Title: | Simulated Stop Signal Task Data |
---|---|
Description: | Stop signal task data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>, Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) <doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) <doi:10.1037/a0035230>. |
Authors: | Mohsen Soltanifar [aut] , Chel Hee Lee [cre, aut] |
Maintainer: | Chel Hee Lee <[email protected]> |
License: | GPL-3 |
Version: | 0.0.5.2 |
Built: | 2024-12-14 06:42:04 UTC |
Source: | CRAN |
Stop signal task data of go and stop trials is generated per participant. The fixed stop signal delay method with underlying exponentially modified Gaussian (ExG) or Shifted Wald (SW) distributions for each of go and stop process is applied. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate different distributional parameters of interest.
simssfixed(pid, block, n, m, SSD.b, dist.go, theta.go, dist.stop, theta.stop)
simssfixed(pid, block, n, m, SSD.b, dist.go, theta.go, dist.stop, theta.stop)
pid |
character vector of size |
block |
numeric vector of size |
n |
numeric vector of size |
m |
numeric vector of size |
SSD.b |
numeric vector of size |
dist.go |
character vector of size |
theta.go |
numeric matrix of size |
dist.stop |
character vector of size |
theta.stop |
numeric matrix of size |
matrix with sum(n)
rows and 8 columns
Gordon D. Logan. On the Ability to Inhibit Thought and Action: A User's Guide to the Stop Signal Paradigm. In D. Dagenbach, & T.H. Carr (Eds.), Inhibitory Process in Attention, Memory and Language. San Diego: Academic Press, 1994.
Dora Matzke, Jonathon Love, Thomas V. Wiecki, Scott D. Brown, and et al. Release the BEESTS: Bayesian Estimation of Ex-Gaussian Stop Signal Reaction Times Distributions. Frontiers in Psychology, 4: Article 918, 2013.
Mohsen Soltanifar. Stop Signal Reaction Times: New Estimations with Longitudinal, Bayesian and Time Series based Methods, PhD Dissertation, Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, 2020.
mySSTdata1 <- simssfixed( pid = c("John.Smith","Jane.McDonald","Jane.McDonald"), n = c(50,100,150), m=c(10,20,30), SSD.b=c(200,220,240), dist.go=c("ExG","ExG","ExG"), theta.go=as.matrix(rbind(c(400,60,30),c(440,90,90),c(440,90,90))), dist.stop=c("ExG","ExG","ExG"), theta.stop=as.matrix(rbind(c(100,70,60),c(120,80,70),c(120,80,70))), block=c(1,1,2)) mySSTdata1
mySSTdata1 <- simssfixed( pid = c("John.Smith","Jane.McDonald","Jane.McDonald"), n = c(50,100,150), m=c(10,20,30), SSD.b=c(200,220,240), dist.go=c("ExG","ExG","ExG"), theta.go=as.matrix(rbind(c(400,60,30),c(440,90,90),c(440,90,90))), dist.stop=c("ExG","ExG","ExG"), theta.stop=as.matrix(rbind(c(100,70,60),c(120,80,70),c(120,80,70))), block=c(1,1,2)) mySSTdata1
Stop signal task data of go and stop trials is generated per participant. The tracking signal delay method with underlying exponentially modified Gaussian(ExG) or Shifted Wald(SW) distributions for each of go and stop process is applied. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate different distributional parameters of interest.
simssgen( pid, block, n, m, SSD.b, dist.go, theta.go, dist.stop, theta.stop, rho, d )
simssgen( pid, block, n, m, SSD.b, dist.go, theta.go, dist.stop, theta.stop, rho, d )
pid |
a character vector of size |
block |
a numeric vector of size |
n |
a numeric vector of size |
m |
a numeric vector of size |
SSD.b |
a numeric vector of size |
dist.go |
a character vector of size |
theta.go |
a numeric matrix of size |
dist.stop |
a character vector of size |
theta.stop |
a numeric matrix of size |
rho |
a numeric vector of size |
d |
a numeric vector of size |
a matrix with sum(n)
rows and (8) columns
Gordon D. Logan. On the Ability to Inhibit Thought and Action: A User's Guide to the Stop Signal Paradigm. In D. Dagenbach, & T.H. Carr (Eds.), Inhibitory Process in Attention, Memory and Language. San Diego: Academic Press, 1994.
Dora Matzke, Jonathon Love, Thomas V. Wiecki, Scott D. Brown, and et al. Release the BEESTS: Bayesian Estimation of Ex-Gaussian Stop Signal Reaction Times Distributions. Frontiers in Psychology, 4: Article 918, 2013.
Mohsen Soltanifar. Stop Signal Reaction Times: New Estimations with Longitudinal, Bayesian and Time Series based Methods, PhD Dissertation, Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, 2020.
mySSTdata1 <- simssgen( pid = c("John.Smith","Jane.McDonald","Jane.McDonald"), block = c(1,1,2), n = c(50,100,150), m = c(10,20,30), SSD.b = c(200,220,240), dist.go = c("ExG","ExG","ExG"), theta.go = as.matrix(rbind(c(400,60,30),c(440,90,90),c(440,90,90))), dist.stop = c("ExG","ExG","ExG"), theta.stop = as.matrix(rbind(c(100,70,60),c(120,80,70),c(120,80,70))), rho = c(0.35,0.45,0.45), d = c(50,65,75)) mySSTdata1
mySSTdata1 <- simssgen( pid = c("John.Smith","Jane.McDonald","Jane.McDonald"), block = c(1,1,2), n = c(50,100,150), m = c(10,20,30), SSD.b = c(200,220,240), dist.go = c("ExG","ExG","ExG"), theta.go = as.matrix(rbind(c(400,60,30),c(440,90,90),c(440,90,90))), dist.stop = c("ExG","ExG","ExG"), theta.stop = as.matrix(rbind(c(100,70,60),c(120,80,70),c(120,80,70))), rho = c(0.35,0.45,0.45), d = c(50,65,75)) mySSTdata1
Stop signal task data of go and stop trials is generated per participant. The tracking signal delay method with underlying exponentially modified Gaussian(ExG) or Shifted Wald(SW) distributions for each of go and stop process is applied. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate different distributional parameters of interest.
simsstrack(pid, block, n, m, SSD.b, dist.go, theta.go, dist.stop, theta.stop)
simsstrack(pid, block, n, m, SSD.b, dist.go, theta.go, dist.stop, theta.stop)
pid |
a character vector of size |
block |
a numeric vector of size |
n |
a numeric vector of size |
m |
a numeric vector of size |
SSD.b |
a numeric vector of size |
dist.go |
a character vector of size |
theta.go |
a numeric matrix of size |
dist.stop |
a character vector of size |
theta.stop |
a numeric matrix of size |
a matrix with sum(n)
rows and (8) columns
Gordon D. Logan. On the Ability to Inhibit Thought and Action: A User's Guide to the Stop Signal Paradigm. In D. Dagenbach, & T.H. Carr (Eds.), Inhibitory Process in Attention, Memory and Language. San Diego: Academic Press, 1994.
Dora Matzke, Jonathon Love, Thomas V. Wiecki, Scott D. Brown, and et al. Release the BEESTS: Bayesian Estimation of Ex-Gaussian Stop Signal Reaction Times Distributions. Frontiers in Psychology, 4: Article 918, 2013.
Mohsen Soltanifar. Stop Signal Reaction Times: New Estimations with Longitudinal, Bayesian and Time Series based Methods, PhD Dissertation, Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada, 2020.
mySSTdata1 <- simsstrack( pid=c("John.Smith","Jane.McDonald","Jane.McDonald"), block=c(1,1,2), n=c(50,100,150), m=c(10,20,30), SSD.b=c(200,220,240), dist.go=c("ExG","ExG","ExG"), theta.go=as.matrix.data.frame(rbind(c(400,60,30),c(440,90,90),c(440,90,90))), dist.stop=c("ExG","ExG","ExG"), theta.stop=as.matrix.data.frame(rbind(c(100,70,60),c(120,80,70),c(120,80,70)))) mySSTdata1
mySSTdata1 <- simsstrack( pid=c("John.Smith","Jane.McDonald","Jane.McDonald"), block=c(1,1,2), n=c(50,100,150), m=c(10,20,30), SSD.b=c(200,220,240), dist.go=c("ExG","ExG","ExG"), theta.go=as.matrix.data.frame(rbind(c(400,60,30),c(440,90,90),c(440,90,90))), dist.stop=c("ExG","ExG","ExG"), theta.stop=as.matrix.data.frame(rbind(c(100,70,60),c(120,80,70),c(120,80,70)))) mySSTdata1