Mind the gap: A review and recommendations for statistically evaluating Dual Systems models of adolescent risk behavior

Abstract

According to Dual Systems models (Casey et al., 2008; Luna and Wright, 2016; Steinberg, 2008), a rapidly-developing socioemotional system and gradually-developing cognitive control system characterize adolescent brain development. The imbalance hypothesis forwarded by Dual Systems models posits that the magnitude of the imbalance between these two developing systems should predict the propensity for engaging in a variety of risk behaviors. The current integrative review argues that the excitement generated by the imbalance hypothesis and its implications for explaining adolescent risk behaviors has not been meet with equal efforts to rigorously test this hypothesis. The goal of the current review is to help guide the field to consider appropriate and rigorous methods of testing the imbalance hypothesis. First, we review the analytic approaches that have been used to test the imbalance hypothesis and outline statistical and conceptual limitations of these approaches. Next, we discuss the utility of two longitudinal analytic approaches (Latent Difference Scores and Growth Mixture Modeling) for testing the imbalance hypothesis. We utilize data from a large community adolescent sample to illustrate each approach and argue that Latent Difference Scores and Growth Mixture Modeling approaches enhance the specificity and precision with which the imbalance hypothesis is evaluated.

Keywords: Dual systems models; Growth mixture modeling; Imbalance hypothesis; Latent difference scores; Self-Regulation; Sensation seeking.

Fig. 1

Adapted with permission from Shulman et al. (2016a). The figure depicts three Dual Systems models and the development of sensation seeking and self-regulation from late childhood to young adulthood according to each of these models. The blue portion in each model represents the imbalance between sensation seeking and self-regulation. The challenge when assessing the imbalance hypothesis is to use a data analytic technique that captures the difference between sensation seeking and self-regulation. Further, each of these Dual Systems model posit systematic changes in sensation seeking and self-regulation across time, therefore, data analytic techniques used to assess the imbalance hypothesis must also be able to capture the proposed developmental differences in sensation seeking and self-regulation from late childhood to young adulthood. A model that captures the dashed line (sensation seeking), either at a single time point or across time, is not assessing the imbalance. Similarly, a model that captures the solid line (self-regulation), either at a single time point or across time, is also not assessing the imbalance. Further, a model that simultaneously models the dashed line (sensation seeking) and solid line (self-regulation), at a singly time point, is not modeling the imbalance. We argue that only data analytic approaches that quantify the imbalance between the dashed and solid lines (the blue portion of each Dual Systems model) and account for developmental changes in imbalance can be rigorous tests of the imbalance hypothesis. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).

Fig. 2

IC = inhibitory control, SR = sensitivity to reward, Imb = the difference (imbalance) between sensitivity to reward and inhibitory control, I = intercept, and S = slope.

Fig. 3

LDS approach to assessing the relationship between the imbalance and the probability of alcohol use and growth in alcohol use across ages 12–20. IC = inhibitory control, SR = sensitivity to reward, Imb = the latent difference (imbalance) between sensitivity to reward and inhibitory control, AU = alcohol use, D = dichotomous use (use vs. no use), C = continuous levels (quantity x frequency) of past year use, I = intercept, and S = slope. Solid two-headed arrows depict significant covariances. Dashed two-headed arrows depict non-significant estimated covariances between the imbalance and alcohol use.

Fig. 4

Final two class solution for the growth mixture model with unique means and shared variances.