EMC2 (version 3.1.1)

DDMGNG: The GNG (go/nogo) Diffusion Decision Model

Description

In the GNG paradigm one of the two possible choices results in a response being withheld (a non-response), which is indicated in the data by an NA for the rt, with the corresponding level of the R (response) factor still being specified. For example, suppose the go response is coded as "yes" and nogo is coded as "no", then for a non-response (R,rt) = ("no",NA) and for a response e.g., (R,rt) = ("yes",1.36). The GNG paradigm must also have a response

Usage

DDMGNG()

Value

A model list with all the necessary functions to sample

window (i.e., a length of time, TIMEOUT period, after which withholding is

assumed).

Details

The model used is described in the following paper, with the addition of modeling the TIMEOUT (which is considered but not used in this paper).

Gomez, P., Ratcliff, R., & Perea, M. (2007). A Model of the Go/No-Go Task. Journal of Experimental Psychology: General, 136(3), 389–413. https://doi.org/10.1037/0096-3445.136.3.389

The likelihood of non-responses requires and evaluation of the DDM cdf, specifically 1 - p(hitting the yes boundary before TIMEOUT).

To use these models three functions must be supplied in the design's function argument with the names TIMEOUT, Rnogo and Rgo. For example, assuming a 2.5 second timeout, and R factor with levels c("no","yes") and "no" mapping to a non-response.

TIMEOUT=function(d)rep(2.5,nrow(d)) Rnogo=function(d)factor(rep("no",nrow(d)),levels=c("no","yes")) Rgo=function(d)factor(rep("yes",nrow(d)),levels=c("no","yes")))

See the help for DDM for further details. At present this model is not fully implemented in C, so is a little slower to use than the DDM, but not greatly.

Examples

dGNG <- design(Rlevels = c("left","right"),
               factors=list(subjects=1,S=c("left","right")),
               functions=list(
               TIMEOUT=function(d)rep(2.5,nrow(d)),
               # no go response level
               Rnogo=function(d)factor(rep("left",nrow(d)),levels=c("left","right")),
               # go response level
               Rgo=function(d)factor(rep("right",nrow(d)),levels=c("left","right"))),
               formula=list(v~S,a~1, Z~1, t0~1),
               model=DDMGNG)

p_vector <- sampled_pars(dGNG)