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Syntax and processing in Seediq: a behavioral study

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Syntax and processing in Seediq: a behavioral study

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Syntactic properties such as word orders are a major factor determining the difficulty of a sentence. In SO-type languages where the subject (S) precedes the object (O) in canonical word order, there is clear evidence that the SO word order is preferred over the OS word order. We investigate to what extent this SO bias is maintained even in typologically diverse languages like Truku, an Austronesian language, in which the Verb-Object-Subject (VOS) word order is canonical and a syntactically basic structure, and SVO is the derived word order and a syntactically more complex structure. It is important to investigate word order preferences in Truku because such inquiries allow us to determine to what extent these widely observed processing preferences are grounded in properties of the linguistic system and/or somewhat more general human cognitive properties. The syntactic complexity account predicts that, in Truku, the derived SVO word order should be more costly, while the saliency account predicts that the word orders in which an agent precedes a theme is preferred. Our auditory comprehension experiment showed that the OS word order was preferred by native speakers of Truku. This indicates that the often-observed SO preference is not a universal feature of language. Furthermore, the lack of a clear indication of the agent-before-theme preference suggests a correlation between the voice property of a given language and the importance of the saliency factor.
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Syntax and processing in Seediq: a behavioral study
Hajime Ono
1
·Jungho Kim
2
·Manami Sato
3
·Apay Ai-yu Tang
4
·
Masatoshi Koizumi
5,6
Received: 1 August 2018 / Accepted: 27 February 2020 / Published online: 12 May 2020
©The Author(s) 2020
Abstract Syntactic properties such as word orders are a major factor determining
the difficulty of a sentence. In SO-type languages where the subject (S) precedes the
object (O) in canonical word order, there is clear evidence that the SO word order is
preferred over the OS word order. We investigate to what extent this SO bias is
maintained even in typologically diverse languages like Truku, an Austronesian
language, in which the Verb-Object-Subject (VOS) word order is canonical and a
syntactically basic structure, and SVO is the derived word order and a syntactically
more complex structure. It is important to investigate word order preferences in
&Hajime Ono
hajime@tsuda.ac.jp
Jungho Kim
kjungho7@gmail.com
Manami Sato
manamisato@gmail.com
Apay Ai-yu Tang
apay@gms.ndhu.edu.tw
Masatoshi Koizumi
koizumi@tohoku.ac.jp
1
Department of English, Tsuda University, Tokyo, Japan
2
Faculty of Letters, Kyoto Women’s University, Kyoto, Japan
3
Department of British and American Language and Culture, Okinawa International University,
Okinawa, Japan
4
Department of Indigenous Language and Communication, National Dong Hwa University,
Hualien, Taiwan
5
Department of Linguistics, Graduate School of Arts and Letters, Tohoku University, Miyagi,
Japan
6
National Institute for Japanese Language and Linguistics, Tokyo, Japan
123
Journal of East Asian Linguistics (2020) 29:237–258
https://doi.org/10.1007/s10831-020-09207-7(0123456789().,-volV)(0123456789().,-volV)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Truku because such inquiries allow us to determine to what extent these widely
observed processing preferences are grounded in properties of the linguistic system
and/or somewhat more general human cognitive properties. The syntactic com-
plexity account predicts that, in Truku, the derived SVO word order should be more
costly, while the saliency account predicts that the word orders in which an agent
precedes a theme is preferred. Our auditory comprehension experiment showed that
the OS word order was preferred by native speakers of Truku. This indicates that the
often-observed SO preference is not a universal feature of language. Furthermore,
the lack of a clear indication of the agent-before-theme preference suggests a cor-
relation between the voice property of a given language and the importance of the
saliency factor.
Keywords Auditory sentence comprehension · Syntactic complexity ·
Filler-gap dependency · Saliency · Truku (Seediq) · Semantic anomaly detection ·
Word order · Voice
1 Introduction
1.1 Word order and sentence comprehension
It has been well established in the sentence processing literature that various
syntactic properties, such as word order and the type/length of syntactic
dependency, have a major influence on the processing cost of a sentence (Aoshima
et al. 2004; Fiebach et al. 2002; Frazier 1987; Nakano et al. 2002; Phillips et al.
2005; Phillips and Wagers 2007; Schlesewsky et al. 2000, among many others). For
instance, in languages where the subject (S) precedes the object (O) in their
canonical word order, which we call “SO-type languages,” it has been repeatedly
observed that the native speakers need more time to comprehend sentences when
they are presented in a non-canonical word order, such as the OS word order, where
the object comes before the subject (Bornkessel et al. 2002; Erdocia et al. 2009;
Hagiwara et al. 2007; Imamura and Koizumi 2008; Kim et al. 2009; Matzke et al.
2002; Mazuka et al. 2002; Sekerina 2003; Tamaoka et al. 2003). In the pair of
sentences in (1), taken from Mazuka et al. (2002), native speakers of Japanese took
more time to read the scrambled sentence in (1b) than the canonical word order
sentence in (1a).
(1) a. SO word order (canonical)
Mariko-ga otooto-o yonda
Mariko-NOM brother-ACC called
b. OS word order (scrambled, non-canonical)
Otooto-o Mariko-ga yonda
brother-ACC Mariko-NOM called
‘Mariko called her brother.’
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Further, Bader and Meng (1999) presented stimulus sentences in German, those
in (2), to the native speakers of German in a speeded grammaticality judgment task,
where the participants were instructed to judge as quickly and accurately as possible
whether or not the sentence was grammatically acceptable. The sentences shown in
(2) involve a relative clause structure, in which the noun phrase die Eltern “the
parents” is locally ambiguous between the subject or the object in the relative clause
until the reader reads the clause-final auxiliary verb hat or haben. This auxiliary
verb indicates whether the subject is singular or plural, which disambiguates the
structure.
(2) a. Subject gap in the relative clause
Maria erza
¨hlte mirvon der Frau, [
CP
die __ die Eltern angerufen hat]
Maria told me about the woman who the parents phoned has
‘Maria told me about the woman, who has phoned the parents.’
b. Object gap in the relative clause
Maria erza
¨hlte mir von der Frau, [
CP
die die Eltern __ angerufen haben]
Maria told me about the woman who the parents phoned have
‘Maria told me about the woman, who the parents have phoned.’
Bader and Meng (1999) found that the participants in their experiment were slow to
judge (2b) to be “acceptable,” compared to (2a). Furthermore, they were less
accurate in making a judgment for (2b) than (2a). This showed that the native
speakers of German have a bias in which the relative pronoun die “who”
corresponds to the subject in the relative clause, so the first noun phrase they
encounter in the relative clause is assumed to be the object. They have a subject-
first/agent-first preference.
The observations from Mazuka et al (2002) and others suggest that syntactic
complexity is one major factor for determining the processing cost of a given
sentence. When readers encounter a sequence of noun phrases in a non-canonical
word order, they must map those phrases into a structure with an extra layer of
projection to accommodate the scrambled noun phrase (Saito 1989; Saito and Fukui
1998; but see Miyagawa 2005 for a different approach). In this sense, the sentences
that use the canonical word order have a simpler syntactic structure, hence they
require a smaller amount of processing resources for comprehension (e.g., O’Grady
1997). Furthermore, the German examples in (2) suggest that sentences with a
longer syntactic dependency are costly to process. The example in (2b) involves a
dependency between the relative pronoun and the object position in the relative
clause, which is longer than the dependency in (2a) (Gibson 2000; Grodner and
Gibson 2005). In terms of dependency, it is possible to regard the processing cost in
(1b) of the non-canonical word order as the cost for a filler-gap dependency. The
displaced object functions as a filler that has to be encoded in the working memory
until it is eventually interpreted at the gap-position; the storage and integration of a
filler require a cognitive cost (Gibson, 2000). Therefore, the presence of a filler-gap
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Seediq Auditory Sentence Comprehension 239
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dependency and its length are factors that increase the processing cost of a given
sentence.
1
There is another approach to the processing costs associated with the OS word
order. It has been observed in many sentence production studies that a perceptual
property in an event, often noted as saliency, has an effect on word order
preferences. Saliency, or properties connected to conceptual accessibility or
animacy, is related to the thematic roles in an event, and some have suggested
that the agent-before-patient or animate-before-inanimate order is preferred (Bock
1982; Bock and Warren 1985; Branigan et al. 2008; McDonald et al. 1993; Tanaka
et al. 2011). For instance, Tanaka et al (2011) used a sentence recall task, and asked
the participants to recall Japanese sentences such as those shown in (3).
(3) a. SO word order
Minato-de ryoosi-ga booto-o hakonda
port-at fisherman-NOM boat-ACC carried
b. OS word order
Minato-de booto-o ryoosi-ga hakonda
port-at boat-ACC fisherman-NOM carried
‘At the port, the fisherman carried the boat.’
Tanaka et al. (2011) observed that, when the participants recalled the sentences,
they were more likely to invert the word order and produce an SO word order such
as (3b). One interpretation of this result is that, in addition to syntactic factors,
native speakers of Japanese prefer to produce sentences in which the agent comes
before the patient. Such an ordering preference may stem from an idea that the agent
is more salient than the theme in an event, because the agent has more control over
the event’s progress.
Similarly, Sauppe et al. (2013) observed the agent preference over the patient in
their picture-description experiment using Tagalog, a language spoken in the
Philippines. They presented participants with a picture depicting a transitive event,
and measured eye gaze patterns while the participants produced a sentence. In the
time-window of the first 600 ms after the presentation of the stimulus picture on the
screen, participants looked at the agent in the picture more often and longer than the
patient in the picture. Also, Hwang (2017) found the tendency among native
speakers of Korean to place agent nouns in the sentence-initial position in a
sentence-assembly task. Crucially, this effect was also present when animacy was
controlled. These observations suggest that there is a universal tendency to place a
salient element, such as an agent noun, before a less salient element. Similar ideas
based on some notions related to human cognitive features and/or discourse features
1
We should also note that Yano and Koizumi (2018) and Yano, Niikuni, Ono, Sato, Tang, and Koizumi
(2019) argue that discourse context is yet another factor for the increased processing cost of the non-
canonical word orders, demonstrating that the processing cost for the non-canonical word order decreases
when the sentence is placed in a supportive discourse context for such a word order. See also Kaiser and
Trueswell (2004) for the effect of discourse factors.
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are found in MacWhinney (1977), Primus (1999), Kemmerer (2012), Cohn and
Paczynski (2013), and Cohn et al. (2017), among others.
The above discussion should make clear that the word order preference of SO
over OS (or agent-theme order over theme-agent order) is observed in a wide range
of paradigms, but at the same time it should also be noted that data samples are quite
limited in terms of the linguistic diversity. Most of the languages that have been
studied and found to have SO and/or agent-before-theme preferences are heavily
biased toward socially, economically, and/or politically “rich” languages (see
related discussions in Anand et al. 2011; Jaeger and Norcliffe 2009; Norcliff et al.
2015). With this in mind, it is important to investigate a much wider range of
languages, to examine to what extent the SO and agent-before-theme preferences
are universal features of language. In particular, in terms of this word order
preference, it is important to investigate those that we call “OS-type languages,” in
which the object comes before the subject in their underlying basic word order.
Koizumi et al. (2014) conducted one of the few studies investigating the nature of
the SO preference, using an OS-type language (see also Kiyama et al. 2013;
Yasunaga et al. 2015). They examined the word order preference in Kaqchikel, a
Mayan language spoken in Guatemala. In Kaqchikel, the canonical word order is
VOS (Ajsivinac Sian et al. 2004, p. 162; Garcı
´aMa
´tzar and Rodrı
´guez Guaja
´n1997,
p. 333; Rodrı
´guez Guaja
´n1994, p. 200; Tichoc Cumes et al. 2000, p. 195; see also
England (1991) and Aissen (1992)), but the language also allows SVO word order.
Like other Mayan languages, Kaqchikel is a head-marking language, in which the
verb carries agreement markers of the dependent elements such as subject and
object (with respect to person and number), in addition to tense/aspect markers.
Sentences in (4) are a sample set of their target sentences; in an auditory semantic
anomaly detection task, they measured participants’ response times in making a
plausibility judgment. In the following examples, the verb shows the person and
number agreement markers; one for the ergative NP (subject), and the other for the
absolutive NP (object).
(4) a. VOS word order (canonical)
X--u-cho
¨y ri cha
¨j ri ajanel
COMPL-ERG.3SG-ABS.3SG-cut DET pine.tree DET carpenter
b. SVO word order (the subject is fronted to the sentence-initial position)
Ri ajanel x--u-cho
¨y ri cha
¨j
DET carpenter COMPL-ERG.3SG-ABS.3SG-cut DET pine.tree
‘The carpenter cut the pine tree.’
Koizumi et al. (2014) observed that VOS sentences were responded to significantly
faster than SVO sentences, at a rate of roughly 150 ms. These results suggest that
the SO preference is not a universal feature of language. Instead, they suggest that
syntactic properties of a given language greatly affect the processing cost in
sentence comprehension, and note that it took more time for native speakers of
Kaqchikel to process sentences with non-canonical word orders, like that in (4b).
Yasunaga et al. (2015) also observed a similar effect in their event-related potential
(ERP) study. Comparing the SVO and VOS sentences, the object in SVO word
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order sentences elicited a P600 effect, suggesting that SVO sentences are more
costly than VOS sentences. These studies suggest that, both in SO- and OS-type
languages, sentences that are syntactically more complex, due to the presence of a
filler-gap dependency, require more processing resources than those sentences with
no filler-gap dependency.
Building upon the findings from Koizumi et al. (2014), this paper reports on an
experiment conducted in the Truku dialect of the Seediq language, which is spoken
in Taiwan. Truku is an OS-type language, in which the canonical word order is
claimed to be VOS (Aldridge 2004; Tsukida 2009). Truku is typologically different
from Kaqchikel, and is not a head-marking language. As is demonstrated in the next
section, verbs in Truku do not carry any person/number agreement markers of the
dependent elements, but the verb is required to have one voice marker indicating
which element in the event is treated as the subject. One might suggest a processing
advantage in the verb-initial structure, due to the agreement markers on the verb. In
fact, Sauppe (2016) conducted an experiment using the visual-world eye-tracking
paradigm in Tagalog and found that, while listening to sentences, native speakers of
Tagalog used verbal semantics to anticipate the upcoming referents and their
thematic roles as soon as the verb was heard. Studying Truku allows us to examine
the role of verbs whose morphological properties are widely different from those in
a head-marking language such as Kaqchikel.
Furthermore, it is important to investigate the word order preferences in Truku,
because such an inquiry allows us to determine to what extent the widely observed
processing preferences, namely the SO and agent-before-theme preferences, are
grounded in properties of the linguistic system or somewhat more general human
cognitive properties (Koizumi et al. 2014; Kubo et al. 2015). Truku allows SVO
word order, but it has also been claimed that SVO is a word order derived from the
more basic VOS word order in this language (Aldridge 2004). In the following
sections, we will see a more detailed discussion of the grammatical properties of
Truku. Under the syntactic complexity hypothesis, that the syntactic complexity has
an impact on the word order preferences for sentences (i.e., it is reflected in
processing costs), the SVO word order should be more costly than the VOS order in
Truku. By contrast, one could propose that, being in the privileged position, the
subject is universally salient over objects. Given a typical transitive event, a subject
is often associated with the agent role, while the object is associated with the theme
role, which accounts for the subject preference. Under this universal saliency
hypothesis, the SO word order preference should also be favored in Truku,
irrespective of its basic word order. Of course, these two accounts of syntactic
complexity and saliency are not in an exclusive relationship, and it is possible that
both of them are found to have a certain effect on the word order preferences in
Truku.
Finally, we should note that the SO word order preference discussed above is,
nevertheless, often conflated with the “agent-before-theme preference” noted in the
previous studies. Using various SO-type languages or even Kaqchikel, it has been
difficult to tease apart the possible source of the saliency noted above. It is possible
that being a subject is an important property to be counted as salient, but it is also
possible that being an agent is important and the agent is taken to be salient. Then,
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we have a case in which the agent is more likely to be promoted to the subject,
yielding the subject preference. As will be seen in greater detail below, Truku has a
symmetrical voice system in which the agent and patient are equally likely to be
promoted to the subject of the sentence (Foley 2008). Given this grammatical
characteristic of Truku, along with other grammatical characteristics, it is possible
to investigate how these two properties, being a subject and being an agent, interact
in comprehending Truku sentences.
1.2 Truku grammar
Truku, a dialect of the Seediq language, is spoken in East Taiwan. Seediq is an
indigenous language and the Seediq people are one of Taiwan’s 16 nationally-
recognized tribes. Seediq belongs to the Atayal group of the Austronesian language
family, and is spoken by approximately 20,000–30,000 people (Covell 2008;
Eberhard et al. 2019). Some grammatical properties of Truku are introduced below,
as these are relevant to the design of the current experiment.
Truku, like many other Austronesian languages, uses the symmetrical voice
system (Aldridge 2004; Foley 2008; Riesberg 2014; Tsukida 2009). Verbs need to
carry one of the three voice markers (agent voice, goal voice, or conveyance voice).
This system is described as “symmetrical” because there is no default or unmarked
voice among the three voice markers (cf., English and Japanese, where the active
voice is clearly unmarked syntactically and morphologically, compared to the
passive voice). The following examples show the basic voice patterns. The two
example sentences in (5) basically represent the same event. The verb carries
different voice markers, and the agent “the cook” is promoted to the subject in (5a),
while the theme “this pineapple” is promoted to the subject in (5b). Truku also has a
third type of voice marker, conveyance voice; in sentences with the verb in the
conveyance voice, elements such as instrument phrases or benefactive NPs are
promoted to the subject position. Examples of the conveyance voice are not shown
here, because sentences of this voice type are not used in the current study. In the
VOS word order, the subject is marked with ka, which is glossed as nom, following
Tsukida (2009), but a slightly different terminology is used in other works
(Aldridge, 2004; Holmer, 2005).
(5) a. Agent voice, VOS word order
qmnilis kalat niyi ka emphapuy
AV-peels pineapple this NOM cook
b. Goal voice, VOS word order
qnlis-an emphapuy ka kalat niyi
peels-GV cook NOM pineapple this
‘The cook peels this pineapple.’
Another relevant feature in Truku is the word order variation. As shown in (5),
the language allows the VOS word order. It also allows the SVO word order, as
shown in (6).
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Seediq Auditory Sentence Comprehension 243
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(6) a. Agent voice, SVO word order
emphapuy o qmnilis kalat niyi
cook TOP AV-peels pineapple this
b. Goal voice, SVO word order
kalat niyi o qnlis-an emphapuy
pineapple this TOP peels-GV cook
‘The cook peels this pineapple.’
In (6), the nominal phrase appearing along with the particle ka in (5) is now placed
in the clause-initial position. When the subject is fronted, the particle ka can no
longer appear, but another particle, o, appears after the fronted subject. It has been
claimed that the VOS word order is the syntactically basic word order, and the SVO
order is syntactically derived from the VOS word order (Aldridge 2004; Tsukida
2009). In this paper, we follow Aldridge (2004) and assume that the VOS word
order is derived by predicate fronting. As for the SVO word order, Yano et al.
(2019) have shown that the SVO order is derived from the VOS, and the subject is
in a higher position, as evidenced by the observation that the fronted subject can
leave its associated quantifier after VO (see the relevant discussion in Sportiche
(1988)). Assuming that the subject fronting involves more functional categories in
the CP domain (Rizzi 1997), the SVO structure is a syntactically more complex one.
The present study investigates the word order preference in Truku, manipulating
voice and word order at the same time. We are particularly interested in whether
SVO sentences produce a large processing cost. In Truku, SVO is a derived word
order, and the syntactic complexity hypothesis predicts that SVO is more costly than
VOS in Truku, because SVO involves a filler-gap dependency. By contrast, if
subject is universally salient over object, and if saliency is a major factor
responsible for the processing cost of the sentence, native speakers of Truku should
process SVO sentences faster than VOS ones. Given that Truku is a symmetrical
voice language, we can manipulate whether the agent role is assigned to the subject
or object, which allows us to examine the interaction of voice and word order
(saliency order) fully. Then, the saliency hypothesis predicts that the SVO word
order is preferred in Agent Voice (AV) sentences, while the VOS is preferred in
Goal Voice (GV) sentences in our auditory semantic anomaly detection experiment,
which is introduced in the next section.
2 Experiment
2.1 Participants
Forty-two native speakers of the Truku dialect of Seediq (9 males and 33 females;
M=60.1 years, SD= 11.1) were paid to participate in the experiment; all signed a
written informed consent document. All reported no hearing or other language-
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related disorders. They live in a village near Hualien City, Taiwan, and all of them
also speak Chinese daily.
2
2.2 Materials
The materials for our experiment consist of 48 sets of sentences. Each set crossed
two types of the verb voice (AV and GV) and two word orders (VOS and SVO),
making four conditions. Among the 48 sets of sentences, 24 sets used a proper name
in the agent phrase, such as Rabay and Abis, which are commonly used names in
Truku, according to our consultants. The other 24 sets used a common noun in the
agent phrase. Those included emphapuy “cook” or knsat “policeman,” etc. In the
AGENT VOICE condition, the verb had the agent voice marker, and the NP with the
agent role appeared as the subject of the sentence. In the GOAL VOICE condition,
however, the verb was marked with the goal voice marker, and the NP with the
theme role appeared as the subject. As for the word order factor, we prepared the
VOS word order, which had the subject at the end of the clause with the nominative
marker ka. The SVO word order was also prepared, in which the subject was fronted
to the sentence-initial position. Each set of target stimuli thus had four versions, and
half of the target stimuli used a proper name, while the other half used a common
name. Those 192 sentences were distributed into four lists by crossing voice and
word order in a Latin-squared design, so that no participant saw more than one
version from each set. Therefore, three factors (voice of the verb, word order, and
noun type) are within-subjects factors. A sample set of the target sentences of the
common noun type is shown below.
(7) a. Agent voice, VOS word order
m-n-hapuy begu niyi ka empsapuh
AV-PRF-cook soup this NOM doctor
‘The doctor cooked this soup.’
b. Agent voice, SVO word order
empsapuh o m-n-hapuy begu niyi
doctor TOP AV-PRF-cook soup this
c. Goal voice, VOS word order
n-puy-an empsapuh ka begu niyi
PRF-cook-GV doctor NOM soup this
‘Lit. This soup was cooked by the doctor.’
d. Goal voice, SVO word order
begu niyi o n-puy-an empsapuh
soup this TOP PRF-cook-GV doctor
In addition to the target sentences, 48 filler sentences were prepared. Those filler
sentences were all semantically anomalous, such as #Simaw planted the bag; #the
2
This study was approved by the Ethics Committee of the Graduate School of Arts and Letters, Tohoku
University.
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Seediq Auditory Sentence Comprehension 245
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chopsticks steamed the vegetables, etc. We made filler sentences in this way
because, as will be seen below, we used a semantic anomaly detection task, and all
of the target sentences should be acceptable to the native speakers of Truku, while
the filler sentences should be responded to as anomalous.
3
One male native speaker of Truku from the village read the sentences aloud and
we recorded him. After the recording, the audio files were trimmed so that the total
length of the sentences was closely matched with respect to the voice and word
order factor. The mean length of sentences was about 3 s, and there was a significant
difference according to the noun-type factor (F(1, 46) =31.54, p\0.01), indicating
that the mean length of sentences with a proper name was shorter than that with a
common noun (the proper name condition, 2589 ms (SD=311); the common noun
condition, 3077 ms (SD=408)). There was no other significant main effect or
interaction.
2.3 Task and procedure
The participants were tested individually. They sat in front of a laptop computer and
wore a headset in a quiet room, and were told to relax. They were instructed by an
experimenter, who is a native speaker of Truku, to listen to the sentences through
the headset, and to decide, as quickly and accurately as possible, whether the
sentences they heard made sense or not (Caplan et al. 2008). Two keys on the
keyboard (“J” and “F”) were assigned for the responses (‘yes’ and ‘no’). The
participants were instructed to use both hands to make their responses. All target
sentences should have been judged as “yes,” and all filler sentences should have
been judged as “no.” The number of “yes” responses, then, was counterbalanced
with the number of “no” responses. After the experimenter provided the instructions
to the participant, there was a practice session with seven trials. In each trial, the
participants briefly saw a small fixation cross in the middle of the screen for
1000 ms, and the sentence was presented auditorily through their headset. Stimulus
sentences were presented in a randomized order for each participant. While the
stimulus sentence was presented auditorily, a pair of the smiley face and non-smiley
face icons was shown on the screen, which would help the participants press the
response keys as they intended. In the practice session, the experimenter provided
feedback to the participants’ responses, to ensure they understood the task and felt
familiar with the procedure. This practice session was repeated if the participants
wanted to practice more. During the experimental session, no feedback was given
for wrong answers. The participant could make a judgment before the sentence
ended, but they usually made their response after the end of the sentence. We
recorded the responses (i.e., “yes” or “no” for the accuracy) and measured the
response times (RTs) from the onset of the sentence. The whole experiment took
about 15 min.
This task required participants to judge the semantic plausibility of each
sentence. The underlying assumption of using RT is that the length of time it took to
complete the task reflected sentence processing difficulties or complexities, such as
3
The entire set of experimental stimuli can be found at: https://osf.io/fzagc/.
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sentences with relatively long syntactic dependencies (Grodner and Gibson 2005)or
ones with a non-canonical word order (Kaiser and Trueswell 2004). Self-paced
measurements have been widely used in various fields, and measuring response
times of the full sentences may not allow us to draw strong conclusions regarding
exactly which word or from what region of the sentence such processing difficulty
has emerged. To assess the time-course of language comprehension, a word-by-
word or phrase-by-phrase self-paced listening task can be set up as a more fine-
grained measurement of processing efficiency (Ferreira et al. 1996). We, however,
employed a whole-sentence measurement, due to concerns for the ecological
validity (Kaiser 2013); that is, people do not hear a sentence segment by segment at
their own pace in a natural setting. Instead, they have to process it as speakers
produce their utterances. Because most, if not all, native speakers of Truku do not
regularly read in Truku, we had to rely on a task measuring their auditory sentence
comprehension, rather than reading.
2.4 Analysis
We removed data gathered from four participants from the analyses because they
only used one hand to make their responses, and such responses are not reliable. The
remaining data from 38 participants were analyzed further. As for the analyses of
the response times, data from the trials in which the participants made an incorrect
response were excluded. In those incorrect responses for the target sentences the
participants answered “no,” and we removed those data from the response time
analysis because such negative responses are known to be disproportionally longer.
We analyzed our remaining data using the lme4 package (Bates et al. 2015b,a) for
the R software (R Core Team 2019). We used logistic mixed-effect models for the
accuracy data (Jaeger 2008), as the dependent measure was categorical, and linear
mixed-effect models for the RT data (Baayen et al. 2008). Following Barr et al.
(2013), the model was initially fit with the maximal random effects structure,
including random slopes for the repeated measures factors and random intercepts for
participants and items. Three repeated measures factors, VOICE,WORD ORDER, and
NOUN TYPE, were entered into the model as fixed factors. For the RT analyses, the
mean correct response rate for each participant, and for each item, were entered as
covariates. The trial order was also included initially, but was later eliminated
because it did not significantly contribute to the improvement of the model’s
performance. Converged models were evaluated, and the optimal model was
selected using backward elimination. Once the optimal model was chosen, we
eliminated the data points that were more than 2.5 SD away from the estimation by
the model, and the model was re-fit to determine the final model. The model
summary and p-values were obtained using the lmerTest package (Kuznetsova et al.
2017).
While we were preparing for the data analysis, we noticed that the RT patterns
from the stimuli with a proper name and those with a common name were largely
different. Roughly, the sentences used in our experiment have three phrases/words:
subject, verb, and object. Although the participants could sometimes detect
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anomalous meanings at the second phrase of the filler sentences, they usually
needed to listen to the third phrase. The participants then needed to listen to the third
phrase to decide whether the stimulus sentence made sense or not. It seemed that the
onset time for the third phrase and its duration had some major influence on the
response time, in addition to the different length of the total sentence. We then
suspected that the length of the third phrase was one of the reasons why the RT
patterns differed depending on the type of agent (a proper name or a common noun).
Proper names used in the stimuli were relatively short, so the onset and the length of
the third phrase differed across conditions. Three research assistants listened to the
audio files and measured the length of the third phrases; they also measured the
onset of the third phrase. We calculated the means of those measurements for each
stimulus. Then, for the RT analyses, the onset time of the third phrase and the length
of the third phrase were also entered into the model as covariates.
4
2.5 Results
The overall correct response rate was 75%. The mean accuracy data by condition is
shown in Fig. 1, and a summary of the statistical analyses of the response accuracy
data is shown in Table 1.
There was a significant main effect of WORD ORDER, showing that the mean accuracy
rate for the SVO condition was lower than that for the VOS condition. Also, a
significant main effect of NOUN TYPE showed that the common noun condition was
more difficult than the proper name condition. There was a three-way interaction
(almost significant) among the three within-subject factors, and further pairwise
comparisons indicated that there was a WORD ORDER NOUN TYPE interaction only in
the AV condition, but not in the GV condition. This interaction was driven by the
contrast within the common noun condition, showing that the AV-SVO condition
was more difficult than the AV-VOS condition. There was no such word order effect
in the GV condition.
The overall mean response times are shown in Fig. 2, and a summary of the
statistical analysis is shown in Table 2.
Table 2shows that the mean RT patterns for the proper name and the common
noun conditions were different, as indicated by a significant NOUN TYPE factor and a
marginally significant three-way interaction among WORD ORDER,VOICE, and NOUN
TYPE. In general, the mean RT for the common noun condition was slower than that
for the proper name condition, which is correlated with the low accuracy for the
common noun condition discussed above. Pairwise comparisons suggest that while
there was no WORD ORDERVOICE interaction in the proper name condition, there was
a significant interaction of such in the common noun condition. Further comparisons
show that, in the GV condition, the VOS condition was significantly faster than the
SVO condition, but no such difference was found in the AV condition.
4
We included these two measures, instead of the total length of the sentence, because they are more fine-
grained, compared to the total length, and should influence the RT more directly.
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Table 1 Summary of the statistical analysis on the response accuracy data
Estimate S.E. Z p-value
Overview
(Intercept) 1.462 0.189 7.727 \.001 ***
Word Order 0.399 0.124 3.205 \.010 **
Voice 0.033 0.125 0.266 0.790
Noun Type 1.212 0.305 3.973 \.001 ***
Word Order * Voice 0.243 0.250 0.972 0.331
Word Order * Noun Type 0.207 0.249 0.834 0.404
Voice * Noun Type 0.113 0.250 0.451 0.652
Word Order * Voice * Noun Type 0.983 0.500 1.967 \.050 *
Within AV
Word Order * Noun Type 0.699 0.352 1.987 \.050 *
Within AV, Common Noun
Word Order 0.616 0.271 2.275 \.030 *
ProperName CommonNoun
VOS SVO VOS SVO
0.00
0.25
0.50
0.75
1.00
Word Order
Mean Accuracy Rate
Voi ce
AV
GV
Fig. 1 The mean response accuracy rates for each condition. The error bars indicate standard errors of
the mean
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Seediq Auditory Sentence Comprehension 249
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Table 2 Summary of the statistical analysis on the response time data
Estimate S.E. t-value p-value
Overview
(Intercept) 6331.54 564.77 11.21 \.001 ***
Word Order 22.19 27.91 0.80 0.427
Voice 18.27 28.09 0.65 0.516
Noun Type 253.45 49.76 5.09 \.001 ***
Onset 3rd Phrase 315.80 19.67 16.06 \.001 ***
Length 3rd Phrase 210.66 21.73 9.70 \.001 ***
Accuracy Rate (Participant) 2431.99 728.06 3.34 \.010 **
Accuracy Rate (Item) 708.68 148.32 4.78 \.001 ***
Word Order * Voice 74.73 71.29 1.05 0.295
Word Order * Noun Type 37.15 56.52 0.66 0.511
Voice * Noun Type 171.54 56.43 3.04 \.010 **
Word Order * Voice * Noun Type 274.53 143.71 1.91 \.060 .
Within Common Noun
Word Order * Voice 228.38 84.80 2.69 \.010 **
Within AV Word Order 72.42 60.82 1.19 0.234
Within GV Word Order 155.97 58.93 2.65 \.010 **
Within Proper Name
Word Order * Voice 46.15 111.89 0.41 0.680
ProperName CommonNoun
VOS SVO VOS SVO
0
2000
4000
Word Order
Mean Response Time (ms)
Voi ce
AV
GV
Fig. 2 The mean response time (ms) for each condition. The error bars indicate standard errors of the
mean
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3 Discussion
The mean response accuracy rates indicated that sentences with a common noun
agent were more difficult than those with a proper name agent. As demonstrated
below, this is also reflected in the RT data. The lower accuracy rates in the common
noun condition indicated that the participants were more willing to reject the
common noun target sentences. In the common noun target sentences, for instance,
they had to decide whether “the carpenter looked for the eggs” was a likely event.
We suspect that some participants thought that there was a more likely agent who
would look for the eggs—a cook, for instance. By contrast, in the proper name
target sentences, they may not have had to examine in too much detail whether a
given agent was a plausible entity to initiate the action, because the agent was just a
name. Of course, they still had to decide whether the eggs are something people
look for in general, but the task demand seemed lower in the proper name condition.
We also observed the general pattern that sentences presented in the SVO word
order were more difficult than those in the VOS word order. This contrast in the
accuracy data was clearly visible in the common noun-AV conditions, suggesting
that the derived SVO sentences were more costly to process than the VOS
sentences.
The results of the RT measure suggest a few major patterns. First, the mean RT in
the proper name condition was faster than that in the common noun in general.
Second, there was no WORD ORDER VOICE interaction in the proper name condition,
but those two factors do interact in the common noun condition. Basically, there was
no RT difference by condition within the proper name condition; in the common
noun condition, the RT for the GV-VOS was faster than the other three conditions.
With respect to the noun type contrast, we suggest the same account as discussed
above; the RT for the proper name condition was faster because it required less
processing demand, compared to the common noun condition, to judge whether the
sentence was semantically plausible or not. Further, the total length of the sentences
with a proper name was shorter than that with a common noun, which is also likely
to be responsible for the RT difference. It seems that the participants made a
response very soon after the sentence finished, so the lack of WORD ORDER VOICE
interaction might be due to the ease of comprehension for the sentences.
As for the WORD ORDER VOICE interaction in the common noun condition, in
Sect. 1, we introduced the syntactic complexity account and the universal saliency
account, but the predictions regarding these are hard to tease apart when testing SO-
type languages. In languages like Truku, the syntactic complexity account predicts
that the SVO word order should take longer to process in general because it is a
derived word order from the more basic VOS word order. Assuming that the SVO
word order involves a more complex syntactic structure (Aldridge 2004; Yano et al.
2019), it is, in general, more costly to process sentences in that word order.
Furthermore, the saliency account suggests that sentences in which the agent comes
before the theme are processed quickly. Among the conditions in this experiment,
sentences in the AV-SVO and the GV-VOS conditions have the agent before the
theme, and are predicted to be processed more quickly.
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Seediq Auditory Sentence Comprehension 251
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To account for the patterns from the accuracy and the response time data
together, we suggest that there is a general VOS preference in Truku. Based on the
common noun conditions, a VOS advantage was found in AV conditions in the
accuracy data. A similar contrast was not seen in the response time data, but we
propose that this exhibits a pattern of speed-accuracy trade-off. The response time in
the AV-VOS condition seemed no faster than that in the AV-SVO condition, but
this could be due to the relatively high accuracy rate. Participants made a better
response by spending a bit longer time to make a response. So, possibly, the
response time in AV-SVO would be faster and the accuracy rate would be slightly
lower. Then, the contrast between the SVO and the VOS word orders would have
been much clearer, showing the VOS advantage. With respect to the pattern in GV
conditions, in a similar way, we would expect a slightly higher accuracy rate for the
GV-VOS condition, if there is a general VOS advantage. We suppose that this
“lower-than-expected” accuracy rate in the GV-VOS is related to the faster response
time in this condition. In sum, although we have to rely on the speed-accuracy trade-
off, it is plausible to hypothesize that Truku has a general VOS preference, because
this can explain the combined pattern of accuracy and response time data.
These interpretations of the results suggest that the often-observed SO preference
is not a universal feature of language (Koizumi and Kim 2016; Koizumi et al. 2014;
Kiyama et al. 2013; Yasunaga et al. 2015; Yano et al. 2017,2019). The SO
preference has been observed in the sentence processing literature, and, most of the
time, the SO-type languages are the targets of investigation. In those languages, the
SO word order is the canonical/basic order, implying that it involves a syntactically
less complex structure than the non-canonical OS word order. In languages like
Truku, the SO word order necessarily involves a more complex structure, so there is
no general preference of the SO word order.
There seems to be an apparent alternative account for the common noun
condition that employs an interaction between syntactic complexity and saliency
(see the relevant illustration in Table 3). This “interaction” account would suggest
that both syntactic complexity and saliency are needed in order to explain the full
picture of the response time results. According to this account, the GV-SVO
sentences took longer time to process than the GV-VOS sentences because: (a) the
SVO word order involves a more complex syntactic structure, and (b) the theme NP
is the subject and placed in the sentence-initial position in the SVO word order
condition. It then precedes the agent NP, which is in the object position. In other
words, the GV-SVO sentence is a costly structure both under the syntactic
complexity account and the saliency account. On the other hand, in the AV
sentences, the AV-SVO sentence is costly to process because it has a more complex
structure than the AV-VOS sentence. The AV-VOS sentence, however, is costly to
process because the agent NP is the subject and placed at the end of the clause. The
theme NP then precedes the agent NP in this sentence type. Assuming that the
magnitude of the costs coming from the syntactic complexity and the saliency are
not largely different from each other, it may be possible to claim that those two
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costs cancel each other out. Then, the reaction times from the two conditions did not
show a significant difference.
5
This account may successfully explain the response
time pattern, but it requires some extra assumptions with respect to the pattern found
in the accuracy data, where there is a clear difference within the AV condition, but
not within the GV condition.
The results from the present study have some implications for the role of head-
marking morphology in sentence processing. Recall that Koizumi et al. (2014) and
their related work indicated that word order preference is largely determined by the
syntactic properties of a given language. They examined Kaqchikel, a Mayan
language, which shares a certain syntactic characteristic with Truku. Both have
VOS as the canonical word order and SVO as a derived word order. Although
Kaqchikel and Truku are typologically different, it is noteworthy that both
languages show the OS word order preference. This indicates that the OS word
order preference is not limited to certain languages that belong to a particular
language family, such as Mayan, but rather this seems to be a property of languages
whose canonical word order is VOS. Some might speculate that, because the verbs
in Kaqchikel carry a lot of agreement morphemes about the dependent nouns, the
head-marking properties in Kaqchikel play a large role for the OS preference. Our
current results suggest that having such a head-marking property is not a necessary
condition for the OS word order preference, because Truku verbs do not have
agreement markers analogous to those in Kaqchikel, yet the language still shows an
OS word order preference.
As discussed in Sect. 2.3, we decided to employ a task to measure the response
time at the end of the sentence, not a word-by-word self-paced listening task, for
example. Therefore, it is a bit difficult to point out exactly at what stage the
processing cost emerges in comprehending sentences, but nevertheless, we would
predict that the processing cost arises at the sentence-initial NP, because it signals
that the sentence is not in the VOS word order (see Yano et al. 2019).
Table 3 The relationship between the two accounts (the Universal Saliency and the Syntactic Com-
plexity) with respect to the processing cost, and the four sentence patterns based on the voice and word
order
Universal Saliency
Salient order
Syntactic Complexity
Subject stay
GV.VOS ✓✓
GV.SVO Costly Costly
AV.VOS Costly
AV.SVO Costly
5
A similar interaction among multiple factors can be found in the previous literature, although the
relevant factors are slightly different. Polinsky, Gallo, Graff, and Kravtchenko (2012) investigated the
processing difficulties in various relative clause (RC) structures in Avar, an ergative language in the
Caucasian language family. They concluded that syntactic structure and case play an important role in
comprehending relative clause structures in Avar.
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Seediq Auditory Sentence Comprehension 253
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Our results also suggest that properties like saliency often have an influence on
the sentence processing costs, but to a different extent in SO- versus OS-type
languages. In quite a few production studies of SO-type languages, it has been found
that cognitive properties such as saliency have an effect on the word order selections
(Branigan et al. 2008; Tanaka et al. 2011). However, our results suggest that
saliency is not a major factor explaining the response pattern in Truku. Note that
Truku is a language that has a rich voice system. It has been claimed that the goal
voice in which a theme argument is promoted to the subject is no more marked than
the agent voice, in terms of frequency (Tsukida 2009). Such a distributional
tendency with respect to the construction types is not common in SO-type languages
(Japanese, English, etc.). Sentences in passive voice are often morphologically more
marked, and arguably less frequent (e.g., Roland et al. 2007). The lack of clear
indication of the agent-before-theme preference suggests a correlation between the
voice property of a given language and the importance of the saliency factor (see
Sauppe (2016) for a relevant discussion). Of course, the availability of the
symmetrical voice system is not restricted to OS-type languages, and the patterns
are much more complicated indeed.
4 Conclusion
Previous experimental results have shown that there is a processing bias whereby
sentences are processed more quickly and easily when the subject appears before the
object. We pointed out that this bias is widespread, but most of the data come from
languages in which the subject precedes the object in their canonical word order. It
is often also confounded that the agent argument precedes the theme argument in a
sentence. The question of whether the SO word order bias is based on the syntactic
complexity of the sentence or on its saliency can be solved by investigating Truku,
whose canonical word order is VOS. The results showed that, with respect to the
response time, there was, superficially, no word order effect in AV sentences with a
common noun agent, but the SVO sentences are processed significantly slower than
the VOS sentences in GV sentences. We argued, however, that there was a general
VOS preference in Truku, further indicating that the syntactic complexity account is
better suited for explaining the pattern found in both response accuracy and
response time data.
In sum, our auditory comprehension experiment suggests that the often-observed
SO preference in SO-type languages is not fully grounded in the universal properties
of human cognition. In Truku, an OS-type language, the OS word order was
preferred. We suggest that the lack of (or at least very weak) saliency effect in this
language may relate to the symmetrical voice system, where promoting a theme
argument to subject does not require a more marked structure, with respect to the
verbal morphology and syntax, than promoting an agent argument. Finally, we
should point out that investigating a typologically wide set of languages is not only
interesting but also necessary for determining the nature of various preferences
found in the psycholinguistic literature. It is not always easy to find a way to
conduct research as we do in our own institutions, but in a way, just looking at
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languages that are spoken and used within an easily-accessible range will invite
unwanted “bias” in our minds.
Acknowledgements We thank anonymous reviewers and Jim Huang for their insightful comments and
suggestions. Various portions of this research were presented at the 34th Annual Meeting of the Japanese
Cognitive Science Society, and the International Workshop on Seediq and Related Languages held at
Harvard-Yenching Institute. We would especially like to thank Colin Phillips and Norvin Richards for
serving as the commentators for the workshop. We also appreciate the kind support from collaborators
and participants in Hualien. Remaining errors are obviously our own. This study was supported by Grant-
in-Aids for Scientific Research from the Japan Society for the Promotion of Science (#15H02603,
#19H05589, PI: Masatoshi Koizumi, #15K02529, #19K00586, PI: Hajime Ono).
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party material in this
article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended
use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by/4.0/
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