Thomas O. Hancock, Stephane Hess, Charisma F. Choudhury
arXiv 22 Jun 2025 · Econometrics
arXiv:2506.18068 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Hancock, T. O., Hess, S., Marley, A., and Choudhury, C. F (2021) An accumulation of preference: Two alternative dynamic models for understanding transport choices self | 1.000 | 18 | 3 | 100% |
| 2 | Paschalidis, E., Choudhury, C. F., and Hess, S (2018) Modelling the effects of stress on gap-acceptance decisions combining data from driving simulator and physiological sensors self | 1.000 | 9 | 4 | 100% |
| 3 | Hancock, T. O., Hess, S., and Choudhury, C. F (2018) Decision field theory: Improvements to current methodology and comparisons with standard choice modelling techniques self | 0.874 | 5 | 2 | 100% |
| 4 | Bansal, P., Kim, E.-J., and Ozdemir, S (2024) Discrete choice experiments with eye-tracking: How far we have come and ways forward | 0.811 | 4 | 2 | 100% |
| 5 | Roe, R. M., Busemeyer, J. R., and Townsend, J. T (2001) Multialternative decision field theory: A dynamic connectionist model of decision making | 0.644 | 4 | 1 | 100% |
| 6 | Krucien, N., Ryan, M., and Hermens, F (2017) Visual attention in multi-attributes choices: What can eye-tracking tell us? | 0.644 | 2 | 2 | 100% |
| 7 | Paschalidis, E (2019) Developing driving behaviour models incorporating the effects of stress | 0.644 | 2 | 2 | 100% |
| 8 | Uggeldahl, K., Jacobsen, C., Lundhede, T. H., and Olsen, S. B (2016) Choice certainty in discrete choice experiments: Will eye tracking provide useful measures? | 0.644 | 2 | 2 | 100% |
| 9 | Cohen, A. L., Kang, N., and Leise, T. L (2017) Multi-attribute, multi-alternative models of choice: Choice, reaction time, and process tracing | 0.585 | 3 | 1 | 100% |
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