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Modeling of decision-making process for moving straight using inverse Bayesian inference.

Youichi Horry1, Ai Yoshinari2, Yurina Nakamoto3

  • 1Matsudo Research Center, Hitachi, Ltd., Japan.

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|December 18, 2017
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Summary

Humans frequently make unreasonable navigation decisions, especially when direction changes. Inverse Bayesian inference models decision-making shifts more effectively than standard Bayesian inference.

Keywords:
Bayesian inferenceDecision-making processInverse bayesian inferenceSense of direction

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Area of Science:

  • Cognitive Psychology
  • Human Navigation
  • Virtual Reality Studies

Background:

  • Humans exhibit suboptimal decision-making, often losing directional sense in real-world navigation.
  • Estimating travel distance and maintaining a straight path can be challenging, leading to deviations.

Purpose of the Study:

  • To investigate the circumstances under which humans lose their sense of direction in a virtual environment.
  • To model human decision-making processes during navigation using Bayesian inference frameworks.

Main Methods:

  • Experimentation in a first-person perspective virtual 3D space.
  • Subjects navigated from a start to a goal position, with deviations recorded.
  • Decision-making processes were modeled using Bayesian inference and inverse Bayesian inference.

Main Results:

  • Participants frequently deviated from the direct centerline path between start and goal points.
  • Directional changes were found to be more influential on navigation errors than distance changes.
  • The angle of turns significantly impacted the deviation from the intended path.

Conclusions:

  • Human navigation decisions can be unreasonable, with directional changes being a key factor.
  • Inverse Bayesian inference provides a more agile model for decision-making shifts compared to standard Bayesian inference.