Negative controls, placebo windows, and temporal leakage

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

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

Predictive and process-feature workflows can accidentally use information that is unavailable at the intended decision boundary. The temporal provenance layer makes availability explicit.

p <- process_feature_time_provenance(c("dwell_pre","rt_final"), c(400,1200), outcome_at=c(1000,1000))
audit_temporal_leakage(p)

Negative controls deliberately break a declared process–outcome relation and rerun the same analysis.

nc <- run_process_negative_controls(data, outcome="y", analysis_fun=analysis_fun, replications=200)
summarise_process_negative_controls(nc)
process_null_benchmark(observed_effect, nc)
plot(nc)

A leakage flag denotes temporal/information contamination, not misconduct. Null-like negative controls are useful diagnostics but do not prove model validity.

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