1. Decision landscapes: visualizing mouse-tracking data
- Author
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Piiroinen, Petri, Aleni, Andrea, Zgonnikov , Arkady, Di Bernardo, Mario, OHora , Denis, Zgonnikov, A., Aleni, A., Piiroinen, P. T., O’Hora, D., and di Bernardo, M.
- Subjects
cognition ,PsyArXiv|Social and Behavioral Sciences|Perception|Motion Perception ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Problem Solving ,Cognition and Perception ,PsyArXiv|Social and Behavioral Sciences|Perception|Vision ,Computer science ,PsyArXiv|Social and Behavioral Sciences|Perception|Touch, Taste, and Smell ,Social and Behavioral Sciences ,computer.software_genre ,0302 clinical medicine ,Dynamical systems ,PsyArXiv|Social and Behavioral Sciences|Perception|Audition ,Psychology ,movements ,lcsh:Science ,choice ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Biases, Framing, and Heuristics ,Multidisciplinary ,mouse tracking ,05 social sciences ,Dynamical system ,FOS: Psychology ,Dynamics (music) ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology ,PsyArXiv|Social and Behavioral Sciences|Perception|Picture Processing ,PsyArXiv|Social and Behavioral Sciences|Perception|Vestibular Systems and Proprioception ,Data mining ,Decision process ,competition ,Mouse tracking ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Learning ,Research Article ,Dynamical systems theory ,task ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Consciousness ,selection ,bepress|Social and Behavioral Sciences|Psychology|Cognition and Perception ,PsyArXiv|Social and Behavioral Sciences|Perception|Embodied Cognition ,Machine learning ,decision making ,050105 experimental psychology ,03 medical and health sciences ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Creativity ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Reasoning ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Judgment and Decision Making ,trajectories ,0501 psychology and cognitive sciences ,PsyArXiv|Social and Behavioral Sciences|Perception ,PsyArXiv|Social and Behavioral Sciences|Perception|Perceptual Organization ,business.industry ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Attention ,Cognitive Psychology ,Potential field ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Memory ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Concepts and Categories ,dynamical systems ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Imagery ,attention ,bepress|Social and Behavioral Sciences|Psychology|Cognitive Psychology ,Visualization ,PsyArXiv|Social and Behavioral Sciences|Cognitive Psychology|Language ,PsyArXiv|Social and Behavioral Sciences ,action dynamics ,bepress|Social and Behavioral Sciences ,PsyArXiv|Social and Behavioral Sciences|Perception|Multisensory Integration ,lcsh:Q ,Artificial intelligence ,business ,Decision making ,computer ,Mathematics ,030217 neurology & neurosurgery ,Motor execution ,PsyArXiv|Social and Behavioral Sciences|Perception|Action - Abstract
Computerized paradigms have enabled gathering rich data on human behaviour, including information on motor execution of a decision, e.g. by tracking mouse cursor trajectories. These trajectories can reveal novel information about ongoing decision processes. As the number and complexity of mouse-tracking studies increase, more sophisticated methods are needed to analyse the decision trajectories. Here, we present a new computational approach to generating decision landscape visualizations based on mouse-tracking data. A decision landscape is an analogue of an energy potential field mathematically derived from the velocity of mouse movement during a decision. Visualized as a three-dimensional surface, it provides a comprehensive overview of decision dynamics. Employing the dynamical systems theory framework, we develop a new method for generating decision landscapes based on arbitrary number of trajectories. This approach not only generates three-dimensional illustration of decision landscapes, but also describes mouse trajectories by a number of interpretable parameters. These parameters characterize dynamics of decisions in more detail compared with conventional measures, and can be compared across experimental conditions, and even across individuals. 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.
- Published
- 2017