Table 3.

Hierarchical regressions of fluid processing ability (composite measure) and crystallized knowledge in older adults

ModelResponseStepPredictor(s)R2dfFp
AFluid processing ability1Age0.00031,170.0050.946
2+ Visual (SVM)0.3082,163.5600.052
R2 increment=0.3081,177.1140.017
BCrystallized knowledge1Age0.0201,170.3390.568
2+ Visual (SVM)0.0202,160.1660.849
R2 increment=0.00071,170.0120.914
CFluid processing ability1Age0.00031,170.0050.946
2+ Visual (Corr)0.2682,162.9240.083
R2 increment=0.2671,175.8430.028
DCrystallized knowledge1Age0.0201,170.3390.568
2+ Visual (Corr)0.0242,160.1950.825
R2 increment=0.0041,170.0700.796
  • In model A, fluid processing ability was explained by age (first step) and the neural specificity measure from the SVM approach (second step). In model B, crystallized knowledge was explained by age (first step) and the neural specificity measure from the SVM approach (second step). R2 increment represents the variance explained by the neural specificity measures beyond age. Models C and D are identical to A and B, respectively, except that the neural specificity measure from the correlation approach was used instead.