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Gaze-informed diffusion-IRT modelling

Confirmatory parameter mapping

Gaze features must be assigned to theoretically defensible diffusion parameters before fitting. A feature cannot be placed simultaneously on drift, boundary, non-decision time, and starting bias in a confirmatory specification.

spec <- gaze_diffusion_spec(
  response = "score",
  response_time = "response_time",
  drift_features = c("evidence_dwell_balance", "verification_transitions"),
  boundary_features = "warning_dwell",
  nondecision_features = "first_fixation_latency",
  starting_features = "initial_option_bias",
  censor_column = "rt_censoring",
  contaminant = TRUE,
  engine = "stan"
)

prepared <- prepare_gaze_diffusion_data(trials, spec)
fit <- fit_gaze_diffusion_irt(trials, spec, seed = 42)

The Stan engine uses the Wiener first-passage likelihood for observed responses, mirrored parameters for the lower boundary, censoring contributions, person/item heterogeneity, and an optional uniform contaminant mixture.

Identification and posterior checks

extract_diffusion_parameters(fit)
diffusion_parameter_diagnostics(fit, correlation_threshold = 0.85)
diffusion_posterior_predictive(fit)
compare_diffusion_accuracy_rt(fit)

The generated predictive RTs are a lightweight diagnostic approximation; likelihood-based inference remains based on the Wiener model.

Simulation programme

programme <- diffusion_identification_study(
  conditions = list(
    n_person = c(50L, 150L, 500L),
    n_item = c(10L, 30L),
    gaze_effect = c(0, 0.20, 0.40),
    contaminant_fraction = c(0, 0.05)
  ),
  replications = 200L
)

Promotion requires identification, parameter recovery, coverage, contaminant and censoring sensitivity, grouped validation, comparison with conventional accuracy–RT models, and empirical reproduction.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.