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Beyond Dual Systems: a Genetically-Informed, Latent Factor Model Of Developmental Cognitive Neuroscience 25 (2017) 221–234 Contents lists available at ScienceDirect Developmental Cognitive Neuroscience j ournal homepage: http://www.elsevier.com/locate/dcn Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking a,b,∗ a a c K. Paige Harden , Natalie Kretsch , Frank D. Mann , Kathrin Herzhoff , c d a,b Jennifer L. Tackett , Laurence Steinberg , Elliot M. Tucker-Drob a Department of Psychology, University of Texas at Austin, Austin, TX, United States b Population Research Center, University of Texas at Austin, Austin, TX, United States c Department of Psychology, Northwestern University, Evanston, IL, United States d Department of Psychology, Temple University, Philadelphia, PA, United States a r t i c l e i n f o a b s t r a c t Article history: The dual systems model posits that adolescent risk-taking results from an imbalance between a cognitive Received 8 March 2016 control system and an incentive processing system. Researchers interested in understanding the devel- Received in revised form opment of adolescent risk-taking use a diverse array of behavioral and self-report measures to index 21 November 2016 cognitive control and incentive processing. It is currently unclear whether different measures commonly Accepted 20 December 2016 interpreted as indicators of the same psychological construct do, in fact, tap the same underlying dimen- Available online 26 December 2016 sion of individual differences. In a diverse sample of 810 adolescent twins and triplets (M age = 15.9 years, SD = 1.4 years) from the Texas Twin Project, we investigated the factor structure of fifteen self-report Keywords: and task-based measures relevant to adolescent risk-taking. These measures can be organized into four Dual systems factors, which we labeled premeditation, fearlessness, cognitive dyscontrol, and reward seeking. Most behav- Cognitive control Reward seeking ioral measures contained large amounts of task-specific variance; however, most genetic variance in each Impulsivity measure was shared with other measures of the corresponding factor. Behavior genetic analyses further Self-control indicated that genetic influences on cognitive dyscontrol overlapped nearly perfectly with genetic influ- Intelligence ences on IQ (rA = −0.91). These findings underscore the limitations of using single laboratory tasks in Delay discounting isolation, and indicate that the study of adolescent risk taking will benefit from applying multimethod Risk-taking approaches. Adolescence © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). “Why do people not think, when they are grown up, as I do when I dren or adults, they are also uniquely vulnerable to being hurt or am young?” killed because of their risky behavior. The top three leading causes of death for adolescents and young adults ages 15–24 are due to “Oh dear,” said Merlin, “You are making me feel confused. Suppose behaviors – unintentional injury (such as falls or car accidents), you wait till you are grown up and know the reason?” suicide, and homicide (Centers for Disease Control, 2015). The long- “I don’t think that is an answer at all,” replied the Wart, justly. standing question, then, is why teenagers are prone to risk-taking behaviors. –T.H. White, The Once and Future King Central to any explanation of the development of adolescent risk-taking is the construct of self-control. Self-control can be 1. Introduction defined as behavioral tendencies that produce “actions aligned with valued, longer-term goals” (Duckworth and Steinberg, 2015) or 1.1. The dual systems model of adolescent risk-taking as “the ability to suppress inappropriate emotions, desires, and actions in favor of alternative, appropriate ones” (Casey, 2015). Adolescence is a health paradox. Although teenagers are Given that adolescent risk-taking generally is not considered val- stronger, fitter, more fertile, and more resistant to disease than chil- ued or appropriate by adult society, it is practically a tautology to say that adolescents are prone to risk-taking because they lack self-control. In the past decade, however, research has begun to ∗ Corresponding author at: 108 E. Dean Keeton Stop #A8000, Austin, TX 78712, push past this tautology and offered new insights into (1) what United States. are the component psychological processes that contribute to E-mail address: [email protected] (K.P. Harden). http://dx.doi.org/10.1016/j.dcn.2016.12.007 1878-9293/© 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4. 0/). 222 K.P. Harden et al. / Developmental Cognitive Neuroscience 25 (2017) 221–234 self-controlled versus risk-taking behavior, (2) how do these com- consistent with this general claim” (van den Bos and Eppinger, ponent processes change, on average, during adolescence, and (3) 2016). what neural systems underlie these developmental changes? In their classic paper, Cronbach and Meehl (1955) proposed that A critical distinction in this work has been between processes the construct validity of a task or set of tasks could be investigated that enable self-controlled behavior versus those processes that by testing a nomological network, the “interlocking system of laws disrupt it (Duckworth and Steinberg, 2015). And, this distinction which constitute a theory.” The nomological network includes laws between “volitional” versus “impulsigenic” processes has also been regarding how “observable properties or quantities” (i.e., observed central to neurodevelopmental theories of adolescent risk-taking. measures) are related to each other, how theoretical constructs are According to the dual systems model (Steinberg, 2010; Shulman related to measures, and how different theoretical constructs relate et al., 2016) and the related maturational imbalance model (Casey to each other. As Pfeifer and Allen (2016) described, “the absence et al., 2011; Casey, 2015), adolescent risk-taking can be understood of such an [nomological network] analysis can be a critical barrier in terms of two separable dimensions – incentive processing and to progress if studies that are putatively investigating a particu- cognitive control – that mature according to separate timescales lar construct are not in fact doing so” (p. 134). Putting this into and that are undergirded by distinct neurobiological circuits. First, modern statistical terms, some predictions of a theorized nomo- an incentive processing system, involving sub-cortical brain regions logical network can be tested as a latent factor model, in which such as the striatum, is hypothesized to undergird impulsigenic the pattern of covariation among multiple measures is accounted processes, such as reward sensitivity, novelty seeking, and sensa- for by a smaller number of latent factors that represent theoreti- tion seeking. The incentive processing system is hypothesized to cal constructs, which may, in turn, be correlated with one another. develop rapidly from early to middle adolescence. Multiple studies Consider, for example, self-reports of sensation seeking and pref- have found, for instance, that adolescents show stronger reactivity erence for immediate rewards on a delay discounting task. Both in the ventral striatum in response to rewards than do children or measures have been used by investigators interested in the rapid adults, and that the extent of ventral striatum reactivity correlates development of an incentive processing system, and thus might be with real-world risk-taking (Galvan, 2010; Shulman et al., 2016). presumed to tap the same underlying construct. In the context of In contrast, a cognitive control system, involving the prefrontal a latent factor model, this presumption can be tested by examin- cortex, is hypothesized to undergird volitional psychological pro- ing whether these measures can be used as indicators of the same cesses that facilitate self-controlled behavior, such as planning, latent factor. abstract reasoning, and impulse control. Unlike the incentive pro- cessing system, the cognitive control system appears to mature 1.3. Dimensions of self-reported impulsivity more slowly from adolescence through emerging adulthood. For example, white matter volume increases until the mid-twenties, The multivariate pattern of associations among self-report and particularly in areas of the brain involved in high-level cogni- behavioral measures commonly used in the study of adolescent tive control (Geier, 2013). This neural maturity gap, therefore, is risk-taking has not yet been widely studied using a latent variable thought to result in adolescents experiencing heightened sensi- approach. However, within the personality literature, there have tivity to rewards and emotions without a concomitant increase in been extensive factor analytic studies of self-report measures that capacity for controlling their behavior. tap tendencies toward self-controlled versus disinhibited behavior. This line of research has identified four dimensions of disinhib- 1.2. Measuring dimensions of self-control: the problem of ited personality traits: (1) urgency, the “tendency to commit rash construct validity or regrettable actions as a result of intense negative affect”; (2) pre- meditation, the “tendency to delay action in favor of careful thinking The dual systems model has become a popular
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