PLOS ONERESEARCH ARTICLE

Sentential inference bridging between lexical/
grammatical knowledge and text
comprehension among native Chinese
speakers learning Japanese
Katsuo Tamaoka1,2, Hiromu Sakai3, Yayoi Miyaoka4, Hajime Ono5, Michiko Fukuda6,
Yuxin Wu7, Rinus G. Verdonschot ID8*

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1 Hunan University, Changsha, China, 2 Nagoya University, Nagoya, Japan, 3 Waseda University, Shinjuku
City, Japan, 4 Hiroshima University of Economics, Hiroshima, Japan, 5 Tsuda University, Kodaira, Japan,
6 Bunkyo University, Koshigaya, Japan, 7 Xi’an International Studies University, Xi’an, China, 8 Max Planck
Institute for Psycholinguistics in Nijmegen, Nijmegen, The Netherlands* rinus.verdonschot@mpi.nl

Abstract
OPEN ACCESS
Citation: Tamaoka K, Sakai H, Miyaoka Y, Ono H,
Fukuda M, Wu Y, et al. (2023) Sentential inference
bridging between lexical/grammatical knowledge
and text comprehension among native Chinese
speakers learning Japanese. PLoS ONE 18(4):e0284331. https://doi.org/10.1371/journal.
pone.0284331
Editor: Lawrence Jun Zhang, The University of
Auckland, NEW ZEALAND
Received: December 1, 2022
Accepted: March 28, 2023
Published: April 13, 2023
Copyright: © 2023 Tamaoka et al. This is an open
access article distributed under the terms of theCreative Commons Attribution License, which
permits unrestricted use, distribution, andreproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data are available on
the Open Science Framework (https://osf.io/k2f8h/).
Funding: This study was supported by the Grantin-Aid (B) - number 23320106 - the Japan Society
for the Promotion of Science (KT) and Grant-in-Aid
for Scientific Research (S) 19H05589 (MK, KT).
The funders had no role in study design, datacollection and analysis, decision to publish, or
preparation of the manuscript.

The current study explored the role of sentential inference in connecting lexical/grammaticalknowledge and overall text comprehension in foreign language learning. Using structural
equation modeling (SEM), causal relationships were examined between four latent variables: lexical knowledge, grammatical knowledge, sentential inference, and text comprehension. The study analyzed 281 Chinese university students learning Japanese as asecond language and compared two causal models: (1) the partially-mediated model, whichsuggests that lexical knowledge, grammatical knowledge, and sentential inference concurrently influence text comprehension, and (2) the wholly-mediated model, which posits thatboth lexical and grammatical knowledge impact sentential inference, which then further
affects text comprehension. The SEM comparison analysis supported the wholly-mediatedmodel, showing sequential causal relationships from lexical knowledge to sentential inference and then to text comprehension, without significant contribution from grammaticalknowledge. The results indicate that sentential inference serves as a crucial bridge between
lexical knowledge and text comprehension.

Introduction
In most written or spoken texts, a specific message is conveyed through a series of semanticallyrelated sentences that form meaningful connections. To understand these texts, one mustgrasp the meaning of the individual words [1–4] and have proficiency in the grammatical rulesthat prescribe word arrangement within sentences [5–9]. As a result, a combination of lexicaland grammatical knowledge is essential for effective sentence comprehension.
However, there is a significant difference between understanding a single sentence andcomprehending the overall message conveyed by a text. A text typically consists of multiple
sentences, and the comprehension of its message relies on the rules of inference that connect

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PLOS ONECompeting interests: The authors have declaredthat no competing interests exist.

Text comprehension among native Chinese speakers learning Japanese

one sentence to another. Sentential inference is thought to facilitate the semantic connections
between previously introduced and new sentence information [10–14], ultimately leading to
the comprehension of a text’s complete message [15–16].
The current study aims to investigate the role of sentential inference as a mediator between
lexical and grammatical knowledge and overall text comprehension.

Background
The role of lexical knowledge during text comprehension
The role of lexical knowledge in text comprehension has been extensively studied in first andsecond language acquisition. Research on language acquisition in native English-speaking children [1, 17–19] has demonstrated that lexical knowledge is a significant contributor to text
understanding and reading comprehension. LaBerge and Samuels [2] introduced the conceptof ‘automaticity,’ which suggests that proficient readers can process words in a text effortlessly.
They suggested this ultimately allows children to focus their cognitive efforts on higher-ordercomprehension skills once they have mastered automatic word processing.
Beck, Perfetti, and McKeown [1] examined the connection between word processing andreading comprehension as well. Over a five-month period, they trained fourth-grade students
to establish semantic networks, leading to improved word processing efficiency and enhanced
reading comprehension compared to a control group. Similarly, using Cantonese, Leong andHo [20] found that lexical knowledge significantly influenced Chinese reading comprehension
in a study involving 361 Chinese secondary school students.
In a study of Japanese language acquisition, Tamaoka, Leong & Hatta [4] found that word
processing speed, an indicator of automaticity, greatly influenced reading comprehension.
They observed significant differences in processing alphabetic loanwords (katakana) and Chinese-origin words (kanji) between skilled and less-skilled readers from grades four to six.
Skilled readers were better at deciphering unknown pronunciations of difficult kanji characters using phonetic cues. This highlights the importance of phonological and orthographic
morphemes in reading comprehension (see also [21]).
Similarly, Takahashi [3] determined that lexical knowledge was the most crucial factor inreading comprehension for Japanese-speaking children in grade five. In a subsequent longitudinal study [22], Japanese-speaking children from grades one to five were studied and it was
discovered that three primary factors—word pronunciation speed, reading memory span, andlexical knowledge—significantly impacted reading comprehension in younger children(grades one to three). However, upon reaching grade five, lexical knowledge emerged as thesole influential factor, with word pronunciation speed and reading memory span no longer
having a significant effect on reading comprehension. Takahashi [22] attributed this change tothe increasing complexity of reading contexts and the expanding range of vocabulary used in
texts as children progress through the grades.
So far, our primary focus has been on the text comprehension of monolinguals. Nevertheless, it seems necessary to point out that also for bilinguals, strategies to text comprehension
might overlap and warrant an investigation. In research exploring the acquisition of English asa second language by native Chinese speakers, Zhang [23] employed Structural Equation
Modeling (SEM) to investigate the contributions of lexical and grammatical knowledge toreading comprehension. Zhang’s findings revealed that lexical knowledge had a significant
impact on reading comprehension, whereas grammatical knowledge offered a more modest
contribution.
Considering the significant role lexical knowledge seems to play in reading comprehension,some have introduced the concept of a ‘lexical threshold’ to determine the minimum lexical

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PLOS ONEText comprehension among native Chinese speakers learning Japaneseknowledge necessary for accurate text comprehension. Hu and Nation [24], Nation [25], andStahl and Nagy [26] posited that understanding 98% of the words in a written English text is
required to achieve accurate comprehension. In the case of native Chinese speakers learning
Japanese, Komori, Mikuni, and Kondo [27] estimated that recognizing 96% of the words in a
general written text is essential for comprehension. Note that this number still indicates thatabout 4% of the vocabulary in a text would be unknown. Additionally, Mikuni et al. [28] calculated the threshold for listening comprehension to be around 93%. As spoken language is typically less lexically dense than written language, it is possible that listening comprehension canbe achieved with a lower lexical threshold than reading comprehension. However, it is evident
that a high level of lexical knowledge is crucial for attaining text comprehension in both spoken and written Japanese.
In a second language study by Zhang, Zhang, Li, and Zhang [29], factors impacting Chinese
reading comprehension were examined by testing 447 English-speaking Chinese learners fromcollege-level study-abroad programs. The study found that morphological awareness and lexical knowledge both influenced reading comprehension through lexical inference. As Chinese
texts are typically composed of morphological (logographic) characters called hànzì, which do
not contain inflections for verbs or adjectives, knowledge of hànzı̀ morphology and compoundwords is essential to understand the text. Approximately 75–80% of Chinese words consist of
two or more hànzı̀ characters [30]. Zhang et al. [29] identified lexical inference as a mediating
factor between morphological/lexical knowledge and reading comprehension, indicating asequential relationship between the understanding of hànzı̀ characters, their compoundwords, and the lexical relations of multiple words. Consequently, morphological/lexical knowledge and lexical inference seem to play crucial roles in Chinese reading comprehension.
Other research has shown that adult native Chinese speakers learning Japanese also makeuse of morphological/lexical knowledge when learning English. For example, Koda (2000)
examined native Chinese and Korean speakers learning English and found that while bothgroups showed similar sensitivity to English morphological structure, Chinese learners weremore efficient in integrating morphological and contextual information during English sentence processing. This strong contribution of morphological awareness to reading comprehension was also evident among native Chinese children in elementary school who learnt English
as a second language. Wang, Cheng, and Chen [31] reported that both Chinese and English
morphological awareness contributed to Chinese reading comprehension in these children,referring to this phenomenon as "cross-language morphological transfer." Specifically, Native
Chinese speakers initially learn to use hànzı̀ morphological awareness for text comprehension
in their first language (Chinese). This morphological/lexical approach, which they acquired in
Chinese, can then potentially be applied through lexical inference to comprehend English text
as well.
Koda’s [32] findings can also be relevant to native English speakers learning Chinese as asecond language. For example, Zhang et al. [29] suggested that morphological awareness andlexical knowledge, through lexical inference, significantly contribute to the comprehension ofChinese text. Due to the linguistic characteristics of the Chinese language, native English
speakers likely rely on hànzı̀ for morphological awareness and utilize lexical inferences tounderstand Chinese text. Consequently, the morphological awareness approach is employed
in both cases: from Chinese as a first language to English as a second language [32], and fromEnglish as a first language to Chinese as a second language [29, 31]. When learning Chinese asa second or foreign language, morphological awareness of hànzı̀ is essential for text comprehension. Note that although there are many inconsistencies in the usage of this terminology,
typically, a “foreign” language is not spoken in the region where it is taught whereas a second

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PLOS ONEText comprehension among native Chinese speakers learning Japanese

language is. Therefore, in both directions of language acquisition, morphological awarenessappears to be a critical factor.
Furthermore, lexical knowledge plays a crucial role in Japanese text comprehension for
native Chinese speakers learning Japanese as a foreign language. Chen [33] found that among
4,600 Japanese kanji-compound words, 54.5% had the same characters and meanings as in
Mandarin Chinese (e.g., 数学 “mathematics”), while 14.9% shared the same characters but
had similar meanings (e.g., 高校 “college” in Chinese; “high school” in Japanese). Only 4.1%of the words had the same characters but different meanings (e.g., 暗算 “mental arithmetic” inJapanese, “to plot” in Chinese). If minor orthographic differences between Chinese hànzı̀ andJapanese kanji characters are disregarded, native Chinese speakers can understand approximately 98.1% of commonly used Japanese kanji before even studying the language [34, 35].
This high degree of similarity explains why native Chinese speakers often rely heavily on kanjibased lexical items to comprehend written Japanese texts. As a result, native Chinese speakers
learning Japanese are likely to leverage their knowledge of hànzı̀ to understand words in Japanese text.
Japanese differs from Chinese in that it utilizes three scripts instead of only the orthographic (hànzı̀) script [36]. Firstly, Japanese text incorporates Chinese-originated words(Kango) written in Chinese morphemic hànzì characters (known as kanji in Japanese) which
predominantly convey conceptual meanings. Additionally, there are two phonetic scripts:
katakana and hiragana. Katakana is primarily used for transcribing alphabetic loanwords,
with word etymology differentiated by the use of kanji and katakana. Hiragana, the third
script, is commonly employed for grammatical functions, although it has other uses as well(e.g., to write specific native Japanese words, such as: きれい,/kirei/ “beautiful”). Given that
lexical and grammatical functions can be visually distinguished to some extent by the three
scripts in Japanese, the causal relationship between morphological awareness, lexical knowledge, and lexical inference leading to text comprehension in Chinese [29] may not operate in
the same manner for understanding Japanese texts.

The role of grammatical knowledge for text comprehension
Despite some researchers (e.g., [5–6, 8–9]) highlighting the significance of grammatical knowledge in text comprehension, it has often been overshadowed by lexical knowledge. Weir [9]reported a strong correlation (r = 0.67) between grammatical knowledge and reading comprehension among non-native English speakers studying English at higher education institutionsin the UK. In a study using SEM with 588 native-Japanese speaking university students learning English in Japan, Shiotsu and Weir [7] found a strong causal relationship between grammatical knowledge and reading comprehension, whereas lexical knowledge did not show sucha relationship. However, the same study also revealed a comparable contribution of lexical andgrammatical knowledge to reading comprehension for 107 native Japanese university students
studying in the UK.
These observed differences in the contribution of lexical and grammatical knowledge toreading comprehension among Japanese students in Japan and the UK could be attributed totheir distinct learning environments. Students in the UK would have more opportunities to
use English in daily life and receive instruction from native English speakers, unlike theircounterparts in Japan. Consequently, Japanese students in English-speaking countries would
encounter various words and expressions regularly, leading to a simultaneous reliance on bothlexical and grammatical knowledge for text comprehension.
On the other hand, Japanese students in Japan have limited exposure to English outside theclassroom and might adopt a strategy of understanding English by translating it into Japanese.

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PLOS ONEText comprehension among native Chinese speakers learning Japanese

Given the significant differences in grammatical features between the two languages, grammatical knowledge becomes vital for translation. As a result, these students might depend
more on grammatical knowledge for text comprehension. The contrasting English learning
environments in Japan and the UK could therefore explain the diverging outcomes in thestudies.
For native Japanese speakers learning English, Yamashita [37] found that while both lexicaland grammatical knowledge had a significant impact on reading comprehension, lexical
knowledge seemed to contribute more. Similarly, Bosser [38] found that both lexical andgrammatical knowledge were important predictors for Dutch reading comprehension among
native Turkish speakers learning Dutch as a second language. However, Bosser [38] also
observed that lexical knowledge was a stronger predictor than grammatical knowledge. Therefore, although grammatical knowledge does contribute to reading comprehension, its influence appears to be less significant than that of lexical knowledge.
Yamato, Tamaoka, and Chu [39] studied the impact of lexical and grammatical knowledge
on the phrase-by-phrase processing of Japanese texts (i.e., self-paced reading) by native Chinese
speakers learning Japanese. To compare reading speeds, participants were divided into threegroups based on their Japanese vocabulary and grammar test scores. They found that lexical
knowledge had a more significant influence on reading speed than grammatical knowledge,
and it contributed to reading speed for both single phrases and continuous sequences ofphrases. However, grammatical knowledge’s impact was limited to certain phrases with complex structures, suggesting that grammar still may play a role in resolving structural difficulties.
Yamato and Tamaoka [40] compared 20 matched pairs of native Chinese and Korean
speakers, who were equally proficient in lexical and grammar knowledge. The study foundthat native Chinese speakers processed words in Japanese kanji characters more quickly than
Korean speakers. Due to the similarity between Chinese and Japanese kanji script [41], Chinese speakers were able to process kanji-presented words faster. This suggests that Japanese
text comprehension may be partially achieved through morphological/lexical knowledge. As
Chinese does not have verb and adjective inflections [42, 43], native Chinese speakers might
primarily focus on Japanese kanji characters for text comprehension, similar to their approachin Chinese.

Bridging between lexical/grammatical knowledge and text comprehension
Lexical and grammatical knowledge are essential for understanding text, but there is a distinction between this knowledge and overall text comprehension [44]. We typically use our life
experiences to deduce the meaning of partially understood texts. In reading and listening comprehension, we make inferences based on cues provided within sentences. This information is
not explicitly stated, so we depend on our experiential knowledge to decipher the text’s meaning. Zhang et al. [29] labeled this process as lexical inference, which involves logical reasoning
within individual sentences.
To fully comprehend a text’s message, a broader form of inference, called sentential inference, is needed to connect different sentences. Kintsch [15] and van Dijk & Kintsch [16] introduced two textual structures: microstructure and macrostructure. Microstructure concerns
lexical and morphosyntax relations within a sentence, while macrostructure deals with intersentential relations. Lo et al. [45] found that cognitive-linguistic skills contribute differently to
microstructure and macrostructure understanding in Chinese elementary school children.
Intra-sentence microstructural comprehension helps in understanding single sentences (lexical inference), while inter-sentence macrostructure comprehension aids in understandinglarger texts (sentential inference).

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PLOS ONEText comprehension among native Chinese speakers learning JapaneseLong et al. [13] discovered that skilled readers outperformed less-skilled readers in identifying theme-related inferences in specific passages. Murray and Burke [14] noted that highskilled readers automatically recalled related lexical items, while low-skilled readers did not.
These first-language understanding studies suggest that the ability to draw lexical and sentential inferences is crucial for reading comprehension.
The current study focused on three aspects of sentential inference, namely: intentional
expressions, co-referential resolution, and causal relations. First, understanding indirect messages in a series of sentences, such as intentions not explicitly stated, is necessary to comprehend the actual message. For example, a listener can infer the unspoken intention of a
speaker’s comment about a cold room and act accordingly, like closing a nearby window. Thisability to grasp indirect speech is important for overall text comprehension.
The second aspect of sentential inference is co-referential resolution, which occurs when
multiple expressions refer to the same person or thing. For example, in “John went to thestore, and he bought some cheese," the words "John" and "he" are co-referential because theyboth refer to the same person. Co-references help link sentences and can refer to a whole sentence’s context, such as a Japanese deictic word like kore ‘this’, sore ‘that’, or are ‘that’ (distal).
Understanding these references is what is denoted as co-referential resolution [46].
The last aspect is causal relation, which deals with cause and effect in sentences. Causal relations develop a text’s message and are often indicated by conjunctions like ‘because’, ‘so’,‘hence’, ‘thus’, or adverbs like ‘accordingly’ and ‘consequently’. In Japanese, causal relations
are indicated by conjunctions and subordinate conjunctive particles. Learners of Japanese as aforeign language may face difficulties with conjunctive particles like -kara and -node due totheir similarities, possibly leading to pragmalinguistic failure [47]. Understanding a series of
causal relations is vital for grasping a text’s overall message, as it involves processing multiple
sentences and their logical reasoning steps.

Two existing models of text understanding
The current study utilizes structural equation modeling (SEM) to examine the relationshipbetween lexical/grammatical knowledge, sentential inference, and text comprehension, specifically focusing on native Chinese speakers learning Japanese. The SEM analysis encompasses
four primary variables: lexical knowledge, grammatical knowledge, sentential inference, andtext comprehension.
Two proposed models outline the key factors in text comprehension. The first, the "partially-mediated" model, posits that lexical/grammatical knowledge and sentential inference
work in tandem to facilitate text understanding (see Fig 1). In this model, second language
learners employ lexical and grammatical knowledge to comprehend sentence meaning, whileboth lexical/grammatical knowledge and sentential inference operate concurrently to aid inunderstanding the entire text. This model anticipates that word meaning, grammatical features, and their integration with sentential relations all play a role in text comprehension.
The second model, the "whole-mediated" model, suggests that lexical and grammatical knowledge offer information to understand individual sentences (see Fig 2). However, comprehending
a single sentence does not necessarily convey a text’s overall message. To understand the sequenceof messages, sentences must be semantically connected to preceding and following sentences.
Consequently, sentential inference may function independently to merge the meanings of multiple sentences, each initially based on lexical/grammatical knowledge. In this model, the final message of a text is understood through the integration of a series of sentential semantic inputs.
Our study tested these two models using a large dataset of native Chinese speakers learning
Japanese at Xi’an International Studies University, China. By comparing the models, we

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PLOS ONEText comprehension among native Chinese speakers learning JapaneseFig 1. The partially-mediated model of text comprehension.
https://doi.org/10.1371/journal.pone.0284331.g001

investigated the causal relationships between lexical/grammatical knowledge and sentential
inference in achieving text comprehension. Notably, since Dixon and Marchman [48] encountered difficulty in determining whether lexical or grammatical knowledge precedes the other,
the present SEM analysis incorporated a correlation between them.

Methodology
Participants
A total of 281 native Chinese-speaking university students learning Japanese at Xi’an International Studies University participated in this study. They had no prior experience studying Japanese before joining the university. At the time of testing, the students had either one year
(N = 146; 124 female) or two years (N = 135; 116 female) of academic Japanese training, andnone of them had lived in Japan. Their ages ranged from 16.1 to 26.7 years, with an averageage of 19.7 ± 1.0 years for those with one-year of learning experience and 20.8 ± 1.1 years for
those with two years of experience. All data collected was securely stored, and participants
were given numerical pseudonyms for privacy. Before beginning the study, participants were
informed about the study objectives, procedures, risks, and benefits, and verbal informed consent was obtained from each participant to ensure ethical research conduct. The study received
approval from the ethics committee of the School of Japanese Culture and Economy at Xi’an
International Studies University.

Fig 2. The wholly-mediated model of text comprehension.
https://doi.org/10.1371/journal.pone.0284331.g002

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PLOS ONEText comprehension among native Chinese speakers learning Japanese

Measurements
The study investigated four main variables, namely: lexical knowledge, grammatical knowledge, sentential inference, and text comprehension through multiple-choice questions (onecorrect answer out of four choices), lasting 180 minutes in total (four 45-minute classes).
Lexical knowledge refers to understanding vocabulary in a language, including individual
word meanings, spelling, connotations, and how words form phrases and sentences. It is a critical component of language proficiency and communication and is measured in nearly all
influential Japanese language proficiency tests (e.g., the Japanese Language Proficiency Test or
JLPT). In this study, lexical knowledge was measured through four tests based on word categories: Chinese-origin words (Kango), Japanese-origin words (Wago), alphabetic loanwords(Gairaigo), and function words, which serve a grammatical function rather than conveying lexical meaning. In Japanese, function words are crucial for constructing coherent and meaningful sentences, as they indicate the grammatical relationships between various sentenceelements. All words in the test were taken from the JLPT [49]. The Chinese-origin words wereselected from two-kanji compound words such as guti (‘complaint’), hukyô (‘recession’), syumi(‘hobby’), yûbô-da (‘promising’), kengaku-suru (‘to visit’) and tyûmon-suru (‘to order’). Examples of Japanese-origin words are arasuzi (‘story’), sakasama (‘upside-down’), yayakosii (‘complicated’), detarame-na (‘nonsense’), hakado-ru (‘to make progress’), and unazu-ku (‘to nod
and agree’). All loanwords were taken from alphabetic languages, such as saizu (‘size’), kyaria(‘career’), dorai-da (‘unsentimental’), rûzu-da (‘loose’), massâzi-suru (‘to massage’) and suttopu-suru (‘to stop’). Function words were defined as grammatical words consisting of morethan two morphemes with no inflections such as ga-hayai-ka (‘no sooner . . . than . . .’), tatokoro-de (‘even if. . .’), itaru-made (‘until. . .’ or ‘up to. . .’), kawa-kiri-ni (‘start by. . .’), andyogi-naku-sa-reru (‘be obliged to. . .’). Twelve words in each lexical category, that is: Chineseorigin, Japanese-origin, and alphabetic loanwords consisted of four nouns, four adjectives andfour verbs. In addition, all twelve words were selected from levels 1 and 2 in the vocabulary listof Japanese Language Proficiency Test (JLPT), cross-matched by JLPT levels 1–2 over threeword categories of Kango, Wago and Gairaigo. As shown in Table 1, Cronbach’s alpha, calculated across 281 participants with 48 test items, was relatively high at 0.739.

Grammatical knowledge
Grammatical knowledge refers to knowledge of the rules and principles that regulate the structure and usage of language, encompassing an awareness of parts of speech, sentence structure,and the interconnectedness of words and phrases. As depicted in Fig 3, grammatical knowledge was measured through three subtests: morphological inflections, local-dependency, andcomplex structure. The subtest for morphological inflections consisted of questions aboutchanges within a single lexical unit. For example, Japanese verbs and adjectives have complex
inflections, and a predicate presented as a single unit in Japanese can be formed by adding various morphemic units indicating negation, past tense, voice, aspect, and mood. For instance,
the verb stem seme-ru ‘to blame’ can be compounded with the negation–nai and the pasttense–ta, resulting in the predicate of seme-nakat-ta ‘did not blame’. In the test, participants
were asked to fill in an empty sentence bracket, such as the following example: Ayamattekabin-o kowasita watasi-o, titi-wa () ‘My father (did not blame) me, who mistakenly broke theflower vase’. Out of four choices: seme-nakat-ta, seme-nai-dat-ta, semeru-nakat-ta, and semenaku-te-dat-ta, seme-nakat-ta was the correct answer for ‘did not blame’.
As depicted in Fig 3, the subtest for local dependency required participants to determine
whether two neighboring units formed a correct expression. For example, the verb tukur-u ‘tocook’ can be modified by the adverb zyôzu-ni ‘very well’ to mean ‘to cook very well.’ However,

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PLOS ONEText comprehension among native Chinese speakers learning Japanese

Table 1. Full scores, means, standard deviations, and correlations for 12 language tests (observed variables).
Language tests

1

2

3

4

5

6

7

8

9

10

11

12

Lexical knowledge (α = 0.739)
1

Function words

1

2

Japanese origin (Wago)

0.47 **

3

Chinese origin (Kango)

0.44 **

0.35 **

1

4

Loanwords

0.36 **

0.33 **

0.39 **

1

1

Grammatical knowledge (α =0.793)
5

Morphological inflection

0.44 **

0.32 **

0.59 **

0.48 **

1

6

Local dependency

0.19 **

0.16 **

0.25 **

0.23 **

0.37 **

1

7

Structural complexity

0.44 **

0.35 **

0.51 **

0.46 **

0.68 **

0.33 **

1

Sentential inference (α = 0.638)
8

Causal relations

0.41 **

0.35 **

0.43 **

0.50 **

0.54 **

0.17 **

0.50 **

1

9

Intentional expressions

0.32 **

0.23 **

0.40 **

0.39 **

0.51 **

0.29 **

0.39 **

0.41 **

1

10

Co-referential resolution

0.32 **

0.29 **

0.40 **

0.35 **

0.43 **

0.10 **

0.43 **

0.39 **

0.37 **1

Text comprehension (α = 0.613)
11

Reading comprehension

0.39 **

0.33 **

0.47 **

0.46 **

0.55 **

0.22 **

0.52 **

0.48 **

0.43 **

0.42 **

1

12

Listening comprehension

0.35 **

0.36 **

0.42 **

0.43 **

0.44 **

0.18 **

0.40 **

0.33 **

0.34 **

0.33 **

0.46 **

1

Full score

12

12

12

12

12

12

12

6

6

6

8

8

Mean

4.66

3.80

6.90

7.20

8.16

10.21

7.57

3.37

4.58

2.76

4.25

4.12

Standard Deviation

1.91

1.96

1.83

2.03

2.35

1.40

2.40

1.43

1.16

1.19

1.88

1.58

Note. N = 281* p < .05** p < .01
α = Cronbach’s Alpha
https://doi.org/10.1371/journal.pone.0284331.t001

Fig 3. Different types of (local) dependencies in Japanese sentence comprehension.
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PLOS ONEText comprehension among native Chinese speakers learning Japanese

when used as an adjective to modify a noun, the particle -na must be used, as in zyôzu-na e ‘awell-drawn picture.’ The difference between these two words is that -ni should be used for theadverb and -na for the adjective. In this subtest, the correct answer for the sentence Kanozyowa itumo tamagoyaki-o () tukuru ‘She always cooks omelets (very well)’ could not be determined by lexical knowledge alone. The correct choice among zyôzu-ni, zyôzu-de, zyôzu-no,and zyôzu-na could only be determined by referencing the verb tukuru (to cook) at the end of
the sentence.
The third subtest for complex structure required participants to refer to an entire sentence
to determine the correct answer, as illustrated in Fig 3 as a long-distance relation. In the example sentence Donnani kanozyo-ga (), ano daigaku-niwa gôkaku sinai darô ‘(No matter) how
hard she (tries), she cannot pass the entrance examination of that university,’ the four choices
were ganbattemo, ganbatte, ganbarunoni, and ganbaruga. Each of these four expressions could
be used to form a correct sentence; however, since the final phrase of the sentence ended witha negative following the prior phrase donnani . . . temo [no matter how . . .], the correct choice
could only be ganbattemo. Cronbach’s coefficient alpha, calculated across 281 participants
with 36 test items, was relatively high at 0.793.
Sentential inference. Sentential inference was measured using three subtests: intentional
expressions, co-referential resolution, and causal relations. A total of eighteen questions wereadministered, six in each of the three categories. All questions in the three subtests consisted ofmore than two sentences. An example of an intentional expression is ‘Shall I fix you a coffee?’with the response ‘If I drink coffee now, I won’t be able to sleep.’ Participants were asked tochoose the best follow-up response from the first speaker: (a) ‘Then, only I will drink coffee.’,(b) ‘How much sugar would you like to have?’, (c) ‘If you take this pill, you can sleep well.’, or(d) ‘I also like coffee very much.’ The appropriate response is (a). The only cue for choosing
the correct response was recognizing the intention of the listener’s statement ‘I cannot sleep.’
Coreferential resolution involves deictic words such as kore (this), sore (that), and are (distal‘that’), which refer to a thing or idea from a previous sentence. For instance, in the sentence"Actually, I am afraid of cats. Please do not tell anyone about this," the phrase "this thing"(kono koto in Japanese) broadly refers to the speaker’s fear of cats rather than a specific wordfrom the previous sentence.
Causal relations questions were created using multiple sentences that followed a logical progression. For example, "Last night, I read a book until 2 a.m. () it was really interesting, so I
couldn’t stop reading." The correct answer was "datte" (because) rather than the other threechoices—"dakara" (therefore), "dakedo" (even though), and "sorede" (then). Based on themeaning of the previous sentence, the causal expression "datte" explains why the speaker reada book until 2 a.m., stating that the book was captivating. These two sentences have a causal
relationship, and this question evaluates understanding of causal relations. Cronbach’s coefficient alpha, calculated for 281 participants with 18 test items, was 0.638 (acceptable).
Text comprehension. Text comprehension is defined as the semantic understanding of awhole text’s theme. It was measured using reading and listening comprehension tests with relatively long, visually-presented (reading) or auditory-presented (listening) texts, each consisting of one or two paragraphs. There were eight texts for reading comprehension and eight for
listening comprehension. Each correct answer was worth one point. To ensure that the total
study workload was manageable, we included only one question per text. This approach wasconsidered reasonable, as high-quality questions probing deeper thinking, connections, andinference generation were used, making them suitable for measuring text understanding.
Cronbach’s coefficient alpha, calculated for 281 participants with 16 test items, was 0.613(acceptable).

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PLOS ONEText comprehension among native Chinese speakers learning JapaneseProcedure
A series of tests was conducted on university students in two ninety-minute classes (180 minutes total) in quiet classrooms. Each Japanese language class had 30 students. Tests wereadministered to second-year students in five classes and third-year students in five classes (10classes total) at the beginning of the academic year (September). Second-year students wereconsidered to have one year of Japanese learning experience, while third-year students had
two years. One or more of the paper’s authors and the students’ Chinese-speaking class
instructors supervised the two test sessions in the 10 classes.

Results
For the 281 participants (146 second-year students and 135 third-year students), the full scores,
means, standard deviations, correlations for the twelve tests, and Cronbach’s coefficient alphas
for the four latent variables are shown in Table 1.

Model comparisons
Using AMOS [50] added on to SPSS Statistics Version 28, a SEM analysis was conducted witha covariance matrix on 281 Chinese students learning Japanese in China. The goodness-of-fit
index (see Table 2) showed that the Chi-squares for both the partially-mediated model [χ2(48)= 72.25, p < .05] and the wholly-mediated model [χ2(50) = 72.88, p < .05] were significant,indicating that neither model ideally fit the data [51]. However, Chi-square tests are inherently
dependent on sample size (e.g., [52–55]). Thus, Jöreskog and Sörbom [54] proposed the χ2/df
index for larger sample sizes, which indicates a good fit for values less than 2.00. Since the current sample size (N = 281) was relatively large, χ2/df was calculated as shown in Table 2. This
resulted in both models fitting well, but the wholly-mediated model (1.46) scored slightly
lower, demonstrating a better fit than the partially-mediated model (1.51).
Table 2 summarizes four model comparison measures [55], and the best-fitting model forthe present data was chosen using these criteria, where a lower value indicates a better fit. As
indicated by all four criteria in Table 2, the wholly-mediated model was a better fit than thepartially-mediated model across all values: AIC [56], CAIC [57], ECVI [58], and BIC [59].
Consequently, the wholly-mediated model was adopted for the present study.

The wholly-mediated model—Fit indicators
We evaluated the fit of the wholly-mediated model to the data obtained in the present study(see Fig 4) using six criteria (for details, see [53, 55]). All six indicators, namely: χ2/df [54],
RMSEA [58], GFI [60], AGFI [55], NFI [61], and CFI [55] demonstrated that the model fit thedata very well. Therefore, the wholly-mediated model depicted in Fig 4 was further investigated using SEM analysis.
Table 2. Comparison indices of the partially-mediated model and the wholly-mediated model.
χ2-indexModel
2Partially-mediated modelχ (48) = 72.25, p = 0.13

Wholly-mediated modelχ2 (50) = 72.88, p = 0.19

χ2/df

AIC

CAIC

ECVI

BIC

1.51

132.25271.400.47

241.40128.88258.750.46

131.61

Saturated Model

--

--

1.46

156.00517.790.56

439.79

Independence Model----

1,268.09

1,323.75

4.53

1,311.75

Note. AIC = Akaike Information Criterion. CAIC = Consistent AIC. ECVI = Expected Cross Validation Index. BIC = Bayes Information Criterion.
https://doi.org/10.1371/journal.pone.0284331.t002

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PLOS ONEText comprehension among native Chinese speakers learning JapaneseFig 4. SEM results of the wholly-mediated model. Note: RMSEA: Root Mean Square Error of Approximation. GFI = Goodness of Fit Index. AGFI = Adjusted
GFI. NFI = Normal Fit Index. CFI = Comparative Fit Index.
https://doi.org/10.1371/journal.pone.0284331.g004

The wholly-mediated model–SEM results
The SEM revealed the component structure for the latent variables within the wholly-mediatedmodel, which was constructed with four latent variables as shown by the ellipses in Fig 4. The
SEM causal structure was established such that grammatical and lexical knowledge connected
to text comprehension via sentential inference. These four latent variables were estimated by
twelve observed variables depicted by rectangles in Fig 4. For the latent variable of lexical
knowledge, all path coefficients indicated that the four observed variables contributed significantly; Japanese-origin words (beta = 0.51), Chinese-origin words (beta = 0.70), alphabetic
loanwords (beta = 0.64), and function words (beta = 0.61) were all important. The latent variable of grammar knowledge was measured by three observed variables. Very high path coefficients were found for morphological inflections (beta = 0.86), and for structural complexity(beta = 0.79), but relatively lower for local dependency (beta = 0.40). Morphological inflections
and, to a lesser degree, structural complexity seemed to greatly contribute to grammar
knowledge.
Similarly, the latent variable of sentential inference was measured by three observed variables, which showed relatively high contributions: causal relations (beta = 0.68), intentional
expressions (beta = 0.60), and co-referential resolution (beta = 0.57). The fourth latent variableof text comprehension was measured by reading comprehension (beta = 0.75) and listening
comprehension (beta = 0.62), which showed a large contribution.
The wholly-mediated model was applied to investigate the influence of sentential inferenceon bridging the gap between lexical/grammatical knowledge and text comprehension. Since
the correlation between lexical knowledge and grammatical knowledge was very high(r = 0.89, p < .001), both factors seemed to be acquired in a mutually-related manner. Native
Chinese speakers displayed a strong causal relation between lexical knowledge and sentential
inference (beta = 0.85, p < .001), but they showed no significant causal relation between

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PLOS ONEText comprehension among native Chinese speakers learning Japanesegrammatical knowledge and sentential inference (beta = 0.15, ns). Moreover, a strong significant causal relation was found between sentential inference and text comprehension(beta = 0.94, p < .001). These SEM results indicate that Chinese speakers learning Japaneserely heavily on their lexical knowledge to support their sentential inference, which in turn contributed greatly to text comprehension. In sum, a clear sequential relation from lexical knowledge via sentential inference to text comprehension was observed in the present study.
The indirect effect from lexical knowledge to text comprehension was significant(beta = 0.72, p < .01) while the indirect effect from grammatical knowledge to text comprehension was not significant (beta = -0.198, ns). The significant probabilities (p-value) of the indirect effects were calculated 500 times by bootstrapping. The indirect effects also provided
evidence for the causal pathway from lexical knowledge, through sentential inference, to text
comprehension in the mediated model.

Discussion
The primary goal of reading or listening to a text is to grasp the intended message. While lexical knowledge and basic grammatical understanding may be sufficient for comprehending
individual sentences, it often falls short when trying to understand a series of sentences in Japanese or other languages. This study aimed to explore the role of sentential inference in bridging the gap between lexical/grammatical knowledge and overall text comprehension. Fourtests (i.e., lexical knowledge, grammatical knowledge, sentential inference, and text comprehension) were conducted on 281 native Chinese-speaking students learning Japanese at a Chinese university to examine the causal relationships among these abilities. Two distinct models
were considered: the partially-mediated model, which views sentential inference as just one ofmany factors contributing to text comprehension on par with lexical/grammatical knowledge;
and the wholly-mediated model, which posits sentential inference as separate from lexical/
grammatical knowledge and serving as a bridge to broader text comprehension (e.g., [10–14]).
In this model, sentential inference is crucial for integrating sentence-level information tounderstand the entire message of a text. Our SEM analysis revealed that the wholly-mediatedmodel better fits the data than the partially-mediated model, as further demonstrated in Fig 4.
This finding suggests a sequential causal relationship that starts with lexical knowledge (butnot grammatical knowledge), moves through sentential inference, and ultimately leads to text
comprehension. The current study highlights the importance of sentential inference as a central mediator of lexical knowledge in facilitating holistic text understanding in Japanese.
In this study, we analyzed participants who had completed one and two years of Japanese
language learning together. However, considering that increasing proficiency could potentiallyaffect the variables being investigated, we re-evaluated the data using the wholly-mediatedmodel for Grade 1 (N = 146) and Grade 2 (N = 135) separately. This analysis produced results
like those obtained when combining all participants (N = 281). Nevertheless, the goodness-offit indices were lower than those for the full sample. Consequently, we opted to retain the single-group structural equation modeling (SEM) analysis with a sample size of N = 281.
The core finding of this study is that sentential inference is essential for bridging the gap
between lexical knowledge and comprehensive understanding of Japanese texts. In a study onlearning Chinese as a foreign language, Zhang et al. [29] discovered that lexical inference
served as a connecting tool between morphological awareness/lexical knowledge and Chinese
reading comprehension for native English speakers. As Chinese texts are entirely written inhànzı̀, English speakers must first acquire knowledge of the morphological units of hànzı̀ characters to read Chinese. Lexical inference is automatically employed as a mediating factor forreading comprehension in Chinese. Consequently, differences may exist in how Japanese and

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PLOS ONEText comprehension among native Chinese speakers learning JapaneseChinese text comprehension occurs. Specifically, sentential inference might act as a bridge
between lexical knowledge and Japanese text comprehension, while lexical inference appears
more involved in understanding Chinese texts. It should be noted that it is also possible that
sentential inference connects Chinese vocabulary knowledge and text comprehension, withthe only difference being that lexical inference is required prior to sentential inference (note:
we like to thank an anonymous reviewer for pointing this out).
Although not examined in our current paper, it is conceivable that individual differences inworking memory capacity also contribute to complete text understanding. For instance, successful inferences must be stored in memory, particularly for long and dense texts, as subsequent interpretation relies on them. Future research is needed to determine if this factor issignificant for our empirical setup.
It is important to note that our findings are currently interpreted in the context of successful and complete text comprehension. Our tests assessed whether whole text understanding
was achieved, and our model was built on this data. This does not imply that every reading situation must follow this exact pattern (e.g., not all text comprehension results in complete
understanding). Undoubtedly, scenarios may exist where, for example, a low-proficiency
L2-learner could still benefit from lexical information aiding text comprehension despite notfully achieving sentential inference. Other cases might involve native speakers confronted withcomplex legal texts, which typically demand significant inferencing skills from readers. However, failure to achieve sentential inference often leads to incomplete overall comprehension,
causing nuances or even essential points to be misunderstood.
Considering the findings from the current study, several implications for L2 acquisition
and teaching could be drawn. First, the results underscore the importance of emphasizing lexical knowledge development in language learning, as it appears to play a more significant rolein sentential inference and text comprehension than grammatical knowledge. Consequently,
L2-educators might consider prioritizing the development of students’ vocabulary, focusing
on both the breadth and depth of their lexical knowledge. Second, the study highlights theneed to focus on sentential inference training in L2-instruction. Educators might therefore
incorporate activities and strategies that enhance students’ abilities to make inferences at thesentence level, such as tasks that involve identifying context clues, drawing connections
between words, and predicting the meaning of unfamiliar words or phrases. Lastly, our findings potentially suggest a reevaluation of the role of grammar instruction in L2-acquisition
and teaching. Given that grammatical knowledge seems not to significantly contribute to text
comprehension through sentential inference, L2-educators may need to place greater emphasison communicative competence and contextual understanding, though further research,
admittingly, needs to explore the role of grammar in different aspects of language learning andproficiency.

Author Contributions
Conceptualization: Katsuo Tamaoka, Rinus G. Verdonschot.
Data curation: Katsuo Tamaoka, Hiromu Sakai, Yayoi Miyaoka, Hajime Ono, Michiko
Fukuda, Yuxin Wu.
Formal analysis: Katsuo Tamaoka, Rinus G. Verdonschot.
Funding acquisition: Katsuo Tamaoka.
Investigation: Katsuo Tamaoka, Yuxin Wu.
Methodology: Katsuo Tamaoka.

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PLOS ONEText comprehension among native Chinese speakers learning Japanese

Project administration: Yuxin Wu.
Supervision: Rinus G. Verdonschot.
Validation: Katsuo Tamaoka, Rinus G. Verdonschot.
Visualization: Katsuo Tamaoka, Rinus G. Verdonschot.
Writing – original draft: Katsuo Tamaoka, Rinus G. Verdonschot.
Writing – review & editing: Katsuo Tamaoka, Hiromu Sakai, Yayoi Miyaoka, Hajime Ono,
Michiko Fukuda, Yuxin Wu, Rinus G. Verdonschot.

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