Zoya Bylinskii

From the moment we wake up to the moment we end our day, we use interfaces built out of the written word. Textual information remains now, as it has for centuries, the cornerstone of human information acquisition. The wide adoption of smartphone, tablets, e-readers and personal computers has shifted the bulk of this reading from inflexible paper to digital content. The control provided by digital displays over how visual information is presented to readers has the potential to improve reading for each and every reader, regardless of ability or diagnosis. This represents a profound shift in how we think about reading because text is no longer rendered immutable by writers, designers, or publishers at a single stage, and human-computer interaction research is key to realizing its potential. Readability research takes a fundamentally individual approach to what each reader needs. Each reader has their own individual needs. Meanwhile, adapting the written word to the individual reader has never been easier, and the goal of maximizing individual reading efficacy is increasingly attainable. No one discipline or field has all the tools or answers, and readability work is inherently interdisciplinary. The authors of this monograph include vision scientists, technology experts, educators, designers, typographers, and data scientists. Together they represent voices from academia, the tech industry, and non-profit institutions, driven by common goals to improve the reading interfaces of today. In this review, they provide a comprehensive introduction to interdisciplinary methodologies, tools, and materials required for readability research focused on the individual reader. They call on the HCI community to contribute to the growing understanding of readers’ needs; to study the interactions between text, user, and task; and to build the tools and interfaces needed to improve reading outcomes for all.

Most research in computer graphics and image synthesis produces outputs for human consumption. In many cases, these algorithms operate largely automatically; in other cases, interactive tools allow professionals or everyday users to author or edit images, video, textures, geometry, or animation.Online crowdsourcing platforms have made it increasingly easy to perform evaluations of algorithm outputs with survey questions like “which image is better, A or B?”, leading to their proliferation in vision and graphics research papers. Results of these studies are often used as quantitative evidence in support of a paper’s contributions. When conducted hastily as an afterthought, such studies can lead to an increase of uninformative, and, potentially, misleading conclusions. On the other hand, in these same communities, user research is underutilized in driving project direction and forecasting user needs and reception.Increased attention is needed in both the design and reporting of user studies in computer vision and graphics papers towards (1) improved replicability and (2) improved project direction. This monograph focusses on these aspects, and an overview of methodologies from user experience research (UXR), human-computer interaction (HCI), and applied perception to increase exposure to the available methodologies and best practices are also presented. Foundational user research methods are included, (e.g., need finding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction. Also, further pointers to the literature for readers interested in exploring other UXR methodologies are given, and broader open issues and recommendations for the research community are described.