Table 3.

Best models, posterior probabilities for the best models and inclusion probabilities for each predictor.

Dependent variable	Predictor	Best model	Pr(β ≠ 0|Y)	Posterior model probability of best model
AS7 Gain	Intercept	•	1.000	0.276
	Age	•	1.000
	Sex	•	1.000
	IQ	•	1.000
	TMT-B1		0.176
	WCST2		0.146
	Stroop		0.284
	LFL3		0.155
	CFL4		0.146
	SWM5		0.145
	RVIP6		0.154
AS Spatial Error	Intercept	•	1.000	0.284
	Age	•	1.000
	Sex	•	1.000
	IQ	•	1.000
	TMT-B		0.164
	WCST		0.151
	Stroop		0.264
	LFL		0.144
	CFL		0.144
	SWM		0.144
	RVIP		0.185
AS Latency	Intercept	•	1.000	0.188
	Age	•	1.000
	Sex	•	1.000
	IQ	•	1.000
	TMT-B	•	0.725
	WCST		0.265
	Stroop		0.211
	LFL		0.278
	CFL		0.155
	SWM		0.253
	RVIP		0.157
AS Error Rate	Intercept	•	1.000	0.249
	Age	•	1.000
	Sex	•	1.000
	IQ	•	1.000
	TMT-B		0.160
	WCST		0.351
	Stroop		0.219
	LFL		0.156
	CFL		0.168
	SWM		0.205
	RVIP	•	0.934

Notes: Bayesian Model Averaging was used to determine inclusion probabilities for the predictors. Thus, regression coefficient estimates are not included. For inference on effect sizes, the estimates for the stepwise regression are reported in the text. ¹Trail Making Task Version B, ²Wisconsin Card Sorting Task, ³Letter Fluency Task, ⁴Category Fluency Task, ⁵Spatial Working Memory Task, ⁶Rapid Visual Information Task, ⁷Antisaccade.