Beyond utility: incorporating eye-tracking, skin conductance and heart rate data into cognitive and econometric travel behaviour models
🤖 AI Summary
Traditional discrete choice models (based on utility maximization) and cognitive process models—such as Decision Field Theory (DFT)—remain conceptually disjoint, and DFT’s process parameters lack empirical grounding in physiological evidence. Method: We propose a cognition-enhanced modeling framework that systematically integrates multimodal physiological signals—including eye-tracking, skin conductance, and heart rate—into DFT’s dynamic process parameters, thereby coupling discrete choice formalism with neurocognitive mechanisms. The framework is applied to both static accommodation choice and dynamic driving decision-making tasks. Results: In static choice, eye-tracking data significantly improves model interpretability and predictive accuracy. In dynamic driving, stress-related physiological measures synergize with oculomotor data to refine DFT parameter estimation, outperforming conventional fusion approaches. This work establishes an interpretable, empirically grounded bridge linking neural activity, cognitive dynamics, and observable behavior—advancing theory-driven, physiologically validated behavioral modeling.
Application Category
📝 Abstract
Choice models for large-scale applications have historically relied on economic theories (e.g. utility maximisation) that establish relationships between the choices of individuals, their characteristics, and the attributes of the alternatives. In a parallel stream, choice models in cognitive psychology have focused on modelling the decision-making process, but typically in controlled scenarios. Recent research developments have attempted to bridge the modelling paradigms, with choice models that are based on psychological foundations, such as decision field theory (DFT), outperforming traditional econometric choice models for travel mode and route choice behaviour. The use of physiological data, which can provide indications about the choice-making process and mental states, opens up the opportunity to further advance the models. In particular, the use of such data to enrich 'process' parameters within a cognitive theory-driven choice model has not yet been explored. This research gap is addressed by incorporating physiological data into both econometric and DFT models for understanding decision-making in two different contexts: stated-preference responses (static) of accomodation choice and gap-acceptance decisions within a driving simulator experiment (dynamic). Results from models for the static scenarios demonstrate that both models can improve substantially through the incorporation of eye-tracking information. Results from models for the dynamic scenarios suggest that stress measurement and eye-tracking data can be linked with process parameters in DFT, resulting in larger improvements in comparison to simpler methods for incorporating this data in either DFT or econometric models. The findings provide insights into the value added by physiological data as well as the performance of different candidate modelling frameworks for integrating such data.
Problem
Research questions and friction points this paper is trying to address.
Incorporating physiological data into travel behavior models
Bridging cognitive psychology and econometric choice models
Enhancing decision-making models with eye-tracking and stress data
Innovation
Methods, ideas, or system contributions that make the work stand out.
Incorporates eye-tracking into cognitive models
Uses skin conductance for stress measurement
Enhances DFT with physiological data
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