Zoya Bylinskii

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. On the one hand we argue that, when conducted hastily as an afterthought, such studies 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. We call for increased attention to both the design and reporting of user studies in computer vision and graphics papers towards (1) improved replicability and (2) improved project direction. Together with this call, we offer 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. We discuss foundational user research methods (e.g., needfinding) that are presently underutilized in computer vision and graphics research, but can provide valuable project direction. We provide further pointers to the literature for readers interested in exploring other UXR methodologies. Finally, we describe broader open issues and recommendations for the research community.

The control provided by digital displays over how visual information ispresented to readers has the potential to improve reading for each and every reader, regardless of ability or diagnosis. On screens, text is fluid,allowing for individual customization based on reader needs, content, and reading task. 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. Targeted changes to the visual characteristics of text on screens increases the ease with which a reader can process and derive meaning. In this review, we provide a comprehensive introductionto interdisciplinary methodologies, tools, and materials required for readability research focused on the individual reader. We call on the HCI community to contribute to our 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.