EasyEyes: Crowded dynamic fixation for online psychophysics

Online vision testing enables efficient data collection from diverse participants, but often requires accurate fixation. When needed, fixation accuracy is traditionally ensured by using a camera to track gaze. That works well in the laboratory, but tracking during online testing with a built-in webcam is not yet sufficiently precise. Kurzawski, Pombo, et al. (2023) introduced a fixation task that improves fixation through hand-eye coordination, requiring participants to track a moving crosshair with a mouse-controlled cursor. This dynamic fixation task greatly reduces peeking at peripheral targets relative to a stationary fixation task, but does not eliminate it. Here, we introduce a crowded dynamic fixation task that further enhances fixation by adding clutter around the fixation mark. We assessed fixation accuracy during peripheral threshold measurement. Relative to the root mean square gaze error during the stationary fixation task, the dynamic fixation error was 55%, whereas the crowded dynamic fixation error was only 40%. With a 1.5° tolerance, peeking occurred on 7% of trials with stationary fixation, 1.5% with dynamic fixation, and 0% with crowded dynamic fixation. This improvement eliminated implausibly low peripheral thresholds, likely by preventing peeking. We conclude that crowded dynamic fixation provides accurate gaze control for online testing.

Authors

Institutions

Publication Details

Journal
Journal of Vision
Published
2026-01-29
DOI
https://doi.org/10.1167/jov.26.1.18
Primary Topic
Gaze Tracking and Assistive Technology
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

EasyEyes: Crowded dynamic fixation for online psychophysics

Denis G. Pelli, Jie Chen, Fengping Hu
Journal of Vision
Gaze Tracking and Assistive Technology
article

EasyEyes: Crowded dynamic fixation for online psychophysics

Denis G. Pelli, Jie Chen, Fengping Hu
article en

Abstract

Online vision testing enables efficient data collection from diverse participants, but often requires accurate fixation. When needed, fixation accuracy is traditionally ensured by using a camera to track gaze. That works well in the laboratory, but tracking during online testing with a built-in webcam is not yet sufficiently precise. Kurzawski, Pombo, et al. (2023) introduced a fixation task that improves fixation through hand-eye coordination, requiring participants to track a moving crosshair with a mouse-controlled cursor. This dynamic fixation task greatly reduces peeking at peripheral targets relative to a stationary fixation task, but does not eliminate it. Here, we introduce a crowded dynamic fixation task that further enhances fixation by adding clutter around the fixation mark. We assessed fixation accuracy during peripheral threshold measurement. Relative to the root mean square gaze error during the stationary fixation task, the dynamic fixation error was 55%, whereas the crowded dynamic fixation error was only 40%. With a 1.5° tolerance, peeking occurred on 7% of trials with stationary fixation, 1.5% with dynamic fixation, and 0% with crowded dynamic fixation. This improvement eliminated implausibly low peripheral thresholds, likely by preventing peeking. We conclude that crowded dynamic fixation provides accurate gaze control for online testing.

Journal of VisionVol. 26(1)
University of Michigan (US), Joyce Foundation (US), Faculty of 1000 (United States) (US), Ann Arbor Center for Independent Living (US), New York University (US)
New York University Abu Dhabi, Tamkeen, National Institutes of Health
Openalex Percentile: Top 77%
Gaze Tracking and Assistive Technology
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

EasyEyes: Crowded dynamic fixation for online psychophysics — Denis G. Pelli, Jie Chen, et al. · Journal of Vision (2026) | TGRS Research Map | TGRS