RDMC {ream} | R Documentation |
Revised Diffusion Model of Conflict Tasks
Description
A DMC-like model which modifies the shape of the controlled and automatic processes
to ensure consistent stimulus representation across the task. It maintains all SDDM
parameters outside the drift rate which is v(x,t) = w_a(t)*d_a + w_c(t)*d_c
, where
w_a(t) = A_0*exp(-k*t)
and w_c(t) = 1 - w_a(t)
.
Usage
dRDMC(rt, resp, phi, x_res = "default", t_res = "default")
pRDMC(rt, resp, phi, x_res = "default", t_res = "default")
rRDMC(n, phi, dt = 1e-05)
Arguments
rt |
vector of response times |
resp |
vector of responses ("upper" and "lower") |
phi |
parameter vector in the following order:
|
x_res |
spatial/evidence resolution |
t_res |
time resolution |
n |
number of samples |
dt |
step size of time. We recommend 0.00001 (1e-5) |
Value
For the density a list of PDF values, log-PDF values, and the sum of the log-PDFs, for the distribution function a list of of CDF values, log-CDF values, and the sum of the log-CDFs, and for the random sampler a list of response times (rt) and response thresholds (resp).
Author(s)
Raphael Hartmann & Matthew Murrow
References
Lee, P.-S., & Sewell, D. K. (2023). A revised diffusion model for conflict tasks. Psychonomic Bulletin & Review, 31(1), 1–31.
Examples
# Probability density function
dRDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.35, 0.5, 7.5, 40.0, 5.0, 5.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Cumulative distribution function
pRDMC(rt = c(1.2, 0.6, 0.4), resp = c("upper", "lower", "lower"),
phi = c(0.35, 0.5, 7.5, 40.0, 5.0, 5.0, 1.0, 0.5, 0.0, 0.0, 1.0))
# Random sampling
rRDMC(n = 100, phi = c(0.35, 0.5, 7.5, 40.0, 5.0, 5.0, 1.0, 0.5, 0.0, 0.0, 1.0))