Reading Time Analysis

Eye-tracking reading measures like FFD, SFD, and GD are computed by applying principled data cleaning steps to first-pass and second-pass fixation data. This yields robust metrics reflecting early reading processing for sentence- and passage-reading experiments.

Spillover effects and ROIs in reading-time analysis are handled through principled framework guidelines for ROI selection and spillover analysis. This ensures valid inferences by properly structuring regions of interest before computing measures.

Eye-tracking reading time data is modeled using linear mixed-effects models (LMMs) with appropriate distribution choices. This statistical modeling approach yields robust inferences for time-course effects and typical reading measure effect sizes.

Go-past time (GPT) and total reading time (TRT) are used in psycholinguistics experiments to capture distinct reading processing stages. GPT measures regressive eye movements, while TRT aggregates all first-pass and second-pass fixation durations.

This reading-time analysis workflow supports sentence- and passage-reading experiments including paragraph comprehension. It applies to eye-tracking data to yield robust measures, properly handling skipped trials and defining ROIs.