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Numeral Legibility and Visual Complexity

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Numeral legibility and visual complexity
BEIER Sofie a*; BERNARD Jean-Baptisteb and CASTET Ericc
a The Royal Danish Academy of Fine Arts, Denmark
b Aix-Marseille Université & Laboratoire de Psychologie Cognitive & Fondation de l’Avenir Visaudio
c Aix-Marseille Université & Laboratoire de Psychologie Cognitive
* Corresponding author e-mail:
doi: 10.21606/dma.2017.246
To enhance the peripheral legibility of numerals we designed three versions of the
digits from 1 through 9 by modifying the complexity of each numeral (equivalent to
their digit skeleton) while controlling for variations in other physical parameters.
Observers identified the different versions of the digits in random three-digit strings,
presented within their peripheral visual field. Our results showed that the digit ‘1’
should have a narrow design without a crossbar at the bottom, the digits ‘3’ and ‘9’
should benefit from open apertures, and the digit ‘7’ should have a straight leg and no
serif at the horizontal bar. The data further demonstrated that crowded digits
presented in the periphery of the visual field generally profit from a short
morphological skeleton. The findings can improve the identifiability of numbers for
readers with normal visions as well as for readers with central visual field loss.
Typefaces, numerals, legibility, inclusive design
1. Introduction
If a reader misreads a number on a road sign, a medicine information leaflet, or an aircraft display, the
potentially flawed action which follows can have severe consequences. With this in mind, it is
important to realise that few studies in the research literature concern numeral legibility. By
identifying visual factors influencing numeral legibility, we seek to add new knowledge that could
benefit both visually impaired readers and readers with normal vision. The findings could help type
designers create legible digits, and could also help graphic designers determine which typeface to
choose when maximum legibility is a priority. Among possible limiting visual factors, we were
interested in studying the effect of the length of the numeral skeleton on numeral legibility. This is
based on previous studies showing the effect of letter skeleton length (potentially measuring letter
complexity) on peripheral letter legibility (Bernard & Chung, 2011; Wang et al., 2014).
Figure 1: The skeleton of a letter or dig it is the basic structure of the character. In this il lustrat ion, the letter s keleton varies
while other parameters, such as stroke weight and width, are identical among the tree letters.
Whilst identifying a letter within a word, the reader will draw on a mental library of all the words he
or she has been exposed to before (Legge, Klitz, & Tjan, 1997). This means that when a reader
encounters an illegible letter, he/she can draw on information from adjacent letters and from the
sentence structure, and thus make an educated guess of what the letter might be (Pelli & Tillman,
2007). This is rarely possible when the target is a digit. In such situations, there will be little or no
additional help from the surrounding digits or the structure of the text. It is therefore essential to
prevent one digit being mistaken for another (Figure 2). This pertains especially to specific visual
conditions that make numerals difficult to identify. For instance, letter/numeral recognition is harder
for small print sizes near the acuity limit because of human optical and neural limitations. When
readers cannot use their central vision (such as patients with age-related macular degeneration
(ARMD)), symbol recognition can be difficult, even for large print sizes. This is due to visual crowding
(Pelli et al., 2004), a phenomenon which impairs symbol recognition when a symbol is surrounded by
other symbols in the peripheral visual field. As previously explained, patients with ARMD,
unfortunately, cannot rely on the general context to improve their limited numeral recognition
Figure 2: Based on word and sentence structure, it is possible to guess the missing letters in the top row. However, there is no
way to guess the missing number in th e bottom row.
The so-called alphanumeric category effect (Hamilton, Mirkin, & Polk, 2006; Jonides & Gleitman, 1972;
Polk & Farah, 1998) describes the fact that in a different-category target search, subjects tend to have
a longer reaction time when detecting a letter among letters than when detecting a letter among
digits, and vice versa. This suggests that digits and letters are, to some degree, independently
processed. Yet, there are indications that this difference is related to habit. As readers often perceive
letters and digits under separate circumstances, it might be more difficult to process them when they
are presented collectively. This idea is demonstrated by Polk and Farah (1998), who found that the
alphanumeric category effect is less evident among Canadian postal workers, who have a daily routine
of sorting postal codes of mixed letters and digits, and by Jonides and Gleitman (1972), who found
that results were affected by whether observers perceived 0 as a digit (zero) or as a letter.
If the phenomenon is due to habit alone, the identification of letters and digits should be equally
difficult. That is, however, not the case. There is substantial evidence suggesting a numeral
identification advantage, with studies demonstrating that it is easier to identify digits than letters
(Schubert, 2016). Further, the vast amount of literature on pure alexia showed that digit naming can
be less impaired than letter naming in certain patients (Starrfelt & Behrmann, 2011). In fact, cases of
digit naming impairment with intact letter naming impairment have yet to be reported (Rath et al.,
One reason for this could be related to the difference in the visual properties of letters and digits. To
investigate this hypothesis, Starrfelt and Behrmann (2011) visually overlapped lowercase letters and
The garden roses are beautifu
1 298 090
digits in the typefaces Times and Arial. They suggested that as there are more letters in the alphabet
than digits, letters have a larger number of possible competitors, and hence, single symbol
identification should be more difficult for letters than for digits. Schubert (2016) focused on scenarios
where letters and digits are mixed. She used uppercase letters and digits of four different typefaces,
separating the character features into different units such as ‘slant’, ‘curve’ and ‘orthogonal’. While
overlapping two characters, she considered position and relative size and found no indications that
digits have more distinctive forms than letters. However, a curve within a typeface can vary highly
between characters (Figure 3).
Figure 3: The fou r typefaces applied i n the study by Schubert (2016). To demonstrate that curves within a typeface can vary
significantly between characters, the curve of the ‘2’ has been rotated and scaled to fit the curve of the ‘D’.
It is also possible that the numeral identification advantage is related to a difference in letter and digit
structure that cannot be detected by measuring the physical overlap of shapes. While uppercase and
lowercase letters originate in the Roman capitals and the Carolingian minuscule, numerals are Hindu-
Arabic. This difference in origin has left a mark on the basic structure of letters and digits. Roman
capital letters were originally cut in stone, and the letter shapes are therefore dominated by straight
horizontal, vertical, and diagonal strokes mixed with clear circular strokes. The vertical stroke survived
in lowercase letters, through the cursive tradition of connecting the downstroke with the upstroke of
the following letter. About 62% of lowercase letters and about 65% of uppercase letters have a vertical
stroke. Compared to this, only 20% of digits have a vertical stroke (Figure 4). It appears that the
downward-upward stroke in lowercase letters contribute to a steady rhythm when the letters are put
into words and sentences (Johnston, 1913).
Figure 4: 1) The cursive writing hand that connects downward and upwards strokes. 2) The vertical strokes of lowercase
letters. 3) The vertical strokes of uppercase letters. 4) The vertical strokes of numerals. Demonstrated in the typeface
Garamond Premier Pro.
The oft-repeated saying that ‘type is a beautiful group of letters, not a group of beautiful letters’
(Carter, 2004), suggests that letters should be designed to be parts of words, not individual units. That
is the essential difference between letters and digits. Since each digit represents a number, their
functions are independent of other symbols. That is not the case for letters. Except for rare exceptions
(for instance, the ‘a’ and ‘i’ in the English language), single letters are only abstract symbols with no
numerical value or semantic meaning. It is when letters are flanked by other letters that they fulfil
their purpose by forming words. Following this, matters related to word readability and the flow of
reading are less relevant in the study of numerals. Research into letter legibility can, however, also
provide useful information for optimising the legibility of numerals.
Times New Roman Arial Consolas Courier
numerals numerals
NUMERALS 1234567890
2. Experiment: the skeleton structure of the digits
Previous research into the foveal and peripheral legibility of numerals have aimed at reinventing the
shapes (Lansdell, 1954), improving the shapes of seven-segment numerals (Van Nes & Bouma, 1980),
or at comparing the digits of different typefaces (Berger, 1944; Fox, Chaparro, & Merkle, 2008; Hind,
Tritt, & Hoffmann, 1976; Smuc, Windhager, Siebenhandl, & Egger, 2007).
Within typeface legibility research there is a tendency to seek answers by comparing different
typefaces in psychophysical experiments. The problem with such an approach is that it is difficult to
isolate one visual feature from another, as different typefaces have different proportions, weights,
contrasts, and styles (Beier, 2016). That makes it difficult to interpret the findings of such studies, as
there are too many typographical variables at play at once. Here, we decided to focus on visual
complexity, a factor that has been shown to influence letter legibility. As several studies have
suggested a link between the visual complexity of symbols and their skeleton length, we chose to
investigate the effect of the skeleton length of a numeral on its legibility. We measured peripheral
legibility, a way to investigate directly how we could improve numeral recognition performance in
patients with central field loss.
1.1. Subjects
Five subjects (two females and three males) with normal or corrected-to-normal vision aged from 21
to 38 years participated in this study. The subjects were students and post-docs from the Aix-Marseille
Université. They were paid 10 euros each for their participation in the experiment. The research
followed the tenets of the Declaration of Helsinki and was approved by the Ethical Committee for
Protection of Human Subjects at the Aix-Marseille Université. Written informed consent was obtained
from each subject after the nature and purpose of the experiment had been explained.
1.2. Apparatus
Stimuli were displayed on a 21-inch CRT color monitor (ViewSonic P227f, refresh rate = 120 Hz,
resolution = 1152 x 854 pixels) driven by a Windows computer running custom software developed in
Python with the Psychopy library. The subjects sat in a comfortable chair with their eyes at a distance
of 40 cm from the monitor in a dimly lit room (screen visual angle: 50.8° x 37.7°). An eye tracker
(Eyelink 1000 Tower Mount distributed by SR Research Ltd., Mississauga, Ont., Canada) was connected
to our system to control the gaze fixation of the subjects. Numerals were displayed in black
(luminance: 0.3 cd/m2) on a light grey background (luminance: 60 cd/m2).
1.3. Design of the numerals
For this experiment, we isolated the variables under investigation by altering one visual feature at a
time. By keeping the test material within one typeface, we can ensure that the findings are related
solely to the matter under investigation. For this purpose, we extended the typeface DejaVu Sans to
contain three variations of each of the numerals from 1-9.
Figure 5 shows the different versions of each numeral. For the numbers 1 and 8, one aspect of interest
was the effect of character width; Fox et al. (2008) found an advantage of a wider ‘1’, and Berger
(1944) and Smuc et al. (2007) both recommended narrow versions of ‘8’. To control the variables, the
only difference between 1a and 1b and between 8v and 8x is the width. We were further interested
in the effect of a cross bar on the numbers ‘1’ and ‘4’; the open and close counter of the numbers ‘2’,
‘3’, ‘5’ and ‘9’; the x-height of the number ‘6’; and the cross sections of the numbers ‘2’ and ‘7’.
Figure 5 : The differen t versions of the digits originate in the typeface DejaVu Sans. Each of the numerals 1, 2, 3, 4, 5, 6, 7, 8
and 9 have been created in three different variants, each having only one visual feature different from another version of the
same number. The variables relate to one of the focus areas described above.
1.4. Experimental Protocol
Each subject ran a single experimental session (total duration of the session: about 1 hour) to test
his/her ability to identify each of the 27 digits in a crowded environment (digits surrounded by other
digits) while using his/her peripheral vision. The session was divided into 6 experimental blocks of 100
trials each, 3 blocks of trials presented in the lower visual field and 3 blocks of trials presented in the
right visual field. Figure 6 schematically describes the temporal course for each trial: observers were
asked to fixate a dot centred on the screen. Gaze location was measured to control for steady fixation
on the fixation target dot. When the subject was ready for the trial, he/she pressed the button on a
hand-held joypad. This triggered an offset correction and initiated the trial: at 10° eccentricity in the
lower visual field, a string of three digits (three digits chosen randomly among the 27 possible ones
with a standard inter-digit spacing) was briefly displayed for 150 milliseconds. The subject’s answer
(three numerals) was stored by the experimenter. We did not ask the subjects to identify which
versions of the numerals were displayed. No pre- or post-masks were displayed before and after each
display. The print size for each subject was obtained in a pre-test session so that the recognition rate
was approximately 50% for the middle digit (print-size average: 0.78°, range: 0.74°0.83°
). On
average, each numeral was presented 67 times for each subject. Approximately 5% of the trials were
discarded because of incorrect fixation. Note that similar to the figure example, different versions of
the same numeral could be part of the same string.
0.74° represents 20 pixels with our viewing distance and screen resolution.
Figure 6 : Description of the experimental protocol: The subject fixated on a dot, pressed a button to display the string of 3
digits and then named the presented numeral.
1.5. Statistical analysis of the individual digits
Statistical analyses were performed using the R language and environment (Team, 2013). For each
numeral from 1 through 9, we investigated the effects of the different versions on recognition
performance by using generalised linear mixed-effects models (function glmer of the lme4 package).
A model was run for each numeral (from 1 through 9). Random effect was the subject factor. Fixed
effects were the version of the numeral (version 1, version 2, or version 3) and the position within the
letter string (left, centre, or right letter). The dependent variable was the letter recognition error
variable (0 or 1). P-values were based on conditional t-tests.
1.6. Individual digits results
Figure 7 shows the different recognition rates for each version of each numeral. First of all, numeral
recognition rates can vary considerably across different numerals. For instance, the numeral ‘1’ has
an average recognition rate of 86% (average across the three different versions) whereas the numeral
‘8’ has an average recognition rate of 56%. This is due to letter confusion that exists only for some
numerals. For example, on average, the digit ‘8’ is confused with the numerals5’ or ‘6’ 20% of the
time, whereas the numeral ‘1’ is confused with the numerals ‘5’ or6’ less than 2% of the time, on
Figure 7 : Recognition rat es for the differen t versions of each numeral 19. A star on the left represents a significant difference
between version 1 and version 2 of the corresponding numeral. A star on the right represents a significant difference between
version 2 and version 3 of the corresponding numeral. A centred star represents a significant difference between version 1
and version 3 of the corresponding numeral.
For each numeral, our linear mixed-effect models show a significant effect of the relative position of
the digit (p<0.00001 for each model): The digit at the centre of the trigram is less often correctly
identified, compared to the digits on the left or on the right of the trigram (53% recognition on
average for the central digit vs. 87% on average for the outside digits). This is because the magnitude
of crowding depends on the number of flankers (Chanceaux, Mathôt, & Grainger, 2014). More
importantly this shows the significance of the differences between each pair of digits based on the
different linear mixed-effect models. The pairs that are significant (across a same numeral) are also
shown in Figure 7. Interestingly, our analysis shows that some versions of the numerals ‘1’, ‘3’, ‘7’
and ‘9’ are significantly easier to identify than other versions.
Table 1: P-values for numeral-pair comparisons. Significant differences are highlighted and marked with a star (p-
value<0.05). The yellow versions are the most legible.
1.7. Complexity analysis
Previous research focusing on the recognition of crowded symbols in the periphery has showed
that symbol complexity (theoretically, the number of visual features of a single symbol) has a
deleterious effect on the recognition of adjacent letters (Bernard & Chung, 2011). Visual complexity
can be measured following different methods that are strongly intercorrelated (Wang, He, & Legge,
2014). Here, we decided to use the length of each digit skeleton for a given print size of 20 pixels (see
Figure 8). This was done by using a custom-written Matlab program and templates for our different
digits. Complexity values for each symbol are shown in Table 2.
Figure 8: Symbol compl exity. The complexity of each cha racter is quantifi ed by the length of eac h digit’s morpholog ical
skeleton (example with a height of 41 pixels). The longer the string, the more complex the character.
For each presented digit (for instance the digit ‘2’ within the trigram ‘123’), we studied (1) the effect
of the complexity of the digit (i.e., the complexity of the digit ‘2’) and (2) the effect of the complexity
of the two adjacent digits (the sum of the complexity of the digit ‘1’ and the complexity of the digit
‘3’). To do so, we ran a new generalised linear mixed-effect model to study the effects of both kinds
of complexity on recognition rate. Random effects were the subject factor and the numeral factor.
Fixed effects were target complexity, flanker complexity, and the digit’s position within the digit string
(left, centre or right letter). The dependent variable was the digit recognition error variable (0 or 1).
Table 2: Complexit y values for the different versions of di gits 1-9.
1.8. Complexity results
The data show a significant effect of the complexity of the displayed target (p<10-4), and a significant
effect of the complexity of the displayed flankers (p<10-3) on target recognition rate. The effect is
stronger for target complexity (0.5% per skeleton pixel) than for flanker complexity (0.2% per
skeleton pixel). To summarise, we found that for a given digit, the recognition rate significantly
increases when the complexity of the digit decreases and the complexity of the adjacent digits
decreases. Finally, the effect of trigram complexity (sum of the complexity of the three letters, i.e., the
sum of both types of complexity) on trigram recognition rates is shown in Figure 9. It clearly exhibits
the negative effect of digit complexity on recognition rate based on the definition of six different
ranges of complexity: [111:120], [121:130], [131:140], [141:150], [151:160] and [161:170]. There are
at least 40 trials per subject for each complexity range.
Figure 9: Effect of trigram complexity (sum of the length of the skeleton for the three digits) on numeral recognition rate.
Recognition rate is averaged across subjects.
3. Discussion
In the following, we will compare the present findings (summarised in Figure 10) with data from other
kinds of experimental designs and discuss the implications.
Figure 10: The characters of the top row were all found to be significantly more legible than the corresponding characters in
the bottom row.
1.9. The digit ‘1’
Our results showed that the narrow version of the digit ‘1’ was more legible than the wider versions.
The finding contradicts previous research by Fox et al. (2008), who investigated the legibility of single
characters of 20 different typefaces and recommended a large surface area for the ‘1’, so that the
character is both tall and wide with a distinctive arm and a crossbar. While our focus is on three-digit
strings, Fox et al. studied single characters. Furthermore, in our study the digits could only be misread
for other digits, while the Fox et al. study also included letters and symbols as possible confusion
material. We argue that except for the reading of codes, in most reading situations involving digits,
possible confusion characters will be other digits. Hence the findings of Fox et al. cannot necessarily
be translated into normal reading.
Our finding that narrowness benefits the digit ‘1’, is supported by a previous study into reading
distances, which found that serifs on the top and bottom of the stem resulted in a greater number of
misreadings between the letters ‘i’ and ‘l’ (Beier & Dyson, 2014). In another distance study, the results
indicated that a serif on top without a large cross bar at bottom made the character more legible
compared to one version with a cross bar and another sans serif version. However, the same study
also concluded that narrow letters such as ‘l’, ‘t’, and ‘j’ benefit from slightly wider designs, yet not
too wide (Beier & Larson, 2010).
It is possible that when characters we expect to be narrow lose their uniqueness as narrow, they
become more difficult to identify, even though they might be easier to spot. Furthermore, while the
alphabet includes several narrow letters that potentially can be misread for each other, there is only
one narrow digit. Hence, the narrower the digit ‘1’ the fewer misreadings for other digits.
1.10. The digit ‘7’
The digit ‘7’ was significantly more legible without a serif at the horizontal crossbar. The effect of serifs
has been a central focus point throughout the history of legibility research. However, many of these
studies lacked both internal and external validity as they often look for answers by comparing different
typefaces (Lund, 1999).
By applying a method of Rapid Serial Visual Presentation of words, Morris et al. (2002) found that a
sans serif version of the typeface Lucida was more legible in very small sizes at distance than a serif
version of Lucida, the typefaces were designed for the study to control all other variables than the
serif. The study by Beier and Dyson (2014) applied a similar approach of controlling other variables
and found that single letters with serifs at the vertical extremes were more legible at great distances
than sans serif letters (see Figure 11).
The different results confirm the notion that legibility-related findings identified under one reading
condition cannot necessarily be translated into another reading condition. In other words, the higher
error rate found in our study for the digit ‘7’ with a serif might not be found if the same character is
tested at greater reading distances or presented in isolation.
Figure 11: By measuring the maximum distance for the identification of single characters, Beier and Dyson (2014) found that
serifs at the vertical extreme enhanced legibility.
1.11. Digits ‘3’ and ‘9
For the digits ‘3’ and ‘9’ our data indicates that versions with open apertures are more legible than
versions with more closed apertures. This finding confirmed a widely voiced opinion by many type
designers, who speak advocate the design of types with open inner counters, as they view this as a
way to improve legibility (Kinneir, 1978, 1980; Unger, 2007). The central function of the open aperture
is demonstrated by an experiment reported by Fiset et al. (2008). Here the researchers blurred
different parts of the letters and found that subjects were better at identifying letters when the stroke
endings were visible. For example, the stroke endings defining the open part of the ‘c’ are essential
for distinguishing it from the letter o’ (Figure 12). Following this, we can conclude that the open
apertures of digits ‘3’ and ‘9’ help to differentiate the characters from similar digits such as ‘8’ and ‘6’.
In 2007, a team of researchers conducted an investigation in connection with the development of the
road traffic typeface ‘Tern’ (Trans-European Road Network) (Smuc et al., 2007); in this study they
compared the distance legibility of a range of different European traffic typefaces. Based on the data,
the team recommended against closed counters and suggested that the digits ‘6’ and ‘9’ should have
a curved tail.
Figure 12: By blurring different parts of the letters, Fiset et al. (2008) found that letters where the stroke endings were visible
were the easiest to identify (our illustration).
There are strong indications that open apertures benefit reading both within the peripheral visual field
and for distance reading. However, that may not be the case when characters are seen in isolation.
Recently, Larson and Carter (2016) published parts of the experimental research they had undertaken
while developing the typeface Sitka. For a brief exposure within the central visual field, their findings
suggest that letters with more open counters performed best when flanked by other letters; however,
the study also found indications to suggest that letters with slightly closed counters performed best
when viewed in isolation.
1.12. Complex and simple skeletons
The data revealed that strings of numerals with simple morphological skeletons were more legible
than strings of numerals with more complex morphological skeleton (Figure 9) when numerals were
presented within the peripheral visual field. This influence of symbol skeleton complexity on
recognition rate was first demonstrated by Bernard & Chung (2011), who tested the typefaces Times
Roman and Courier and the script typefaces Edwardian and Aristocrat at an eccentricity of 10° in the
peripheral vision. They found that letter identification error rate increases with flanker complexity, up
to a certain value.
This is an interesting finding in relation to the design of legible characters and numerals for subjects
who cannot use their central vision. Yet, it somewhat contradicts the approach applied by several
renowned type designers whose focus on ensuring differentiation between characters may result in
added features, such as cross bars and tails (Herrmann, 2012; Johnston, 1913; Spiekermann, 2007).
For the London Underground typeface, Johnston created a loop in the lowercase ‘lto differentiate
the character from the capital ‘I’ (Walter, 1986). This resulted in a more complex letter skeleton, which
in theory would lower legibility. However, the issue is not straightforward. As mentioned above, Beier
and Larson (2010) found that at greater reading distances, a tail on the ‘l’ results in fewer errors.
Further, a two-storey ‘a’ and ‘g’ also have more complex letter skeletons than single-storey versions.
However, Beier and Larson (2010) established that the two-storey ‘a’ is more legible than the single-
storey ‘a’, as the latter produced a high number of misreadings, being confused with ‘o’ and ‘q’ (Figure
Figure 13: Beier and Larson (2010) found that with brief exposure and at distance reading, the two-storey ‘a´’ (left) was more
legible than the single-storey ‘a’ (right).
It appears that simple letter and digit skeletons generally improve legibility; however, this is only the
case when the simplicity does not result in character shapes that are easily misread for others.
Numerals are a great example of a set of symbols whose complexity can be significantly reduced: there
are only 10 different symbols, and thus fewer confusion pairs compared to the Roman alphabets with
their 26 or more characters. Based on the findings of this paper, we recommend the design of digit
skeletons that follow the typeface of the top row in Figure 14, while the bottom row shows a typeface
with less legible designs.
Figure 14: The digits in the top row were col lectively more legibl e than the digits in the bottom row.
4. Conclusion
The purpose of this study was to identify the most legible digit skeletons for readers relying on their
peripheral field of vision. The experiment produced significant findings for four out of the nine
numbers tested. The results showed that the digit ‘1’ should be narrow without a crossbar at the
bottom, the digit ‘3’ may either have open apertures or a triangular upper part, the digit ‘7’ should be
designed with a straight leg with no serif at the vertical bar, and the digit ‘9’ should have open
apertures with the bowl being somewhat straight and not too round.
The results further showed that a simple morphological digit skeleton facilitates greater peripheral
legibility than more complexed skeletons by (1) increasing its own legibility and (2) increasing the
legibility of adjacent digit skeletons. Based on previous research, it is argued that this is only relevant
in situations where the simple digit skeleton will not result in a greater number of misreadings for
other characters.
As the stimuli were designed for this specific experiment we were able to isolate the variable of digit
skeleton for investigation, a methodological approach that improves the external validity of the
findings and generates a set of usable ‘rules of thumb’ that can be easily implemented in the design of
new typefaces.
Our results are highly relevant for the design of numerals that would be of specific benefit to
individuals with central field loss, such as age-related macular degeneration. It has also been
suggested that improved peripheral letter legibility could benefit reading performance for subjects
who are able to use their central vision when they read running text (Rayner & Pollatsek, 1989). Thus,
our findings could also benefit normally sighted individuals.
5. References
Beier, S. (2016). Letterform Research: an academic orphan. Visible Language, 50(2), 64.
Beier, S., & Dyson, M. C. (2014). The influence of serifs on 'h' and 'I': useful knowledge from design-led
scientific research. Visible Language, 47(3), 74-95.
Beier, S., & Larson, K. (2010). Design Improvements for Frequently Misrecognized Letters. Information Design
Journal, 18(2), 118-137.
Berger, C. (1944). I. Stroke-width, form and horizontal spacing of numerals as determinants of the threshold of
recognition. Journal of Applied Psychology, 28(3), 208.
Bernard, J.-B., & Chung, S. T. (2011). The dependence of crowding on flanker complexity and targetflanker
similarity. J Vis, 11(8).
Carter, M. (2004). An Exercise in Versatility. In L. Cabarga (Ed.), Logo, Font & Lettering Bible: A comprehensive
guide to the design, construction and usage of alphabets, letters and symbols (pp. 200): Davis &
Chanceaux, M., Mathôt, S., & Grainger, J. (2014). Effects of number, complexity, and familiarity of flankers on
crowded letter identification. J Vis, 14(6), 7-7.
Fiset, D., Blais, C., Ethier-Majcher, C., Arguin, M., Bub, D., & Gosselin, F. (2008). Features for identification of
uppercase and lowercase letters. Psychol Sci, 19(11), 1161-1168.
Fox, D., Chaparro, B. S., & Merkle, E. (2008). Examining the Legibility of the Number" 1" and the"÷" Symbol.
Usability news, 10(1).
Hamilton, J. P., Mirkin, M., & Polk, T. A. (2006). Category-level contributions to the alphanumeric category
effect in visual search. Psychon Bull Rev, 13(6), 1074-1077.
Herrmann, R. (2012). The Design of a signage typeface. Retrieved from
Hess, R. F., Dakin, S. C., & Kapoor, N. (2000). The foveal ‘crowding’effect: physics or physiology? Vision Res,
40(4), 365-370.
Hind, P., Tritt, B., & Hoffmann, E. (1976). Effects of level of illumination, strokewidth, visual angle and contrast
on the legibility of numerals of various fonts. Paper presented at the Australian Road Research Board
(ARRB) Conference, 8th, 1976, Perth.
Johnston, E. (1913). Writing & illuminating, & lettering: Macmillan.
Jonides, J., & Gleitman, H. (1972). A conceptual category effect in visual search: O as letter or as digit.
Attention, Perception, & Psychophysics, 12(6), 457-460.
Kinneir, J. (1978). The practical and graphic problems of road sign design. In R. Easterby & H. Zwaga (Ed.),
Information design: the design and evaluation of signs and technical information (pp. 341-358):
Chichester: John Wiley and Sons Ltd.
Kinneir, J. (1980). Words and buildings: the art and practice of public letterings. London: The Architectural
Lansdell, H. (1954). Effects of form on the legibility of numbers. Canadian Journal of Psychology/Revue
canadienne de psychologie, 8(2), 77.
Larson, K., & Carter, M. (2016). Sitka: a collaboration between type design and science.
Legge, G. E., Klitz, T. S., & Tjan, B. S. (1997). Mr. Chips: an ideal-observer model of reading. Psychol Rev, 104(3),
Liu, L., & Arditi, A. (2001). How crowding affects letter confusion. Optometry & Vision Science, 78(1), 50-55.
Lund, O. (1999). Knowledge Construction in Typography: The case of legibility research and the legibility of sans
serif typefaces. (PhD).
Marinus, E., Mostard, M., Segers, E., Schubert, T. M., Madelaine, A., & Wheldall, K. (2016). A Special Font for
People with Dyslexia: Does it Work and, if so, why? Dyslexia, 22(3), 233-244.
Morris, R. A., Aquilante, K., Yager, D., & Bigelow, C. (2002). P-13: Serifs Slow RSVP Reading at Very Small Sizes,
but Don't Matter at Larger Sizes. Paper presented at the SID Symposium Digest of Technical Papers.
Pelli, D. G., & Tillman, K. A. (2007). Parts, wholes, and context in reading: A triple dissociation. PLoS One, 2(8),
Polk, T. A., & Farah, M. J. (1998). The neural development and organization of letter recognition: Evidence
from functional neuroimaging, computational modeling, and behavioral studies. Proceedings of the
National Academy of Sciences, 95(3), 847-852.
Rath, D., Domahs, F., Dressel, K., Claros-Salinas, D., Klein, E., Willmes, K., & Krinzinger, H. (2015). Patterns of
linguistic and numerical performance in aphasia. Behavioral and Brain Functions, 11(1), 2.
Rayner, K., & Pollatsek, A. (1989). The Psychology of Reading: Lawrence Erlbaum Associates.
Schubert, T. (2016). Why are digits easier to identify than letters? Neuropsychologia.
Smuc, M., Windhager, F., Siebenhandl, K., & Egger, S. (2007). Impaired Visibility Typeface TestReport. Report
In-Safety A, 2.
Spiekermann, E. (2007). How does the serif on a sans-serif i increase legibility/readability? ( Comment in the
discussion ). Retrieved from
Starrfelt, R., & Behrmann, M. (2011). Number reading in pure alexiaA review. Neuropsychologia, 49(9), 2283-
Team, R. D. C. (2013). R: A language and environment for statistical computing R Foundation for Statistical
Computing, Vienna, Austria
Unger, G. (2007). While You’re Reading, . New York: Mark Batty Publisher.
Van Nes, F. L., & Bouma, H. (1980). On the legibility of segmented numerals. Human Factors: The Journal of the
Human Factors and Ergonomics Society, 22(4), 463-474.
Walter, T. (1986). Letters of credit (a view of type design): Gordon Fraser, London.
Wang, H., He, X., & Legge, G. E. (2014). Effect of pattern complexity on the visual span for Chinese and
alphabet characters. J Vis, 14(8), 6-6.
About the Authors:
Sofie Beier is a type designer and associate p rofessor specialised in typeface
legibility. She is the author of the books ‘Reading Letters: Designing for legibility’
and ‘Type Tricks: Your personal guide to type design’.
Jean-Baptiste Bernard is a postdoctoral researcher specialized in the visual aspects
of reading. His recent works concern the study of visual factors limiting letter
recognition, word recognition and reading in normal and peripheral vision.
Eric Castet is a CNRS research director specialized in visual perception and eye
movements at the Laboratoire de Psychologie Cognitive. His recent works concern
the influence of visuo-attenti onal and psycho-linguistic factors that determine
reading performance in normal and low vision.
Sofie Beier1, Jean-Baptiste Bernard2,3,4, Eric Castet2,3
1The Royal Danish Academy of Fine Arts, Denmark; 2Aix-Marseille Université; 3 Laboratoire de Psychologie Cognitive; 4 Fondation de
l’Avenir, Visaudio
On receipt of ‘Top 15 Recommended Best Paper’ by reviewers in a double-blind review process.
This Best paper Award is presented for your outstanding paper entitled:
Numeral legibility and visual complexity
Published in the Design Research Society International Conference 2018 Proceedings
Keelin Leahy
DRS2018 Co-Chair
Muireann McMahon
DRS2018 Co-Chair
Cristiano Storni
Programme Committee Chair
... Multiple experiments within design and vision research have demonstrated that font style can affect both letter and word identification. Examples include serifs at vertical extremes improving distance letter recognition (Beier and Dyson, 2014), small-size sans serif resulting in faster reading speed (Morris et al., 2002), simple letter shapes causing faster recognition of trigrams (Beier et al., 2018), and greater letter differentiation improving letter recognition (Beier and Larson, 2010;Bernard et al., 2016). One typographical feature that is yet to be investigated is the impact on the perception of bold fonts of high stroke contrast on letter recognition. ...
... The experimental paradigm was based on the methodology by Beier et al. (2018). Participants took part in a partial report trigram recognition task, where they were shown a string of three letters and were asked to report only the middle letter. ...
... where n is the current trial number (excluding the first eight trials), x n is the x-height of the current trial, x n+1 is the x-height of the subsequent trial, m shift is the number of shifts in response category that occurred after the first eight trials (from correct to incorrect or vice versa), z n is equal to 1 if the response for the current trial is correct and 0 if the response in the current trial is incorrect, c is the initial step size of 0.11 • at 350 cm distance or 0.19 • at 200 cm distance. The final response accuracy was based on the methodology previously used by Beier et al. (2018), such that the staircase was terminated after 19 reversals, which yielded a response accuracy of approximately 50% (foveal x-height average: 0.10 • (33.81 pixels); STD: 0.03 • (9.56 pixels); range: 0.07 • -0.21 • (24-70 pixels); parafoveal x-height average: 0.23 • (43.50 pixels); STD: 0.05 • (10.5 pixels); range: 0.14 • -0.37 • (26-71 pixels)). ...
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To make graphical user interfaces look more fashionable, designers often make use of high-stroke-contrast fonts. We are yet to understand how these fonts affect reading. We examined the effect of letter-stroke contrast on three bold fonts, one with extreme contrast between thick and thin strokes, one with no contrast, and one in between. The fonts were designed for this experiment to enable control of font variables. Participants identified the middle letter in a lowercase letter trigram in each trial, briefly presented in the parafovea (at 2° left and right of fixation) and at the foveal fixation point. There was evidence for letter recognition impairment for the font with high stroke contrast compared to the fonts with low and medium stroke contrast, while there was no significant difference in performance between the medium- and low-stroke-contrast fonts. The results suggest that bold fonts with high stroke contrast should not be considered for designs where letter recognition is a priority.
... The aim of the present study was to investigate the balance between details and simplification of shapes. Guided by findings on letter recognition that showed that simple letters are more easily recognized than complex letters (Beier et al., 2018;Bernard & Chung, 2011;Pelli et al., 2006), we hypothesised that simplifying the shapes, while maintaining the same level of details, would improve the legibility of pharmaceutical pictograms. ...
... While previous findings have demonstrated that decreased perimetric complexity improves digit and letter recognition (Beier et al., 2018;Bernard & Chung, 2011;Pelli et al., 2006), our experiment has not succeeded in demonstrating that the positive effect of stroke simplicity is also evident in pictogram design. ...
The purpose of pharmaceutical pictograms is to help patients manage their medicinal treatment. However, the pictograms often lack perceptual clarity. While they are frequently tested for aspects such as comprehension, little attention has been paid to their legibility. This paper presents the conception and results of an experiment adapted from the ISO ‘Method for testing perceptual quality’ (ISO 9186-2:2008) to measure the visibility of pictogram elements in two sets: 15 American USP pictograms and 15 redesigned versions reduced in complexity. The statistical analysis did not show reliable significant differences, which indicates that there are more factors at stake.
... However, since typefaces have different contrasts, proportions, styles, and weights it is difficult to isolate any specific visual feature (Beier, 2016). Since there are too many typographical variables at play at once Beier, Bernard, and Castet (2018) focused on visual complexity in a study of legibility of digits. Among possible limiting visual factors, the authors studied the effect of the length of the numeral skeleton on numeral legibility. ...
... The skeleton of a letter or a digit is the basic structure of the character. Beier, Bernard, and Castet (2018) designed three versions of the digits from 1 through 9. They modifying the complexity of each numeral (equivalent to their digit skeleton) while controlling for variations in other physical parameters. ...
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In this book the focus is on graphic design. The practice of graphic design is as old as recorded history. The purpose of work with graphic design is to find a suitable presentation for the content with respect to the receiver, the subject matter, the medium, and the financial situation. Within a given area, such as a page in a book, a poster, a label, a computer screen, or a projected image the designer may alter the design of headings, margins, ornaments, pictures, space, symbols, and text. Graphic design is used as an important “tool” in the other four parts of message design. The most fundamental design technique is reduction. In graphic design the main objective is to provide functional, aesthetic, and organised structure to all kinds of information sets. You can download the previous edition of this book from IIID Public Library < > (almost at the bottom of the page). IIID will soon upload the new editions here./Rune Pettersson
... The research literature shows almost no interest in this typographic characteristic. Except for a recent study into stroke contrast in bold serif fonts, which found that hairline strokes lower letter recognition (Beier & Oderkerk, 2021), other studies concerned with the effects of font style have mainly looked into letter complexity (Beier et al., 2018;Bernard & Chung, 2011;Pelli et al., 2006) and letter boldness (Beier & Oderkerk, 2019;Bernard et al., 2013;Burmistrov et al., 2016;Chung & Bernard, 2018;Macaya & Perea, 2014;Pelli et al., 2006;Sheedy et al., 2005). The main aim of the present paper was to isolate the two typographic features of serifs and stroke contrast and investigate whether a given difference in reading performance between serif and sans-serif fonts is attributable to serifs or to stroke contrast, and in addition being able to isolate these two features. ...
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Aim It is a long-lasting dispute whether serif or sans serif fonts are more legible. However, different fonts vary on numerous visual parameters, not just serifs. We investigated whether a difference in word identification can be attributed to the presence or absence of serifs or to the contrast of the letter stroke. Method Participants performed a word-recognition two-interval, forced-choice task (Exp. 1) and a classic lexical decision task (Exp. 2). In both experiments the word stimuli were set with four new fonts, which were developed to isolate the stylistic features of serif and letter-stroke contrast. Two measures (i.e., font-size threshold & sensitivity) were analysed. Results The threshold measure of both experiments yielded a single significant main effect of stroke contrast such that low stroke contrast elicited lower than high stroke contrast. The sensitivity measure of Experiment 1 yielded a single significant effect of the interaction between serifs and stroke contrast. Specifically, at the sans-serif level, low stroke contrast revealed better sensitivity, relative to high stroke contrast. At the serif level, the opposite stroke contrast pattern was observed. Conclusion Sans serif fonts with low stroke contrast yield better performance and if a serif font is used, high stroke contrast yields better performance than low stroke contrast. Limitations and future directions are discussed.
... Few studies have looked into the effects that font style might have on letter recognition and on lexical processing in glance reading. In letter recognition research, the focus is on the effects of letter structure (Beier, Bernard, and Castet 2018;Beier and Dyson 2014;Beier and Larson 2010;Bernard, Aguilar, and Castet 2016) and letter weight (Beier and Oderkerk 2019). In research on the lexical processing of words, a similar focus is seen on effects of letter structure (Dobres et al. , 2015 and letter weight (Dobres, Reimer, and Chahine 2016), and also letter width (Dyson and Beier 2016;Sawyer et al. 2017). ...
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Most text on modern electronic displays is set in fonts of regular letter width. Little is known about whether this is the optimal font width for letter recognition. We tested three variants of the font family Helvetica Neue (Condensed, Standard, and Extended). We ran two separate experiments at different distances and different retinal locations. In Experiment 1, the stimuli were presented in the parafovea at 2° eccentricity; in Experiment 2, the stimuli were presented in the periphery at 9° eccentricity. In both experiments, we employed a short-exposure single-report trigram paradigm in which a string of three letters was presented left or right off-centre. Participants were instructed to report the middle letter while maintaining fixation on the fixation cross. Wider fonts resulted in better recognition and fewer misreadings for neighbouring letters than narrower fonts, which demonstrated that wider letter shapes improve recognition at glance reading in the peripheral visual view. Practitioner summary: Most of the text is set in fonts of regular letter width. In two single-target trigram letter recognition experiments, we showed that wider letter shapes facilitate better recognition than narrower letter shapes. This indicates that when letter identification is a priority, it is beneficial to choose fonts of wider letter shapes.
... Low stroke contrast improves word recognition (Minakata et al. 2020). Simple letter skeletons result in greater letter recognition (Beier & Larson 2010;Beier et al. 2018). Condensed fonts impair letter recognition (Oderker & Beier 2020), and so do heavy and light letter weight fonts (Beier & Oderkerk 2019a), which also slow down reading speed (Chung & Bernard 2018). ...
Readability is on the cusp of a revolution. Fixed text is becoming fluid as a proliferation of digital reading devices rewrite what a document can do. As past constraints make way for more flexible opportunities, there is great need to understand how reading formats can be tuned to the situation and the individual. We aim to provide a firm foundation for readability research, a comprehensive framework for modern, multi-disciplinary readability research. Readability refers to aspects of visual information design which impact information flow from the page to the reader. Readability can be enhanced by changes to the set of typographical characteristics of a text. These aspects can be modified on-demand, instantly improving the ease with which a reader can process and derive meaning from text. We call on a multi-disciplinary research community to take up these challenges to elevate reading outcomes and provide the tools to do so effectively.
... Isolation of a given variable requires manipulation of this variable only, while the others are kept constant. When researchers are able to alter the test fonts so that only one variable is changed, they may succeed in identifying the effect of specific typographical features such as serifs (Arditi & Cho, 2005;Beier & Dyson, 2014;Morris et al., 2002), letter skeleton (Beier et al., 2018;Beier & Larson, 2010;Larson & Carter, 2016) and letter boldness (Beier & Oderkerk, 2019b). The present experiment employs this methodological paradigm to demonstrate that isolated font variables alone can induce significant differences in reading acuity in AMD patients. ...
Full-text available
Low vision readers depend on magnification, but magnification reduces the amount of text that can be overviewed and hampers text navigation. In this study, we evaluate the effects that font variations letter spacing, letter width, and letter boldness have on low vision reading. We tested 20 low-vision patients with age-related macular degenera-tion (AMD) and used the Radner Reading Chart, which measures reading acuity (logRAD), maximum reading speed, and critical print size. The results demonstrated a small, but measurable effect of letter spacing and letter width on reading acuity near critical font sizes.
... Another contemporary example is the Centre for Visibility Design at The Royal Danish Academy of Fine Arts, which includes researchers with backgrounds in graphic design and in psychology Oderkerk 2019a, 2019b). Within the research community, several newer experiments employing a paradigm of classic psychophysics 3 are results of collaborations between type designers and vision scientists (Beier, Bernard, and Castet 2018;Beier and Dyson, 2013;Dobres, Reimer, and Chahine 2016;Sawyer et al. 2017;Xiong et al. 2018), and typographers and neuroscientists (Keage et al. 2014;Thiessen et al. 2015). These novel multidisciplinary methodological approaches are showing that choice of typeface has cognitive and perceptual implications that can impact on behaviours and abilities to sustain reading activities over time. ...
Full-text available
Recent debate has seen the proposition that difficult to read, or disfluent, typefaces can improve certain learning conditions. This is counterintuitive for typography where it is the aim to support reading acts by creating texts that are as clear and as easy to read as possible. We explore recent literature on the disfluency effect in an effort to contextualize the results for typography research that is grounded in functional readability. What is evident is that the discussion about whether or not disfluent reading materials support learning is far from resolved. Further research is needed in key areas such as those related to the typographic principles of visual cuing and emphasis as well as other broader areas such as how we may be able to determine threshold for disfluency, benefit over time, and what impact environmental distractions have on the disfluency effect.
... Within letterform research there is a growing body of knowledge on typeface legibility (for example Chung, Mansfield and Legge 1998, Sawyer et al. 2017, Beier, Bernard and Castet 2018, Thiessen et al. 2015) that could form the basis of determining what constitutes pictogram legibility. Hence, from some visual aspects, pictograms and letters can be compared, because both consist of black and white shapes, counters and strokes. ...
The design of medicinal information in leaflets and labels is often criticized for not meeting patients’ needs. For that reason, there is an increasing focus on how the use of pictures, such as pictograms, may benefit patients on their medical journey. However, before a pictogram can be comprehended it must be legible, which may be a challenge when pharmaceutical information has to be conveyed. Within a limited space many visual details need to be included in order to clarify the intended meaning. While we have abundant information about the comprehension of pictograms, we know very little about the legibility – the ability to visually identify objects – of pictograms. By looking at legibility research into pharmaceutical pictograms from a design perspective, this paper demonstrates that legibility is not prioritized either in theory or in practice. In order to proceed with the use and implementation of pictograms in, for example, patient information leaflets and labels, we need to know more about the features that constitute legibility. To create a research foundation, this paper draws on knowledge of visibility and legibility from related domains. This forms the basis of a discussion of the need for future research to focus on legibility issues, amongst others by incorporating design knowledge into experiments.
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Beginning with Dejerine's report of pure alexia in 1892, numerous researchers have noted that individuals with acquired impairments of reading may show spared digit identification performance. This digit advantage has also been found in unimpaired adult readers across a number of tasks, and five main hypotheses have been proposed to explain how it arises. In this paper I consider these hypotheses in the context of recent theories of a unified alphanumeric character identification system, and evaluate them according to relevant empirical evidence. Despite some promising findings, none of the hypotheses currently provide a sufficient explanation of the digit advantage. Rather than developing new hypotheses to explain a categorical difference between digit and letter performance, I argue that future work should consider factors that affect identification performance specific to individual characters.
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We recognize words by first recognizing individual letters, then using the letters to build a word [Larson, 2004; Rayner et al. 2012]. Words become more readable by making each of the individual letters more recognizable. This chapter is about the development process for a new typeface named Sitka. During the typeface's development, we tested how well peo-ple could read each of the letters in the typeface, and used the test results to inform design decisions. While the test results needed to be applied conscientiously, we discovered that typeface design could be successfully integrated with scientific legibility testing. © World Scientific Publishing Co. Pte. Ltd. All rights reserved.
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This paper looks into the history of letterform research and discusses why the discipline has yet to make the big break within design research. By highlighting two of the most popular focus areas (letter distinctiveness and the role of serifs) and by discussing various forms of methodological shortcomings, the paper suggests that future research into letterforms should (1) draw on results from the field of reading research (2) be based on test material informed by design knowledge and (3) move away from the former tendency of looking for universal answers.
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Background Empirical research on the relationship between linguistic and numerical processing revealed inconsistent results for different levels of cognitive processing (e.g., lexical, semantic) as well as different stimulus materials (e.g., Arabic digits, number words, letters, non-number words). Information of dissociation patterns in aphasic patients was used in order to investigate the dissociability of linguistic and numerical processes. The aim of the present prospective study was a comprehensive, specific, and systematic investigation of relationships between linguistic and numerical processing, considering the impact of asemantic vs. semantic processing and the type of material employed (numbers compared to letters vs. words).MethodsA sample of aphasic patients (n¿=¿60) was assessed with a battery of linguistic and numerical tasks directly comparable for their cognitive processing levels (e.g., perceptual, morpho-lexical, semantic).Results and conclusionsMean performance differences and frequencies of (complementary) dissociations in individual patients revealed the most prominent numerical advantage for asemantic tasks when comparing the processing of numbers vs. letters, whereas the least numerical advantage was found for semantic tasks when comparing the processing of numbers vs. words. Different patient subgroups showing differential dissociation patterns were further analysed and discussed. A comprehensive model of linguistic and numerical processing should take these findings into account.
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The typographical naivety of much scientific legibility research has caused designers to question the value of the research and the results. Examining the reasons underlying this questioning, the paper discusses the importance of designers being more accepting of scientific findings, and why legibility investigations have value. To demonstrate how typographic knowledge can be incorporated into the design of studies to increase their validity, the paper reports on a new investigation into the role of serifs when viewed at a distance. The experiment looks into the identification of the lowercase letters ‘j’, ‘i’, ‘l’, ‘b’, ‘h’, ‘n’, ‘u’, and ‘a’ in isolation. All of the letters originate in the same typeface and are presented in one version with serifs and one version without serifs. Although the experiment found no overall legibility difference between the sans serif and the serif versions, the study showed that letters with serifs placed on the vertical extremes were more legible at a distance than the same letters in a sans serif. These findings can therefore provide specific guidance on the design of individual letters and demonstrate the product of collaboration between designer and scientist on the planning, implementation, and analysis of the study.
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We tested identification of target letters surrounded by a varying number (2, 4, 6) of horizontally aligned flanking elements. Strings were presented left or right of a central fixation dot, and targets were always at the center of the string. Flankers could be other letters, digits, symbols, simple shapes, or false fonts, and thus varied both in terms of visual complexity and familiarity. Two-alternative forced choice (2AFC) speed and accuracy was measured for choosing the target letter versus an alternative letter that was not present in the string. Letter identification became harder as the number of flankers increased. Greater flanker complexity led to more interference in target identification, whereas more complex targets were easier to identify. Effects of flanker complexity were found to depend on visual field and position of flankers, with the strongest effects seen for leftward flankers in the left visual field. Visual complexity predicted flanker interference better than familiarity, and better than target-flanker similarity. These results provide further support for an excessive feature-integration account of the interfering effects of both adjacent and nonadjacent flanking elements in horizontally aligned strings.
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In 2008 Christian Boer, a Dutch artist, developed a special font ("Dyslexie") to facilitate reading in children and adults with dyslexia. The font has received a lot of media attention worldwide (e.g.,,,, USA Today, and Interestingly, there is barely any empirical evidence for the efficacy of Dyslexie. This study aims to examine if Dyslexie is indeed more effective than a commonly used sans serif font (Arial) and, if so, whether this can be explained by its relatively large spacing settings. Participants were 39 low-progress readers who were learning to read in English. They were asked to read four different texts in four different font conditions that were all matched on letter display size (i.e., x-height), but differed in the degree to which they were matched for spacing settings. Results showed that low-progress readers performed better (i.e., read 7% more words per minute) in Dyslexie font than in standardly spaced Arial font. However, when within-word spacing and between-word spacing of Arial font was matched to that of Dyslexie font, the difference in reading speed was no longer significant. We concluded that the efficacy of Dyslexie font is not because of its specially designed letter shapes, but because of its particular spacing settings. Copyright © 2016 John Wiley & Sons, Ltd.
Reading is a highly complex skill that is prerequisite to success in many societies in which a great deal of information is communicated in written form. Since the 1970s, much has been learned about the reading process from research by cognitive psychologists. This book summarizes that important work and puts it into a coherent framework. Note that the full-text of this book is not available.