Automated Detection of Decision-Making Style, Based on Users’ Online Mouse Pointer Activity
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000597408Publication status
publishedExternal links
Book title
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 4: BIOSIGNALSPages / Article No.
Publisher
SciTePressEvent
Subject
Decision-making style; Personality; Computational recognition; Computer mouse; Pointer; Machine learningOrganisational unit
03987 - Hölscher, Christoph / Hölscher, Christoph
Notes
Conference lecture on February 17, 2023.More
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ETH Bibliography
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