Proceedings of the Annual Meeting of the Cognitive Science Society

The Stroop test evaluates the ability to inhibit cognitive interference. This interference occurs when the processing of onestimulus characteristic affects the simultaneous processing of another attribute of the same stimulus. Eye movements are anindicator of the individual attention load required for inhibiting cognitive interference. We used an eye tracker to collecteye movements data from more than 60 subjects each performing four different but similar tasks (some with cognitiveinterference and some without). After the extraction of features related to fixations, saccades and gaze trajectory, wetrained different Machine Learning models to recognize tasks performed in the different conditions (i.e. with interference,without interference). The models achieved good classification performances when distinguishing between similar tasksperformed with or without cognitive interference. This suggests the presence of characterizing patterns common amongsubjects, despite of the individual variability of visual behavior. The results open up interesting investigations.