End-to-End Models for the Analysis of System 1 and System 2 Interactions based on Eye-Tracking Data
This work addresses the need for empirical validation of dual-system theories in cognitive science, though it is incremental as it applies existing methods to a specific domain.
The authors tackled the problem of quantitatively confirming interactions between System 1 and System 2 cognitive systems by proposing a computational method using eye-tracking data from a modified Stroop test, achieving good classification accuracy for distinguishing tasks.
While theories postulating a dual cognitive system take hold, quantitative confirmations are still needed to understand and identify interactions between the two systems or conflict events. Eye movements are among the most direct markers of the individual attentive load and may serve as an important proxy of information. In this work we propose a computational method, within a modified visual version of the well-known Stroop test, for the identification of different tasks and potential conflicts events between the two systems through the collection and processing of data related to eye movements. A statistical analysis shows that the selected variables can characterize the variation of attentive load within different scenarios. Moreover, we show that Machine Learning techniques allow to distinguish between different tasks with a good classification accuracy and to investigate more in depth the gaze dynamics.