Running head: TRIMORPHEMIC COMPOUND RECOGNITION

Visual trimorphemic compound recognition in a morphographic script

1

Koji Miwa, 2 Gary Libben, 3 Yu Ikemoto

1

Seminar für Sprachwissenschaft, Eberhard Karls Universität Tübingen, Germany

2

Department of Applied Linguistics and Department of Psychology, Brock University,

Canada3

School of Human Welfare Studies, Kwansei Gakuin University, Japan

Address for the correspondence:
Koji Miwa
Seminar für Sprachwissenschaft
Wilhelmstraße 19, Raum 3.22
Eberhard Karls Universität Tübingen
72074 Tübingen, Germany
E-mail: kojimiwa4@gmail.com
Phone: +49 7071 29-73952

Acknowledgment:This study was supported by the Izaak Walton Killam predoctoral grant from the Killam
trusts to the first author.

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Abstract

This lexical decision with eye-tracking study investigated how Japanesetrimorphemic compounds (e.g., 体温計 ‘clinical thermometer’) are recognized. The
questions answered were, in the course of decomposing and composing Japanesetrimorphemic compounds, (1) whether recognition processes are tuned for a specificbranching direction, (2) whether the morphological processing proceeds in a bottom-up
combinatorial manner, and (3) whether the three constituents of trimorphemic
compounds are equally important and processed serially. Mixed-effects regression
analyses of response times and fixation durations revealed that a left-branching advantage
appears in a late time frame and that, although there was early processing of the whole
compound from the first fixation, a character frequency effect was also observed.
Furthermore, the first and the third, but not the second, constituent frequencies
contributed to compound recognition. This bathtub-like effect was further supported bycorpus-based evidence: the conditional probability for the second constituent isincomparably high.

Keywords:

morphological processing, trimorphemic compound, lexical decision with
eye-tracking, JapaneseWord count: Abstract (147) + Main text (10072) + References (1874)

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Visual trimorphemic compound recognition in a morphographic script

Bimorphemic compound recognition
There has been a sizable number of studies on processing of bimorphemic
compounds (e.g., Kuperman, Bertram, & Baayen, 2008; Libben, 2006; Marelli &Luzzatti, 2012; Pollatsek, Hyönä, & Bertram, 2000; see also Frost, Grainger, & Carreiras,
2008 for a review). This is not surprising because complex words provide an opportunity
to study how processing efficiency is achieved in human mind and what efficiency meansin the first place (McClelland & Patterson, 2002a, 2002b; Pinker & Ullman, 2002a,
2002b). Accumulated evidence suggests, at least in the context of visual processing, thatprocessing efficiency is achieved not by decreasing the mount of processing nor byminimizing the number of representations but instead by maximizing opportunities forprocessing (Libben, 2006). For instance, in recognizing a compound word, such as
baseball, frequencies of occurrences of all morphological constituents (i.e., thecompound baseball, the first constituent base, and the second constituent ball) codetermine response speed. Current morphological processing models differ with respect
to the order of processing: whole-then-part (Giraudo & Grainger, 2001), part-then-whole(Taft, 2004; Taft & Nguyen-Hoan, 2010), or part-and-whole (Diependaele, Duñabeitia,
Morris, & Keuleers, 2011; Kuperman, Schreuder, Bertram, & Baayen, 2009; Pollatsek etal., 2000).
For languages with different writing systems, such as Chinese and Japanese, it has
similarly been reported that lexical distributional properties of morphological constituents

4
contribute to compound word recognition processes. Note that, because morphographic
scripts used in these languages represent morphemes (Rogers, 2005), bimorphemic
compounds are written with two orthographic symbols, such as 体温 taion ‘temperature’(hereafter, an individual morphological constituent represented by a morphographic
symbol is called character). Evidence of character component processing in compound
recognition came from a large number of studies (e.g., Taft, Zhu, & Peng, 1999;
Tamaoka, 2005, 2007; Tamaoka & Hatsuzuka, 1998; Miwa, Libben, Dijkstra, & Baayen,
2014; Nakayama, Sears, Hino, & Lupker, 2014; Zhou, Marslen-Wilson, Taft, & Shu,1999). Although these studies all agree on the point that constituent characters areactivated during morphographic compound recognition, consensus has not been reachedregarding to what extent compound words are decomposed at the very initial stage oflexical access. In the multilevel model of Taft et al. (1999), compound words andcomplex characters are initially decomposed not into characters but into even smaller
components called radicals, and word recognition proceeds in a bottom-up manner.
Indeed, in many cases, it is possible to decompose characters into smaller sub-charactercomponents, one of which indicates a basic category meaning (e.g., 体 ‘body’ consists ofcomponents イ + 本, where the semantic radical イ implies that the whole character
meaning relates to a human body). In a character-driven processing model of Miwa,
Libben et al. (2014), on the other hand, compound words are recognized predominantly
from characters, instead of radicals. The evidence came from lexical decision with eyetracking experiments, showing that the early fixation durations were co-determined bylarge character frequency effects and small radical frequency effects. Given that thistrend remained unchanged across different task demands and different font sizes, they5concluded that characters serve as the primary processing units in the earliest time frame,
although radical-level representations are assumed in this model as well.

Trimorphemic compound recognition
Given the accumulated results on bimorphemic compound processing, it isreasonable to question to what extent trimorphemic compound recognition processes canbe predicted from our knowledge of bimorphemic compound recognition. On the surface,extension from bimophemic to trimorphemic compounds appears to be simple (i.e., only
one additional morpheme). However, trimorphemic compounds provide a unique
opportunity to disentangle the problem of composition from the problem ofdecomposition.
It is expected that two processes are at work in recognizing compound words — aprocess to identify constituents (decomposition) and a process to understand the wholeword meaning (composition) — regardless of whether these processes work in sequence
or in parallel. For a bimorphemic compound AB, once the word is decomposed into A
and B, there is only one possible structural choice regarding how to compose thesemorphemes for proper interpretation: [AB]. For this reason, it is difficult to investigate acomposition process using bimorphemic compounds. On the contrary, for trimorphemic
compounds ABC, upon decomposition of words into morphemes A, B, and C, there are
multiple possibilities for composition: [[AB][C]] or [[A][BC]] or [[A][B][C]]. For
example, after a trimorphemic compound pork back rib is decomposed into threemorphemes, these morphemes may be composed as [[pork back][rib]] or [[pork][backrib]] or [[pork][back][rib]]. Depending on how constituent morphemes are composed, the6resulting whole word carry different meanings: ‘a rib of a pork back’ or ‘a back rib of apork’ or ‘a pork back rib.’ Thus, trimorphemic compounds are qualitatively different
from bimorphemic compounds and possess characteristics of both words and sentences.
This morphological problem has been conceptualized in the form of hierarchical tree
structure in theoretical linguistics (Spencer, 1997). A question yet to be answered iswhether such hierarchical structures are actually used online during several hundred
milliseconds of visual word recognition process.
Compared to the large number of bimorphemic compound processing research,trimorphemic compound research is rather limited, and the limited number of past studies
do not point to a single conclusion with respect to effects of branching directions. An
early study of Libben (1993) reported no significant difference between left- and rightbranching words in a naming experiment with nonsense multimorphemic words, but anonsense word naming task with a Broca's patient showed a right-branching advantage(Libben, 1994). On the contrary, Pollatsek, Drieghe, Stockall, and de Almeida’s (2010)
study on ambiguous trimorphemic compound recognition in sentential reading reported aleft-branching advantage. Krott et al. (2004), too, reported left-branching advantage forGerman and Dutch languages and stated that the result is consistent with lexical statistics
in these languages, indicating that there are more left-branching words than rightbranching words. de Almeida and Libben (2005) tested ambiguous trimorphemic
compound processing in sentential reading and pointed out that morphological structures
may not be constructed from the beginning; it is possible that early processing ismorphologically blind and a morphological structure is chosen later. These
inconsistencies may come from various sources: languages, participants, or tasks.

7
Although the above studies were done with languages with alphabetic scripts,
cross-linguistic orthographic differences should be taken into account in investigating theissue of morphological decomposition and composition. That is, morphologicaldecomposition is clearly more challenging in alphabetic scripts than that inmorphographic scripts. If the readers are not familiar with German or related languages,
for example, it must be nearly impossible to decompose multimorphemic German
compounds (e.g., Fußballweltmeisterschaft ‘soccer world cup’) into constituents (e.g.,
Fuß-ball-welt-meister-schaft). However, without any knowledge of the Japanese
language, readers can decompose multimorphemic words (e.g., 非課税証明書 ‘acertificate of tax exemption’) into constituents properly (e.g., 非-課-税-証-明-書) due tothe inter-character spacing and the orthographical scripts representing a morpheme.
Effects of a visual cue in segmenting compounds were tested by Inhoff, Radach andHeller (2000). In their study, spacing inserted into German compounds facilitatedmorphological segmentation in an early phase. Similarly, Bertram, Kuperman, Baayen,and Hyönä (2011) inserted a hyphen in trimorphemic compounds. Although this is notconventional in the tested languages Dutch and Finnish, hyphenation was received
favorably by readers, particularly towards the end of the experiment, as long as it did not
motivate incorrect interpretation of words. Given these findings, in processing Japanesetrimorphemic compounds, readers are expected to take advantage of inter-character
spacing inherent in the morphographic script, which may motivate qualitatively different
processes. For example, if the third character happens to be crucial for its status as amorphological head (e.g., 体温計), then there is no need to process from left to right inlocating the third character, as it always occurs in the same physical location separated

8
from the preceding character by space. To the best of our knowledge, there is nochronometric experimental study that investigated how and when lexical properties atdifferent morphological levels contribute to trimorphemic compound recognition in amorphographic script. Using multivariate analyses for response time and eye movement
data, we tested multiple hypotheses and aimed to provide a bird’s-eye view of thecontributions of 12 lexical predictors.

Possible processing architectures for Japanese trimorphemic compounds
A great number of studies investigated processing of bimorphemic compounds inmorphographic scripts. It is not clear, however, whether Japanese trimorphemic
compound processing can be predicted from our knowledge of bimorphemic compoundprocessing. On one hand, the two are comparable because it is efficient to process the two
types of compounds using the same cognitive architecture. On the other hand, acomposition process, which is crucial in three-character compound processing, has notbeen investigated. Japanese is considered to be a left-branching language with respect toits syntactic structure. Because linguistic structures tend to pattern in a systematicmanner, apparently motivated by cognitive reasons, such as processing andrepresentational efficiency (Whaley, 1997), it is possible to expect that the morphologicalstructure of Japanese is similarly left-branching. That is, a left-branching morphologicalstructure embedded in a left-branching syntax is intuitively less costly than rightbranching morphology in left-branching syntax. An inspection of the Balanced Corpus ofContemporary Written Japanese (BCCWJ, Maekawa et al., 2014) confirmed that there
are more left-branching compounds (61.8%) in Japanese than right-branching compounds

9(24.5%) and those without any branching structure (13.7%). If a hierarchical architectureis psychologically real, and if the word recognition system is tuned for a left-branching
structure, processing three-character compounds from left to right on a character-bycharacter basis may fail for some types of words (e.g., right-branching words). Yet,native speakers of Japanese recognize familiar three-character compounds effortlessly.
To conceptualize the problem more clearly, several possible processing architectures arepresented in Figure 1. Throughout the sections that follow, individual morphographic
constituents are called characters, a two-character compound is called a bimorphemic
compound, and a three-character compound is called a trimorphemic compound.

(Figure 1 about here)

Hypothetical processing architectures
Left-branching architecture. Miwa, Libben et al. (2014) manipulated the initial
fixation point in lexical decision and concluded that, for bimorphemic compounds, it is
optimal to process from the left character to the right character. If trimorphemic
compound recognition similarly proceeds from left to right, then one possible architectureis the one tuned for a left-branching structure, in which a hierarchical structure can beestablished by connecting the incoming constituent to the existing constituent (Figure 1
Panel a). This approach is, for example, applicable to a trimorphemic complex words
science fiction writer, which can be correctly recognized as science, science fiction, and10then science fiction writer. Similarly, a Japanese trimorphemic compound 体温計
taionkei ‘thermometer,’ which can be correctly recognized sequentially as 体 ‘body,’ 体温 ‘body temperature,’ and then 体温計 ‘clinical thermometer.’ This architecture isanalogous to the late closure and minimal attachment principles in sentence processing(Frazier, 1978 as cited in Frazier & Rayner, 1982), although the working-memory-related
cost saving is expected to be negligible in compound processing, relative to sentence
processing.
It is also possible to expect a left-branching-tuned processing, because Japanese istypologically considered to be a left-branching language and because there are more leftbranching words, as mentioned above. Experimental investigation of branching directionsof languages dates back to Forster’s (1966, 1968) studies on sentence completion. In
these studies, participants’ performance to complete sentences, in which either the
beginning or the end were missing, was affected by the branching direction of theparticipants’ language. A drawback of such a left-branching-specific processing is alikely misanalysis of right-branching words. Compound words such as Toronto drug
store cannot be properly recognized (i.e., *Toronto drug + store). For the same reason,
Japanese compounds with a right-branching structure, such as 北半球 kitahankyu‘northern hemisphere (literally, north-half-sphere)’ cannot be decomposed into *北半 and
球. According to this left-branching architecture, assuming that words are processed fromleft to right, we expect a large character frequency effect in an early time frame, together
with a moderate compound frequency effect and a lesser or null effect of whole word
(trimorphemic compound) frequencies. Processing advantage for words with the leftbranching structure is also expected.

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Right-branching architecture. The architecture tuned for right-branching words isvisualized in Figure 1 Panel b. Although this is intuitively not appropriate for a leftbranching language, the architecture is in line with the prefix stripping hypothesis (Taft &Forster, 1975), in which a complex word’s initial morphological constituent to be
automatically stripped off. In this architecture, the right-branching word 北半球 ‘northernhemisphere’ can be correctly recognized as a combination of 北 ‘north’ and 半球‘hemisphere.’ However, if such initial constituent-stripping were the language-generalrule, trimorphemic compound recognition would fail for the majority of Japanese words
at an early processing stage. According to this right-branching architecture, we can
expect processing advantage for words with the right-branching structure.
Architecture without any intermediate compound level. If the processing
architecture is strictly tuned for a particular branching structure as in Panels (a) and (b),extra processing cost has to be expected for words with an incompatible branching
structure. If we assume the same processing mechanism operating for trimorphemic
compounds of all kinds, then one solution is not to assume any mechanisms to distinguishdifferent branching directions (Panel c). Although this architecture may not be appealingfrom the perspective of theoretical linguistics, there is cognitive motivation: there is noapparent cost arising from a misanalysis. According to this architecture, a bimorphemic
compound frequency effect is not expected to appear at any point in time, not to mention
an effect of branching directions.
Both-branching architecture. It is also possible to construct all possible
morphological structures from the beginning, and an appropriate interpretation is chosen
at a later stage (Figure 1, Panel d). This appears to create redundancy. However,12
redundancy maximizes opportunities for further processing and reduces cost of later
misanalysis (Libben, 2006). Like the previous architecture (Panel c), processing
advantage is not assumed for a specific branching structure. Unlike the previous
architecture, however, a bimorphemic compound processing is expected. In an early timeframe, the magnitude of a bimorphemic compound frequency effect is expected to bebigger than that of a trimorphemic compound frequency effect but smaller than that of acharacter frequency effect.
Supralexical and sublexical processing. We have so far provided several possible
processing architectures assuming that morphological word recognition starts from thecharacter level. However, it should be noted that there are other possibilities not depicted
in Figure 1: supralexical and sublexical processing. If trimorphemic compounds areprocessed in a top-down manner, as predicted by a supralexical model (Giraudo &Grainger, 2001), then we should observe a large whole word frequency effect before
morphological constituent frequency effects. In contrast, if trimorphemic compounds areprocessed in a strictly bottom-up manner from sub-character radical constituents, aspredicated by sublexical model (Taft et al., 1999), then we should observe large radical
frequency effects before character and whole word frequency effects arise. Although we
did not ignore these possibilities during the forthcoming data analyses, it should be notehere that a supra-lexical model has never been proposed in morphographic word
recognition research and that character effects were incomparably stronger than radical
and whole compound effects in the early time frame of two-character word recognition
process (Miwa, Libben et al., 2014). Therefore, it was predicted that strictly bottom-up

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and top-down processes were less likely than the above mentioned character-driven
processes in three-character compound recognition as well.

Methodological consideration and aims of this study
Use of eye-tracking in a lexical decision experiment
Response time has been a commonly used dependent variable in word recognition
research (Libben & Jarema, 2002). Whenever response times are measured, an implicit
assumption is that response times reflect all processes from the moment of stimulus
perception to the moment of a button-press, following the mental chronometry ofDonders (1969/1868). However, this should be kept in mind in interpreting results
because response times do not necessarily capture the entire processing from perception
to recognition to response. One solution to this problem is recording of eye movements
during a word recognition experiment, which provides more dependent measures andallows researchers to tap into a time-course of word recognition process (Hyönä, Laine,& Niemi, 1995; Kuperman et al., 2009; Miwa, Libben et al., 2014; Miwa, Dijkstra,
Bolger, & Baayen, 2014; see Bertram, 2011 for a review of eye-tracking studies onmorphological processing). In previous studies, the results in a response time analysis
were qualitatively more similar to those in a later time frame than those in an early timeframe, although early and late measures both showed a decent correlation with response
times (Miwa, Libben et al., 2014, Miwa, Dijkstra et al., 2014).
In this lexical decision with eye-tracking study, participants’ eye movements (i.e.,
fixation duration, fixation count, and saccade amplitude) were measured, as well response
times (RT), and analyzed using linear mixed-effects regression modeling (Baayen, 2008;

14
Baayen, Davidson, & Bates, 2008). Emphasis was placed on response times and fixation
durations because they can be interpreted in a like manner, given the shared unit ofmeasurement in milliseconds. Analyses of early and late fixation durations allow us tosee what variables co-determine lexical processing in early and late time frames
respectively. Saccade amplitudes were analyzed to safeguard against the possibility thatdifferent eye movement measures point to overly different conclusions. Althoughanalyses of fixation durations motivated Miwa, Libben et al. (2014) to propose characterdriven processing, where to fixate next may depend more on the properties of the whole
compound (i.e., trimorphemic compound frequency and branching direction).

Research questions
Using a regression design, we tested multiple hypotheses. First, we investigatedwhether recognition processes are tuned for a specific branching direction and, if so,when a branching-direction effect manifests itself. If there is a language-specificmorphological tree or something equivalent entrenched in memory (Figure 1 Panels a andb), then an effect of branching directions should be observed. We expected left-branchingadvantage because Japanese is a left-branching language with the majority of threecharacter words being left-branching, as well as because processing advantage was foundin a reading task for left-branching words even in a right-branching language (Pollatsek
et al., 2010). If Japanese word recognition system is strictly tuned to optimize leftbranching processing, then the branching effect should co-determine the first fixation
durations. However, following the maximization of opportunity principle (Libben, 2006),

15
it is possible to predict that the early processing is blind to branching structures (Panels cand d).
Second, we investigated whether the morphological processing proceeds in abottom-up combinatorial manner. Because bimorphemic compound recognition starts
with activation of constituent characters, accompanying a small yet significant
contribution of a whole compound unit (Miwa, Libben et al., 2014), it is similarly
expected that, in trimorphemic compound recognition, the first fixation durations are codetermined by large character frequency effects and, if there is any, a small whole
trimorphemic compound frequency effect. However, if the lexical processing proceeds ina bottom-up manner, we should also expect a frequency effect of a bimorphemic
compound inside a trimorphemic compound (e.g., 体温 in 体温計). This second question
relates to the first question because a morphology-sensitive architecture implies bottomup processing.
Third, we investigated whether the three characters of trimorphemic compounds
are equally important and processed serially. Note that there may be different answers tothis question at different levels. At a lexical level, morphological headedness (Libben,
Gibson, Yoon, & Sandra, 2003) and left-to-right preferential processing in Japanese(Miwa, Libben et al., 2014) deserve attention. Because Japanese compounds are
predominantly right-headed (e.g., 体温計 ‘thermometer, literally body-temperaturemeasure’ is a kind of a ‘measure,’ as opposed to a kind of a ‘body.’ see also Kageyama,
2010 for Japanese morphology), the third character frequency may contribute more thanfrequencies of the first and the second characters. Indeed, in multivariate studies on
Japanese bimorphemic compound recognition (Miwa, Libben, & Baayen, 2012; Miwa,

16
Libben et al., 2014), the contribution of the second character frequency to response times
was always more than or equal to that of the first character frequency. With respect toprocessing direction, although a morphological head is located at the right-hand side, it isexpected that Japanese trimorphemic compound words are read from left to right. Miwa,
Libben et al. (2014) manipulated the initial fixation position (left, right, and centre) in
their lexical decision with eye-tracking experiment, but frequency of the left character codetermined early fixation durations more than that of the right character regardless.
At a feature level, we coded visual complexity for each character separately,
instead of considering the complexity of a whole trimorphemic compound because visualcomplexity does not always inhibit processing as is commonly believed. The direction ofvisual complexity effects depends on the eye position. In Miwa, Libben et al.’s (2014)
lexical decision study, during an early time frame, visual complexity of the fixated region
inhibited processing and that of unfixated region facilitated processing. Such fixationdependent asymmetrical effects of visual complexity were in line with a magnetic
attraction account proposed by Hyönä and Bertram (2004). The present study moreclearly tests the magnetic force account of a parafoveal-on-foveal effect (note, however,that this is a within-word parafoveal-on-foveal effect. For more commonly investigatedbetween-word parafoveal-on-foveal effects, see Drieghe, 2011 for a review). If theasymmetrical effects of character strokes observed in Miwa, Libben et al. (2014) are not
two-character-word-specific phenomena, then at the first fixation on a trimorphemic
compound, complexity of the first character in the foveal area should lead to inhibition
and that of the second character should lead to facilitation, possibly accompanying
slightly weaker facilitatory effect of visual complexity of the third character.

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Predictors considered in this study
Table 1 shows descriptive statistics of lexical predictors considered in this study.
Branching is a factor with two levels of LeftBranching and RightBranching, referring to
the morphological branching direction (e.g., science fiction writer is left-branching).

(Table 1 about here)

The log-transformed frequencies of occurrences for various morphological units(LogTrimorphCompFreq LogBimorphCompFreq, LogFirstCharFreq
LogSecondCharFreq, and LogThirdCharFreq) were collected from the Balanced Corpus
of Contemporary Written Japanese (BCCWJ) listing approximately 104 million words(Maekawa et al., 2014). Although LogTrimorphCompFreq and LogBimorphCompFreq
correlated decently (r = .46 p < .01), we included the original predictors together without
residualizing and observed the consequence, following suggestions by Wurm and
Fisicaro (2014) and York (2012).
Although NTT frequencies (Amano & Kondo, 2003), based on newspaper texts
published in the years from 1985 to 1998, were popular among many past studies on
Japanese processing (e.g., Hino, Kusunose, Lupker, & Jared, 2013; Miwa et al., 2012;
Miwa, Libben et al., 2014; Nakayama et al., 2014; Tamaoka, 2007; White, Hirotani, &Liversedge, 2011), we opted for the BCCWJ frequency measures in this study. The

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BCCWJ frequency appears to fit lexical decision data better, based on a goodness-of-fit
test, possibly because the BCCWJ is more contextually diverse and more up-to-date.
When NTT frequency and BCCWJ frequency were compared as a sole fixed-effect inmixed-effects models fitted to response time data for 474 bimorphemic Japanesecompounds tested in Miwa, Libben et al. (2014) study, the latter was identified to be abetter frequency measure (ΔAIC = -55.2, p < 0.0001 in a likelihood ratio test).
Not to neglect the possibility that morphographic words are initially decomposed
into radical units (Taft et al, 1999), we considered radical combinability.
LogFirstCharacterRadicalCombinability, LogSecondCharacterRadicalCombinability,and LogThirdCharacterRadicalCombinability were log-transformed radical
combinability measures taken from Tamaoka and Makioka’s (2004) lexical database forkanji characters. These were type frequencies of radicals, indicating how many charactersshare a given semantic radical (e.g., the radical 氵 ‘water’ is seen in 103 characters,
including 海 ‘sea’, 港 ‘port’, and 滝 ‘waterfall’).
Because visual word recognition necessarily starts with decoding of visualfeatures, visual complexity was considered by means of character stroke counts(FirstCharacterStrokes, SecondCharacterStrokes, and ThirdCharacterStrokes). We
considered stroke counts for the three characters separately, instead of stroke counts ofentire trimorphemic compounds. Because the direction of visual complexity effects canbe facilitatory or inhibitory depending on where the eye is fixating (Miwa, Libben et al.,2014), this allows us to study visual complexity effects more precisely.
In addition to the above lexical predictors, we also considered participants’months of stay in Canada and several task-related predictors (Trial,

19
PreviousResponseCorrect, invPreviousRT, and PreviousFixationDuration). Trial
indicates at what point in the experiment a particular trial took place.
PreviousResponseCorrect (levels: Correct, Incorrect) is a factor coding response
accuracy in the previous trial (reference level = Correct). invPreviousRT refers to
inversely transformed response time (-1000/RT) in an immediately preceding trial. In thesecond subgaze duration reported below, logPreviousFixationDuration (i.e., logtransformed first fixation duration) was included as a covariate. All the above numericalpredictors were centered by subtracting the mean value from their individual values.
In this study, we opted for mixed-effects regression techniques (Baayen, 2008;
Baayen, Davidson, & Bates, 2008) to assess participants, items, and task effects
simultaneously in a single statistical model. With this technique, there was no need forextensive pre-experimental matching and dichotomization of numerical variables, which
are not optimal for statistical reasons (Baayen & Milin, 2010; MacCallum, Zhang,Preacher, & Rucker, 2002).

A lexical decision with eye-tracking experiment
Methods
Participants. Twenty-one native speakers of Japanese (14 females, 19 righthanded) were tested. They were 23.6 years old on average (median = 22 years,
interquartile range = 7). At the time of the experiment, the majority of the participants
had stayed in Canada for less than half a year (median = 3 months, IQR = 10), and they
were exposed to Japanese 42.4% of the time (SD = 18.4). The participants’ logtransformed months of stay in Canada correlated strongly with log-transformed age (r =

20.80, p < .01). To safeguard against potential effects of their expatriate status on lexical
processes, their log-transformed months of stay in Canada was tested in the forthcoming
regression models.
Materials. The lexical decision experiment consisted of 200 existing
trimorphemic words and 200 non-existing nonwords (see Appendix A). The 200 existing
trimorphemic either had a left-branching structure (100 words) or a right-branching
structure. Because the whole compound frequency is considered to be the most influential
predictor in Japanese compound recognition (see Miwa, Libben, & Baayen, 2012 for arandom forest variable importance ranking), the frequencies of occurrence were matched
for the left- and the right-branching words, t(198) = -0.26, p = .79, so that the two groups
of words become comparable with respect to ease of processing. For all other lexical
properties between the left- and right-branching words, we opted for post-experimental
statistical control by including relevant predictors as covariates in regression models. If
left- and right-branching words are inherently different with respect to specific lexical
distributional properties, this can be statistically controlled by including an interaction thelexical effects and the factor Branching.
In order to elicit “no” responses, 200 nonwords were created by randomly
combining kanji characters. The existing words and the nonwords were matched withrespect to stroke counts, t(398) = 0.125, p = .90, not to motivate responses purely basedon visual complexity.
Apparatus. The experiment was designed with Experiment Builder software (SRResearch, Canada). Words were presented on a 24-inch LCD display. An EyeLink II
head-mounted eye-tracker (SR Research, Canada) was used to track eye movements, in21
the pupil-only mode with a sampling rate of 250 Hz. Tracking was done binocularly, but
the recording was done only for the right eye for all participants.
Procedure. In this experiment, participants were instructed to decide as quickly
and accurately as possible whether a presented word is an existing legitimate word inJapanese (= Yes) or not (= No), by pressing an appropriate button of a Microsoft
SideWinder game-pad. The Yes-button was pressed by the index finger of their dominanthand. Participants were instructed, in each trial, to fixate on a fixation point, which also
served as a drift-correct point. Given that the first character is more likely to be fixatedthan the second and the third characters when reading Japanese trimorphemic words insentential reading (Kajii, Nazir, & Osaka, 2001), the fixation mark was placed at the firstcharacter position. Words were presented in white Mincho font on a black background.
From a viewing distance of 70 cm, the visual angle for each kanji character wasapproximately 1.3 degree (the physical width of each character was 16 mm). The
experiment started with 12 practice trials and lasted approximately an hour, including two
breaks. Participants received feedback about accuracy and speed of their responses after
the practice and at each break point.

Results and discussion
In the sections that follow, (generalized) linear mixed-effects analyses wereconducted in R version 3.2.2 (R Development Core Team, 2015), using the R packages
lme4 (Bates, Maechler, Bolker, & Walker, 2014) with p-values computed withSatterthwaite approximations to degrees of freedom with lmerTest package (Kuznetsova,
Brockhoff, & Christensen, 2015). The partial effects were then visualized using

22
languageR package (Baayen, 2013). Transformations of outcome variables were based on
visual inspection of Q-Q normality plots and Box-Cox transformation with MASS
package (Box & Cox, 1964; Venables & Ripley, 2002).
Response time analysis. All participants responded with relatively high accuracy(range 89.4 - 99.5%) and were therefore kept for the analyses. Four words were removed
from the analyses because average response accuracy to these words was less than 70%.
In addition, incorrect responses and responses faster than 300 ms were removed. The
above trimming procedure removed 6% of the data, and 3,897 data points were kept.
We fitted linear mixed-effects models to inversely transformed response times (1000/RT). In the final model, after outliers with absolute standardized residuals
exceeding 2.5 standard deviation units were removed (2.4% of the data), random
intercepts for subjects (SD = 0.15) and item (SD = 0.09), together with random slopes for
subjects with respect to PreviousRT (SD = 0.05) and Trial (SD = 0.03) were included.
Random slopes involving other predictors were also tested but not justified by likelihood
ratio tests. While there are competing proposals regarding an optimal random-effects
structure (Barr, Levy, Scheepers, & Tily, 2013; Bates, Kliegl, Vasishth, & Baayen, 2015;
Matuschek, Kliegl, Vasishth, Baayen, & Bates, 2015), we opted for a forward-fitting
procedure and kept only those random intercepts and random slopes justified bylikelihood ratio tests throughout this study. The standard deviation of the residual error of
the model was 0.23. The R2 calculated for the correlation between the fitted and observed
values was .46.
Fixed-effects were selected by backward elimination based on p < .05 and AIC
values, and the final list of fixed-effects is summarized in Table 2. Figure 2 visualizes

23partial effects of the lexical predictors. In Figure 2, it is notable that although Panels (b),(c), (d), and (f) all show a downward slope, Panel (e) does not, meaning that allmorphological constituents (i.e., trimorphemic compound, bimorphemic compound, andcharacter) contributed to RTs, except the second character. RTs were not significantly codetermined by branching directions (Figure 2, Panel a), stroke counts (Panels g, h, and i),radical combinability measures, nor subjects’ months of stay in Canada. However, it is
too hasty to conclude that these predictors did not contribute at all in the recognition of
trimorphemic compounds. Their effects might have appeared and disappeared well before
responses, or their effects were inhibitory at one point and facilitatory at another point.
To see more accurate pictures, the participants’ eye movements were analyzed next.

(Table 2 and Figure 2 about here)

Fixation count analysis. Fixation counts were analyzed first because only trials
with multiple fixations are informative for the purposes of studying the time course of
lexical effects: multiple fixations can dissect the recognition process into smaller timeframes. The words and trials removed in the above response time analysis were removed
here as well. After removing trials with a blink, 3643 data points were at our disposal(12% of the data was removed in this trimming procedure).
Given the font size typical in lexical decision experiments, one may predict thatwords could be read with a single fixation. This was true. 10% of the trials were read

24
with a single fixation. However, 70% of the trials were read with two fixations (17% withthree fixations, and 2% with four fixations), implying that the participants could readwith a single fixation but did not most of the time. This is not surprising because evenbimorphemic compounds were read with two fixations in the context of a lexical decisionexperiment (Miwa, Libben et al., 2014).
Generalized linear mixed-effects models with a Poisson distribution were fitted to
fixation counts. The final model comprised of the random-effects of subjects (SD = 0.12)
and, as fixed-effects, significant facilitatory effects of Trials and logThirdCharFreq(Table 3). This indicates that participants made fewer fixations for words with highlogThirdCharFreq and for trials in later parts of the experiment (effect sizes = -0.2 and 0.3 fixation counts respectively). In the sections below, only a subset of trials withexactly two or three fixations was used in fixation duration analyses (87% of the data).
First fixation duration analysis. The words and trials removed in the responsetime analysis were removed here as well, and there were 3,195 data points at our
disposal. Linear mixed-effects models were fitted to log-transformed first fixation
durations, and outliers with standardized residuals exceeding 2.5 standard deviation units
were removed (2.9% of the data) for the final model. The random-effects structure of the
final model comprised random intercepts for subjects (SD = 0.15) and random slopes per
subjects for Trial (SD = 0.02), logFirstCharFreq (SD = 0.01), and FirstCharacterStrokes(SD = 0.01). Random slopes involving other predictors were also tested but not justified
by likelihood ratio tests. The standard deviation of the residual error was 0.14. The R2
calculated for the correlation between the fitted and observed values was 0.44. The fixedeffects were identified through backward elimination based on p < .05 and AIC values,

25
and the final list of fixed-effects is summarized in Table 3. Significant partial effects of
the lexical predictors are visualized in Figure 3.

(Table 3 and Figure 3 about here)

Because visual word recognition is expected to start with perception of visualfeatures, the first fixation durations, not surprisingly, reflected analyses of stroke features.
The visual complexity of the first character had an inhibitory effect (Figure 3, Panel g),
but visual complexity of the second and third character had a facilitatory effect (Panels hand i). This asymmetrical pattern of results may appear to be counterintuitive, but theresults are in line with Hyönä and Bertram’s (2004) magnetic force account thatprocessing difficulty in a parafoveal region attracts an eye movement, with the magnitude
of attraction depending on the distance between the current eye position and the source ofdifficulty. Recall that the fixation mark was placed at the first character position in thisexperiment. Consequently, participants fixated at the center of the first character without
exception (M = 1.52, SD = 0.17, given that 1.0 and 2.0 refer to the left edges of the firstcharacter and the second character respectively). The facilitatory effect of visualcomplexity in a yet-to-be fixated parafoveal region was also observed in bimorphemic
compound recognition (Miwa, Libben et al., 2014), and the present results indicate thateven the visual complexity at the third character position facilitates processing.
Interestingly, this asymmetrical pattern was almost completely reversed in the saccade

26
amplitude analysis (Appendix B, Panels g, h, i) in which saccade amplitudes weredecreased by the visual complexity of the second and third characters but increased bythat of the third character. That is, the visual complexity of the third character elicited theeye to travel further. These results indicate that feature-level analyses are done for all
three characters simultaneously during the earliest time frame.
Figure 3 also shows a facilitatory logFirstCharFreq (Panel d), accompanying aneffect of logTrimorphCompFreq (Panel b). It is notable that there was no effect ofBranching (Panel a) and LogBimorphCompFreq (Panel c). Because there is an effect oflogTrimorphCompFreq without an effect of LogBimorphCompFreq, the result is notconsistent with an architecture that postulates a compound frequency effect (Figure 1 a).
However, this is still consistent with the architectures that assumes a direct link from thefirst character to the whole trimorphemic compound level (Figure 1 b and c).
Although some past studies proposed decomposition of characters into radicals inan early time frame, radical combinability measures did not contribute to the first fixation
durations. This is consistent with the character-driven, not radical-driven, processing
proposed by Miwa, Libben et al. (2014): early contribution of radicals remain negligible
compared to that of characters even in trimorphemic compound recognition.
Second subgaze duration analysis. To study lexical processes in a later timeframe, mixed-effects models were fitted to square-root-transformed second subgaze
durations. For trials with three fixations, second subgaze durations were the sum of thesecond and third fixation durations, and second subgaze durations were identical tosecond fixation durations for trials with exactly two fixations. As in the first fixation
duration analysis, with the words and trials removed in the response time analysis

27
excluded, there were 3,195 data points to start with. Note that, in this later time frame, theeye moved to the second character region: Median = 2.33 (IQR = 0.33) for the second
fixations and Median = 2.81 (IQR = 1.22) for the third fixations, given that positions 2.0
and 3.0 refer to the left edges of the second character and the third character respectively.
It is clear that even when three fixations were made, the eye rarely traveled to the third
character.
With outliers with standardized residuals exceeding 2.5 standard deviation units
removed (2.6% of the data), the final model was fitted. The random structure of the final
model comprised random intercepts for subjects (SD = 0.15), per-subject random slopes
for Trial (SD = 0.02), per-subject random slopes for logFirstCharFreq (SD = 0.01), andper-subject random slopes for FirstCharacterStrokes (SD = 0.01). Random slopes
involving other predictors were also tested but not justified by likelihood ratio tests. The
standard deviation of the residual error of the model was 0.14. The R2 calculated for thecorrelation between the fitted and observed values was 0.54.
The fixed-effects of the model are summarized in Table 3, and the partial effectsof the lexical predictors are visualized in Figure 4. In this late timeframe, a main effect ofBranching was observed, favoring a left-branching structure (Figure 4, Panel a). The
second subgaze durations were also co-determined by large facilitatory effects oflogTrimorphCompFreq and logBimorphCompFreq (Panels b and c) and a facilitatory
effect of logThirdCharFreq (Panel f). The inhibitory effect of SecondCharacterStrokes(Panel h) is reasonable because this is where the eye was fixating.
The lack of the first character frequency effect (Panel d) may be because firstcharacter processing was more or less completed and because the eye moved away from28
the first character. The lack of the second character frequency effect (Panel e) is,
however, more counter-intuitive. In bimorphemic compound recognition, both first andsecond character frequency effects were observed (Miwa, Libben et al., 2014). Similarly,
in trimorphemic compound recognition, it is natural to expect all characters to become
activated for successful recognition of the word. Yet, this was not the case.
Radical combinabilities and participants’ months of stay in Canada were notsignificant co-determinants of the second subgaze durations either.

(Figure 4 about here)

Lexical statistics: predictability of characters at different positions

Thus far, neither the RT analysis nor the eye movement analyses captured acontribution of logSecondCharFreq. In other words, recognition of trimorphemic
compounds was achieved with activation of the first and the third characters. This thengenerates questions as to whether the second character is qualitatively special and why
successful trimorphemic compound recognition is achievable with the first and the third
characters only. One possible scenario is that native speakers of Japanese have lexical
distributional information entrenched in memory and make use of it during word
recognition, just as native speakers of Dutch and German make use of lexical29
distributional statistics of interfixes inside trimorphemic compounds as a probabilistic cue
in processing compounds (Krott et al., 2004).
In order to understand this bathtub-like effect, we calculated conditional
probabilities for characters at different positions to test the hypothesis that the threecharacters in trimorphemic compounds are not equally predictable. From the frequency
list of Balanced Corpus of Contemporary Written Japanese (BCCWJ) containing
2,434,619 words (National Institute for Japanese Language and Linguistics, 2013), we
sampled 112,854 Japanese trimophemic compounds categorized as general nouns. Thesewords were then morphologically segmented by MeCab-0.996 (Kudo, 2013) and the
majority of the words were identified to have a left-branching structure (61.8% for leftbranching, 24.5% for right-branching, and 13.7% for others). This classification isconsistent with the linguistic typological understanding of Japanese as a left-branching
language and shows that the vast majority of Japanese trimorphemic compounds have aclear branching structure (i.e., not ambiguous).
In Figure 5, the violin plots depicted with vioplot R-package (Adler, 2005)
visualize, for the 112,854 trimorphemic compounds, distributions of conditional
probability for the first character, the second character, and the third character
respectively (Panels a, b, and c). The conditional probability measures predictability ofeach character when the other characters are known. For the trimorphemic word 動物園‘zoo,’ for example, the conditional probabilities of individual characters at different
positions are as follows:(1)

P 1 2 3 = P    = P( ∩  ) P   = 0.82

30
(2)

P 2 1 3 = P    = P( ∩  ) P   = 1.00

(3)

P 3 1 2 = P    = P( ∩  ) P   = 0.59

As shown in (1), when the second character 物 and the third character 園 are given, thetrimorphemic compound is predicted to be 動物園 82% of the time (it can also be 植物
園 ‘botanical garden’). When the first character 動 and the third character 園 are given asin (2), however, the second character is always 物 without an exception. When the firstcharacter 動 and the second character 物 are given as in (3), it is more difficult to predict
the trimorphemic compound because there are many possibilities at the third character
position: 動物学 ‘zoology,’ 動物界 ‘animal kingdom,’ 動物食 ‘animal diet,’ 動物臭‘animal odor,’ 動物名 ‘animal name’ and more. All in all, for the word 動物園, thepredictability of the second character is incomparably higher than that of the first andthird characters
Interestingly, this seems to be a general trend across all trimorphemic words. The
panels (a), (b), and (c) in Figure 5 indicate that conditional probability of the second
character (median = .83) is generally higher than that of the first and the third characters(medians = .50 and .16) to a statistically significant extent (p < 0.01 in a generalized
linear mixed-effects model for the binomial family with a logit link function). This means
that, if native speakers of Japanese are given the first and the third characters, they cancorrectly guess the second character most of the time. These lexical statistics help us tounderstand why second character frequency effects were not observed at all in the present
lexical decision experiment: the second character can be predicted once the first and the31third characters are recognized. These lexical statistics also help us to re-interpret Kajii etal.’s (2001) report that Japanese speakers tend to fixate on the first character but not thesecond character of trimorphemic compounds in sentential reading. Perhaps, readers
avoid fixating on the second character (i.e., the word center) because the second characteris least informative.

(Figure 5 about here)

The panels (d), (e), and (f) of Figure 5 show conditional probabilities forcharacters at different positions separately for 69,723 left-branching words and 27,699
right-branching words, excluding 15,432 words that MeCab-0.996 identified to beneither. It is clear that predictability of the first and third characters depends on whether aword is left-branching or right-branching. For right-branching words, the first character isdifficult to predict (Panel d), but the third character is highly predictable (Panel f). Thispattern reverses for left-branching words: the first character is more or less predictable,
but the third character is not. One interpretation is that, when trimorphemic compounds(e.g., 北半球 ‘northern hemisphere’) are morphologically interpreted to be combinationof a monomorphemic word (e.g., 北 ‘north’) and a bimorphemic compound (e.g., 半球‘hemisphere’), the monomorphemic word is difficult to predict. It might be the case thatthe second character is most predictable because it is always part of a bimorphemic
compound.

32
Because conditional probabilities differ greatly between left- and right-branching
structures, the response time and eye movement data were reanalyzed with, as covariates,
conditional probabilities for the first, second, and third characters. Conditional
probability measures, however, did not emerge as significant predictors, and the mixedeffects models reported thus far remained unchanged. The lack of significant additive
contribution of conditional probability to on-going eye movements is perhaps expected,
given the insignificant additive contribution of conditional co-occurrence probability
reported by Ong and Kliegl (2008), although their conditional probability measure wasfor prediction of an upcoming word in sentential reading. This implies that conditional
probability was not computed online for individual compounds in each trial. Instead,conditional probability served as a probabilistic cue to set a global processing strategy forthe entire experiment session: it is important to process the first and third characters
because they are more informative than the second character.

General discussion

This lexical decision with eye-tracking study revealed that, in recognition ofJapanese trimorphemic compounds presented in isolation, the first character and thewhole trimorphemic compound unit become active in an early time frame. During thisearly time frame, visual complexity of the second and third characters shortened fixation
durations (see also comparable finding in a saccade amplitude analysis in Appendix B).
This facilitatory effect of visual complexity in a parafoveal region can be consideredsomething analogous to magnetic force (Hyönä & Bertram, 2004). According to this

33
account of a parafoveal-on-foveal effect, strength to attract eye movements depends onthe distance from the current fixation and the source of complexity. At the first fixation,
there was no significant effect of branching direction. The early effect of trimorphemic
compound frequencies should deserve attention as well, with respect to Bertram andHyönä’s (2003) visual acuity hypothesis, which claims that longer words are more likely
to be processed from parts. In this study, holistic processing was still at work formorphographic trimorphemic compounds from the very first fixation, possibly because
Japanese trimorphemic compounds are not long enough to exceed the visual acuity
limitation. If so, a question that remains for future research is whether four- or fivemultimorphemic words are processed from constituent morphemes.
In a late time frame, after a saccade was made rightward at the second subgaze,the frequency of the trimorphemic compound unit co-determined eye fixation durations
to a great extent, accompanying activation of the third character and the bimorphemic
compound units. It is apparent that weight of processing shifted to the right and also to
the higher level of the morphological representations. Processing advantage for leftbranching words was also observed in this later time frame. Recall that the majority oftrimorphemic compounds have a left-branching structure, and the recognition system, or
more specifically a composition stage, is apparently sensitive to such lexical statistics.
The sum of the above lexical effects in these early and later time frames roughly
corresponded to those observed in the response time analysis, in which all themorphological units contributed to lexical decision responses except the second
character. Successful recognition of compound words without explicit activation of aconstituent may appear to be counter-intuitive. However, the result is compatible with34
any dual- or multiple-routes models of lexical processing. As de Almeida and Libben(2002) stated, “prelexical activation helps but is not necessary [… because] whenconstituent activation is disrupted, it doesn't have the opportunity to make the difference
it otherwise would” (p. 113).

Possible motivations behind the bathtub-like effect
The above results overall show that activation of a complex word does not require
activation of its all constituent units. In the case of the present experiment, a second
character frequency effect was not observed anywhere. There remains a question: why
was the second character not actively processed? Here, we present five possibilities.
First, the importance of peripheral components evokes an association with thebathtub effect in word recall (Aitchison, 1987); the first and last units are recalled better
than the middle unit. It is, however, not likely that this was the sole motivation because
compounds are too short to be memory-demanding, and the underlying motivation is notexpected to be found in short-term memory
Second, the focus on peripheral components also evokes an association with aterminal feature effect in letter identification (Fiset et al., 2008). Terminal features are
known to be particularly important when alphabetic letters (e.g., A) are identified, andtrimorphemic compounds might have been perceived as a coherent unit like an alphabetic
letter. However, this is not likely either because second character was processed at thefeature level (Figure 3 and 4, Panel h).
Third, because the motivation for peripheral constituent processing cannot be
attributed to short-term memory nor visuoperceptual attention, we considered the35morphological transcendence hypothesis (Libben, 2015). From this perspective, twomorphologically transcended variants of the second characters are assumed, and theymight have been both activated in qualitatively different manners. More specifically, in atriconstituent compound structure composed of constituents ABC, A keeps its
morphological role as modifier, no matter what the configuration of the compound (inright-headed languages like Japanese and English), and C keeps its role as head.
However, the constituent B needs to change its roles. For left branching, it gives up its
head role to be part of a modifier; for right branching, it gives up its modifier role to A,
which now functions as the modifier of the ABC compound. To check this hypothesis,
we considered positional family sizes of the second character so that we could see
whether they interact with the factor Branching (e.g., perhaps for left-branchingcompounds, it is the modifier family size of B in the embedded compound AB that plays
a role). However, reanalyses with mixed-effects modeling showed that this was not thecase.
Fourth, the lack of any second character frequency effect might have been due to
optimization of processing based on conditional probability calculated in each trial. In
order to test this hypothesis, we reanalyzed with conditional probabilities of characters at
different positions. They, however, did not emerged as significant predictors.
Finally, although conditional probability did not affect processing locally in eachtrial, it might have affected lexical processing globally throughout the experiment. In a
corpus analysis, it became evident that the second character is highly predictable given
the other two characters. It might have been the case that such uneven conditional
probabilities of characters at different positions were known beforehand and that readers

36
utilized them as part of their processing strategy throughout the experiment. With allother motivation above being less likely, this is at present the best explanation. It is
important to note that the bathtub-like effect we observed was not a byproduct of aparticular response strategy developed during the experiment because there was nointeraction between logSecondCharFreq and Trial (p = .98 in a mixed-effects model).

Revisiting the hypothetical morphological architectures
It is now time to revisit the processing architectures presented in Figure 1. The
model tuned for left-branching words (Panel a) is not likely because, at the first fixation,
there was no effect of LogBimorphCompFreq, although an effect of
LogTrimorphCompFreq was already observed. The architecture tuned for rightbranching words (Panel b) can accommodate early effect of LogTrimorphCompFreq
without LogBimorphCompFreq, assuming that characters were processed from left toright. However, this is not likely either because there was a left-branching advantage at
the second fixation. The architecture without any hierarchical morphological structure(Panel c) similarly can accommodate the lack of any LogBimorphCompFreq effect at thefirst fixation. However, this is not perfectly correct either because there were effects of
LogBimorphCompFreq and Branching at the second subgaze. Finally, the both-branchingarchitecture (Panel d) is not likely either because an effect of LogTrimorphCompFreq
was observed before that of LogBimorphCompFreq.
All in all, because none of these architectures is perfectly compatible with theresults, it is not possible to pick one model and discard the others. Yet, conversely
speaking, each of these models is partially compatible with the results. One way to think

37
about this issue is that early pre-lexical decomposition processes follow the branchlessarchitecture (Panel c) and late post-lexical composition processes are sensitive to lexical
statistics favoring a left-branching structure (Panel a) more than a right-branching
structure (Panel b). The architecture to build all possible morphological trees (Panel d)seems to be least likely, and this cast another vote to de Almeida and Libben’s (2005)
conclusion that two morphological structures are not simultaneously built fortrimorphemic compounds. Yet, another approach to this issue is to question the linguistic
structure or any stable and well-defined representations. Libben (2014, 2015) claims, justas an electron can be paradoxically perceived to be both particles and a wave in aquantum physics, a word, too, may possess duality. In other words, there is no word in
the mind, not to mention structures, as a stable or well-defined representational unit. If
one is to proceed along this line, a research question is, according to Libben (2014), not“how are compound words represented in the mind?” but “what mental representations
correspond to compound words?”

Cross-linguistic differences and similarities
The present study investigated trimorphemic compound processing in amorphographic script, leaving us with a question: what are similar and what are different
across different writing systems? Recall that the morphographic script used in Japanese
has two unique characteristics: individual symbols are considered to encode morphemes,
and there are inter-morpheme spaces in trimorphemic compounds. The former
characteristics offers physically fixed word length for compounds consisting of the same
number of morphemes. The latter characteristics segment compounds into morphemes

38
and makes the morphological constituents immediately identifiable even at thevisuoperceptual level. Given these unique characteristics, at least the earliest processing
stage is expected to be language-specific. We predict that facilitatory visual complexity
effects of characters in parafoveal regions are language-specific because the second andthird character are always found in the same regions in a distinguishable manner, thanks
to inter-character space and constant word length.
Although the early lower level processing is expected to language-specific, thehigher level cognitive processes are expected to be language-general. In this study, weobserved a bathtub-like effect (i.e., frequency effects of the 1st and 3rd, but not 2nd,
constituents) and a left-branching advantage. Although specific lexical effects may differ
across languages, patterns of lexical processing go hand in hand with lexical
distributional statistics and gradually tuned to optimize processing (see Krott et al., 2004
for Dutch and German speakers’ awareness of conditional probabilities of interfixes).
In addition, multimorphemic compound processing studies of Kuperman (2008,2009), Miwa, Libben et al. (2014), and the present study provides a functional overlap;
the first constituent's contribution is largest in the early time frame, and a whole
compound frequency effect appears before the second/third constituent frequency effects,
irrespective of the number of morphemes (i.e., bimorphemic or trimorphemic) and thetarget language (i.e., Dutch, Finnish, or Japanese).

Revisiting use of eye-tracking in a lexical decision experiment
Following previous lexical decision with eye-tracking studies (Kuperman et al.,2009; Miwa, Dijkstra et al., 2014, Miwa, Libben et al., 2014), the present study, too,39
demonstrated that an eye-tracking technique successfully complements a lexical decisionexperiment with response time as a sole dependent variable. Lexical decision has been
the most popular task in psycholinguistics. An ideal scenario believed in the field is thatwhat we see in a RT analysis is the sum of what we see in an early fixation analysis andin a late fixation analysis. Interestingly, this was more or less true for the character
frequency effects (i.e., Panel b to f in Figure 2 ≈ Panel b to f in Figure 3 + Panel b to f in
Figure 4; note that all the plots in Figures 2, 3, 4 have comparable y-axes with an 80millisecond range). However, this is not always the case. In this study, for example, theeffects of stroke counts were not observed in the RT analysis, but they clearly codetermined eye movements. It is possible that the effects appeared and disappeared
before a response (in the case of the first and third character stroke effects) or that an
early inhibitory effect was cancelled out by a late facilitatory effect (in the case of thesecond character stroke effect). Inconsistencies are sometimes observed between an eye
movement analysis and a response time analysis. This is why combining the multiple
measures is valuable.

Limitation of the present study
In this study, responses and eye movements were measured in the context of avisual lexical decision task with words presented in isolation. For future studies, wesummarize three obvious limitations of the study.
Task-induced effects. We did not study eye movements in a context of natural
reading. We studied recognition processes for trimorphemic compounds presented inisolation by means of eye movements. There were some positive aspects of this design –40(1) the results were not colored by sentence contexts, (2) the first fixation position was
perfectly controlled, and (3) the results are more or less generalizable to isolated wording
reading in real life (e.g., reading signs, advertisements, and simple descriptions). The
design becomes problematic only when one attempts to generalize the results to what iscalled ‘natural’ reading (see Kuperman, Drieghe, Keuleers, & Brysbaert, 2013 for acomparison of lexical decision and reading). Because of the nature of the isolated readingtask with the lexical decision task demand, span of effective vision might have beenchanged, and there was no parafoveal preview benefit in this study. We predict thatreaders are not expected to make as many multiple fixations if the same trimorphemic
compounds appear in a sentential context.
Legitimacy of stimuli. In this study, we opted for constructing a stimulus set, as
opposed to sampling randomly from a large data base. We confirmed that the set of items
used in this study were, although the items were chosen from high frequency words, lognormally distributed and qualitatively comparable to randomly selected samples withrespect to word frequencies between left- and right-branching types (see Appendix C for
details).
Potentially influential other variables. As Simmons, Nelson, & Simonsohn (2011)
warned, there is a researcher degree of freedom with respect to what predictors to be
included in analyses. The present study considered, for parsimony, only 12 lexical
predictors, one participants’ trait, and three task-related variables although there are manyother potentially influential lexical variables: for example, familiarity, imageability,
semantic transparency, orthographic neighbors, and homophone counts. Some of thesevariables are known to correlate highly with the predictors used in this study, and41
investigation of their independent contributions requires further statistical care (e.g.,principal component analysis) at the expense of parsimony and interpretation ease. We
therefore leave it to future research. Interested readers are encouraged to test efficacy ofother predictors, using our response time and eye fixation duration data published online.
At the moment, because Miwa and Libben et al.’s (2014) Japanese bimorphemic
compound study did not observe these unconsidered variables modulating compound andcharacter frequency effects, we expect that inclusion of these variables is not going tochange the pattern of results reported in the present study.

Conclusion
This regression study was a modest attempt to understand how multiple lexical
distributional predictors contribute to the visual recognition of Japanese trimorphemic
compounds. A lexical decision with eye-tracking experiment revealed several notablephenomena: (1) late left-branching advantage, (2) character-driven, as opposed to radicaldriven, bottom-up processing, and (3) uneven and asymmetrical contributions ofconstituent characters reflected in the bathtub-like constituent frequency effect and theearly within-word parafoveal-on-foveal effect. While the character-driven processing andthe within-word parafoveal-on-foveal effect were also found in a previous bimorphemic
Japanese compound processing study, the left-branching advantage and the bathtub-like
effect are specific to trimorphemic compounds and in line with lexical distributional
characteristics of trimorphemic compounds found in a corpus. Native speakers ofJapanese apparently make use of lexical distributional statistics in the course ofrecognizing compounds in a brief second.

42

Acknowledgments

The response time and fixation duration data in a long format, accompanying participant
and item properties, are downloadable from the first author’s website(http://kojimiwa.com/publication.html). The descriptions of the variables and data
trimming procedures are found in this paper. The frequency data obtained from Balanced
Corpus of Contemporary Written Japanese (BCCWJ) are published with permission of
the National Institute for Japanese Language and Linguistics. We thank Denis Drieghe
and an anonymous reviewer for their comments on the earlier version of this paper.

43
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63

Table 1. Descriptive statistics of lexical predictors considered in this study. The mean
was calculated before the centering procedure. Comp = compound, Char =character, Trimorph = Trimorphemic compound, Bimorph = bimorphemic
compoundLexical predictors
BranchingType
Branching

Range

Levels, Mean (SD)
LeftBranching,
RightBranching

LogTrimorphCompFreq
LogBimorphCompFreq
LogFirstCharFreq
LogSecondCharFreq
LogThirdCharFreq
LogFirstCharRadicalCombinability
LogSecondCharRadicalCombinability
LogThirdCharRadicalCombinability
FirstCharacterStrokes
SecondCharacterStrokes
ThirdCharacterStrokes

Trimorph
BimorphCharacter
Character
Character
Radical
Radical
RadicalFeatureFeatureFeature2.57 : 7.87
3.22 : 11.12
6.91 : 13.39
6.88 : 13.39
7.81 : 13.14
0 : 4.69
0 : 4.69
0 : 4.69
1 : 201 : 21
3 : 215.01 (1.1)
7.93 (1.5)
10.84 (1.2)
10.96 (1.3)
10.78 (1.2)
2.77 (1.3)
2.91 (1.4)
2.80 (1.2)
8.59 (3.7)
8.68 (3.6)
9.63 (3.8)

64Table 2. The fixed-effects structure of the linear mixed-effects model for response times

Estimate Std.Error

t-valuep-value(Intercept)
Trial
PreviousResponseCorrect (Incorrect)
invPreviousRT
logTrimorphCompFreq
logBimorphCompFreq
logFirstCharFreq-1.874-0.0450.096
0.133-0.053-0.015-0.018

0.034
0.008
0.017
0.017
0.008
0.006
0.006

-53.682-5.1035.308
7.100-6.006-2.726-2.392< 0.001< 0.001< 0.001< 0.001< 0.001
0.007
0.018

logThirdCharFreq-0.0150.006-2.2900.023

65Table 3. The fixed-effects structure of the (generalized) linear mixed-effects models for
fixation counts, first fixation durations, and second subgaze durations.
(a) Fixation counts
Estimate Std.Error(Intercept)
Trial
logThirdCharFreq

t-valuep-value

0.029
0.010
0.009

25.247-2.840-2.945< 0.001
0.005
0.003

Estimate Std.Error

t-valuep-value

0.033 166.759
0.005
1.951
0.011
-2.8470.002-2.6030.003-2.923
0.001
7.376
0.001
-3.8940.001-2.578< 0.001
0.066
0.004
0.009
0.007
< 0.001< 0.001
0.010

0.730
-0.029-0.028(b) First fixation durations(Intercept)
Trial
PreviousResponseCorrect (Incorrect)
logTrimorphCompFreq
logFirstCharFreq
FirstCharacterStrokes
SecondCharacterStrokes
ThirdCharacterStrokes

5.650
0.012
-0.035-0.005-0.0080.011-0.004-0.002(c) Second subgaze durations
Estimate Std.Error(Intercept)
logPreviousFixationDuration
Trial
PreviousResponseCorrect (Incorrect)
invPreviousRT
Branching (RightBranching)logTrimorphCompFreq
logBimorphCompFreq
logThirdCharFreq
FirstCharacterStrokes
SecondCharacterStrokes

19.021-4.646-0.413
1.205
1.025
0.383
-0.318-0.174-0.157-0.025
0.054

0.385
0.117
0.049
0.271
0.121
0.146
0.071
0.052
0.057
0.020
0.018

t-valuep-value

46.153-35.189-7.319
3.761
7.173
2.120
-4.711-3.344-2.428-1.6012.775< 0.001< 0.001< 0.001
0.001< 0.001
0.036< 0.001
0.001
0.016
0.114
0.006

66

Figure captions

Figure 1. Hypothetical processing architectures for Japanese trimorphemic compounds.
Note: To be concise, sub-character radical representations and the response/decisionsystem are assumed but not depicted.

Figure 2. Partial effects of lexical predictors in the mixed-effects model fitted to inversetransformed response times. Note: The rugs represent a distribution of predictor values.
The RTs were back-transformed to the original scale for the visualization. Trimorph =trimorphemic compound, Bimorph = bimorphemic compound.

Figure 3. Partial effects of lexical predictors in the mixed-effects model fitted to logtransformed first fixation durations. Note: The rugs represent a distribution of predictor
values. The first fixation durations were back-transformed to the original scale for thevisualization.

Figure 4. Partial effects of lexical predictors in the mixed-effects model fitted to squareroot-transformed second fixation durations. Note: The rugs represent a distribution ofpredictor values. The second fixation durations were back-transformed to the original
scale for the visualization. A line is drawn in Panel (a) simply to make the effect more
visible, and it is does not imply any continuity between the LeftBranching andRightBranching types.

Figure 5. Violin plots showing distributions of conditional probability for characters at
different positions. Note: The white dots and the corresponding values represent a median.

67
Figure 1

(a)

(b)

(c)

(d)

68

Figure 2

540
500

RT (ms)

540
−2

0

1

2

3

−3

−1

1

3

(e) Character(f) Character

−4

−2

0

2

500

500

RT (ms)ns

540(d) Character

RT (ms)logBimorphCompFreq

540

logTrimorphCompFreq

540

Branching

500

−4

−2

0

2

−3

−1

1

2

(h) Feature(i) Feature

−5

0

5

10

FirstCharacterStrokes

ns

500

ns

500

500

ns

540

(g) Feature

RT (ms)logThirdCharFreq

RT (ms)logSecondCharFreq

540

logFirstCharFreq

540

RT (ms)LeftBranching RightBranching

RT (ms)(c) Bimorph

500

RT (ms)

540

●

ns

●

(b) Trimorph

500

RT (ms)(a) Branching

−5

0

5

10

SecondCharacterStrokes

−5

0

5

10

ThirdCharacterStrokes

69

Figure 3

0

1

2

340
300
260

1st fixation duration (ms)

340
300
−2

ns

3

−3

−1

1

3

(d) Character(e) Character(f) Character

−2

0

2

−4

−2

0

300

ns

260

340
300
260

340
300
−4

ns

340

logBimorphCompFreq

1st fixation duration (ms)logTrimorphCompFreq

1st fixation duration (ms)

Branching

260

2

−3

−1

1

2

(g) Feature(h) Feature(i) Feature

0

5

10

FirstCharacterStrokes

−5

0

5

10

SecondCharacterStrokes

300
260

340
300

340
300
260
−5

340

logThirdCharFreq

1st fixation duration (ms)logSecondCharFreq

1st fixation duration (ms)logFirstCharFreq

260

1st fixation duration (ms)LeftBranching RightBranching

1st fixation duration (ms)(c) Bimorph

260●(b) Trimorph
1st fixation duration (ms)

340
300

ns

●

260

1st fixation duration (ms)(a) Branching

−5

0

5

10

ThirdCharacterStrokes

70

0

1

2

280
240

280
−2

(c) Bimorph

3

−3

−1

1

3

(f) Character

−4

−2

0

2

−4

−2

0

240

ns

240

ns

280

(e) Character2nd subgaze duration (ms)(d) Character

280

logBimorphCompFreq

2nd subgaze duration (ms)logTrimorphCompFreq

280

Branching

240

2

−3

−1

1

2

(h) Feature(i) Feature

−5

0

5

10

FirstCharacterStrokes

−5

0

5

10

SecondCharacterStrokes

ns

240

240

240

ns

280

(g) Feature

2nd subgaze duration (ms)logThirdCharFreq

280

logSecondCharFreq

280

logFirstCharFreq
2nd subgaze duration (ms)

2nd subgaze duration (ms)LeftBranching RightBranching

2nd subgaze duration (ms)

2nd subgaze duration (ms)(b) Trimorph

240

●
●

2nd subgaze duration (ms)280(a) Branching240

2nd subgaze duration (ms)Figure 4

−5

0

5

10

ThirdCharacterStrokes

71
Figure 5

1.0
0.8
0.6
0.4

0.16
0.0

0.0

LeftBranching RightBranching

●

0.8
0.6
0.0

LeftBranching RightBranching

0.98

0.4

0.4

0.6

0.75

●

0.2

●

Conditional probability P(3|1 2)

1.0(f) Predictability of the 3rd Character

1.0
0.8

0.92

0.0

0.11

0.0

●

●

0.2

0.2

0.4

0.6

0.75

Conditional probability P(2|1 3)●

0.2

Conditional probability P(3|1 2)

0.8
0.4

0.6

0.83

(e) Predictability of the 2nd Character

1.0
0.8

●

0.2

Conditional probability P(2|1 3)

0.8
0.6
0.2

0.4

0.5

0.0

Conditional probability P(1|2 3)●(d) Predictability of the 1st Character

Conditional probability P(1|2 3)(c) Predictability of the 3rd Character

1.0(b) Predictability of the 2nd Character

1.0(a) Predictability of the 1st Character

●

0.06

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