To understand why we make decisions the way we do, decision-making researchers have mainly focused on what people choose, by proposing cognitive processes that would give rise to the observed choice outcome distributions. Despite this continuous practice, our decisions often leave much to be desired.
#Processing python mousex professional#
The decision landscape visualization approach is a novel tool for analysing mouse trajectories during decision execution, which can provide new insights into individual differences in the dynamics of decision making.Įvery minute of every day, we make decisions that affect our personal and professional lives, sometimes to a great extent. These parameters characterize dynamics of decisions in more detail compared with conventional measures, and can be compared across experimental conditions, and even across individuals. This approach not only generates three-dimensional illustration of decision landscapes, but also describes mouse trajectories by a number of interpretable parameters. Employing the dynamical systems theory framework, we develop a new method for generating decision landscapes based on arbitrary number of trajectories. Visualized as a three-dimensional surface, it provides a comprehensive overview of decision dynamics. A decision landscape is an analogue of an energy potential field mathematically derived from the velocity of mouse movement during a decision.
Here, we present a new computational approach to generating decision landscape visualizations based on mouse-tracking data. As the number and complexity of mouse-tracking studies increase, more sophisticated methods are needed to analyse the decision trajectories. These trajectories can reveal novel information about ongoing decision processes. Computerized paradigms have enabled gathering rich data on human behaviour, including information on motor execution of a decision, e.g.
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