Unity and diversity of executive functions: Individual differences as a window on cognitive structure

Abstract

Executive functions (EFs) are high-level cognitive processes, often associated with the frontal lobes, that control lower level processes in the service of goal-directed behavior. They include abilities such as response inhibition, interference control, working memory updating, and set shifting. EFs show a general pattern of shared but distinct functions, a pattern described as "unity and diversity". We review studies of EF unity and diversity at the behavioral and genetic levels, focusing on studies of normal individual differences and what they reveal about the functional organization of these cognitive abilities. In particular, we review evidence that across multiple ages and populations, commonly studied EFs (a) are robustly correlated but separable when measured with latent variables; (b) are not the same as general intelligence or g; (c) are highly heritable at the latent level and seemingly also highly polygenic; and (d) activate both common and specific neural areas and can be linked to individual differences in neural activation, volume, and connectivity. We highlight how considering individual differences at the behavioral and neural levels can add considerable insight to the investigation of the functional organization of the brain, and conclude with some key points about individual differences to consider when interpreting neuropsychological patterns of dissociation.

Keywords: Behavioral genetics; Executive control; Individual differences; Prefrontal cortex.

Copyright © 2016 Elsevier Ltd. All rights reserved.

Figures

Figure 1

Latent variable models of executive functions (EFs). Inhibiting tasks require avoiding a dominant or prepotent response (eye movements, categorization, or word reading for antisaccade, stop-signal, and Stroop, respectively); Updating tasks require continuously updating the contents of working memory, adding new information and removing no-longer-relevant information (with category exemplars, letters, or spatial locations for keep track, letter memory, and spatial 2-back, respectively), and Shifting tasks require switching between two subtasks according to a cue that appears before each trial (between categorizing numbers as odd/even or letters as consonant/vowels, shapes as red/green or circle/triangle, or words as living/nonliving or big/small for the number–letter, color–shape, and category-switch tasks, respectively); see Friedman et al. (2008) for more details. In the correlated factors parameterization (panel A), three latent variables (represented with ellipses) each predict separate tasks (represented with rectangles). The numbers on the single-headed arrows are standardized factor loadings, and the numbers on the curved double-headed arrows are correlations between the latent variables. All of the correlations are significantly larger than zero (indicating unity), but none of the factors can be collapsed without significantly harming model fit (indicating diversity). In the bifactor parameterization (panel B), unity is captured with a common factor that predicts all nine tasks, and diversity is captured by orthogonal factors that capture remaining correlations among the updating and shifting tasks, respectively, once the Common EF variance is removed. Parameters taken from Friedman et al. (2011); all p<.05. Letter = letter memory; S2back = spatial 2-back; Number = number–Letter; Color = Color–shape; Category = category switch.

Figure 2

Twin model estimates for the bifactor unity/diversity model (parameters taken from Friedman et al., 2011). Each latent variable variance is decomposed into additive genetic (A), shared environmental (C), and nonshared environmental (E) variances (percentages at top of figure), as are the residual variances for each task (i.e., variances not explained by the EF factors; percentages at bottom of figure). Numbers on arrows are standardized factor loadings. Boldface type and solid lines indicate p < .05. Anti = antisaccade, Stop = stop-signal, Keep = keep track; Letter = letter memory, Sback = spatial 2-back, Num = number–letter, Col = color–shape, Cat = category-switch.

Figure 3

General structure and connectivity of the prefrontal cortex (PFC) basal ganglia (BG) computational model used to simulate executive function (EF) tasks in Herd et al. (2014). Each box represents a layer or set of layers, and arrows indicate connectivity. Layer properties and connectivity incorporate extensive physiological data (Frank et al., 2001). The BG learn whether incoming information (in the sensory input layer) should be gated into the PFC, based on dopaminergic signals representing learned reward values associated with those inputs generated by the primary value–learned value (PVLV) system. If information is deemed relevant, active maintenance currents within PFC are turned on to enable that information’s representation to persist in the absence of input. That information can then be used to bias ongoing processing and select response mappings (learned by the posterior cortex). I/O = input/output.