# TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data

arXiv: https://arxiv.org/abs/2001.04461 · HTML: https://mlsanity.com/paper/2001.04461/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices](https://mlsanity.com/paper/2307.00122/) (2023)
- [FreeGaze: Resource-efficient Gaze Estimation via Frequency Domain Contrastive Learning](https://mlsanity.com/paper/2209.06692/) (2022)
- [Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention](https://mlsanity.com/paper/2508.03713/) (2025)

## Abstract (arXiv)

Eye movements provide insight into what parts of an image a viewer finds most salient, interesting, or relevant to the task at hand. Unfortunately, eye tracking data, a commonly-used proxy for attention, is cumbersome to collect. Here we explore an alternative: a comprehensive web-based toolbox for crowdsourcing visual attention. We draw from four main classes of attention-capturing methodologies in the literature. ZoomMaps is a novel "zoom-based" interface that captures viewing on a mobile phone. CodeCharts is a "self-reporting" methodology that records points of interest at precise viewing durations. ImportAnnots is an "annotation" tool for selecting important image regions, and "cursor-based" BubbleView lets viewers click to deblur a small area. We compare these methodologies using a common analysis framework in order to develop appropriate use cases for each interface. This toolbox and our analyses provide a blueprint for how to gather attention data at scale without an eye tracker.