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

Abstract

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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 eye-tracking 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
• eye-movement
• cognitive model
• decision-making
• drift-diffusion model (DDM)
• Bayesian