Beyond dual systems: A genetically-informed, latent factor model of behavioral and self-report measures related to adolescent risk-taking

Abstract

The dual systems model posits that adolescent risk-taking results from an imbalance between a cognitive control system and an incentive processing system. Researchers interested in understanding the development of adolescent risk-taking use a diverse array of behavioral and self-report measures to index cognitive control and incentive processing. It is currently unclear whether different measures commonly interpreted as indicators of the same psychological construct do, in fact, tap the same underlying dimension of individual differences. In a diverse sample of 810 adolescent twins and triplets (M age=15.9years, SD=1.4years) from the Texas Twin Project, we investigated the factor structure of fifteen self-report and task-based measures relevant to adolescent risk-taking. These measures can be organized into four factors, which we labeled premeditation, fearlessness, cognitive dyscontrol, and reward seeking. Most behavioral measures contained large amounts of task-specific variance; however, most genetic variance in each measure was shared with other measures of the corresponding factor. Behavior genetic analyses further indicated that genetic influences on cognitive dyscontrol overlapped nearly perfectly with genetic influences on IQ (r_A=-0.91). These findings underscore the limitations of using single laboratory tasks in isolation, and indicate that the study of adolescent risk taking will benefit from applying multimethod approaches.

Keywords: Adolescence; Cognitive control; Delay discounting; Dual systems; Impulsivity; Intelligence; Reward seeking; Risk-taking; Self-control.

Fig. 1

Behavior Genetic Model. Note: Double-lined arrows indicate that all correlations between A factors and all correlations between E factors were estimated. Self-report measures in yellow; behavioral measures in green. A = additive genetic, C = shared environmental, E = non-shared environmental. Age and sex were controlled by regressing these variables from the factors. All factor loadings were estimated in the final model to be significantly different from zero at p < 0.05, except for the loading of delay discounting on reward seeking (shown with a dashed line).