Predicting Decision-Making during an Intelligence Test via Semantic Scanpath Comparisons

Fluid intelligence is considered to be the foundation to many aspects of human learning and performance. Individuals' behavior while solving intelligence tests is therefore an important component in understanding problem-solving strategies and learning processes. We present preliminary results of a novel eye-Tracking-based approach to predict participants' decisions while solving a fluid intelligence test that utilizes semantic scanpath comparisons. Normalizing scanpaths and applying a knn classifier allows us to make individual predictions and combine them to predict final scores. We evaluated our proposed approach on the TuEyeQ dataset published by Kasneci et al. containing data of 315 university students, who worked on the Culture Fair Intelligence Test. Our approach was able to explain 39.207% of variance in the final score and predictions for participants' final scores showed a correlation of τ = 0.65759 with participants' actual scores. Overall, the proposed method has shown great potential that can be expanded on in future research.