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Related Concept Videos

Muscles of the Eye01:20

Muscles of the Eye

The muscles of the eye are sophisticated structures that control eye movement and focus, allowing for the precise and rapid adjustments necessary for vision. The human eye is controlled by ten muscles — six extraocular muscles, three intraocular muscles, and one primary eyelid retractor muscle.
Extraocular Muscles
The six extraocular muscles surround the eyeball and control its movements. They are responsible for a wide range of eye motions, including looking up, down, left, right, and rotating...

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Related Experiment Video

Updated: May 14, 2026

VisualEyes: A Modular Software System for Oculomotor Experimentation
10:41

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Bayesian Dynamical Modeling of Fixational Eye Movements.

Lisa Schwetlick1,2, Sebastian Reich3,4, Ralf Engbert5,4

  • 1Department of Psychology, University of Potsdam, Potsdam, Germany. lisa.schwetlick@epfl.ch.

Biological Cybernetics
|June 9, 2025
PubMed
Summary
This summary is machine-generated.

This study models human eye movements during fixation using a statistically self-avoiding random walk (SAW) model. Findings reveal a link between the model

Keywords:
Bayesian data assimilationFixational eye movementsMicrosaccadesPhysiological driftSelf-avoiding walkVisual fixation

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Vision Science

Background:

  • Human eye movements are constant, even during fixation, involving slow (drift, tremor) and fast (microsaccades) components.
  • The statistically self-avoiding random walk (SAW) model has been proposed to describe the complex dynamics of physiological drift.

Purpose of the Study:

  • To implement a data assimilation approach for the SAW model to analyze fixational eye movements and microsaccades.
  • To investigate the relationship between the SAW model's activation and microsaccade occurrence using Bayesian parameter estimation.
  • To explore individual differences in fixational eye movement behavior.

Main Methods:

  • Utilized a data assimilation approach to implement the SAW model.
  • Applied Bayesian parameter estimation to experimental high-resolution eye-tracking data.
  • Analyzed the SAW model's likelihood function for individual human observers.

Main Results:

  • Established a relationship between SAW model-predicted activation and microsaccade occurrence.
  • Demonstrated that the model's latent activation correlates with microsaccade onsets and offsets.
  • Provided evidence supporting a triggering mechanism for microsaccades.

Conclusions:

  • The SAW model effectively captures individual variations in fixational eye movements.
  • The SAW model serves as a valuable tool for studying the interplay between physiological drift and microsaccades.
  • Findings enhance understanding of individual differences in microsaccade behavior and visual processing.