Eye-tracking data analysis using hidden semi-Markovian models to identify and characterize reading strategies
Résumé
Textual information search is not a homogeneous process in time, neither from a cognitive perspective nor in terms of eye-movement patterns (Simola, 2008). The research objective is to analyze eye-tracking signals acquired through participants achieving a reading task and simultaneously aiming at making a binary decision: whether a text is related or not to some theme given a priori. This activity is expected to involve several phases with contrasted oculometric characteristics, such as normal reading, scanning, careful reading, associated with different cognitive strategies, such as creation and rejection of hypotheses, confirmation and decision. We propose an analytical data-driven method based on hidden semi-Markov models (Yu, 2010) which are composed of two stochastic processes. The former is observed, and corresponds to eye-movement features over time, while the latter is a latent semi-Markov chain, which preconditions the first process, and is used to uncover the information acquisition strategies. Four interpretable strategies were highlighted: normal reading, fast reading, careful reading, and decision making. This interpretation was derived using the model properties such as dwell times, inter-phase transition probabilities, and emission probabilities, which characterize the observed process. More importantly, model selection was performed using both, information theory criterion and some covariates, which were also used to reinforce the interpretation.