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Neurocognitive Signatures of
Naturalistic Reading of Scientifc
Texts: A Fixation-Related fMRI
Stud
Chun-Ting Hsu 1,2, Roy Clariana3, Benjamin Schloss1 & Ping
How do students gain scientifc knowledge while reading expository text? This study examines the
underlying neurocognitive basis of textual knowledge structure and individual readers’ cognitive
diferences and reading habits, including the infuence of text and reader characteristics, on outcomes
of scientifc text comprehension. By combining fxation-related fMRI and multiband data acquisition,
the study is among the frst to consider self-paced naturalistic reading inside the MRI scanner. Our
results revealed the underlying neurocognitive patterns associated with information integration of
diferent time scales during text reading, and signifcant individual diferences due to the interaction
between text characteristics (e.g., optimality of the textual knowledge structure) and reader
characteristics (e.g., electronic device use habits). Individual diferences impacted the amount of
neural resources deployed for multitasking and information integration for constructing the underlying
scientifc mental models based on the text being read. Our fndings have signifcant implications for
understanding science reading in a population that is increasingly dependent on electronic devices.
Reading expository texts remains a primary means for students to acquire scientifc knowledge. Learning from
such texts crucially depends on the reader’s ability to construct a mental representation that can maximally cap- ture the knowledge structure (KS) inherent in the text. Te text’s KS refects the author’s conceptual knowledge
associations, and the text KS interacts with the reader’s cognitive abilities that together impact the learning out- come of the reader’s representation of the scientifc knowledge afer reading1,2
. Te current study is designed to
examine this interaction, specifcally how the KS of the text (referred to as textual KS henceforth) interacts with
the individual reader’s abilities in executive function and his or her reading habits (including electronic device
usage). To understand this complex interaction properly, we studied expository science text reading at both the
behavioural and the neurocognitive level, combining methods of network analyses of the reading material with
statistical analyses of the data collected from self-paced naturalistic reading.
Until now, neurocognitive studies of reading comprehension have focused on narrative texts, and the major
theories in the feld have also been based on analyses of narrative texts3
. When reading expository texts, the read- er’s task is to identify the diferent possible relationships among ofen quite abstract concepts. Tese relationships
can be correlational, temporal sequential, causal, or hierarchical, and can exist between pairs or clusters of con- cepts. Comprehension of these relationships in the text (in addition to understanding the meaning of words and
facts about the world) is thus key to the reader’s success in expository text comprehension4
.
An infuential model of reading comprehension, the Construction-Integration model5,6
, suggests that text
comprehension is organized in cycles, roughly corresponding to short sentences or phrases7,8
. Te construction
process takes place early in the cycle, in which the reader forms concepts and propositions from the linguistic
input. Later in the cycle, the integration process establishes an elaborated propositional representation that is
internally coherent and reasonably consistent with the discourse context and with the reader’s world knowledge.
However, this early construction vs. late integration dual-stage processing view has been recently challenged by
1
Department of Psychology and Center for Brain, Behavior, and Cognition, Pennsylvania State University, Universit
Park, PA, 16802, USA. 2
Kokoro Research Center, Kyoto University, 46 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto,
606-8501, Japan. 3Department of Learning and Performance Systems, Pennsylvania State University, University
Park, PA, 16802, USA. Correspondence and requests for materials should be addressed to P.L. (email: pingpsu@
gmail.com)
Received: 14 February 2019
Accepted: 10 July 2019
Published: xx xx xxxx
O
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the view of parallel integration of information at diferent levels and scales. For example, Kuperberg and Jaeger9
proposed that during reading, predictive candidates are activated before the incoming new information is pro- cessed (i.e., top-down processing). Te predictive pre-activation encompasses multiple levels of representations
including syntactic, semantic, phonological, orthographic and perceptual. From the perspective of memory,
Hasson et al.
10 argued that all cortical circuits are involved in information accumulation in a hierarchical organ- ization. Te primary perceptual-motor systems have short process memory, while the higher order areas such as
temporoparietal junction, angular gyrus, and medial prefrontal cortex have long process memory. Te primary
process areas are modulated by the fronto-parietal network of attentional control, while the higher-order areas
by the medial temporal lobe (hippocampal) circuit of binding and consolidation. Terefore, the construction
and integration processes might not be temporally dissociable, and instead, it is the brain regions that integrate
information at diferent time scales of memory processes that should and can be empirically diferentiated, such
as distinct neural networks involved in the integration of local and global contexts11.
For expository texts, the information integration process encompasses analogous transfer12,13 or knowledge
revision3
, where updated situation models14 or mental models15 are generated. Te extent to which the reader
generates an appropriate situation model, an integrative mental representation of the text knowledge, depends
on the one hand on how the knowledge is conveyed to them (e.g., text properties) and on the other, the reader’s
cognitive abilities, including abilities to retain information in memory, sustain attention during reading, and
formulate abstract concept relations (i.e., reader characteristics). Tese knowledge-specifc and reader-specifc
characteristics can be examined, as in this study, under the umbrella of textual KS, and executive function and
reasoning abilities, respectively.
Knowledge Structure as Network Maps
Textual KS refers to how concepts/units of information are organized in an expository text16. Kintsch and van
Dijk frst proposed this idea using graphs to represent the network of coherent propositions8
in texts and Ferstl
and Kintsch were among the frst to apply network measures to estimate a reader’s situation model17. Network
maps are one common explicit visual representation of KS, which consist of pairs of concepts (represented as
nodes) joined by link lines (represented as edges) indicating relationships between pairs of concepts. Tis type
of KS representation is now well established in the literature (see Kinchin et al.
18 for discussion). In this study, we
extracted the textual KS as network maps according to Clariana19. Tis process involves several steps as described
in the Methods section.
Among numerous network metrics that could be derived, centrality has been proposed as one of the most
basic and pragmatic ways to describe network maps20. Te centrality of a node in a network indicates the relative
importance of that node in relation to all other nodes, and this measure has been used as a way to quantify the
structure or shape of concept maps21. For example, Kinchin et al.
18 categorized concept maps in terms of the
network topologies of spoke, chain, and net (Fig. 1), and a major discriminating criterion was graph centrality.
Te spoke type concept map has a large graph centrality value, meaning that one central concept is connected
to a large proportion of all other concepts. It represents a KS of simple associations, with no hierarchy and little
integration of concepts. On the opposite end of this spectrum is the chain type map, which has a small graph
centrality value, representing a sequential KS of isolated conceptual understanding with few associations among
the concepts. Such concept maps are susceptible to “meltdown” from a single broken link, and are unlikely to then
reorganize. In between these two extreme types is the net type map that has a medium graph centrality value, rep- resenting a KS of higher integrity with several levels of hierarchy and with complex interactions between levels.
Reorganization of KS by incorporating would-be knowledge is well supported in maps of the net type, and miss- ing links can more easily be compensated with redundant paths. For example, four behavioural studies22–25 that
have considered this relationship have reported an ‘inverted U-shape function’ (as suggested by Rikers et al.
26)
for network graph centrality (abscissa, x-axis) and post-test measures (ordinate, y-axis), with the function’s maxi- mum agreeing with the network graph centrality of the experts’ network. In sum, the shape of the network (spoke,
net, chain) is related to the degree of conceptual integrity in the KS, and can be represented by diferent centrality
scores.
Applying this logic in this study, we consider network maps with medial graph centrality values to represent
near optimal textual KS, whereas maps with (extremely) high or low graph centrality values represent sub-optimal
textual KS. Specifcally, we use the maximal betweenness centrality (MBC)27 value, the highest betweenness cen- trality values of all nodes in a network to describe the characteristic of each textual network map. Also, we use
the quadratic terms of the mean-centred/normalized MBC values as a measure of textual KS optimality: higher
quadratic centrality values (further away from zero) indicate sub-optimal KS, while lower quadratic centrality val- ues (closer to zero) indicate more optimal KS; the adoption of the quadratic terms is based on several established
observations in previous studies22–25. Note that the defnition of sub-optimal text here does not automatically
mean a ‘bad’ or ‘incoherent’ text. Rather, the text KS typology depends on the nature of the domain knowledge;
for example, optimal KS here refers to texts that have a KS structure hierarchically organized as central versus
peripheral concepts.
Executive Function, Reasoning, and Text Comprehension
Text comprehension results from how executive functions and analogical reasoning are employed by the reader
to process the textual information28. Executive functions consist of a set of dissociable processes that coordinate
cognition and facilitate goal-oriented behaviour29. Follmer’s30 meta-analysis showed positive correlations between
reading comprehension and the following components of executive function: working memory, shifing, inhibi- tion, and sustained attention and monitoring. In particular, working memory is needed to maintain and update
textually relevant information on a constant basis, thereby facilitating the reader’s development of a mental rep- resentation of the text31. In the current study, we assess these four important components of executive functions
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through widely used standardized tests, the ‘attention network test’32 for measuring shifing and inhibition and
the ‘letter-number sequencing test’33 for measuring working memory.
Another cognitive ability, analogical reasoning12, also signifcantly afects reading comprehension, although it
is less well examined as compared with executive function. In analogical transfer12, the existing KS serves as the
source or reference, and the newly formed textual KS is the target in the analogical process. For example, in chem- istry classes, the solar system is ofen used as the source/referential analogy when explaining atomic structure
(target concept). Analogical reasoning is also involved in reading when readers revise or update existing KS based
on the new textual KS through reading comprehension. Tey compare and detect any inconsistency between the
two, and if successful, further convert and incorporate the text information into prior knowledge for future use3
.
In this study, we assess analogical reasoning by using a standardized test, the Raven’s Progressive Matrices34.
Although no neuroimaging work has examined text comprehension based on the reader’s analogical reasoning
ability, there is a sizable literature on the neural correlates of analogical reasoning. An aggregated meta-analysis of
7 studies35 showed neural correlates of semantic analogy in lef IFG, MFG, frontopolar cortex (FPC), dorsolateral
prefrontal cortex (DLPFC), and bilateral caudate heads. In particular, the lef FPC is also involved in analogical
reasoning of matrix problem tasks (e.g., based on Raven’s task) and visuospatial domains. Tis fnding is consist- ent with the proposal that FPC is critical for integrating the outcomes of separate cognitive operations to facilitate
long-term goal oriented behaviour36,37.
Electronic Device and Readin
Individual diferences also exist in areas other than executive function and analogical reasoning, and in a recent
study, Follmer et al.
38 investigated how diferent reading background variables relate to the individual’s read- ing comprehension of STEM (Science, Technology, Engineering, Mathematics) texts. Using a large sample
of Mechanical Turk participants, they showed that STEM text comprehension was negatively correlated with
reported frequency of reading on electronic devices (e.g., smartphones, tablets, computers) as well as with
reported frequency of non-reading behaviour on electronic devices (e.g., watching television). At the same time,
STEM text comprehension was positively correlated with self-reported level of reading attitudes and preferences
(e.g., enjoyment of challenging books, learning difcult things via reading). Tese disturbing fndings provided
initial evidence of how the emerging electronic reading habits may fundamentally alter readers’ comprehension
of expository scientifc texts39.
Previous studies have investigated the efect of paper vs. screen-based reading comprehension (see Sidl et al.
40
for a review), with fndings indicating that reading on a screen, as compared with reading on paper, may lead to
Figure 1. Tree main concept map structures (reproduced from Kinchin et al.
18). (A) Spoke – a radial structure
in which all the related aspects of the topic are linked directly to the core concept, but are not directly linked
to each other. (B) Chain – a linear sequence of understanding in which each concept is only linked to those
immediately above and below. (C) Net – a highly integrated and hierarchical network
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worse performance (or more reading time for achieving the same level of performance). Such discrepancies have
been attributed to aspects of the technology such as visual fatigue and less convenient navigation, and also to the
impact of electronic devices on metacognitive processes (e.g., overconfdence and reduced self-regulation and
monitoring). However, recent studies have taken personal preferences of platforms into consideration: when not
under time pressure, some readers who prefer the electronic platform actually show an efect of screen superi- ority41. In the current study, our focus with regard to the relationship between electronic device and reading will
be on individual diferences in the habits (and daily duration) of using electronic device, and the efect of these
diferences on reading comprehension.
The Current Stu
Tis study systematically investigates the relationships among executive functions, analogical reasoning, and elec- tronic and non-electronic reading behaviour, and their impact on reading comprehension at both the behavioural
and the neurocognitive levels. As previously mentioned, most neurocognitive studies of text comprehension have
focused on narrative texts28,42,43. For example, the Extended Language Network hypothesis42 suggests that the
classic language network, the semantic control and integration network, and the executive function network are
simultaneously engaged during narrative text comprehension. Swett et al.
44 was among the frst to investigate the
neural correlates of expository text comprehension. Consistent with the idea of multiple networks, Swett et al.
reported patterns of co-activation in the brain’s key regions of cognitive control, visual processing, and language/
semantic integration. Specifcally, expository text comprehension also engages the core semantic-processing net- work for integrating word- and sentence-level semantic information, and additional multi-modal regions that
create and update the situation/mental models for the text being read. Te authors further reported diferent
patterns for central versus peripheral text concepts, which implies that good readers notice and use the implicit
textual KS of the expository text by focusing on the central and peripheral concepts diferently (i.e., recruiting
diferent regions of the brain).
In fMRI studies of reading, it is important to know the exact onset time of words and phrases to convolve
the hemodynamic response function (HRF) with specifc task-related variance and isolate it from unexplained
variance. To this end, we employed a paradigm called “fxation-related fMRI”45 (see Methods for more details).
Previous neuroimaging studies of texts dealt with the stimulus timing issue by controlling the presentation rate
of the stimuli, typically with individual words, phrases, or sentences shown in a rapid-serial-visual-presentation
or RSVP paradigm46. But reading every word for half a second in succession of one another is not a natural
reading experience. To overcome this problem, we have taken advantage of an emerging paradigm that explores
simultaneous eye-tracking and fMRI data acquisition (fxation-related fMRI). With this paradigm, participants
are allowed to self-pace materials during reading in the scanner in a more naturalistic manner than reading via
RSVP47. To match the fast speed of eye-movements and the cognitive processes during reading, we further used
the multiband echo-planar imaging (EPI) acquisition technique48 to reduce the fMRI repetition time (TR) to
400ms, in contrast to the typical TR of 2000 ms used in task-based fMRI studies. Multiband EPI provides greater
within-participant statistical power with a higher sampling rate, a higher temporal Nyquist frequency to detect
fast oscillatory neurally generated BOLD signals49, and better removal of spurious non-BOLD high frequency
signal content50. By integrating eye-movement and high sampling-rate fMRI data in a naturalistic paradigm, our
study is poised to provide neurocognitive insights into naturalistic scientifc text comprehension.
To analyse the data collected from fxation-related fMRI, we incorporated a parametric modulator of the index
of word position in sentences (starting from 1) in our fMRI GLM analysis. Tis approach aims to capture the
variance in the HRF that changes along the time course of sentential processing across the text. It corresponds to
the hypothesis of the Construction-Integration model5,6
that cycles of text comprehension roughly corresponds
to short sentences or phrases7,8
. Note that such a regressor, even though it is temporally based, would also capture
variances associated with other concomitant cognitive processes which evolve along the time course of sentence
reading (e.g., predictive pre-activation at syntactic, phonological, orthographic and perceptual levels9
). Neural
patterns negatively correlated with this regressor would be more involved in the early stage of sentential process- ing, which could be associated with the construction phase of the cycle or the integration of local information
within the sentence. Neural patterns positively correlated with this regressor would be involved in the late stage
of sentential processing, which could be associated with the integration phase, as well as the integration of the
sentential information with more global context of the current textual representation or world knowledge. Te
beta images of this regressor (variance along the time course of reading a sentence) could be further used to inves- tigate the efects of stimuli (e.g., textual KS) and individual diferences (e.g., executive function). In a naturalistic
reading paradigm such as used in the current study, these concomitant cognitive processes are not dissociable,
and they are indeed vital in language comprehension9
.
Given the approaches reviewed thus far, we make the following hypotheses. First, regarding the efects of tex- tual KS, we hypothesize that when processing expository scientifc texts with sub-optimal KS, cognitive demands
of executive function should be higher due to the construction of a situation/mental model from the text; as a
result, the associated neural correlates will be refected as stronger activation in the executive control network,
including the prefrontal cortex and the cingulate cortex. Second, regarding the efects of reader characteristics
and individual diferences, we hypothesize that executive function, analogical reasoning, and positive reading
attitude will be positively correlated with reading comprehension performances. Neurocognitively, such cor- relations should be refected as co-activation in areas including the lef IFG, MFG, FPC, dorsolateral prefrontal
cortex (DLPFC), and bilateral caudate heads, areas that are critical for executive function, analogical reasoning,
and linguistic-semantic integration when processing scientifc text28,35,42–44.
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Results
Behavioural performances and individual differences. Participants read five expository texts in
the scanner. Every participant made at least six correct answers to the 10 multiple-choice assessment questions
at the end of each text during in-scanner reading. Te accuracy for the questions for each text was as follows
(mean% ± SD, n = 51): Mathematics, 94.71 ± 7.84, GPS, 90.98 ± 9.22, Mars, 91.76 ± 9.10, Electric Circuit,
95.10±7.03, and Supertanker, 88.40±11.14. ANOVA showed signifcant diferences among participants’ perfor- mance accuracy on the texts (F(4,250)=5.32, p=0.0004). Specifcally, post-hoc Tukey’s HSD test showed that per- formance accuracy difered signifcantly between Electric Circuit and Supertanker (lower and upper confdence
limit=2.17, 11.94, p=0.0009) and between Mathematics and Supertanker (LCL=1.78, UCL=11.55, p=0.002).
Participants’ mean performance accuracy varied depending on the individual diference scores: it was pos- itively correlated with GSRT scores (n=49, ρ=0.65, p<0.0001), with Raven’s score of analogical reasoning
(n=49, ρ=0.28, p=0.027), and with reading preference index (n=49, ρ=0.27, p=0.029). Further, the GSRT
was also positively correlated with the working memory LNS task (n=46, ρ=0.36, p=0.007) and with Raven’s
scores (n=49, ρ=0.33, p=0.011). GSRT scores also showed a positive trend though not signifcant correlation
with the reading preference index (n=49, ρ=0.23, p=0.059).
fMRI Results: Main efects and individual diferences of integrative processing. Neural correlates
of reading (Content Word fxation) were refected in the strong activity in bilateral visual cortex and medial sup- plementary motor area (SMA), along with lef precentral gyrus, superior and middle temporal gyrus (STG and
MTG), anterior temporal lobe (aTL), inferior frontal gyrus (IFG) pars triangularis, and hippocampus (Table 1,
Fig. 2).
Neural correlates of Integrative processing were refected in two diferent patterns: the frst one, negatively
correlated with the word position regressor, was associated with strong activities in bilateral occipital pole, poste- rior cingulate cortex (PCC), pregenual anterior cingulate cortex (pgACC), as well as lef fusiform and precentral
gyrus (Table 1, Fig. 3, blue); the second, positively correlated with the word position regressor engaged DLPFC,
IFG pars triangularis, precuneus, lingual gyrus, MTG and ITG, as well as lef IPL, medial SMA, insula, and the
parahippocampal gyrus (PHG) (Table 1, Fig. 3, red). One cluster in the lef insula and IFG pars triangularis
showed negative correlation between the E-device reading index and Integrative processing (MNI: [27 20 18];
Table 1, Fig. 4).
fMRI Results: Main efects and individual diferences of KS optimality. Afer the linearly correlated
variance of MBC (maximum betweenness centrality) was partialled out, the quadratic term of MBC represented
the optimality of textual KS (with values closer to 0 being more optimal; see Introduction). Neural correlates of
the processing of texts with optimal KS revealed strong activity in the lef DLPFC and lef middle STG, while the
processing of sub-optimal KS led to greater activity in the lef frontopolar cortex (FPC) and bilateral dorsal ACC
(Table 2, Fig. 5A,B). Furthermore, lef FPC and bilateral SMA were correlated with the processing of sub-optimal
KS texts among participants with higher GSRT scores (Fig. 5C,D), suggesting an interaction between textual KS
properties and reader characteristics (e.g., of high-vs-low reading competence). Finally, this text-reader interac- tion was also refected in the regression results of E-device reading index: during processing of sub-optimal KS
texts, neural responses in the lef temporoparietal junction (TPJ, Fig. 5E) increased with E-device reading index,
while responses in the right claustrum (Fig. 5F) decreased. Tese interactions have signifcant implications for
student science concept learning, as discussed below.
Discussion
Te current study investigated the neurocognitive processes underlying the interaction between properties of
expository texts and characteristics of the reader, specifcally between the textual KS (network structure of the
texts to be read) and the individual readers’ executive function, reasoning, and reading habits. Our study also
showed that readers’ electronic device usage is negatively correlated with the involvement of key brain regions for
integrative information processing. To our knowledge, this study is the frst systematic behavioural and neurocog- nitive investigation of expository texts of scientifc concepts with a naturalistic reading paradigm that combines
both fMRI and eye-tracking.
First, at the behavioural level, we found that student performance in reading comprehension is correlated
with individual diferences in executive functions, analogical reasoning, and positive reading attitude. Te GSRT
general reading ability scores are correlated with analogical reasoning and positive reading attitude, for both
in-scanner performance and immediate post-test assessment questions. GSRT scores were also correlated with
individual diferences in working memory. Tese patterns are in line with previous studies that have identifed
the relationships between reading comprehension and executive functions30 and between comprehension and
reading behaviour38.
Te relationships among reading comprehension and executive function, reasoning, and reading attitudes
are not one-to-one, but are multidirectional and complex. For example, better executive function might lead to
superior reading comprehension, and conversely, better reading experience could improve readers’ reasoning,
attention, and working memory. Readers with a positive reading attitude engage in more reading activity, which
leads to more rewarding experiences and in turn more positive reading attitude. Diferent reader characteristics
could also be related to each other: for example, reasoning has been proposed to require working memory capac- ity in the mental model theory15, engaging working memory’s underlying executive processes51. Finally, reading
comprehension performance may be correlated with the student’s success in other domain disciplines: reasoning
abilities have been found to be predictive of academic achievements in Mathematics, Biology, Physics, History,
and English52,53. Our fndings that scientifc text reading comprehension is correlated with individual diferences
in working memory and analogical reasoning are consistent with these general fndings but also more specifcally
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demonstrate that individual diference variables impact scientifc reading. Although these correlations could have
underlying causal relations, the current study was not designed to test causal relationships, which need to be
investigated in future studies.
Second, at the neurocognitive level, we found dynamic neural correlates of integrative information processing,
suggesting a local predictive focus on surface form analysis (visual cortex and fusiform gyrus) and a global pre- dictive focus on semantic, syntactic analysis and integration (frontoparietal network and SMA). Such change in
focus across the time course of processing is in line with the diferent time-scale analysis of text comprehension.
At the beginning of reading a new sentence, integration of local information within the sentence takes place,
which demanded primary perceptual-motor areas with short process memory. Te more the reader proceeds
along the sentence, the more the integration of sentential information with global context of the current textual
representation or world knowledge takes place, which demanded higher order areas with long process memory, as
predicted by models of memory and text comprehension6,9–11. Tus, temporal ordering and integrative processing
may be related at multiple levels and time scales, although the predictive pre-activation hypothesis9
emphasizes
that integrative processing is due to parallel integration rather than staged processing across time.
With regard to the impact of text properties, texts that have optimal textual KS recruit regions associated
with linguistic, semantic (IFG and temporal lobe), and integrative processing (DLPFC). Texts with sub-optimal
textual KS recruit regions that are critical for dual-tasking, monitoring, and attention (FPC and dACC), suggest- ing that these texts elicit more efortful processing during mental model construction. Furthermore, reading
H Regions Voxel p T B.A. [x, y, z]
Neural Correlates of Reading*
B Cuneus & lingual gyrus 1455 <0.001 15.16 23 & 18 12 −76 10
B SMA 140 <0.001 9.04 6 & 32 −9 11 54
L Precentral gyrus and MFG 112 <0.001 7.98 6 −42 −7 62
L MTG & STG 179 <0.001 7.88 21 −57 −25 −2
L IFG pars triangularis 30 0.001 6.5 45 −48 20 18
L Hippocampus 8 0.002 6.25 −24 −31 −6
L aTL 5 0.002 6.23 38 −48 17 −26
Neural Correlates of Early/Local Integrative Processing*
R IOG 49 <0.001 8.05 27 −91 −6
B PCC & Precuneus 139 <0.001 7.82 31 −9 −52 26
L Precentral 22 <0.001 7.64 6 −48 −4 42
R VMPFC & pgACC 113 <0.001 7.4 10 & 32 9 53 −10
R Cuneus 21 <0.001 6.92 17 12 −85 2
L Fusiform, lingual & IOG 86 <0.001 6.74 19, 18 & 17 −24 −79 −18
B pgACC 47 <0.001 6.72 32 −9 44 −2
L IOG 9 0.012 5.72 19 −30 −88 14
R Insula 5 0.012 5.7 13 42 −31 −6
Neural Correlates of Late/Global Integrative Processing*
R Lingual gyrus and cerebellum 212 <0.001 9.84 19 24 −61 −6
L IPL & Supramarginal gyrus 126 <0.001 8.51 40 −39 −46 42
B SMA 48 <0.001 8.14 32 −3 23 46
L DLPFC & IFG pars triangularis 223 <0.001 8.12 6 & 46 −21 17 58
L Insula 28 <0.001 7.82 13 −42 −7 10
L Cuneus 49 <0.001 7.73 18 −6 −97 −6
L Parahippocampal gyrus 44 <0.001 7.45 36 −30 −37 −14
L MTG & ITG 50 <0.001 7.15 37 & 20 −57 −55 −10
R DLPFC 38 <0.001 6.83 6 27 8 58
R Precuneus 33 <0.001 6.74 31 27 −79 22
R IFG pars triangularis 8 0.001 6.39 46 54 41 14
L Precuneus & Angular gyrus 17 0.002 6.29 19 −30 −73 42
L Cuneus 7 0.011 5.75 19 −9 −85 26
Neural Correlates of Integrative Processing for Individuals with lower E-device Usage**
L Insula & IFG pars triangularis 70 0.01 4.89 13 & 47 −30 17 14
Table 1. Neural Correlates of Reading and Integrative Processing. *
Voxel-level FWE corrected p-values.
**Cluster-level FWE corrected p-values with CDT p=0.001 uncorrected. Abbreviations: DLPFC=dorsolateral
prefrontal cortex; IFG=inferior frontal gyrus; IOG=inferior occipital gyrus; IPL=inferior parietal
lobule; ITG=inferior temporal gyrus; MTG=middle temporal gyrus; PCC=posterior cingulate cortex;
SMA=supplementary motor area; VMPFC=ventromedial prefrontal cortex; H=hemisphere; L=lef;
R=right; p=p-value; T=T-value; B.A.=Brodmann area; x, y, z=MNI coordinates.
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competence (as measured by GSRT scores) is refected clearly in the processing of texts with sub-optimal KS:
high-competence readers activate regions in integrative information processing in the SMA and FPC, as well as
regions for linguistic processing in IFG, insula and STG, suggesting the engagement of multiple brain networks
for conceptual integration.
Due to the nature of the hemodynamic response function, we used the content word fxation regressor to cap- ture the variance of neural responses throughout text reading. Neural correlates of this regressor included the typ- ical fronto-temporal circuit engaged in language, syntactic and semantic processing (IFG, STG, MTG, aTL)42,54,
but also the SMA and hippocampus. SMA, including the supplementary eye feld (SEF) and the pre-SMA which
has traditionally been implicated in motor planning and motor learning55. However, in the context of semantic
retrieval, Danelli et al.
56 found the SMA, premotor, and lef IFG to be involved in both grapheme-to-phoneme and
lexical-semantic routes of lexical access. Further, pre-SMA has been proposed to be part of a network including
thalamus and caudate nucleus that govern aspects of semantic retrieval of object memories, supported by EEG
data57. Te lef SMA is also associated with syntactic processing as shown in a recent meta-analysis54. Te SMA
and pre-SMA activity could be part of the on going predictive pre-activation process across multiple levels during
reading comprehension9
. In addition, Duf and Brown-Schmidt58 proposed that the hippocampal declarative
memory system is a critical contributor to language use and processing because of its capacity for relational bind- ing, representational integration, fexibility, and maintenance. In Hasson et al.’s memory processing hierarchy10,
the medial temporal hippocampal region would also interact with regions with long process memory, and facil- itate binding and consolidation of incoming information with global context and world knowledge. Given these
fndings in the literature, it is not surprising that SMA and the hippocampus both play crucial roles in expository
text comprehension as shown in our current study, since the predictive and integrative processes take place irre- spective of the text genre (i.e., narrative or expository).
Augmented by the high-sampling rate (400 ms TR, a Nyquist frequency of 800 ms) of multiband EPI acqui- sition in our current design (see Methods), the parametric modulator of word position in sentences successfully
captured the dynamic change of neurocognitive integrative processes along diferent time scales during reading
comprehension (mean reading time for each sentence=3.33±0.86 s). Our results indicated that the temporal
evolution of integrative processes shifed from relatively shallow, form-oriented and local processing (e.g., involv- ing the occipital cortex and fusiform gyrus) to more global processing that involves semantic retrieval, informa- tion integration, and situational/mental model updating that engage the DLPFC, IFG, IPL, and SMA. Previous
work based on narrative text reading has implicated the frontoparietal network in situation model building, an
integrative mental representation of the text, with a rough division of labour in situation model construction
Figure 2. Neural correlates of content word processing. (A) Lateral view of the lef hemisphere. (B) Medial view
of the lef hemisphere. Both showing signifcant voxels in the lef visual cortex, SMA, precentral gyrus, IFG and
STG and MTG. (C) Medial view of the right hemisphere showing the right visual cortex and SMA. (D) Sagittal
section with cross hair at MNI [−24 −31 −6] highlighting the signifcant voxels in the lef hippocampus. (E)
Sagittal section with cross hair at MNI [−48 17 −26] highlighting the signifcant voxels in the lef aTL.
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(the posterior parietal and anterior temporal regions) and situation model maintenance (frontal regions)46. Our
fnding of the dynamic changes at the sentential level, although from scientifc rather than narrative text reading,
is consistent with the theoretical framework that the situation/mental model is constantly updated as reading
comprehension unfolds in time14,59. Such dynamic changes are seen in cognitive domains other than language
or reading: for example, Fangmeier et al.
60 showed a similar pattern of shif in neural correlates during diferent
stages of reasoning in which the initial processing of the premise involves occipital and temporal regions, whereas
the validation of a given conclusion based on the premise engages the frontoparietal network (DLPFC, IPL, and
precuneus).
By modelling the knowledge structure of a text as network maps (e.g., textual KS), we were able to capture the
diferences in the neural correlates of expository science text reading as a function of text structure. Specifcally,
the graph-theoretical measure MBC (referred to as graph centrality) of a textual KS network allowed us to repre- sent texts with optimal (network-like maps) vs. sub-optimal (spoke- or chain-like) KS18, and such KS diferences
directly impact the neurocognitive substrates of reading. Previous behavioural studies22–25 have suggested an
inverted U-shape function between network graph centrality of knowledge structure and reading comprehension
performances. By using the U-shaped quadratic term of knowledge structure as regressor, we found that the pro- cessing of optimal KS texts recruits classical language processing brain regions (lef M/STG), along with regions
that involve situation/mental model construction and information integration (lef DLPFC), whereas processing
of sub-optimal KS texts engaged activities in the lef FPC and bilateral dorsal ACC.
In the context of multitasking research, FPC and ACC have been proposed to serve complementary but dis- sociable roles in allocating resources for cognitive control of the primary and subgoals/tasks61,62. While ACC has
been frequently implicated in language processing (especially confict monitoring in bilingual speech produc- tion)63, the role of FPC (Brodmann Area 10) has been traditionally linked to a variety of higher-order cognitive
functions based on human and primate research64. Specifcally, FPC has been associated with the ability to hold
a primary goal while performing concurrent subgoals, playing an important role in multitasking and multiple
resource allocation61,65–67, including reasoning and integration of multiple disparate mental relations68. Given
this role of FPC in integrative processing, it is no surprise that we see it involved in the processing of sub-optimal
KS texts that have (1) spoke-like KS, where a core concept is associated with multiple isolated concepts, and (2)
chain-like KS, where concepts are serially associated one by one. In these cases, multitasking is required of the
reader so as to retain the core concept while processing and integrating multiple isolated sub-concepts across
Figure 3. Neural correlates of integrative processing (word position efect). Surface rendering of negative
(green) and positive (red) correlation with the word position index in sentences. (A) lef hemisphere lateral
view, (B) right hemisphere lateral view, (C) lef hemisphere sagittal section of MNI x=−5, (D) right
hemisphere sagittal section of MNI x=5. Green regions are more activated in the beginning of sentences
(negatively correlated with the word position index in sentences) including bilateral visual cortex, PCC and
pgACC and lef precentral gyrus. Red regions are more activated towards the end of sentences including
bilateral DLPFC and IFG, lef IPL, M&ITG and SMA.
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the text. Note that the quadratic efect of graph centrality (as measured with MBC) in FPC and ACC in our data
cannot be accounted for by its relation with other psycholinguistic variables such as word length or word fre- quency, although the latter have also been shown to have curvilinear/quadratic efect on both behavioural69 and
neuroimaging correlates of reading70. It is important to note that MBC measures of the texts are largely collinear
with the text-wise mean values of key psycholinguistic variables such as word frequency, AoA, and word length
(see Section Materials in Method). However, in our subject-level regression model, we included both linear and
quadratic terms of MBC, and the linear term was included as a covariate of non-interest. Terefore, the confound- ing linear efects of the psycholinguistic variables were partialled out before the group-level multiple regression.
Te impact of electronic device usage is evident in our results. Across all texts, we found a negative correlation
between frequency in electronic device usage and BOLD activity in lef insula and IFG pars triangularis. Te
anterior insula is part of the salience network71, which responds to the degree of information saliency (and subse- quent attention) in a variety of domains including cognitive and emotional processing72–74. Sridharan et al.
75 used
Grainger Causality to estimate efective connectivity, proposing that the fronto-insular cortex plays a critical and
causal role in switching between the central-executive network and the default-mode network. In addition, our
data indicate that individuals with higher electronic device usage, on the one hand, have decreased engagement
in insula and IFG, and on the other, recruit more lef TPJ and less right claustrum when processing texts with
sub-optimal KS. Te claustrum has the highest connectivity in the brain by regional volume, especially with the
frontal lobe and cingulate regions76, and it has been proposed to be the “gate keeper” of neural information for
conscious awareness77. Considering the potential negative efects of excessive daily usage of electronic device
(especially texting on smartphones), the neural patterns in our data regarding insula and claustrum, along with
the behavioural data of Follmer et al.
38, could point to the readers’ reduced or inefcient coordination of cognitive
resources and switching between the central executive and default mode networks. At the same time, the result of
over-engagement of the TPJ, part of the executive network71, might suggest that these same readers required more
efortful processing, especially for texts with sub-optimal KS of the spoke or chain types.
Finally, we found that individuals with higher GSRT scores engage the lef FPC and bilateral SMA more
strongly when reading texts with sub-optimal KS. As discussed above, FPC and SMA may be signifcant for
expository text comprehension given their important roles in multi-tasking, cognitive resource allocation, and
visuospatial processing. Our neurocognitive patterns suggest that better reading ability is associated with the
engagement of neural substrates responsible for highly integrative cognitive processes as well as for reasoning. By
Figure 4. Neural correlates of integrative processing negatively correlated with individual E-device usage.
Te sections show the signifcant cluster in lef insula and IFG pars triangularis in which the beta estimates for
integrative processing were negatively correlated with the individual E-device usage reported in the RBQ. Te
crosshair highlights the peak in the cluster, MNI: [27 20 18].
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contrast, readers who report excessive daily electronic device usage may not activate these critical brain regions
for integrative cognitive processing. As discussed in the Introduction, behavioural work on the immediate efect
of media (paper vs. screen) has, by and large, shown that excessive use of screen-based devices is associated with
lower quality of metacognitive processes40,41. Our fndings provided the frst neurocognitive evidence that habit- ual electronic device usage might adversely afect high-level cognitive processing required for scientifc text com- prehension. Future investigation is needed to identify the causal relationships among reading habits, preferences
of media types, metacognitive performances, and expository text comprehension.
Methods
Participants. Sixty-two right-handed native English speakers were recruited. Seven participants did not
fnish the frst session due to eye-tracker or MR scanner technical issues. One participant was excluded due
to very low accuracy (50%) for an in-scanner comprehension test and poor behavioural testing results outside
the scanner. One participant was found to be lef-handed afer the behavioural session, leaving 51 participants
aged between 18 and 40 years in our analysis. Eye-tracking data were missing for one participant during one
run containing one text, leading to its exclusion for the analysis for KS. Forty-nine out of the 51 participants
completed the behavioural testing session, of which only 46 correctly performed the Letter Number Sequencing
task. Terefore, behavioural data analysis included 49 participants (23 males, mean age ±SD=22.69±4.57).
fMRI data for neural correlates of Reading and Integrative Processing included 51 participants (24 males, mean
age±SD=22.67±4.52). Forty-six participants (21 males, mean age±SD=22.84±4.63) were included in the
fMRI multiple regression models for neural correlates of individual diferences in Integrative Processing. Forty- fve participants (21 males, mean age±SD=22.47±3.88) were included in fMRI regression models for neural
correlates of individual diferences in sentential processing of texts with diferent KS optimality.
All participants had normal or corrected to normal vision, and had no history of mental or neurological dis- order. Te study was approved by the Pennsylvania State University Institutional Review Board (IRB) and was
performed in accordance with the ethical standards described in the IRB. Written informed consent was obtained
from all participants before they took part in the study.
Materials. Prior to the experiment, fve expository texts of STEM contents were modifed from previous
research stimuli (see Follmer38 for details): Mathematics (Permutations and Combinations, 28 sentences, 306
words, maximal betweenness centrality/MBC=0.34), GPS (28 sentences, 307 words, MBC=0.29), Mars (31
sentences, 310 words, MBC=0.59), Electric Circuit (30 sentences, 302 words, MBC=0.16), and Supertanker (31
sentences, 302 words, MBC=0.72). Texts were controlled for the mean word count per sentence (10.4±0.62)
and the mean character count per sentence including spaces (62.48±1.92). Furthermore, psycholinguistic var- iables of the lexical properties (word frequency, length, etc.) of each text were derived from the English Lexicon
Project78 the Kuperman age-of-acquisition (AoA) database79, the MRC Database80 and the Brysbaert concreteness
database81. Bootstrapped One-way ANOVAs revealed no signifcant diference between the average values across
all fve texts for the average number of syllables (NSyll, F=0.05, p=0.99), lexical decision time (LDT, F=1.07,
p=0.38), log frequency (F=0.25, p=0.91), naming response time (NRT, F=1.41, p=0.23), orthographic neigh- bourhood density (OLD, F=0.04, p=0.99), phonological neighbourhood density (PLD, F=0.34, p=0.85), con- creteness (F=0.24, p=0.91), and number of phonemes (NPhon, F=0.02, p=0.99). However, one-way ANOVAs
for average word length and AoA were signifcant at p<0.05 (F=3.27, F=3.32, respectively). Text-wise, mean
values of psycholinguistic variables were linearly correlated with the linear term of MBC (OLD, r=0.92 p=0.025;
PLD, r=0.92, p=0.0285; NSyll, r=0.92, p=0.0268; NPhon, r=0.88, p=0.0487; LDT, r=0.9, p=0.0356; NRT,
H Regions Voxel p T B.A. [x, y, z]
Neural Correlates of Integrative Processing for Texts with Optimal KS
L MTG & STG 122 0.001 4.81 22 & 21 −51 −46 −2
L DLPFC (SFG & MFG) 92 0.003 4.66 6 & 8 −48 11 50
Neural Correlates of Integrative Processing for Texts with Sub-optimal KS
B dACC 204 <0.001 5.21 32 3 35 26
L FPC (MFG) & IFG pars triangularis 66 0.015 4.42 46 & 10 −39 44 10
Neural Correlates of Sub-optimal KS Processing in Individuals with higher GSRT
L FPC (MFG) 77 0.007 5.19 10 −30 38 26
L SMA 70 0.01 5.09 6 −12 −1 62
Neural Correlates of Sub-optimal KS Processing in Individuals with higher E-device Usage
L TPJ (MTG & Angular gyrus) 50 0.039 4.21 22 & 39 −36 −58 18
Neural Correlates of Sub-optimal KS Processing in Individuals with lower E-device Usage
R Claustrum 53 0.031 5.81 27 20 18
Table 2. Neural Correlates of Optimality of KS. *Cluster-level FWE corrected p-values with CDT p=0.001
uncorrected. Abbreviations: dACC=dorsal anterior cingulate cortex; DLPFC=dorsolateral prefrontal cortex;
FPC=frontopolar cortex; IFG=inferior frontal gyrus; MFG=middle frontal gyrus; MTG=middle temporal
gyrus; SMA=supplementary motor area; STG=superior temporal gyrus; TPJ=temporoparietal junction;
H=hemisphere; L=lef; R=right; p=p-value; T=T-value; B.A.=Brodmann area; x, y, z=MNI coordinates.
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r=0.91, p=0.034; frequency, r=0.89, p=0.0453; AoA, r=0.88, p=0.049; length, r=0.87, p=0.053, concrete- ness, r=0.88, p=0.0487). Stimuli were presented using E-Prime 2.082, sentence by sentence onto a screen which
was then projected onto a refective mirror mounted above the participants’ eyes in the MRI scanner (see section
Eye-tracking Data Acquisition and Processing for details).
KS quantifed as maximal betweenness centrality (MBC, Graph Centrality). Fifeen key terms
were selected as nodes from each of the fve texts38, along with their synonyms and metonyms. Te key terms
were aggregated from a key-term generating task of a previous Amazon MTurk study of 403 participants38 and a
key-term generating task of the authors of the current study (with a general overlap of 88%). Te edges between
the nodes are defned as proximity associations between nodes, operationalized as follows: a forward pass is made
through the text without regard to sentence boundaries, and for every key term that is found, it is linked to the
immediate previous key term by entering a “1” (binary coding) in a 15 by 15 term proximity array, indicating
Figure 5. Neural correlates of KS optimality and individual diferences. (A,B) Neural correlates of integrative
processing for texts with diferent KS optimality. Red: neural correlates of texts with sub-optimal KS (quadratic
MBC values away from 0), including bilateral dACC and lef FPC and IFG pars triangularis. Green: neural
correlates of texts with optimal KS (quadratic MBC values closer to 0), including lef DLFPC, STG and MTG.
Panel B showed the sagittal section of MNI x=3. (C,D) Neural correlates of texts with sub-optimal KS in
individuals with higher GSRT scores, including lef FPC and SMA. Panel D showed the sagittal section of MNI
x=3. (E) Lef IPL correlated with sub-optimal KS processing in individuals with higher E-device usage. (F)
Right claustrum (crosshair MNI=[27 20 18]) correlated with sub-optimal KS processing in individuals with
lower E-device usage.
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that there is a link (edge) between the two terms. Textual network maps were thus generated with Analysis of
Lexical Aggregates Reader (ALA-Reader)19. Maximal Betweenness Centrality per map/text as a measure of graph
centrality (and measure of KS) was calculated using the NodeXL sofware (Microsof Inc., 2018). For a node k
in a network, its partial betweenness with respect to the other two nodes i and j is defned as the probability that
node k falls on a randomly selected path linking nodes i and j. Te betweenness centrality value of node k is the
sum of the partial betweenness values in respect to all pairs of nodes in the network except for k27. Each node in a
network has a betweenness centrality value. Note that the betweenness centrality measure depends on the num- ber of nodes in the graph27, and the absolute value of MBC per se does not indicate the optimality of KS. In the
current study, the lengths of all the texts were made comparable (roughly 300 words), and we used 15 nodes (key
concepts) to construct concept maps for all fve texts so that the graph centrality values and the range of optimal
KS values are also comparable across the texts. To operationalise the optimality of textual KS, the centrality values
were normalised and quadratic terms were calculated. Higher quadratic centrality values (further away from zero,
which is the average in the normalised distribution) indicate sub-optimal KS, while lower quadratic centrality
values (closer to zero) indicate more optimal KS.
Procedure. Afer providing consent, participants underwent a structural MRI scan, followed by a practice
session for self-paced reading in the scanner. Tey were instructed to click a button to advance from one sen- tence to the next. Each sentence was presented for up to 8 seconds afer which the next sentence automatically
appeared on the screen. At the end of each text they answered 10 comprehension questions. Once the practice
session ended, the participants completed fve self-paced reading sessions, during which time simultaneous fMRI
and eye-tracking data were collected. On a second visit, which was usually one week afer the in-scanner reading
session, participants completed a battery of behavioural tests.
Behavioural data collection and processing. In the behavioural session, the Gray Silent Reading Test,
Raven’s Progressive Matrices, Letter Number Sequencing and Attention Network tests were presented to par- ticipants via E-Prime 2.0, and the Reading Background Questionnaire was completed on an internet browser.
Detailed information of each test is as below.
Gray Silent Reading Test (GSRT). Te GSRT test measures reading comprehension competence83. Up to 13 nar- rative texts were provided the in GSRT, and each text was presented alongside fve assessment questions. Adult
participants started with Text No. 8 (a text of middle-level difculty) and were tested downward (e.g., Text No. 7)
until the basal was reached (i.e., when all fve questions were answered correctly), and upward (e.g., Text No. 9)
until the ceiling was reached (i.e., 3 out of 5 answers were wrong). Because all participants were in the same age
group (18 and beyond), conversion of scores to quotient according to age groups was not necessary, and the raw
scores were used.
Raven’s progressive matrices. Te Raven’s test measures analogical reasoning34. In each of the sixty-fve tests, a
matrix of relations, from which part is omitted, is presented. Subjects have to choose, from a group of six or eight
alternatives, the one which completes the matrix. Te problems are arranged in fve sets, each of which has a
distinctive theme: (A) continuous patterns, (B) analogies between pairs of fgures, (C) progressive alterations of
patterns, (D) permutations of fgures and (E) resolution of fgures into constituent parts. Te frst problem in a set
is intended to be self-evident, and it is succeeded by twelve problems of increasing difculty. Te testing time was
limited to 10minutes, and the number of corrected trials was used as the score.
Letter number sequencing (LNS). Te LNS task measures working memory. Te task was adapted from the
Wechsler Adult Intelligence Scale (WAIS-III)33. Participants heard a series of alternating letters and numbers
and were asked to recall the numbers frst in ascending order and then the letters in alphabetical order. Te task
began with a set size of two (one letter plus one number) and increased by one for every three trials until a set size
of eight was reached. Te participants’ outputs were corrected for using capital letters (if lower-case letters were
the targets) and accidental usage of arrow keys. To properly refect the difculty of diferent items, size-weighted
scores were calculated as the summation of correct items’ set size. For example, if the participant was correct in
three items with the size of two, one item with the size of three, and two items with the size of four, the score will
be calculated as 3×2+1×3+2×4.
Attention network test (ANT). Te ANT tests measure the alerting and orienting skills of attention and the
inhibitory control ability of executive function32. It consisted of a fanker test in which a central arrow was pre- sented with congruent or incongruent fanking arrows, and the participants were asked to give indicate the direc- tion of the central arrow as fast and as accurately as possible. Te row of arrows could appear above or below
the fxation cross. In some trials before the arrows appeared, one or two asterisks would appear. Tey could
either alert the participants that the arrows will appear soon but without orienting the location of the arrows, or
alert them that the arrows will appear soon and direct attention to the correct location (orienting). Tree scores
were derived according to Fan et al.
32, refecting the RT diferences caused by alerting, orienting, and conficting
manipulations; for example, the higher the confict efect on RT, the lower the participant’s inhibitory control is.
Reading Background Questionnaire (RBQ). Participants were administered 20 questions constructed based on
previous research84,85 to assess readers’ general reading habits and background, using a Google Form38. Te items
asked about participants’ reading habits on electronic media (e.g., computers, smartphones), their electronic
non-reading behaviour (e.g., time spent texting friends, watching television), and their reading habits (amount
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of time spent on reading), preferences (e.g., enjoyment of types of books, enjoyment of books about diferent cul- tures), attitudes towards reading, and reading ability. Items were administered using either a 4-point or a 5-point
Likert scale.
Correlational analyses showed signifcant correlations between E-device reading and non-reading time, and
pair-wise correlations among reading time, reading preference and reading attitude/ability (see also Follmer et
al.’s analyses38 of how these variables impact reading). Exploratory factor analysis yielded two factors: Factor
1 explains 34.45% of variance, including reading preference (loading = 0.90), reading attitude/ability (load- ing=0.66) and reading time (loading=0.64); Factor 2 explains 21.67% of variance, including E-device reading
(loading=0.97) and non-reading time (loading=0.31). Given these two factors, we simplifed the RBQ variables
into two scores: E-device reading index (summation of E-device reading and non-reading time) and reading
preference index (summation of reading preference, reading attitude/ability and reading time).
Behavioural data analyses. To test what cognitive measures contribute to participants’ reading compre- hension behaviourally, we performed non-parametric correlation tests checking correlations between GSRT or
question-answering accuracy with the Raven’s scores, LNS scores, the ANT Alerting, Orienting, and Confict
scores, the RBQ E-device reading and reading preference indices. Because the mean accuracy of the perfor- mance assessment scores and the GSRT scores violated the assumption of normality (Shapiro-Wilk W test, both
ps<0.01), one-tailed non-parametric Spearman’s correlations were used.
Eye-tracking data acquisition and processing. Te basic idea of fxation-related fMRI paradigm, as
frst explored by Marsman et al.
86, is to use self-paced eye-movements to convolve the hemodynamic responses
and model the psychological regressors to analyse fMRI data of visual processing. Later studies45,87 have further
demonstrated the validity of simultaneous eye-tracking and fMRI paradigms in naturalistic word and text read- ing. Eye movements were recorded with an Eye-Link 1000 Plus long-range mount MRI eye tracker (SR-Research)
with a sampling rate of 1kHz. Te camera was placed at the rear end of the scanner bore, and captured eye move- ments via a refective mirror above the head coil. Te distance between the camera and the participant’s eyes via
the refective mirror was 120 cm. Recording was monocular (from the right eye), and the participant’s head was
stabilized in the head coil. A 13-point calibration routine preceded the experiment. Before each reading session,
a validation procedure is performed, and re-calibration is done when the validation error is larger than 1 degree.
Data adjustment was later performed to address drifing issues caused by the calibration accuracy decline over
time. For fxations falling outside (above or below) the range of predefned target regions, manual adjustment was
performed using the Data Viewer sofware. Instead of using auto-adjustment which brings all fxations onto one
horizontal line, we performed trial-by-trial correction adjusting all of the fxations in a single try only along the y
axis (vertical adjustment) so as to maintain readers’ original eye fxation patterns.
MRI data acquisition. Data were acquired using a 3 T Siemens Magnetom Prisma Fit scanner with a
64-channel phased array coil. We acquired a MPRAGE scan with T1 weighted contrast [176 ascending sagit- tal slices with A/P phase encoding direction; voxel size = 1 mm isotropic; FOV = 256 mm; TR = 1540 ms;
TE=2.34ms; acquisition time=216 s; fip angle=9°; GRAPPA in-plane acceleration factor=2; brain coverage
is complete for cerebrum, cerebellum and brain stem]. Afer the T1, we acquired fve functional runs of T2*
weighted echo planar sequence images [30 interleaved axial slices with A/P phase encoding direction; voxel
size=3×3×4mm; FOV=240mm; TR=400ms; TE=30ms; acquisition time varied on the speed of self-paced
reading, maximal 306 s; multiband acceleration factor for parallel slice acquisition=6; fip angle=35°; brain
coverage misses the top of the parietal lobe and the lower end of the cerebellum]. Additionally, we collected a
pair of spin echo sequence images with A/P and P/A phase encoding direction [30 axial interleaved slices; voxel
size=3×3×4mm; FOV=240mm; TR=3000ms; TE=51.2ms; fip angle=90°] to calculate distortion correc- tion for the multiband sequences88.
fMRI data preprocessing and analyses. Data preprocessing and analysis were performed in SPM12
v6906 (http://www.fl.ion.ucl.ac.uk/spm). Functional imaging preprocessing consisted of correction of feld
inhomogeneity artefacts with the HySCO toolbox (Hyperelastic Susceptibility Artifact Correction)89 using the
pair of spin echo sequence images and realignment for motion correction. Te structural image was coregistered
to the mean functional image, and segmented into grey matter, white matter, cerebrospinal fuid, bone, sof tis- sue, and air/background to estimate the forward deformation parameters to MNI space. Images were normal- ized with the 4th degree B-Spline Interpolation algorithm and further smoothed with a Gaussian kernel of 8mm
full-width-at-half-maximum (FWHM).
In the GLM analysis, the design matrix contained one psychological regressor of interest, the “Content Word”
condition, specifying the onsets and gaze durations of frst pass fxations and regressions for content words
(informed by eye-tracking data). Te index of word position in sentences (starting from 1) was incorporated
as a parametric modulator of the “Content Word” condition. We also included two psychological regressors of
non-interest: “Non-Content Word” and “Instructions”: the “Non-Content Word” condition modelled fxations
on non-content (function) words and ocular regressions, and the “Instructions” condition modelled two sec- onds of instructions presented at the beginning of each run. Because of the self-paced reading, all psychological
regressors at the frst level were subject-specifc. Finally, we included six motion parameters and three physio- logical regressors (white matter, ventricular, and non-ventricular CSF space signal). We then applied a high pass
flter with a cut of period of 128 s, and the temporal autocorrelation was accounted for with the FAST option in
SPM1290. Ten, we calculated fxed efects across all runs for each subject. At the group level, two random-efect
one sample t-tests (N=51) were performed for the efects of reading in general (Content Word fxation), and
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Integrative Processing (parametric efect of word positions). We applied peak-level family-wise error (FWE) cor- rection of p<0.05, minimal cluster size=5 voxels, for the main efects of both one sample t-tests.
At the group-level, the beta maps of the Integrative Processing obtained at the subject-level were entered into
one multiple regression model as the dependent variable (N=46). Te following eight independent variables
were included to checked the efect of individual diferences: (1) GSRT, (2) Raven’s, (3) span-weighted LNS, the
(4) Alerting, (5) Orienting, and (6) Confict efects of the ANT, (7) the RBQ E-device reading index and (8) the
RBQ reading preference index. At the whole brain level, we applied cluster-level FWE-correction p<0.05, using
a cluster-defning threshold of p=0.001.
To further investigate Integrative Processing due to the efects of textual KS (measured as MBC, see Materials
in the Methods), the beta maps of Integrative Processing of each text were entered into a subject-level regression
model including the linear and quadratic terms of MBC as the independent variable. At the group level, the beta
maps of quadratic MBC correlates of the Integrative processes were entered into an one-sample t-test (N=50) for
the main efect and a multiple regression model (N=45) with the same eight independent variables for individ- ual diferences as mentioned before. We applied cluster-level FWE-correction p<0.05, using a cluster-defning
threshold of p=0.001, for the main efects of the one-sample t-test and for each individual diference in the mul- tiple regression model of MBC.
Data Availability
All behavioural, eye-tracking and neuroimaging data (with personal information de-identifed) have been made
available on OpenNeuro (https://openneuro.org/datasets/ds001980/versions/1.0.1) and on the PI’s lab website
(http://blclab.org/).
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Acknowledgements
Tis study was supported by grants from the National Science Foundation (BCS-1633817, BCS-1533625) to
P.L. and R.C. We thank current and former members of the Reading Brain project, the Brain, Language, and
Computation Lab for their assistance and contribution to this project (especially Shin-Yi Fang, Rose Yuratovac,
Jake Follmer, Lena Kremin and Tanner Quiggle). We also thank the Center for NMR Research of the Penn State
Milton S. Hershey Medical Center that has enabled the smooth collection of data reported in this study (especially
Emma Cartisano, YunQing Li, Sebastian Rupprecht, Chris Sica, Qing X. Yang, Jef Vesek, and Jian-li Wang).
Author Contributions
C.-T.H., R.C., B.S. and P.L. conceptualized the overall project, interpreted the results and edited the manuscript.
C.-T.H. and B.S. established the fMRI experimental paradigm, collected the behavioural and fMRI data, and
analysed fMRI data. R.C. performed the textual KS visualisation and the calculation of MBC values. C.-T.H.
conducted behavioural data analysis and prepared all fgures. C.-T.H. and P.L. wrote the manuscript.
Additional Information
Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-47176-7.
Competing Interests: Te authors declare no competing interests.
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