This function creates a drift_dm object that corresponds to the Diffusion Model for Conflict Tasks by Ulrichetal.2015;textualdRiftDM.
Usage
dmc_dm(
var_non_dec = TRUE,
var_start = TRUE,
instr = NULL,
obs_data = NULL,
sigma = 1,
t_max = 3,
dt = 0.001,
dx = 0.001,
b_coding = NULL
)
Arguments
- var_non_dec, var_start
logical, indicating whether the model should have a normally-distributed non-decision time or beta-shaped starting point distribution, respectively. (see
nt_truncated_normal
andx_beta
in component_shelf). Defaults areTRUE
. IfFALSE
, a constant non-decision time and starting point is set (seent_constant
andx_dirac_0
in component_shelf).- instr
optional string with additional "instructions", see
modify_flex_prms()
and the Details below.- obs_data
data.frame, an optional data.frame with the observed data. See obs_data.
- sigma, t_max, dt, dx
numeric, providing the settings for the diffusion constant and discretization (see drift_dm)
- b_coding
list, an optional list with the boundary encoding (see b_coding)
Value
An object of type drift_dm
(parent class) and dmc_dm
(child class),
created by the function drift_dm()
.
Details
The Diffusion Model for Conflict Tasks is a model for describing conflict tasks like the Stroop, Simon, or flanker task.
It has the following properties (see component_shelf):
a constant boundary (parameter
b
)an evidence accumulation process that results from the sum of two subprocesses:
a controlled process with drift rate
muc
a gamma-shaped process with a scale parameter
tau
, a shape parametera
, and an amplitudeA
.
If var_non_dec = TRUE
, a (truncated) normally distributed non-decision with
mean non_dec
and standard deviation sd_non_dec
is assumed. If
var_start = TRUE
, a beta-shaped starting point distribution is assumed
with shape and scale parameter alpha
.
If var_non_dec = TRUE
, a constant non-decision time at non_dec
is set. If
var_start = FALSE
, a starting point centered between the boundaries is
assumed (i.e., a dirac delta over 0).
Per default the shape parameter a
is set to 2 and not allowed to
vary. The model assumes the amplitude A
to be negative for
incompatible trials. Also, the model contains the custom parameter
peak_l
, containing the peak latency ((a-2)*tau
).