knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
Pupil diameter is treated as a physiological time series. It is not automatically labelled cognitive load, effort, surprise, or arousal. The model preserves preprocessing choices and includes nuisance adjustment before any substantive interpretation.
spec <- functional_pupil_irt_spec( df = 6L, basis = "natural_spline", response = "score", engine = "stan", alignment = "event", latency_ms = 200, baseline_window = c(-500, 0), baseline_method = "subtract", time_window = c(-200, 2000), luminance_column = "luminance", gaze_x_column = "x", gaze_y_column = "y", blink_column = "blink", interpolated_column = "interpolated", max_interpolated_fraction = 0.20, ar1 = TRUE, participant_effect = TRUE, item_effect = TRUE ) prepared <- prepare_functional_pupil_data(pupil_trials, spec) basis <- functional_pupil_basis(prepared$data$time, df = 6) fit <- fit_joint_functional_pupil_irt(pupil_trials, spec, seed = 42)
The Stan engine jointly models the binary item outcome and pupil trajectory using shared person/item effects, functional bases, luminance and gaze-position covariates, and optional AR(1) residual structure.
extract_functional_pupil_parameters(fit) functional_pupil_diagnostics(fit) compare_functional_scalar_models(fit)
Scalar peak or area-under-the-curve summaries are retained as transparent baselines rather than assumed to be inferior.
grid <- pupil_preprocessing_grid( baseline_windows = list(c(-500, 0), c(-200, 0)), latency_ms = c(100, 200, 300), basis_df = c(4L, 6L, 8L), baseline_methods = c("subtract", "percent"), max_interpolated_fraction = c(0.10, 0.20) ) sensitivity <- pupil_preprocessing_sensitivity(pupil_trials, grid, spec) plot(sensitivity)
Promotion requires recovery under autocorrelation, luminance confounding, blink/interpolation variation, baseline uncertainty, and external experimental validation.
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