DDM: The Diffusion Decision Model
| DDM | R Documentation |
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).
