Individuated Reading Experiences Increase Reading Speed without Affecting Comprehension

 

Readability Matters obtained a pre-print of a forthcoming ACM Transactions on Computer-Human Interaction (TOCHI) paper. Shaun Wallace and his colleagues report that participants’ reading speeds (measured in words per minute – WPM) increased by 35% when comparing their fastest and slowest fonts without affecting reading comprehension. Further, they note that high variability in results across the fonts tested suggests that one font does not fit all.

They provide font recommendations related to higher reading speed and discuss the need for individuation, allowing digital devices to match their readers’ needs in the moment. The study results and recommendations are from one of the most significant online reading efforts to date. To complement this research, the authors release their materials and tools with the paper.

 

 

Towards Individuated Reading Experiences:
Different Fonts Increase Reading Speed for Different Individuals

Shaun Wallace, Zoya Bylinskii, Jonathan Dobres, Bernard Kerr, Sam Berlow, Rick Treitman, Nirmal Kumawat, Kathleen Arpin, Dave B. Miller, Jeff Huang, and Ben D. Sawyer

 

ABSTRACT
In our age of ubiquitous digital displays, adults often read in short, opportunistic interludes. In this context of Interlude Reading, we consider if manipulating font choice can improve adult readers’ reading outcomes. Our studies normalize font size by human perception and use hundreds of crowdsourced participants to provide a foundation for understanding which fonts people prefer and which fonts make them more effective readers. Participants’ reading speeds (measured in WPM) increased by 35% when comparing fastest and slowest fonts without affecting reading comprehension. High WPM variability across fonts suggests that one font does not fit all. We provide font recommendations related to higher reading speed and discuss the need for individuation, allowing digital devices to match their readers’ needs in the moment. We provide recommendations from one of the most significant online reading efforts to date. To complement this, we release our materials and tools with this paper.

Access the published paper from ACM Transactions on Computer-Human Interaction (TOCHI) here.

Shaun Wallace, Zoya Bylinskii, Jonathan Dobres, Bernard Kerr, Sam Berlow, Rick Treitman, Nirmal Kumawat, Kathleen Arpin, Dave B. Miller, Jeff Huang, and Ben D. Sawyer. 2022. Towards Individuated Reading Experiences: Different Fonts Increase Reading Speed for Different Individuals. ACM Trans. Comput-Hum. Interact. 29, 4, Article 38 (August 2022), 56 pages. https://doi.org/10.1145/3502222

 

Press Coverage

NN/g: Jakob Nielsen renowned web design and usability guru of the Nielson Norman Group (NN/g) recently reviewed the research published in ACM’s Transactions on Computer Human Interaction (TOCHI) in his recent report, “Best Font for Online Reading: No Single Answer.” https://www.nngroup.com/articles/best-font-for-online-reading/

Fast Company: Suzanne Labarre, writes that “The study’s findings underscore a design problem that stretches far beyond typography: There is no such thing as an “average” user. Even if there were, designing for that person would exclude countless others.” Labarre further summarizes that “Individual differences were more significant than any overall effect.” https://www.fastcompany.com/90747518/are-fonts-ageist

 

About ACM Transactions on Computer-Human Interaction (TOCHI): TOCHI covers the software, hardware and human aspects of interaction with computers. Topics include hardware and software architectures; interactive techniques, metaphors, and evaluation; user interface design processes; and users and groups of users. Those within the artificial intelligence, object-oriented systems, information systems, graphics and software engineering communities, will benefit from the high quality research papers in TOCHI concerning information and ideas directly related to the construction of effective human-computer interfaces.

About Shaun Wallace: Shaun Wallace is a Ph.D. candidate at Brown University and a researcher with a focus on HCI and Big Data systems. Visit Shaun’s website