[Preview] CogTool+ Modeling Human Performance at Large Scale
Haiyue Yuan, Shujun Li, Patrice Rusconi
CHI'22: ACM Conference on Human Factors in Computing Systems
Session: Cognition and Computational Collaboration
Abstract
Cognitive modeling tools have been widely used by researchers and practitioners to help design, evaluate, and study computer user interfaces (UIs). Despite their usefulness, large-scale modeling tasks can still be very challenging due to the amount of manual work needed. To address this scalability challenge, we propose CogTool+, a new cognitive modeling software framework developed on top of the well-known software tool CogTool. CogTool+ addresses the scalability problem by supporting the following key features: (1) a higher level of parameterization and automation; (2) algorithmic components; (3) interfaces for using external data; and (4) a clear separation of tasks, which allows programmers and psychologists to define reusable components (e.g., algorithmic modules and behavioral templates) that can be used by UI/UX researchers and designers without the need to understand the low-level implementation details of such components. CogTool+ also supports mixed cognitive models required for many large-scale modeling tasks and provides an offline analyzer of simulation results. In order to show how CogTool+ can reduce the human effort required for large-scale modeling, we illustrate how it works using a pedagogical example, and demonstrate its actual performance by applying it to large-scale modeling tasks of two real-world user-authentication systems.
WEB:: https://programs.sigchi.org/chi/2022/program/content/70491
Presentation Video:: https://www.youtube.com/watch?v=VTS0soS9ne4
DOI:: https://doi.org/10.1145/3447534
Video previews for CHI 2022 Interactivity