A Computational Approach for Prediction of Problem-Solving Behavior Using Support Vector Machines and Eye-Tracking Data

Abstract

Inference about high-level cognitive states during interaction is a fundamental task in building proactive intelligent systems that would allow effective offloading of mental operations to a computational architecture. We introduce an improved machine-learning pipeline able to predict user interactive behavior and performance using real-time eye-tracking. The inference is carried out using a support-vector machine (SVM) on a large set of features computed from eye movement data that are linked to concurrent high-level behavioral codes based on think aloud protocols. The differences between cognitive states can be inferred from overt visual attention patterns with accuracy over chance levels, although the overall accuracy is still low. The system can also classify and predict performance of the problem-solving users with up to 79 % accuracy. We suggest this prediction model as a universal approach for understanding of gaze in complex strategic behavior. The findings confirm that eye movement data carry important information about problem solving processes and that proactive systems can benefit from real-time monitoring of visual attention.