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

Published in final edited form as: Psychol Rev. 2017 Oct 16;125(1):33–46. doi: 10.1037/rev0000080

Table 1.

Diffusion Model Parameters from Fits to Data

Discrimination task	a	T_er	η	s_z	s_t	v₁	v₂	v₃	G²
Numerosity	0.129	0.411	0.124	0.068	0.197	0.074	0.225	0.344	43.0
Numerosity (random)	0.127	0.396	0.153	0.073	0.178	0.072	0.220	0.348	39.4
Letter	0.111	0.381	0.209	0.032	0.160	0.104	0.260	0.364	58.2
Motion	0.098	0.438	0.156	0.062	0.238	0.098	0.139	0.191	43.0
Static brightness	0.104	0.431	0.164	0.044	0.208	0.173	0.274		31.0
Dynamic brightness	0.113	0.423	0.157	0.070	0.230	0.135	0.246		31.4

The parameters were: Boundary separation a (starting point z = a/2), mean nondecision component of response time, T_er, SD in drift across trials η, range of the distribution of starting point s_z and range of the distribution of nondecision times, s_t. 95% critical values of chi-squares (G² is asymptotically distributed χ² square) are 37.6 for 25 degrees of freedom for the numerosity, letter, and motion discrimination tasks and 25.0 for 15 degrees of freedom for the brightness discrimination tasks. Values of the mean χ² between 1 and 2 times the critical value are representative of adequate fits (Ratcliff and Childers, 2015). For drift rates, v₁ represents the most difficult condition, v₂ easier, and v₃ the easiest condition.