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

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

Published on: March 18, 2019

Bayesian microsaccade detection.

Andra Mihali¹, Bas van Opheusden², Wei Ji Ma³

¹Center for Neural Science, New York University, New York, NY, USAalm652@nyu.edu.

Journal of Vision

|January 24, 2017

Summary

This summary is machine-generated.

A new Bayesian microsaccade detection (BMD) method accurately identifies fixational eye movements. BMD outperforms traditional thresholding methods, especially in noisy data, improving perception and cognition research.

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Area of Science:

Neuroscience
Computational Neuroscience
Ophthalmology

Background:

Microsaccades are rapid eye movements crucial for visual perception and cognition.
Current detection relies on thresholding smoothed eye velocity, which can be imprecise.

Purpose of the Study:

To introduce and evaluate a novel Bayesian microsaccade detection (BMD) method.
To compare BMD's performance against traditional methods using simulated and real eye-tracking data.

Main Methods:

Developed Bayesian microsaccade detection (BMD) using a statistical model of eye position with hidden states (drift, microsaccade).
BMD infers the posterior probability distribution over eye state time series.
Evaluated BMD on simulated data and eye-tracking data from EyeLink and Dual Purkinje Image (DPI) trackers.

Main Results:

BMD demonstrated superior accuracy in recovering true microsaccades from simulated data, particularly under high noise conditions.
BMD identified nearly all microsaccades found by the default method in EyeLink data, plus additional potential candidates.
BMD showed 54% fewer errors than the default algorithm when tested on DPI data with added noise comparable to EyeLink data.

Conclusions:

BMD offers a more robust and accurate method for microsaccade detection compared to traditional thresholding.
BMD provides probabilistic outputs and adaptability for future model refinements.
The BMD algorithm is available as a software package for researchers.