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Table 1.

Estimated Parameters, Standard Errors, Confidence Intervals, and t Test (or z Test) Statistics for Each Predictor in the Linear (or Beta) Regression Model

Outcome	v		a		t₀		Accuracy		RT
Predictors	b (SE) [95% CI]	t	b (SE) [95% CI]	t	b (SE) [95% CI]	t	b (SE)	z	b (SE) [95% CI]	t
Intercept	0.22***(0.05) [0.12 0.31]	4.43, p < .001 p_adj < .001	1.55***(0.09) [1.36 1.74]	16.47, p < .001 p_adj < .001	0.77***(0.04) [0.70 0.86]	19.77, p < .001 p_adj < .001	0.89***(0.16)	5.63, p < .001 p_adj < .001	1.24***(0.04) [1.15 1.33]	28.95, p < .001 p_adj < .001
N2ac	−0.22*(0.08) [−0.38 −0.06]	−2.79, p = .008 p_adj = .032	−0.32 (0.15) [−0.62 −0.01]	−2.10, p = .042 p_adj = .126	0.08 (0.06) [−0.05 0.21]	1.29, p = 0.203 p_adj = .407	−1.02***(0.26)	−3.93, p < .001 p_adj < .001	0.08 (0.07) [−0.06 0.22]	1.14, p = .261 p_adj = .407
ALI	−2.52 (6.77) [−16.20 11.16]	−0.37, p = .712 p_adj = 1.067	−10.87 (13.05) [−37.22 15.49]	−0.83, p = .410 p_adj = 1.230	10.18 (5.45) [−0.83 21.19]	1.87, p = .069 p_adj = .345	−33.48 (21.81)	−1.54, p = .124 p_adj = .499	3.73 (5.94) [−8.27 15.74]	0.63, p = .533 p_adj = 1.230
Age	−0.14*(0.05) [−0.24 −0.04]	−2.86, p = .007 p_adj = .020	−0.07 (0.09) [−0.26 0.12]	−0.76, p = .452 p_adj = .452	0.12*(0.04) [0.03 0.20]	3.00, p = .005 p_adj = .018	−0.48*(0.16)	3.03, p = .002 p_adj = .012	0.08 (0.04) [−0.01 0.16]	1.82, p = .075 p_adj = .151
N2ac*Age	−0.16 (0.08) [−0.32 0.00]	−2.02, p = .050 p_adj = .160	−0.11 (0.15) [−0.41 0.20]	−0.72, p = .474 p_adj = .474	0.15 (0.06) [0.03 0.28]	2.44, p = .018 p_adj = .095	−0.53 (0.26)	−2.05, p = .039 p_adj = .160	0.09 (0.04) [−0.04 0.24]	1.43, p = .160 p_adj = .319
ALI*Age	−4.67 (6.78) [−18.35 9.02]	−0.69, p = .495 p_adj = .965	−10.06 (13.05) [−36.41 16.30]	−0.77, p = .445 p_adj = 1.336	11.23 (5.45) [0.22 22.24]	2.06, p = .046 p_adj = .229	−15.30 (21.78)	−0.70, p = .483 p_adj = 1.336	9.19 (5.94) [−2.81 21.19]	1.55, p = .130 p_adj = .519
Adjusted/pseudo-R²	.26		.03		.32		.36		.14
F-statistic	F(5, 41) = 4.15, p = .004		F(5, 41) = 1.25, p = .302		F(5, 41) = 5.29, p = .001		–		F(5, 41) = 2.46, p = .048

Outcome	v		a		t₀		Accuracy		RT
Predictors	b (SE) [95% CI]	t	b (SE) [95% CI]	t	b (SE) [95% CI]	t	b (SE)	z	b (SE) [95% CI]	t
Intercept	0.22***(0.05) [0.12 0.31]	4.43, p < .001 p_adj < .001	1.55***(0.09) [1.36 1.74]	16.47, p < .001 p_adj < .001	0.77***(0.04) [0.70 0.86]	19.77, p < .001 p_adj < .001	0.89***(0.16)	5.63, p < .001 p_adj < .001	1.24***(0.04) [1.15 1.33]	28.95, p < .001 p_adj < .001
N2ac	−0.22*(0.08) [−0.38 −0.06]	−2.79, p = .008 p_adj = .032	−0.32 (0.15) [−0.62 −0.01]	−2.10, p = .042 p_adj = .126	0.08 (0.06) [−0.05 0.21]	1.29, p = 0.203 p_adj = .407	−1.02***(0.26)	−3.93, p < .001 p_adj < .001	0.08 (0.07) [−0.06 0.22]	1.14, p = .261 p_adj = .407
ALI	−2.52 (6.77) [−16.20 11.16]	−0.37, p = .712 p_adj = 1.067	−10.87 (13.05) [−37.22 15.49]	−0.83, p = .410 p_adj = 1.230	10.18 (5.45) [−0.83 21.19]	1.87, p = .069 p_adj = .345	−33.48 (21.81)	−1.54, p = .124 p_adj = .499	3.73 (5.94) [−8.27 15.74]	0.63, p = .533 p_adj = 1.230
Age	−0.14*(0.05) [−0.24 −0.04]	−2.86, p = .007 p_adj = .020	−0.07 (0.09) [−0.26 0.12]	−0.76, p = .452 p_adj = .452	0.12*(0.04) [0.03 0.20]	3.00, p = .005 p_adj = .018	−0.48*(0.16)	3.03, p = .002 p_adj = .012	0.08 (0.04) [−0.01 0.16]	1.82, p = .075 p_adj = .151
N2ac*Age	−0.16 (0.08) [−0.32 0.00]	−2.02, p = .050 p_adj = .160	−0.11 (0.15) [−0.41 0.20]	−0.72, p = .474 p_adj = .474	0.15 (0.06) [0.03 0.28]	2.44, p = .018 p_adj = .095	−0.53 (0.26)	−2.05, p = .039 p_adj = .160	0.09 (0.04) [−0.04 0.24]	1.43, p = .160 p_adj = .319
ALI*Age	−4.67 (6.78) [−18.35 9.02]	−0.69, p = .495 p_adj = .965	−10.06 (13.05) [−36.41 16.30]	−0.77, p = .445 p_adj = 1.336	11.23 (5.45) [0.22 22.24]	2.06, p = .046 p_adj = .229	−15.30 (21.78)	−0.70, p = .483 p_adj = 1.336	9.19 (5.94) [−2.81 21.19]	1.55, p = .130 p_adj = .519
Adjusted/pseudo-R²	.26		.03		.32		.36		.14
F-statistic	F(5, 41) = 4.15, p = .004		F(5, 41) = 1.25, p = .302		F(5, 41) = 5.29, p = .001		–		F(5, 41) = 2.46, p = .048

v, a, t₀, and RT denote drift rate, threshold separation, non-decision time, and mean RTs, respectively. SE = standard error, CI = confidence interval. Adjusted R² is given for linear regression models (v, a, t₀, and RT); pseudo-R² is given for beta-regression (accuracy). p denotes uncorrected p values; p_adj denotes p values corrected for multiple comparison using a Bonferroni–Holm correction procedure (Holm, 1979). Asterisks denote significant estimates with adjusted p values as *p_adj < .05, ***p_adj < .001.

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