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Confidence in predicted position error explains saccadic decisions during pursuit.

Jonathan D Coutinho¹, Philippe Lefèvre^1,2,3, Gunnar Blohm¹

¹Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada.

Journal of Neurophysiology

|December 28, 2020

Summary

Humans coordinate eye movements to track targets using pursuit and saccades. A new probabilistic model explains saccade timing during pursuit, improving our understanding of motor control and sensory uncertainty.

Keywords:

Bayesian Kalman Filter bounded accumulation modeling motor coordination

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Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity

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Published on: March 18, 2019

Area of Science:

Neuroscience
Motor Control
Computational Neuroscience

Background:

Coordinating pursuit and saccadic eye movements is crucial for tracking moving objects.
The precise mechanisms and variability in saccade triggering during pursuit remain poorly understood.
Existing models lack a comprehensive explanation for saccade frequency and timing distributions.

Purpose of the Study:

To develop a predictive, probabilistic model for saccade decisions during pursuit eye movements.
To explain the variability in saccade frequency and trigger times.
To investigate the influence of sensory uncertainty on saccade decisions.

Main Methods:

Proposed a predictive, probabilistic model using Bayesian inference and Kalman filtering.
Modeled the decision to trigger saccades based on predicted position error and uncertainty.
Estimated confidence using log-probability ratio and a fixed accumulation threshold.

Main Results:

The model qualitatively explains observed saccade frequency and trigger time distributions across various target trajectories.
Demonstrated that saccade decisions are sensitive to uncertainty for small position errors, with diminishing influence for larger errors.
Provided a quantitative framework for understanding pursuit-saccade coordination.

Conclusions:

The predictive, confidence-based model offers a fundamental principle for probabilistic neural control of coordinated movements.
This model accounts for noise and delays in the sensorimotor system.
Novel predictions regarding the role of sensory uncertainty in saccade decisions were made.