Figure 2.

Two complementary parameterizations of the EF data, updated from Friedman et al. (2008). Numbers on arrows are standardized factor loadings, those under the smaller arrows are residual variances, and those on curved double-headed arrows are inter-factor correlations. In the correlated factors model (Panel A), there are three correlated EF latent variables predicting three tasks each. In the nested factors model (Panel B), there is a Common EF latent variable on which all nine executive function tasks load, as well as two “nested” latent variables on which the updating and shifting tasks, respectively, also load. The Common EF variance turned out to be isomorphic with the Inhibiting latent variable (see Friedman et al., 2008), so there was no inhibiting-specific variance. Because the Common EF factor captures the variance common to all three EFss, the Updating-specific and Shifting-specific factors capture the variance that is unique to updating and shifting, respectively. Hence, they are uncorrelated with the Common EF factor and with each other. All parameters were statistically significant (p < .05). Antisac = antisaccade, Stop = stop-signal, Letter = letter memory, S2back = spatial 2-back, Number = number-letter, Color = color-shape, Category = category-switch.