Using mouse-tracking data to visualise decision landscapes

Abstract

Computerised paradigms have enabled decision making researchers to gather rich data on human behaviour, including information on motor execution of a decision, e.g., by tracking mouse cursor trajectories. As the number and complexity of mouse-tracking studies rapidly increase, more sophisticated methodology is needed to analyse the decision trajectories. Here we present a new computational approach to generating decision landscape visualisations based on mouse-tracking data. Decision landscape is an analogue of energy potential field mathematically derived from velocity of mouse movement during a decision. Visualised as a 3D surface, it provides a comprehensive overview of motor evolution of decisions. Employing the dynamical systems theory framework, we develop a new method for generating decision landscapes based on arbitrary number of trajectories. The decision landscape visualisation have potential to become a novel tool for analysing mouse trajectories during decision execution, which can provide new insights into the dynamics of decision making.


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