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While theories postulating a dual cognitive system take hold, quantitativeconfirmations are still needed to understand and identify interactions betweenthe two systems or conflict events. Eye movements are among the most directmarkers of the individual attentive load and may serve as an important proxy ofinformation. In this work we propose a computational method, within a modifiedvisual version of the well-known Stroop test, for the identification ofdifferent tasks and potential conflicts events between the two systems throughthe collection and processing of data related to eye movements. A statisticalanalysis shows that the selected variables can characterize the variation ofattentive load within different scenarios. Moreover, we show that MachineLearning techniques allow to distinguish between different tasks with a goodclassification accuracy and to investigate more in depth the gaze dynamics.

End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data