Automated Detection of Decision-Making Style, Based on Users’ Online Mouse Pointer Activity

Marcus Cheetham, Catia Cepeda, Hugo Gamboa, Christoph Hoelscher, Seyed Valizadeh

2023

Abstract

Decision-making (DM) and online activity go hand in hand in many domains of everyday life (e.g., consumer behaviour, financial and investment choices, career development, health and psychological well-being). DM style refers to consistent behavioural patterns in the way different individuals approach DM situations. In this study, we explored the feasibility of inferring DM style from the trace of mouse cursor (or pointer) activity that users generated while performing an online task (the task required no explicit DM). We focussed on maximizing and satisficing DM style. Based on a set of spatial, temporal and spatial-temporal features that were extracted from mouse activity data and on measures of DM style assessed with a conventional self-report questionnaire, we modelled DM style in a supervised machine learning approach. The results show that the models of DM style have between good and high predictive accuracy. Guided by these results, we propose that this mouse-based method might play a useful role in computational recognition of DM style and merits further development. Future work will test the ability of pointer-based models to meaningfully link psychological measures of DM style to objective measures and outcomes of real-world DM situations.

Paper Citation

in Harvard Style

Cheetham M., Cepeda C., Gamboa H., Hoelscher C. and Valizadeh S. (2023). Automated Detection of Decision-Making Style, Based on Users’ Online Mouse Pointer Activity. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS; ISBN 978-989-758-631-6, SciTePress, pages 281-286. DOI: 10.5220/0011716700003414

in Bibtex Style

@conference{biosignals23,
author={Marcus Cheetham and Catia Cepeda and Hugo Gamboa and Christoph Hoelscher and Seyed Valizadeh},
title={Automated Detection of Decision-Making Style, Based on Users’ Online Mouse Pointer Activity},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS},
year={2023},
pages={281-286},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011716700003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALS
TI - Automated Detection of Decision-Making Style, Based on Users’ Online Mouse Pointer Activity
SN - 978-989-758-631-6
AU - Cheetham M.
AU - Cepeda C.
AU - Gamboa H.
AU - Hoelscher C.
AU - Valizadeh S.
PY - 2023
SP - 281
EP - 286
DO - 10.5220/0011716700003414
PB - SciTePress