 10) and extending through the second decade of life. We distinguish "late adolescence" (ages 18-21) as a period lacking full adult status despite legal majority.

We use "reward sensitivity" and "cognitive control" to denote neurobiological constructs measured through brain structure and function, while "sensation seeking" and "self-regulation" refer to their respective psychological manifestations (Smith et al., 2013). "Sensation seeking" encompasses the pursuit of novel and intense experiences, often involving risk-taking (Zuckerman, 1994). "Self-regulation" refers to the ability to modulate thoughts, feelings, and actions towards planned goals, encompassing impulse control, response inhibition, emotion regulation, and attentional control.

3. Are Adolescents Particularly Prone to Risk Taking?

While the notion of adolescence as a period of risky behavior is pervasive, empirical evidence for a mid-adolescent peak is not definitive. Epidemiological data show peak ages for various risk behaviors vary, with late adolescence often exhibiting the highest levels (Willoughby et al., 2013).

We argue that while the dual systems model proposes peak risk-taking propensity in mid-adolescence, its expression depends on contextual factors (e.g., opportunities, social norms). Late adolescents, while biologically less predisposed, may exhibit higher real-world risk-taking due to increased opportunity and autonomy.

3.1. Risk Taking in the Laboratory

Laboratory studies, employing tasks like gambling games and driving simulations, offer greater control over contextual factors. However, findings are inconsistent, with some reporting greater adolescent risk-taking (e.g., Burnett et al., 2010), others finding no age effects (e.g., Bjork et al., 2007), and some even showing less risk-taking in adolescents compared to children (Paulsen et al., 2011). This suggests that heightened adolescent risk-taking propensity may be context-dependent (Defoe et al., 2014).

Recent research has incorporated elements of real-world risk-taking, such as ambiguous risk probabilities. Tymula et al. (2012) found adolescents to be more risk-averse than adults under known risk conditions but more risk-prone under ambiguous risk conditions, mirroring real-life scenarios where probabilities are often unknown.

Emotional arousal, often present in real-world risk-taking, has also been shown to influence adolescent risk behavior. Figner et al. (2009) found adolescents more likely to engage in risk-taking and exhibit poorer risk-assessment than adults only under emotionally arousing conditions.

Furthermore, studies manipulating social context by including peers (Chein et al., 2011; Gardner & Steinberg, 2005) have found peer presence to increase adolescent risk-taking more than adults. Neuroimaging data suggests that peer presence increases affective arousal, as evidenced by greater socioemotional system activation (Chein et al., 2011). This heightened peer influence on adolescent risk-taking is supported by studies on driving behavior (Lambert et al., 2014) and a rodent model showing increased alcohol consumption in adolescent mice in the presence of peers (Logue et al., 2014).

In conclusion, evidence for heightened adolescent risk-taking exists, but this tendency is contingent upon specific conditions, including emotional arousal, ambiguous risk, and peer presence. The increased risk-taking in arousing contexts and the observed heightened activity in the socioemotional system during peer interactions underscore the significance of reward processing in adolescent decision-making.

4. The Development of Sensation Seeking and Reward Sensitivity

The dual systems model posits that heightened socioemotional system activation and reactivity during adolescence contribute to increased risk-taking. This section explores psychological and neurological evidence for this heightened responsiveness, even in non-risk-taking scenarios.

Central to the model is the hypothesis that the socioemotional system's development is closely tied to pubertal development (Smith et al., 2013). Pubertal hormones, beginning around ages 11-12, trigger significant neural changes, particularly in dopamine-rich limbic regions associated with reward processing (Blakemore et al., 2010; Sinclair et al., 2014). This hormonal surge is thought to sensitize the adolescent brain to reward (Forbes & Dahl, 2010; Peper & Dahl, 2013), a finding corroborated by animal studies (Alexander et al., 1994; Clark et al., 1996; Miele et al., 1988). We posit that the subsequent decline in reward sensitivity during late adolescence and adulthood results from desensitization to these hormones (Smith et al., 2013).

4.1. Sensation Seeking

Sensation seeking, a psychological manifestation of socioemotional reactivity, often predicts self-reported risk-taking (Kong et al., 2013; MacPherson et al., 2010). Longitudinal and cross-sectional studies consistently find peak sensation seeking around mid-adolescence, declining into adulthood (Harden & Tucker-Drob, 2011; Peach & Gaultney, 2013; Quinn & Harden, 2013; Romer & Hennessy, 2007; Shulman et al., 2014a, 2014b; Steinberg & Chein, 2015; Steinberg et al., 2008). Longitudinal studies tracking individuals from childhood into adolescence report increases in sensation seeking (Collado et al., 2014; Lynne-Landsman et al., 2011; MacPherson et al., 2010). Harden and Tucker-Drob (2011) and Shulman et al. (2014a, 2014b), analyzing data from the National Longitudinal Study of Youth 1979, found an inverted-U shaped trajectory for sensation seeking, peaking in mid- to late adolescence and declining thereafter. Notably, females exhibit an earlier peak than males.

This pattern aligns with the dual systems model's prediction of an inverted-U shaped developmental trajectory for the socioemotional system. Moreover, studies consistently link more advanced pubertal status to higher sensation seeking, particularly in boys (Castellanos-Ryan et al., 2013; Gunn & Smith, 2010; Martin et al., 2002; Quevedo et al., 2009; Resnick et al., 1993; Steinberg et al., 2008; Urošević et al., 2014). This pubertal influence extends to behaviors associated with sensation seeking, such as substance use (Castellanos-Ryan et al., 2013; de Water et al., 2013; Gunn & Smith, 2010), delinquency (Collado et al., 2014; Kretschmer et al., 2014), and laboratory risk-taking (Collado et al., 2014; Kretsch & Harden, 2014; Steinberg et al., 2008).

4.2. Behavioral Manifestations of Reward Sensitivity

Compared to sensation seeking, behavioral studies on reward sensitivity development are less extensive and methodologically diverse. One large-scale study utilizing a modified Iowa Gambling Task (IGT) found that mid-adolescents (14-17 years) and older adolescents (18-21 years) learned to select advantageous decks faster than younger adolescents (10-13 years) or adults (22-25 years), suggesting heightened reward sensitivity between 14 and 21 years of age (Cauffman et al., 2010; Steinberg & Chein, 2015). This finding was further corroborated by Steinberg (2010) who found that sensation seeking, but not impulsivity, predicted overall reward-seeking behavior in the IGT.

Other studies have investigated reward sensitivity by incorporating rewarding stimuli into traditional inhibitory control tasks. Somerville et al. (2011), using an Emotional Go/No-Go task with happy and calm faces as stimuli, found adolescents (13-17 years) exhibited more difficulty inhibiting responses to happy faces (rewarding) compared to calm faces, suggesting heightened reward sensitivity. However, this pattern was not replicated by Tottenham et al. (2011) using a similar paradigm, highlighting the need for further investigation and the advantages of incorporating neuroimaging methods to detect subtle differences in socioemotional system engagement.

4.3. Neuroimaging of Reward Sensitivity

Neuroimaging studies have investigated the neural mechanisms underlying adolescent reward sensitivity, particularly focusing on the ventral striatum, a region implicated in reward processing (Knutson et al., 2001; Luciana & Collins, 2012). The dual systems model posits that increased striatal engagement during decision-making biases adolescents towards rewarding choices. Additionally, the nucleus accumbens, a key ventral striatum structure, exhibits increased volume in early adolescence followed by a decrease thereafter (Luciana & Collins, 2012).

While initial neuroimaging studies yielded mixed results regarding striatal engagement during reward processing (Bjork et al., 2004; Galvan et al., 2006; Steinberg, 2008), more recent reviews have highlighted methodological inconsistencies contributing to these discrepancies (Galvan, 2010; Richards et al., 2013). Current evidence largely supports greater striatal activation in adolescents compared to children and adults during reward anticipation and receipt (Barkley-Levenson & Galvan, 2014; Christakou et al., 2011; Galvan & McGlennen, 2013; Galvan et al., 2006; Geier et al., 2010; Hoogendam et al., 2013; Jarcho et al., 2012; Padmanabhan et al., 2011; Silverman et al., 2015; Van Leijenhorst et al., 2010b). This heightened adolescent striatal response, particularly during reward receipt, aligns with the dual systems model's assertion of increased reward sensitivity. A longitudinal study by Qu et al. (2015) found that ventral striatal activation during a risk-taking task declined across mid-adolescence and correlated with self-reported real-world risk-taking.

However, some studies report dampened striatal responses in adolescents (Bjork et al., 2004; Bjork et al., 2010; Hoogendam et al., 2013; Lamm et al., 2014) or no age differences (Krain et al., 2006; Teslovich et al., 2014; Van Leijenhorst et al., 2006), specifically during reward anticipation. These inconsistencies may be attributable to methodological variations, such as the timing of event modeling and the reliability of reward cues. Studies employing cues that reliably predict reward delivery tend to find increased adolescent striatal activation during anticipation (Barkley-Levenson & Galvan, 2014; Van Leijenhorst et al., 2010b). Conversely, studies using cues that signal reward possibility but not likelihood yield less consistent results (Bjork et al., 2007; Teslovich et al., 2014).

Moreover, studies incorporating behavioral measures of reward sensitivity, such as reaction times and reward-related errors, consistently report both heightened striatal activation during reward anticipation and greater behavioral reward sensitivity in adolescents (Barkley-Levenson & Galvan, 2014; Christakou et al., 2011; Geier et al., 2010; Padmanabhan et al., 2011; Somerville et al., 2011). This underscores the importance of including behavioral measures in neuroimaging studies to ensure observed neural activity translates to actual differences in reward processing.

Our lab's research (Smith et al., 2015) supports the influence of social context on adolescent reward processing. We found that peer presence during a reward-processing task (without risk) amplified striatal activation in adolescents, exceeding activation levels observed in adults, highlighting the modulating effect of social context on reward processing in adolescence. This finding aligns with our previous work demonstrating adolescents' stronger preference for immediate rewards in the presence of peers (O'Brien et al., 2011; Weigard et al., 2014).

Recent neuroimaging studies also link pubertal development to changes in striatal reward sensitivity (Braams et al., 2015; Forbes et al., 2010; Op de Macks et al., 2011). Braams et al. (2015), in a large-scale longitudinal study, found that nucleus accumbens activation in response to monetary reward peaked in mid-adolescence and correlated with both self-reported pubertal stage and salivary testosterone levels, supporting the role of pubertal hormones in heightened socioemotional system responsiveness during adolescence.

Beyond the striatum, studies have also examined developmental changes in other reward-processing regions, including the dorsal striatum (Benningfield et al., 2014; Hoogendam et al., 2013; Lamm et al., 2014), medial prefrontal cortex (Christakou et al., 2011), orbitofrontal cortex (Galvan et al., 2006; Galvan & McGlennen, 2013; Hoogendam et al., 2013; Van Leijenhorst et al., 2010b), and anterior insular cortex (Galvan & McGlennen, 2013; Van Leijenhorst et al., 2010b).

Our lab (Smith et al., 2014b) suggests that inconsistencies in striatal recruitment during adolescent reward processing may be due to ongoing maturation of connectivity between the striatum and anterior insular cortex, a region influencing both control and reward processing networks.

Cho et al. (2012), using resting state data, found similar functional connectivity between the striatum, thalamus, and anterior insular cortex in adolescents and adults during reward anticipation. This suggests that bottom-up reward processing is mature by adolescence. Therefore, developmental differences in reward sensitivity may arise from differences in top-down influences on reward valuation rather than immature connectivity. Future research adopting a network-based approach, examining the coordinated activity within the socioemotional system, is crucial for understanding developmental changes in reward processing.

In summary, despite some inconsistencies, evidence from self-report, behavioral, and neuroimaging studies supports peak reward sensitivity during adolescence. Adolescents exhibit greater striatal engagement, particularly during reward receipt, and this heightened sensitivity is influenced by pubertal development and social context.

5. The Development of Self-Regulation and Cognitive Control

5.1. Self-Reported Impulsivity

The dual systems model posits that cognitive control develops linearly throughout adolescence, reaching full maturity only in adulthood. Impulsivity, often considered a hallmark of adolescence, gradually declines with age, as evidenced by both cross-sectional (Leshem & Glicksohn, 2007; Steinberg et al., 2008) and longitudinal (Harden & Tucker-Drob, 2011) studies. Importantly, this decline continues into young adulthood, with individuals in their early twenties reporting less impulsivity than those aged 18-19 (Vaidya et al., 2010). This suggests that while adults may engage in impulsive acts, the frequency stabilizes at lower levels compared to adolescence (Quinn & Harden, 2013; Steinberg et al., 2008). Quinn and Harden (2013) found a linear decrease in self-reported impulsivity between 15 and 21 years old, with no further age-related changes observed between 21 and 25 years old.

5.2. Behavioral Measures of Self-Regulation

Response inhibition, a key aspect of cognitive control, involves overriding automatic responses in favor of goal-directed behavior (Casey et al., 2002). Behavioral tasks assessing response inhibition, such as the Go/NoGo, antisaccade, and Stroop paradigms, consistently demonstrate improved self-regulation from childhood to adulthood (Bezdjian et al., 2014; Bunge et al., 2002; Casey et al., 1997, 2002; Durston et al., 2002; Marsh et al., 2006; Paulsen et al., 2015; Rubia et al., 2006, 2013; Smith et al., 2011a; Tamm et al., 2002; Velanova et al., 2009; Veroude et al., 2013).

While adolescents generally outperform children on these tasks, differences between adolescents and adults are less consistent unless the task demands are high. For instance, traditional Stroop tasks often reveal no age differences (Andrews-Hanna et al., 2011), while emotional Stroop tasks, requiring greater emotional interference control, demonstrate continued improvement from adolescence to adulthood (Veroude et al., 2013). This suggests that while basic inhibitory control mechanisms may be mature by adolescence, more demanding self-regulation skills continue to develop into the early twenties. This developmental pattern extends to proactive inhibitory control, involving anticipatory planning and monitoring (Vink et al., 2014).

The Tower of London task, requiring strategic planning, further illustrates this point. Albert and Steinberg (2011) found no age differences in performance on easy trials, but on difficult trials, performance improved with age from childhood to adulthood, accompanied by increased deliberation time, indicating ongoing self-regulation development throughout adolescence and into early adulthood.

Importantly, the protracted development of self-regulation appears independent of pubertal development (Smith et al., 2013). Steinberg et al. (2008) found no association between pubertal status and self-reported or behavioral self-regulation, while sensation seeking was strongly linked to pubertal development, supporting the notion of independent developmental trajectories.

Interestingly, adolescents' self-control may be more context-dependent than adults, particularly regarding reward availability. Studies using incentivized antisaccade tasks found that rewards improved adolescent performance to adult levels (Geier et al., 2010; Jazbec et al., 2006; Padmanabhan et al., 2011). This suggests that when heightened reward sensitivity serves to motivate improved performance without disrupting cognitive processes, it can be advantageous for adolescents. Even in certain risk-taking scenarios, where riskier choices yield higher expected value, greater reward sensitivity can be beneficial (Strang et al., 2013).

In conclusion, self-regulation, as measured by behavioral tasks and self-reports, improves with age throughout adolescence and into adulthood. However, adolescents' ability to self-regulate appears more sensitive to contextual factors, such as task difficulty, reward salience, and reward presentation. While adolescent self-regulation may appear adult-like under ideal conditions by age 15, its continued maturation is reflected in the consistency of self-control across varying contexts.

5.3. Neuroimaging of Cognitive Control

Neuroimaging studies have provided insights into the neural substrates of cognitive control development, particularly focusing on the lateral prefrontal cortex (lPFC). The dual systems model attributes improved cognitive control to the maturation of the lPFC and its connectivity with other cortical and subcortical regions involved in motor, affective, and executive functions.

Individuals of all ages who perform well on response inhibition tasks exhibit greater lPFC activation (Durston et al., 2006; Rubia et al., 2006, 2013; Velanova et al., 2009). Across adolescence, improvements in response inhibition coincide with either linear increases in lPFC recruitment (Adleman et al., 2002; Bunge et al., 2002; Durston et al., 2006; Marsh et al., 2006; Paulsen et al., 2015; Spielberg et al., 2015; Tamm et al., 2002; Velanova et al., 2009; Vink et al., 2014) or significant increases from adolescence to adulthood (Rubia et al., 2000, 2006, 2013; Veroude et al., 2013). Several studies directly link age-related increases in lPFC activation to successful cognitive control (Adleman et al., 2002; Andrews-Hanna et al., 2011; Bunge et al., 2002; Casey et al., 1997; Durston et al., 2006; Rubia et al., 2006, 2007, 2013; Velanova et al., 2009).

While the link between lPFC maturation and cognitive control from adolescence to adulthood is well-established, the relationship from childhood to adolescence is less consistent (Alahyane et al., 2014; Booth et al., 2003; Braet et al., 2009; Casey et al., 1997; Durston et al., 2002). Some studies find that children utilize more diffuse frontal regions than adults for successful response inhibition, suggesting that cognitive control development may involve a transition from diffuse to focal neural activation (Durston et al., 2002). This shift towards focal lPFC recruitment during inhibition reflects increased neural efficiency resulting from synaptic pruning and strengthening of essential connections (Poldrack, 2014).

Effective cognitive control relies not only on regional activation but also on the strength of connectivity within the cognitive control network and its connections to other brain areas (Hwang et al., 2010; van Belle et al., 2014). Hwang et al. (2010) found increased connectivity between the prefrontal cortex and other cortical areas from childhood into adolescence, with some connections continuing to strengthen into adulthood. This highlights the importance of investigating developmental changes in connectivity, not just activation, to understand the complex processes underlying cognitive control. Future research should prioritize network-based approaches, examining the efficiency and strength of connections within and between brain systems (Satterthwaite et al., 2013).

6. Is Risk Taking During Adolescence Related to Heightened Reward Sensitivity and Immature Cognitive Control?

The dual systems model attributes adolescent risk-taking to the confluence of heightened socioemotional reactivity and underdeveloped cognitive control. However, testing this hypothesis requires disentangling the independent contributions of these two systems, which is complicated by the high correlation between their psychological manifestations (e.g., sensation seeking and impulsivity), despite their theoretical and empirical separability (Duckworth & Kern, 2011; Duckworth & Steinberg, 2015; Shulman & Cauffman, 2014; Steinberg et al., 2008). This overlap may be partly artifactual, reflecting measurement challenges, but also partly due to the dynamic interplay between these systems during decision-making.

Studies examining the independent contributions of sensation seeking and impulse control to risk-taking are limited but generally support the dual systems model. Higher sensation seeking and lower impulse control independently predict greater risk-taking (Cyders et al., 2009; Donohew et al., 2000; Quinn & Harden, 2013). For example, Cyders et al. (2009) found that sensation seeking uniquely predicted alcohol use frequency, while Donohew et al. (2000) reported that both sensation seeking and impulsive decision-making independently predicted various risk behaviors in ninth-graders. Unpublished data from our lab (Shulman & Cauffman, 2014) also supports the equal contribution of impulse control and sensation seeking to self-reported risk-taking.

While limited by reliance on self-report, these studies suggest independent contributions of socioemotional and cognitive control systems to risk-taking. Neuroimaging studies offer the advantage of directly measuring activity in these systems. However, few studies have investigated the simultaneous engagement of both systems during adolescent decision-making. Chein et al. (2011) found both increased socioemotional system activation and decreased cognitive control system activation during a simulated driving task with adolescents, supporting the dual systems model. Paulsen et al. (2011) also observed age-related differences in both PFC and striatum activation during a risk-taking task, but inconsistent striatal activation patterns make interpretation difficult.

Van den Bos et al. (2015) investigated age differences in impatience during a temporal discounting task. They found that delayed gratification was associated with greater self-reported future orientation and increased frontoparietal control circuitry activation, but not with present hedonism (reward sensitivity). However, methodological limitations may have biased the study against finding links between reward sensitivity and impatience (Steinberg & Chein, 2015).

While studies have not consistently demonstrated simultaneous heightened socioemotional activation and dampened cognitive control within the same task, some have observed increased striatal activation during reward receipt following a decision (Braams et al., 2014, 2015). Cascio et al. (2015) found that greater engagement of cognitive control circuitry during a response inhibition task predicted safer driving behavior in a simulated driving task, suggesting that individuals with better cognitive control are more resistant to socioemotional arousal and peer influence on risk-taking. However, this study did not compare different age groups.

Van Duijvenvoorde et al. (2015b) found that adolescents, but not children or adults, exhibited greater activation of control circuitry (including the dmPFC) as the perceived risk of decisions increased during a risk-taking task. They suggest that this heightened recruitment reflects a compensatory mechanism to manage increased emotional responses to risk during adolescence.

While evidence suggests that both socioemotional and cognitive control systems contribute to adolescent risk-taking, a comprehensive study confirming their joint effects is still needed.

7. Unresolved Questions and Future Directions

Several unresolved questions warrant further research. First, the modest effects of socioemotional and cognitive control system maturation on real-world risk-taking are challenging to disentangle from contextual factors. Future studies should utilize ecologically valid laboratory tasks that control for opportunities and constraints on risk-taking across age groups.

Second, the mechanism behind the decline in socioemotional system responsiveness between adolescence and adulthood remains unclear. Luciana and Collins (2012) propose that experience and learning lead to lower dopamine levels, while Casey et al. (2008) imply that cognitive control maturation dampens socioemotional reactivity. However, Shulman et al. (2014b) found no evidence for a prospective effect of increased impulse control on decreased sensation seeking, suggesting independent development. Neuroimaging data from van Duijvenvoorde et al. (2015a) support this notion, indicating that decreases in reward sensitivity relate to age-related changes within the socioemotional system rather than influences from the cognitive control system. Longitudinal studies with more frequent assessments and sensitive measures are needed to clarify this relationship.

Third, the operationalization of "mature" cognitive control requires further refinement. While some aspects of the cognitive control system appear mature by mid-adolescence (Luna et al., 2014), others suggest ongoing development into adulthood, challenging the notion of early maturation (Luna & Wright, 2016). We argue that focusing on structural and functional connectivity within the cognitive control network and its connections to other brain regions offers a more informative index of maturation (Strang et al., 2013). Research correlating self-regulation measures with connectivity indices is crucial. Current findings linking increased control behaviors to enhanced frontostriatal connectivity (van den Bos et al., 2015; van Duijvenvoorde et al., 2015a; Vink et al., 2014) need further investigation, especially given findings of increased risk-taking in individuals with more developed cortico-subcortical connections (Berns et al., 2009; DeWitt et al., 2014).

Finally, future models should consider the development and interaction of additional brain systems beyond the dual systems framework, as exemplified by Ernst's triadic model (2014) and our work on anterior insular cortex involvement (Smith et al., 2014b). Understanding developmental shifts in connectivity between the ventral striatum, prefrontal cortex, and anterior insular cortex, particularly the transition from stronger insula-striatum connections in adolescence to stronger PFC-insula connections in adulthood, may be crucial for understanding the trajectory of risk-taking behavior.

8. Concluding Comment

The dual systems model provides a compelling framework for understanding adolescent risk-taking, positing that it arises from the interplay of a hyper-responsive socioemotional system and an underdeveloped cognitive control system. Despite some inconsistencies in the literature, the model is largely supported by self-report, behavioral, and neuroimaging evidence. While future research should address unresolved questions and refine its components, the dual systems model remains a valuable heuristic for guiding research and informing interventions aimed at mitigating adolescent risk-taking.

It is crucial to emphasize that the model does not portray adolescents as universally risky or incompetent decision-makers. Instead, it highlights their heightened sensitivity to rewards and the ongoing development of their cognitive control abilities. Under conditions that minimize socioemotional arousal and allow for deliberate decision-making, adolescents demonstrate reasoning and judgment comparable to adults (Chein et al., 2011; Figner et al., 2009; Van Leijenhorst et al., 2008). The key tenet of the dual systems model is that adolescents are particularly prone to risk-taking in contexts that activate the socioemotional system, such as those involving peers, emotional arousal, or immediate rewards.

In conclusion, the dual systems model, while acknowledging contextual influences, provides a more nuanced explanation for adolescent risk-taking compared to previous models that attributed recklessness to cognitive deficits. The model's continued refinement and elaboration will further advance our understanding of adolescent development and inform strategies for promoting healthy and safe decision-making during this critical period.

Link to Article

Adolescence, Risk-Taking, and the Developing Brain: Examining the Dual Systems Model

It's no secret that teenagers are known for taking risks. Social scientists and psychologists have long observed that the period between childhood and adulthood is marked by an increase in risky behavior. Even though teens are generally healthy, they experience higher rates of injury and even death compared to younger children, largely due to this risk-taking. Why are adolescents seemingly hardwired for recklessness? This question has puzzled researchers for generations, but recent advancements in developmental psychology and neuroscience, particularly in the field of brain development, are providing new clues.

For years, psychologists believed that teenagers' risky behavior stemmed from cognitive limitations, such as irrational thinking, poor information processing, and a lack of understanding about risks. However, research has debunked these theories, showing that by age 15, teenagers generally demonstrate adult-level abilities in logical reasoning, information processing, and risk perception (Steinberg, 2008).

1. The Rise of Dual Systems Models

Around a decade ago, the emerging field of developmental cognitive neuroscience started to shed light on how brain development might explain adolescent decision-making (Dahl, 2004). In 2008, researchers at Temple University (Steinberg, 2008; Steinberg et al., 2008) and Cornell University (Casey et al., 2008) independently proposed similar "dual systems" models to explain this phenomenon. These models propose that two brain systems, maturing at different rates, contribute to adolescent risk-taking:

• Socioemotional System: This system, located in areas like the striatum and parts of the prefrontal cortex, drives our motivation to seek rewards and pleasurable experiences. It matures early, peaking in adolescence.

• Cognitive Control System: This system, encompassing areas like the lateral prefrontal cortex and anterior cingulate cortex, acts as a brake on impulsive behavior, promoting thoughtful decision-making. It matures more slowly, reaching full maturity in adulthood.

The dual systems model suggests that risk-taking peaks in adolescence because the socioemotional system, already highly active, pushes teenagers towards exciting and novel experiences, while the cognitive control system, still under development, is not yet equipped to consistently rein in these impulses.

Several variations of the dual systems model exist, with slight differences in how they characterize the developmental trajectories of these systems (Casey et al., 2008; Duckworth & Steinberg, 2015; Evans & Stanovich, 2013; Luna & Wright, 2016; Metcalfe & Mischel, 1999; Steinberg, 2008). For example, some models propose that the socioemotional system's activity peaks in mid-adolescence and plateaus, while others suggest an inverted-U shaped trajectory, where responsiveness to reward declines in early adulthood. Despite these variations, a core principle remains: the imbalance between these two systems during adolescence contributes to increased risk-taking.

Another model, known as the triadic model (Ernst, 2014), builds on this concept by adding a third system centered on emotional intensity and avoidance. While intriguing, evidence for this model is still emerging and falls outside the scope of this review.

2. Examining the Evidence

This article reviews research conducted since 2008, evaluating its support for the dual systems model. We focus on three key propositions:

1. Reward sensitivity peaks in adolescence.

2. Cognitive control increases linearly during adolescence.

3. Heightened adolescent risk-taking stems from increased reward-seeking and weaker cognitive control.

We address criticisms of the model and clarify terminology, using "sensation seeking" to describe the desire for novel and intense experiences, which aligns with reward sensitivity at the neural level. "Self-regulation" refers to the ability to control impulses and actions, reflecting the cognitive control system. We acknowledge that both sensation seeking and self-regulation can be beneficial depending on the context.

3. Are Adolescents Really More Likely to Take Risks?

While the idea of the reckless teenager is a common trope, evidence for a peak in risk-taking specifically during mid-adolescence is not always clear-cut. Some research suggests that risk-taking peaks in late adolescence or early adulthood, depending on the behavior (Willoughby et al., 2013). For example, binge drinking, a highly risky behavior, is most common in the early twenties (Chassin et al., 2002).

However, proponents of the dual systems model argue that these findings don't necessarily contradict the model's core tenets. They propose that while mid-adolescents might be biologically predisposed to greater risk-taking, their actual engagement in risky behaviors is often limited by a lack of opportunity. Late adolescents and young adults, in contrast, have greater freedom, resources, and legal access to engage in risky behaviors. Essentially, teenagers may want to take more risks, but they often lack the means to do so.

3.1. Studying Risk-Taking in the Lab

To isolate risk-taking propensity from real-world opportunities, researchers use controlled laboratory tasks, such as gambling games and driving simulations. These studies have produced mixed findings, with some showing greater risk-taking in adolescents compared to adults, while others show no difference or even less risk-taking in adolescents (e.g., Bjork et al., 2007; Burnett et al., 2010; Mitchell et al., 2008). This suggests that age-related differences in risk-taking might only emerge under specific conditions (Defoe et al., 2014).

Recent studies have tried to make these laboratory tasks more realistic. For example, research has shown that adolescents are more likely to take risks when the odds of a positive or negative outcome are unknown, reflecting the ambiguity of real-life risky situations (Tymula et al., 2012).

Additionally, researchers have found that emotional arousal – a key factor in real-life risk-taking – can amplify age differences. Adolescents tend to take more risks and make poorer decisions than adults when tasks are designed to be more emotionally arousing (Figner et al., 2009).

Finally, because adolescent risk-taking often happens in groups, researchers have explored the impact of peers on decision-making in the lab. Studies show that adolescents are more influenced by the presence of peers and take more risks when they believe their friends are watching, a finding supported by neuroimaging studies showing heightened activity in the socioemotional system during peer observation (Chein et al., 2011; Gardner & Steinberg, 2005; Smith et al., 2014a).

In conclusion, while laboratory research provides some support for heightened risk-taking propensity in adolescents, the findings are not always consistent. The context of the decision, including factors like emotional arousal and social influence, appears to play a crucial role.

4. How Sensation Seeking and Reward Sensitivity Develop

The dual systems model proposes that the socioemotional system is not only more easily activated during adolescence but is also more sensitive to rewards in general. This heightened sensitivity is believed to be linked to pubertal development, which causes surges in hormones that affect brain regions associated with reward processing, like the striatum (Forbes & Dahl, 2010).

4.1. Sensation Seeking Over Time

One way to study the socioemotional system is through self-reported sensation seeking. As predicted by the dual systems model, studies consistently show that sensation seeking peaks around mid-adolescence and then declines into adulthood (Harden & Tucker-Drob, 2011; Peach & Gaultney, 2013; Quinn & Harden, 2013; Romer & Hennessey, 2007; Shulman et al., 2014a, 2014b; Steinberg & Chein, 2015; Steinberg et al., 2008). This pattern is also linked to pubertal development, with more physically mature adolescents reporting higher levels of sensation seeking (Castellanos-Ryan et al., 2013; Gunn & Smith, 2010; Martin et al., 2002; Quevedo et al., 2009).

4.2. Measuring Reward Sensitivity in the Lab

Behavioral studies on reward sensitivity are less common, but some support the idea of heightened sensitivity in adolescence. For instance, research using modified versions of the Iowa Gambling Task suggests that adolescents are quicker to learn and capitalize on rewarding choices compared to children and adults (Cauffman et al., 2010; Steinberg, 2010; Steinberg & Chein, 2015). Other studies have found that adolescents have a harder time controlling their impulses when faced with rewarding stimuli, such as happy faces, compared to neutral stimuli (Somerville et al., 2011).

4.3. Brain Imaging and Reward

Neuroimaging studies provide further evidence. Many studies show that adolescents exhibit greater activity in the striatum, a key reward center, compared to children and adults, particularly when receiving a reward (Galvan & McGlennen, 2013; Hoogendam et al., 2013; Van Leijenhorst et al., 2010b). However, findings for striatal activity during reward anticipation are less consistent. These inconsistencies might be due to variations in how tasks are designed, particularly the reliability of cues predicting rewards (Barkley-Levenson & Galvan, 2014; Geier et al., 2010).

Additionally, research suggests that the adolescent brain is more responsive to rewards in social contexts. A study using a card-guessing task found that adolescents showed greater striatal activity when they received rewards in the presence of peers compared to when they were alone (Smith et al., 2015). This highlights the powerful influence of social context on adolescent reward processing.

Finally, neuroimaging studies support the link between puberty and reward sensitivity. Research has shown that during adolescence, higher levels of pubertal hormones are associated with greater activation in reward-related brain regions (Braams et al., 2015; Forbes et al., 2010; Op de Macks et al., 2011).

In summary, research on sensation seeking, reward sensitivity, and brain imaging paints a consistent picture: the adolescent brain is highly attuned to rewards, and this sensitivity peaks during this developmental period, likely due in part to the influence of puberty.

5. The Development of Self-Regulation and Cognitive Control

The dual systems model also proposes that cognitive control, the ability to regulate impulses and make thoughtful decisions, continues to develop throughout adolescence and into early adulthood. This development is thought to be less influenced by puberty and follows a more linear trajectory.

5.1. Impulsivity Declines with Age

Self-report measures consistently show that impulsivity, the tendency to act without thinking, decreases with age throughout adolescence and young adulthood (Harden & Tucker-Drob, 2011; Leshem & Glicksohn, 2007; Steinberg et al., 2008; Vaidya et al., 2010). While adults can be impulsive, by their early twenties, impulsive behaviors tend to stabilize at lower frequencies compared to adolescence (Quinn & Harden, 2013; Steinberg et al., 2008).

5.2. Behavioral Measures of Self-Control

Researchers use behavioral tasks requiring response inhibition, the ability to suppress automatic responses, to study self-regulation. These tasks show that self-control improves from childhood to adulthood (e.g., Bezdjian et al., 2014; Bunge et al., 2002; Casey et al., 1997). While adolescents generally perform better than children, differences between adolescents and adults are often only apparent in more complex tasks that require higher levels of cognitive control (e.g., Veroude et al., 2013). This suggests that while basic impulse control emerges earlier, the ability to handle demanding situations continues to mature throughout adolescence and into the twenties.

Studies using tasks like the Tower of London, which requires planning and strategic thinking, support this idea. Research shows that adolescents' self-regulation improves with age, particularly in more challenging versions of the task (Albert & Steinberg, 2011). This supports the notion that complex self-regulation skills continue to develop even when basic impulse control seems mature.

Furthermore, unlike reward sensitivity, self-regulation doesn't appear to be directly tied to pubertal development. Studies have found that while sensation seeking is linked to pubertal stage, self-regulation is more strongly correlated with age (Steinberg et al., 2008).

Interestingly, adolescents' self-control can be influenced by rewards. Studies using tasks like the antisaccade task have shown that when incentives are offered for better performance, adolescents can achieve adult-level self-regulation (Geier et al., 2010; Jazbec et al., 2006; Padmanabhan et al., 2011). This suggests that adolescents are not incapable of good self-control; rather, their ability to exercise it can be influenced by contextual factors, such as the presence of rewards.

In conclusion, self-regulation, like reward sensitivity, follows a developmental trajectory, improving with age. However, the development of self-regulation appears to be less dependent on puberty and more related to the maturation of specific brain regions and cognitive skills.

5.3. Neuroimaging and Cognitive Control

Brain imaging studies help pinpoint the neural mechanisms underlying these behavioral changes. The lateral prefrontal cortex (lPFC) plays a crucial role in cognitive control, and its development is linked to improved self-regulation.

Studies consistently show that greater activity in the lPFC is associated with better performance on response inhibition tasks across all ages (Durston et al., 2006; Rubia et al., 2006, 2013; Velanova et al., 2009). Longitudinal research also shows that lPFC activity increases with age throughout adolescence and into adulthood, supporting its role in the development of self-control (Adleman et al., 2002; Bunge et al., 2002; Durston et al., 2006).

Interestingly, some studies suggest that younger children may show more diffuse activation in frontal brain regions compared to adults during response inhibition tasks (Durston et al., 2002). This has led to the theory that as the brain matures, neural connections become more refined, resulting in more focused and efficient activation patterns in the lPFC.

Beyond simply looking at activity in individual brain regions, researchers are also studying how connections between different regions change over time. Effective cognitive control relies on well-coordinated communication between the lPFC and other brain areas involved in motor control, emotional processing, and action execution (Hwang et al., 2010; van Belle et al., 2014).

Studies have shown that connectivity between the prefrontal cortex and other cortical areas increases from childhood to adolescence and continues to strengthen into adulthood (Hwang et al., 2010). This suggests that the development of cognitive control is not just about individual brain regions maturing, but also about these regions becoming more interconnected and working together more efficiently.

6. Connecting the Dots: How Reward Sensitivity and Cognitive Control Influence Risk-Taking

The dual systems model posits that the unique combination of heightened reward sensitivity and still-developing cognitive control in adolescence explains why teenagers are more prone to take risks. This section explores the evidence for this claim.

One challenge in testing this model is that sensation seeking and impulsivity, the psychological counterparts of these two brain systems, are often highly correlated. This makes it difficult to separate their individual contributions to risk-taking. Some of this overlap might stem from limitations in measurement tools, but it could also reflect a dynamic interplay between these systems in the brain (Duckworth & Kern, 2011; Duckworth & Steinberg, 2015; Shulman & Cauffman, 2014).

Despite this challenge, studies that have measured both sensation seeking and impulsivity generally find that both independently contribute to risk-taking (Cyders et al., 2009; Donohew et al., 2000; Quinn & Harden, 2013). For example, sensation seeking is linked to increased alcohol use, while impulsivity is associated with a greater likelihood of engaging in sexual behavior (Cyders et al., 2009; Donohew et al., 2000). However, it's important to note that most of these studies rely on self-report, which can be biased.

Neuroimaging studies offer a more direct way to examine the activity of the socioemotional and cognitive control systems during decision-making. However, only a handful of studies have investigated this directly in the context of risk-taking (e.g., Cascio et al., 2015; Chein et al., 2011; Kahn et al., 2015; Paulsen et al., 2011; van Duijvenvoorde et al., 2015b).

One study that directly measured both systems during a simulated driving task found that adolescents showed greater activation in reward-related brain regions and reduced activity in areas associated with cognitive control compared to adults when making risky decisions (Chein et al., 2011). This provides some support for the simultaneous influence of both systems on adolescent risk-taking.

Another study found that while adolescents generally did not differ from other age groups in their overall risk-taking on a gambling task, they did show increased activity in the prefrontal cortex, a key area for cognitive control, as the riskiness of the decisions increased (van Duijvenvoorde et al., 2015b). The authors suggest that this heightened activity reflects adolescents' efforts to regulate their emotional responses to risk, further supporting the interplay between these systems.

While more research is needed, these findings suggest that the dual systems model offers a plausible explanation for adolescent risk-taking. It appears that the heightened responsiveness of the socioemotional system and the still-maturing cognitive control system both contribute to this behavior.

7. Open Questions and Future Directions

Despite growing support for the dual systems model, several questions remain unanswered, and future research should address these gaps:

• Real-World Relevance: Given the constraints on adolescent opportunities to engage in many risky behaviors, it's crucial to develop more ecologically valid research methods that can disentangle the influence of brain development from contextual factors. This could involve designing laboratory tasks that better mimic real-life situations or conducting longitudinal studies that track both brain development and real-world behavior over time.

• Why Does Reward Sensitivity Decline?: While the model proposes a decline in socioemotional system activity in adulthood, the reasons for this decline are still unclear. Further research could explore potential explanations, such as changes in dopamine levels, the impact of learning and experience, or shifts in social roles and responsibilities.

• Defining Mature Cognitive Control: There's no single definition of what constitutes "mature" cognitive control. Future research should focus on developing more nuanced measures of cognitive control that go beyond simple activation patterns in specific brain regions. Investigating the development of functional connectivity within the cognitive control network and between this network and other brain areas might offer a more comprehensive understanding of its maturation.

• Expanding the Model: The dual systems model provides a valuable framework, but it might be overly simplistic. Incorporating additional brain systems, such as those involved in emotional intensity and avoidance, as proposed by the triadic model, could offer a more complete picture of adolescent risk-taking. Additionally, future research should explore how individual differences in factors like genetics, temperament, and environmental influences interact with brain development to shape risk-taking behavior.

8. Conclusion

The dual systems model has emerged as a compelling explanation for the increase in risk-taking during adolescence. Research overwhelmingly supports its key propositions, demonstrating that reward sensitivity is heightened during this developmental period, likely due to influences of puberty, while cognitive control continues to mature throughout adolescence and into early adulthood.

It's crucial to remember that the dual systems model does not depict adolescents as inherently irrational or incapable of making sound judgments. Instead, it highlights the powerful influence of context on adolescent decision-making. When teens are in situations that activate their reward system, such as being with friends or facing exciting opportunities, they might be more inclined to take risks, particularly as their ability to regulate these impulses is still under development.

Future research should focus on refining the model, addressing its limitations, and exploring its real-world implications. A deeper understanding of the complex interplay between brain development, social context, and individual differences will be crucial for developing effective prevention and intervention programs aimed at reducing risky behavior and promoting healthy development during adolescence.

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Why Are Teens So Reckless? Understanding Risk-Taking in Teenagers

Everyone knows that teenagers take more risks than kids or adults. But why are teens so into risky behavior? For a long time, psychologists thought that teens just weren't good at thinking things through – that their brains weren't developed enough to make good decisions. But recent research suggests a different answer.

Two Systems in The Brain

New discoveries in brain science are giving us a clearer picture of how teenagers make decisions. One theory, called the "dual systems" model, suggests that two different systems in the brain develop at different speeds.

• The Reward System: This system (located in areas like the striatum and prefrontal cortex) drives a person to seek out exciting and rewarding experiences. Think of it like the gas pedal in a car.

• The Control System: This system (located in areas like the lateral prefrontal cortex) helps a person think ahead, control impulses, and make careful choices. Think of this as the brakes in a car.

The dual systems model proposes that teenagers are more likely to take risks because their "gas pedal" (the reward system) develops much faster than their "brakes" (the control system). Basically, teens are wired to find excitement and rewards very appealing, but their ability to stop and think before they act is still catching up.

Scientists have proposed different versions of this dual systems model. Some believe the reward system peaks in early adolescence, while others argue it continues developing until adulthood. There's also debate about whether the control system develops steadily or plateaus in mid-adolescence. Regardless of these details, all the versions agree on one thing: the interaction between these two systems is key to understanding teenage risk-taking.

Exploring the Evidence

So, what does the research actually say about this dual systems idea?

First, is it even true that teenagers are the biggest risk-takers?

Well, it's complicated. While common sense says yes, real-life data shows a more mixed picture. For example, binge drinking is riskier than having a single beer, and it's actually more common among young adults than teenagers. But this might be because younger teens have fewer opportunities to access alcohol.

To figure out if teens are biologically more prone to risk-taking, scientists create experiments in controlled settings. These studies often use games or simulations that allow teens to make risky choices without real-life consequences. These studies sometimes show that teens take more risks than adults, but not always.

Interestingly, studies suggest that teens are more likely to take risks in situations that:

• Have unclear risks: Like when a person doesn't know the exact chances of winning or losing.

• Are exciting or stressful: Like when with friends or doing something thrilling.

What about the reward system? Is it really super active in teenagers?

Research supports this idea:

• Surveys: Teens consistently score higher on measures of "sensation seeking" – the desire for intense, exciting experiences.

• Brain Studies: When teens anticipate or receive rewards, brain regions associated with pleasure and excitement (like the striatum) show more activity than in children or adults.

• Hormones: The hormonal changes of puberty seem to play a role in making the reward system more sensitive, especially in boys.

And what about the control system? Is it really lagging behind in teenagers?

Again, research supports this:

• Surveys: Teens report being more impulsive than adults.

• Behavioral Tasks: Teens often perform worse than adults on tasks that require self-control, especially when the tasks are difficult or involve resisting tempting rewards.

• Brain Studies: Brain regions involved in planning, decision-making, and impulse control (like the prefrontal cortex) continue to develop throughout adolescence and into the early twenties.

So, do these two systems actually work together to influence teen risk-taking?

More research is needed, but some studies show that:

• Teens with high sensation-seeking and low impulse control take the most risks. This suggests that both systems contribute to real-life risky behavior.

• Brain imaging studies show that, when teens take risks, there is increased activity in reward-related areas and decreased activity in control-related areas. This supports the idea that an imbalance between these systems drives risky choices.

What's Next?

There's still a lot we don't know about how these systems interact. For example, why does the reward system become less sensitive in adulthood? And how do individual differences in these systems influence behavior?

Future research needs to:

• Develop more realistic experiments: We need to study risk-taking in ways that better reflect real-life situations and account for differences in opportunity between teens and adults.

• Improve measurement: We need better ways to measure how these systems are functioning in the brain and how they relate to real-life behavior.

• Explore other brain regions: The dual systems model is a good starting point, but other parts of the brain are probably involved in risk-taking, too.

Conclusion: It's Not Just About Being "Risky"

The dual systems model helps us understand that teen risk-taking isn't just about being reckless or irrational. It's about a specific brain-based imbalance between reward-seeking and self-control.

Importantly, this doesn't mean that teens are doomed to make bad decisions. In fact, when teens are calm, focused, and not distracted by rewards, they can be just as good as adults at making smart choices.

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Why Do Teenagers Take So Many Risks?

People have wondered about this for a long time! Psychologists and scientists who study the brain are starting to understand why teenagers seem more likely to take risks.

For a while, people thought teenagers made bad decisions because they weren't as smart as adults. But studies show that by around age 15, teenagers think logically and understand risks just as well as adults do.

1. Two Parts of the Brain: The Gas Pedal and the Brakes

About ten years ago, scientists began to study how the teenage brain develops, and they made a fascinating discovery. There are two main systems in our brains that work together when we make decisions. Imagine one system is like a gas pedal in a car, pushing us towards things we want, like rewards. The other system is like the brakes, helping us stop and think before acting impulsively.

The "gas pedal" system (scientists call it the "socioemotional system") develops quickly during the teenage years. This means teenagers are easily excited by new and thrilling experiences. The "brake" system (the "cognitive control" system), which helps us make calm and thoughtful choices, develops much more slowly. It doesn't fully mature until our twenties.

Scientists believe this difference in development explains why teenagers take more risks. The "gas pedal" is pushing hard, but the "brakes" aren't strong enough yet to always stop us.

There are different ideas about exactly how these systems develop. Some scientists think the "gas pedal" is strongest in middle adolescence, while others believe it stays strong until adulthood. More research is needed to know for sure.

2. What This Article Explores

This article examines studies about how these two brain systems develop and whether they truly explain why teenagers take more risks.

Here are the main questions we'll explore:

1. Do teenagers REALLY take more risks? We'll look at whether teenagers are naturally more risky or if other factors play a role.

2. How strong is the "gas pedal" in the teenage brain? We'll examine how sensitive teenagers are to rewards and excitement.

3. How do the "gas pedal" and the "brakes" work together in teenagers? We'll see how the development of these two systems might explain risky choices.

We'll also consider some criticisms of this theory and discuss what future research can tell us.

It's important to remember that scientists use specific words like "sensation-seeking" and "self-regulation" to talk about these brain systems and how they affect our behavior.

3. Are Teenagers REALLY Risk-Takers?

We've all heard that teenagers are impulsive. But is this always true? Studies show that the age at which people take the most risks actually depends on the type of risk. For example, drinking too much alcohol is most common in the early twenties, not the middle teens.

Why does this happen? Scientists believe teenagers might be biologically prone to taking more risks, but they often don't have the opportunity. Think about it: younger teens usually have more supervision from adults, less money, and fewer legal rights to do risky things like driving, drinking, or gambling.

3.1. Studying Risk in a Safe Environment

To study risk-taking without the influence of opportunities, scientists create games and simulations in a laboratory. These studies sometimes show that teenagers take more risks than adults, and sometimes they don't.

However, scientists have discovered that teenagers ARE more likely to take risks in specific situations, especially:

• When they don't know the odds: In a game where they don't know their chances of winning. Teenagers are more likely to take risks in these uncertain situations than adults.

• When emotions run high: When teenagers feel excited or scared, they are more likely to take risks.

• When friends are around: Teenagers take more risks when they're with their friends than when they're alone. This might be because being with friends is exciting and rewarding.

These findings show that teenagers might not always be risky, but they are more likely to take risks in situations that make their "gas pedal" system work harder.

4. The Power of the "Gas Pedal"

One of the main ideas in the "two brain systems" theory is that the "gas pedal" system is strongest during the teenage years. Scientists have been studying this by looking at how sensitive teenagers are to rewards.

One way to measure this sensitivity is to ask teenagers how much they enjoy exciting experiences. As the theory predicts, studies show that teenagers, especially those in middle adolescence, report feeling the strongest desire for new and intense experiences.

4.1. Excitement Seekers

Scientists also study a personality trait called "sensation seeking." People high in sensation seeking love new, intense, and even risky experiences. Studies have shown that sensation seeking increases throughout childhood and peaks in mid-adolescence, just as the theory suggests. Then, as people enter adulthood, their desire for sensation seeking decreases.

Interestingly, scientists have found a link between puberty and sensation seeking. Teenagers who go through puberty earlier also tend to be higher in sensation seeking. This makes sense, as the hormonal changes during puberty might be like adding fuel to the "gas pedal" system.

4.2. The Brain's Reward Center

Scientists can now study the brain directly using special machines. This has allowed them to observe the "reward center" of the brain (called the striatum) in action.

Many studies have shown that the striatum is more active in teenagers than in children or adults when they're anticipating or receiving a reward. This suggests that teenagers' brains are wired to find rewards extra exciting.

4.3. The Brain: A Network of Connections

Scientists are learning that the "gas pedal" system isn't just one part of the brain, but a network of connected areas. These areas communicate with each other to process information about rewards. Understanding how these connections develop in the teenage brain is an important area for future research!

To sum up, many studies on sensation-seeking and brain activity support the idea that the "gas pedal" system is strongest during the teenage years, which could explain why teenagers are drawn to rewards and exciting experiences.

5. The Development of the "Brakes"

The "two brain systems" theory also proposes that the "brake" system, responsible for self-control, develops much more gradually than the "gas pedal" system. This system continues to mature throughout adolescence and into early adulthood.

5.1. Learning to Resist Impulses

As teenagers grow older, they get better at resisting impulses. Studies show that self-reported impulsivity decreases throughout adolescence. This means that older teenagers are better at thinking before they act and making thoughtful decisions.

Interestingly, even young adults in their late teens and early twenties still report being more impulsive than those in their mid-twenties. This suggests that the "brakes" are still being fine-tuned even after we officially become adults!

5.2. Testing Self-Control

Scientists also study self-control by giving people tasks that require them to stop themselves from acting impulsively. For example, in one task, participants have to quickly press a button when they see certain images but stop themselves from pressing it when they see others.

These studies consistently show that self-control improves as we get older, but it's important to note that teenagers can be just as good as adults at these tasks when:

• The task is simple: Teenagers have no problem controlling their impulses when the task is easy.

• They are rewarded for good performance: When teenagers know they'll get a reward for doing well, their self-control improves significantly.

These findings suggest that teenagers can control their impulses, but it might take more effort or motivation for them to do so compared to adults.

5.3. The Brain's Control Center

The brain region most responsible for self-control is called the prefrontal cortex (PFC). As teenagers age, their PFC becomes more active and connected with other brain areas. This increased connectivity allows for better communication within the brain, leading to improved self-control.

Think of it like this: as the PFC matures, it becomes a more skilled conductor, coordinating the different parts of the brain involved in making decisions and controlling impulses.

6. Connecting the Dots: Rewards, Control, and Risk-Taking

The "two brain systems" theory suggests that teenage risk-taking results from both a strong "gas pedal" and weak "brakes." But do studies actually show that these two systems work together to influence risky behavior?

Scientists have found that teenagers who report high sensation seeking (strong "gas pedal") AND low impulse control (weak "brakes") are the most likely to engage in risky activities like drinking, smoking, and breaking rules. This means that both systems play a role in teenage risk-taking!

Brain imaging studies also provide some support for this idea. For example, one study found that teenagers who showed strong activity in the reward center of their brains while driving a simulated car were more likely to take risks, especially when their friends were watching. This study shows how the "gas pedal" system can overpower the "brakes" in certain situations.

While more research is needed to fully understand how these systems work together, current evidence suggests that both increased reward sensitivity and developing self-control contribute to teenage risk-taking.

7. Unanswered Questions and Future Discoveries

Although the "two brain systems" theory has helped us understand teenage risk-taking, there's still much to learn! Here are some unanswered questions:

• Real-world risks: Most studies on risk-taking are done in a laboratory. How do these findings apply to real-life situations where opportunities and consequences are different?

• Why does the "gas pedal" weaken?: We know that sensation seeking decreases as we enter adulthood, but we don't fully understand why.

• How do we measure mature self-control?: Scientists are still figuring out the best ways to measure how developed a person's self-control is.

Future studies will continue to explore these questions and refine our understanding of the developing brain.

8. The Takeaway

The "two brain systems" theory offers a valuable framework for understanding why teenagers sometimes make risky choices. It reminds us that:

• Teenage brains are still developing: The part of the brain responsible for self-control develops gradually throughout adolescence and into early adulthood.

• Excitement and rewards are powerful: The teenage brain is wired to find rewards and new experiences exciting, which can lead to impulsive decisions.

• Context matters: Teenagers are more likely to take risks in certain situations, such as when they're with friends or feeling emotional.

This doesn't mean that all teenagers are constantly making risky choices. They are capable of making responsible decisions, especially when they're calm, focused, and the "brakes" are given a chance to work.

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