TYPE Original Research
PUBLISHED 24 April 2024
DOI 10.3389/fpsyg.2024.1360191

OPEN ACCESSEDITED BY

Hye Pae,
University of Cincinnati, United States
REVIEWED BY

Omid Khatin-Zadeh,
University of Electronic Science andTechnology of China, China
Eriko Sato,
Stony Brook University, United States*CORRESPONDENCE

Rinus G. Verdonschot
rinus.verdonschot@mpi.nl
RECEIVED 22 December 2023
ACCEPTED 25 March 2024
PUBLISHED 24 April 2024
CITATION

Tamaoka K, Yu S, Zhang J, Otsuka Y, Lim H,
Koizumi M and Verdonschot RG (2024)
Syntactic structures in motion: investigating
word order variations in verb-final (Korean)
and verb-initial (Tongan) languages.
Front. Psychol. 15:1360191.
doi: 10.3389/fpsyg.2024.1360191
COPYRIGHT© 2024 Tamaoka, Yu, Zhang, Otsuka, Lim,
Koizumi and Verdonschot. This is an openaccess article distributed under the terms of
the Creative Commons Attribution License(CC BY). The use, distribution or reproduction
in other forums is permitted, provided theoriginal author(s) and the copyright owner(s)are credited and that the original publicationin this journal is cited, in accordance withaccepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.

Syntactic structures in motion:
investigating word ordervariations in verb-final (Korean)
and verb-initial (Tongan)
languages
Katsuo Tamaoka 1,2, Shaoyun Yu 3, Jingyi Zhang 4, Yuko Otsuka 5,
Hyunjung Lim 6, Masatoshi Koizumi 7 and Rinus G. Verdonschot 8*
School of Foreign Language Studies, Shanghai University, Shanghai, China, 2 Graduate School of
Humanities, Nagoya University, Nagoya, Japan, 3 Department of Chinese and Bilingual Studies, Hong
Kong Polytechnic University, Hong Kong, China, 4 Organization for Global Collaboration Center for
Language and Cultural Studies, University of Miyazaki, Miyazaki, Japan, 5 Faculty of Foreign Studies,
Sophia University, Tokyo, Japan, 6 Faculty of International Studies, Yamaguchi Prefectural University,
Yamaguchi, Japan, 7 Graduate School of Arts and Letters, Tohoku University, Sendai, Japan,
8
Neurobiology of Language, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
1This study explored sentence processing in two typologically distinct languages:
Korean, a verb-final language, and Tongan, a verb-initial language. The firstexperiment revealed that in Korean, sentences arranged in the scrambled
OSV (Object, Subject, Verb) order were processed more slowly than those in
the canonical SOV order, highlighting a scrambling effect. It also found that
sentences with subject topicalization in the SOV order were processed as swiftlyas those in the canonical form, whereas sentences with object topicalizationin the OSV order were processed with speeds and accuracy comparable toscrambled sentences. However, since topicalization and scrambling in Korean
use the same OSV order, independently distinguishing the effects of topicalizationis challenging. In contrast, Tongan allows for a clear separation of word orders
for topicalization and scrambling, facilitating an independent evaluationof topicalization effects. The second experiment, employing a maze task,
confirmed that Tongan’s canonical VSO order was processed more efficiently
than the VOS scrambled order, thereby verifying a scrambling effect. The third
experiment investigated the effects of both scrambling and topicalization in
Tongan, finding that the canonical VSO order was processed most efficiently
in terms of speed and accuracy, unlike the VOS scrambled and SVO topicalizedorders. Notably, the OVS object-topicalized order was processed as efficiently
as the VSO canonical order, while the SVO subject-topicalized order was
slower than VSO but faster than VOS. By independently assessing the effects of
topicalization apart from scrambling, this study demonstrates that both subject
and object topicalization in Tongan facilitate sentence processing, contradicting
the predictions based on movement-based anticipation.
KEYWORDS

topicalization, scrambling, word order, verb (head)-final language, verb (head)-initial
language

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1 Introduction(3) SOV: Subject topicalized order.
Eom-ma -neun sa-gwa -leul meog -eoss-da.
NP(mother) TOP NP(apple) ACC V(eat) PST.
엄마는 사과를 먹었다.

In languages spoken worldwide, sentence structures vary, with theverb sometimes positioned at the beginning or the end of a sentence.
Irrespective of the verb’s position, the topic of discourse often precedes
it, a phenomenon known as topicalization. This flexibility allows for theselection of various topics within a sentence. For example, in English,
the object ‘my brother’ in the sentence ‘I am proud of my brother’ canbe topicalized to form ‘My brother, I am proud of.’ Chomsky (1977)
proposed that a topicalized phrase (topicP) is moved to a higher
position within a sentence, typically at the specifier (Spec) of acomplementizer phrase (CP), thus creating a more complex syntactic
structure. Similarly, in Japanese, Shibatani (1990) noted that topicP
syntactically belongs to a CP positioned higher than an Inflectional
Phrase (IP). Moreover, Kuroda (1987) suggested that object
topicalization in Japanese involves topicalization and scrambling
movements, further complicating the sentence structure. We generally
assumed that increased structural complexity leads to a heavier
processing load (Holmes and O’Regan, 1981; Ford, 1983; King and Just,
1991; Just et al., 1996; Caplan et al., 1998; Bates et al., 1999; Gibson,
2000; Sekerina, 2003). Therefore, this study aims to investigate theprocessing of topicalized sentences in Korean, a verb-final language,
and Tongan, a verb-initial language. We focus on multiple word orders
created through syntactic movement, with the goal of understanding
the processing dynamics of topicalized sentences in these languages.

(4) OSV: Object topicalized order.
Sa-gwa -neun eom-ma-ga meog -eoss-da.
NP(apple) TOP NP(mother) NOM V(eat) PST.
사과는 엄마가 먹었다.
In Sentence (1), ‘(My) mother ate (an) apple,’ follows the SOVorder with ‘(my) mother’ marked by the nominative case marker -ga(NPNOM), ‘(an) apple’ marked by the accusative case marker -leul(NPACC), and the past tense verb (V-PST) meog-eoss-da ‘ate’ at the end.
In Sentence (2), the positions of NPNOM and NPACC are scrambled, as
is characteristic of the OSV order. Once again, the final verb ‘ate’
appears at the end of the sentence. The OSV order is formed bymoving the object to the beginning of the sentence. This word order
was termed scrambled by Ross (1967), who primarily discussed thisphenomenon in relation to Germanic languages (Neeleman, 1994;
Broekhuis, 2008).
Several psycholinguistic studies conducted in Japanese, whichshares similar syntactic features with Korean, have investigatedsentence processing. These studies (e.g., Mazuka et al., 2002; Ueno andKluender, 2003; Koizumi and Tamaoka, 2004, 2010; Miyamoto and
Takahashi, 2004; Tamaoka et al., 2005, 2014; Imamura et al., 2016;
Witzel and Witzel, 2016; Tamaoka and Mansbridge, 2019) consistently
found that the canonical SOV order is processed faster than thescrambled OSV order. For instance, in the methodology employed by
Tamaoka et al. (2005), participants were tasked with evaluating thecorrectness of each sentence, considering both its semantic coherence
and grammatical accuracy. This approach is hereafter referred to as a‘sentence correctness decision task.’ Utilizing this task, the study
measured the processing time for both canonical SOV and scrambled
OSV sentence orders in Japanese, employing sentences analogous to
Korean Sentences (1) and (2). Without any preceding context, thestudy found that Japanese canonical SOV sentences were processedboth more rapidly and accurately compared to their scrambled OSV
counterparts. Drawing on these results, the current study predicts asimilar processing advantage for canonical sentences in Korean overtheir scrambled alternatives.
The processing inefficiency observed in both accuracy and speed
for scrambled sentences is commonly referred to as the scrambling
effect. One potential explanation for the delay in processing thescrambled OSV order in Japanese comes from the gap-filling parsing
model (Frazier and Rayner, 1982; Stowe, 1986; Frazier, 1987; Frazierand Clifton, 1989; Frazier and Flores D’Arcais, 1989). According tothis model, native Korean speakers likely identify the initial NPACC
marked by -leul as the filler and subsequently search for its original
position in the specifier of the gap to establish the filler-gap
dependency. Given that the OSV scrambled order entails a
syntactically more complex structure than its corresponding SOVorder, the process of gap-filling parsing for the OSV order is expected
to be slower than for the SOV order. Therefore, we anticipate observing
similar processing dynamics in Korean as those identified in Japanese,
where scrambled sentences are processed less efficiently compared to
canonical sentences.
In the processing of scrambled word order in Japanese andpotentially in Korean, the filler-gap dependency is activated. While the

1.1 Scrambling and topicalization in Korean
Korean, the native language of both South Korea and North
Korea, is estimated to have more than 80 million people in the world
who speak Korean as a first, second, or heritage language (Pae, 2024).
Korean is a verb-final language with the canonical order of subject,
object, and verb (SOV). The order of subject and object is relatively
flexible. A simple transitive sentence in Korean can have one of two
basic orders: either SOV or OSV. Noun phrases (NPs) are marked by
one of three case markers (or particles): the subject or nominative
marker-이/가, −i/ka (NPNOM), the object or accusative marker-을/를,−eul/leul (NPACC), or the topic marker-은/는, −eun/neun (NPTOP). In
this study, we represent sentences in Korean using standard
romanization. These combinations enable the creation of four types ofsentences (Lee and Ramsey, 2000; Pae, 2024). These, with either SOVor OSV, are illustrated in Sentences (1) to (4). All sentences
fundamentally carry the same meaning. Sentence (1) is the canonicalorder, which employs the scrambled order OSV, as shown in Sentence(2). Topicalization in Korean is achieved by adding the marker -eun/
neun to a noun phrase, as illustrated in Sentence (3) with subject
topicalization and Sentence (4) with object topicalization.
(1) SOV: Canonical order.
Eom-ma-ga
sa-gwa -leul meog -eoss-da.
NP(mother) NOM NP(apple) ACC V(eat) PST.
엄마가 사과를 먹었다.
‘(My) mother ate (an) apple.’(2) OSV: Scrambled order.
Sa-gwa-leul
eom-ma-ga meog -eoss-da.
NP(apple) ACC NP(mother) NOM V(eat) PST.
사과를 엄마가 먹었다.
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subject typically appears first in a sentence, the object precedes it in ascrambled sentence. Given that Korean and Japanese are null-subject(or pro-drop) languages, the subject of a sentence can be omitted.
Consequently, native speakers of Korean and Japanese may initiallyinterpret the sentence as having a null subject. However, in ascrambled sentence, the subject follows the object (OS order). Native
speakers of Korean and Japanese will then search for the gap1,
representing the position where the object would occur in the SOV
canonical order. They establish the filler-gap dependency between theobject and the gap as O1 S gap1 before encountering the verb to fullycomprehend the scrambled sentence. This additional processing step
may prolong the processing time and potentially lead tocomprehension errors.
In Korean, canonical and scrambled word orders overlap with
topicalization orders. The subject in the SOV canonical order istypically positioned at the beginning of the sentence unless a pro-drop
occurs. Therefore, the canonical SNOMOV and the topicalized STOPOV
share the same word order. For instance, SNOMOV for 경찰이 범인을
잡았다 (Gyeong-chal-i beom-in-eul jab-ass-da, ‘The police caught the
culprit’) and STOPOV for 경찰은 범인을 잡았다 (Gyeong-chal-eun
beom-in-eul jab-ass-da) both have the subject in the initial position,
which is canonical. It is important to note that Korean lacks definite
and indefinite articles, so nouns in a sentence are typically denoted
without an article. Similarly, OACCSV and OTOPSV also share the sameword order. For example, OACCSV for 범인을 경찰이 잡았다 (Beomin-eul gyeong-chal-i jab-ass-da, ‘The culprit, the police caught’) and
OTOPSV for 범인은 경찰이 잡았다 (Beom-in-eun gyeong-chal-i
jab-ass-da) both have the object in the scrambled position. Since theword orders of subject and object topicalization share the same pattern
as canonical and scrambling word orders, it is difficult to measure theeffect of subject and object topicalization independently from theprocessing of canonical and scrambling structures.
In Korean, noun phrases can be topicalized using the marker-eun/neun (NPTOP). As topicalization is a discourse feature, NPTOP ispositioned at the beginning of a sentence to indicate the topic. A
subject or an object can be topicalized using the marker -eun/neun.
Sentence (3) exemplifies a subject-topicalized (NPSUB-TOP) sentence in
the SOV canonical order, beginning with ‘Speaking of (my) mother.’
Subject topicalization also implies an exclusionary meaning, referring
specifically to ‘mother’ and not other family members. Sentence (4)illustrates an object-topicalized (NPOBJ-TOP) sentence in the OSV
scrambled order, commencing with ‘Speaking of (the) apple.’ Object
topicalization similarly implies an exclusionary meaning, specificallyreferring to ‘(the) apple’ and not other fruits.
It is also notable that the distinct grammatical particles (ormakers) used for the subject (−이/가, −i/ga) and the object (−을/를,−eul/leul) differ from auxiliary particles, such as -은/는, −eun/neun.
In particular, auxiliary particles that serve functions beyond
topicalization may interfere with processing due to their multiple
functions. For instance, the marker -eun/neun can be employed foremphasis, as in 그는 영어는 잘 한다 (Neun yeong-eo-neun jal
han-da), which translates to ‘(He is not so good at other subjects, but)he is good at English.’ Another usage is to indicate contrast, as seen in인생은 짧고 예술은 길다 (In-saeng-eun jjalb-go ye-sul-eun gil-da),meaning ‘Life is short, art is long.’ Additionally, it can function as a
pseudo-subject particle in certain contexts where the subject has
already been mentioned. In such cases, its function may notnecessarily be as a topic marker. Additionally, the Korean language isFrontiers in Psychology

also a highly contextual language. Depending on the use of theauxiliary particle, the nuance of a given sentence varies because it
carries semantic information rather than the syntactic function. Thus,
it is essential to be cautious of the multiple functions of the particle(marker) -eun/neun when interpreting the results of an experiment.
In verb-final languages such as Korean and Japanese, the sentenceending verb also plays a crucial role in properly understanding asentence, particularly when there is no animacy contrast between thesubject and the object. This processing tendency can be described asa backward argument-verb dependency (Tamaoka and Mansbridge,2019). In the absence of an animacy contrast, native Japanese speakers
rely on information provided by the verb at the end of the sentence toestablish the structural properties of scrambled constituents. Previous
studies (Hsiao and Gibson, 2003; Pu, 2007; Vasishth et al., 2013; Kwon
et al., 2019 for Mandarin Chinese; Mak et al., 2002 for Dutch) haveindicated that animacy features affect the processing of relative
clauses. Similarly, an animacy contrast may influence the processingof Korean and Japanese sentences, given that native speakers of theselanguages encounter the verb only at the end of the sentence. In suchcases, animacy information might play a crucial role in constructing
sentence structure, particularly in verb-final languages such as Korean
and Japanese. Therefore, a semantically driven analysis for sentence
processing would be considered an important additional factor.
It is widely accepted that both Korean and Japanese belong to thegroup of Altaic languages, sharing many linguistic features (Lee andRamsey, 2000; Pae, 2024), although the debate over the designation of
language family remains unsettled. In both Korean and Japanese,
simple transitive sentences can exhibit either SOV or OSV word
orders. In Japanese, noun phrases (NPs) are denoted by one of threecase markers: the nominative -ga (NPNOM), the accusative -o (NPACC),
or the topicalization -wa (NPTOP). This system mirrors Korean in thatthe word orders demonstrate overlap in two primary aspects. First,
sentences with a topicalized subject adhere to the same SOV structure
as canonical sentences. Second, sentences where the object is
topicalized align with the OSV order typical of scrambled sentences.
A study on topicalization in Japanese by Imamura et al. (2016)
found the order of processing speed to be as follows: canonical
SNOMOACCV (M = 1,410 ms) = subject topicalized STOPOACCV (M =1,414 ms) < scrambled OACCSNOMV (M = 1,512 ms) < object topicalized
OTOPSNOMV (M = 1,626 ms). This study showed that the processing
time for the subject topicalized word order of STOPOACCV was the sameas that of the canonical SNOMOACCV. Therefore, as Imamura et al. (2016)
suggested, this order seems to be commonly used, resulting in both
structures being easily processed within a short time. A simpler
explanation may be that when the nominative marker -ga is used, thetopic marker -wa in the SOV order appears to identify the subject or
its equivalent. Another possible explanation is that a sentence topic
placed at the beginning of a sentence may speed up processing byinitially providing an overall theme of the sentence. This discoursefeature may explain equivalencies in processing speed. Additionally,
it may be that a combination of these factors speeds up processingtime.
However, the processing speed of object topicalized sentences was
slower than their equivalent canonical sentences, and furthermore,even slower than their scrambled sentences. Thus, as Kuroda (1987)
suggested, object topicalization could involve the process of both
scrambling and topicalization. Additionally, it is possible to add the
explanation that object topicalization focuses on ‘(an) apple’. This03

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scrambled sentences in the following manner: The noun phrase ‘a eika ‘the fish’ after the verb is an absolutive case marked (‘a) nounphrase indicating the object (O). Native Tongan speakers perceive thatthe object should come after the subject (S) to conform to the VSO
canonical order. The object is temporarily kept in working memory(Chen, 1986; Carpenter and Just, 1989; King and Just, 1991) while theoriginal object position of gap1 is identified as following S. Finally,
speakers establish a relationship between O1 and gap1, and ‘the fish’ isfilled into the gap1 position. The multiple steps required to use theactive filler strategy should account for the extra processing timeneeded for the VOS scrambled order compared to the canonical
VSO order.
In Tongan, there is a distinction in the topicalization of the subject
and object. In the case of subject topicalization, a resumptive pronounsuch as ‘ne’ (third person singular) appears in place of a gap in therelative clause (Otsuka, 2006), positioned after the past tense na’a(phonological change from na’e) and before the verb kai ‘eat’. In the
English sentence, ‘This is the girl that I do not know what she said,’‘she’ in a relative clause refers to the previously mentioned noun ‘thegirl’. Similarly, the resumptive pronoun ne in Tongan in Sentence (7)refers to e fefine ‘the woman’. In fact, the resumptive pronoun ne isnecessary in the relativization of ergative NPs. Thus, topicalization
behaves similarly to relativization in this aspect, indicating that it
involves A-bar movement.

focus implies an exclusionary meaning of ‘an apple’ and ‘not any otherfruits.’ Consequently, this focus may further delay the processing
speed compared to corresponding scrambled sentences. Once again,
because subject/object topicalization shares the same word order withcanonical/scrambling, the effect of subject/object topicalization
cannot be measured independently from canonical/scrambling.

1.2 Scrambling and topicalization in
TonganIn the Austronesian language of Tongan, the canonical word orderis VSO (Verb-Subject-Object), commonly used in transitive sentences.
However, a VOS (Verb-Object-Subject) order is also grammaticallypossible (Churchward, 1953; Dixon, 1979, 1994; Otsuka, 2000,
2005a,b; Custis, 2004). Tongan, being an ergative language (Otsuka,
2005a,b, 2010), marks both the subject of an intransitive sentence andthe object of a transitive sentence with the same absolutive (ABS)
marker ‘a, while the subject of a transitive sentence is marked by theergative (ERG) marker ‘e (i.e., ERG/ABS case marking pattern).
Tongan verbs have limited inflectional morphology (e.g., no inflection
for tense). The present study focuses only on transitive sentences.
In Tongan, a noun phrase (NP) that is topicalized is positioned
before the verb in either SVO or OVS orders, marked with the topic
marker ‘ko’. This results in four potential word orders for transitive
sentences: VSO, VOS, SVO, or OVS. Unlike in Japanese and Korean,
when topicalization occurs in Tongan, the resulting word order does
not overlap with either the VSO canonical or scrambled orders. To
observe the topicalization effect as an independent phenomenon, thepresent study experimentally investigated processing times for thesefour orders of transitive sentences. A verb-initial language such as
Tongan is ideal for investigating this processing function.
In Tongan, a sentence denoting ‘the woman ate the fish’ is assumed
to be in the canonical order, as shown in Sentence (5), where NP refers
to the noun phrase, PST refers to the past tense, ERG (‘e) refers to theergative case marker, ABS (‘a) refers to the absolutive case marker, andREF refers to the referential/specific article (Anderson and Otsuka,
2006; Macdonald, 2014).

(7) S1Vgap1O: Subject topicalized transitive Tongan sentenceKoe fefinei
na’a nei kai‘a eika
TOP REF NP (woman) PST PRO V(eat) ABS REF NP (fish)‘(It is) the woman that (she) ate the fish.’
Custis (2004) posits that topicalization results from a movement
marked by ko. Therefore, according to the gap-filling parsing model,
a topicalized subject phrase is shifted in front of the verb. Native
Tongan speakers identify the initial topicalized phrase as the filler andthen seek its original position in the specifier of the gap to establish
filler-gap dependency. As this dependency extends beyond the verb,
processing an SVO subject topicalized sentence might require evenmore processing time than a VOS scrambled sentence. It is noteworthy
that both subject and object topicalization in Tongan also convey an
exclusionary meaning, similar to what is seen in Korean and Japanese.
The object can also be topicalized by moving it in front of the verb.
As with subject topicalization, the object is also marked by the topic
marker ko, as seen in ko e ika (‘the fish’) in Sentence (8), illustrating
object topicalization (OTOP). However, unlike subject topicalization,
object topicalization does not require a resumptive pronoun. Both
Sentences (7 and 8) follow the structure ko NP1 V NP2. The object
topicalization in Sentence (8) does not necessitate aresumptive pronoun.

(5) VSO: Canonical transitive Tongan sentence.
Na’e kai‘ehe fefine‘a eika.
PST V(eat) ERG REF NP (woman) ABS REF NP (fish)‘The woman ate the fish.’
The VOS scrambled order is derived from the VSO canonicalorder by moving the object (O) between the verb (V) and the subject(S), as shown in Sentence (6), constructing a more complex structure(VO1Sgap1) than the canonical (VSO) order. The VOS scrambled orderretains the same meaning of ‘The woman ate the fish.’(8) O1VSgap1: Object topicalized transitive Tongan sentence.
Koe ika
na’e kai‘e hefefine.
TOP REF NP (fish) PST V(eat) ERG REF NP (woman).
‘About the fish, the woman ate.’(6) VO1Sgap1: Scrambled transitive Tongan sentence.
Na’e kai ‘ae ika‘ehe fefine.
PST V(eat) ABS REF NP (fish) ERG REF NP (woman)‘The fish, the woman ate.’

Object topicalization in Tongan may involve an even longer
distance movement (O1VSgap1) than subject topicalization (S1Vgap1O)
because the object moves ahead of the subject and the verb. Native
Tongan speakers may read a topicalized sentence to find gap1 at the

According to the gap-filling parsing model (Frazier and Rayner,
1982; Stowe, 1986; Frazier, 1987; Frazier and Clifton, 1989; Frazier andFlores D’Arcais, 1989), native Tongan speakers possibly process VOS

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end of the sentence as O1VSgap1. Once they establish the relationshipbetween O1 and gap1, the topicalized O1 is filled in gap1. The distance
between filler and gap in O1VSgap1 is longer than the distance between
filler and gap in either VO1Sgap1 or S1Vgap1O. Therefore, based on thesyntactic structure (moved distance), Tongan speakers keep O1 inworking memory longer for O1VSgap1 than VO1Sgap1 andS1Vgap1O. Thus, the gap-filling parsing model predicts that an OVS
topicalized order will take longer to process than either a VOS
scrambled or SVO topicalized order. Additionally, if native Tongan
speakers can obtain the argument information from the verb at the
beginning of the sentence, they could easily construct a wholesentence based on that information. The processing of argument-verb
dependency would function well to enable the construction of a whole
syntactic structure of VSO and VOS. Since the subject and the object
are placed before the verb in topicalization, native Tongan speakers
may have to process the backward verb-argument dependency. Insuch a case, SVO and OVS orders may be disadvantaged in processing
compared to VSO and VOS orders: a distinct difference in reaction
times and possibly in accuracy may be observed between the verbinitial orders (VSO and VOS) and the verb-second orders (SVO
and OVS).
From the perspective of syntactic complexity based on filler-gap
dependency, processing difficulties among the four-word orders in
Tongan sentences can be predicted as follows: The VSERG OABS
canonical order is processed the fastest. In the V OABS1 SERG gap1
structure, the object is moved in front of the subject, while in the
topicalized STOP1 V gap1 OABS sentence, the subject phrase is moved
before the verb. Due to the distance of one phrase movement, thespeed of sentence processing would be assumed to be similar between
the scrambled VOABS1 SERG gap1 and topicalized S1Vgap1O orders.
However, in object topicalization, the object phrase moves beyond
both the verb and the subject phrases. Therefore, the OTOP1 V SERG gap1
object topicalized order has a much longer distance of filler-gap
dependency, making it syntactically more complex than other word
orders. This syntactically complex order would require the longest
processing time among the four orders.

shown by various studies (e.g., Mazuka et al., 2002; Ueno andKluender, 2003; Koizumi and Tamaoka, 2004, 2010; Miyamoto and
Takahashi, 2004; Tamaoka et al., 2005, 2014; Imamura et al., 2016;
Witzel and Witzel, 2016; Tamaoka and Mansbridge, 2019),
we expected OTOP1 SNOM gap1V to be slower than both the canonical
SNOM OACC V and topicalized STOP OACC V orders. Furthermore,according to Imamura et al. (2016), the topicalized order of OTOP1 SNOM
gap1V was even slower than the scrambled order of OACC1 SNOM gap1V
in Japanese, suggesting a similar result may be observed in Korean.
Therefore, based on the filler-gap dependency (the movement-based
anticipation), the speed of Korean sentence processing was predicted
as follows:
Prediction 1: Korean Sentence Processing Based on a Filler
Gap Dependency.
SNOM O ACC V =STOP O ACC V < O ACC1 SNOM gap1V < OTOP1 SNOM gap1V.

Experiment 2 involved a phrase-by-phrase processing experimentusing a maze task to verify whether the VSO order is indeed canonicalin the verb-initial language of Tongan. Similar to Experiment 1 in
Korean, this experiment aimed to ascertain the canonical word orderin Tongan sentence structure. Participants were presented withsentences having both VSO and VOS orders, broken down into
phrases, and their processing speed for each phrase was measured.
The results were then analyzed to determine whether the VSO orderdemonstrated superior processing efficiency compared to the VOS
scrambled order, confirming its canonical status in Tongan
sentence construction.
Experiment 3 in Tongan was conducted in a similar manner
to Experiment 1 in Korean, enabling a comparative analysis ofsentence processing strategies across the two languages. This
experiment was designed to delve deeper into the effects of both
scrambling and topicalization in Tongan sentence processing
using a sentence correctness decision task. Specifically, it aimed
to separate the influence of topicalization from the impact ofcanonical and scrambled orders. By comparing sentence
processing across four different orders—VSO, VOS, SVO, andOVS—participants’ reaction times and accuracy rates wererecorded and analyzed. This comprehensive investigation allowed
for an understanding of how subject (SVO) and object
topicalizations (OVS), in conjunction with canonical (VSO) andscrambled (VOS) orders, affect sentence processing in Tongan.
Based on the filler gap dependency (movement-based
anticipation), the speed of Tongan sentence processing waspredicted as follows:
Prediction 2: Tongan Sentence Processing Based on a Filler
Gap Dependency.

1.3 Outline of the present study
The present study conducted three experiments to investigate
differences in word orders resulting from scrambling andtopicalization. For native speakers, the accuracy of sentence
correctness decisions tends to be consistently higher. This makes thereaction time required to determine whether a sentence is correct amore sensitive indicator. Therefore, sentence processing for various
word orders can be predicted by processing speed.
Experiment 1 focused on (verb-final) Korean to compare thefindings with those of Imamura et al. (2016) on Japanese. Given thesyntactic similarities between Korean and Japanese, this experiment
aimed to determine if a similar processing trend exists between the
two languages. We anticipated that sentences in the SNOM OACC V orderwould be processed most quickly because they are in the canonicalorder. Next, since topicalized sentences of STOP OACC V also have thesame word order as the canonical order, we expected that they would
be processed at the same speed as the canonical order, similar to
Japanese sentences. Given that the scrambled order of OACC1 SNOM
gap1V is known to be significantly slower than the canonical order, as

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V SERG O ABS < V O ABS1 SERG gap1 =STOP1 V gap1 O ABS < OTOP1 V SERG gap1.

The findings from these three experiments would contribute toour understanding of sentence processing mechanisms in both verbfinal and verb-initial languages, shedding light on the universality andlanguage-specific aspects of sentence structure and comprehension.

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2 Experiment 1: processing of Korean
scrambled and topicalized sentences
2.1 Method
2.1.1 Participants
Forty-eight native Korean speakers (32 female and 16 male) wererecruited from Busan National University in Korea. The mean age of theparticipants was 23.3 ± 2.6 years (range: 20–31 years). All collected
information was stored in a secure location, and the participants were
given numerical pseudonyms to ensure privacy. The present experiment
involving human participants was reviewed and approved by the
Research Ethics Committee of Busan National University. Participants
signed informed consent forms before the experiment, and at the endof the experiment, they received payment and were debriefed.

FIGURE 1

A single trial of the Korean sentence correctness decision task.

2.1.2 Stimulus sentences
Initially, 32 Korean SOV canonical sentences were created.
Subsequently, 128 sentences corresponding to the four-word orders(32 × 4 = 128) were constructed following the format illustrated in
Sentences (1) to (4). The complete list of stimulus sentences isprovided in Supplementary material. As part of the control, 32
semantically incorrect sentences were formulated, ensuring that halfof all sentences were semantically infelicitous and/or grammaticallyincorrect (hereafter referred to as ‘incorrect’). Each list thus consistedof 32 correct and 32 incorrect sentences, totaling 64 sentences.
Examples of incorrect Korean sentences included 아이가 커튼을헤엄쳤다 (A-i-ga keo-teun-eul he-eom-chyeoss-da), meaning ‘The
child swam through the curtain,’ and 남동생이 사진을 운동했다(Nam-dong-saeng-i sa-jin-eul un-dong-haess-da), meaning ‘Myyounger brother exercised a photo.’ The incorrect sentences are notincluded in Supplementary material. To ensure that the same sentencewith different word orders would not be assigned to a single
participant, these 128 sentences were counterbalanced into four lists,
each to be distributed among the four participant groups.

TABLE 1 Result of the LME model analysis for accuracy.

EstimateSEz value

Pr(>|t|)pvalue(Intercept)

5.11

0.57

9.03

p < 0.001***

STOP OACCV

−0.08

0.54

−0.14

p = 0.886

OACC SNOMV

−1.33

0.46

−2.88

p < 0.01**

OTOP SNOMV

−1.59

0.45

−3.53

p < 0.001***

trial.z

−0.62

0.19

−3.22

p < 0.001***

Participants = 48. Item = 32. Total observation = 1,536.
glmer(acc ~ wordorder + trial.z + (0 + trial.z|participant) + (1|participant) + (1|item), data,family = binomial).

(LME) models (Baayen et al., 2008) and the lme4 package (Bates et al.,2016). The two fixed effects were trial and word order (four sentence
conditions). The random effects were participants and stimulus
sentences. The data for reaction times consisted only of data fromtrials with correct judgments. Satterthwaite’s approximations(Satterthwaite, 1946) were used via the lmerTest package to generatep-values for each model (Kuznetsova et al., 2017) using the restrictedmaximum likelihoods (Harville, 1977).

2.1.3 Procedure
In Experiment 1, a Korean sentence correctness decision task was
conducted on 48 native Korean speakers using their personal
computers connected to the online experimental platform “Pavlovia”.1
As depicted in Figure 1, an eye fixation symbol (++++++++) was
initially presented at the center of the computer screen for 500 ms,
after which a target sentence replaced it. Participants were thenrequired to decide whether the sentence was a correct Korean sentence(pressing the YES key for correct and the NO key for incorrect). The
next trial appeared after a 200 ms interval. All stimulus sentences wererandomly presented to each participant. Participants were instructedto complete the task as quickly and accurately as possible. Ten practice
items were provided to each participant before the commencement of
the actual experiment.

2.1.5 Results of LME model analyses for accuracy
data
A total of 1,536 responses (48 participants × 32 semantically andgrammatically correct items) were analyzed. The fixed factors weretrial and word order. The trial was centralized into z-values, coded as“trial.z.” The two random factors were participant and stimulus
sentences. According to model comparisons using AIC (Anderson
et al., 2000), the final best-fit LME model was glmer(acc ~ wordorder+ trial.z + (0 + trial.z|participant) + (1|participant) + (1|item), data,family = binomial). The result of the best-fit LME model is reported inTable 1. The factor trial was significant [z = −3.22, p < 0.001]. This
indicated that, as the experiment progressed, the accuracy of task
performance decreased. The reference for word order was set as the
canonical order of SNOMOACCV. As shown in Table 1, the result
indicated that SNOMOACCV sentences were processed as accurately as
STOP OACCV topicalized sentences [z = −0.14, ns] but more accurately
than OACC SNOMV scrambled sentences [z = −2.88, p < 0.01] and OTOPSNOMV topicalized sentences [z = −3.53, p < 0.001].

2.1.4 Analysis
The accuracy and reaction times data collected from the sentence
correctness decision task were analyzed using the linear mixed effect

1 https://pavlovia.org/

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To examine word order differences, accuracies of the four word
orders were compared using the lsmeans (least-squares means;
Searle et al., 1980) R package. The means and standard deviations arereported, and the result of multiple comparisons is shown in
Figure 2. The result indicated that SNOMOACCV (M = 97.40%) andSTOPOACCV (M = 97.92%) were processed with the same level ofaccuracy. Both SNOMOACCV and STOPOACCV orders were moreaccurately processed than both OACC SNOMV (M = 93.75%) and OTOPSNOMV (M = 91.67%) orders. OACC SNOMV and OTOP SNOMV orders wereequally accurate.

For a detailed examination of the differences in reaction times
among word orders, the times for the four word orders were comparedusing the R package of lsmeans (least-squares means; Searle et al., 1980).
The means and standard deviations (1,428 responses) were reported,and the result of multiple comparisons is shown in Figure 3. The result
indicated that SNOMOACCV (M = 1,270 ms) and STOPOACCV (M = 1,267 ms)orders were processed at the same speed. Both SNOMOACCV andSTOPOACCV orders were processed faster than both OACC SNOMV(M = 1,551 ms) and OTOP SNOMV (M = 1,571 ms) orders. OACC SNOMV andOTOP SNOMV orders were processed at roughly the same speed.

2.1.6 Results of LME model analyses for reaction
time data

2.2 Discussion

There were no stimulus sentences processed faster than 500 ms or
slower than 6,000 ms. After removing 74 incorrectly answered items
from the 1,536 semantically and grammatically correct items, theremaining 1,462 correctly answered items were analyzed on reaction
times. Based on the Box-Cox power transformation technique (Boxand Cox, 1964; Venables and Ripley, 2002), a logarithmictransformation (natural log) was applied to the reaction times toattenuate any skewness in their distribution. Reaction times wereanalyzed with the lmer function using the restricted maximum
likelihood (Harville, 1977). Satterthwaite’s approximations(Satterthwaite, 1946) were used via the lmerTest package to generatep-values for each model (Kuznetsova et al., 2017).
According to model comparisons using AIC (Anderson et al.,2000), the best-fit LME model was lmer (log(rt) ~ wordorder +trial.z + (1|participant) + (1|item), data). Based on this best-fit LME
model, potentially influential outliers with absolute standardized
residuals exceeding 2.5 standard deviation were removed. Of the 1,462
responses, 34 responses were removed. The result of the LME model
analysis for the 1,428 responses is reported in Table 2. Unlike for
accuracy, the trial was not a significant factor [t(1104.00) = −0.34, ns].
The reference for word order was set as SNOMOACCV. As shown in
Table 2, this result indicated that SNOMOACCV order was processed at
the same speed as STOP OACCV topicalized order [t(1346.00) = −0.09,ns], but faster than OACC SNOMV scrambled order [t(1355.00) = 8.64,
p < 0.001] and OTOP SNOMV topicalized order [t(1355.00) = 9.79,
p < 0.001].

The results of the Korean sentence correctness decision task from
Experiment 1 demonstrated a pattern similar to that observed in the
study by Imamura et al. (2016) in Japanese, largely confirming
Prediction 1, with the exception of no difference observed between
OACC SNOMV and OTOP SNOMV. First, akin to Japanese, the scrambled
order in Korean was processed more slowly than the canonical order.
Second, consistent with the findings of the previous Japaneseexperiment (Imamura et al., 2016), the subject topicalized order wasprocessed at a comparable speed to the canonical order. Thisobservation suggests that similar to Japanese, the STOPOACCV subject
topicalized order in Korean might have been construed simply as the
subject, akin to the canonical ordered sentence SNOMOACCV. Third,
unlike Japanese, there was no discernible difference in processing time
between object topicalized and scrambled sentences. This discrepancy
between Japanese and Korean could be attributed to the animacy
effect, as Korean stimulus sentences featured animacy contrast, unlike
the study by Imamura et al. (2016). Further elaboration on this aspect
will be provided in the General Discussion section. Additionally,
similar to Japanese, the SOV canonical order in Korean overlappedwith the subject topicalized order, while the OSV scrambled order in
Korean overlapped with the object topicalized order. Consequently,
neither Japanese nor Korean could distinguish between the effects ofscrambling and topicalization. Hence, a more comprehensive
investigation is warranted in Tongan, which exhibits a distinct word
order for canonical/scrambled and subject/object topicalization.

3 Experiment 2: verification of the
VSO canonical order in Tongan
This experiment aimed to verify whether the VSO order is indeed
canonical in sentence processing by native Tongan speakers.

3.1 Methods
3.1.1 Participants
Forty-eight native Tongan speakers (33 female and 15 male) wererecruited from the Tonga Institute of Education located on the mainisland of Tongatapu, Tonga. The mean age of the participants was
22.8 ± 4.6 years (range 17–35 years). All participants received monetarycompensation in exchange for their participation and provided
written informed consent. The present experiment involving humanparticipants was reviewed and approved by the Research Ethics

FIGURE 2

Accuracies of the four word orders of Korean sentences. The values
after ± refer to standard errors.

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TABLE 2 Result of the LME model analysis for reaction times.

Variables

EstimateSEdf

t value

Pr(>|t|)p-value(Intercept)

0.17

0.04

78.36

STOP OACCV−0.0020.02

1,346.00

4.02

p < 0.001

***

−0.09

p = 0.927

OACC SNOMV

0.19

0.02

1,355.00

8.64

p < 0.001***

OTOP SNOMV

0.21

0.02

1,355.00

9.79

p < 0.001***

trial.z−0.0030.01

1,104.00−0.34

p = 0.733

Participants = 48. Item = 32. Total observation = 1,428 responses (74 incorrectly answered responses and 34 outliers were removed from the total observation of 1,536 responses).
lmer(log(rt) ~ wordorder + trial.z + (1|participant) + (1|item), data).

ηp2 = 0.40]; finally, writing skills between Tongan (M = 5.31, SD = 0.78)
and English (M = 4.60, SD = 1.11) also differed significantly [F(1,47) = 14.16, p < 0.001, ηp2 = 0.23]. In summary, both indexes of language
use percentages and subjective language skill judgments indicated ahigh level of proficiency in Tongan and good proficiency (but to alesser degree than Tongan) in English.

3.1.2 Stimulus sentences
Thirty transitive sentences were composed. Each sentence
contained four phrases, including an initially presented adverb (Adv)mahalo ‘maybe’, a verb (V), and two noun phrases. The frequently used
first names of Taniela and Kaufusi were used in all the “two noun”
phrases. An example of this is the sentence, Na‘e talitali ‘e Taniela ‘a
Kaufusi meaning ‘Maybe Taniela welcomed Kaufusi’. Based on the
canonical order of AdvVSO illustrated in Sentence (9), the 30 AdvVOS
ordered scrambled sentences were created by exchanging theabsolutive marker ‘a and ergative marker ‘e for the two first names,
resulting in an AvdVOS scrambled order as seen in Sentence (10).

FIGURE 3

Reaction times of the four word orders of Korean sentences. The
values after ± refer to standard errors.

(9) AdvVSO canonical order.
Phrase 1
Phrase 2
Phrase 3
Phrase 4.
Mahalona‘e talitali‘e Taniela ‘a Kaufusi.
Adv(maybe) V(welcome)-PAST NP-ERG (Taniela) NP-ABS(Kaufusi).
‘Maybe Taniela welcomed Kaufusi.’

Committee of Tohoku University. All collected information was stored
in a secure location, and the participants were given numericalpseudonyms to ensure privacy. The participants signed informed
consent forms before the experiment, and at the end of the experiment,
they received payment and were debriefed.
Both Tongan and English are the official languages in Tonga. As aresult of globalization, English is frequently used in Tonga (Otsuka,2007). However, in daily life, native Tongans use Tongan more
frequently than English; therefore, Tongan is considered to be theirfirst language. To analyze how native Tongans use the two languages,
the present study conducted a questionnaire survey on 48 participants
regarding their use of the two languages and their perceptions of theirown language proficiencies. The survey found that the mean use
percentage of Tongan in daily life was 79.98% (SD 15.35%), whereas
the mean use for English was 23.92% (SD 15.26%). It should be notedthat a question for Tongan or English daily use was asked
independently, so usage percentages of Tongan and English do not
perfectly sum up to 100% (79.98% + 23.92% = 103.9%). This 64.63%
usage difference between Tongan and English was significant [F(1,47) = 138.91, p < 0.001, ηp2 = 0.75]. Subjective proficiency judgments offour important language skills (speaking, listening, reading, andwriting) for Tongan and English were measured using a 0-to-6 point
scale (0 ‘none’ to 6 ‘very high’): Speaking skills between Tongan(M = 5.60, SD = 0.64) and English (M = 4.38, SD = 0.91) differedsignificantly [F(1, 47) = 62.57, p < 0.001, ηp2 = 0.57]; listening skills
between Tongan (M = 5.50, SD = 0.74) and English (M = 4.58,
SD = 1.09) differed significantly [F(1, 47) = 38.17, p < 0.001, ηp2 = 0.45];
reading skills between Tongan (M = 5.56, SD = 0.60) and English(M = 4.81, SD = 0.98) differed significantly [F(1, 47) = 31.67, p < 0.001,
Frontiers in Psychology(10) AdvVOS scrambled order.
Phrase 1
Phrase 2
Phrase 3
Phrase 4.
Mahalona‘e talitali‘a Taniela‘e Kaufusi.
Adv(maybe) V(welcome)-PAST NP-ABS (Taniela) NP-ERG(Kaufusi).
‘Maybe Kaufusi welcomed Taniela.’
As shown in Sentences (9) and (10), the first names “Taniela andKaufusi” were kept in the same position within the sentence. Throughthis manipulation, the differences in each region of the two casemarkers for the noun phrases could be directly compared using themaze task (Forster et al., 2009; Forster, 2010, the method is explainedin the following section). The experimental stimuli (correct lexical
sentences) are listed in Supplementary material. Incorrect lexical items
are not included in Supplementary material. The stimuli werecounterbalanced to ensure that participants would see only one format
for each sentence per experimental session.

3.1.3 Measuring reaction times with the lexical
maze task
The present study employed a measurement tool for sentence
processing known as the “maze task” (Forster et al., 2009 for English;
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Witzel and Witzel, 2016 for Japanese; and Qiao et al., 2012 for
Mandarin Chinese). This experiment was conducted individually bya native Tongan speaker in a classroom at the Tonga Instituteof Education.
In this experiment, both a real word and a non-word are
simultaneously presented on the left and right sides of a computer
screen. As shown in Figure 4, initially ‘+++++’ and Mahalo (Maybe)with a capitalized first letter were presented. Participants were
instructed to choose Mahalo by pressing the right key. This was theinitial lexical decision across all trials. After 200 ms, na‘e talitali(‘welcomed’; a real word) and na‘e sakula (non-word) were presented.
The participants were directed to select the real word in Tongan. The
past tense of na‘e was kept as a constant, so that participants would
make a lexical decision based only on their assessment of the verb
form of talitali ‘welcome’. In this case, a correct decision would havebeen made by pressing the left key. After another 200 ms, the two firstnames of Taniela and Hakela, both with the absolutive case marker ‘e,were presented. The first name Taniela is a real name in Tongan, while
Hakela is not, so participants were expected to press the left key.
Finally, after a further 200 ms, Hokapoi and Kaufusi, both with theergative case maker ‘a, were presented. The first name Kaufusi is thereal name, so the participant was expected to press the right key. As
the case markers of the third (P3) and fourth (P4) phrases were kept
the same on both the left and right sides, the participant had to decide
whether these were correct expressions in Tongan solely by focusing
on the first name. Within the flow of lexical decision-making, theseries of correct real words constructed a four-phrase real sentence,i.e., the AvdVSO canonical ordered sentence, Mahalo na‘e talitali ‘aKaufusi ‘Maybe Taniela welcomed Kaufusi’ P1 to P4 in Figure 4
illustrates the series.
If the participant made a mistake before the final phrasal set, thetrial was stopped, and the next trial started 600 ms later. Two stimulus
phrases were randomly positioned on the left and right sides of thescreen in each trial. The participants were asked to perform the mazetask as quickly and accurately as possible. Before the actual experiment
began, eight practice trials were given.

FIGURE 4

A series of lexical decisions in the lexical maze task.

FIGURE 5

Reaction times for processing first phrase (P1) to fourth phrase (P4)of canonical AdvVSO and scrambled AdvVOS ordered sentences.
The values after ± refer to standard errors.

attenuate skewness in the distribution. Reaction times were analyzed
with the lmer function with the restricted maximum likelihood(Harville, 1977). Satterthwaite’s approximations (Satterthwaite, 1946)
were used via the lmerTest package to generate p-values for eachmodel (Kuznetsova et al., 2017). After that, the best-fit LME model
was found based on model comparisons using AIC (Akaike’s
Information Criterion compared by the maximum likelihood;
Anderson et al., 2000). Outliers with absolute standardized residuals
exceeding 2.5 standard deviations were removed, and then the same
best-fit LME model was reapplied to the resulting data set. This LME
analysis procedure was repeated for the reaction times from the firstphrase (P1-Adverb) to the fourth phrase (P4-O/S). The means andstandard errors from all phases are depicted in Figure 5.
LME analysis was conducted on reaction times for the first phrase(P1-Adverb). After 26 responses (1.95%) were removed from 1,331
total correct responses, 1,305 responses were used for the final
analysis. LME command was lmer (−1,000/rt ~ (0 + trial.
z|participant) + (1|participant) + (1|item) + wordorder + trial.z, data).
The trial order was centralized as z values (trail.z). Reaction times forthe first phrase with the adverb Mahalo (P1-Adverb) showed asignificant trial order effect [t(45.52) = −4.23, p < 0.001], but nosignificant difference [t(1215.31) = −1.16, ns] between the canonical3.1.4 Results
3.1.4.1 Data from the lexical maze task
The reaction times for the lexical maze task were analyzed using
a linear mixed effect (LME) model (Baayen et al., 2008) and the lme4
package (Bates et al., 2016). For every analysis of the four phrases of
P1 to P4, the fixed variables were phrasal order (AdvVSO canonicalorder versus AdvVOS scrambled order) and trial (centralized as
z-vales). The random variables were participants and stimulus
sentences. In the lexical maze task, any mistake made during theperformance terminated the processing. There were 109 incorrect
responses out of 1,440 total trials (92.43% correct rate or 7.57%
incorrect rate), meaning that 1,331 correct responses were used foranalysis. The reaction times of the four phrases were analyzed.
3.1.4.2 Results of LME model analyses
The Box-Cox power transformation technique (Box and Cox,
1964; Venables and Ripley, 2002) indicated that a reciprocaltransformation (−1,000/rt; rt. referring to reaction times) for the firstphrase (P1) and a square root transformation for the second phrase(P2) to the fourth phrase (P4) was applied to the reaction time data to

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3.2 Discussion

AdvVSO (M = 674 ms, SD = 257 ms, SE = 10 ms) and scrambled
AdvVOS orders (M = 693 ms, SD = 302 ms, SE = 12 ms). Since the same
adverb Mahalo was presented in both the canonical and scrambled
orders, no differences were expected.
Similarly, an LME analysis was conducted on reaction times forthe second phrase (P2-Verb). After 32 responses (2.40%) were
removed from 1,331 total correct responses, 1,299 responses wereused for the final analysis. The trial order was centralized as z values(trail.z). LME command was lmer (sq_rt2 ~ wordorder +trial.z + (0 + trial.z|subject) + (1|subject) + (1|item), data). Reaction
times for the second phrase of the verb (P2-Verb) showed a significant
trial order effect [t(46.65) = −2.52, p < 0.05], but, once again, there
were no significant differences in word order [t(1203.92) = −1.08, ns].
Because the same verbs were presented as the correct choice for thelexical decisions in both the canonical AdvVSO (M = 1,525 ms,
SD = 647 ms, SE = 25 ms) and scrambled AdvVOS (M = 1,566 ms,
SD = 648 ms, SE = 26 ms) orders, a null result for order was also
expected for this phrase of the sentence.
As shown in Table 3, reaction times for the third S/O phrase(P3-S/O) showed a significant difference between recognizing the
subject for the canonical AdvVSO (M = 1,452 ms, SD = 657, SE = 26)
order and the object for scrambled AdvVOS (M = 1,648 ms,
SD = 644 ms, SE = 25) order [t(1212.90) = −7.42, p < 0.001], as well as
for the trial order effect [t(45.55) = −4.42, p < 0.001]. A noun phrasewith the ergative marker ‘e (subject) would be expected to come after
the verb in the canonical VSO order in Tongan. This result supports
the linguistic proposal that VSO is the canonical order in Tongan.
As shown in Table 4, reaction times for the fourth O/S phrase(P4-O/S) showed a significant difference between recognizing theobject for the canonical AdvVSO (M = 1,319 ms, SD = 540 ms,
SE = 21 ms) order and the subject for the scrambled AdvVOS(M = 1,382 ms, SD = 530 ms, SE = 21 ms) order [t(1203.97) = −2.60,
p < 0.01] as well as for the trial order effect [t(47.05) = −6.11, p < 0.001].
Noun phrases with the absolutive marker ‘a (object) were processed
faster than noun phrases with the ergative marker ‘e (subject), eventhough the final phrase would automatically be understood to include
the object for the canonical and the subject for the scrambled order.
This difference in the final fourth phase further supports the VSO
canonical order advantage against its VOS scrambled counterpart.

Experiment 2 utilized a maze task to assess the processing of
Tongan AdvVSO canonical and AdvVOS scrambled orders phraseby-phrase. The results concerning the third and fourth crucial
phrases occurring after the verb indicated that native Tongan
speakers process the VSO order faster than the VOS order. Consistent
with previous linguistic studies (Churchward, 1953; Dixon, 1979,
1994; Otsuka, 2000, 2005a,b, 2010), the present psycholinguistic
investigation confirmed that the VSO order is canonical in Tongan.
Experiment 2 provided support for the scrambling effect (V SERGOABS < V OABS1 SERG gap1) as depicted in Prediction 2 based on
filler-gap dependency.

4 Experiment 3: observing
topicalization and scrambling effects
independently
Building upon the findings of Experiment 2, which demonstrated
that the canonical order in Tongan is VSO and the scrambled order is
VOS, Experiment 3 aimed to conduct a comprehensive investigation
into the effects of canonical/scrambled word orders and subject/object
topicalization. Specifically, this experiment sought to assess theprocessing efficiency, including both speed and accuracy, of the four
word orders (VSO, VOS, SVO, and OVS) in Tongan sentences.

4.1 Methods
4.1.1 Participants
Forty native Tongan speakers (28 female and 12 male) wererecruited on the main island of Tonga (Tongatapu) by a native Tongan
experimenter hired to conduct Experiment 3. The mean age of theparticipants was 29.3 ± 6.4 years (range: 21–38 years). All participantsreceived monetary compensation in exchange for their participationand provided written informed consent. The present experiment
involving human participants was reviewed and approved by the
Research Ethics Committee of Tohoku University. All collected

TABLE 3 Result of the LME analysis for reaction times for the third phrase (P3-S/O).

Variables

EstimateSEdf

t value

Pr(>|t|)p-value(Intercept)

40.01

0.57

75.51

70.70

p < 0.001***

Phrasal order

−2.37

0.32

1212.90−7.42

p < 0.001***

trial.z

−1.07

0.24

45.55

−4.42

p < 0.001***

lmer (sq_rt3 ~ wordorder + trial.z + (0 + trial.z|participant) + (1|participant) + (1|item), data). After 28 responses (2.10%) were removed from 1,331 total correct responses, 1,303 responses wereused for the final analysis. The trial order was centralized as z values (trail.z).

TABLE 4 Result of the LME analysis for reaction times for the fourth phrase (P4-O/S).

Variables

EstimateSEdf

t value

Pr(>|t|)p-value(Intercept)

36.60

0.67

75.93

54.90

p < 0.001***

Phrasal order

−0.78

0.30

1203.97−2.60

p < 0.01**

trial.z

−1.28

0.21

47.05

−6.11

p < 0.001***

lmer (sq_rt4 ~ wordorder + trial.z + (0 + trial.z|participant) + (1|participant) + (1|item), data). After 34 responses (2.55%) were removed from 1,331 total correct responses, 1,297 responses wereused for the final analysis. The trial order was centralized as z values (trial.z).

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information was stored in a secure location, and the participants were
given numerical pseudonyms to ensure privacy. Participants from
Experiment 2 did not participate in Experiment 3. As indicated by the
result of the questionnaire survey in Experiment 2 (see ‘participants’section in Experiment 2), Tongatapu residents are highly proficient in
Tongan as their first language and also have good proficiency inEnglish as their second language.

to the online experimental research environment “Pavlovia” (seefootnote 1).

4.1.4 Results
4.1.4.1 Analysis
The same LME analyses used in Experiment 1 were applied inExperiment 3.

4.1.2 Stimulus sentences
Thirty-two VSO canonical, VOS scrambled, SVO subject
topicalized, and OVS object topicalized sentence pairs (32 × 4 = 128
items) were created as shown in example Sentences (5) to (8). All thesemantically and grammatically correct sentences are listed in
Supplementary material. Each sentence consisted of three phrases
consisting of a transitive verb and two noun phrases. Transitive verbs
were all expressed in the past tense na‘e. Frequently used first names
or human nouns were used as the subject with the ergative casemarker ‘e, whereas the frequently used inanimate or non-humannouns with the absolutive case marker ‘a were used as the object.
When the object was topicalized, the topicalization maker ko wasused. In sentences in which a first name was not used, all animate andinanimate nouns were marked by the definite article e/he. The 128
experimental sentences were counterbalanced to ensure thatparticipants would see only one format of each sentence perexperimental session.
An equal number of 32 semantically and/or grammaticallyincorrect sentences were also created, comprising 8 VSO, 8 VOS, 8
SVO, and 8 OVS-ordered sentences (32 in total). An example of anincorrectly ordered VSO sentence is Na′e inu ‘e he tahi ‘a e vaka,meaning ‘The sea drank the boat.’ Since incorrect responses were notused for analysis, only one set was prepared with no counterbalance.
Additionally, 10 correct and 10 incorrect sentences unrelated to thepresent study were included. These 20 dummy sentences were also not
used for analysis. Therefore, each participant received a total of 84
sentences: (1) counterbalanced sentences of VSO, VOS, SVO, andOVS ordered correct sentences (N = 8 each), (2) non-counterbalanced
VSO, VOS, SVO, and OVS ordered incorrect sentences (N = 8 each),and (3) 20 dummy sentences consisting of 10 correct and 10 incorrectsentences. An equal proportion of 42 correct sentences and 42
incorrect sentences were used to ensure the same number of correctand incorrect stimuli. The stimulus sentences listed in
Supplementary material do not include the incorrect sentences ordummy sentences.

4.1.4.2 Results of LME model analyses for accuracy data
A total of 1,280 responses (40 participants × 32 semanticallyand grammatically correct items) were analyzed. The fixed factors
were trial and word order. The trial was centralized into z-values,
coded as trial.z. The two random factors were participants andstimulus sentences. According to model comparisons using AIC(Anderson et al., 2000), the final best-fit LME model was
glmer(acc ~ wordorder + trial.z + (1|participant) + (1|item), data,family = binomial). The result of the best-fit LME model is reported
in Table 5. Trial was not a significant factor [z = −0.62, ns]. Basedon the reference of VSERGOABS canonical order, the result in Table 5
indicated that VSERGOABS canonical sentences were processed as
accurately as OTOPVSERG object topicalized sentences [z = −1.35, ns],
but more accurately than VOABSSERG scrambled sentences [z = −2.80,
p < 0.01] and STOPVOABSV subject topicalized sentences [z = −2.29,
p < 0.05].
To examine differences among word orders more in depth, theaccuracies of the four sentence conditions were compared using the R
package of lsmeans (Searle et al., 1980). The means and standard
deviations are reported, and the results of multiple comparisons areshown in Figure 6. The result indicated that VSERGOABS (M = 99.38%),
STOPVOABS (M = 96.56%), and OTOPVSERG (M = 98.13%) were processed
equally accurately. Both subject and object topicalized sentences were
processed as accurately as canonical sentences. By contrast, VSERGOABS
canonical sentences were processed more accurately than theircorresponding VOABSSERG scrambled sentences (M = 95.31%). This
result illustrates the scrambling effect. Since the multiple comparisonsby lsmeans did not show a significant difference in results for
VSERGOABS and STOPVOABS, we interpret the difference shown in Table 5of the LME analysis as a significant tendency rather than as asignificant difference.
4.1.4.3 Results of LME model analyses for reaction time
data
There were no items processed faster than 500 ms or more slowly
than 5,000 ms. After removing 34 incorrectly answered items from the

4.1.3 Procedure
As in Experiment 1, Experiment 3 employed a sentence
correctness decision task. The eye fixation of ‘++++++++’ was initiallypresented at the center of the computer screen for 500 ms, and thenreplaced by a target sentence. Participants were required to decide
whether the presented sentence was grammatically and semanticallycorrect in Tongan by pressing the YES key for correct or the NO key
for incorrect. After pressing either key, the next trial started after
200 ms. The participants were asked to perform the sentence
correctness decision task as quickly and accurately as possible. All
stimulus sentences were randomly presented for each participant.
Before the experiment started, 10 practice sentences were given.
Experiment 3 was conducted individually face-to-face in a quiet room
by a native Tongan experimenter using her own computer connected

Frontiers in Psychology

TABLE 5 Result of the LME model analysis for accuracy.

Variables

Estimate

SE

z
value

Pr(>|t|)pvalue(Intercept)

6.13

0.89

6.86

p < 0.001***

VOABS SERG

−2.17

0.77

−2.80

p < 0.01**

STOPVOABS

−1.81

0.79

−2.29

p < 0.05*

OTOPVSERG

−1.13

0.84

−1.35

p = 0.178

trial.z

−0.12

0.19

−0.62

p = 0.533

Participants = 40. Items = 32. Total Observations = 1,280.
glmer(acc ~ wordorder + trial.z + (1|participant) + (1|item), data, family = binomial).

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4.2 Discussion
Experiment 3 addressed the primary objective of this study:
examining the effects of scrambling and topicalization in Tongan
sentences. The efficiency of processing for the four word orders (VSO,
VOS, SVO, and OVS) was measured in terms of speed and accuracy.
Similar to Experiment 2, the results indicated that VSO canonicalsentences were processed more rapidly and accurately than VOS
scrambled sentences, supporting the existence of a scrambling effect.
Interestingly, OVS object topicalized sentences were processed as
swiftly and accurately as VSO canonical sentences, while SVO subject
topicalized sentences were processed more slowly than VSO canonicalsentences but faster than VOS scrambled sentences. Both SVO subject
and OVS object topicalized sentences were processed more efficiently
than VOS scrambled sentences. The object topicalization sentences
exhibited a positive (efficient) processing effect, whereas the subject
topicalization sentences were processed less efficiently than object
topicalization sentences but more efficiently than VOS scrambled
sentences. These findings did not entirely align with Prediction 2
based on the filler-gap dependency. Further details were discussed in
the subsequent General Discussion section.

FIGURE 6

Accuracies for the four word orders of Tongan sentences. The values
after ± refer to standard errors.

1,280 semantically and grammatically correct items, the remaining
1,246 correctly answered items were analyzed for reaction times.
Based on the Box-Cox power transformation technique (Box and Cox,
1964; Venables and Ripley, 2002), a logarithmic transformation(natural log) was applied to the reaction times to attenuate skewness
in their distribution. Reaction times were analyzed with the lmer
function using the restricted maximum likelihood (Harville, 1977).
Satterthwaite’s approximations (Satterthwaite, 1946) were used via the
lmerTest package to generate p-values for each model (Kuznetsova
et al., 2017).
According to model comparisons using AIC (Anderson et al.,2000), the best-fit LME model was lmer (log(rt) ~ wordorder +trial.z + (0 + trial.z|participant) + (1|participant) + (1|item),data).
Based on this best-fit LME model, potentially influential outliers withabsolute standardized residuals exceeding 2.5 standard deviation were
removed. In this operation, 28 responses were removed. The result of
the LME model analysis for 1,218 responses is reported in Table 6. Aswith accuracy, trial was not a significant factor [t(50.06) = −0.85, ns].
Based on the reference canonical order of VSERGOABSV, the result inTable 6 indicated that VSERGOABS canonical sentences were processed
equally as quickly as OTOPVSERGV object topicalized sentences[t(1131.53) = 1.93, ns] but more quickly than VOABSSERG scrambled
sentences [t(1127.06) = 5.53, p < 0.001] and STOPVOABS subject
topicalized sentences [t(1122.14) = 2.77, p < 0.01].
To examine differences among word orders more in-depth,
reaction times for the four word orders were compared using the R
package lsmeans (Searle et al., 1980). The means and standard
deviations are reported, and the results of multiple comparisons areshown in Figure 7. The result indicated that OTOPVSERG object
topicalized sentences (M = 1,665 ms) were processed equally as quickly
as VSERGOABS canonical sentences (M = 1,643 ms). However, STOPVOABS
subject topicalized sentences (M = 1,690 ms) were processedsignificantly more slowly than VSERGOABS canonical sentences.
Furthermore, STOPVOABS sentences were processed significantly fasterthan VOABSSERG scrambled sentences (M = 1,753 ms). Thus, scrambled
sentences were processed more slowly than both object and subject
topicalized sentences.

Frontiers in Psychology

5 General discussion
The present study conducted three experiments to investigate
differences in the effects of canonical/scrambled and subject/object
topicalization on the verb-final language of Korean and the verb-initial
language of Tongan. Experiment 1 focused on Korean to compare thefindings with those of Imamura et al. (2016) on Japanese. Experiment
1 supported Prediction 1 with the exception of no difference observed
between OACC SNOMV and OTOP SNOMV. Consistent with the findings ofthe previous Japanese experiment (Imamura et al., 2016), the OSV
scrambled order in Korean was processed more slowly than the SOV
canonical order (i.e., scrambling effect). The SVO subject topicalizedorder was processed at a comparable speed to the SOV canonical order,
suggesting the SOV subject topicalized order in Korean might haveprocessing such as the canonical sentence. However, unlike Prediction
1, there was no noticeable difference in processing time between object
topicalized and scrambled sentences in Korean. This discrepancy
between Japanese and Korean could be attributed to the animacy effect.
Given that the SOV canonical order in Korean overlapped withthe subject topicalized order, and the OSV scrambled order in Korean
overlapped with the object topicalized order, Tongan exhibits adistinct word order for canonical/scrambled and subject/object
topicalization. Consequently, the study proceeded to investigate thehead-initial language of Tongan. Experiment 2 employed a phrase-byphrase processing experiment to determine whether the VSO order iscanonical in Tongan. The results of Experiment 2 indicated that native
Tongan speakers process the VSO order faster than the VOS order,
confirming the canonical status of the VSO order in Tongan(scrambling effect, V SERG OABS < V OABS1 SERG gap1), which aligns withprevious linguistic studies (Churchward, 1953; Dixon, 1979, 1994;
Otsuka, 2000, 2005a,b, 2010). Experiment 2 provided support for thescrambling effects outlined in Prediction 2.
The primary focus of the present study was Experiment 3, whichinvestigated the effect of subject/object topicalization separately fromthe canonical/scrambled effect. Processing efficiency, in terms of both

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TABLE 6 Result of the LME model analysis for reaction times.

Variables

EstimateSEdf

t value

Pr(>|t|)p-value(Intercept)

0.35

VOABS SERG

0.12

0.03

95.52

12.96

p < 0.001

***

0.02

1127.06

5.53

p < 0.001***

STOPVOABS

0.06

0.02

1122.142.77

p < 0.01**

OTOPVSERG

0.04

0.02

1131.53

1.93

p = 0.054

trial.z

−0.01

0.02

50.06

−0.85

p = 0.401

Participants = 40. Item = 32. Total Observation = 1,218. lmer(log(rt) ~ wordorder + trial.z + (0 + trial.z|participant) + (1|participant) + (0 + trial.z|participant) + (1|item), data).

object in front of the subject, as in O1SVgap1. The result of Experiment
1 indicated that the scrambled O1SVgap1 order in Korean was processedmore slowly and less accurately than the SOV canonical order. This
result confirmed the existence of the scrambling effect, as also found inprevious studies on Japanese sentence processing (e.g., Mazuka et al.,
2002; Ueno and Kluender, 2003; Koizumi and Tamaoka, 2004, 2010;
Miyamoto and Takahashi, 2004; Tamaoka et al., 2005, 2014; Imamura
et al., 2016; Witzel and Witzel, 2016; Tamaoka and Mansbridge, 2019).
The processing delay for the syntactic structure of the O1SVgap1
scrambled order in both Japanese and Korean could be explained by the
gap-filling parsing model (Frazier and Rayner, 1982; Stowe, 1986; Frazier,
1987; Frazier and Clifton, 1989; Frazier and Flores D’Arcais, 1989)
which was presented in the introductory section of this article.
Regarding topicalization, in agreement with Imamura et al.
(2016), the subject topicalized order in Korean was processed equallyas quickly as the canonical order. However, it is still uncertain whether
the processing speed of subject topicalization is accelerated by the
influence of the SOV canonical order. Additionally, there were nodifferences in processing speed between the object topicalized orderand the scrambled order in Korean. Once again, since the OSV
scrambled order overlaps with the object topicalized order, the nulldifference result may have been caused by the influence of the OSV
scrambled order and bears no relation to the object topicalization
effect. It is quite possible that the scrambling effect may override thetopicalization effect. It is also conceivable that the particle -eun/neuncan be a pseudo-subject. This might be a partial reason for the fact thatno difference has been found in this study between SNOMOV andSTOPOV in accuracy and reaction time.
One difference was found between Japanese and Korean. There
was no difference in processing speed between the object topicalized
and the scrambled order in Korean. By contrast, the object topicalizedorder was slower than the scrambled order in Japanese. This different
result may be created by the animacy effect (Mak et al., 2002; Hsiao
and Gibson, 2003; Pu, 2007; Vasishth et al., 2013; Kwon et al., 2019).
In the Japanese study (Imamura et al., 2016), proper nouns such as
Satoo and Suzuki were used for both subjects and objects. For example,
サトウがスズキを褒めた (Satoo-ga Suzuki-o home-ta) meaning‘Sato praised Suzuki’ contains two animate nouns. These two animate
proper nouns can function as either subject or object. Unlike theJapanese study, the stimulus sentences used in the current Experiment
1 using Korean were mostly constructed by pairing an animate noun
with an inanimate noun, such as in 여성이 물을 삼켰다 (Yeoseong-i
mul-ul sam-keossda) meaning ‘The woman drank water.’ This animacy
contrast between the subject and the object in Korean may havefacilitated the processing of OSV object topicalized order, which is a
plausible explanation for why there was no difference in the speed ofsentence processing between OVS object topicalized and OSV
scrambled orders. However, the animacy of noun phrases could

FIGURE 7

Reaction times of the four word orders of Tongan sentences. The
values after ± refer to standard errors.

speed and accuracy, was assessed for the four word orders (VSO, VOS,
SVO, and OVS). The findings of Experiment 3, outlined below, shedlight on Tongan sentence processing:Results of experiment 3: Tongan sentence processing:
V SERG O ABS =OTOP1 V SERG gap1 < STOP1 V gap1 O ABS < V O ABS1 SERG gap1.
The results of Experiment 3 did not entirely align with Prediction
2. A notable discrepancy was observed: OVS object topicalized
sentences were unexpectedly processed as swiftly and accurately as
VSO canonical sentences. Additionally, VSO canonical sentences were
processed more rapidly and accurately than VOS scrambled sentences,whereas SVO subject topicalized sentences were processed more
slowly than VSO canonical sentences but faster than VOS scrambled
sentences. Both SVO subject and OVS object topicalized sentenceswere processed more efficiently than VOS scrambled sentences.
Consequently, Prediction 2, which was based on syntactic complexity
according to the filler-gap dependency, or in other words, themovement-based anticipation, was not supported. Further detailed
discussion will be provided in the following sections.

5.1 Korean sentence processing incomparison to Japanese
Various syntactic similarities are found between Korean andJapanese. In both languages, a scrambled order is created by moving the
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be closely related to thematic assignments and argument structures(Comrie, 1981; Mak et al., 2002; Traxler et al., 2002, 2005; BornkesselSchlesewsky and Schlesewsky, 2006; Gennari and MacDonald, 2008;
Wagers and Phillips, 2014; Ness and Meltzer-Asscher, 2017). This issue
should be investigated in future studies.

Furthermore, some Tongan verbs can be used both as transitiveand intransitive verbs (i.e., ambitransitivity). Since Tongan is anergative language and the absolutive case marker ‘a also marks the
subject of intransitive sentences, the VOABS order can be interpreted as
being an intransitive canonical sentence in VSABS order. When a nounphrase marked by the ergative case e’ follows, the sentence isconsidered a transitive VOABSSERG scrambled sentence. In that case, it
may be that the delay in processing a VOABSSERG ordered sentence iscaused by verb transitivity confusion. Nine of 32 verbs in Experiment
3 fall into this ambitransitivity category; these are kai ‘eat’, talitali ‘wait’,ui ‘call’, tuli ‘chase’, huo ‘hoe’, fo ‘wash clothes’, inu ‘drink’, tui ‘wear’, andlau ‘read.’ However, the nouns used in Experiment 3 are basically apair of animate nouns for the subject (e.g., ‘woman’, ‘teacher’, ‘children’)and inanimate nouns (e.g., ‘clothes’, ‘bicycle’, ‘money’) for the object.
Transitivity confusion could be avoided by using animacy contrast in
the stimulus sentences in Experiment 3. Thus, we do not consider the
possibility of the influence of ambitransitivity to be very likely. Hence,
it would be parsimonious to conclude that the differences in accuracy
and speed were primarily driven by the effect of scrambling order:
specifically, VOS scrambled order exhibited a 4.07% lower accuracy
rate and was 110 ms slower in speed compared to the VSO
canonical order.

5.2 The scrambling effect in Tongan
Previous studies (e.g., Churchward, 1953; Dixon, 1979, 1994;
Otsuka, 2000, 2005a,b, 2010; Custis, 2004) have suggested that the
canonical order in Tongan is VSO, while the scrambled order is
VOS. However, no large-scale corpus study has been conducted inTongan to verify that the VSO order is dominant. Thus, before
investigating the processing of subject/object topicalized sentences, amaze task was conducted in Experiment 2 to determine whether VSO
is truly the canonical order of Tongan transitive sentences. The lexical
maze task is a unique task in which participants make a series ofcontinuous lexical decisions that reveal how they are processing
sentences without the participants’ awareness of this background
operation. This task makes it possible to compare phrase-by-phrase
sentence processing between VSO and VOS orders.
Because the first phrase consisting of the adverb mahalo (‘maybe’)
and the second phrase containing a verb is the same in AdvVSO
canonical and AdvVOS scrambled sentences in the lexical maze task,
no differences in processing speed for the first and second phrases
were expected. The phrases of interest appear after the verb. In thethird subject/object phrase, the noun with the ergative ‘e marker (S)was processed faster than the noun with the absolutive ‘a marker (O).
Since VSO is considered the most used order, the subject would
be expected to appear after the verb. In the following fourth object/subject phrase, the noun with the absolutive ‘a marker (O) was
processed faster than the noun with the ergative ‘e marker (S). This
result was surprising because after the third phrase was identified, thefollowing final fourth phrase would automatically have been an object
for the canonical or a subject for the scrambled order. Even after theprocessing of the third phrase, scrambling continues to affect theprocessing of the sentence-final subject. This could be due to thesearch for the gap after the subject in the AdvVO1Sgap1. As anticipatedby Prediction 2, the processing difference between VSO and VOS in
the third and fourth phrases in Experiment 2 supports the proposalthat VSO is the canonical order.
The effect of scrambling in Experiment 2 was further confirmed
in Experiment 3. The VSO canonical order was processed more
quickly and accurately than the VOS scrambled order. As Otsuka(2005b, 2010) proposed, VOS scrambled order in Tongan isunderstood as movement (VO1Sgap1) motivated by a new information
focus. Otsuka argues that the position immediately following the verb
is reserved for new information. In other words, given a context inwhich the object is new information and the subject is old information(e.g., answering an object wh-question), the object is placed before
the subject. According to this proposal, as in Japanese and Korean,
gap-filling parsing (Stowe, 1986; Frazier, 1987; Frazier and Clifton,
1989; Frazier and Flores D’Arcais, 1989; Traxler and Pickering, 1996)
may have been used for processing the Tongan VOS scrambled order.
Consequently, Experiments 2 and 3 provided clear evidence for the
linguistic claim that the VSO order is canonical and the VOS isscrambled in Tongan (Churchward, 1953; Dixon, 1979, 1994; Otsuka,
2000, 2005a,b, 2010).
Frontiers in Psychology

5.3 Processing SVO/OVS topicalized
sentences in Tongan
The SVO and OVS sentence-fronting topicalized orders are always
ko-marked (Custis, 2004), as shown in Sentences (7) and (8) above.
Custis (2004) considers that both SVO and OVS orders involve A-bar
movement similar to relativization. If that is the case, then both orders
would be predicted to be recognized by native Tongan speakers as amore complex syntactic structure than the VSO canonical order.
According to the gap-filling parsing model (Stowe, 1986; Frazier,
1987; Frazier and Clifton, 1989; Frazier and Flores D’Arcais, 1989;
Traxler and Pickering, 1996), the processing of Tongan topicalized
sentences is described as follows: a topicalized subject phrase is moved
in front of the verb; Tongan speakers recognize the initial topicalized
phrase as the filler, and then look for its original position in thespecifier of gap to establish the filler-gap dependency. In thisframework, the OVS order should have a longer processing time thanthe SVO order because the movement involved in the object
topicalization O1VSgap1 is greater in distance than the movement
involved in the subject topicalization S1Vgap1O. In addition, the object
relative clause (O1VSgap1 in Tongan) for many of the world languages
has been found to be more difficult to process and comprehend than
the subject-extracted (S1Vgap1 in Tongan) relative clause (e.g., Staub,
2010; Jäger et al., 2015).
However, an unexpected result was found in Experiment 3. There
was no significant difference in processing speed and accuracy
between the OVS object tropicalized order and the VSO canonicalorder (OVS=VSO). By contrast, the processing of the SVO subject
topicalized order was slower than for the VSO canonical order(VSO < SVO) but faster than for the VOS scrambled order(SVO < OVS). The contrasting result between SVO and OVS may
be related to the prominence of the topic. Custis (2004) considers
Tongan to be a topic-marking language. A topic can be placed in oneof two positions in Tongan—either immediately before the verb (SVO/
OVS) or immediately after the verb (VOS). The placement of a topic
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Ethics statement

is pragmatically motivated and syntactically results in one of the threeword orders of SVO, OVS, or VOS, while the VSO canonical order ispragmatically neutral. Custis (2004) observed (using the corpus data)that SVO and OVS orders are used to mark less salient topics and VOS
to mark more salient topics in discourse. By contrast, the VOS
scrambled order is used for presenting a salient topic. The activationlevels infer cognitive status reflecting the frequency of a discourse
topic: namely, how often would it be talked about? When the NP isfrequently mentioned in discourse, it is more accessible and hence lesssalient in the discourse. This is the case for the fronted subject of SVO
or the fronted object of OVS orders. On the other hand, a low
activation level indicates that the topic is less frequently talked about
so that it would be more salient and less accessible. This is the case forthe object of VOS scrambled order.
The idea of cognitive status by Custis (2004) may account for the
result of the processing speed in VSO < SVO < VOS. The pragmaticallyneutral VSO is the fastest, while the VOS scrambled order is theslowest. The SVO subject topicalized order falls in between the VSO
and VOS orders. Then, what causes the difference in processing speed
between SVO and OVS orders? One possibility is that, as shown in
Sentence (7), the resumptive pronoun ne appears after the tense andbefore the verb in the case of subject topicalization, so this visible
syntactic feature may require extra processing time than the OVS
object topicalized order with no resumptive pronoun. Prediction 2(i.e., VSO < VOS = SVO < OVS) was constructed according to thesyntactic complexity constructed by the filler-gap dependency (Stowe,1986; Frazier, 1987; Frazier and Clifton, 1989; Frazier and Flores
D’Arcais, 1989; Traxler and Pickering, 1996), regarding the word orderin Tongan scrambling and topicalization. However, the results ofExperiment 3 (i.e., VSO = OVS < SVO < VOS) indicated that word
orders of both subject and object topicalization facilitated in sentence
processing, deviating from the movement-based anticipation ofPrediction 2.
In conclusion, the present study sheds light on the processing
dynamics of Tongan sentence structures, offering insights into the
cognitive mechanisms underlying language comprehension. The
observed processing speeds, with VSO being the fastest, followed by
SVO and then VOS, suggest a nuanced interplay between syntactic
structure and cognitive processing. The unexpected processing
efficiency of OVS orders compared to the predicted pattern raises
intriguing questions about the underlying mechanisms at play. The
discrepancy between the observed results and Prediction 2 highlights
the limitations of relying solely on syntactic complexity metrics suchas the filler-gap dependency. Instead, the findings suggest that thefacilitation effect of both subject and object topicalization in Tongan
may be better explained by a broader consideration of cognitive status
and pragmatic factors. Further investigations into the processing
dynamics of topicalized word orders in Tongan, as well as comparative
studies across different languages, are warranted to deepen our
understanding of how linguistic structure interacts with cognitive
processing mechanisms. This study opens avenues for future research
into the intricate interplay between syntax, cognition, andlanguage comprehension.

The studies involving humans were approved by Research Ethics
Committee of Busan National University (Exp 1) Research Ethics
Committee of Tohoku University (Exp 2 and 3). The studies wereconducted in accordance with the local legislation and institutional
requirements. The participants provided their written informed
consent to participate in this study.

Author contributions
KT: Conceptualization, Data curation, Formal analysis, Investigation,
Methodology, Project administration, Resources, Software, Supervision,
Validation, Visualization, Writing – original draft, Writing – review &editing. SY: Data curation, Formal analysis, Methodology, Writing –original draft, Writing – review & editing. JZ: Data curation, Writing –original draft, Writing – review & editing. YO: Conceptualization, Data
curation, Investigation, Writing – original draft, Writing – review &editing. HL: Data curation, Writing – original draft, Writing – review &editing. MK: Funding acquisition, Resources, Writing – original draft,
Writing – review & editing. RV: Resources, Supervision, Validation,
Writing – original draft, Writing – review & editing.

Funding
The author(s) declare financial support was received for the research,authorship, and/or publication of this article. This study was supported
by a Grant-in-Aid for Japan Society for the Promotion of Science(#19H05589, PI: Masatoshi Koizumi at Tohoku University, Japan).

Acknowledgments
The authors appreciate the kind support from Korean and Tongan
participants of the experiments.

Conflict of interest
The authors declare that the research was conducted in theabsence of any commercial or financial relationships that could
be construed as a potential conflict of interest.

Publisher’s noteAll claims expressed in this article are solely those of the authorsand do not necessarily represent those of their affiliated organizations,or those of the publisher, the editors and the reviewers. Any productthat may be evaluated in this article, or claim that may be made by its
manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

Data availability statement

The Supplementary material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fpsyg.2024.1360191/
full#supplementary-material

The raw data supporting the conclusions of this article will
be made available by the authors, without undue reservation.
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References
Anderson, D. R., Burnham, K. P., and Thompson, W. L. (2000). Null hypothesis
testing: problems, prevalence, and an alternative. J. Wildl. Manag. 64, 912–923. doi:
10.2307/3803199

Jäger, L., Chen, Z., Li, Q., Lin, C.-J. C., and Vasishth, S. (2015). The subject relativeadvantage in Chinese: evidence for expectation-based processing. J. Mem. Lang. 79-80,
97–120. doi: 10.1016/j.jml.2014.10.005

Anderson, V., and Otsuka, Y. (2006). The phonetics and phonology of “definitive
accent” in Tongan. Oceanic Linguistics 45, 21–42. doi: 10.1353/ol.2006.0002

Just, M. A., Carpenter, P. A., Keller, T. A., Eddy, W. F., and Thulborn, K. R. (1996).
Brain activation modulated by sentence comprehension. Science 274, 114–116. doi:
10.1126/science.274.5284.114

Baayen, R. H., Davidson, D. J., and Bates, D. M. (2008). Mixed-effects modelling withcrossed random effects for subjects and items. J. Mem. Lang. 59, 390–412. doi: 10.1016/j.
jml.2007.12.005

King, J., and Just, M. A. (1991). Individual differences in syntactic processing: the roleof working memory. J. Mem. Lang. 30, 580–602. doi: 10.1016/0749-596X(91)90027-H

Bates, E., Devescovi, A., and D’Amico, S. (1999). Processing complex sentences: across-linguistic study. Language and Cognitive Processes 14, 69–123. doi:
10.1080/016909699386383

Koizumi, M., and Tamaoka, K. (2004). Cognitive processing of Japanese sentenceswith ditransitive verbs. Gengo Kenkyu (Journal of the Linguistic Society of Japan) 125,
173–190. doi: 10.11435/gengo1939.2004.125_173

Bates, D., Maechler, M., Bolker, B., and Walker, S. (2016). lme4: Linear mixed-effects
models using ‘Eigen’ and S4 (R package, lme4 version 1.1-11)

Koizumi, M., and Tamaoka, K. (2010). Psycholinguistic evidence for the VP-internal
subject position in Japanese. Linguistic Inquiry 41, 663–680. doi: 10.1162/LING_a_00016

Bornkessel-Schlesewsky, I., and Schlesewsky, M. (2006). The extended argument
dependency model: a neurocognitive approach to sentence comprehension acrosslanguages. Psychol. Rev. 113, 787–821. doi: 10.1037/0033-295X.113.4.787

Kuroda, S. (1987). Movement of noun phrases in Japanese. T. Imai and M. Saito (Eds.)
Issues in Japanese linguistics (pp. 229–271), Dordrecht: Foris.
Kuznetsova, A., Brockhoff, P. B., and Christensen, R. H. B. (2017). lmerTest
package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26. doi: 10.18637/
jss.v082.i13

Box, G. E. P., and Cox, D. R. (1964). An analysis of transformations. J. R. Stat. Soc. B
26, 211–243. doi: 10.1111/j.2517-6161.1964.tb00553.x
Broekhuis, H. (2008). Derivations and evaluations: Object shift in the Germanic
languages. Berlin: de Gruyter Mouton.

Kwon, N., Ong, D., Chen, H., and Zhang, A. (2019). The role of animacy and structural
information in relative clause attachment: evidence from Chinese. Front. Psychol.
10:1576. doi: 10.3389/fpsyg.2019.01576

Caplan, D., Alpert, N., and Waters, G. (1998). Effects of syntactic structure andpropositional number on patterns of regional cerebral blood flow. J. Cogn. Neurosci. 10,
541–552. doi: 10.1162/089892998562843

Lee, I., and Ramsey, S. R. (2000). The Korean language. Albany, NY: State Universityof New York Press.

Carpenter, P. A., and Just, M. A. (1989). “The role of working memory in language
comprehension” in Handbook of psycholinguistics. eds. D. Klahr and K. Kotovsky (San
Diego, CA: Academic Press), 1075–1122.

Macdonald, C. M. (2014). Functional projections and non-local relations in Tongan
nominal phrases. Toronto: Doctoral dissertation University of Toronto.
Mak, W. M., Vonk, W., and Schriefers, H. (2002). The influence of animacy on relative
clause processing. J. Mem. Lang. 47, 50–68. doi: 10.1006/jmla.2001.2837

Chen, H. C. (1986). Effects of reading span and textual coherence on rapid-sequential
reading. Mem. Cogn. 14, 202–208. doi: 10.3758/BF03197693

Mazuka, R., Itoh, K., and Kondo, T. (2002). Costs of scrambling in Japanese sentence
processing. Mineharu Nakayama (Ed.), Sentence processing in east Asian languages (pp.
131–166), Stanford, CA: CSLI Publications.

Chomsky, N. (1977). “On Wh-movement” in Formal syntax. eds. P. W. Cullicover, T.
Wasow and A. Akmajian (New York: Academic Press), 71–132.
Churchward, C. M. (1953). Tongan grammar. Oxford: Oxford University.

Miyamoto, E. T., and Takahashi, S. (2004). Filler-gap dependencies in the processingof scrambling in Japanese. Lang. Ling. 5, 153–166.

Comrie, B. (1981). Language universals and linguistic typology: Syntax and morphology.
Chicago: University of Chicago Press.

Neeleman, A. (1994). Scrambling as a D-structure phenomenon. N. Corver andRiemsdijkH. Van (Eds.) Studies on scrambling: Movement and non-movement approaches
to free word-order phenomena (pp. 387–430). Berlin: de Gruyter Mouton

Custis, T. (2004). Word order variation in Tongan: A syntactic Analysis. Minneapolis:
Doctoral dissertation at the University of Minnesota.

Ness, T., and Meltzer-Asscher, A. (2017). Working memory in the processing of longdistance dependencies: interference and filler maintenance. J. Psycholinguist. Res. 46,
1353–1365. doi: 10.1007/s10936-017-9499-6

Dixon, R. M. W. (1979). Ergativity. Language 55, 59–138. doi: 10.2307/412519
Dixon, R. M. W. (1994). Ergativity. Cambridge: Cambridge University.
Ford, M. (1983). A method for obtaining measures of local parsing complexity
throughout sentences. J. Verbal Learn. Verbal Behav. 22, 203–218. doi: 10.1016/
S0022-5371(83)90156-1

Otsuka, Y. (2000). Ergativity in Tongan. Oxford: Doctoral dissertation Universityof Oxford.

Forster, K. I. (2010). Using a maze task to track lexical and sentence processing. Mental
Lexicon 5, 347–357. doi: 10.1075/ml.5.3.05for

Otsuka, Y. (2005a). Two derivations of VSO: a comparative study of Niuean andTongan. Andrew Carnie, Heidi Harley and Sheila Ann Dooley (Eds.) Verb first: On thesyntax of verb-initial languages (pp. 281–302), Amsterdam: John Benjamins.

Forster, K. I., Guerrera, C., and Elliot, L. (2009). The maze task: measuring forced
incremental sentence processing time. Behav. Res. Methods 41, 163–171. doi: 10.3758/
BRM.41.1.163

Otsuka, Y. (2005b). Scrambling and information focus: VSO-VOS alternation in
Tongan. Joachim Sabel and Mamoru Saito (Eds.) The free word order phenomenon: Its
syntactic sources and diversity (pp. 243–279) Berlin: Mouton de Gruyter.

Frazier, L. (1987). Syntactic processing: evidence from Dutch. Nat. Lang. Linguistic
Theory 5, 519–559. doi: 10.1007/BF00138988

Otsuka, Y. (2006). Syntactic ergativity in Tongan. Ergativity (pp. 79–107). Dordrecht:
Springer Netherlands.

Frazier, L., and Clifton, C. (1989). Identifying gaps in English sentences. Lang. Cogn.
Processes 4, 93–126. doi: 10.1080/01690968908406359

Otsuka, Y. (2007). Making a case for Tongan as an endangered language. Contemp.
Pac. 19, 446–473. doi: 10.1353/cp.2007.0064

Frazier, L., and Flores D’Arcais, G. B. (1989). Filler–driven parsing: a study of gap
filling in Dutch. J. Memory Lang. 28, 331–344. doi: 10.1016/0749-596X(89)90037-5

Otsuka, Y. (2010). DP ellipsis in Tongan: is syntactic ergativity real? Nat. Lang. Ling.
Theory 28, 315–342. doi: 10.1007/s11049-010-9096-y

Frazier, L., and Rayner, K. (1982). Making and correcting errors during sentencecomprehension: eye movements in the analysis of structurally ambiguous sentences.
Cogn. Psychol. 14, 178–210. doi: 10.1016/0010-0285(82)90008-1

Pae, H. K. (2024). Analyzing the Korean alphabet: The science of Hangul Springer.
Pu, M. (2007). The distribution of relative clauses in Chinese discourse. Discourse
Process. 43, 25–53. doi: 10.1080/01638530709336892

Gennari, S. P., and MacDonald, M. C. (2008). Semantic indeterminacy in object
relative clauses. J. Mem. Lang. 58, 161–187. doi: 10.1016/j.jml.2007.07.004

Qiao, X., Shen, L., and Forster, K. I. (2012). Relative clause processing in mandarin:
evidence from the maze task. Lang. Cogn. Processes 27, 611–630. doi:
10.1080/01690965.2011.578394

Gibson, E. (2000). The dependency locality theory: a distance-based theory of
linguistic complexity. Alec Marantz, Yasushi Miyasita and Wayne O’Neil (eds.), Image,language, brain: Papers from the first mind articulation project symposium (pp. 95–126).
Cambridge, MA: MIT Press.

Ross, J. R. (1967). Constraints on variables in syntax. Cambridge, MA: Doctoral
dissertation Massachusetts Institute of Technology.
Satterthwaite, F. E. (1946). An approximate distribution of estimates of variancecomponents. Biom. Bull. 2, 110–114. doi: 10.2307/3002019

Harville, D. A. (1977). Maximum likelihood approaches to variance component
estimation and to related problems. J. Am. Stat. Assoc. 72, 320–338. doi:
10.1080/01621459.1977.10480998

Searle, S. R., Speed, F. M., and Milliken, G. A. (1980). Population marginal means in
the linear model: an alternative to least squares means. Am. Stat. 34, 216–221. doi:
10.1080/00031305.1980.10483031

Holmes, V. M., and O’Regan, J. K. (1981). Eye fixation patterns during the reading ofrelative-clause sentences. J. Verbal Learn. Verbal Behav. 20, 417–430. doi: 10.1016/
S0022-5371(81)90533-8
Hsiao, F., and Gibson, E. (2003). Processing relative clauses in Chinese. Cognition 90,
3–27. doi: 10.1016/S0010-0277(03)00124-0

Sekerina, I. A. (2003). Scrambling and processing: dependencies, complexity andconstraints. Simin Karimi (Ed.), Word order and scrambling (pp. 301–324). Malden, MA:
Blackwell.

Imamura, S., Sato, Y., and Koizumi, M. (2016). The processing cost of scrambling andtopicalization in Japanese. Front. Psychol. 7:531. doi: 10.3389/fpsyg.2016.00531

Shibatani, M. (1990). The languages of Japan. Cambridge: Cambridge
University Press.

Frontiers in Psychology

16

frontiersin.org

Tamaoka et al.

10.3389/fpsyg.2024.1360191

Staub, A. (2010). Eye movements and processing difficulty in object relative clauses.
Cognition 116, 71–86. doi: 10.1016/j.cognition.2010.04.002

Traxler, M. J., and Pickering, M. J. (1996). Plausibility and the processing of
unbounded dependencies: an eye-tracking study. J. Mem. Lang. 35, 454–475. doi:
10.1006/jmla.1996.0025

Stowe, L. A. (1986). Parsing WH-constructions: evidence for on-line gap location.
Language and Cognitive Processes 1, 227–245. doi: 10.1080/01690968608407062

Traxler, M. J., Williams, R. S., Blozis, S. A., and Morris, R. K. (2005). Working memory,
animacy, and verb class in the processing of relative clauses. J. Mem. Lang. 53, 204–224.
doi: 10.1016/j.jml.2005.02.010

Tamaoka, K., Asano, M., Miyaoka, Y., and Yokosawa, K. (2014). Pre-and post-headprocessing for single-and double-scrambled sentences of a head-final language asmeasured by the eye tracking method. J. Psycholinguist. Res. 43, 167–185. doi: 10.1007/
s10936-013-9244-8

Ueno, M., and Kluender, R. (2003). Event-related brain indices of Japanese scrambling.
Brain Lang. 86, 243–271. doi: 10.1016/S0093-934X(02)00543-6

Tamaoka, K., and Mansbridge, M. P. (2019). An eye-tracking investigation of pre-headand head-driven processing for scrambled Japanese sentences. Gengo Kenkyu (Journalof the Linguistic Society of Japan) 155, 35–63. doi: 10.11435/gengo.155.0_35

Vasishth, S., Chen, Z., Li, Q., and Guo, G. (2013). Processing Chinese relative clauses:
evidence for the subject-relative advantage. PLoS One 8:e77006. doi: 10.1371/journal.
pone.0077006

Tamaoka, K., Sakai, H., Kawahara, J., Miyaoka, Y., Lim, H., and Koizumi, M. (2005).
Priority information used for the processing of Japanese sentences: thematic roles, caseparticles or grammatical functions? J. Psycholinguist. Res. 34, 281–332. doi: 10.1007/
s10936-005-3641-6

Wagers, M. W., and Phillips, C. (2014). Going the distance: Memory and control
processes in active dependency construction. Q. J. Exp. Psychol. 67, 1274–1304.

Venables, W. N., and Ripley, B. D. (2002). Modern applied statistics with S (4th ed.).
New York: Springer.

Witzel, J., and Witzel, N. (2016). Incremental sentence processing in Japanese: a maze
investigation into scrambled and control sentences. J. Psycholinguist. Res. 45, 475–505.
doi: 10.1007/s10936-015-9356-4

Traxler, M. J., Morris, R. K., and Seely, R. E. (2002). Processing subject and object
relative clauses: evidence from eye movements. J. Mem. Lang. 47, 69–90. doi: 10.1006/
jmla.2001.2836

Frontiers in Psychology

17

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