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.