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

Marcus Cheetham, Cátia Cepeda, Hugo Gamboa, Christoph Hoelscher, Seyed Abolfazl Valizadeh

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