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Scientific Reports | (2019) 9:10678 | https://doi.org/10.1038/s41598-019-47176-7
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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