Our objective is to analyze scanpaths acquired through participants achieving a reading task
aiming at answering a binary question: Is the text related or not to some given target topic?
We propose a data-driven method based on hidden semi-Markov chains to segment scanpaths
into phases deduced from the model states, which are shown to represent different
cognitive strategies: normal reading, fast reading, information search, and slow confirmation.
These phases were confirmed using different external covariates, among which semantic
information extracted from texts. Analyses highlighted some strong preference of specific
participants for specific strategies and more globally, large individual variability in
eye-movement characteristics, as accounted for by random effects. As a perspective, the
possibility of improving reading models by accounting for possible heterogeneity sources
during reading is discussed.