Drift Diffusion Model (DDM) — rddm • cogmod

The Drift Diffusion Model (DDM) is a widely used model for decision-making tasks. It assumes that evidence accumulates over time until it reaches one of two decision boundaries.

The 'ddm' family in this package is not a full re-implementation of the DDM, but uses of the brms wiener family. It is simply a reparametrized version for consistency with the other race models in the package.

Usage

rddm(n, drift, bs, bias, ndt, backend = "rtdists", ...)

dddm(x, drift, bs, bias, ndt, response, log = FALSE, backend = "Rwiener", ...)

ddm_lpdf_expose()

ddm_stanvars()

ddm(
  link_mu = "identity",
  link_bs = "softplus",
  link_bias = "logit",
  link_tau = "logit",
  link_minrt = "identity"
)

log_lik_ddm(i, prep, ...)

posterior_epred_ddm(prep)

posterior_predict_ddm(i, prep, ...)

Arguments

n: Number of simulated trials. Must be a positive integer.
drift: Drift rate. Can take any real value.
bs: Decision threshold (boundary separation). Must be positive.
bias: Starting point bias (proportion of boundary separation). Must be in (0, 1).
ndt: Non-decision time. Must be non-negative.
backend: The backend to use for the simulation. Based on local benchmarks, "rtdists" is much faster for simulation but "Rwiener" is faster for density computation.
...: Other arguments to be passed to brms::rwiener() or brms::dwiener(),
x: The observed reaction time (RT). Must be greater than ndt.
response: The decision indicator (0 or 1). 0 for choice 0, 1 for choice 1.
log: Logical; if TRUE, returns the log-density. Default: FALSE.
link_mu, link_bs, link_bias, link_tau, link_minrt: Link functions for the parameters.
i, prep: For brms' functions to run: index of the observation and a brms preparation object.

Examples

# Simulate data
# data <- rddm(1000, drift = 0.2, bs = 1, bias = 0.5, ndt = 0.3)
# hist(data[data$response == 0, "rt"], breaks = 50, main = "Reaction Times", xlab = "RT")
# hist(data[data$response == 1, "rt"], breaks = 50, add = TRUE, col = rgb(1, 0, 0, 0.5))

# Compute density
# dddm(x = c(0.5, 0.7), drift = 0.2, bs = 1, bias = 0.5, resp = c(0, 1), ndt = 0.3)