Skip to main content
. Author manuscript; available in PMC: 2021 Aug 15.
Published in final edited form as: Neuroimage. 2021 May 2;237:118134. doi: 10.1016/j.neuroimage.2021.118134

Table 2.

Model fit for univariate growth models of neural and behavioral cognitive control.

Model Label χ2 df p(exact) RMSEA [90% CI] CFI Δχ2 Δdf p(d)
Fronto-parietal regions
a. Linear growth model 555.501 339 <0.001 0.063 [0.054, 0.073] 0.918
b. Latent basis growth model rACC 541.413 337 <0.001 0.062 [0.052, 0.071] 0.923 14.088 2 <0.001
a. Linear growth model 16.581 8 0.034 0.082 [.021, 0.138] 0.494
b. Latent basis growth model 2.545 6 0.863 0.009 [.000, 0.054] 1.000 14.036 2 <0.001
Intraindividual standard deviations
a. Linear growth model 30.407 8 <0.001 0.130 [.083, 0.180] 0.860
b. Latent basis growth model 4.913 6 0.555 0.000 [.000, 0.090] 1.000 25.494 2 <0.001

Note. rACC = the rostral anterior cingulate cortex; RMSEA = root mean square error of approximation; CI = confidence interval; CFI = comparative fit index; Δχ2 = difference in likelihood ratio tests; Δdf = difference in df; p(d) = probability of the difference tests. Best-fitting model in boldface.