言語研究（Gengo Kenkyu）155: 35–63（2019）

doi: 10.11435/gengo.155.0_35

An Eye-tracking Investigation of Pre-head and Head-driven
Processing for Scrambled Japanese Sentences
Katsuo Tamaoka   Michael P. Mansbridge
Nagoya University

Nagoya University

Abstract: The syntactic movement of scrambled Japanese sentences is often
attributed to pre-head anticipatory processing prior to reading the head verb.
However, previous studies have not compared nouns within the same sentence
position; furthermore, studies have compared different noun types, influencing processing via semantic activation. Thus, this study only presented highly
frequent first names and maintained each noun in the same position by only
manipulating the case markings. Under this strictly controlled condition, scrambling was investigated using two eye-tracking experiments. The results indicatedthat pre-head processing begins resolving the filler-gap dependency at the critical
NP (noun phrase) containing the gap, as revealed by significantly longer go-pasttimes for scrambled conditions. However, without additional semantic cues,
case marking did not provide sufficient information, as seen by the difficulties
under scrambled conditions during late-processing stages at the critical NP
after reading the verb. Without semantic cues, Japanese speakers mostly usedverb information to establish the structural properties of scrambled constituents.
Consequently, the relative strength of pre-head and head-driven processing varies
depending on the cues available.*
Key words: eye-tracking, head-driven processing, pre-head processing, scrambling

1. Introduction
Wh-movement and other long-distance dependencies occur in many of the world’s
languages. For example, wh-movement in English involves moving an NP to form
a question or, in some cases, a relative clause (Chomsky 1977). Since wh-movement in English is obligatory, it can be difficult to directly compare its structure
to a corresponding non-movement structure and to visualize syntactic movement
experimentally. Here, scrambling comes into play. Many languages have a fundamental word order and sentence structure, termed the canonical word order. In* The authors would like to express their sincere gratitude to the anonymous reviewers fortheir insightful and constructive comments to complete this paper. They would like to extend their gratitude to Professor Masatoshi Sugiura at Nagoya University for kindly allowing us to use his eye-tracker. This study was funded by the Japan Society for the Promotionof Sciences ( JSPS) Grant-In-Aid granted to Michael P. Mansbridge (15J03336).

36   Katsuo Tamaoka and Michael P. Mansbridge

addition to the canonical order, some languages exhibit a variety of non-canonicalword orders (Ross 1967). Studies of theoretical linguistics (e.g., Saito 1985) havepresented syntactic evidence for transformational accounts of free word order inJapanese. According to these accounts, canonical word order is reordered by atransformation called scrambling, which was originally proposed by Ross (1967).
Japanese is a head-final language with a canonical SOV (Subject-ObjectVerb) word order with case particles, including the nominative -ga (NP-NOM),
accusative -o (NP-ACC) and dative -ni (NP-DAT), which attach to the ends ofnouns. Except for the verb, Japanese allows for flexible word order (e.g., Kondo
and Yamashita 2011, Miyagawa 2003, Nakayama 1995, Saito 1992), partly because
these particles mark the role of the NP within the clause. The syntactic movementof scrambled Japanese sentences has frequently been discussed as an attributionof pre-head incremental processing prior to reading the predicate (e.g., Aoshima el
al. 2004, Aoshima et al. 2009, Kamide and Mitchell 1999, Mazuka et al. 2002,
Miyamoto and Takahashi 2004). This processing is implemented bit by bit while
reading a sentence from the beginning (Miyamoto 2006). However, a recent
eye-tracking study by Tamaoka et al. (2014) indicated heavy head-driven processing (or post-head processing after seeing the verb) in Japanese scrambled sentences.
Although native Japanese speakers read sentences incrementally as sequences of
input, they still require the head verb information to complete the phrasal structure, especially when nouns do not provide sufficient information to construct aphrasal structure. With eye-tracking, we can investigate processing responses forboth before and after the participant sees the verb; accordingly, this study aimed
to clarify when the understanding of phrasal structures in scrambled word orders
is resolved.
2. Scrambling in Japanese sentences and previous studies
Native Japanese speakers seem to use the canonical (or unmarked) word orderas the base structure for sentence processing. An example of a canonical active(SOV) sentence with a transitive verb is Sanae-ga banana-o tabe-ta ‘Sanae ate abanana’. This sentence can also be scrambled if the accusative noun phrase bananao ‘a banana’ is moved to the initial position of the sentence, creating the scrambled
OSV sentence banana-o Sanae-ga tabe-ta. The scrambled order requires a syntactic
operation of phrasal movement from a gap (gap1) in the canonical position to thesentence initial position (NP-ACC1) as in [IP′ NP-ACC1 [IP NP-ga [ VP gap1 V]]].
Because subjects NP-NOM(-ga) are often omitted in Japanese, native speakers
will not expect scrambling order when initially seeing the NP-ACC(-o). However,since the NP-NOM follows the NP-ACC, the gap is usually expected. Thus, thesequential word order of NP-ACC and NP-NOM could create delays in reading
time to establish a filler-gap dependency, while this order could also be a relative
clause (Miyamoto 2006).
Tamaoka et al. (2005) found an inhibitory trend (i.e., a scrambling effect) in
the processing speed (reaction times) for scrambled sentences across four different sentence types. This result suggests an extra cognitive cost for the scramblingAn Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   37operation. In psycholinguistic studies, the scrambling effect has frequently beenoffered as an explanation of gap-filling parsing (Frazier and Clifton 1989, Stowe1986). This parsing mechanism is especially useful in identifying a wh-filler inEnglish. A sentence-initial wh-phrase triggers a search for its gap to establish afiller-gap dependency. This mechanism was also supported by ERP and fMRI
studies on the processing of German wh-questions (Fiebach et al. 2001). The strategy of gap-filling parsing has been applied to explain the processing of other typesof scrambling in Japanese (e.g., Koizumi and Tamaoka 2004, 2010, Miyamoto and
Takahashi 2004).
Some previous studies (e.g., Koizumi and Tamaoka 2004, 2010, Tamaoka et
al. 2005) have used a sentence correctness decision task because the same nouns
are not easily placed in the same phrasal position of the sentence. For example,
the canonical SOV sentence Sanae-ga banana-o tabe-ta ‘Sanae ate a banana’ isscrambled as banana-o Sanae-ga tabe-ta. In the first phrasal position, the proper
noun Sanae, a woman’s name, is compared to the general (or common) noun
banana. In the second phrasal position, the common noun banana is compared tothe proper noun Sanae. Only the final verb is positioned in the same sentence-final
position in both the canonical and scrambled sentences. The filler and gap sharesyntactic and semantic information essential for successful sentence interpretation. Sentence processing is a sequential operation, so a filler (banana-o) in thescrambled sentence cannot be assigned to its gap (after Sanae-ga) until sentence
processing has occurred. Assuming that all possible factors equally influence thespeed (reaction time) of sentence processing, reading times for the sentence correctness decision task were compared to judge the processing difference between
canonical and scrambled sentences. However, as Miyamoto (2008), Miyamoto andNakamura (2005), and Witzel and Witzel (2016) have stated, this task, strictly
speaking, cannot provide detailed evidence at the crucial NP, where the gap-filling
dependency occurs. Consequently, previous studies (e.g., Koizumi and Tamaoka
2004, 2010, Tamaoka et al. 2005) using this task could only assume that native
Japanese speakers use gap-filling parsing for scrambled sentences, compared to
canonical sentences. Although the sentential decision task is useful for comparing
a minimal-paired condition at the sentence level, the explanatory limitation of thetask is unavoidable once one attempts to describe the processing mechanism ofscrambled order in detail.
Using eye-tracking, Mazuka et al. (2002) examined phrase-by-phrase parsing of Japanese canonical and scrambled sentences. These authors reported that an
extra cognitive load was required to process simple transitive SOV-structured sentences with scrambled word order. This result was found in both the first-pass andre-reading time at the crucial NP of the second argument position immediately
before the verb. Mazuka et al. (2002) created paired sentences, as in Mariko-ga
otooto-o yon-da [IP NP(Mariko)-NOM [ VP NP(brother)-ACC V-PAST]], meaning ‘Marito called [her] younger brother’, and its OSV scrambled minimal
pair Otooto-o Mariko-ga yon-da [IP′ NP(brother)-ACC1 [IP (Mariko)-NOM [ VP
t1 V-PAST]]]. In this pair, the second NP of the scrambled sentence (i.e.,38   Katsuo Tamaoka and Michael P. Mansbridge

NP(Mariko)-NOM) is crucial to establishing a filler-gap dependency. However,when a different NP was placed in the same position in the baseline canonical sentence, it resulted in comparing a common noun to a proper noun.
Similarly, the eye-tracking study by Mazuka et al. (2002) also created a nountype mismatch issue. Common nouns (e.g., banana) denote general conceptual
characteristics, while proper nouns (e.g., Sanae) refer to individual entities. As
Kripke (1980) and Searle (1958) discussed, proper nouns seem to occupy a uniquelinguistic status distinguished from common nouns. In relation to this position,
multiple experimental studies (e.g., Semenza 2009, Van Lancker and Ohnesorge2002) have suggested that proper nouns possess a special neuropsychologicalstatus different from common nouns. The difference in processing speed could
be the result of comparing different noun types (proper noun vs. common noun).
Consequently, unless a study directly compares the same noun in an NP and possibly its collocational frequencies, the mechanism for establishing a filler-gap dependency remains unclear. It is also possible that a contrast in noun types prior to thereading of the predicate could act as a semantic expectation cue for the predicate
and its argument relationship, which would also account for pre-head processing.
In addition, the contrast of a proper noun (e.g., Sanae <agent>) and a common
noun (e.g., banana <theme>) often provides a relation of agent and theme (i.e.,
spreading activation, Collins and Loftus 1975), such as Sanae acting on banana,resulting in raised activation levels of possible following verbs, such as muku (peal),taberu (eat), and kiru (cut), which would come at the end of a Japanese sentence.
Tamaoka et al. (2014) also used eye-tracking to investigate how scrambled
sentences are processed. Three paired, simple types of Japanese active sentenceswith ditransitive verbs were used: (1) SO1O2V [IP NP-NOM [ VP NP-DAT [ VP′
NP-ACC V]]] canonical; (2) SO2O1V single-scrambled; and (3) O1O2SV doublescrambled order. First-pass reading times indicated that the third noun phrase
immediately before the verb in both single- and double-scrambled sentences
required longer reading times than canonical sentences. Tamaoka et al. (2014)
proposed the possibility that a single filler-gap dependency could be resolved via
pre-head processing by enabling language users to incrementally construct syntactic structures without seeing the head verb (e.g., Kamide and Mitchell 1999,
Miyamoto and Takahashi 2004). However, like Mazuka et al. (2002), Tamaoka etal. (2014) also compared different types of nouns (proper noun vs. common noun)at the crucial NP position before the verb; the nouns in NPs before the final verb
were common nouns for the canonical order but proper nouns for short- and longdistance scrambling. Therefore, although both Mazuka et al. (2002) and Tamaoka
et al. (2014) revealed a similar result, this claim should be investigated further withconsistent noun types in the crucial position.
The focus of Tamaoka et al. (2014) mainly centered on head-driven processing(Pritchett 1988, 1991). They investigated how participants analyze the structure
after seeing a sentence-final verb. Their claim was that pre-head processing is not
sufficient to process a double-scrambled sentence. Instead, a sentence involving
multiple scrambling requires the information encoded in the head-final verb toAn Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   39resolve double scrambling (two movements). In fact, Tamaoka et al. (2014) directlycompared single-scrambled sentences with SO2O1V orders (e.g., [IP NP-NOM[ VP′ NP-ACC1 [ VP′ NP-DAT [ VP t1V]]]) with double-scrambled sentences withO1O2SV orders (e.g., [IP′′ NP-DAT1 [IP′ NP-ACC2 [IP NP-NOM [ VP′ t1 [ VP
t2V]]]). Because both sentence types have the same nouns in the same region(e.g., Satoo-san-ga kaban-o Suzuki-san-ni azuke-ta ‘Mr. Satoo left (his) bag to
Mr. Suzuki’ vs. Satoo-san-ni kaban-o Suzuki-san-ga azuke-ta ‘Mr. Suzuki left (his)bag to Mr. Satoo’), they could directly compare noun phrases of two single- anddouble-scrambled orders. Re-reading times showed that all noun phrases, including the crucial phrase before the verb in double-scrambled sentences, required
longer re-reading times than noun phrases in the same regions in single-scrambled
sentences.
Head-driven processing (Pritchett 1988, 1991) suggests that syntactic phrasal
structures are established by the head verb with argument information. To explain
scrambling effects, Tamaoka et al. (2005) proposed that neither thematic roles
nor case particles can provide fully satisfactory information for phrase order:
only grammatical functions offer satisfactory information in canonical orders for
active, passive, potential, and causative sentences. Because grammatical functions
basically imply the agreement information provided by the sentence-final verb,
head-driven processing might better explain the processing of scrambling in headfinal languages, such as Japanese. Also using eye-tracking, Tamaoka et al. (2014)
suggested that a sentence with two filler-gap dependencies in Japanese was likely
solved using verb information via head-driven processing. Evidence for this headdriven model was also found in Ikuta et al. (2009), in which functional magnetic
resonance imaging (fMRI) showed activation differences between canonical andscrambled Japanese sentences at the head verb in brain areas related to syntactic
processing. Similarly, a study using event-related potentials (ERPs) (Woff et al.
2008) indicated processing difficulty in scrambling sentences at the sentence-finalverb.
Witzel and Witzel (2016), using the Maze Task (Forster 2010), controlled
the noun types to be proper first names. Their results supported the pre-headprocessing account; however, their items included an adverb region that might
have acted as a cue that could elicit the anticipation of a verbal head based on collocation (e.g., Ellis et al. 2009). Thus, the information provided by this cue might
initiate the filler-gap integration mechanism in combination with case-marking
cues. Accordingly, it is unclear which cues were responsible for the pre-verbal
difficulties.
3. Eye-tracking measurements and possible indices for movement
During reading, there are two basic components of eye movements: saccades andfixations (Rayner 2009). Eye-tracking provides a detailed method to observe thecomplex cognitive activity of reading. This method has been frequently used inexperiments that involve the processing of complex phrasal structures, such as
ambiguous sentences (e.g., Binder et al. 2001, Clifton et al. 2007, Frazier and40   Katsuo Tamaoka and Michael P. Mansbridge

Rayner 1982, Rayner et al. 1983, Rayner and Frazier 1987, 1989).
Reading time measures in eye-tracking can be collected for each phrase of thesentence. The measurements for early processing are first-fixation time and first-passtime. These early measurements generally are sensitive to the early stages of sentence processing, such as lexical access and early integration of information. Firstfixation time refers to the very first fixation made in an interest region from which
the region was first visually entered. First-pass reading time is composed of all
fixations made within an interest region from when an eye movement first enteredfrom the left until it exits in either direction. First-pass times are the essential
index for the early stage of sentence processing. The late processing measurementsof eye-fixation durations are re-reading time and go-past time (or regression-path
duration). The late measurements are the late stages of sentence processing, such as
structural re-analysis, recovery from processing difficulties and discourse integration (Rayner 2009). Go-past time is the total reading time for an interest region
before it is exited to the right for the first time; it also includes any regressive readings out of the region to the left before going right.
For example, in a three-phrase sentence with SOV or OSV order (S or O as
in Region 1 and/or Region 2 and V as in Region 3), although there is no region
to the right of a verb in Region 3, go-past time at the end of a sentence (a verb inJapanese) would be a useful index for understanding total reading time, including
the time spent within and moving out of that region (i.e., the sentence wrap-up
effect). Thus, the difference in go-past time with null difference in first-pass timein Region 2 could be an important index (or measurement) to judge pre-headanticipatory processing, used for processing scrambled OSV ordered sentences.
Re-reading time is the sum of all fixations after the first-pass for an interest region.
Re-reading time in Region 2 is especially important for identifying post-head processing (after participants see the verb). The total measurement in each region is
dwell time, which is the sum of all fixations made in an interest region. Accordingly,
dwell time is composed of both early and late measurements. Regressions are saccades that move backward. Two important regression measurements are regressionin and regression-out. Regression-in expresses whether the participant regresses
back into the region. Regression-out indicates whether there is an eye movementout of a region into a region to its left during the first-pass through a region.
4. The present investigation
While scrambling effects were observed in the aforementioned studies, they used
gap-filling as the assumed mechanism for various types of sentences with a scrambled word order, which is resolved chiefly via the so-called pre-head anticipation
processing. This anticipation processing is often used for interpreting various movement operations, including processing scrambled sentences and subject/object relative clauses. However, these studies did not precisely investigate detailed phrasal
processing by keeping all noun phrases of the same type in the same sequential
position by altering case markers and/or allowing participants to read backwhen necessary. By not doing so, the filler-gap dependency model has remainedAn Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   41a hypothetical explanation. Therefore, we investigated, using two eye-tracking
experiments, the detailed processing of OSV single-scrambled simple sentencesin Experiment 1, and short-distance and long-distance scrambling in complex
sentences in Experiment 2. Using ditransitive verbs, which allow all arguments
to be proper nouns, all nouns were kept the same across NP positions in stimulus
sentences, with only case marking varying between NP pairs. While this condition would change the meaning between sentences, it is only a subtle change since
the noun type is the same. Experiment 1 used simple SOV and OSV sentenceswith two high-frequency first name proper nouns (e.g., Kenta, Naoko) and a verb.
Although some degree of the pre-head processing is involved in the processing ofscrambled sentences in Japanese, the agreement information from the head verb
will play an important role in forming the final syntactic structure. Because thesentences in the present experiments no longer contain strong anticipatory cues,
the verb argument information might be necessary for the correct interpretationof the phrasal relation. In such a case, the indices of re-reading times and possibly
regression-in/-out frequencies will provide evidence for head-driven processing.
Experiment 2 used scrambled sentences embedded in complex sentencesin the three conditions of canonical [S [SOV] V], short-distance scrambling
within an embedded sentence [S [O1 S gap1 V] V], and long-distance scrambling
beyond an embedded sentence [O1 S [S gap1V] V]. For the processing of complex
sentences with short- and long-distance scrambling, we predict that, despite thecomplex sentence structure, the general processing pattern will be similar to that of
the simple sentence.
5. Experiment 1
5.1. Methods
5.1.1. Participants
Forty-two native Japanese speakers were recruited from Nagoya University inJapan. Four participants were excluded due to excessive eye-tracking errors, leaving 38 participants (N = 38; Female = 24). The ages ranged from 18 years and 3
months old to 23 years and 3 months old. The mean age of the participants was
19 years and 3 months old. All participants received monetary compensation in
exchange for their participation and provided written informed consent. All collected information was stored in a secure location, and the participants were given
numerical pseudonyms to ensure privacy.
5.1.2. Materials
Thirty-six canonical and scrambled experimental item pairs were created. Each
item contained two NPs and a verb, and each item came in two variants: a canonical SOV order and a corresponding scrambled OSV counterpart. The nouns of
two NPs were chosen from a list of the top ten most frequently used first names
given to newborn babies between 1980 and 2014 (http://www.tonsuke.com/nebin.
html retrieved on April 15, 2015, Web accessibility checked in March 2019). An
example set of SOV- and OSV-ordered sentence pairs is presented below.

42   Katsuo Tamaoka and Michael P. Mansbridge(1)		a.		 SOV canonical order					 b. OSV scrambled order				 Region 1		 Region 2 Region 3		 Region 1 Region 2			 Region 3
				Kenta-ga		 Naoko-o		 sasot-ta			 Kenta-o Naoko-ga			 sasot-ta
				NP-NOM NP-ACC V-PAST		 NP-ACC1 NP-NOM gap1 V-PAST			‘Kenta invited Naoko.’						‘Naoko invited Kenta.’

The Regions in (1a) and (1b) refer to the visual area where the eye-tracker
measures eye-fixations and movements. Based on the SOV canonical ordered sentence in (1a), a scrambled ordered sentence was created by moving the accusativemarked (-o) NP to the front of sentence (1b), resulting in OSV scrambled order [IP′NP-ACC1 [IP NP-NOM [ VP gap1 V]]] with a single gap. As shown in the example
set (1a–b), the names Kenta and Naoko were kept in the same position (i.e., region).
With this manipulation, each region of the two NPs can be directly comparedusing multiple indices of eye-tracking. In the OSV scrambled sentence, the gap
is placed between Region 2 and Region 3. The experimental items (36 canonicalsentences and 36 scrambled sentences; stimulus items in Appendix 1) were counterbalanced with an additional 112 filler items to ensure that participants would
only see one condition of each item per experimental session. All experimental
sentences for Experiment 1 are downloadable from the web site http://tamaoka.
org/en/scholarly/ under article #169 [accessed March 2019]. The 112 filler items
include various sentences: those with manner or resultative adverbs, numericalclassifiers, and subject/object-modified relative clauses.
5.1.3 Procedure
The present study employed a verification task. A participant was instructed toread the target sentence at his or her natural pace, such as Tomoko-ga Tatoo-ohometa ‘Tomoko praised Taro’. Once they finished reading and comprehending
the sentence (within a time limit of 8,000 ms), the participants were instructed topress any button on a gamepad to replace the sentence with a verification question,such as ‘Did Taro praise?’ The participant answered the question by pressing either
a TRUE- or FALSE-marked button on the gamepad. In this case, the correct
answer is FALSE because Taro was the person praised. All questions were constructed in canonical order. The participant’s right eye was periodically calibrated
to the eye-tracker camera using a 9-point calibration and validation method.
Before the display of an experimental trial, a drift-checking mask was presented on the far-left center of the screen, indicated by ‘◎’. Once a participant
accurately fixated on the mask, the experimenter then accepted the fixation toallow the presentation of a trial item that replaced the mask. Stimulus sentenceswere displayed horizontally on the center-left of a 17-inch LCD monitor. All
characters were displayed in Japanese (MS Gothic 30 pt). Eye movements wererecorded using an EyeLink 1000 Core System (SR Research Ltd., Ontario,
Canada). Eight practice items were given prior to the experiment proper to ensurethat the participants understood the task.

An Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   435.2. Results
5.2.1. Data and analysis
All fixations shorter than 80 ms were merged into a neighboring fixation within a
one-radian distance, and the remaining fixations shorter than 80 ms and fixations
exceeding 1,000 ms were removed (796 fixation points or 6.83% of all fixations).
This adjustment was performed to remove points representing eye-tracker loss,
blinks or moments of non-reading. Data outliers (reading time data only) were
trimmed based upon ± 2.5 standard deviations of the predicted model, which
resulted in the elimination of 1.54% of the data; see below for data analyses. The
means and standard errors of the reading times and the regression-out/-in ratios
are reported in Table 1.
The collected reading times and binomial data (i.e., accuracy and regressionout/-in) were analyzed using a linear mixed effect model (Baayen et al. 2008)
and the lme4 package (Bates et al. 2014) within R (R Core Team 2014). For
every analysis, the fixed effect was sentence type (canonical SOV word order vs.
scrambled OSV word order). The random effects were participants and items(random intercept and slopes). In addition to accuracy, all other analyses were
performed only using data from trials with correct judgments. Reading times weretransformed using natural logarithms and analyzed with the lmer function withmaximum likelihoods. Satterthwaite’s approximations were used via the lmerTest
package to generate p values for each model (Kuznetsova et al. 2014). For binomial
data, the glmer function (logit link function) was used to calculate the z distribution using maximum likelihoods and Laplace approximations. The detailed resultsof the linear mixed effect modeling are shown in Table 2.
5.2.2. Overall sentence processing
The total reading time for whole sentences revealed that SOV canonical sentenceswere read faster than scrambled OSV sentences (p < .001). Additionally, SOV
sentences had significantly higher judgment accuracies than OSV scrambled sentences (p < .001).
Region 1 (NP-NOM vs. NP-ACC)
In the first region of the sentence, there were no significant differences
between conditions in first-fixation time (p = .45), first-pass (p = .76), re-reading
time (p = .39), dwell time (p = .84), or regression-in ratio (p = .67).
Region 2 (NP-NOM vs. NP-ACC) – Crucial NP for OSV Scrambled Sentences
There were no significant differences between SOV and OSV sentences infirst-fixation duration (p = .30) or first-pass time (p = .96). However, the go-pasttime (p < .01) between the two sentence types was significant, suggesting thatparticipants were likely to spend longer in Regions 1 and 2 before going beyond
Region 2 in scrambled sentences. Re-reading time (p < .001) in Region 2 showeda significant difference; OSV scrambled sentences had longer re-reading times
than SOV canonical sentences. In addition, eye movements (or saccades) were44   Katsuo Tamaoka and Michael P. Mansbridge

significant in their frequency ratios for regression-in (p < .001) into Region 2 fromthe verb of Region 3, showing that OSV scrambled sentences more frequently
involved looking back to Region 2 than SOV canonical sentences. Because theverb is the only sentence element that follows the NP in Region 2, this regressionin is only possible from the sentence-ending verb into the NP in Region 2. The
dwell time (p < .001) in Region 2 was also significant, reflecting the significant
re-reading time result.
Table 1. Means and standard errors for SVO and OSV ordered sentences
Eye-tracking indexes

Canonical SOV

Scrambled OSV

M

SE

M

SE

25
1.1%

2,035
72.5%

32
1.7%

8.45E-08
6.63E-07

3
8
14
17
0.01

205
412
568
910
0.86

4
10
18
20
0.02

.454
.758
.386
.836
.665

3
8
20
14
20
0.02
0.02

220
331
780
636
780
0.49
0.32

3
10
26
19
26
0.02
0.02

.297
.960
.005
6.56E-06
1.57E-08.081
1.43E-05

5
7
26
14
10
0.01

234
273
1,201
322
373
0.88

5
7
35
18
12
0.02

.087
.221
.002
.316
.021
.213

Whole sentence
Total time
1,819
Accuracy
90.6%
Region 1: NP-NOM or NP-ACC
First-fixation time
200
First-pass time
410
Re-reading time546
Dwell time896
Regression-in
0.89
Region 2: NP-NOM or NP-ACC
First-fixation time
223
First-pass time325
Go-past time651
Re-reading time489
Dwell time
653
Regression-out
0.43
Regression-in
0.19
Region 3: Verb
First-fixation time
228
First-pass time271
Go-past time
1,061
Re-reading time294
Dwell time
345
Regression-out
0.91

Note: N=38. SE=standard errors. Bold numbered p values indicate significance.

p

Region 3 (Verb)
There were no significant differences in the reading of the head verb between
SOV and OSV sentences in first-fixation duration (p = .09), first-pass time (p =.22), and re-reading time (p = .32); however, dwell time was significant (p < .05),
showing that OSV sentences were read for a longer time than SOV sentences.
Additionally, go-past time (since nothing comes after the verb, this time is composed of first-pass reading time at the verb plus any fixation duration after leaving
the verb for all regions) was also significant (p < .01), indicating that OSV sentences were longer than SOV sentences. Nevertheless, regression out (p = .23) was
not significant.

An Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   45Table 2. Results of linear mixed effect (LME) modeling
Eye-tracking Indexes

Estimate

Whole sentence
Total reading time
0.11
Accuracy-1.99
Region 1: NP-NOM or NP-ACC
First-fixation time
0.02
First-pass time
0.01
Re-reading time
-0.04
Dwell time
-0.01
Regression-in-0.17
Region 2: NP-NOM or NP-ACC
First-fixation time
-0.03
First-pass time
0.00
Go-past time
0.15
Re-reading time
0.24
Dwell time
0.24
Regression-out
0.30
Regression-in
0.72
Region 3: Verb
First-fixation time
0.06
First-pass time
0.05
Go-past time
0.11
Re-reading time
0.08
Dwell time
0.11
Regression-out
-0.43

SE

DF

t/z value

p

0.02
0.40

39
1,368

6.59
−4.97

***
***

0.02
0.03
0.05
0.04
0.40

36
32
37
38
1,112

0.76
0.31
-0.88
-0.21
-0.43

0.02
0.04
0.05
0.05
0.03
0.17
0.17

34
39
23
38
40
1,108
1,108

-1.06
0.05
3.12
5.22
7.06
1.74
4.34

0.03
0.04
0.03
0.07
0.05
0.34

30
38
34
31
27
838

1.77
1.25
3.35
1.02
2.45
-1.25

**
***
***
***

**
*

Note: N=38. * p<.05. *** p<.001. SE=standard error. DF=degree of freedom.

5.3. Discussion
Experiment 1 indicated a clear difference in processing between SOV canonicaland OSV scrambled sentences. As indicated by previous studies (e.g., Koizumi
and Tamaoka 2004, 2010, Mazuka et al. 2002, Miyamoto and Takahashi 2004,
Tamaoka et al. 2005, 2014), SOV canonical sentences were processed more quickly
and accurately than their OSV counterparts. The involvement of pre-head incremental processing (e.g., Aoshima et al. 2004, Aoshima et al. 2009, Kamide et al.
2003, Kamide and Mitchell 1999, Miyamoto 2006, Mazuka et al. 2002, Witzel
and Witzel 2016) was indicated by the go-past time in the crucial NP of Region
2. However, we consider this processing to be minimal since multiple indices haveclearly indicated heavy head-driven processing (Ikuta et al. 2009, Woff et al. 2008).
The re-reading time for the crucial NP (NP-NOM) in OSV scrambled sentencesin Region 2 was significantly longer than for the NP (NP-ACC) with the samenoun in SOV canonical sentences. Since the gap in OSV scrambled sentences isplaced between the crucial NP in Region 2 and the head verb in Region 3, the re-46   Katsuo Tamaoka and Michael P. Mansbridge

reading time suggested that the participants read back to the crucial NP in Region
2 after seeing the head verb. This trend was further supported by the significantly
higher regression-in frequency for OSV scrambled sentences in Region 2 from theverb. In summary, although the word order of NP-ACC(-o) and NP-NOM(-ga)
will trigger, to an extent, pre-head processing for preparing to establish the dependency between the NP-ACC filler and its gap located close to NP-NOM, theOSV sentence with a single instance of scrambling was basically read up to thehead verb and then read back to the crucial NP closest to the gap. We suggest that
this step of reading backwards resolved the filler-gap dependency.
6. Experiment 2
6.1. Methods
6.1.1. Participants
Fifty-four native Japanese speakers were recruited from a university in Japan; none
participated in Experiment 1. Due to eye-tracking errors, eight participants were
removed (N = 46; Female = 24). The mean age of the participants was 19 years
and 8 months old, ranging from 18 years and 3 months to 20 years and 7 months.
The participation consent, compensation and privacy measures were the same as
Experiment 1.
6.1.2. Materials
After embedding the stimulus sentences used in Experiment 1 into complex
sentences, 36 pairs of canonical, short-scrambled and long-scrambled complex
sentences were created. Each item contained three NPs and two verbs. As with
Experiment 1, the three NPs were obtained from a top ten list of frequently usedbaby names in Japan. An example set of the three conditions is as follows.
(2)		a.		[S[SOV]V] complex canonical ordered sentence
Region 1		Region 2		Region 3		Region 4				Region 5
Kenji-ga		 Masato-ga Keiko-o			tasuketa				to			 kii-ta
NP-NOM NP-NOM NP-ACC		 V(help)-PAST Comp V(hear)-PAST‘Kenji heard that Masoto helped Keiko.’		b.		[S[O1Sgap1V]V] short-distance scrambling ordered sentence
Region 1		Region 2		Region 3				Region 4			Region 5
Kenji-ga		 Masato-o Keiko-ga				tasuketa			 to			 kii-ta
NP-NOM NP-ACC1 NP-NOM gap1 V(help)-PAST Comp V(hear)-PAST‘Kenji heard that Keiko helped Masoto.’
		c.		[O1[S[Sgap1V]V]] long-distance scrambling ordered complex sentence
Region 1		Region 2		Region 3				Region 4			 Region 5
Kenji-o		Masato-ga Keiko-ga				tasuketa			 to		 kii-ta
NP-ACC1 NP-NOM NP-NOM gap1 V(help)-PAST	  Comp V(hear)-PAST‘Masato heard that Keiko helped Kenji.’

An Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   47Each Region in (2a–c) referred to the visual area where the eye-tracker measured eye fixations and movements. Based on the canonically ordered complex
sentence in (2a), a short-distance scrambled sentence (2b) and a long-distancescrambled sentence (2c) were created by moving the accusative-marked (-o) NP.
As shown in the example set (2a–c), the three first names Kenji, Masato and Keiko
were kept in the same positions (or the same regions) across all sentences. Due tothis manipulation, these three NP regions can be directly compared using multiple
indices of the eye-tracker. In both short-distance and long-distance scrambled
sentences, the gap was placed in the same position between Region 3 and Region
4 in the subordinate clause. The experiment included 104 filler items and 36 sets ofcomplex sentences (36 canonical, 36 short-distance and 36 long-distance; stimulus
items in Appendix 2). All experimental sentences for Experiment 2 are downloadable from the web site http://tamaoka.org/en/scholarly/ under article #169. These
items were placed into three counterbalanced lists to ensure that the participants
would only see one condition of each item per experimental session. The filler
items included sentences with numerical classifiers, ga-no conversion and relative
clauses.
6.1.3. Procedure
The procedure of Experiment 2 was the same as that of Experiment 1. As with
Experiment 1, eight practice items were given to the participants in Experiment 2.
6.2. Results
6.2.1. Data and analysis
Data editing was the same as for Experiment 1. In Experiment 2, 1,938 fixation
points (7.52% of all fixations) were cleaned, and 1.53% of the reading time data
were removed as data outliers. For the main effects in the LME models, the conditions were coded as a continuous variable: the canonical condition was coded as 1,
short-scrambling was 2, and long-scrambling was 3. In other words, we predicted
a linear relationship between these variables with the hypothesis that the canonicalcondition would be the easiest to parse, the short-scrambling condition more difficult and the long-distance scrambling condition engendering the most processing
difficulty. For the simple comparisons between the conditions, the three conditions were recoded as categorical. Following the initial LME analysis, a post hoc
multiple comparison (Tukey’s contrasts) was conducted for each model to provide
simple comparisons between conditions. We do not believe that this approach
would differ substantially were the conditions releveled within the initial LME
analysis. The means and standard errors for reading times and regression-out/-inratios in each region are reported in Table 3. The data analyses were the same as forExperiment 1. The detailed results of the linear mixed effect model are shown in
Table 4.
6.2.2. Overall sentence processing
The main effect of sentence type was significant for total reading time (p < .001)

48   Katsuo Tamaoka and Michael P. Mansbridge

and accuracy (p < .001). Multiple comparisons revealed that canonical complex
sentences had shorter reading times than complex sentences with short-distancescrambling. Sentences with short-distance scrambling, in turn, had shorter reading
times than sentences with long-distance scrambling (see Table 3 for details of themeans and Table 4 for the results of Tukey’s contrasts). The canonical sentenceswere much more accurately processed than those with both short-distance andlong-distance scrambling. Although the processing speed was slower for the sentences with long-distance scrambling, the accuracy results did not differ between
sentences with short-distance and long-distance scrambling.
6.2.3. Main effects throughout three complex sentence types
The scrambling effect was analyzed with the main effects in the five regions. Main
effects were found in all five regions. In the first NP of Region 1, re-reading time(p < .001), dwell time (p < .001) and regression-in (p < .001) were all highly significant. In the second NP of Region 2, go-past time (p < .05), re-reading time (p< .001), and dwell time (p < .001) were significant. As with Region 1, re-reading
time (p < .001), dwell time (p < .001), and regression-in (p < .001) were highly
significant in the third NP of Region 3. In Region 4, where the verb in the subordinate clause is located, go-past time (p < .001), re-reading time (p < .001), anddwell time (p < .001) were highly significant. In the main clause verb of Region 5,
go-past time (p < .001) was highly significant. However, these main effects only
indicate the overall differences among the three sentence types. Thus, multiple
comparisons were performed on the three sentence types after these significant
main effects were discovered.

An Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   49Table 3. Means of canonical, short-distance scrambling and long-distance scrambling
Eye-tracking indexes

Canonical

M

SE

Whole sentence
Total time
3,263
46
Accuracy
91.2%
1.2%
Region 1: NP
First-fixation time
208
3
First-pass time406
10
Re-reading time471
18
Dwell time
720
17
Regression-in
0.69
0.02
Region 2: NP
First-fixation time229
4First-pass time358
9Go-past time549
17
Re-reading time882
22
Dwell time
1,208
23
Regression-out
0.27
0.02
Regression-in
0.89
0.01
Region 3: NP
First-fixation time
225
3
First-pass time324
8Go-past time617
24
Re-reading time605
18
Dwell time849
19
Regression-out
0.34
0.02
Regression-in
0.32
0.02
Region 4: Verb in the subordinate clauseFirst-fixation time
224
3
First-pass time296
6Go-past time
1,340
44
Re-reading time346
14
Dwell time475
12
Regression-out
0.74
0.02
Regression-in
0.04
0.01
Region 5: Verb in the main clauseFirst-fixation time
197
6
First-pass time204
7
Go-past time
1,693
82
Re-reading time179
12
Dwell time237
10
Regression-out
0.94
0.02

Short-distanceM

SE

Long-distance
M

SE

3,598
75.2%

62
1.9%

4,408
73.5%

77
2.0%

2.66E-15
1.84E-07

201
390
546
818
0.75

3
10
20
22
0.02

212
417
763
1,100
0.91

4
12
23
26
0.01

.538
.513
2.71E-10
1.51E-14
1.01E-11

226
326
515
1,025
1,307
0.27
0.85

4
9
17
28
29
0.02
0.02

227
357
598
1,138
1,437
0.33
0.85

4
11
21
33
34
0.02
0.02

.435
.908
.019
1.13E-05
1.47E-04
.077
.156

221
313
676
765
992
0.33
0.34

4
7
33
23
23
0.02
0.02

232
307
654
974
1,223
0.30
0.51

4
8
36
31
32
0.02
0.03

.378
.471
.456
1.34E-10
5.44E-10.206
9.07E-07

223
303
1,599
398
496
0.70
0.05

4
8
66
18
15
0.02
0.01

233
310
2,099
509
646
0.77
0.05

4
7
81
22
20
0.02
0.01

.146
.110
6.85E-06
6.73E-11
5.11E-09
.434
.614

188
191
2,051
194
228
0.94

7
8
103
15
11
0.02

200
208
2,810
212
251
0.96

9
11
155
21
14
0.02

.613
.442
2.96E-05
.464
.82
.407

p

Note: N=46. Bold p values indicate statistical significance. M=mean. SE=standard error.

50   Katsuo Tamaoka and Michael P. Mansbridge

Table 4. Results of linear mixed effect (LME) modeling and multiple comparisons
Eye-tracking indexes Estimate

SE

Whole sentence
Total reading time
0.15
0.01
Accuracy-0.57
0.11
Region 1: NP
First-fixation time
0.01
0.01
First-pass time
0.01
0.02
Re-reading time
0.26
0.03
Dwell time
0.21
0.02
Regression-in
0.79
0.12
Region 2: NP
First-fixation time
-0.01
0.01
First-pass time
0.00
0.02
Go-past time
0.05
0.02
Re-reading time
0.13
0.03
Dwell time
0.08
0.02
Regression-out
0.14
0.08
Regression-in
-0.21
0.15
Region 3: NP
First-fixation time
0.01
0.01
First-pass time
-0.01
0.02
Go-past time
-0.02
0.03
Re-reading time
0.25
0.02
Dwell time
0.18
0.02
Regression-out
-0.12
0.09
Regression-in
0.46
0.09
Region 4: Verb in the subordinate clauseFirst-fixation time
0.02
0.01
First-pass time
0.02
0.02
Go-past time
0.20
0.04
Re-reading time
0.18
0.03
Dwell time
0.15
0.02
Regression-out
0.08
0.10
Regression-in
-0.15
0.30
Region 5: Verb in the main clauseFirst-fixation time
-0.01
0.02
First-pass time
-0.02
0.03
Go-past time
0.25
0.05
Re-reading time
0.04
0.06
Dwell time
-0.01
0.03
Regression-out
0.27
0.32

DF

t/z valuep

C vs. S S vs. L C vs. L

45
11.7
1,592 −5.215

***
***

C<S
C>S

S<L
S=L

C<L
C>L

***
***
***

C=S
C=S
C<S
C<S
C=S

S=L
S=L
S<L
S<L
S<L

C=L
C=L
C<L
C<L
C<L

C=S
C=S
C=S
C<S
C=S
C=S
C=S

S=L
S=L
S<L
S<L
S<L
S=L
S=L

C=L
C=L
C<L
C<L
C<L
C=L
C=L

C=S
C=S
C=S
C<S
C<S
C=S
C=S

S=L
S=L
S=L
S<L
S<L
S=L
S<L

C=L
C=L
C=L
C<L
C<L
C=L
C<L

C=S
C=S
C=S
C<S
C=S
C=S
C=S

S=L
S=L
S<L
S<L
S<L
S=L
S=L

C=L
C=L
C<L
C<L
C<L
C=L
C=L

C=S
C=S
C<S
C=S
C=S
C=S

S=L
S=L
S<L
S=L
S=L
S=L

C=L
C=L
C<L
C=L
C=L
C=L

59
48
40
47
1,266

0.619
0.659
8.362
10.92
6.805

674 -0.781
29 -0.116
43 2.448
34 5.142
37 4.227
1,275 1.767
1,275 -1.419
44 0.891
30 -0.731
35 -0.754
22 11.18
34 8.534
1,274 -1.265
1,274 4.911
46 1.478
972 1.601
34 5.313
584
6.65
42 7.315
1,191 0.782
1,191 -0.505
107 -0.507
105 -0.772
23 5.168
57 0.737
27 -0.229
395
0.83

*
***
***

***
***
***

***
***
***

***

Note: N=46. * p<.05. ** p<.01. *** p<.001. SE=standard error. C=canonical. S=short-distancescrambling. L=long-distance scrambling.

An Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   516.2.4. Comparisons of sentences between canonical order and short-distancescrambling
Differences in sentence processing were first examined by comparing two types ofcomplex sentences with canonical and short-distance scrambling. Refer to Table
4 for the comparison results of canonical and short-distance scrambling (C vs. S).
Region 1 (NP-NOM vs. NP-NOM)
At the reading of the first region of the sentence, the later-processing index
of re-reading time was significant; sentences with short-distance scrambling had
longer re-reading times than canonical sentences. Additionally, dwell time was alsosignificant; sentences with short-distance scrambling had longer dwell times thancanonical sentences. Dwell time is the total duration of all fixations in this region
including first-pass times and re-reading times. However, since the first-pass time
was not significant, it only reflects later processing stages.
Region 2 (NP-NOM vs. NP-ACC)
In Region 2, re-reading time was significant. Sentences with short-distancescrambling had longer re-reading times in this region than canonical sentences.
The significance of dwell time also confirmed this result. Additionally, no differences were found in dwell time, regression-in or regression-out.
Region 3 (NP-ACC vs. NP-NOM) Crucial NP for Short-distance Scrambling
The crucial NP (assumed to include the gap at the same region) is located in
Region 3. Re-reading time significantly differed between canonical sentences andshort-distance scrambling sentences. Additionally, dwell time revealed a similar pattern between canonical sentences and sentences with short-distance scrambling.
Region 4 (Verb in the Subordinate Clause)
At the subordinate clause verb, only the re-reading index revealed a difference
between these two conditions: short-distance scrambling sentences required longer
re-reading times than canonical sentences.
Region 5 (Verb in the Main Clause)
Here, the only difference was found during go-past time. Canonical sentenceswere shorter than short-distance scrambled sentences.
6.2.5. Comparisons between canonical/short-distance and long-distancescrambling
The processing of complex sentences with long-distance scrambling was further investigated by comparing these sentences to their corresponding canonicalsentences and sentences with short-distance scrambling. Multiple comparisons(Tukey’s contrasts) in sentence processing were performed between canonical andlong-distance scrambling (C vs. L) and between short-distance and long-distancescrambling (S vs. L). Please refer to the comparison results in Table 4.

52   Katsuo Tamaoka and Michael P. Mansbridge

Region 1 (the first NP)
In complex sentences with long-distance scrambling, the NP in Region 1 isthe filler NP (NP-ACC) that removed from the NP in the subordinate clause (i.e.,
syntactic movement). This gap (or trace) is placed between the NP of Region 3 andthe verb of Region 4. Re-reading time significantly differed between canonical andlong-distance scrambling and between short-distance and long-distance scrambling. Furthermore, regression-in showed differences between canonical and longdistance scrambling and between short-distance and long-distance scrambling.
Dwell time also differed significantly among the three types of complex sentences,reflecting differences in reading time.
Region 2 (the second NP)
Differences were found in the go-past time between canonical and longdistance scrambling and between short-distance and long-distance scrambling.
Differences were also seen in the re-reading time between canonical and longdistance scrambling and between short-distance and long-distance scrambling.
In addition, a difference in dwell time was found between canonical and longdistance scrambling and between short-distance and long-distance scrambling.
Participants read the second NP in complex sentences with long-distance scrambling much more slowly than with canonical and short-distance scrambled
sentences.
Region 3 (the third NP)
Region 3 is the crucial NP region assumed to include the gap. As with shortdistance scrambling in simple OSV sentences from Experiment 1, re-reading
time clearly indicated significant differences between canonical and long-distancescrambling and between short-distance and long-distance scrambling; complex
sentences with a long-distance scrambling required longer re-reading times thanthe other two sentence types. Significance in regression-in frequency ratios among
the three sentence types also indicated that participants read back into this crucial
NP-NOM region from either the verb of the subordinate clause or the verb of themain clause.
Region 4 (Verb in the Subordinate Clause)
Go-past time, re-reading time and dwell time significantly differed between
canonical sentences and sentences with long-distance scrambling, as well as
between sentences with short-distance and long-distance scrambling (see Table 4for details). All indices indicated that longer reading times were needed to process
the verb in Region 4 within long-distance scrambling sentences.
Region 5 (Verb in the Main Clause)
Differences were found for re-reading times in complex sentences between
canonical and long-distance scrambling. Given that go-past time was the only
index to show a difference in processing cost for long-distance scrambling at theAn Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   53main-clause verb in complex sentences, it must show that participants did notneed to spend any extra time understanding the main-clause verb itself. Despite
having already spent more time processing the previous three NPs and the subordinate-clause verb prior to reaching the final main-clause verb (see Region 4
go-past), participants spent a considerable amount of time revisiting the prior four
regions after seeing the subordinate-clause verb, which might reflect the global
comprehension difficulty associated with long-distance scrambling.
6.3. Discussion
Experiment 2 compared the processing of complex sentences with three different word orders: canonical, short-distance scrambling, and long-distance scrambling. For the reading time of the whole sentence, Experiment 2 clearly indicatedthat canonical complex sentences were more quickly and accurately processedthan their corresponding counterparts with short- or long-distance scrambling.
Moreover, complex sentences with short-distance scrambling were processed more
quickly than their long-distance counterparts. First, when complex sentences withshort-distance scrambling were compared to their canonical counterparts, it was
revealed that subordinate clauses with short-distance scrambling had significantly
longer re-reading times at the crucial NP (NP-NOM) in Region 3. Recall that
Region 3 contains the gap site immediately before the verb, consistent with theresults of Experiment 1. Second, complex sentences with long-distance scrambling
showed even longer re-reading times and frequent regressions-in, compared with
their counterparts with short-distance scrambling. Even greater differences were
found when scrambled sentences were compared with sentences with canonicalword order. These results were found across multiple eye-tracking measurements.
Only minimal evidence was found between short-/long-distance scrambling andtheir canonically ordered counterparts for first fixation, first-pass time or go-pasttime in the first NPs. Therefore, as indicated by the previous eye-tracking study of
double-scrambled sentences with ditransitive verbs (Tamaoka et al., 2014), heavy
head-driven processing is most likely involved in the processing of scrambled
complex sentences. That is, once the involvement of factors, such as noun type, thesemantic likelihood of verb-object/subject combination, and the semantic natureof the subject/object, are controlled, the results might suggest head-driven processing. Both indices of re-reading time and regression-in were found to be significant
in the crucial NP-NOM region in both short-distance and long-distance scrambling and the filler NP-ACC in long-distance scrambling. Third, two consecutive
NP-NOMs appeared in canonical sentences, while the second NP was marked
as NP-ACC for short-distance scrambling. According to Miyamoto (2002), thisword order difference was expected to induce longer reading times in Region 2 forcanonical sentences during the early stage indices of first pass time and go-pasttime, compared to short-distance scrambling sentences. However, nothing was
significantly differed between canonical and short-distance scrambling. As such, aclause boundary might not require that much of an extra cognitive load.

54   Katsuo Tamaoka and Michael P. Mansbridge

7. General Discussion
Using an eye-tracking technique, the present study aimed to clarify when andwhere filler-gap dependencies in scrambled word orders become resolved inJapanese, both before and after the participants see the head-verb. Experiment 1
focused on the processing of simple intransitive sentences, while Experiment 2
focused on the same type of simple sentences embedded in complex sentences.
These two experiments used high-frequency first names across the same NP positions in paired stimulus sentences to exclude the involvement of influences suchas noun type, semantic likelihood of verb-object/subject combination (collocation
frequency effects) and semantic nature of subject/object (e.g., animacy). This stimulus manipulation made it possible to directly compare scrambled and canonicallyordered sentences. Here, we discuss the main question of this study: at what point(or to what degree) are scrambled word orders resolved before seeing the verb (i.e.,
pre-head) and after seeing the verb (head-driven)?
7.1. Processing of scrambled simple intransitive sentences
The whole sentence processing data in Experiment 1 showed that canonicallyordered simple intransitive SOV sentences were processed more quickly and moreaccurately than OSV scrambled counterparts. Consistent with previous studies (e.g., Koizumi and Tamaoka 2004, 2010, Mazuka et al. 2002, Miyamoto and
Takahashi 2004, Tamaoka et al. 2005, 2014), the scrambling effect was observed inExperiment 1. According to the results of the eye-tracking measurements, Figure
1 depicts the processing of OSV scrambled sentences. These simple intransitive
sentences consisted of three regions: two NPs and the verb. The details of thesethree phrases (or regions) were examined using multiple indices of eye-tracking
measurements.

Figure 1. The processing of a simple scrambled intransitive sentence

No index of eye-tracking showed significance in the first NP region of
NP- ACC1. In the second NP region of NP-NOM, the go-past time was significant when comparing canonical and scrambled sentences. This significant
go-past time could imply pre-head processing that initiated the construction of
the phrasal structure of the two NPs. As depicted in Figure 1, the NP-ACC andAn Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   55NP-NOM order or the o-and-ga order in Regions 1 and 2 might initiate scrambling resolution by partially constructing the syntactic structure based on thetwo noun phrases. In this sense, we could call this time-triggered processing initiated by reading the o-and-ga order. However, in the present study, there was no
semantic contrast of subject/object, such as an animacy contrast, so the o-and-ga
order did not provide sufficient information to establish a filler-gap dependency
in the NP-NOM of Region 2. Because the nouns of NP-ACC1 and NP-NOM
consisted of popular, highly frequent first names, such as Tomoko, Taroo, and Kenji,
these nouns could not provide sufficient information to properly construct thephrasal structure, including a filler-gap dependency at the early processing stage inRegion 2; native Japanese speakers must seek the verb information in Region 3 tocomplete the relationship between the two NPs and create a filler-gap dependency.
Re-reading time and regression-in in Region 2 were highly significant (seeTables 1 and 2); native Japanese speakers must read ahead to the ending verb to
obtain the argument information to establish the relationship between two NPs.
Because the verb in Region 3 is in the only region after Region 2, it implies thatnative Japanese speakers read back to NP-NOM close to gap1 in Region 2 fromthe head verb. Participants were likely to stay in Region 2 longer at the late processing stage. Consequently, we consider pre-head processing to be insufficient,although it could be initiated by the o-and-ga order.
Importantly, the verb agreement information is required to establish a fillergap dependency to complete a whole sentence structure and understand a sentence
once all nouns are controlled. Using argument information from the verb, native
Japanese speakers read back to the crucial NP-NOM region close to gap1 toresolve the dependency of the filler NP (NP-ACC1) and gap1. Gap-filling parsing would be performed around the crucial NP-NOM region after processing thehead verb (i.e., head-driven processing).
It should be noted that, as in [φ (Watashi-wa ‘I’) Kenji-o [Masumi-ga kitatoki] yobi-yose-ta] ‘[I] called Kenji when Mayumi came’, the scrambled orderof NP-ACC (Kenji-o) and NP-NOM (Masumi-ga) could be interpreted as acomplex sentence with a subordinate clause having an empty subject (Miyamoto
2006). Since the go-past time of NP-NOM in Region 2 was significant, thisresult implies the initiation of the construction of a phrasal structure. Thus, we
assume that native Japanese speakers are unlikely to expect a relative clause by the
NP-ACC and NP-NOM order; rather, they initiate the gap-filling parsing by theo-and-ga order despite the highly frequent first names of the first two NPs used inExperiment 1.
7.2. Processing of short-distance scrambled complex sentences
The scrambling effect found with simple sentences in Experiment 1 was further
examined by comparing different types of scrambling in complex sentences inExperiment 2. Whole sentence processing in Experiment 2 indicated that canonical complex sentences were processed more quickly and accurately than theirshort-distance scrambling counterparts. The scrambling effect (e.g., Koizumi

56   Katsuo Tamaoka and Michael P. Mansbridge

and Tamaoka 2004, 2010, Mazuka et al. 2002, Miyamoto and Takahashi 2004,
Tamaoka et al. 2005, 2014) was again observed in Experiment 2.
No indices of early stage processing including go-past time showed significance in the three NPs from Regions 1-3 when compared to canonical sentences(see Table 4). As shown in Figure 2, in complex sentences with short-distancescrambling, the crucial NP (NP-NOM) closest to gap1 immediately before theverb in subordinate clauses is located in Region 3. Both NP-ACC1 in Region
2 and NP-NOM in Region 3 in short-distance scrambled sentences had significantly longer re-reading times than their corresponding canonical complex
sentences (see Table 4). This outcome suggests that Japanese speakers must readthrough from the first NP-NOM to the verb in the subordinate clause and readback to the second and third NP.
In short-distance scrambling, the early sequence of NP-NOM and NP-ACC1
in Regions 1 and 2 indicates the canonical order of a simple SOV sentence. The
following NP-NOM in Region 3 follows this order, which partially creates the
NP-ACC1 and NP-NOM or the o-and-ga order. However, unlike in Experiment
1, no significant go-past time was observed in short-distance scrambling. Given
three sequential NPs before seeing the verb, native Japanese speakers did not havesufficient information to establish both the filler and gap dependency and theclause boundary; they needed to have the verb argument information in Region
4. Consequently, they read ahead to see the verb in the subordinate clause without
initiating filler-gap parsing (i.e., no pre-head processing).

Figure 2. The processing of a complex sentence with short-distance scrambling   The re-reading time in the verb of the subordinate clause in Region 4 was significant (see Table 4), so they must read back and forth between the verb and the
crucial NP-NOM closest to gap1 and the NP-ACC1. Unlike with OSV-orderedsimple sentences shown in Figure 1, OSV-ordered subordinate clauses required
native Japanese speaker to re-read NP-ACC1, shown in Figure 2 as the extension of the re-reading time required for both the NP-ACC1 and the NP-NOM
of the subordinate clause. The verb in the main clause in Region 5 only showedsignificance in the go-past time. This go-past time in the sentence final positionindicates the overall reading time because nothing comes after it. Thus, this result
suggests a longer wrapping-up time for the processing of short-distance scrambled
sentences. Nevertheless, the basic mechanism for the processing of short-distancescrambled sentences would be quite similar to the processing of the OSV-orderedsimple sentences. After processing the head verb of the subordinate clause, native
Japanese speakers would perform gap-filling parsing (Frazier and Clifton 1989) toAn Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   57establish a filler-gap dependency within the subordinate clause to understand ascrambled intransitive sentence embedded in a complex sentence.
As shown in Figure 3, a canonical SOV-ordered intransitive sentence embedded in a complex sentence displays two consequential NP-NOMs or two subjects.
In such a case, since the second NP-NOM indicates the clause boundary between
the main and subordinate clauses, the second NP-NOM slows down to establish
the clause boundary (Miyamoto 2002). In contrast, the sequential NP-NOM
and NP-ACC order appears in a scrambled OSV-ordered intransitive sentence
embedded in a complex sentence. In this order, native Japanese speakers will wait
until seeing the third NP-NOM to establish the clause boundary. Therefore, we
expected that canonical sentences would have longer reading times in the second
NP-NOM region, compared to the same region in scrambled sentences. However,Experiment 2 demonstrated that neither first-pass time nor go-past time significantly differed between canonical and short-distance scrambling (see Table4). Thus, the detection delay for the clause boundaries (Miyamato 2002) might
require little processing effort, at least during early stage processing.

Figure 3. No extra processing load in establishing a clause boundary
in sequential NP-NOMs during early stage processing
7.3. Processing of long-distance scrambled complex sentences
Long-distance scrambling in complex sentences was examined by comparing sentences with long-distance scrambling to counterparts with either canonical word
order or an instance of short-distance scrambling. Whole sentence processing inExperiment 2 indicated that both canonical and short-distance complex sentenceswere processed more quickly than sentences with long-distance scrambling; however, only the canonical complex sentence had higher accuracy. The syntactic structure of long-distance scrambling is [IP′ NP-ACC1 [IP NP-NOM [CP [IP NP-NOM[ VP t1 V]] C] V]], consisting of three NPs and two verbs. The detailed results ofphrase-by-phrase processing indicated by eye tracking are depicted in Figure 4.

58   Katsuo Tamaoka and Michael P. Mansbridge

Figure 4. The processing of a complex sentence with long-distance scrambling
No differences were found between long-distance scrambling and shortdistance scrambling or between long-distance scrambling and canonical word
order in first fixation, first-pass or go-past time in the first NPs, except for the
go-past time in Region 2 (see Table 4). As shown in Figure 4, the long-distancescrambling in Experiment 2 displays the NP-ACC1 and NP-NOM sequence or
the o-and-ga order in Regions 1 and 2.
Here, the reader could begin to partially construct a phrasal structure toprepare for the establishment of the filler and gap dependency. This result wasconsistent with the o-and-ga order of the simple scrambled intransitive sentencesin Experiment 1 (see Figure 1). After the o-and-ga order comes NP-NOM, thesequential NP-NOMs in Regions 2 and 3 could begin to establish the clauseboundary. However, we did not find any reading delay in Region 3 at the early processing stage, which could indicate that no extra processing load is required for theclause boundary (see Figure 2). Otherwise, as with Experiment 1, highly frequent
first names of the three NPs cannot provide sufficient information to establish
both the filler-gap dependency and the clause boundary. Thus, native Japanese
speakers read ahead until they reach the verb in the subordinate clause located in
Region 4.
In contrast, the later stage processing measurements for re-reading time andregression-in were found to be highly significant, compared to sentences with bothcanonical and short-distance scrambling (see Table 4). These significances wereshown in the crucial NP-NOM region in long-distance scrambling. Therefore,
native Japanese speakers read through the sentence from the first NP-ACC1, thesecond NP-NOM, the third NP-NOM and the two verbs. Then, as shown in
Figure 4, they read back to NP-ACC1 and the crucial NP-NOM (close to thegap). This pattern is especially salient after they see the verb in the subordinate
clause (supported by the significant re-reading times in Regions 2, 3 and 4). The
filler NP-ACC1 is placed in the sentence-initial position of Region 1. Due to thislonger scrambling distance, native Japanese speakers showed a strong trend towardreading back to the filler NP-ACC1 after seeing the verb in Region 4. This processing trend was shown for two indices: re-reading time in the sentence-initial
position of the filler NP-ACC1 and regression-in into this NP region (see Table

An Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   594). It is also possible to read the final verb in the main clause before reading back,as the go-past time was significant in Region 5. However, the re-reading time atthe verb of the subordinate clause was highly significant, so native Japanese speakers probably read the verb of the subordinate clause longer to obtain the verbal
argument information required to resolve the filler-gap dependency.
7.4. Ending remarks: Mechanism for the processing of scrambled sentences
Previous studies have proposed pre-head anticipation processing for even head-final
languages such as Japanese. When the NP-ACC and NP-NOM order or theo-and-ga order was apparent, the indication of pre-head processing was foundduring the go-past time in Experiment 1 and Experiment 2 (only long-distancescrambling). Eye-tracking, however, clearly showed longer re-reading times in
the crucial NP region and frequent regression-in from the verb to the crucial
NP-NOM in Experiments 1 and 2 and the filler NP-ACC in Experiment 2.
These results indicated heavy head-driven processing. Although the gap is not visually presented in a sentence, native Japanese speakers are still able to locate thegap in a sentence. The crucial NP was located near the gap before the verb: in the
NP-NOM in Region 2 of Experiment 1 and in Region 3 of Experiment 2. To
perform gap-filling parsing, native Japanese speakers must establish a relationshipbetween the filler NP-ACC and the gap by reading back to the crucial NP after
obtaining the verb agreement information. Because all nouns in Experiments 1
and 2 were controlled as the same noun type of highly frequent first names, thehead verb argument information was necessary to construct phrasal structure. If
so, semantic factors, such as the semantic likelihood of verb-object/subject combinations and/or the semantic nature of the subject/object, might act as cues for
pre-head processing. When no such pre-head cues are available, the pre-head processing does not function appropriately. Thus, we can assume that native Japanese
speakers can perform both pre-head and head-driven (or post-head) processing,depending upon the availability of the processing cues. In other words, the relative
strength of the two sets of pre-head and head-driven processing types varies basedon the cues available.
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Appendix
All experimental sentences for Experiments 1 and 2 are downloadable from theweb site http://tamaoka.org/en/scholarly/ under article #169 [accessed March2019]. The following are the materials used to perform the experimental sentencesfor Experiment 1.
The nouns used were the following proper names: 友子, 太郎, 次郎, 順子, 和子,
and 健二. The verbs used were the following: ほめた, 助けた, 殴った, 雇った, だました,

殺した, 憎んだ, 許した, 産んだ, 信じた, 指導した, 疑った, 叩いた, 追いかけた, 尊敬した, 逃がした, 突き飛ばした, 驚かした, 蹴った, 投げ飛ばした, 刺した, 縛った, 呼び止めた, 引っ掻いた, 起こした, 誤解した, 背負った, にらんだ, 突き落とした, 見つけた, 脅した, 見送った, 捕まえた, 呼んだ, 泣かせた, and 押した.

Sentence 1 contains a scrambled variation (3b) of the canonical (3a) word
order. The interest regions are designated between asterisks.
(3)		a.		Canonical		 *友子が*太郎を*ほめた。*
		b.		Scramble		 *友子を*太郎が*ほめた。*

The following are the materials used to create the experimental sentences forExperiment 2. The nouns used the following proper names: アキラ, アケミ, アサミ, アス

カ, オサム, カズヤ, クミコ, ケイコ, ケンジ, サチコ, サトコ, シゲル, ススム, タカシ, タクマ, タツヤ, ツ
トム, ツヨシ, テツヤ,トオル,トモコ, ナオキ, ナナミ, ハナコ, ヒデキ, ヒロシ, マサト, マナブ, マユミ,
ミチコ,ミハル, メグミ, ユウコ, ユカリ, ユタカ, ユミコ, and ヨウコ.

The embedded verbs used were the same verbs as in Experiment 1. The matrix
verb phrase was always と聞いた. Sentence 1 contains both the short scrambled andlong scrambled variations of the canonical word order. The interest regions are designated between asterisks.

An Eye-tracking Investigation of Pre-head and Head-driven Processing for Scrambled Japanese Sentences   63(4)		a.		Canonical			*ケンジが*マサトが*ケイコを*助けた*と聞いた。*
		b.		Short Scrambled *ケンジが*マサトを*ケイコが*助けた*と聞いた。*
		c.		Long Scrambled *ケンジを*マサトが*ケイコが*助けた*と聞いた。*
Author’s contact information:[Received 31 December 2016;
Katsuo Tamaoka
Accepted 24 July 2018]
Graduate School of Humanities, Nagoya University
Furo-cho, Chikusa-ku, Nagoya-shi, Aichi-ken, 464-8601, Japan
E-mail: tamaoka@nagoya-u.jp

【要 旨】
日本語のかき混ぜ文の主要部前と主要部駆動処理に関する視線計測研究
玉岡 賀津雄名古屋大学

Michael P. Mansbridge
名古屋大学

動詞を読む前の予測処理がかき混ぜ文の処理に影響すると報告されている。しかし，これらの研究は，文の同じ位置で名詞を比較しておらず，名詞の種類も異なっていた。そこ
で，高使用頻度の人名を文の同じ位置に配置して，他動詞の単文とそれらを埋め込んだ複
文の 2 つの実験で，短距離・長距離のかき混ぜを句ごとに視線計測した。NP-ACC（ヲ）と
NP-NOM（ガ）が連続して現れる場合は，2 つ目の名詞句の NP-NOM（ガ）の部分で，両実験のかき混ぜ文の通過時間が有意に長くなった。これは，埋語補充解析が始まることを示唆している。しかし，その後，動詞を読んでからの再読時間と読み戻り頻度が，埋語が想定される付近の名詞句を中心に観察された。意味的な手掛かりが欠如する場合には，動詞の情報に準拠した主要部駆動処理に強く依存することが示された。手掛かりの有無によって，主要部前処理か主要部駆動処理かの依存の度合いが異なってくると考えられる。