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The Relationship Between Environmental Statistics and Predictive Gaze Behaviour During a Manual Interception Task:

David Harris1, Sam Vine1, Mark Wilson1

  • 1School of Public Health and Sport Sciences, Medical School, University of Exeter, St Luke's Campus, Exeter, EX1 2LU UK.

Computational Brain & Behavior
|July 16, 2026
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Summary

Human eye movements in a dynamic task followed environmental statistics but did not perfectly optimize predictions. Bayesian learning models better explained gaze changes than simple associative models.

Keywords:
BayesianComputationalEye trackingGazeInterceptionPredictive processing

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Vision

Background:

  • Human observers often exhibit Bayes-optimal decision-making.
  • The visual system may utilize probabilistic mental models for environmental processing.
  • Investigating eye movements in dynamic tasks can reveal underlying probabilistic computations.

Purpose of the Study:

  • To determine if eye movements during a dynamic interception task align with Bayesian inference principles.
  • To assess whether the gaze system adheres to environmental statistics and probabilistic models.
  • To compare Bayesian learning models with associative learning models in explaining gaze dynamics.

Main Methods:

  • Forty-one participants performed a virtual reality racquetball interception task.
  • The probability of the ball's onset location was manipulated across five conditions.
  • Gaze positions and trial-to-trial changes were analyzed to evaluate adherence to Bayesian principles.

Main Results:

  • Pre-onset gaze positions tracked the true distribution of ball onset locations, indicating adherence to environmental statistics.
  • Eye movements did not minimize distance to the fovea based on an optimal probabilistic model, instead reflecting a 'best guess'.
  • Bayesian learning models (hierarchical Gaussian filter) better explained trial-to-trial gaze changes than associative learning models.
  • Pupil dilations and gaze variability correlated with precision of beliefs and prediction errors from Bayesian models.

Conclusions:

  • The gaze system appears to spontaneously adhere to environmental statistics during dynamic interception.
  • While Bayesian learning influences gaze adjustments, eye movements may not fully optimize predictions in real-time.
  • Probabilistic context significantly influences spontaneous gaze dynamics, as evidenced by pupil responses and gaze variability.