DDM: The Diffusion Decision Model

In EMC2: Bayesian Hierarchical Analysis of Cognitive Models of Choice

The Diffusion Decision Model

Description

Model file to estimate the Diffusion Decision Model (DDM) in EMC2.

Usage

DDM()

Details

Model files are almost exclusively used in design().

Default values are used for all parameters that are not explicitly listed in the formula argument of design().They can also be accessed with DDM()$p_types.

Parameter	Transform	Natural scale	Default	Mapping	Interpretation
v	-	[-Inf, Inf]	1		Mean evidence-accumulation rate (drift rate)
a	log	[0, Inf]	log(1)		Boundary separation
t0	log	[0, Inf]	log(0)		Non-decision time
s	log	[0, Inf]	log(1)		Within-trial standard deviation of drift rate
Z	probit	[0, 1]	qnorm(0.5)	z = Z x a	Relative start point (bias)
SZ	probit	[0, 1]	qnorm(0)	sz = 2 x SZ x min(a x Z, a x (1-Z))	Relative between-trial variation in start point
sv	log	[0, Inf]	log(0)		Between-trial standard deviation of drift rate
st0	log	[0, Inf]	log(0)		Between-trial variation (range) in non-decision time

a, t0, sv, st0, s are sampled on the log scale because these parameters are strictly positive, Z, SZ and DP are sampled on the probit scale because they should be strictly between 0 and 1.

Z is estimated as the ratio of bias to one boundary where 0.5 means no bias. DP comprises the difference in non-decision time for each response option.

Conventionally, s is fixed to 1 to satisfy scaling constraints.

See Ratcliff, R., & McKoon, G. (2008). The diffusion decision model: theory and data for two-choice decision tasks. Neural computation, 20(4), 873-922. doi:10.1162/neco.2008.12-06-420.

Value

A model list with all the necessary functions for EMC2 to sample

Examples

design_DDMaE <- design(data = forstmann,model=DDM,
                           formula =list(v~0+S,a~E, t0~1, s~1, Z~1, sv~1, SZ~1),
                           constants=c(s=log(1)))
# For all parameters that are not defined in the formula, default values are assumed
# (see Table above).

EMC2 documentation built on April 11, 2025, 5:50 p.m.