Skip to main content

REVIEW article

Front. Behav. Econ.
Sec. Neuroeconomics
Volume 3 - 2024 | doi: 10.3389/frbhe.2024.1384713
This article is part of the Research Topic Choice Process Data in Economic Decision Making View all 5 articles

Unraveling information processes of decision-making with eye-tracking data Authors

Provisionally accepted
  • Department of Psychology and Hamburg Center of Neuroscience, Institute of Psychology, Faculty of Psychology and Human Movement Science, University of Hamburg, Hamburg, Germany

The final, formatted version of the article will be published soon.

    Eye movements are strongly linked to the perception of visual information and can be used to infer mental processes during decision-making. While eye-tracking technology has been available for several decades, the incorporation of eye-tracking data into computational models of decision making is relatively new in neuroeconomics. This review article provides an overview of the interaction between eye movement and choices, highlighting the value of eyetracking data in decision-making research. First, we provide an overview of empirical work studying the interaction between eye movement and choices. In the second part, we present existing models that incorporate eye-tracking data into process models of decision-making, emphasizing their assumptions regarding the role of attention in choice formation and contrasting models that use gaze data to inform behavioral predictions with those that attempt to predict eye movements themselves. Additionally, we discuss the potential of using cognitive models to understand the connection between choice and gaze patterns and normative aspects of decision-making. Overall, this review underscores the significant role of eye-tracking data in understanding decision-making processes, particularly in the field of neuroeconomics, and its potential to provide valuable insights into individual differences in decision-making behavior.

    Keywords: Attention, cognitive model, eye-movement, Drift-diffusion model (DDM), decision-making, Bayesian

    Received: 10 Feb 2024; Accepted: 24 Jul 2024.

    Copyright: © 2024 Ting and Gluth. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Chih-Chung Ting, Department of Psychology and Hamburg Center of Neuroscience, Institute of Psychology, Faculty of Psychology and Human Movement Science, University of Hamburg, Hamburg, Germany

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.