Effects of working memory and relevant knowledge on reading texts and infographics

Abstract

Infographics are a new type of reading material comprising textual and visual information that has been used worldwide. Nonetheless, there has been limited research investigating people’s infographic-reading performance and the characteristics of superior readers. This study adopted Chinese texts and infographics as materials and employed eye-tracking technology to assess how working memory and relevant knowledge affected 137 college students’ reading comprehension, as indicated by reading accuracy (ACC), and reading efficiency, which in turn was indicated by reading time (RT) and total fixation duration (TFD). For texts, verbal working memory (VWM) exhibited no effects on individuals’ reading performance; visuospatial working memory (VSWM) exerted positive effects on both ACC and TFD, and participants with higher knowledge demonstrated better ACC. For infographics, higher-VWM participants showed greater ACC, and higher-VSWM participants displayed a longer RT and TFD, though the effect of knowledge was limited. Moreover, a significant interaction effect of VWM and relevant knowledge on the TFD of infographics was observed, indicating that individuals’ prior knowledge or experience might structure schemas in an infographic and then act with VWM to accelerate reading speed. This study improves our understanding of how working memory and relevant knowledge impact the processing of materials with different synthesized levels, and its implications for instruction and research are discussed.

Full text

Vol.:(0123456789)
Reading and Writing
https://doi.org/10.1007/s11145-022-10368-1
1 3
Effects ofworking memory andrelevant knowledge
onreading texts andinfographics
Chia‑YuLiu1 · Chao‑JungWu2
Accepted: 28 September 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022
Abstract
Infographics are a new type of reading material comprising textual and visual infor-
mation that has been used worldwide. Nonetheless, there has been limited research
investigating people’s infographic-reading performance and the characteristics of
superior readers. This study adopted Chinese texts and infographics as materials
and employed eye-tracking technology to assess how working memory and relevant
knowledge affected 137 college students’ reading comprehension, as indicated by
reading accuracy (ACC), and reading efficiency, which in turn was indicated by
reading time (RT) and total fixation duration (TFD). For texts, verbal working mem-
ory (VWM) exhibited no effects on individuals’ reading performance; visuospatial
working memory (VSWM) exerted positive effects on both ACC and TFD, and par-
ticipants with higher knowledge demonstrated better ACC. For infographics, higher-
VWM participants showed greater ACC, and higher-VSWM participants displayed a
longer RT and TFD, though the effect of knowledge was limited. Moreover, a signif-
icant interaction effect of VWM and relevant knowledge on the TFD of infograph-
ics was observed, indicating that individuals’ prior knowledge or experience might
structure schemas in an infographic and then act with VWM to accelerate reading
speed. This study improves our understanding of how working memory and relevant
knowledge impact the processing of materials with different synthesized levels, and
its implications for instruction and research are discussed.
Keywords Eye-tracking methodology· Infographic· Relevant knowledge· Working
memory
* Chao-Jung Wu
cjwu@ntnu.edu.tw
Chia-Yu Liu
leave1756@gmail.com
1 Research Center forTesting andAssessment, National Academy forEducational Research, No.
2, Sanshu Rd., Sanxia Dist., NewTaipeiCity237201, Taiwan
2 Department ofEducational Psychology andCounseling/Institute forResearch Excellence
inLearning Sciences, National Taiwan Normal University, No. 162, Sec. 1, Heping E. Rd.,
Taipei106209, Taiwan

C.-Y.Liu, C.-J.Wu
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Introduction
Reading is critical for success in school and in daily activities. The Organisation for
Economic Cooperation and Development (2005) proposed a precise definition of its
essential role—to reach one’s goals, realize one’s potential, and engage in society,
one must comprehend, utilize, reflect on, and engage with written texts. In addition
to text, visual information is also a critical reading material. The Association of Col-
lege and Research Libraries defined the ability to interpret and employ visual infor-
mation as visual literacy and affirmed its essentiality for citizens in the multimedia
world (Brown etal., 2016). Infographics (a blend of “information” and “graphic”)
are a new type of reading material defined as synthesizing both textual information
(e.g., keywords) and visual stimulus (e.g., diagrams and statistical charts) to sche-
matize the synopsis of an idea (Hernandez-Sanchez et al., 2020). Compared with
illustrated texts, which often separate texts and graphs, infographics contain a higher
level of text-graph synthesis. Infographics have not only been used as a pedagogical
tool for several years (e.g., Agley etal., 2021; Liu et al., 2019; Ozdamli & Ozdal,
2018) but have also played a role in international assessment. For example, the Pro-
gramme for International Student Assessment (PISA) (2021) examined how stu-
dents employ infographics to express their ideas visually. It revealed the necessity
to explore how learners with different characteristics process this reading material to
devise empowerment instruction.
Several researchers have employed the individual-differences approach to clarify
the roles of working memory (WM) and relevant knowledge in reading activities
(e.g., Wu & Liu, submitted; Harvey & Walker, 2018; Strobel etal., 2019; Yang,
2017). WM has its foundation in language processing, which has been examined
by eye-tracking, comprehension tests, and reading time (RT) (Pérez et al., 2015;
Sanchez & Wiley, 2006; Strobel etal., 2019). Relevant knowledge concerning indi-
viduals’ majors (Jian, 2022; Yang, 2017) or prior knowledge (Wu & Liu, submit-
ted; Jian, 2022; Kendeou & van den Broek, 2007) often enables readers to construct
rich and interrelated concepts, and thus offers several reading advantages. Clarify-
ing the contributions of these characteristics to understanding reading materials with
distinctive formats (i.e., texts and infographics) can not only facilitate the design
of effective instructions for struggling comprehenders but also help us identify the
critical cognitive processes that determine reading. Hence, this study addressed two
research questions: How do WM and relevant knowledge impact reading perfor-
mances for texts and infographics reading materials, respectively?
Literature review
Passage types andtheir effects onreaders
Texts, which comprise expository texts, narratives, mechanical texts, and persua-
sive texts, have been extensively studied in reading research for several decades

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Effects ofworking memory andrelevant knowledge onreading…
(Clinton, 2019; Snell et al., 2018). Snell et al. (2018) indicated two advanced
lines in their meta-analysis study for text reading: word recognition (e.g., McClel-
land & Rumelhart, 1981) and eye-tracking (e.g., Reilly & Radach, 2006). The
eye-tracking line was especially critical because it could reveal how learners
processed information while reading texts, facilitating the design of effective
remediation. Eye tracking has been extensively used in the educational domain
(Rayner, 2009). Several eye-movement indicators have been proposed to inform
researchers about learners’ cognitive processes (Brusnighan & Folk, 2012; Yang,
2017). First, total fixation duration (TFD) measures the total time of all fixations
within an area of interest. This reflects the degree of cognitive effort required to
process material. For example, Yang (2017) found that participants with relevant
knowledge exhibited longer TFDs for areas of data and claims when reading an
unfamiliar passage, revealing that they invested greater cognitive effort in these
critical areas. Second, first gaze duration (GD) was calculated by summing all
fixations on a word region until the fixation point left. This indicator represents
an individual’s decoding ability during initial reading, where a longer gaze dura-
tion is indicative of the greater cognitive effort that must be exerted in decoding
words (Brusnighan & Folk, 2012).
Infographics are new reading material in addition to text. They integrate both
texts and visual elements to convey complex information in a more understandable
manner (Hernandez-Sanchez et al., 2020). In a good infographic, visual elements
should be able to describe a visualized story with the lowest requirement for textual
explanation (Lankow etal., 2012). Moreover, arranging the text and visualizations
in a coherent layout is necessary to direct viewers to understand the infographic
appropriately (Majooni et al., 2018). With a greater text-graph synthesized level
than other reading materials (e.g., illustrated texts), comprehending infographics
may require significantly different processing patterns and needs. Several studies
adopted self-reporting to investigate readers’ affective experiences of infographics
(Agley etal., 2021; Brogan-Hewitt etal., 2021). For example, Agley et al. (2021)
used web-based assessments to investigate whether individuals’ beliefs in misinfor-
mation were affected by inspecting an infographic concerning scientific processes
and determined that their trust in science did increase.
Relatively few studies have examined how learners process infographics using
eye-tracking methodologies (Wu & Liu, submitted; De Haan etal., 2018; Liu etal.,
2019). Liu etal. (2019) examined how 52 advanced second-language English learn-
ers read an e-book for vocabulary acquisition and comprehension. The e-book com-
prised three micro-level support features for learning specific words (e.g., vocabu-
lary focus and footnotes) and three macro-level support features for understanding
global information (e.g., infographics and illustrations). The results indicated that
the participants frequently referred to the micro-level support features when read-
ing for vocabulary acquisition, whereas they intensely inspected macro-level sup-
port features when reading for comprehension. Among the macro-level support
features, learners intensively inspected illustrations rather than infographics, which
should provide optimal support for global understanding. They speculated that
these learners might consciously avoid attending to the infographics, which would
require much more cognitive effort than a sketchy illustration. De Haan etal. (2018)

C.-Y.Liu, C.-J.Wu
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explored how news consumers used and perceived Dutch newspapers, which con-
sisted of articles and infographics on news websites, e-newspapers on tablets, and
print newspapers, using eye-tracking, focus groups, and surveys. The results showed
that the participants used infographics, but their reading time was much less than
that of the articles. The participants also appreciated the infographics once the info-
graphics corresponded to the articles’ content. Notably, De Haan etal. adopted a
relatively loose definition that only a statistical diagram could be classified as an
infographic instead of a text-graph synthesized material.
Generally speaking, compared to text reading, which has been extensively studied
in the past decade, few studies have used eye-tracking methodology to objectively
reveal how learners processed an infographic, and the inconsistent definitions in
previous studies invite controversy. Moreover, we still know little about why some
people are superior infographic readers than others, raising another critical issue:
What individual characteristics could explain the variations in infographic-reading
performance?
Effects ofWM andrelevant knowledge onpassage reading
Several studies have adopted the individual-differences approach to clarify the char-
acteristics that influence information processing and make individuals better readers
(e.g., Freed etal., 2017; Harvey & Walker, 2018; Strobel et al., 2019). One of the
prominent characteristics is WM, which has long been argued to be crucial in adults’
reading performance (Daneman & Carpenter, 1980; Talwar etal., 2018). WM com-
prises multiple components that can temporarily maintain information about pas-
sages while retrieving relevant information from readers’ long-term memory to
facilitate integration (Cain et al., 2004). Baddeley and his colleagues (Baddeley,
2003; Baddeley & Hitch, 1974) proposed a WM model that contains one central
system (i.e., the executive system), two distinct but correlated subsystems (i.e., the
phonological loop and the visuospatial sketchpad), and a newly proposed subsystem
(i.e., the episodic buffer) to explain various cognitive processes. The phonological
loop is responsible for processing verbal information and is known as verbal work-
ing memory (VWM), whereas the visuospatial sketchpad manipulates visuospatial
information and is known as visuospatial working memory (VSWM). Studies have
determined that VWM is related to VSWM but with weak correlations ranging from
0.21 to 0.34 (e.g., Freed etal., 2017; Oakhill etal., 2011). The other characteris-
tic is relevant knowledge, which has been indicated as critical for impacting adults’
reading performance (Wu & Liu, submitted; Jian, 2022; Kendeou & van den Broek,
2007; Yang, 2017).
For text reading, the positive effects of VWM on adults’ reading performance
have been confirmed by empirical studies (Harvey & Walker, 2018; Pérez etal.,
2015) and meta-analyses (Kudo et al., 2015; Peng et al., 2018), although a few
studies have indicated that it is insufficient for successful reading (Van Dyke etal.,
2014). Peng etal. (2018) collected 197 studies with 2026 effect sizes and determined
that VWM demonstrated the greatest relationship with the reading ability of mature
readers. Harvey and Walker (2018) investigated how VWM impacted 30 college

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Effects ofworking memory andrelevant knowledge onreading…
students’ performance in reading static and horizontally scrolling texts. For static
texts, they found that higher VWM supported better comprehension and the lower
VWM readers frequently returned to previous reading parts to reinstate information,
resulting in longer RT. Nonetheless, VWM’s effect was not observed for horizon-
tally scrolling text. In contrast, Van Dyke etal. (2014) executed a comprehensive
skill battery and examined whether it predicted 65 adults’ reading comprehension
and reading time. The results indicated that VWM’s effects were limited, and only
the predictor of receptive vocabulary knowledge was significant.
The role of the VSWM in adults’ text-reading performance has been a matter of
dispute (Pérez etal., 2015; Weng etal., 2016). Pérez etal. (2015) conducted an eye-
tracking study to explore how 40 readers read narrative texts, including concepts
that the readers expected and those they did not expect. Nonetheless, the results
showed that VSWM had no effect on readers’ reading performances. Conversely,
Weng etal. (2016) examined the connection among WM components, Chinese read-
ing comprehension, and rapid automatized naming with 55 college students and
found that VSWM was significantly correlated with reading comprehension. The
different effects of visuospatial skills on reading may be related to the language’s
“orthographic breadth” (Landerl et al., 2022). Alphabetic languages such as Eng-
lish rely on limited letters, and their shallow orthography may allow VSWM to play
a rather limited role. In contrast, non-alphabetic languages, such as Chinese, often
contain high visual complexity and extensive orthography, which highlights the role
of VSWM in reading (Xu et al., 2020). In short, readers with a good VSWM are
more likely to comprehend Chinese characters accurately and effectively.
The role of relevant knowledge in text processing has frequently been discussed
(Kendeou & van den Broek, 2007; Yang, 2017). According to the information
reduction hypothesis (Haider & Frensch, 1996), individuals with more expertise
exhibit excellent attentional control ability and an effective visual processing strat-
egy by focusing more on thematically relevant information, thus improving compre-
hension. Yang (2017) assigned participants into relevant and irrelevant background
knowledge groups based on whether they majored in earth science and explored
how they read two scientific news reports. She found that the relevant-background-
knowledge group attributed their attention to the most critical parts of the materials
more efficiently than the irrelevant-background-knowledge group. Moreover, Kend-
eou and van den Broek (2007) also found that readers with misconceptions made
more incorrect inferences and spent more time reading refutation texts than readers
without misconceptions.
For infographics, we chose to focus on and review both infographics and illus-
trated texts because studies conducted with individual characteristics and infograph-
ics were still rare, although infographics did contain higher text-graph synthesized
levels than illustrated texts. For instance, Sanchez and Wiley (2006) investigated
whether an undergraduate’s ability to control attention (i.e., VWM) influenced their
processing of scientific text with seductive images, conceptually relevant images,
and no images. The results showed that low-VWM readers developed a worse under-
standing of the text and frequently attended to seductive images than high-VWM
readers, which might be because they had difficulty dealing with distracting infor-
mation. However, there were no significant differences in the overall RT between

C.-Y.Liu, C.-J.Wu
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the two VWM groups. Moreover, Strobel etal. (2019) used graph tasks that involved
illustrations and texts as material and found that a higher level of VSWM enabled
individuals to process the material more thoroughly and comprehend it better.
Regarding relevant knowledge, Eitel and Scheiter’s (2015) meta-analysis revealed
that learners’ levels of relevant knowledge determined how well the representation
of texts and figures were connected with the existing semantic network and, in turn,
constructed comprehension. Jian (2022) determined that undergraduates with more
knowledge demonstrated better reading comprehension and longer RT in processing
a scientific text with multiple representations than those with less knowledge. More-
over, Wu and Liu (submitted) found that infographic readers who scored higher on
an infographic comprehension test demonstrated significantly more TFD and transi-
tions between areas than their counterparts.
The current study
In the current study, we examined how WM and relevant knowledge impacted indi-
viduals’ reading performance. For the first research question regarding texts, three
hypotheses were proposed. Most studies have confirmed the association between
VWM and reading performance (Daneman & Carpenter, 1980; Kudo etal., 2015;
Peng etal., 2018; Talwar etal., 2018). Thus, we predicted that individuals with more
VWM would demonstrate better reading comprehension, as indicated by ACC, and
greater reading efficiency, as indicated by RT and TFD (Hypothesis 1–1). Based on
the mixed results for the effects of VSWM on individuals’ reading performance in
the literature (Oakhill etal., 2011; Pérez etal., 2015; Weng etal., 2016), we made
a weak prediction that individuals high in VSWM would perform with higher ACC
and shorter RT and TFD (Hypothesis 1–2). Studies have shown that individuals
who majored in relevant subjects (Yang, 2017) and those who performed better in
a prior knowledge test (Kendeou & van den Broek, 2007); that is, with more rel-
evant knowledge, they often possess an effective visual processing strategy, thereby
comprehending texts accurately and efficiently (Haider & Frensch, 1996; Kendeou
& van den Broek, 2007; Yang, 2017). The content of the texts used in this study
was related to literature, one of the main courses that non-science major students in
Taiwan must learn after they enter high school. Thus, we supposed that non-science
major students had more relevant knowledge in these texts and would comprehend
them with a higher ACC and shorter RT and TFD (Hypothesis 1–3).
For the second research question concerning infographics, three hypotheses
were proposed. As mentioned previously, we made hypotheses based on studies of
infographics and illustrated texts since there are hardly any studies examining how
WM and relevant knowledge impact infographic reading. According to Sanchez and
Wiley (2006), we predicted that higher-VWM individuals would have higher ACC
but would show identical RT and TFD on infographics as lower-VWM individuals
(Hypothesis 2–1). Strobel etal. (2019) reported that high VSWM is associated with
fewer mistakes and longer processing times. Therefore, we predicted that individu-
als with higher VSWM would show better ACC and longer RT and TFD, as they
would inspect infographics more thoroughly (Hypothesis 2–2). Relevant knowledge

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Effects ofworking memory andrelevant knowledge onreading…
positively impacted comprehension for infographics, but it did not increase reading
efficiency—individuals with more relevant knowledge spent more time processing
infographics (Wu & Liu, submitted; Jian, 2022). Considering that infographics often
relate to science disciplines, which makes them easier to comprehend for science
majors who have more relevant knowledge. Thus, we predicted that science majors
would show higher ACC but longer RT and TFD on infographics (Hypothesis 2–3).
Method
Participants
The participants were 137 undergraduate and graduate students (121 undergraduate
students) in Taiwan. The sex ratio of males to females was 40:97, and the ratio of
non-science/science majors was 106:31. The average age was 22.82 (SD = 3.35). All
the participants had normal or corrected-to-normal vision.
Materials
Texts andassociated comprehension tests
Three expository texts from graduate program entrance exams at National Taiwan
Normal University were used in this study. Several departments required students
to take the entrance exams in order to be considered for admission to the univer-
sity. Therefore, the test construction and design typically have excellent quality
and ought to be used as an appropriate text resource for college students. The three
texts were written in vernacular Chinese with no illustrations, and their topics were
smiles, animal protection, and butterfly. The average number of Chinese characters
in each passage was 393 (range: 317–440). To confirm the readability of the three
texts, the Chinese Readability Index Explorer (Sung etal., 2016) was adopted. The
data indicated that the texts were readable by adult readers in the indicators of words
(average: 222.33), sentences (average: 22.33), average sentence length (average:
10.54), content words (average: 170.33), content word frequency (average: 0.76),
and sentences with complex semantic categories (average:10.00).
Two to four multiple-choice questions were created for each text to assess stu-
dents’ comprehension of the reading materials. There were nine questions in total,
including five surface and four inferential questions, resulting in a total possible
score ranging from 0 to 9. We adopted the two-parameter logistic model of Item
Response Theory to estimate the texts’ psychometric quality: The reliability was
0.44, the item discrimination ranged from 0.26 to 0.53 logits, and the item difficulty
ranged from − 3.60 to − 1.10 logits. It was revealed that the reliability was inter-
mediate, the item discrimination was good enough to distinguish between students
with low and high comprehension, and the item difficulty was relatively easy (Baker,

C.-Y.Liu, C.-J.Wu
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2001). The passage was displayed on the left side of the screen, and the question was
displayed on the right side.
Infographics andassociated comprehension tests
This study included three infographics from a pool of published questions from
PISA. The selection principle was that the passage meets the definition of infograph-
ics in this study and that the question did not require calculation. The topics of the
three infographics were braking, tall buildings, and balloons (Figs.1 and 2). The
average number of Chinese characters was 175 (range: 35–251). To assess the stu-
dents’ comprehension of the infographics, five multiple-choice questions and eight
short-response questions were created. There were 13 questions, including seven
surface and six inferential questions, resulting in a total possible score ranging from
0 to 13. The two-parameter logistic model revealed that its reliability was 0.62, the
item discrimination ranged from 0.37 to 0.98 logits, and the item difficulty ranged
from − 3.55 to 1.77 logits. According to Baker (2001), the infographics contained
intermediate reliability, good item discrimination, and medium item difficulty. The
passage was displayed on the left side of the screen, and the question was displayed
on the right side (Fig. 1). Appendix A displays the Chinese version of the three
infographics.
Working memory tests
Two WM span tests were administered. Adapting from Lin etal. (2013) and Turner
and Engle (1989), the reading span test is a VWM test in which participants read
several sentences and made a correct/wrong judgment on each (e.g., solar power
Fig. 1 The infographic of braking and a sample item. Note. Adapted from “Braking (M215),” 2006, PISA
2006 released items

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Effects ofworking memory andrelevant knowledge onreading…
Fig. 2 The Infographic of Tall Buildings (a) and Balloon (b). Note. Adapted from “Tall buildings
(R419)” and “Balloon (R417),” 2009, PISA 2009 released items

C.-Y.Liu, C.-J.Wu
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could be used to generate electricity). After each judgment, participants were given
a word in Chinese that was irrelevant to the earlier sentence (e.g., tea leaf) and were
required to memorize it. After the trial was completed, the participants sequentially
recalled the target words. An individual’s span size and score were determined by
the number of words that he or she could correctly memorize. The span size ranged
from three to eight, and each size included three sets of trials. The coefficient of
internal consistency reliability was 0.66, indicating acceptable reliability.
Adapting from Chen and Li (2005) and Swanson (1999), the simple visuospatial
span test is a VSWM test in which participants inspected a geometric figure and its
position within a matrix. After each inspection, they solved an arithmetic problem
(e.g., 54 + 7) and were asked to speak the answer. After the trial was completed, the
participant identified the correct geometric figure from several figures and recalled
its position within the matrix. An individual’s span size and score were determined
by the greatest matrix size that its figures could be correctly identified, and their
position could be recalled. The matrices varied in size from four to twenty-five
squares, and each size included four sets of trials. The coefficient of internal consist-
ency reliability was 0.81, indicating acceptable reliability.
Apparatus
Eye movements were recorded using an EyeLink 1000 video-based eye tracker (SR
Research Ltd., Mississauga, Ontario, Canada) at a sampling rate of 1000Hz. Par-
ticipants were seated with their heads stabilized on a chinrest and at a distance of
approximately 70cm from the monitor. The passages were presented on a 22-inch
LCD monitor with a resolution of 1920 × 1080 pixels. The passage was approxi-
mately 945 × 935 pixels in area and the question was approximately 784 × 278 pixels
in area.
Procedure
Data collection took place in two sessions, and the participants were seated at
individual computers in a quiet room in all sessions. In the first session, they were
tested using two WM tests. The order of the tests for each participant was randomly
selected. Each test was completed within approximately 20min.
The second session involved individual experiments using an eye-tracker. After
the instructions were provided, a nine-point calibration and validation of eye move-
ments were executed for each participant. For practice, participants were asked to
read one passage carefully on the screen and to give their answers out loud as they
read the comprehension questions on the screen. They were instructed to press the
space bar to switch the display when they finished reading or answering the ques-
tions and were not allowed to return and revise their answers. The formal experi-
mental process was the same as that used in practice. Each participant was given
the three texts and three infographics, and the sequence of the passages was fixed.
To create a natural reading condition, participants were allowed to read the material
and complete the tests at their own pace. The average time for a participant to finish
reading the material and complete the tests was approximately 30min.

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Effects ofworking memory andrelevant knowledge onreading…
Data analysis
The reading comprehension scores for the passages were converted into percentages
as ACC. For the VWM test, the possible scores ranging from 0 to 18, M = 4.69,
SD = 1.96, and the number of participants below or above the mean score = 65/72
participants. For the VSWM test, the possible scores ranging from 0 to 24,
M = 15.41, SD = 2.47, and the number of participants below or above the mean
score = 64/73 participants. The total time spent on each passage was calculated as
RT.
The eye-movement data of some participants were excluded owing to apparent
drift or low valid ratios below 40%. Therefore, each passage or infographic and the
data included in the analysis were as follows: smile (n = 127), animal protection
(n = 131), butterfly (n = 132), braking (n = 135), tall buildings (n = 134), and balloon
(n = 134). Two eye-movement indicators (see the Eye Tracking to Measure the Pas-
sage Reading Process section) were adopted in this study. First, the TFD of each
passage was calculated. Second, the GD of the texts was averaged and calculated
after excluding eye-movement data of less than 100ms and more than 1000ms. We
assigned it as a control variable representing individuals’ lexical-level reading abil-
ity. Notably, one of the texts’ GD was not included because this text contained sev-
eral blank brackets, which might have hindered the participants’ reading fluency.
The data structure of repeated measures that nested within participants and pas-
sages was clustered (Snijders & Bosker, 2012), which would yield an inflated type
I error rate if regular analysis of variance models were adopted. To account for this
clustered data structure, we performed linear-mixed models (LMM) of the relation-
ships between individual characteristics and outcome measures using passages with
R (version 3.4.3 for Windows; R Core Team, 2017). Separate models were fitted
to the outcome measures, including ACC, RT, and TFD. The models included ran-
dom effects for students and passages and fixed effects for individual characteris-
tics, which included their sex (SEX), education background (EDU), age (AGE), GD,
reading span test scores (VWM), visuospatial span test scores (VSWM), and majors
(MAJ), as well as passage types that included texts (TEXT) and infographics (IG).
Results
Relationship betweenindividual characteristics andreading performances
To obtain an overview of the relationships between individual characteristics, pas-
sage types, and outcome measures, correlations were computed (Table1). Overall,
for the relationships between background variables (i.e., SEX, EDU, AGE), corre-
lations ranging from − 0.24 to 0.19 were observed, representing weak correlations
under Evans’s (1996) criteria. There was a weak positive correlation between VWM
and VSWM, r(137) = 0.34, p < 0.001. A weak positive correlation between the two
passage types’ outcome measures was found for ACC, r(137) = 0.37, p < 0.001;
a moderate positive correlation was found for RT, r(137) = 0.56, p < 0.001; and a
strong positive correlation was found for TFD, r(135) = 0.63, p < 0.001.

C.-Y.Liu, C.-J.Wu
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Table 1 Correlations of individual characteristics, passage types, and outcome measures
a 0 = female and 1 = male
b 0 means undergraduate student and 1 means graduate student
c 0 means non-science majors and 1 means science majors
*p < .05; **p < .01
SEXaEDUbAGE MAJcVWM VSWM GD TEXT IG
ACC RT TFD ACC RT TFD
GD −.02 −.03 .08 .04 .04 .16
VWM .06 .02 −.16 .06 .34**
VSWM .19* −.11 −.10 .07
TEXT
ACC −.08 −.08 −.19* −.20* .06 .18* −.12 .37**
RT .03 .03 .10 .07 −.02 .12 .48** −.04 .56**
TFD −.01 .04 .05 .10 .01 .21* .49** .21* .72** .63**
IG
ACC .08 .03 −.24** < .00 .34** .23** −.16 – – –
RT −.20* −.12 −.17 −.07 .06 .18* .19* – – – .18*
TFD −.03 −.14 −.18* .02 .09 .22** .15 – – – .30** .76**

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Effects ofworking memory andrelevant knowledge onreading…
Under the condition of reading TEXT, the more GD the participants exhibited,
the more RT and TFD they demonstrated, r(135) = 0.48, p < 0.001; r(135) = 0.49,
p < 0.001, respectively. The higher the participants’ VSWM scores, the greater their
ACC and TFD, r(137) = 0.18, p = 0.034 and r(136) = 0.21, p = 0.014, respectively.
Non-science majors (M = 0.84, SD = 0.23) had a higher ACC than science majors
(M = 0.76, SD = 0.27), r(137) = − 0.20, p = 0.019. In addition, there was a weak pos-
itive correlation between ACC and TFD, r(136) = 0.21, p = 0.016, and a strong posi-
tive correlation between RT and TFD, r(136) = 0.72, p < 0.001.
Under the IG reading condition, the more time the GD the participants spent,
the greater their RT, r(135) = 0.19, p = 0.032; the higher the participants’ VWM
scores, the higher their ACC, r(137) = 0.34, p < 0.001; the higher participants’ the
VSWM scores, the greater their ACC, the longer their RT, and the higher their
TFD, r(137) = 0.23, p = 0.008; r(137) = 0.18, p = 0.033; r(135) = 0.22, p = 0.010,
respectively. In addition, there was a very weak positive correlation between ACC
and RT, r(137) = 0.18, p = 0.039, and a weak positive correlation between ACC and
TFD, r(135) = 0.30, p < 0.001. Meanwhile, a strong positive correlation was found
between RT and TFD, r(135) = 0.76, p < 0.001. The means and standard deviations
of the outcome measures for each passage are presented in Table2.
LMM analysis
The initial model contained all the variables of the individual characteristics and
passages. The least significant variables were then gradually eliminated to obtain a
reduced model that best described the effects of individual characteristics on reading
different types of passages. This study’s independent variables were individual char-
acteristics (GD, VWM, VSWM, and MAJ) and passage types (TEXT and IG), and
the dependent variables were outcome measures (ACC, RT, and TFD).
To confirm whether individual characteristics are related to their processing
of passage types, we first examined their interactions (Table3). For ACC, there
were significant interactions between passage type and VWM, estimate = 2.40,
t = 2.94, p = 0.003, as well as passage type and MAJ, estimate = 7.35, t = 2.13,
p = 0.034. For RT, significant interactions were also found for passage type and
GD, estimate = − 0.07, t = − 4.20, p < 0.001 as well as passage type and MAJ,
Table 2 Means of ACC, RT (seconds), and TFD (seconds) by TEXT and IG (standard deviations in
parentheses)
Text IG
Text 1 Text 2 Text 3 IG 1 IG 2 IG 3
ACC .82 (0.30) .83 (0.20) .82 (0.23) .80 (0.23) .63 (0.21) .74 (0.22)
RT 43.15 (15.78) 39.05 (15.05) 47.36 (18.57) 42.21 (25.88) 39.47 (19.78) 53.85 (33.41)
TFD 35.80 (14.12) 32.36 (12.85) 39.35 (16.09) 35.01 (21.85) 32.69 (16.77) 46.29 (29.67)

C.-Y.Liu, C.-J.Wu
1 3
estimate = − 2.32, t = − 2.18, p = 0.030. For TFD, there was a significant interac-
tion between passage type and GD, estimate = − 0.09, t = − 2.56, p = 0.011. Thus,
we used LMM to separately estimate the relationships between individual charac-
teristics and outcome measures for the texts and infographics.
Influencing paths fromindividual characteristics totext‑reading performance
The results for individual characteristics and outcome measures for the texts are
presented in Table4. The values of the estimates of the fixed effects reflect the
effect of Text 1 and Text 2 in comparison to Text 3.
Concerning ACC, parameter estimates indicated that there was a significant
main effect of VSWM, estimate = 1.51, t = 2.16, p = 0.033, demonstrating that
for each additional point of VSWM, the participant’s ACC increased by 1.51%.
In addition, the main effect of major was also significant, estimate = − 8.43,
t = − 2.49, p = 0.014, indicating that science majors’ ACC was 8.43% lower
than that of non-science students. Concerning RT, a significant main effect of
GD was found, estimate = 0.12, t = 6.34, p < 0.001, such that for each additional
Table 3 Fixed and random effects in the LMM for ACC, RT, and TFD
The baseline category is TEXT
* p < .05. ** p < .01. *** p < .001
Fixed effect ACC RT TFD
Estimate SE t-value Estimate SE t-value Estimate SE t-value
Intercept 84.40 2.51 33.66*** 25.54 1.98 12.88** 35.953 3.42 10.40**
IG − 11.88 1.71 − 6.96*** −0.05 0.52 0.10 1.26 1.13 1.12
GD − 0.09 0.05 − 1.87 0.11 0.02 5.75*** 0.22 0.05 4.54***
VWM − 0.13 0.73 − 0.18 − 0.21 0.29 − 0.74 − 0.50 0.70 − 0.71
VSWM 1.55 0.58 2.68** 0.16 0.23 0.73 0.87 0.56 1.57
MAJ −8.47 3.09 − 2.74** 0.74 1.21 0.61 2.17 2.99 0.73
IG * GD − 0.02 0.06 − 0.42 − 0.07 0.02 − 4.20*** − 0.09 0.04 − 2.56*
IG * VWM 2.40 0.82 2.94** 0.12 0.25 0.49 0.70 − 0.54 1.30
IG * VSWM − 0.50 0.65 − 0.77 0.32 0.20 1.62 0.47 0.43 1.09
IG * MAJ 7.35 3.46 2.13* − 2.32 1.06 − 2.18* − 2.51 2.29 − 1.10
Random
effect
Variance SD Variance SD Variance SD
Student 88.20 9.39 22.60 4.75 156.40 12.51
Passage 11.90 3.45 10.70 3.27 28.50 5.34
Residual 444.80 21.90 42.00 6.48 194.20 13.93

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Effects ofworking memory andrelevant knowledge onreading…
millisecond the participants spent on GD, their RT increased by 0.12 ms. For
TFD, the main effect of GD was significant, estimate = 0.23, t = 6.28, p < 0.001,
such that for each additional millisecond of the participant’s GD, their TFD
increased by 0.23ms. There was also a main effect for VSWM, estimate = 0.91,
t = 1.98, p = 0.050, such that for each additional point of VSWM, participants’
TFD increased by 0.91ms.
Influencing paths fromindividual characteristics toinfographic‑reading
performance
The results for individual characteristics and outcome measures for the infograph-
ics are presented in Table5. The values of the estimates of the fixed effects reflect
the effects of IG 1 and IG 2 compared with IG 3.
Concerning ACC, the main effects of GD were estimated to be − 0.11,
t = − 2.33, p = 0.022, showing that for each additional millisecond the partici-
pants devoted to GD, their ACC decreased by 0.11%. Moreover, there was also
a main effect of VWM, estimate = 2.44, t = 3.11, p = 0.002, such that for each
Table 4 Parameter estimates of the fixed and random effects for texts
The baseline category is TEXT 3
*p < .05; **p < .01; ***p < .001
Fixed effect ACC RT TFD
Estimate SE t-value Estimate SE t-value Estimate SE t-value
Intercept 84.26 1.76 47.89*** 25.48 1.62 15.70** 35.59 2.35 15.15***
GD − 0.09 0.06 − 1.69 0.12 0.02 6.34*** 0.23 0.04 6.28***
VWM − 0.15 0.89 − 0.17 0.04 0.20 0.12 − 0.18 0.58 − 0.31
VSWM 1.51 0.70 2.16* 0.06 0.24 0.25 0.91 0.46 1.98*
MAJ − 8.43 3.39 − 2.49* 0.85 1.15 0.74 2.39 2.22 1.07
VWM *
VSWM
0.07 0.31 0.23 0.03 0.11 0.29 − 0.07 0.21 − 0.36
VWM *
MAJ
0.09 2.02 0.04 − 1.25 0.69 − 1.83 − 1.62 1.32 − 1.22
VSWM *
MAJ
0.05 1.74 0.03 0.45 0.59 0.76 − 0.25 1.14 − 0.22
Random
effect
Variance SD Variance SD Variance SD
Student 1.23e + 02 11.09 21.75 4.66 95.60 9.78
Passage 1.36e − 05 0.00 6.83 2.61 12.64 3.56
Residual 4.68e + 02 21.62 31.41 5.60 73.09 8.55

C.-Y.Liu, C.-J.Wu
1 3
additional point of VWM, the participant’s ACC increased by 2.44%. For RT,
there was a main effect for GD, estimate = 0.05, t = 2.25, p = 0.026, demonstrating
that for each additional millisecond spent on GD, the participant’s RT increased
by 0.05ms. In addition, for VSWM, a marginally significant effect was observed,
estimate = 0.54, t = 1.97, p = 0.051, such that for each additional point of VSWM,
the participant’s RT increased by 0.54 ms. Finally, for TFD, there was a main
effect for VSWM, estimate = 1.69, t = 2.14, p = 0.034, such that for each addi-
tional point of VSWM, the participant’s TFD increased by 1.69ms. In addition,
Table 5 Parameter Estimates of the Fixed and Random Effects for Infographics
The baseline category is IG 3
✝ p = .051; * p < .05; **p < .01; ***p < .001
Fixed effect ACC RT TFD
Estimate SE t-value Estimate SE t-value Estimate SE t-value
Intercept 72.69 5.02 14.49** 25.54 2.92 8.73** 37.45 4.61 8.12**
GD − 0.11 0.05 − 2.33* 0.05 0.02 2.25* 0.12 0.06 1.86
VWM 2.44 0.78 3.11** 0.19 0.35 0.55 1.15 1.01 1.15
VSWM 1.12 0.62 1.82 0.54 0.27 1.97✝1.69 0.79 2.14*
MAJ 5.04 24.16 0.21 9.44 10.75 0.88 32.67 31.50 1.04
VWM * VSWM − 0.09 0.28 − 0.31 0.03 0.12 0.27 0.02 0.36 0.06
VWM * MAJ − 0.84 1.78 − 0.47 − 1.41 0.79 − 1.78 − 5.01 2.32 − 2.16*
VSWM * MAJ − 0.39 1.54 − 0.25 − 0.69 0.69 − 1.01 − 2.02 2.01 − 1.01
Random effect Variance SD Variance SD Variance SD
Student 98.00 9.90 30.40 5.51 286.00 16.92
Passage 68.20 8.26 24.20 4.92 52.00 7.21
Residual 359.00 18.95 38.10 6.17 209.00 14.45
Table 6 Summary of the effects of individual characteristics for passage-reading performance
TEXT IG
ACC RT TFD ACC RT TFD
VWM − − − + 2.44 − −
VSWM + 1.51 − + 0.91 − + 0.54 + 1.69
MAJ − 8.43 − − − − −
VWM * VSWM − − − − − −
VWM * MAJ − − − − − − 5.01
VSWM * MAJ − − − − − −

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Effects ofworking memory andrelevant knowledge onreading…
an interaction effect between VWM and MAJ was found, such that for each addi-
tional point of VWM, science majors’ TFD decreased by 5.01ms more than did
that of non-science students. Table6 summarizes the effects of individual charac-
teristics on text- and infographic-reading performances: With increasing VWM,
VSWM, and MAJ, participants’ ACC, RT, and TFD would increase or decrease,
respectively.
Discussion andconclusion
We investigated how readers’ WM and relevant knowledge influence their read-
ing performance in texts and infographics. This study makes a new contribution
to reading research regarding whether individual characteristics exert different
influences on materials with distinct synthesized levels of texts and graphs. We
show that the individual-differences approach is necessary for exploring what
distinguishes successful and unsuccessful readers, in order to develop more
powerful interventions. Regarding reading texts, the results showed that VWM
had limited effects on all outcome indicators (Hypothesis 1–1), whereas VSWM
had positive effects on ACC and TFD (Hypothesis 1–2). In addition, the effect
of relevant knowledge was observed only for ACC (Hypothesis 1–3). Regard-
ing reading infographics, VWM had a positive effect on ACC but not on RT or
TFD (Hypothesis 2–1). Moreover, the effect of VSWM was found only for RT
and TFD (Hypothesis 2–2). Finally, the results also indicated that students with
science and non-science majors did not demonstrate different performances in
reading infographics (Hypothesis 2–3).
For the first research question regarding texts, Hypothesis 1-1 was not sup-
ported, while Hypotheses 1–2 and 1–3 were partially supported. In assessing
the effect of VWM, we obtained similar results with twice the sample size of
Van Dyke et al. (2014). This is probably because the words in the texts were
linear, making them easy for readers with any VWM capacity to follow and
read. Two alternative explanations may be proposed. First, the floor effect of
the VWM scores provided restricted variance of the participants’ capacities
and thus might constrain the effect of VWM. Another explanation may be that
VWM plays different roles in various language systems. According to Leong
etal. (2011), VWM differentiates Chinese students from other language users
in processing Chinese expository passages. Regarding the effect of VSWM,
the significant relationship between VSWM and text-reading accuracy cor-
responded to Weng etal.’s (2016) findings. This revealed that readers with a
higher ability to read and distinguish various visual features can process the
extensive orthography of Chinese better, as indicated by Landerl etal. (2022)
and Xu etal. (2020). We unexpectedly found that higher VSWM was associated

C.-Y.Liu, C.-J.Wu
1 3
with readers’ higher TFD on texts. One interpretation is that individuals with
better VSWM ability might rely more heavily on it, and thus spend more time
manipulating visual and spatial information. Regarding the effect of relevant
knowledge, consistent with previous studies (Haider & Frensch, 1996; Kendeou
& van den Broek, 2007; Yang, 2017), we determined that the relevant knowl-
edge of texts possessed by non-science majors might allow them to access task-
relevant information and ignore task-irrelevant information, thus leading to bet-
ter comprehension, although its effect on RT and TFD was limited.
For the second research question regarding infographics, Hypothesis 2–1
was fully supported, Hypothesis 2–2 was partially supported, and Hypothesis
2–3 was not supported. We found that VWM was beneficial for accurately com-
prehending infographics, which corresponds to the findings of Sanchez and
Wiley (2006). Contrary to a previous study (Wu & Liu, submitted), we deter-
mined that relevant knowledge did not affect infographic-reading performance.
However, the significant interaction between VWM and major on TFD indi-
cated that although relevant knowledge and experiences are not sufficient to
ensure infographic-reading performance, they might provide specific schemas
to construct meanings/patterns in an infographic and then interact with read-
ers’ ability to temporarily store and process verbal information together as they
integrate information, thus facilitating reading efficiency. Consistent with the
findings of Strobel et al. (2019), we confirmed a strong effect of VSWM in
increasing the processing time of infographics, which was nearly twice as long
as that of texts. This shows that if readers possess a better capacity to store and
handle visual and spatial information, they tend to make a greater cognitive
effort in reading infographics that contain combined visual and spatial informa-
tion, perhaps out of caution or interest. Furthermore, we also determined the
trend that a higher level of VSWM allowed readers to accurately comprehend
infographics, although its effect was not significant.
Regarding the correlations between variables, our findings were consistent with
those of Freed etal. (2017) and Oakhill etal. (2011), who found a weak correla-
tion between VWM and VSWM, demonstrating that it is reasonable to separately
examine the effects of the two distinct but correlated WM constructs on reading
performance. For the correlations between the outcome measures of the two pas-
sage types, the moderate to strong correlations for RT and TFD demonstrated that
participants who devoted more time to texts would also spend more time on info-
graphics. However, for ACC, only a weak correlation was found, indicating that the
ability to comprehend the two passage types should be different. Moreover, we also
determined that the longer the RT and TFD that readers exhibited, the higher their
ACC, and for infographics in particular. This accorded with Wu and Liu (submitted)
and revealed that more cognitive effort would be needed to successfully comprehend
infographics.

1 3
Effects ofworking memory andrelevant knowledge onreading…
Two hypothetical propositions were constructed based on the results of how indi-
vidual characteristics impacted infographic reading. The first hypothetical propo-
sition concerns VWM’s positive effects on reading comprehension. To process an
infographic, readers must hold words that are often incomplete and scattered while
integrating them with adjacent figures to construct a micro-structured representa-
tion. Subsequently, these micro-structured representations are retained and incor-
porated into macro-structured representations, leading to comprehension. The bet-
ter VWM capacity readers have, the more they are able to successfully retain and
manifest these representations. Moreover, VWM interacts with relevant knowledge
to exert effects on reading efficiency. The second hypothetical proposition concerns
VSWM’s positive impact on reading efficiency, indicating that the greater the abil-
ity of readers to hold and use visualizations, the more willing they are to spend time
processing infographics. Unexpectedly, we found a limited impact of VSWM on
reading comprehension. An alternative explanation is that our measurement might
not be sufficiently sensitive to detect readers’ comprehension levels. Future studies
could verify VSWM’s effect with other measurements, such as summarizing argu-
ments in an infographic.
There is some discrepancy between the reading processes of illustrated texts
and infographics, although the current study reviewed both materials due to the
rarity of infographic research using an individual-differences approach. The
first discrepancy may be the timing of word-figure integration. Since the words
and figures in illustrated texts are often spatially separated, word-figure inte-
gration may either happen after readers have read all the words and figures or
occur as readers inspect some words and then construct mental models utilizing
figures (e.g., Hegarty & Just, 1993). In contrast, infographics, which include
scattered words and their corresponding figures, stimulate word-figure integra-
tion at an earlier stage (Wu & Liu, submitted). The second discrepancy may
be due to the guidance for reading sequences. For illustrated texts, the linear
sequence of words directs individuals’ reading sequences, which was confirmed
by research (Lee & Wu, 2018). By contrast, the nonlinear and scattered words
in infographics barely direct reading sequences; thus, readers often encounter
challenges in comprehending infographics. Future studies could compare how
readers process illustrated texts and infographics to verify and explore the dis-
crepancies between the two materials.
Several directions for future instruction and research on infographics should
be considered. First, both the weak correlation between the ACC of the two
passage types and the correlations between infographics’ ACC and RT revealed
that comprehending infographics requires skills/strategies that are not com-
pletely identical to those needed for processing texts. Educators should help
students learn how to read infographics and even use them to communicate
with the visual world. For example, Smith and Robertson (2021) developed
an infographic instruction framework comprising four phases: exploration,

C.-Y.Liu, C.-J.Wu
1 3
investigation, creation, and integration. Second, we adopted reading span and
simple visuospatial span tests and examined how individuals’ WM capacity
influenced their performance. However, more types of working memory tests
(e.g., operation span tests with numerical stimuli) should be considered to fully
account for WM-reading performance associations. Moreover, it is important
to identify WM types that may be more relevant in different language systems.
Third, we defined the participants’ relevant knowledge based on their majors
and examined their majors’ effects on performance. Although it is difficult to
define the relevant knowledge of popular science articles like those used in
this study, future studies could investigate participants’ experiences with the
topic of texts or infographics. Fourth, though we did clarify how readers with
different characteristics processed the new reading material of infographics,
infographic-related research is still insufficient. Other variables involved in the
infographic-reading processes, such as mind wandering and a mediating role
between WM and reading comprehension (McVay & Kane, 2012), should also
be further explored soon.
Appendix A: The infographics used inthis study

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Effects ofworking memory andrelevant knowledge onreading…

C.-Y.Liu, C.-J.Wu
1 3
Funding This research was financially supported by the Ministry of Science and Technology (project
number: MOST 108-2410-H-003-046-MY2; 108-2410-H-656-009-MY3; 111-2410-H-003-089-MY2;
111-2410-H-656-010-) as well as the “Institute for Research Excellence in Learning Sciences” of
National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within
the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
Data availability Data available on request from the authors.
Declarations
Conflict of interest Not applicable.
Ethical approval This study has received ethics approval from the Research Ethics Committee, National
Taiwan Normal University (Approval Number: 201906HM004).
Permission to reproduce material from other sources The passages were in the public domain that no
exclusive intellectual property rights were applied.
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