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Related Concept Videos

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Decision Making: Traditional Method01:14

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Updated: Feb 16, 2026

Integrating Visual Psychophysical Assays within a Y-Maze to Isolate the Role that Visual Features Play in Navigational Decisions
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Decision landscapes: visualizing mouse-tracking data.

A Zgonnikov1, A Aleni2, P T Piiroinen3

  • 1School of Psychology, Statistics and Applied Mathematics, National University of Ireland, Galway, Ireland.

Royal Society Open Science
|January 2, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces decision landscapes, a novel computational method for analyzing mouse-tracking data. These visualizations offer new insights into decision-making dynamics and individual differences.

Keywords:
decision makingdynamical systemsmouse tracking

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Human-Computer Interaction

Background:

  • Computerized studies generate extensive human behavior data, including mouse cursor trajectories.
  • Analyzing mouse trajectories offers insights into decision-making processes.
  • Increasing study complexity necessitates advanced trajectory analysis methods.

Purpose of the Study:

  • To present a novel computational approach for decision landscape visualization using mouse-tracking data.
  • To develop a method for generating 3D decision landscapes from multiple trajectories.
  • To identify interpretable parameters characterizing decision dynamics.

Main Methods:

  • Utilizing dynamical systems theory to model mouse movement velocity.
  • Developing a computational approach to generate decision landscapes from arbitrary numbers of trajectories.
  • Deriving interpretable parameters from mouse trajectories.

Main Results:

  • A new method for generating 3D decision landscape visualizations from mouse-tracking data.
  • Identification of parameters that offer detailed characterization of decision dynamics.
  • Demonstration of the approach's ability to compare dynamics across conditions and individuals.

Conclusions:

  • Decision landscape visualization is a novel tool for analyzing mouse trajectories during decision execution.
  • This approach provides deeper insights into the dynamics of decision-making.
  • The method can reveal individual differences in decision-making dynamics.