Gaze-informed diffusion-IRT modelling

In eyeprocess: Harmonize Eye-Tracking, Pupillometry, Biometrics, and Psychometric Process Data

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

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.

eyeprocess documentation built on Sept. 28, 2026, 5:08 p.m.