Model | Response | Step | Predictor(s) | R2 | df | F | p |
---|---|---|---|---|---|---|---|
A | Fluid processing ability | 1 | Age | 0.0003 | 1,17 | 0.005 | 0.946 |
2 | + Visual (SVM) | 0.308 | 2,16 | 3.560 | 0.052 | ||
R2 increment | =0.308 | 1,17 | 7.114 | 0.017 | |||
B | Crystallized knowledge | 1 | Age | 0.020 | 1,17 | 0.339 | 0.568 |
2 | + Visual (SVM) | 0.020 | 2,16 | 0.166 | 0.849 | ||
R2 increment | =0.0007 | 1,17 | 0.012 | 0.914 | |||
C | Fluid processing ability | 1 | Age | 0.0003 | 1,17 | 0.005 | 0.946 |
2 | + Visual (Corr) | 0.268 | 2,16 | 2.924 | 0.083 | ||
R2 increment | =0.267 | 1,17 | 5.843 | 0.028 | |||
D | Crystallized knowledge | 1 | Age | 0.020 | 1,17 | 0.339 | 0.568 |
2 | + Visual (Corr) | 0.024 | 2,16 | 0.195 | 0.825 | ||
R2 increment | =0.004 | 1,17 | 0.070 | 0.796 |
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.