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Related Experiment Video

Updated: Apr 16, 2026

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

Continuous psychophysics: Target-tracking to measure visual sensitivity.

Kathryn Bonnen¹, Johannes Burge², Jacob Yates¹

¹Department of Psychology and Institute for Neuroscience, University of Texas at Austin, Austin, TX, USA.

Journal of Vision

|March 22, 2015

Summary

This summary is machine-generated.

This study presents a new method for measuring visual sensitivity using a continuous tracking task and a Kalman filter model. The novel approach offers a faster, more engaging alternative to traditional psychophysics for assessing human visual performance.

Keywords:

Kalman filter manual tracking psychophysics vision

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Last Updated: Apr 16, 2026

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

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A Method to Quantify Visual Information Processing in Children Using Eye Tracking

A Method to Quantify Visual Information Processing in Children Using Eye Tracking

Published on: July 9, 2016

Area of Science:

Visual perception and psychophysics
Computational neuroscience
Human-computer interaction

Background:

Traditional psychophysical methods for assessing visual sensitivity can be time-consuming and may not be suitable for all populations.
There is a need for more efficient and engaging methods to measure human visual performance.

Purpose of the Study:

To introduce and validate a novel framework for estimating visual sensitivity using a continuous target-tracking task.
To compare the proposed method with traditional psychophysical measures.

Main Methods:

Developed a continuous target-tracking task where observers followed a moving target in dynamic visual noise.
Applied a Kalman filter model, assuming Bayesian ideal observer behavior, to human tracking data.
Correlated estimates of sensory noise from the model with traditional psychophysical sensitivity measures.

Main Results:

Kalman filter estimates of human sensory noise showed a high correlation (R2 > 97%) with traditional psychophysical measures.
The continuous tracking task effectively functions as numerous
minitrials
, significantly reducing assessment time.
Summary statistics from tracking data accurately predicted relative visual sensitivity.

Conclusions:

The novel framework provides a fast, engaging, and accurate method for estimating visual sensitivity.
This approach is suitable for diverse populations, including children and clinical patients.
The modeling framework yields comparable estimates to traditional methods, offering a more efficient alternative.