Advancing understanding of executive function impairments and psychopathology: bridging the gap between clinical and cognitive approaches

Abstract

Executive function (EF) is essential for successfully navigating nearly all of our daily activities. Of critical importance for clinical psychological science, EF impairments are associated with most forms of psychopathology. However, despite the proliferation of research on EF in clinical populations, with notable exceptions clinical and cognitive approaches to EF have remained largely independent, leading to failures to apply theoretical and methodological advances in one field to the other field and hindering progress. First, we review the current state of knowledge of EF impairments associated with psychopathology and limitations to the previous research in light of recent advances in understanding and measuring EF. Next, we offer concrete suggestions for improving EF assessment. Last, we suggest future directions, including integrating modern models of EF with state of the art, hierarchical models of dimensional psychopathology as well as translational implications of EF-informed research on clinical science.

Keywords: executive function; inhibition; methods; psychopathology; shifting; transdiagnostic; working memory.

FIGURE 1

Unity/diversity model of EF. Two complimentary ways of representing the unity/diversity model (Adapted from Friedman et al., 2011). In both latent variable models, individual tasks are combined to form latent factors. Numbers on arrows are standardized factor loadings (range 1 to -1), that indicate the extent to which each task is predicted by the latent factor. Those on curved double-headed arrows are correlations between the latent variables, which indicate how strongly they are related. (A) The updating, shifting, and inhibition components are substantially correlated (unity), but are separable (i.e., not correlated 1.0; diversity). (B) Unity and diversity are more clearly shown with a bifactor model. All nine tasks load onto a common EF factor (unity), and updating and shifting tasks also load onto their respective specific factors (diversity). Note that there is no inhibition-specific factor (i.e., inhibition task variance is fully accounted for by common EF).

FIGURE 2

Task impurity problem. A score on an EF task is composed of (A) systematic variance attributable to the specific aspect of EF targeted by that task (e.g., shifting-specific or updating-specific variance), (B) systematic variance attributable to common EF (i.e., variance shared across multiple types of EF tasks, hypothesized to be related to task-goal maintenance), (C) systematic variance attributable to non-EF aspects of the task (e.g., articulation speed, visual processing), and (D) non-systematic (error) variance. Use of single tasks to measure EF is thus problematic because this task-impurity makes interpreting the results difficult and because the amount of variance attributable to EF (A,B) can be relatively small compared to non-EF variance (C,D).