Diffusion Decision Model

Usage

ddm(rt, response, links = NULL, ...)

Arguments

rt: Name of the reaction time variable coding reaction time in seconds in the data.
response: Name of the response variable coding the response numerically (0 = lower response / incorrect, 1 = upper response / correct)
links: A list of links for the parameters. For positive parameters (e.g. bound, ndt), "softplus" is available as an alternative to the default "log" link that grows linearly for large values and avoids the numerical blow-up of exp().
...: used internally for testing, ignore it

Value

An object of class bmmodel

Details

Domain: Decision Making / Response times
Task: Two-Alternative Force Choice RT
Name: Diffusion Decision Model
Citation:
- Ratcliff, R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59-108. https://doi.org/10/fjwm2f;
Requirements:
- The response time should be in seconds and represent the time between onset of the target stimulus until the response execution

The response should be coded numerically: 0 = lower response, 1 = upper response

Parameters:
- drift: Drift rate = Average rate of evidence accumulation of the decision processes
- bound: Boundary separation = Distance between the decision boundaries that need to be reached
- ndt: Non-decision time = Additional time required beyond the evidence accumulation process
- zr: Relative starting point = Starting point between the decision thresholds relative to the upper bound.
Fixed parameters:
- zr = 0
- mu = 0
Default parameter links:
- drift = identity; bound = log; ndt = log; zr = logit
Default priors:
- drift:
  - main: cauchy(0,1)
  - effects: normal(0,0.5)
- bound:
  - main: normal(0,0.5)
  - effects: normal(0,0.5)
- ndt:
  - main: normal(-1.5,0.5)
  - effects: normal(0,0.3)
- zr:
  - main: normal(0,0.5)
  - effects: normal(0,0.3)

Default behavior

By default, zr is fixed at 0. If you want to estimate zr, add a formula for zr in your bmf() call.

Examples

if (FALSE) { # isTRUE(Sys.getenv("BMM_EXAMPLES"))
# Simulate data for one subject using rddm
set.seed(123)
n_trials <- 500

# Simulate DDM data with fixed parameters
sim_data <- rddm(
  n = n_trials,
  drift = 1.5,    # drift rate
  bound = 1.2,    # boundary separation
  ndt = 0.3,      # non-decision time
  zr = 0.5        # relative starting point
)

# Prepare data frame
dat <- data.frame(
  rt = sim_data$rt,
  response = sim_data$response
)

# Define formula (intercept-only model)
ff <- bmmformula(
  drift ~ 1,
  bound ~ 1,
  ndt ~ 1
)

# Specify the DDM model
model <- ddm(rt = "rt", response = "response")

# Fit the model
fit <- bmm(
  formula = ff,
  data = dat,
  model = model,
  cores = 4,
  iter = 1000,
  backend = "cmdstanr"
)

# Check parameter recovery
summary(fit)
}