TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data
| Autor: | Zoya Bylinskii, Barry A. McNamara, Anelise Newman, Camilo Fosco, Nam Wook Kim, Pat Sukhum, Matthew Tancik, Yun Bin Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Interface (Java) business.industry Computer science 05 social sciences ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Computer Science - Human-Computer Interaction Eye movement 020207 software engineering 02 engineering and technology Crowdsourcing Toolbox Human-Computer Interaction (cs.HC) Salient Mobile phone Human–computer interaction 0202 electrical engineering electronic engineering information engineering Visual attention Web application Eye tracking 0501 psychology and cognitive sciences Zoom business 050107 human factors |
| Zdroj: | CHI |
| Popis: | 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. To appear in CHI 2020. Code available at http://turkeyes.mit.edu/ |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|