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Modelling human navigation and decision dynamics in a first-person herding task.

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Summary
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Dynamical perceptual-motor primitives (DPMPs) effectively modeled human navigation and target selection in a herding game. This approach accurately simulated and predicted player behavior in complex scenarios.

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Area of Science:

  • Cognitive Science
  • Robotics
  • Human-Computer Interaction

Background:

  • Understanding human navigation and decision-making is crucial for developing intuitive human-machine interfaces.
  • Dynamical perceptual-motor primitives (DPMPs) offer a framework for modeling complex motor behaviors.

Purpose of the Study:

  • To investigate the applicability of DPMPs for capturing human navigation and target selection in a first-person herding task.
  • To assess the predictive power of a combined DPMP and heuristic policy model for human behavior.

Main Methods:

  • Human participants engaged in a first-person herding game, corralling virtual targets.
  • Movement trajectories and target-selection decisions were recorded and modeled using DPMPs and a heuristic policy.

Main Results:

  • A DPMP navigation model accurately reproduced participants' movement trajectories.
  • A heuristic policy captured approximately 80% of target-selection decisions.
  • The combined model successfully simulated and predicted behavioral dynamics in novel contexts.

Conclusions:

  • DPMPs provide a robust framework for modeling complex human perceptual-motor behaviors, including navigation and decision-making.
  • Integrating DPMPs with heuristic policies enhances the simulation and prediction of human behavior.
  • Findings have implications for designing advanced artificial agents for human-machine interaction.