. Author manuscript; available in PMC: 2019 Nov 1.

Published in final edited form as: Psychol Rev. 2018 Nov;125(6):888–935. doi: 10.1037/rev0000117

Table A1:

Mean and SDs in SCDM parameters from simulation studies

Data set	Param	T_er	S_t	a	s_a	s_w	r	S_d	d₁	d₂	d₃	d₄	G²
Experiment 1	Mean	177.5	33.1	15.4	5.9	37.0	14.0	0.859	42.7	31.3	16.7		89.2
Sim. data N=4000		176.0	26.6	14.1	4.2	36.7	20.6	0.721	44.8	33.5	18.5		54.4
Sim. data N=200		181.0	25.1	13.8	4.4	37.6	22.9	0.761	46.4	34.5	18.8		37.2
Experiment 3		177.7	48.1	12.3	5.5	26.0	34.0	0.821	26.3	12.2	14.6	5.1	102.1
Sim. data N=4000		179.9	58.3	12.7	6.3	25.8	31.5	0.917	27.2	13.1	15.1	6.0	46.2
Sim. data N=200		182.8	55.7	12.7	6.3	26.0	31.1	0.945	28.3	14.2	15.7	6.6	34.4
Experiment 1	SD	12.4	13.2	2.6	2.5	2.4	10.0	0.47	11.2	5.9	3.9
Sim. data N=4000		2.0	1.9	0.2	0.4	0.5	1.0	0.06	1.0	0.8	0.5
Sim. data N=200		4.9	2.6	0.3	0.5	1.5	2.0	0.10	2.7	2.3	1.6
Experiment 3		20.7	15.7	1.8	0.9	2.2	5.5	0.25	3.2	2.6	2.8	2.4
Sim. data N=4000		4.9	5.0	0.2	0.2	0.6	1.6	0.07	1.1	0.9	0.6	0.5
Sim. data N=200		8.4	5.5	0.4	0.2	1.2	3.2	0.13	2.1	2.1	1.2	1.4

T_er is nondecision time, s_t is the range in nondecision time, a is the boundary setting, s_a is the range in the boundary setting, s_w is the SD in the drift rate distribution, r is the Gaussian process kernel parameter, s_d is the range in the height of the drift rate distribution, d_t is the height of the drift rate function, and G² is the multinomial maximum likelihood statistic.