This study examined the comprehension of expository and narrative texts read on screens, and its association with executive functions, reading habits, and reading media of choice (paper or screen) for study or recreational purposes, in university students. Participants were ninety-eight university students (76.8% women, mean age: M = 20.6, DS = 5.24 years), who completed a screen-based expository and narrative text comprehension task, a computerized executive functions assessment (working memory, cognitive flexibility and inhibition), a survey on reading habits and an author recognition test. Working memory was a general predictor of comprehension, while flexibility was specifically linked to expository text comprehension. Fiction exposure specifically contributed to narrative text comprehension. Students who preferred to study on screens showed better performance on expository text comprehension than those who chose to study on paper. The contribution of executive functions to reading comprehension on screens was similar to that observed in paper-reading studies. The effect of study reading media of choice suggests that practice might compensate the more superficial reading mode that is typically observed in digital media reading studies.
Article Details
How to Cite
Tabullo, Ángel-J., & Pulifiato-Hamann, E.-S. (2024). Digital Reading in University Students: contributions of Executive Functioning and Reading Habits. Ocnos. Journal of reading research, 23(2). https://doi.org/10.18239/ocnos_2024.23.2.424
Tabullo and Pulifiato-Hamann: Digital reading in university students: contributions of executive functioning and
reading habits
Introduction
Reading comprehension is a fundamental skill for academic success (), yet a recent meta-analysis indicates that most of Latin American university students
only achieve a literal level of comprehension (). In Argentina, psychology university students have shown an average performance
lower than 70% in a reading comprehension task (). On the other hand, the PISA studies point out the tendency of progressive penetration
of digital media and displacement of books. This phenomenon was accelerated due to
the lockdown restrictions and rise of virtual education modalities during the Covid-19
pandemic (). Moreover, better performance was observed for reading texts presented on paper,
and the preference of this format among adolescents is associated with a higher frequency
of recreational reading (). In this context, it is of interest to analyze the factors that explain the individual
differences in reading comprehension presented via digital media among university
students.
The model known as Simple View of Reading () suggests that comprehension depends on the coordinated integration of two components:
word recognition and language comprehension. These components represent processes
linked to the detection and decoding of the orthographic information and the access
to the meaning and integration with previous knowledge, respectively. Two abilities
were identified as the main predictors to success for comprehension: vocabulary and
reading fluency (for a review, see ). In turn, the multicomponential approach () proposes that comprehension depends on a series of subcomponents, whose coordinated
interaction allows for prioritizing relevant information and constructing a mental
model of its meaning: text content (basic text schema, facts and sequences, and lexical
semantics), elaboration (syntactical structure, cohesion and inferences), and metacognition
(text genre recognition, flexible reading strategy and detection of inconsistencies).
In addition to these linguistic skills, comprehension requires general cognitive processes,
such as the executive functions (), which have been formally incorporated into theoretical models of reading, such
as the ‘Active View of Reading’ (). On the other hand, different lines of research indicate a consistent link between
individual reading experience (volume, diversity, frequency) and text comprehension
(; ). In the following sections we will present briefly the evidence on the characteristics
of reading in digital media, and the contribution of the executive functions and reading
habits to comprehension.
Reading comprehension in digital media
It has been suggested that the presentation of texts on screens promotes skimming,
scanning, multitasking, and attention volatility, favoring a more superficial reading
strategy (). Recent meta-analyses have confirmed the existence of an advantage in the comprehension
of expository texts (but not narrative ones) when comparing reading on paper with
screens, which increases with the length of the texts or when a time limit is imposed
(; ). Furthermore, the increase in the size of this effect in recent years has been interpreted
as a consequence of the increased frequency of reading in digital media (). On the other hand, it has been observed that the difference between formats is
reduced or disappears when interventions are made to increase the perceived importance
of reading by students () or to encourage deeper semantic processing (for example, by suggesting making summaries
or identifying key words) (). It should be noted that these studies examined reading traditional texts, or linear-structure
texts, on screen, and did not include hypertext formats or web environments, which
involve a series of additional competencies and cognitive demands (see for a review).
The role played by executive functions in reading comprehension
The term “executive functions” refers to a set of cognitive processes involved in
the planning, execution, monitoring, and adaptation of goal-oriented behavior (). The model of executive functions that has most influenced reading research is that
proposed by , in which they are conceptualized as three main components or functions: inhibition,
cognitive flexibility, and working memory (WM) (). WM provides a dynamic space to integrate visual text input with information evoked
from long-term memory systems and allow the development of mental representations
of the text at its different levels. Cognitive flexibility allows switching between
different focuses of attention in the text, reading strategies, and mental levels
of representation, facilitating the generation of inferences. Regarding inhibition,
while it has been hypothesized to contribute to freeing up processing resources by
suppressing internal or external interferences, the results have been less consistent
(), and it has been interpreted that these variations depend on age and the type of
inhibitory process studied (). The relative contribution of executive functions may vary depending on the characteristics
of the text. In the study by , cognitive flexibility was a significant predictor of comprehension of scientific
expository texts with low cohesion, while working memory predicted the comprehension
of highly cohesive texts. For their part, found that general domain executive functions have a direct effect on text comprehension
in university students, as well as an indirect effect, mediated by their language
skills (vocabulary and reading fluency).
While empirical evidence has established the contribution of executive functions to
reading comprehension in paper format (for a meta-analysis, see: ), the demands that the presentation of linear texts on screen might pose to executive
processes have not yet been systematically investigated. In this sense, it has been
found that the study of reading digital hypertexts (which typically present a non-linear
structure), or in internet environments, generates an additional cost of executive
processing, linked to the challenges posed by navigation ().
Reading habits and text comprehension
There is ample evidence of the link between reading experience (the amount of time,
volume and/or diversity of texts read) and the ability to understand texts (see the
meta-analyses by ; ). One of the most commonly used measures to objectively assess print exposure is
the Author Recognition Test (ART) (), a task that examines how many names of fiction (or non-fiction) authors subjects
are able to recognize. Studies conducted on the Argentine university population showed
that the ART is a robust predictor of reading comprehension among students (Tabullo
et al., , ). found moderate to strong correlations between the score on this task and measures
of text comprehension, an effect that increased with age. The results were interpreted
as indicators of reciprocal causality: just as good comprehenders were avid readers,
their reading experience contributed to improving their reading skills. This explanation
is known as the Matthew effect (). Several mechanisms have been proposed to explain the contribution of reading habits
to comprehension: the increase in vocabulary improves lexical representations (), the automatization of low-level processes, such as word decoding (), or training in inference generation (). In the case of digital reading, it has been proposed that the growing (and increasingly
earlier) exposure to screens could have a negative impact on comprehension (), and better performances have been found in children who prefer paper (). However, a large-scale study found no influence of the frequency of digital media
reading on text comprehension in adolescents (). Lastly, a recent meta-analysis found an interaction between the educational stage
and digital reading habits: while negative effects are observed in primary school,
the association becomes positive in secondary school and university ().
Despite the extensive evidence on the role of executive functions and reading habits
on text comprehension, the contribution of these factors to screen reading has not
yet been systematically examined in university students. The present study aimed to
analyze the relationship between the comprehension of expository and narrative texts
presented on screen, executive functions, and reading habits in university students,
considering in particular their exposure to fiction (measured with an Author Recognition
Test) and their favourite medium for reading for study or recreational purposes (paper,
mobile phone screen, or personal computer screen).
Methodology
Design
The present research had a quantitative, non-experimental, correlational and transversal
study design.
Participants
The study sample was selected using a non-probabilistic convenience sampling method.
Ninety-eight first-year university psychology students from the Pontifical Catholic
University of Argentina (Mendoza) participated in the study (76.8% women, Mean Age:
M = 20.6, SD= 5.24 years). To verify if reading comprehension was similar between
screen and paper reading medias, we compared our participants’ performance with that
of a different group of students, who took part in a previous study reading the same
texts on paper instead of screens (control group data: expository text group: N = 62, 74.2% women, Age M = 19.9, SD = 4.3 years; narrative text group: N = 56,
69.6% women , age M = 20.4 years, SD = 6.36 years). The students comprising these
control groups came from the same career in the same university and were taking the
same courses than the study sample when they were evaluated, and were also similar
in age (T(211) = 0.600, p = .549) and gender ( = 0.707, p = .401). All of them completed an informed consent form, which explained
that the activity was voluntary and anonymous, and that they could withdraw from the
study at any time, without any negative consequence. This study was designed and conducted
according to ethics normative 5344/99 of the National Council of Scientific and Technical
Research, and all its proceedings were in accord with the 1975 Helsinki Declaration
and its subsequent amendments.
Instruments
Expository and narrative text comprehension test (). A test from a previous digital reading comprehension study was applied (). The test consisted in reading an expository text (a science communication article
titled “Mathematics, brain and dyscalculia”, by Valeria Abusamra) and a narrative
text (a story titled “The coffee cups”, by Mario Benedetti). The narrative text was
1117 words long and told the story of a love triangle between a husband, his wife
and one of his friends. The instrument INFLESZ () was applied to determine its reading difficulty, which was classified as “rather
easy”. On the other hand, the expository text was 1113 words long, and explained the
links between children’s brain development and mathematical skills. The text was written
to make it accessible for non-specialized readers. According to the INFLESZ scale,
its reading difficult was “rather difficult”. After reading each text, participants
answered 12 multiple-choice questions (one correct answer and three wrong but semantically-related
alternatives). These questions covered the most relevant aspects of comprehension,
according to the multicomponential model. The task was administered on a PC screen
through the Google Forms platform.
TAC Neuropsychological battery (Tareas de autorregulación cognitiva - Cognitive Self-regulation Tasks) (). The following tests from the computerized neuropsychological battery were administered:
- Visual Search, which evaluates perpetual inhibition
- Fingers task, which evaluates cognitive flexibility
The visual search task requires participants to indicate the presence or absence of
a target stimuli (blue square) among a variable number of distractors, pressing Z
or M keyboard keys, respectively.
Performance is operationalized as the difference between mean response times between
high (32 distractors) and low cognitive load (4 distractors) trials, so higher scores
indicate higher cognitive costs (and therefore, worse performance).
The finger task displays a hand drawing on the left or right side of the screen, with
its index finger pointing downwards (same side) or to the other side of the screen.
Participants are instructed to press a key located on the same side as the hand (congruent
trial) or on the opposite side (incongruent trial). Performance is operationalized
as an Inverse Efficiency score (Mean response time / (1 - error proportion)), calculated
over those trials where response type (ipsilateral or contralateral) and response
side (left, right) are different from the previous trial.
The TAC battery has shown adequate internal and external validity in adults ()
Running span task (). We administered an adapted version of the running span task, from the computerized
BIMeT-V working memory battery, to evaluated verbal working memory. The task requires
participants to remember a letter sequence of variable length (presented one at a
time) and to recall the last 2, 3, 4, 5 or 6 letters shown. The length of the sequence
is unknown to the participant in each trial. When the word “recall” appears on the
screen, the participant must input the letters in the same order through the keyboard.
As the task progresses, the number of letters to be remembered increases. Performance
was operationalized as the number of correctly recalled letter sequences.
Reading habits survey. An ad hoc survey was administered to examine the students’ reading habits, including:
number of books in their personal library, on paper and digital formats (Likert-scale
responses from 0 to 5: 0 = none, 1 = 1 to 10, 2 = 10 to 30, 3 = 30 to 50; 4 = 50 to 100, 5 = more than 100) and number of books read for recreational purposes within the last six months. The
survey also asked about the weekly frequency of the following reading-related activities:
web surfing (reading websites for non-study purposes, does not include social networks),
social networks (Facebook, Instagram, etc.), recreational reading (novels, stories,
fiction and non-fiction), study reading (Likert-scale responses from 0 to 6: 0 = does not do it / almost does not do it on a weekly basis, 1 = a couple days a week, 2 = one hour per day, 3 = 1-2 hours per day, 4 = 2-3 hours per day, 5 = 3-4 hours per day 6 = 4 hours or more per day). In addition, participants indicated their preferred reading media for recreational
and study purposes (paper, PC or laptop screen, smartphone). This survey was based
on a previous study conducted on the local university population. ().
Author Recognition Test. To evaluate fiction print exposure, a local version of the Author Recognition Test
(ART) that had been designed and validated for the national context, and previously
applied in the local university population, was used (Tabullo et al., ; ). This task was based on the most widely applied and validated instrument to measure
fiction literature exposure, the Author Recognition Test designed by , which has proven to be a consistent predictor of reading comprehension and other
language skills (). The task consists in identifying the names of ten literary fiction authors (including
Literature Nobel Prize winners, such as Albert Camus, Haruki Murakami or Mario Vargas
Llosa) from a list that includes another ten fake author names. Performance is calculated
as the number of correctly identified authors, minutes the number of incorrectly selected
fake author names.
Procedure
The task was conducted in the university’s computer room, during class hours. The
students were informed that the purpose of the study was to examine the relationship
between reading texts on screen and cognitive abilities. The students performed the
tasks in the following order: text comprehension task, TAC battery tests, verbal working
memory task, reading survey, and ART. The study was conducted in a single session
and lasted approximately one hour. The comprehension test, the survey, and the author
recognition test were administered via the Google Forms platform.
Data Analysis
To examine if reading comprehension was similar between paper and screen formats,
the percentage of correct responses for each text was compared between the study and
control groups with a Students’ T test. An exploratory analysis of the associations
between study variables was conducted with Spearman correlations. An ANOVA was applied
to examine differences in reading comprehension by gender and text genre, and MANOVAs
were conducted to examine performance differences according to the preferred recreational
and study reading media. The contribution of executive functions and reading habits
to expository and narrative text comprehension was analyzed through hierarchical multiple
linear regression analyses, including the following predictors in successive steps
of the model: demographic variables (age, gender), verbal WM, inhibition and flexibility
scores, ART scores and those reading habit variables with the most significant associations
with reading comprehension, according to the correlation analysis. In the regression
analysis, assumptions of normality (non-significant Shapiro-Wilk test), homocedasticity
and linearity assumptions (visual inspection of residual plots) were verified. The
error independence assumption was confirmed using the Durbin-Watson coefficient (1.91
< DW < 2.06), and variance inflation factor analysis did not indicate the presence
of multicollinearity risks (1.05 < FIV < 1.20). Statistical analysis was conducted
using JAMOVI software.
Results
Descriptive statistics and associations between study variables
Descriptive statistics of study variables are synthesized in Table 1. To examine if text comprehension on screens was similar to paper, the percentage
of correct responses was compared between the study and control groups with Students
T tests. No comprehension differences were found for the expository (T(158) = -0.334, p = .738) or narrative text (T(152) = -0.427, p = .670) as a function of format (Table 2). Considering the Shapiro-Wilk test did indicate a violation of the normality assumption
in this case (W = .968, p = .001), the Mann-Whitney U-test was applied to verify the T-test results. Once again,
we did not find significant differences (Expository text: U = 2920, p = .678; Narrative text: U = 2682, p = .756).
Table 1Descriptive Statistics of Study Variables
M / Med
SD / IQR
Minimum
Maximum
Expository
55.61
17.25
25.000
100.00
Narrative
44.24
18.00
8.333
91.67
WM
4.86
2.44
0.500
12.00
Inhibition
566.99
299.99
71
2085
Flexibility
10.31
3.29
4.920
26.32
Libpaper
2.00
2.00
0.000
5.00
Libdigital
1.00
1.00
0.000
5.00
Book6m
2.00
2.50
0.000
10.00
SocialN
4
2.00
0
6
Web
3
2.00
0
6
Recreational
2
3.00
0
6
Study
3
1.00
0
6
Note: Mean (M) and standard deviations (SD) of continuous variables (reading comprehension
and executive functions); and median (Med) and interquartile range (IQR) of ordinal
variables (reading habits) are shown. Expository: % of correct responses in the expository
text. Narrative: % of correct responses in the narrative text. WM: verbal working
memory (number of correct responses). Inhibition: visual search task score (response
times difference between 32 and 4 distractor conditions). Flexibility: Fingers task
score (inverse efficiency). Libpaper: Amount of paper books in personal library. Libdigital:
number of digital books in personal library. Book6m: number of books read within the
last 6 months. SocialN: weekly frequency of social network use. Web: weekly frequency
of web surfing. Recreational: weekly frequency of recreational reading. Study: weekly
frequency of study reading.
Table 2Percentage of success in the text comprehension task by reading format
Text
Medium
N
M
SD
Expository
Screen
98
55.4
17.4
Paper
62
56.3
18.2
Narrative
Screen
98
43.9
17.9
Paper
57
45.2
17.9
Note: Mean (M) and standard deviations (SD) of the % of correct responses in the reading
comprehension task are presented for those groups of subjects who read the texts on
screens or paper.
Reading comprehension scores for both texts were compared with a text × gender repeated
measures ANOVA with age as a covariate. A significant interaction between gender and
text type was found (F(1,92) = 7.345, p = .008). Men obtained significantly better scores in the expository (M = 56.4%, DE = 16.3%) text compared to the narrative text (M = 36.1%, DE = 16.5%) (p = .018), while women outperformed men in the latter (M = 46.4%, DE = 18.4%) (p = .021).
Spearman correlations matrix is shown in Table 3. Expository text comprehension was associated with: WM (rho (96) = .247, p = .016), cognitive flexibility (rho (96) = -.214, p = .035) and the number of digital books (rho (96) = .229, p = .025). Narrative text comprehension was associated with: working memory (rho (96) = .247, p = .040), ART scores (rho (96) = .348, p < .001), number of recently read books (rho (96) = .209, p = .042) and the frequency of recreational reading (rho (96) = .215, p = .037).
Table 3Matrix of Spearman correlations among study variables
Note: Expository: % of correct responses in the expository text. Narrative: % of correct
responses in the narrative text. WM: verbal working memory (number of correct responses).
Inhibition: visual search task score (response times difference between 32 and 4 distractor
conditions). Flexibility: Fingers task score (inverse efficiency). Libpaper: Amount
of paper books in personal library. Libdigital: number of digital books in personal
library.. Book6m: number of books read within the last 6 months. SocialN: weekly frequency
of social network use. Web: weekly frequency of web surfing . Recreational: weekly
frequency of recreational reading. Study: weekly frequency of study reading.
* p < .05
** p < .01
*** p < .001
Regarding the preferred reading medium for studying, most of the students chose the
computer (58.2%), paper came in second place (32.7%) and the smallest proportion used
the smartphone (9%). In the case of recreational reading, most preferred paper (50%),
in second place, smartphone (29.6%) and then the computer (9.2%), while the remaining
10.2% answered that they did not read for recreational purposes. To examine the association
between preferred reading medium and text comprehension performance, subjects were
classified in one of the following groups: reading on paper for studying (n = 32)
vs reading on computers (n = 57), and recreational reading on paper vs smartphones (n = 29). We decided to choose the more frequent category instead of
collapsing both screen reader groups, since previous studies reported differences
in reading comprehension when comparing smartphones and computer media (; ).
Regression analysis of reading comprehension
Expository text comprehension. Explained variance increased with the inclusion of
flexibility (R2 = .051, p = .031) and WM (R2 = .047, p = 0.034) (R2 = .071, F(5,86) = 2.397, p = 0.044), while ART and the number of digital books did not have significant effects
(see Table 4). Expository text comprehension improved with flexibility scores ( = -.220, p = .034, IC [-0.424, -0.017]) and WM ( = .228, p = .029, [0.024, 0.433]).
Narrative text comprehension. Explained variance increased with the inclusion of WM
(R2 = .075, p = .007) and ART (R2 = .057, p= .015) (R2 = .165, F(6,85) = 4.00, p = 0.001), while no effects were observed for other EFs or the number of books read
recently (see table 4). Narrative text comprehension improved with WEM ( = .273, p = .006, IC [0.079, 0.467]) and ART scores ( = .247, p = .015, IC [0.050, 0.442]), and was lower among men ( = -.570, p = .017, IC [-1.035, -0.106]).
Table 4Regression analysis of reading comprehension scores
Note: Expository: % of correct responses in the expository text. Narrative: % of correct
responses in the narrative text. WM: verbal working memory (number of correct responses).
Inhibition: visual search task score (response times difference between 32 and 4 distractor
conditions). Flexibility: Fingers task score (inverse efficiency). Libdigital: number
of digital books in personal library.. Book6m: number of books read within the last
6 months.
* p < .05
** p < .01
*** p < .001
Effects of preferred reading media on text comprehension
Preferred reading media for studying. A significant main effect of preferred reading media for studying was found (Wilk’s = .886, F(2,88) = 4.27, p = 0.017). Expository text was better among students who preferred studying on computer
screens (M = 59.5%, SD = 43.6%) than in those who prefer studying on paper (M = 51.2%, SD = 16.9%) (F(1, 89) = 5.066, p = .027), while no significant differences were observed in narrative text comprehension
(F(1, 89) = 0.265, p = .608). This effect was still significant after controlling for gender, flexibility
and WM as covariates (Wilk’s = .906, F(2,79) = 4.12, p = .020).
Preferred reading media for recreational purposes. No effects of preferred recreational reading media on reading comprehension were
observed (Wilk’s = 0.996, F(2,75)= 0.15, p = .860).
Discussion
This study has been the first to analyze and compare the joint contribution of the
executive functions and reading habits to the comprehension of expository and narrative
texts read on screen, in university students. These texts were considerably difficult
for the students, however their performance was not significantly lower than those
of students that completed the task on paper. When EFs were considered, WM contributed
to the comprehension of both texts, while cognitive flexibility was a specific predictor
of the expository text. Among the variables describing reading habits, exposition
to literary fiction (ART score) was the main predictor of narrative text comprehension.
In addition, we found that those students who preferred studying on screens performed
better in the expository text than those who preferred doing it on paper, while no
differences were observed based on the preferred medium for recreational reading.
These findings are discussed below.
The role of executive functions in the comprehension of expository and narrative texts
on screen
In accordance with previous studies of reading on paper (; ) and digital hypertexts (Wylie et al., 2918), the comprehension of linear expository
and narrative texts on screen was significantly associated with the executive functioning
of university students. In particular, a general contribution of WM to both texts,
and a specific contribution of flexibility to the expository text, were found.
The contribution of WM to the comprehension of both types of text is consistent with
theoretical models () and previous evidence. Follmer’s meta-analysis () indicated that this effect was significant across all age ranges considered (children,
adolescents, adults). WM provides a workspace for integrating current input with the
ongoing mental representation of the text, and the information recalled from long-term
memory. Additionally, the updating component of WM (assessed in our work through the
running span task) is an even stronger predictor of comprehension than measures more closely linked
to WM capacity (such as digit span), since it considers the ability to sustain relevant
information and to actively exclude the irrelevant. WM updating contributes to building
coherent representations of the text (). Furthermore, this updating component could be more intimately linked to specific
comprehension processes, such as inference generation (). Lastly, we cannot dismiss the possibility that the observed contribution of WM
is totally or partially mediated by linguistic skills, such as vocabulary or reading
fluency, as has been observed in adults (), adolescents, and children (; ).
The contribution of flexibility to the comprehension of expository texts is also consistent
with theory () and previous evidence (). Various studies have identified flexibility as a predictor of comprehension in
children (), adolescents (), and adults (). Specific associations with expository text have also been reported in children
(), and adults (). In addition, a measure of flexibility applied to the processing of phonological
and semantic aspects of words explained an additional portion of the variance of text
comprehension, when compared to general domain EF measures in adults (). Flexibility is implied in inference generation, and it has been observed that inferential
comprehension processes are consistently more difficult in expository texts, across
all ages (see ). A study in adults showed that cognitive flexibility was a better predictor of comprehension
of scientific texts (above other EFs) when they had lower referential cohesion (). Moreover, and similarly to WM, we cannot rule out the possibility of a total or
partial mediation of this flexibility effect by linguistic skills, as seen in previous
studies (; ; ).
Unlike what was observed in other works (; ), we did not find effects of inhibition on the comprehension of either text. This
difference could be due to these studies using a specific measure of inhibition of
verbal responses (Hayling test), while we examined inhibition at the perceptual level
(visual selective attention). Furthermore, it has been proposed that the inconsistencies
found in the contribution of inhibition could be due to the variability of measures
and processes considered, as well as verbal specificity of the processes ().
Effects of reading habits in the comprehension of expository and narrative texts
Despite the low performance of the sample, no differences were found when they were
compared to the controls who read on paper. In the case of the expository text, this
does not align with what was observed in the meta-analyses by and . It has been proposed that screens promote a more superficial mode of reading, less
focused and more biased by overconfidence and lack of self-monitoring. On the other
hand, this effect is more clearly observed when longer texts are examined (Singer
& Alexander, ; ) or when working under time constraints (), which could explain the absence of differences in our case, since we did not introduce
these conditions. Additionally, another study conducted in adults () failed to find any differences in comprehension between reading mediums.
In the analyses of reading habits, significant correlations were observed between
measures of: exposure to written fiction, frequency of recreational reading, recent
reading volume, and comprehension of narrative text on screen, while the regression
analysis pointed to TRA as the main predictor of performance. There is ample evidence
that reading fiction experience measured through TRA is a robust predictor of text
comprehension throughout life (; ). As most of these works have examined reading on paper, our results allow the extension
of these conclusions to the comprehension of narrative texts read on screens. Regarding
the exclusivity of the observed advantage for narrative texts, it is possible that
a higher previous experience with fiction literature facilitates the comprehension
of characters, actions, events, and context of the presented narrative, thereby contributing
to the construction of the situation model of the text (). It is worth noting that no digital media reading frequency effects (social networks,
web browsing, digital library size) were found for narrative texts. In the case of
the expository text (which dealt with developmental psychology), the specificity of
its demands in terms of technical vocabulary and previous knowledge about the subject
may have limited the contribution of fiction exposure to performance.
Even though we have not found consistent effects of reading habits on expository text
comprehension, we did observe interesting effects of reading medium: those students
accustomed to studying on screens exhibited a better performance. At first sight,
this result might seem contrary to the negative effect of screens for expository text
comprehension observed in the meta-analyses by and . On another hand, it has been observed that the difference between digital and paper
formats diminishes or disappears when interventions are made in order to increase
the perceived importance of reading by students () or when a deeper semantic processing is encouraged (e.g., suggesting summarizing
or identifying keywords) (). It could then be argued that those students accustomed to studying the contents
of the university curriculum on screen (computer) could have more developed compensatory
strategies (attentional focus, deep processing, strategic reading), turning out more
successful when performing comprehensive reading of an expository text in this format.
Students accustomed to studying on paper, on the other hand, might experience the
opposite effect: greater difficulties in identifying and retaining relevant information
due to the properties of the screen format that hinder the depth of reading. This
familiarity or adaptation mechanism to the medium would be similar to the role of
internet navigation skills as predictors of comprehension of digital hypertexts ().
Study limitations
Among the limitations of the present study, we must point to the relatively small
sample size and low proportion of men. In addition, voluntary participation suggests
possible selection bias. Regarding the expository text, students' previous knowledge
of the subject was not controlled, but all the students were in their first year of
degree and none of them had yet studied developmental psychology or another relevant
subject (in the case of the narrative text, on the other hand, none had read the story
previously). Furthermore, reading times and their possible contribution to comprehension
performance were not systematically evaluated. Self-reporting measures of reading
frequency (for screen activities, recreational reading, or study reading) were used,
and could be more accurate if complemented with more objective variables. Other possibly
relevant variables, such as text interest or motivation, were not considered either.
Finally, although a control group was included in order to verify that the reading
performance on screens was similar to that on paper, future research should compare
the contribution of EFs and reading habits to reading texts on paper and screen, with
the purpose of detecting potential differences in the cognitive processes involved.
Conclusion
Different contributions of EFs and reading habits to the comprehension of expository
and narrative texts on screen were found. Both the general association with working
memory and the specific role of flexibility in the expository text, as well as the
effect of fiction exposure in the narrative text are consistent with previous studies
of reading on paper. No effects of digital media reading frequency (social network,
web) on comprehension were found. The effect of the preferred reading medium for study
on the comprehension of expository texts on screen is interesting and relevant for
the academic field, so it should be replicated in a larger sample and analyzed more
deeply in future research. Likewise, considering the difficulties of reading found
at the secondary level of education in the Latin American and local contexts, the
comparative study of the cognitive demands of reading on paper and digital mediums
in university students and adolescents could inform decision-making; as well as pedagogical
interventions aimed at compensating this deficit and improving academic performance.
Lastly, it is recommended to try and replicate these findings on larger samples, with
a wider age range, and better representation of both genders in order to strengthen
our conclusions.
Author contributions
Ángel Javier Tabullo: Project administration; Formal analysis; Conceptualization; Data curation; Writing
- original draft; Writing - review & editing; Investigation; Methodology; Resources;
Software; Supervision; Validation; Visualization.
Abusamra, V., Cartoceti, R., Ferreres, A., De-Beni, R., & Cornoldi, C. (2009). La
comprensión de textos desde un enfoque multicomponencial. El Test “Leer para comprender”.
Ciencias Psicológicas, 3(2), 193-200. https://doi.org/10.22235/cp.v3i2.151
2
Abusamra, V., Difalcis, M., Martínez, G., Low, D. M., & Formoso, J. (2020). Cognitive
skills involved in reading comprehension of adolescents with low educational opportunities.
Languages, 5(3), 34. https://doi.org/10.3390/languages5030034
3
Acheson, D. J., Wells, J. B., & MacDonald, M. C. (2008). New and updated tests of
print exposure and reading abilities in college students. Behavior Research Methods, 40(1), 278-289. https://doi.org/10.3758/BRM.40.1.278
4
Ackerman, R., & Lauterman, T. (2012). Taking reading comprehension exams on screen
or on paper? A metacognitive analysis of learning texts under time pressure. Computers in Human Behavior, 28(5), 1816-1828. https://doi.org/10.1016/j.chb.2012.04.023
5
Alrizq, M., Mehmood, S., Mahoto, N. A., Alqahtani, A., Hamdi, M., Alghamdi, A., &
Shaikh, A. (2021). Analysis of skim reading on desktop versus mobile screen. Applied Sciences, 11(16), 7398. https://doi.org/10.3390/app11167398
6
Altamura, L., Vargas, C., & Salmerón, L. (2023). Do new forms of reading pay off?
A meta-analysis on the relationship between leisure digital reading habits and text
comprehension. Review of Educational Research, online first. https://doi.org/10.3102/00346543231216463
7
Ball, R., & Hourcade, J. P. (2011). Rethinking reading for age from paper and computers.
International Journal of Human–Computer Interaction, 27(11), 1066-1082. https://doi.org/10.1080/10447318.2011.555319
8
Barreyro, J. P., Injoque-Ricle, I., Formoso, J., & Burin, D. I. (2019). Computerized
working memory battery (BIMeT-V): Studying the relation between working memory, verbal
reasoning and reading comprehension. Trends in Psychology, 27(1), 53-67 https://doi.org/10.9788/TP2019.1-05
9
Barrio-Cantalejo, I.M., Simón-Lorda, P., Melguizo, M., Escalona, I., Marijuán, M.I.,
& Hernando, P. (2008). Validación de la Escala INFLESZ para evaluar la legibilidad
de los textos dirigidos a pacientes. Anales del Sistema Sanitario de Navarra, 31(2), 135-152. https://recyt.fecyt.es/index.php/ASSN/article/view/1953/1390
Butterfuss, R., & Kendeou, P. (2018). The role of executive functions in reading comprehension.
Educational Psychology Review, 30(3), 801-826. https://doi.org/10.1007/s10648-017-9422-6
13
Cartwright, K. B., Lee, S. A., Barber, A. T., DeWyngaert, L. U., Lane, A. B., & Singleton,
T. (2020). Contributions of executive function and cognitive intrinsic motivation
to university students’ reading comprehension. Reading Research Quarterly, 55(3), 345-369. https://doi.org/10.1002/rrq.273
14
Clinton-Lisell, V. (2019). Reading from paper compared to screens: A systematic review
and meta-analysis. Journal of Research in Reading, 42(2), 288-325. https://doi.org/10.1111/1467-9817.12269
15
Clinton-Lisell, V., Taylor, T., Carlson, S. E., Davison, M. L., & Seipel, B. (2022).
Performance on reading comprehension assessments and college achievement: A meta-analysis.
Journal of College Reading and Learning, 52(3), 191-211. https://doi.org/10.1080/10790195.2022.2062626
16
Cotton, A., Benedetti, P., & Abusamra, V. (2023). Reading comprehension on smartphones:
A comparison with computers. Cuadernos de Lingüística Hispánica, 41, e16032. https://doi.org/10.19053/0121053X.n41.2023.16032
17
De-la-Peña, C., & Luque-Rojas, M. J. (2021). Levels of reading comprehension in higher
education: Systematic review and meta-analysis. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.712901
18
Delgado, P., Vargas, C., Ackerman, R., & Salmerón, L. (2018). Don't throw away your
printed books: A meta-analysis on the effects of reading media on reading comprehension.
Educational Research Review, 25, 23-38. https://doi.org/10.1016/j.edurev.2018.09.003
19
Demagistri, M. S., Richards, M. M., & Canet-Juric, L. (2014). Incidencia del funcionamiento
ejecutivo en el rendimiento en comprensión lectora en adolescentes. Electronic Journal of Research in Educational Psychology, 12, 343-370. https://doi.org/10.25115/ejrep.33.13146
20
Duke, N. K., & Cartwright, K. B. (2021). The science of reading progresses: Communicating
advances beyond the simple view of reading. Reading Research Quarterly, 56(S1), S25–S44. https://doi.org/10.1002/rrq.411
21
Duncan, L. G., McGeown, S. P., Griffiths, Y. M., Stothard, S. E., & Dobai, A. (2016).
Adolescent reading skill and engagement with digital and traditional literacies as
predictors of reading comprehension. British Journal of Psychology, 107(2), 209-238. https://doi.org/10.1111/bjop.12134
Follmer, D. J., & Sperling, R. A. (2018). Interactions between reader and text: Contributions
of cognitive processes, strategy use, and text cohesion to comprehension of expository
science text. Learning and Individual Differences, 67, 177-187. https://doi.org/10.1016/j.lindif.2018.08.005
24
Georgiou, G. K., & Das, J. P. (2016). Direct and indirect effects of executive function
on reading comprehension in young adults. Journal of Research in Reading, 41(2), 243-258. https://doi.org/10.1111/1467-9817.12091
25
Hahnel, C., Goldhammer, F., Naumann, J., & Kröhne, U. (2016). Effects of linear reading,
basic computer skills, evaluating online information, and navigation on reading digital
text. Computers in Human Behavior, 55 (Part A), 486-500. https://doi.org/10.1016/j.chb.2015.09.042
26
Hoover, W. A., & Tunmer, W. E. (2018). The simple view of reading: Three assessments
of its adequacy. Remedial and Special Education, 39(5), 304-312. https://doi.org/10.1177/0741932518773154
27
Introzzi, I., & Canet-Juric, L. (2019). TAC: Tareas de Autorregulación Cognitiva. https://tac.com.ar
28
Kieffer, M. J., Vukovic, R. K., & Berry, D. (2013). Roles of attention shifting and
inhibitory control in fourth-grade reading comprehension. Reading Research Quarterly, 48(4), 333-348. https://doi.org/10.1002/rrq.54
29
Kieffer, M. J., Mancilla-Martinez, J., & Logan, J. K. (2021). Executive functions
and English reading comprehension growth in Spanish-English bilingual adolescents.
Journal of Applied Developmental Psychology, 73, 101238. https://doi.org/10.1016/j.appdev.2021.101238
30
Kintsch, W. (1988). The role of knowledge in discourse comprehension: A construction-integration
model. Psychological Review, 95(2), 163-182. https://doi.org/10.1037/0033-295X.95.2.163
31
Lauterman, T., & Ackerman, R. (2014). Overcoming screen inferiority in learning and
calibration. Computers in Human Behavior, 35, 455-463. https://doi.org/10.1016/j.chb.2014.02.046
32
Mani, N., & Huettig, F. (2014). Word reading skill predicts anticipation of upcoming
spoken language input: A study of children developing proficiency in reading. Journal of Experimental Child Psychology, 126, 264–279. https://doi.org/10.1016/j.jecp.2014.05.004
33
Mar, R. A., Li, J., Nguyen, A. T. P., & Ta, C. P. (2021). Memory and comprehension
of narrative versus expository texts: A meta-analysis. Psychonomic Bulletin & Review, 28(3), 732-749. https://doi.org/10.3758/s13423-020-01853-1
34
Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., & Howerter, A. (2000).
The unity and diversity of executive functions and their contributions to complex
"frontal lobe" tasks: A latent variable analysis. Cognitive Psychology, 41(1), 49-100. https://doi.org/10.1006/cogp.1999.0734
35
Mol, S. E., & Bus, A. G. (2011). To read or not to read: A meta-analysis of print
exposure from infancy to early adulthood. Psychological Bulletin, 137(2), 267-296. https://doi.org/10.1037/a0021890
36
Ober, T. M., Brooks, P. J., Plass, J. L., & Homer, B. D. (2019). Distinguishing direct
and indirect effects of executive functions on reading comprehension in adolescents.
Reading Psychology, 40(6), 551-581. https://doi.org/10.1080/02702711.2019.1635239
37
OECD (2021). 21st-Century Readers: Developing literacy skills in a digital world. OECD Publishing, Paris. https://doi.org/10.1787/a83d84cb-en
Potocki, A., Sánchez, M., Ecalle, J., & Magnan, A. (2017). Linguistic and cognitive
profiles of 8- to 15-year-old children with specific reading comprehension difficulties:
The role of executive functions. Journal of Learning Disabilities, 50(2), 128-142. https://doi.org/10.1177/0022219415613080
40
Rayner, K., & Reichle, E. D. (2010). Models of the reading process. Wiley Interdisciplinary Reviews: Cognitive Science, 1(6), 787-799. https://doi.org/10.1002/wcs.68
41
Richards, M. M., Krzemien, D., Valentina, V., Vernucci, S., Zamora, E. V., Comesaña,
A., García Coni, A., & Introzzi, I. (2021). Cognitive flexibility in adulthood and
advanced age: Evidence of internal and external validity. Applied Neuropsychology: Adult, 28(4), 464-478. https://doi.org/10.1080/23279095.2019.1652176
42
Sidi, Y., Shpigelman, M., Zalmanov, H., & Ackerman, R. (2017). Understanding metacognitive
inferiority on screen by exposing cues for depth of processing. Learning and Instruction, 51, 61-73. https://doi.org/10.1016/j.learninstruc.2017.01.002
43
Singer, L. M., & Alexander, P. A. (2017a). Reading on paper and digitally: What the
past decades of empirical research reveal. Review of Educational Research, 87(6), 1007-1041. https://doi.org/10.3102/0034654317722961
44
Singer, L. M., & Alexander, P. A. (2017b). Reading across mediums: Effects of reading
digital and print texts on comprehension and calibration. Journal of Experimental Education, 85(1), 155-172. https://doi.org/10.1080/00220973.2016.1143794
45
Spencer, M., Richmond, M. C., & Cutting, L. E. (2020). Considering the role of executive
function in reading comprehension: A structural equation modeling approach. Scientific Studies of Reading, 24(3), 179-199. https://doi.org/10.1080/10888438.2019.1643868
46
Stanovich, K. E. (1986). Matthew effects in reading: Some consequences of individual
differences in the acquisition of literacy. Reading Research Quarterly, 21(4), 360-407. https://www.jstor.org/stable/747612
47
Stanovich, K. E., & West, R. F. (1989). Exposure to print and orthographic processing.
Reading Research Quarterly, 24(4), 402-433. https://doi.org/10.2307/747605
48
Tabullo, Á. J., Chiófalo, M. F., & Wainselboim, A. J. (2024). Reading comprehension
in undergraduates during the Covid-19 pandemic: Associations with executive function
difficulties, reading habits, and screen times. Reading Psychology, 45(1), 1-30. https://doi.org/10.1080/02702711.2023.2246972
49
Tabullo, A. J., Navas-Jiménez, V. A., & García, C. S. (2018). Associations between
fiction reading, trait empathy, and theory of mind ability. International Journal of Psychology & Psychological Therapy, 18(3), 357-370. https://psycnet.apa.org/record/2019-31832-008
50
Tabullo, A. J., Pithod, M., & Moreno, C. B. (2020). Associations between reading,
comprehension, print exposure, executive functions, and academic achievement in Argentinean
university students. Revista Neuropsicología, Neuropsiquiatría y Neurociencias, 20(2), 15-48. http://revistaneurociencias.com/index.php/RNNN/article/view/117
51
Wylie, J., Thomson, J. M., Leppänen, P. H. T., Ackerman, R., Kanniainen, L., & Prieler,
T. (2018). Cognitive processes and digital reading. In M. Barzillai, J. Thomson, S.
Schroeder, & P. Van-den-Broek (Eds.), Learning to read in a digital world (pp. 57-90). Amsterdam, The Netherlands: John Benjamins Publishing Co. https://doi.org/10.1075/swll.17.03wyl
52
Wu, Y., Barquero, L. A., Pickren, S. E., Barber, A. T., & Cutting, L. E. (2020). The
relationship between cognitive skills and reading comprehension of narrative and expository
texts: A longitudinal study from Grade 1 to Grade 4. Learning and Individual Differences, 80, 101848. https://doi.org/10.1016/j.lindif.2020.101848
