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brain
sciences
Article
How Visual Word Decoding and Context-Driven Auditory
Semantic Integration Contribute to Reading Comprehension:
A Test of Additive vs. Multiplicative Models
Yu Li 1, Hongbing Xing 2, Linjun Zhang 3,*, Hua Shu 4and Yang Zhang 5,*
Citation: Li, Y.; Xing, H.; Zhang, L.;
Shu, H.; Zhang, Y. How Visual Word
Decoding and Context-Driven
Auditory Semantic Integration
Contribute to Reading
Comprehension: A Test of Additive
vs. Multiplicative Models. Brain Sci.
2021,11, 830. https://doi.org/
10.3390/brainsci11070830
Academic Editor: Clara Casco
Received: 16 May 2021
Accepted: 21 June 2021
Published: 23 June 2021
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Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1Division of Science and Technology, BNU-HKBU United International College, Zhuhai 519087, China;
yuli@uic.edu.cn
2Institute on Education Policy and Evaluation of International Students, Beijing Language and Culture
University, Beijing 100083, China; xinghb@blcu.edu.cn
3Beijing Advanced Innovation Center for Language Resources and College of Advanced Chinese Training,
Beijing Language and Culture University, Beijing 100083, China
4State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University,
Beijing 100875, China; shuh@bnu.edu.cn
5Department of Speech-Language-Hearing Sciences and Center for Neurobehavioral Development,
University of Minnesota, Minneapolis, MN 55455, USA
*Correspondence: zhanglinjun@blcu.edu.cn (L.Z.); zhang470@umn.edu (Y.Z.)
Abstract:
Theories of reading comprehension emphasize decoding and listening comprehension as
two essential components. The current study aimed to investigate how Chinese character decoding
and context-driven auditory semantic integration contribute to reading comprehension in Chinese
middle school students. Seventy-five middle school students were tested. Context-driven auditory
semantic integration was assessed with speech-in-noise tests in which the fundamental frequency
(F
0
) contours of spoken sentences were either kept natural or acoustically flattened, with the latter
requiring a higher degree of contextual information. Statistical modeling with hierarchical regression
was conducted to examine the contributions of Chinese character decoding and context-driven
auditory semantic integration to reading comprehension. Performance in Chinese character de-
coding and auditory semantic integration scores with the flattened (but not natural) F
0
sentences
significantly predicted reading comprehension. Furthermore, the contributions of these two factors
to reading comprehension were better fitted with an additive model instead of a multiplicative
model. These findings indicate that reading comprehension in middle schoolers is associated with
not only character decoding but also the listening ability to make better use of the sentential context
for semantic integration in a severely degraded speech-in-noise condition. The results add to our
better understanding of the multi-faceted reading comprehension in children. Future research could
further address the age-dependent development and maturation of reading skills by examining
and controlling other important cognitive variables, and apply neuroimaging techniques such as
functional magmatic resonance imaging and electrophysiology to reveal the neural substrates and
neural oscillatory patterns for the contribution of auditory semantic integration and the observed
additive model to reading comprehension.
Keywords:
reading comprehension; speech-in-noise recognition; nature F
0
contours; flattened F
0
contours; Chinese character decoding
1. Introduction
Reading comprehension involves the construction of literal and inferred meanings
from linguistic input in print, which requires cognitive operations at lower-level processes
of visual feature extraction and word decoding as well as higher-level processes of lexical
access, syntactic analysis, and semantic integration. While different levels of cognitive and
linguistic processing might have unique contributions to reading comprehension
[1–5]
,
Brain Sci. 2021,11, 830. https://doi.org/10.3390/brainsci11070830 https://www.mdpi.com/journal/brainsci
Brain Sci. 2021,11, 830 2 of 13
these processes can be simplified to two separate components, i.e., decoding (recognizing
printed words) and linguistic comprehension (taking lexical information and deriving
sentence and discourse interpretations with listening comprehension most often mea-
sured as a substitute) [
1
,
6
]. Specifically, the Simple View of Reading (SVR) postulates
that once isolated printed words are recognized, the remaining processes in reading com-
prehension are analogous to those involved in listening comprehension. At the neural
level, functional magnetic resonance imaging studies have revealed that in addition to
modality-specific brain activation, the two modalities of language comprehension activate
common brain regions of the temporal and inferior frontal cortices associated with semantic
processing
[7–9],
thus providing neural evidence for shared cognitive processes underlying
reading and listening comprehension. Of these cognitive processes, semantic integration is
an essential bridging element by which listeners integrate lexical–semantic information
with pragmatic and sociolinguistic knowledge to reach an interpretation that reflects se-
mantic representations [
10
]. Given the critical role of auditory semantic integration in the
context of discourses in listening comprehension, the abilities to recognize printed words
and make use of top-down contextual semantic information could serve as good predictors
for reading comprehension.
During speech recognition and understanding, top-down semantic integration is
particularly important in adverse conditions in which acoustic–phonetic analysis alone
is insufficient for the identification of particular words because of suboptimal listening
backgrounds and/or poor speech signal quality. Specifically, the presence of various
types of interference such as broadband noise and one-/multi-talker babbles deteriorates
speech intelligibility dramatically, but listeners are able to use semantic context to offset
the detrimental effects to a great extent [
11
–
14
]. Similarly, semantic context is also used
by listeners to aid speech recognition and comprehension when the speech signal itself
is degraded [
15
,
16
]. Furthermore, when a degraded speech signal is presented against
interference, listeners benefit even more from semantic context. For example, the intelli-
gibility difference between acoustically degraded sentences and semantically unrelated
words is much greater when they are presented in suboptimal listening backgrounds than
in quiet, indicating that listeners rely more on the top-down semantic context to aid speech
recognition and comprehension in adverse conditions [16–18].
The development of the auditory semantic integration ability to help speech recogni-
tion and comprehension occurs during early age and continues to improve over the entire
childhood. While children are able to use semantic context to assist speech recognition in
quiet as early as 2 years old [
19
], the ability to use semantic context in adverse listening back-
grounds continues to improve into adolescence. Previous studies on speech intelligibility of
high- and low-predictability sentences in children at 5–17 years of age consistently showed
that contextual semantic cues assisted children of all ages in identifying words masked by
babbles, with older children performing better than younger children in the high-context
conditions [
20
–
22
]. For example, elementary school children aged 11 and 13 years per-
formed significantly poorer than middle school children aged 15 and 17 years on speech
recognition in noise, and this difference occurred primarily for high-predictability sentences
presented at the 0 dB signal-to-noise (SNR) ratio [
20
]. In addition, there are developmental
changes in children’s ability to use semantic context to decode degraded signals in quiet
and interfering backgrounds [
23
,
24
]. For example, children aged 5–7 years required as
much acoustic–phonetic information with high-predictability as with low-predictability
contexts to identify target words presented in quiet. By contrast, children aged 8–10 years
required shorter portions of the target words (i.e., less acoustic–phonetic information) for
recognition in high-predictability than in low-predictability sentences [
24
]. Our previous
study [
25
] showed that elementary school children aged 10 years could not make better
use of semantic context in recognizing speech with flattened fundamental frequency (F
0
)
contours compared to speech with natural F
0
contours. By contrast, middle school children
aged 14 years benefited more from semantic context when natural F
0
contours were altered,
regardless of whether the spoken sentences were presented in quiet or suboptimal listening
Brain Sci. 2021,11, 830 3 of 13
backgrounds. These findings suggest that semantic context can be better used to facilitate
listening comprehension and speech recognition by middle school students compared with
younger children.
Reading is a cultivated process that maps written symbols to phonological and seman-
tic representations, and learning to read universally involves solid and stable interactions
between written and spoken languages through a large amount of training in schools or
other learning experiences. Chinese has a logographic writing system, which is markedly
different from alphabetic languages. In alphabetic languages, graphemes that correspond
to phonemes of spoken languages are used as visual symbols and word decoding follows
grapheme-to-phoneme conversion rules. In written Chinese, however, characters are used
as basic writing units with no parts in a character corresponding to phonemes. It is never
the case in the Chinese writing system that a phonetic component maps onto a sub-syllabic
phonological representation in the way that a letter maps onto a part of a word’s phono-
logical form in an alphabetic system [
26
]. Furthermore, the layout of graphemes in an
alphabetic language is linear, whereas a Chinese character is composed of intricate strokes
packed into a square configuration. Full literacy in Chinese requires knowledge of between
3000 and 4000 different characters and it relies to a large degree on rote memory, which is in
sharp contrast to that of alphabetic languages which use a relatively limited number of sym-
bols (typically 22–30 or so letters of the alphabet) to produce all words in the languages [
27
].
Therefore, learning to read can present a much greater challenge for Chinese-speaking
children than for children who speak alphabetic languages, and there is evidence that the
challenge persists into the late stage of reading development. It takes children a longer
time to achieve decoding accuracy when learning to read in a deep orthography compared
with a shallow orthography [
28
]. Moreover, studies on alphabetic languages have revealed
that the relation between decoding and reading comprehension decreases sharply after
age 9–10 years, indicating a developmental transition [
29
,
30
]. Considering the opaqueness
and visual complexity of Chinese orthography, the transition may be much delayed, and
probably occurs after elementary schooling. Indeed, previous studies of Chinese reading
have suggested that decoding is still a strong predictor of reading comprehension in middle
school children aged 13–14 years [31–33]. The correlation coefficients (0.55–0.70) reported
in these studies are higher than those found in English-speaking children with similar age
(0.41 in [
30
]). However, during the development of Chinese reading ability, it remains
unclear how the processes of character decoding and contextual semantic integration for
speech recognition may independently or interactively contribute to reading comprehen-
sion. This critical issue is particularly pertinent to the middle school students who have
already got through the early stage of learning to read with a sizable vocabulary [
34
] but
are still facing the challenge of reading development in Chinese.
Based on these extant studies on the development of auditory semantic integration
ability and character decoding and the SVR theory, it can be predicted that these two
abilities can serve as good predictors of reading comprehension in Chinese middle school
children. However, to our knowledge, no previous studies have investigated this issue
systematically. This model-testing study aimed to fill this gap by examining the relative
contributions of the two subskills to reading comprehension. The Chinese language is
particularly interesting and suitable for our purpose due to its special orthographic system
that takes considerable amounts of time to learn and a phonological system that deploys
pitch contour variations at each syllable (known as the lexical tones) for different words.
Specifically, to assess the role of contextual semantic integration in speech recognition, we
introduced acoustic manipulations in two speech-in-noise conditions, one with natural F
0
contours kept in the target sentences presented against interfering background speech, and
the other with flattened F
0
contours that disrupted the critical cue for Chinese lexical tones
for proper word recognition. This speech-in-noise test protocol with Mandarin Chinese
materials was previously adopted in a number of studies on elementary school students,
middle school students, and adults, including the elderly population [
16
,
18
,
25
], and the
results demonstrate that greater auditory semantic integration at the sentence level is
Brain Sci. 2021,11, 830 4 of 13
required to recognize the words in the F
0
-degraded condition. Furthermore, two statistical
models, i.e., the multiplicative model (product of the two subskills) and additive model
(sum of the two subskills), were tested to clarify how the subskills would predict the
variance in reading comprehension. The multiplicative model predicts that if an individual
performs poorly on one subskill, reading comprehension would also be poor no matter
how he or she performs on the other subskill. The additive model predicts that reading
comprehension is adequately explained by the linear combination of the two subskills.
There has been mounting evidence from studies of reading comprehension that the additive
model of decoding and linguistic comprehension fitted data well and could provide a
better fit than the multiplicative model of the two subskills, and beyond the additive
model, the multiplicative model did not provide further significant contribution to reading
comprehension in alphabetic languages [
35
–
37
] and Chinese [
38
]. Thus, we hypothesized
that the additive model might provide better predictive modeling results for our data.
The findings of the present study will add to our understanding of the cognitive model
of reading to further delineate the developmental trajectory of reading skills, which can
provide the foundation to examine the brain mechanisms underlying the acquisition of
normal reading skills in children as well as the disruptive neural correlates accompanying
developmental dyslexia [39–44].
2. Methods and Materials
2.1. Participants
Seventy-five middle school students (37 females; mean age, 14.2 years; age range,
13.25 to 15.44 years; 59 Grade 8, 16 Grade 9) were recruited from a middle school located in
Beijing to participate in this study. Each had normal or corrected-to-normal vision, and
had no known hearing impairments or history of neurological disorders. Participation was
voluntary with consent obtained from their legal guardians. This study was approved by
the Institute Review Board of Beijing Normal University in compliance with the Declaration
of Helsinki for the protection of human subjects.
2.2. Test Protocols and Measures
Three independent tests were carried out, measuring Chinese character decoding,
semantic integration during speech recognition against interfering background speech,
and reading comprehension, respectively. The children were individually tested in quiet
rooms with ambient noise level below 45 dB(A). Details of these test procedures are
described below.
2.3. Chinese Character Decoding Test
The test that was used to estimate children’s decoding ability consisted of 150 charac-
ters. The first 40 items were taken from a Chinese character decoding test for kindergarten
children [
45
]. One hundred of the remaining 110 characters were taken from Chinese
language textbooks for elementary school children with 20 items from each grade level
from Grades 2 to 6 [
46
]. The last 10 characters had not been introduced in textbooks
for both kindergarten and elementary school children. There were 42 regular phonetic
characters, 62 irregular phonetic characters, and 46 non-phonetic characters in the test. Of
these characters, low-frequency (less than 10 per million) characters accounted for 15.3%,
i.e., 23 characters, intermediate-frequency (between 10 and 100 per million) characters
48.7%, i.e., 73 characters, and high-frequency (above 100 per million) characters 36%, i.e.,
54 characters. Four Mandarin tones were all covered in the test. The number of strokes
in a character ranged from 2 to 19 and the mean stroke number was 10.2. During the
test, children were asked to read the characters aloud one by one and the testing was
terminated after reading 15 consecutive items incorrectly. Each character was worth one
mark. The performance score was the number of correctly pronounced characters. This
test was widely used in our previous studies of Chinese child reading and demonstrated
excellent reliability and validity [40,47,48].
Brain Sci. 2021,11, 830 5 of 13
2.4. Auditory Semantic Integration during Speech Recognition against Interference
Performance in speech recognition against interference was used to indicate auditory
semantic integration ability. Stimuli in this speech recognition test were taken from a
corpus used in our previous study [
25
] and only a brief description is provided here. In
this speech-in-noise test, two types of sentences, i.e., sentences with natural and flattened
F
0
contours, were used as targets. Sentences with natural F
0
contours were read by a male
native Chinese speaker and the monotonous sentences with flat F
0
contours were created
by flattening the natural F
0
contours at each sentence’s mean F
0
. Masker stimuli were
consonant-misplaced sentences, which were constructed based on the normal sentences.
Specifically, the initial consonant of every syllable in each normal sentence was replaced
with another consonant as long as the phonotactic rules of Chinese were not violated.
Consonant-misplaced sentences were unintelligible at both word and sentence levels, thus
having minimal effect of informational masking. For example, the consonant-misplaced
sentence “zhi
1
ping
1
zai
4
tui
4
zhen
1
di
3
kao
3
” is constructed based on the normal sentence
“chi
1
qing
1
cai
4
dui
4
shen
1
ti
3
hao
3
(Eating vegetables is good for health)”. A female native
Mandarin speaker read the masker sentences, which was to enable easy separation of the
target message read by a male from the interfering speech (Figure 1). The target and masker
sentences were first edited to be at sound pressure levels of 75 and 70 dB, respectively, and
then combined to form the speech against interference stimuli with SNR level set at +5 dB
(refer to the supplementary materials for the sample stimuli).
Brain Sci. 2021, 11, x FOR PEER REVIEW 6 of 13
were from our early work (Lei et al., 2011) and the last 10 items were developed after-
wards. There were more than 3000 characters contained in all items and all participants
were familiar with the characters and words. Of these 100 items, 50 items were semanti-
cally correct. Semantically incorrect sentences were made by contradicting inferences
from the sentences with world knowledge or knowledge of syntax (e.g., the sun rises from
the west every day). In the task, each child was given 3 min to read as many items as
possible and indicate whether they were semantically correct or not using ‘√’ or ‘×’ symbol
at the end of each item. Three examples were given to ensure understanding of the task
before the actual test. The performance score was the number of correctly judged items.
Excellent reliability and validity were demonstrated in our previous work on children
with dyslexia and typically developing children [40,47,51].
2.6. Data Analysis
Partial correlation analyses after controlling for age and sex were first carried out to
examine the relationship between different measures. Hierarchical regression analyses
were then conducted to extract the relative contributions of character decoding and audi-
tory semantic integration to reading comprehension, with different orders of the perfor-
mance in the tests entered into the analyses and the relative contributions of the additive
model and multiplicative model. The additive model was defined as the total variance of
character decoding and speech recognition no matter what order entered into the hierar-
chical regression analyses was used, and the multiplicative model as the variance of the
multiplication of the two factors (see [52] for the same approach).
Figure 1. Acoustic features of sample speech stimuli. (A) Broadband spectrograms (SPG: 0 to 5 kHz)
and fundamental frequency (F
0
: 50 to 600 Hz) contours are displayed for a sentence with natural F
0
contours and its pitch-flattened counterpart; (B) An interfering sentence with natural F
0
contours.
Note: The samples are transcribed in Pinyin (the official Romanization system for Standard Chinese)
and the figures in the upper right corners signify lexical tones.
3. Results
Performance scores in the Chinese character decoding, speech recognition against
interference, and reading comprehension are summarized in Table 1. A paired-samples t-
test analysis revealed that recognition of speech with normal F
0
contours was significantly
Figure 1.
Acoustic features of sample speech stimuli. (
A
) Broadband spectrograms (SPG: 0 to 5 kHz)
and fundamental frequency (F
0
: 50 to 600 Hz) contours are displayed for a sentence with natural F
0
contours and its pitch-flattened counterpart; (
B
) An interfering sentence with natural F
0
contours.
Note: The samples are transcribed in Pinyin (the official Romanization system for Standard Chinese)
and the figures in the upper right corners signify lexical tones.
The speech stimuli were delivered via a pair of Edifier R18 loudspeakers with the
sound level set at 65 dB SPL calibrated at the approximate center position of the listener’s
head. The participants were instructed to listen to the target sentences in the male voice
carefully and verbally repeat what they heard for the sentences with natural F
0
contours
and what the sentences should be for the sentences with natural F
0
contours, as commonly
administered in a typical speech intelligibility test. Before the actual test phase, all children
participated in a brief practice session representing samples of the experimental conditions.
This test protocol adopted a self-paced paradigm, and the participants were encouraged
Brain Sci. 2021,11, 830 6 of 13
to take a guess when they were not sure which words they had heard. Each child was
presented with a total of 28 sentences, half with natural F
0
contours and the other half
with flat F
0
contours. The speech materials were prepared to ensure that each participant
did not listen to the same sentence with natural or flat F
0
contours and the order of F
0
patterns was counterbalanced across the participants. The responses were recorded and
scored by the first author of this paper and checked by an independent auditor blind to the
experiment. A strict score standard was adopted. Specifically, only words with consonants,
vowels, and lexical tones all correctly identified were considered correct answers. The
performance score was the proportion of the number of correctly reported words, which
was then converted to rationalized arcsine transform units (RAU) [
49
] for the final statistical
analyses. The use of RAU transformation helps to reduce ceiling-level saturation and
restore the homoscedasticity required of normal distribution for parametric statistical tests,
as the distribution of the scores, i.e., proportion data, was not strictly Gaussian.
2.5. Reading Comprehension Test
The test, developed by following the procedure in Moll, Fussenegger, Willburger, and
Landerl [
50
], consists of 100 sentences or short passages with the number of characters
(including punctuations) in each item gradually increasing from 7 to 159. The first 90 items
were from our early work (Lei et al., 2011) and the last 10 items were developed afterwards.
There were more than 3000 characters contained in all items and all participants were
familiar with the characters and words. Of these 100 items, 50 items were semantically
correct. Semantically incorrect sentences were made by contradicting inferences from the
sentences with world knowledge or knowledge of syntax (e.g., the sun rises from the west
every day). In the task, each child was given 3 min to read as many items as possible and
indicate whether they were semantically correct or not using ‘
√
’ or ‘
×
’ symbol at the end
of each item. Three examples were given to ensure understanding of the task before the
actual test. The performance score was the number of correctly judged items. Excellent
reliability and validity were demonstrated in our previous work on children with dyslexia
and typically developing children [40,47,51].
2.6. Data Analysis
Partial correlation analyses after controlling for age and sex were first carried out to ex-
amine the relationship between different measures. Hierarchical regression analyses were
then conducted to extract the relative contributions of character decoding and auditory
semantic integration to reading comprehension, with different orders of the performance in
the tests entered into the analyses and the relative contributions of the additive model and
multiplicative model. The additive model was defined as the total variance of character
decoding and speech recognition no matter what order entered into the hierarchical regres-
sion analyses was used, and the multiplicative model as the variance of the multiplication
of the two factors (see [52] for the same approach).
3. Results
Performance scores in the Chinese character decoding, speech recognition against
interference, and reading comprehension are summarized in Table 1. A paired-samples
t-test analysis revealed that recognition of speech with normal F
0
contours was significantly
better than that of speech with flattened F
0
contours (t(74) = 9.69, p< 0.001), indicating that
the latter was more difficult for the listeners.
Table 1. Means, ranges, and standard deviations of each measure.
Measures M Range SD
Chinese character decoding 137 118–148 7.07
Recognition of speech with normal F0contours 0.93 0.31–1 0.12
Recognition of speech with flattened F0contours 0.80 0.23–1 0.14
Reading comprehension 74.51 46–91 9.57
Brain Sci. 2021,11, 830 7 of 13
Table 2depicts the partial correlations between different measures after controlling
for age and sex. The results show that both Chinese character decoding and reading
comprehension were positively correlated with recognition of speech with flattened F
0
con-
tours, but not significantly correlated with recognition of speech with normal F
0
contours.
Accuracy scores on the two speech recognition measures were positively correlated.
Table 2. Correlations between variables after controlling for age and sex.
1 2 3 4
1. Chinese character decoding -
2. Recognition of speech with normal F0contours a0.126 -
3. Recognition of speech with flattened F0contours a0.362 ** 0.547 *** -
4. Reading comprehension 0.384 *** 0.182 0.39 ** -
Note. a, transformed data. ** p< 0.01. *** p< 0.001.
Hierarchical regression analyses further revealed that Chinese character decoding
significantly accounted for the most variance in reading comprehension when it was en-
tered before recognition of speech with normal F
0
contours (13.1%), and the contribution
decreased slightly (11.7%) when speech recognition was entered before character decoding.
Recognition of speech with normal F
0
contours did not significantly contribute to the
variance in reading comprehension no matter if it was entered before or after Chinese char-
acter decoding (Table 3A,B). However, both Chinese character decoding and recognition of
speech with flattened F
0
contours significantly or marginally significantly accounted for
the variance in reading comprehension, with either of them contributing more when first
entered (Table 4A,B).
Table 3.
Hierarchical regression analyses predicting reading comprehension from Chinese character
decoding and recognition of speech with normal F0contours.
Reading Comprehension
Step Variables R2∆R2
1 Age 0.112 0.112 *
Sex
A
2 Chinese character decoding 0.243 0.131 ***
3 Recognition of speech with normal F0contours a0.252 0.009
B
2 Recognition of speech with normal F0contours a0.135 0.023
3 Chinese character decoding 0.252 0.117 ***
C
2Chinese character decoding ×
Recognition of speech with normal F0contours a0.219 0.106 **
Note. a, transformed data. * p< 0.05. ** p< 0.01. *** p< 0.001.
The hierarchical regression analyses also revealed that the additive model contributed
to 14.0% of the variance in reading comprehension for recognition of speech with normal
F
0
contours and the percentage increased to 16.3% for recognition of speech with flattened
F
0
contours. By comparison, the multiplicative model contributed to 10.6% of the vari-
ance in reading comprehension for recognition of speech with normal F
0
contours and
the percentage increased to 14.8% for recognition of speech with flattened F
0
contours
(Tables 3C and 4C)
. These results indicate that the additive model accounted for more
variance than the multiplicative model.
Brain Sci. 2021,11, 830 8 of 13
Table 4.
Hierarchical regression analyses predicting reading comprehension from Chinese character
decoding and recognition of speech with flattened F0contours.
Reading Comprehension
Step Variables R2∆R2
1 Age 0.112 0.112 *
Sex
A
2 Chinese character decoding 0.243 0.131 ***
3 Recognition of speech with flattened F0contours a0.275 0.032 Ψ
B
2 Recognition of speech with flattened F0contours a0.200 0.088 **
3 Chinese character decoding 0.275 0.074 **
C
2Chinese character decoding ×
Recognition of speech with flattened F0contours a0.261 0.148 ***
Note. a, transformed data. Ψp< 0.09. * p< 0.05. ** p< 0.01. *** p< 0.001.
4. Discussion
Reading comprehension encompasses various components and the past decades have
witnessed great progress in the understanding of its complexity. Based on the Simple View
of Reading (SVR) framework, the present study examined the contribution of Chinese
character decoding and auditory semantic integration to reading comprehension among
Chinese middle school students who had largely passed the early stage of learning to read
during elementary schooling and were at the stage of reading to learn new knowledge [
34
].
The results show that Chinese character decoding significantly contributed to reading
comprehension irrespective of the extent of auditory semantic integration, indicating that
children who know a greater amount of characters have better reading comprehension.
Recognition of speech with natural F
0
contours did not significantly contribute to reading
comprehension; however, a significant contribution was observed when natural F
0
con-
tours were flattened, indicating that children who can make better use of sentence-level
contextual semantic integration in a more adverse condition have better reading ability.
Furthermore, based on the predictive power results, the contributions of the two subskills
to reading comprehension fitted better with the additive model than the multiplicative
model. By examining the contribution of auditory semantic integration to reading, the
current study highlights the complexity of reading comprehension.
Because of the lack of clear grapheme-to-phoneme mapping and complex layout of
characters in the Chinese logographic writing system, the number of recognized characters
plays a critical role in early reading development. For example, Joshi, Tao, Aaron, and
Quiroz [
53
] found that the performance in Chinese character decoding explained 22% of
the variance in reading comprehension among Grade 2 (about 8 years old) children and
32% in Grade 4 (about 10 years old) children. Together with their findings, the results of
the current study confirm that Chinese character decoding contributed significantly to
reading comprehension for middle school students, which suggests that the role of Chinese
character decoding persists throughout the reading development process before maturity.
Because different materials were adopted for the measurement of Chinese character decod-
ing and reading comprehension in the current study and Joshi et al.’s study, it is impossible
to make direct comparisons between the contributions of character decoding to reading
comprehension and obtain the developmental trend from elementary school to middle
school in children. Chung et al. [
31
–
33
] recently found high correlations
(rs > 0.50
) between
Chinese character decoding and reading comprehension in middle school children. These
findings indicate that it is still hard to establish a developmental trend in Chinese, at least
not in elementary and middle school children. Studies in alphabetic languages, however,
indicate an overall trend in which the importance of word decoding in reading comprehen-
sion decreases from elementary school to middle school [
30
]. Further investigations are
needed to explore the developmental trend in the Chinese logographic writing system. It
Brain Sci. 2021,11, 830 9 of 13
is noteworthy that Chinese character decoding ability can be measured with both accuracy
and fluency. In the current study, only accuracy scores were obtained. There has been some
evidence that Chinese character decoding fluency contributes to reading comprehension in
elementary school children [
53
,
54
]. Because middle school children are at the intermediate
stage from premature readers to mature readers [
34
], future longitudinal studies need to
examine which measure is a stronger predictor of reading comprehension.
As auditory semantic integration in the sentential context is essential to listening
comprehension [
10
], we predicted that it could serve as a good predictor for reading
comprehension. In the present study, the extent of semantic integration needed for recog-
nizing the target spoken sentences was manipulated by presenting two types of stimuli.
Specifically, in suboptimal listening conditions, greater auditory semantic integration is
required for successful recognition of speech with flattened F
0
contours compared with
speech with nature F
0
contours [
16
,
18
]. Interestingly, our results show that recognition
of speech with flattened F
0
contours contributed to reading comprehension, while the
contribution decreased dramatically when the natural F
0
contours in the target spoken
sentences were intact. These results indicate that the more auditory semantic integration is
involved in a speech recognition task, the more the listening task contributes to reading
comprehension. There are developmental changes for children to use semantic context to
aid speech recognition in adverse conditions. For example, our previous study showed that
middle school children could make better use of semantic context in recognizing speech
with flattened F
0
contours compared to speech with natural F
0
contours, but elementary
school children could not, although they could utilize semantic context when recognizing
speech with natural F
0
contours [
25
]. How the ability to make use of semantic information
to aid recognition of speech with natural F
0
contours contributes to reading comprehension
among elementary school children needs further investigation.
Regarding the statistical modeling issue of how word decoding and linguistic com-
prehension combine to explain the variance in reading comprehension, previous studies
have found that the additive model performed as well as or better than the multiplicative
model in explaining the variance in reading comprehension [
35
–
37
]. However, this issue
is still rarely examined in Chinese. A recent study on Chinese reading comprehension
among elementary school children revealed that, overall, the multiplicative model did not
contribute a significant amount of variance to reading comprehension in addition to the
additive model [
38
]. Although our findings are not meant to be directly compared with
those of the previous studies because different subskills were measured, all these studies
collectively provide evidence in support of the additive model from a general perspective.
Interestingly, the respective contributions of character decoding and recognition of speech
with flattened F
0
contours to reading comprehension depended on which one was first
entered. When speech recognition was first entered, the relative contribution of character
decoding was still significant but much reduced, and vice versa. These results echo the
multiplicative model, showing a higher contribution of the two components than either
of them. However, similar patterns were not observed for the recognition of speech with
natural F
0
contours. When the F
0
contours are flattened, speech recognition depends
more on the semantic context, which has been demonstrated in our previous studies cov-
ering both children and adults [
16
,
25
]. In particular, older children (middle-school age)
have been shown to be more adult-like, and benefit more from the semantic context than
younger ones when the F
0
contours are flattened. In the current study, the flattened F
0
condition appears to be better suited than the natural F
0
condition to tap into the top-down
processing mode of semantic integration, revealing the significant association between
visual word decoding and semantic integration for speech comprehension in the auditory
modality. There may be additional common mechanisms and cognitive resources that
mediate the presence or absence of a significant association between word decoding and
auditory comprehension. Uncovering these mediating cognitive processes is beyond the
scope of the current investigation, and we hope to clarify them in future studies.
Brain Sci. 2021,11, 830 10 of 13
Although the present study specifically examined the relative contributions of Chinese
character decoding and auditory semantic integration in adverse conditions to reading
comprehension in middle school students, it is limited in several aspects. Firstly, our
study was narrowly focused on testing the multiplicative vs. additive models based on
the theoretical framework of SVR. We measured only Chinese character decoding and
auditory semantic integration abilities but did not measure other reading-related linguistic
subskills (e.g., phonological awareness and vocabulary) and cognitive competence (e.g.,
non-verbal IQ and executive function) that also contribute to reading comprehension. The
relative contributions of these interrelated linguistic and cognitive subskills to reading
comprehension in Chinese middle school students were examined in Chinese primary
school students [
38
,
55
,
56
] and Chinese middle school children [
31
–
33
]. These findings
extended the original version of SVR on which we framed the current experimental design,
adding to the better understanding of the complexity of reading comprehension. Further
investigations are needed to understand how auditory semantic integration and character
decoding contribute to reading comprehension after controlling for these linguistic or
cognitive subskills with a more sophisticated multi-level statistical modeling approach,
including both fixed and random (such as test item and participant) factors. Secondly,
reading comprehension can be measured at various levels such as sentence, paragraph,
and passage. Long paragraph and passage comprehension require greater semantic inte-
gration than sentence or short passage comprehension used in the present study, which
might increase the contribution of auditory semantic integration during speech-in-noise
recognition. This issue needs further investigation.
The last two decades have witnessed fast development in neuroimaging techniques
such as event-related potentials and magnetic resonance imaging and the application
of these techniques in revealing neural correlates of cognitive processing. Previous re-
search has shown that the left posterior fusiform gyrus is much involved in visual word
form processing after the basic visual processing [
39
,
57
] and that listening and reading
comprehension is supported by widely distributed brain regions in the temporal and
inferior frontal cortices [
7
–
9
]. How these regions work in concert to efficiently support
the reading processes from visual word form analysis to semantic integration is still an
open question [
42
,
43
]. Neuroimaging techniques can be employed to reveal and charac-
terize functional and structural connections between the posterior fusiform gyrus and
the distributed temporal and frontal regions in typically developing children [
58
], and
thus could serve as a powerful tool to provide neural evidence for the additive model
of reading comprehension revealed here. Neuroimaging investigations may also help
to reveal how the disruption of these functional and structural connections is associated
with aberrant reading behaviors in children with dyslexia who are widely thought to
have word reading deficits [
41
,
59
]. Furthermore, cognitive neuroscience research has
revealed that neural oscillations play an important role in language processing [
60
–
62
].
For example, it has been shown that distinct neural oscillatory activities are involved in
speech comprehension [
60
,
63
–
65
], semantic integration for sentence reading [
66
], and story
reading [
67
]. Future studies could investigate neural oscillatory patterns in association
with the findings of the present study, specifically by examining neural oscillation evidence
for how visual word decoding and auditory semantic integration jointly contribute to
reading comprehension.
5. Conclusions
In conclusion, our findings reveal that in Chinese middle school students, Chinese
character decoding and auditory semantic integration during speech-in-noise recogni-
tion contributed to reading comprehension in an additive manner. The current study
emphasizes that in addition to decoding and linguistic comprehension measures that are
traditionally thought to be reliable predictors of reading comprehension, children’s perfor-
mance in speech recognition under some adverse conditions can also serve as a predictor of
reading comprehension, because a high degree of semantic integration is involved in these
Brain Sci. 2021,11, 830 11 of 13
conditions. These new findings from middle school students enrich our understanding
of the complexity and decomposition of reading comprehension from a developmental
perspective, which supports the additive model for conducting further cognitive brain
research with potential clinical implications for intervention with dyslexia.
Author Contributions:
Y.L., L.Z., H.S., and Y.Z. conceived and designed the experiment. Y.L., H.X.,
and L.Z. implemented the experiment, and collected and analyzed the data. Y.L., H.X., L.Z., H.S.,
and Y.Z. interpreted the data. Y.L., L.Z., H.S., and Y.Z. prepared and finalized the manuscript. All
authors have read and agreed to the published version of the manuscript.
Funding:
This research was supported by grants from the Humanities and Social Sciences Fund of
the Ministry of Education of China (20YJCZH079) and the United International College Research
Foundation (R202011, R72021207) to Y.L., and the Social Science Fund of Beijing (17YYA004), the
Discipline Team Support Program (JC201901), and the Science Foundation of Beijing Language and
Culture University (Fundamental Research Funds for the Central Universities) (18PT09) to L.Z. Y.Z.
was additionally funded by the Brain Imaging Grant from the College of Liberal Arts, University
of Minnesota.
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Institutional Review Board of Beijing Normal University
on 30 November 2018 (ICBIR_A_0021_014).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
The summary report of original data is included in the article; further
inquiries of raw data can be directed to the corresponding authors.
Acknowledgments: We thank all the children who participated in this study.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Gough, P.B.; Tunmer, W.E. Decoding, reading, and reading disability. Remedial Spec. Educ. 1986,7, 6–10. [CrossRef]
2.
Perfetti, C.A.; Landi, N.; Oakhill, J.V. The acquisition of reading comprehension skill. In The Science of Reading: A Handbook;
Snowling, M.J., Hulme, C., Eds.; Blackwell Publishing Ltd.: Oxford, UK, 2005; pp. 227–247.
3.
Vellutino, F.R.; Tunmer, W.E.; Jaccard, J.J.; Chen, R.S. Components of reading ability: Multivariate evidence for a convergent skills
model of reading development. Sci. Stud. Read. 2007,11, 3–32. [CrossRef]
4. McNamara, D.S.; Magliano, J. Toward a Comprehensive Model of Comprehension. Psychol. Learn. Motiv. 2009,51, 297–384.
5. Perfetti, C.; Stafura, J. Word Knowledge in a Theory of Reading Comprehension. Sci. Stud. Read. 2014,18, 22–37. [CrossRef]
6. Hoover, W.A.; Gough, P.B. The simple view of reading. Read. Writ. 1990,2, 127–160. [CrossRef]
7.
Buchweitz, A.; Mason, R.A.; Tomitch, L.M.; Just, M.A. Brain activation for reading and listening comprehension: An fMRI study
of modality effects and individual differences in language comprehension. Psychol. Neurosci. 2009,2, 111–123. [CrossRef]
8.
Regev, M.; Honey, C.J.; Simony, E.; Hasson, U. Selective and invariant neural responses to spoken and written narratives. J.
Neurosci. 2013,33, 15978–15988. [CrossRef]
9.
Deniz, F.; Nunez-Elizalde, A.O.; Huth, A.G.; Gallant, J.L. The Representation of Semantic Information Across Human Cerebral
Cortex During Listening Versus Reading Is Invariant to Stimulus Modality. J. Neurosci.
2019
,39, 7722–7736. [CrossRef] [PubMed]
10.
Cutler, A.; Clifton, C.J. Comprehending spoken language: A blueprint of the listener. In The Neurocognition of Language; Brown, C.,
Hagoort, P., Eds.; Oxford University Press: Oxford, UK, 1999; pp. 123–166.
11.
Dubno, J.R.; Ahlstrom, J.B.; Horwitz, A.R. Use of context by young and aged adults with normal hearing. J. Acoust. Soc. Am.
2000
,
107, 538–546. [CrossRef]
12.
Scott, S.K.; Rosen, S.; Wickham, L.; Wise, R.J. A positron emission tomography study of the neural basis of informational and
energetic masking effects in speech perception. J. Acoust. Soc. Am. 2004,115, 813–821. [CrossRef]
13.
Golestani, N.; Rosen, S.; Scott, S.K. Native-language benefit for understanding speech-in-noise: The contribution of semantics.
Biling-Lang. Cogn. 2009,12, 385–392. [CrossRef]
14.
Calandruccio, L.; Dhar, S.; Bradlow, A.R. Speech-on-speech masking with variable access to the linguistic content of the masker
speech. J. Acoust. Soc. Am. 2010,128, 860–869. [CrossRef] [PubMed]
15.
Patel, A.D.; Xu, Y.; Wang, B. The role of F0 variation in the intelligibility of Mandarin sentences. In Proceedings of the Speech
Prosody 2010-Fifth International Conference, Chicago, IL, USA, 10–14 May 2010.
16.
Wang, J.J.; Shu, H.; Zhang, L.J.; Liu, Z.X.; Zhang, Y. The roles of fundamental frequency contours and sentence context in
Mandarin Chinese speech intelligibility. J. Acoust. Soc. Am. 2013,134, El91–El97. [CrossRef] [PubMed]
Brain Sci. 2021,11, 830 12 of 13
17.
Binns, C.; Culling, J.F. The role of fundamental frequency contours in the perception of speech against interfering speech. J.
Acoust. Soc. Am. 2007,122, 1765. [CrossRef] [PubMed]
18.
Jiang, W.; Li, Y.; Shu, H.; Zhang, L.J.; Zhang, Y. Use of semantic context and F0 contours by older listeners during Mandarin
speech recognition in quiet and single-talker interference conditions. J. Acoust. Soc. Am.
2017
,141, El338–El344. [CrossRef]
[PubMed]
19.
Fernald, A. Making use of semantic context in early language understanding. In Proceedings of the Society for Research in Child
Development, Minneapolis, MN, USA, 19–22 April 2001.
20.
Elliott, L.L. Performance of children aged 9 to 17 years on a test of speech intelligibility in noise using sentence material with
controlled word predictability. J. Acoust. Soc. Am. 1979,66, 651–653. [CrossRef] [PubMed]
21.
Fallon, M.; Trehub, S.E.; Schneider, B.A. Children’s perception of speech in multitalker babble. J. Acoust. Soc. Am.
2000
,108,
3023–3029. [CrossRef]
22.
Stelmachowicz, P.G.; Hoover, B.M.; Lewis, D.E.; Kortekaas, R.W.; Pittman, A.L. The relation between stimulus context, speech
audibility, and perception for normal-hearing and hearing-impaired children. J. Speech Lang. Hear. Res.
2000
,43, 902–914.
[CrossRef]
23.
Cole, R.A.; Perfetti, C.A. Listening for mispronunciations m a children’s story: The use of context by children and adults. J. Verbal
Learn. Verbal Behav. 1980,19, 297–315. [CrossRef]
24.
Craig, C.H.; Kim, B.W.; Rhyner, P.M.; Chirillo, T.K. Effects of word predictability, child development, and aging on time-gated
speech recognition performance. J. Speech Hear. Res. 1993,36, 832–841. [CrossRef]
25.
Zhou, H.; Li, Y.; Liang, M.; Guan, C.Q.; Zhang, L.; Shu, H.; Zhang, Y. Mandarin-Speaking Children’s Speech Recognition:
Developmental Changes in the Influences of Semantic Context and F0 Contours. Front. Psychol.
2017
,8, 1090. [CrossRef]
[PubMed]
26.
Perfetti, C.A.; Liu, Y.; Tan, L.H. The lexical constituency model: Some implications of research on Chinese for general theories of
reading. Psychol. Rev. 2005,112, 43–59. [CrossRef]
27.
Li, H.; Shu, H.; McBride-Chang, C.; Liu, H.Y.; Peng, H. Chinese children’s character recognition: Visuo-orthographic, phonological
processing and morphological skills. J. Res. Read. 2012,35, 287–307. [CrossRef]
28.
Seymour, P.H.K. Early reading development in European orthographies. In The Science of Reading: A Handbook; Snowling, M.J.,
Hulme, C., Eds.; Blackwell Publishing Ltd.: Oxford, UK, 2005; pp. 296–315.
29.
Florit, E.; Cain, K. The Simple View of Reading: Is It Valid for Different Types of Alphabetic Orthographies? Educ. Psychol. Rev.
2011,23, 553–576. [CrossRef]
30.
Garcia, J.R.; Cain, K. Decoding and Reading Comprehension: A Meta-Analysis to Identify Which Reader and Assessment
Characteristics Influence the Strength of the Relationship in English. Rev. Educ. Res. 2014,84, 74–111. [CrossRef]
31.
Chung, K.K.; Ho, C.S.; Chan, D.W.; Tsang, S.M.; Lee, S.H. Contributions of syntactic awareness to reading in Chinese-speaking
adolescent readers with and without dyslexia. Dyslexia 2013,19, 11–36. [CrossRef] [PubMed]
32.
Chung, K.K.H.; Lo, J.C.M.; Ho, C.S.H.; Xiao, X.Y.; Chan, D.W. Syntactic and discourse skills in Chinese adolescent readers with
dyslexia: A profiling study. Ann. Dyslexia 2014,64, 222–247. [CrossRef] [PubMed]
33.
Chung, K.K.H.; Ho, C.S.H.; Chan, D.W.; Tsang, S.M.; Lee, S.H. Cognitive skills and literacy performance of Chinese adolescents
with and without dyslexia. Read. Writ. 2011,24, 835–859. [CrossRef]
34. Chall, J.S. Stages of Reading Development; McGraw-Hill Book: New York, NY, USA, 1983.
35.
Chen, R.S.; Vellutino, F.R. Prediction of reading ability: A cross-validation study of the simple view of reading. J. Lit. Res.
1997
,
29, 1–24. [CrossRef]
36.
Savage, R. Reading comprehension is not always the product of nonsense word decoding and linguistic comprehension: Evidence
from teenagers who are extremely poor readers. Sci. Stud. Read. 2006,10, 143–164. [CrossRef]
37.
Georgiou, G.K.; Das, J.P.; Hayward, D. Revisiting the “simple view of reading” in a group of children with poor reading
comprehension. J. Learn. Disabil. 2009,42, 76–84. [CrossRef]
38.
Yeung, P.S.; Ho, C.S.H.; Chan, D.W.O.; Chung, K.K.H. A componential model of reading in Chinese. Learn. Individ. Differ.
2016
,
45, 11–24. [CrossRef]
39.
McCandliss, B.D.; Cohen, L.; Dehaene, S. The visual word form area: Expertise for reading in the fusiform gyrus. Trends Cogn. Sci.
2003,7, 293–299. [CrossRef]
40.
Zhang, Y.J.; Zhang, L.J.; Shu, H.; Xi, J.; Wu, H.; Zhang, Y.; Li, P. Universality of categorical perception deficit in developmental
dyslexia: An investigation of Mandarin Chinese tones. J. Child Psychol. Psychiatry 2012,53, 874–882. [CrossRef] [PubMed]
41.
Finn, E.S.; Shen, X.; Holahan, J.M.; Scheinost, D.; Lacadie, C.; Papademetris, X.; Shaywitz, S.E.; Shaywitz, B.A.; Constable, R.T.
Disruption of functional networks in dyslexia: A whole-brain, data-driven analysis of connectivity. Biol. Psychiatry
2014
,76,
397–404. [CrossRef]
42. Protopapas, A.; Parrila, R. Is Dyslexia a Brain Disorder? Brain Sci. 2018,8, 61. [CrossRef] [PubMed]
43. Stein, J. What is Developmental Dyslexia? Brain Sci. 2018,8, 26. [CrossRef]
44.
Lohvansuu, K.; Torppa, M.; Ahonen, T.; Eklund, K.; Hamalainen, J.A.; Leppanen, P.H.T.; Lyytinen, H. Unveiling the Mysteries of
Dyslexia-Lessons Learned from the Prospective Jyvaskyla Longitudinal Study of Dyslexia. Brain Sci. 2021,11, 427. [CrossRef]
45.
Shu, H.; Peng, H.; McBride-Chang, C. Phonological awareness in young Chinese children. Dev. Sci.
2008
,11, 171–181. [CrossRef]
Brain Sci. 2021,11, 830 13 of 13
46.
Shu, H.; Chen, X.; Anderson, R.C.; Wu, N.N.; Xuan, Y. Properties of school Chinese: Implications for learning to read. Child Dev.
2003,74, 27–47. [CrossRef]
47.
Xue, J.; Shu, H.; Li, H.; Li, W.; Tian, X. The stability of literacy-related cognitive contributions to Chinese character naming and
reading fluency. J. Psycholinguist. Res. 2013,42, 433–450. [CrossRef]
48.
Xia, Z.C.; Hoeft, F.; Zhang, L.J.; Shu, H. Neuroanatomical anomalies of dyslexia: Disambiguating the effects of disorder,
performance, and maturation. Neuropsychologia 2016,81, 68–78. [CrossRef]
49. Studebaker, G.A. A “rationalized” arcsine transform. J. Speech Hear. Res. 1985,28, 455–462. [CrossRef]
50.
Moll, K.; Fussenegger, B.; Willburger, E.; Landerl, K. RAN is not a measure of orthographic processing. Evidence from the
asymmetric German orthography. Sci. Stud. Read. 2009,13, 1–25. [CrossRef]
51.
Xia, Z.C.; Zhang, L.J.; Hoeft, F.; Gu, B.; Gong, G.L.; Shu, H. Neural correlates of oral word reading, silent reading comprehension,
and cognitive subcomponents. Int. J. Behav. Dev. 2018,42, 342–356. [CrossRef] [PubMed]
52.
Johnston, T.C.; Kirby, J.R. The contribution of naming speed to the simple view of reading. Read. Writ.
2006
,19, 339–361.
[CrossRef]
53.
Joshi, R.M.; Tao, S.; Aaron, P.G.; Quiroz, B. Cognitive component of componential model of reading applied to different
orthographies. J. Learn. Disabil. 2012,45, 480–486. [CrossRef]
54.
Ho, C.S.H.; Zheng, M.; McBride, C.; Hsu, L.S.J.; Waye, M.M.Y.; Kwok, J.C.Y. Examining an extended simple view of reading in
Chinese: The role of naming efficiency for reading comprehension. Contemp. Educ. Psychol. 2017,51, 293–302. [CrossRef]
55.
Shu, H.; McBride-Chang, C.; Wu, S.; Liu, H.Y. Understanding Chinese developmental dyslexia: Morphological awareness as a
core cognitive construct. J. Educ. Psychol. 2006,98, 122–133. [CrossRef]
56.
Chik, P.P.M.; Ho, C.S.H.; Yeung, P.S.; Chan, D.W.O.; Chung, K.K.H.; Luan, H.; Lo, L.Y.; Lau, W.S.Y. Syntactic skills in sentence
reading comprehension among Chinese elementary school children. Read. Writ. 2012,25, 679–699. [CrossRef]
57.
Dehaene, S.; Cohen, L. The unique role of the visual word form area in reading. Trends Cogn. Sci.
2011
,15, 254–262. [CrossRef]
[PubMed]
58.
Li, Y.; Zhang, L.; Xia, Z.; Yang, J.; Shu, H.; Li, P. The Relationship between Intrinsic Couplings of the Visual Word Form Area with
Spoken Language Network and Reading Ability in Children and Adults. Front. Hum. Neurosci. 2017,11, 327. [CrossRef]
59. Lyon, G.R.; Shaywitz, S.E.; Shaywitz, B.A. A definition of dyslexia. Ann. Dyslexia 2003,53, 1–14. [CrossRef]
60.
Peelle, J.E.; Davis, M.H. Neural oscillations carry speech rhythm through to comprehension. Front. Psychol.
2012
,3, 320.
[CrossRef] [PubMed]
61. Murphy, E. The Oscillatory Nature of Language; Cambridge University Press: Cambridge, UK, 2020.
62.
Goswami, U. A neural oscillations perspective on phonological development and phonological processing in developmental
dyslexia. Lang. Linguist. Compass 2019,13, e12328. [CrossRef]
63.
Kosem, A.; van Wassenhove, V. Distinct contributions of low- and high-frequency neural oscillations to speech comprehension.
Lang. Cogn. Neurosci. 2017,32, 536–544. [CrossRef]
64.
Meyer, L. The neural oscillations of speech processing and language comprehension: State of the art and emerging mechanisms.
Eur. J. Neurosci. 2018,48, 2609–2621. [CrossRef]
65.
Prystauka, Y.; Lewis, A.G. The power of neural oscillations to inform sentence comprehension: A linguistic perspective. Lang.
Linguist. Compass 2019,13, e12347. [CrossRef]
66.
Bastiaansen, M.; Hagoort, P. Frequency-based Segregation of Syntactic and Semantic Unification during Online Sentence Level
Language Comprehension. J. Cognit. Neurosci. 2015,27, 2095–2107. [CrossRef]
67.
Kujala, J.; Pammer, K.; Cornelissen, P.; Roebroeck, A.; Formisano, E.; Salmelin, R. Phase coupling in a cerebro-cerebellar network
at 8–13 Hz during reading. Cereb. Cortex 2007,17, 1476–1485. [CrossRef]
