Advanced pupillometry representations and confound control

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Frequency/activity features

freq <- pupil_frequency_features(
  samples,
  by = c("person_id", "trial_id"),
  time = "time_ms", pupil = "pupil_gaze_corrected_bc",
  sampling_rate_hz = 60
)
plot(freq)

pupil_activity_index() exposes transparent velocity, low/high-frequency contrast, and RIPA-style proxy representations. The package deliberately avoids presenting these as pure cognitive-load measures.

Event-related pupil deconvolution

deconv <- fit_pupil_event_deconvolution(
  samples,
  by = c("person_id", "trial_id"),
  time = "time_ms", pupil = "pupil_gaze_corrected_bc",
  events = list(stimulus = 0, information = "information_onset_ms", action = "response_time_ms")
)

pupil_event_effects(deconv)
plot(deconv, type = "observed_fitted")
plot(deconv, type = "effects")
compare_pupil_kernels(samples, tmax_values = c(512, 930),
                      by = c("person_id", "trial_id"),
                      time = "time_ms", pupil = "pupil_gaze_corrected_bc",
                      events = list(stimulus = 0))

Luminance and trial-order adjustment

conf <- fit_pupil_confound_model(
  trial_data,
  pupil = "pupil_peak",
  luminance = "screen_luminance",
  trial_order = "trial_sequence",
  theta = "theta_hat",
  person = "person_id", item = "item_id"
)

adjust_pupil_confounds(conf)
pupil_confound_effects(conf)
compare_raw_adjusted_pupil(conf)
plot(conf, type = "raw_adjusted")
plot(conf, type = "theta_luminance_surface")

Adjusted values remain model-dependent and should be described as luminance/fatigue-adjusted, not as cognition isolated from all confounding.

Robust filtering

f <- filter_pupil_signal(raw_pupil, width = 9)
audit_signal_filter(f)
plot(f)
compare_signal_filters(raw_pupil)

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.