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.
Highlights
Every minute of every day, we make decisions that affect our personal and professional lives, sometimes to a great extent.2017 The Authors
Decision landscape: plot V (x, y, cij)/t be described by a dynamical system of a specific form, which incorporates a parametrized function describing the shape of the two-attractor landscape
We describe the x- and y-components of a decision trajectory by a system of differential equations τ x and τ y where x = x(t) and y = y(t) are the positions of the mouse along the x- and y-coordinates, and x = dx/dt and y = dy/dt are the time derivatives of x and y, respectively, τ > 0 is the time-scale parameter expressed in seconds, V(x, y) is an unknown function describing the decision landscape, which defines the dynamics of the system, and ∂V/∂x and ∂V/∂y are partial derivatives of V
Summary
Every minute of every day, we make decisions that affect our personal and professional lives, sometimes to a great extent. One class of paradigms, including eye tracking [2] and different variations of the information search paradigm [3], taps attentional processes, trying to answer the question of what information is attended to in the course of a decision Another strand of research, focused on hand or mouse tracking, examines how decisions are executed through the motor system. Response trajectories provide rich continuous data, but the vast majority of available studies use a few relatively simple measures These include latency measures (initiation or response times), consistency measures (e.g. changes in the x-direction (x-flips) or sample entropy) and trajectory curvature measures (maximum deviation of the trajectory from an ideal, straight-line trajectory, or area under the curve of difference between actual and ideal trajectories). More advanced analysis and visualization methods can enable us to get deeper insights from the rich data provided by the mouse-tracking paradigm
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