Predicting choice behaviour in economic games using gaze data encoded as scanpath images.

ABSTRACT: Eye movement data has been extensively utilized by researchers interested in studying decision-making within the strategic setting of economic games. In this paper, we demonstrate that both deep learning and support vector machine classification methods are able to accurately identify participants' decision strategies before they commit to action while playing games. Our approach focuses on creating scanpath images that best capture the dynamics of a participant's gaze behaviour in a way that is meaningful for predictions to the machine learning models. Our results demonstrate a higher classification accuracy by 18% points compared to a baseline logistic regression model, which is traditionally used to analyse gaze data recorded during economic games. In a broader context, we aim to illust

SUBMITTER: Byrne SA 

PROVIDER: S-EPMC10036613 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature