Hi my name is Shaun Wallace I'm a phd candidate at brown university advised by Jeff Huang
this work began during a research internship with adobe where i was advised by Zoya Bylinskii I'm
grateful to my many co-authors who came together to study fonts and remote readability we set out
to study whether the average adult reader can see improvements in reading performance simply
by changing the text format we began by looking at what prior research says about font choices
font size and the relationship to users reading speed and font preferences each row in the table
shows if there is a relationship between the two variables in the header first study for example
in the far right column we can see there is a relationship between font size and font preference
on the first column we can see prior research shows font does affect speed but it's complicated
it's complicated because font choices in the devices we read with have evolved over the
past two decades most importantly when comparing two fonts rendered at the same size say 16 pixels
they may not necessarily be the same visual size previous research and interviews with typographers
show us the font size needs to be controlled we found in preliminary studies when all fonts were
16 pixels that size predicts preference so for example many fonts are designed to have a larger
height for a lowercase x than others this is a lowercase x and Times at 16 pixels and here's a
lowercase x and Montserrat also at 16 pixels when these are overlaid the lowercase x and Montserrat
is clearly larger however we can decrease the size of Montserrat so its lowercase x has the same size
as Times we used Times as our control font and computed new pixel values for all 16 fonts in
our study based upon three normalization methods lowercase x height the average character width
and the average character height we recruited paid crowd workers to compare the size of fonts
rendered using these three methods in addition to applying normalization and voted on which method
was most similar in size to Times resulting in new normalized pixel values to render each font app
here's a list of the 16 fonts we used in our final study with the normalized text size to the right
we selected these fonts for their common usage across multiple different mediums we developed
several open source tools and web applications to run large remote studies to study interlude
reading a reading mode focusing on typical reading tasks such as reading forum posts or blogs to
evaluate these tools and fonts we've run several small to large scale studies with paid crowd
workers our focus is to understand if some fonts are preferred and effective across a population
or are there large individual differences if one font increase reading speed for all
or does everyone have a different font that helps them read faster our studies feature participants
that mirror the general digital reading population while participants are very comfortable reading
english they represent many different countries and languages so how do we find someone's fastest
font maybe people can select their fastest fonts based on their preferences to determine someone's
preference we develop a web-based toggle test that should be familiar to anyone who's ever
done an eye exam during the font preference toggle test users will see the same text rendered in two
different fonts and they can toggle between the two and then they select the font they preferred
the most all of our interfaces are developed with an e-reader aesthetic they are designed
to be consistent in presentation across all devices so how do we measure reading speed we
split a passage into two screens so we can get two different measurements we capture words per minute
by starting a timer when the reader first sees the screen and in the background we're also tracking
mouse movements on desktop devices and motion controls on mobile devices as a reader proceeds
through the two screens we capture a reading speed measurement each time the study design for our
large remote study features preference and reading speed tests to help determine an individual's best
and worst fonts in this study all participants read in Times and Noto Sans because those fonts
performed well in our preliminary work for our results we only use reading speed measurements
between 100 and 650 words per minute to ensure readers are not skimming or memorizing content we
found that participants reading speeds increased by 35 percent when comparing their best and worst
fonts so let's break this finding down further our readers who experience the largest gains
in reading speed could read a 700 word article over one minute faster imagine you were reading
something that normally took you an hour to read with the right font you could get back
20 minutes of your time so we saw the right font can increase reading speed wouldn't it be
great if user's preference for a font was also predictive of their reading speed our findings
show the participants do not know what is best for them 73 percent of participants believe their most
preferred font would also be their most effective to read them that only happened at chance however
amongst reading speed preferences a post survey found that participants described their preferred
font as bold and modern with natural character spacing while many older participants wanted a
font the decreased eye strain younger participants preferred fonts with tighter character spacing
believing this would help them read faster we found that certain fonts predictably affect
reading speed making it possible to make some general recommendations for people over the age
of 35. for instance EB Garamond and Montserrat were particularly fast fonts while Avant-Garde
and Open Sans were slow however looking across the whole population fonts do not affect people
in the same way a font that is effective for one person is not necessarily effective for another
it can be deceiving to rely on averages to understand a font's effect on reading speed
for example EB Garamond was the fastest on average of 312 words per minute however individual
participants only read faster and at 48 percent of the time compared to the other fonts they read in
in contrast Open Sans was the slowest font on average while individual participants read faster
in it 51 percent of the time so maybe certain fonts might help readers with different abilities
now when looking at an individual's fastest font it was at random if a participant read fastest
in their most preferred font, Times, or Noto Sans each black line in this figure represents
the distance between a single user's slowest and fastest fonts these lines are ordered by average
reading speed per user most importantly while there is no one font that worked for everyone
participants could experience large games and reading speed with the right font we
found that for 94 percent of our participants they experienced medium to large gains in reading speed
when comparing their fastest and slowest fonts this figure shows the reading speed measurements
for our fastest readers it shows the potential that font alone can have on reading speed
hence being able to quickly and accurately find someone's optimal font can be massively beneficial
in their daily lives the most effective font for reading speed varies across participants
fonts with different attributes and anatomies are effective for different people hence the
need for personalized reading experiences while a participant's most preferred font
was not always the fastest our recent work shows that if you generally prefer a font
you will also most likely read faster with it so preference in a font will not hurt your
reading speed feel free to read in the fonts you prefer you might read faster because of it
we found that different fonts work best for different people and also changing font
alone produces the biggest gains in reading speed compared to design manipulations such
as changing character spacing among all of our studies one thing remains constant
different settings work for different people hence the need to study personalized reading
to help engineer future personalized reading experiences we've developed a collaborative
community of organizations to create the virtual readability lab whereas we call
it the VRL we help rapidly prototyping around new personalized readability experiments through this
joint effort we are creating the largest data set of reading studies ever and these results
will help individuals find their fastest fonts and inform future research thank you very much