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Create the Diffusion Model for Conflict Tasks

Source: R/models.R
dmc_dm.Rd

This function creates a drift_dm object that corresponds to the Diffusion Model for Conflict Tasks by Ulrich et al. (2015) .

Usage

dmc_dm(
  var_non_dec = TRUE,
  var_start = TRUE,
  instr = NULL,
  obs_data = NULL,
  sigma = 1,
  t_max = 3,
  dt = 0.0075,
  dx = 0.02,
  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 and x_beta in component_shelf). Defaults are TRUE. If FALSE, a constant non-decision time and starting point is set (see nt_constant and x_dirac_0 in component_shelf).

instr

optional string with "instructions", see modify_flex_prms().

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 parameter a, and an amplitude A.

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. This is because the derivative of the scaled gamma-distribution function does not exist at t = 0 for a < 2. Currently, we recommend keeping a fixed to 2. If users decide to set a != 2, then a small value of 1e-05 is added to the time vector t_vec before calculating the derivative of the scaled gamma-distribution as originally introduced by Ulrich et al. (2015) . Note that varying a can lead to large numerical inaccuracies if both tau and a are small and/or dt is large.

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).

Note

The scaling of the parameters in dRiftDM is different to Ulrich et al. (2015) . This is because dRiftDM works in seconds and with a diffusion constant of 1, while the original DMC parameterization is in milliseconds and with a diffusion constant of 4. We describe how to convert the parameters on our website.

References

Ulrich R, Schröter H, Leuthold H, Birngruber T (2015). “Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.” Cognitive Psychology, 78, 148–174. doi:10.1016/j.cogpsych.2015.02.005 .

Examples

# the model with default settings
my_model <- dmc_dm()

# the model with no variability in the starting point and a finer
# discretization
my_model <- dmc_dm(var_start = FALSE, dt = .005, dx = .01)