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Quarterly Journal of Experimental Psychology

Japanese morphographic word recognition 1
Running head: JAPANESE MORPHOGRAPHIC WORD RECOGNITION

The time-course of lexical activation in Japanese morphographic word recognition:
Evidence for a character-driven processing model!
Koji Miwa1, Gary Libben2, Ton Dijkstra3, Harald Baayen14

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Department of Linguistics, University of Alberta, Canada

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

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Donders Institute for Brain, Cognition, and Behaviour
Radboud University Nijmegen, The Netherlands

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Seminar fuer Sprachwissenschaft, Universität Tübingen, Germany

Address for correspondence:
Koji Miwa
4-32 Assiniboia Hall, University of Alberta
Edmonton, Alberta, T6G2E7 Canada
E-mail: kmiwa@ualberta.ca
Phone: +1(780)492-3434,

FAX: +1(780)492-0806

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Japanese morphographic word recognition 2

Abstract

This lexical decision study with eye-tracking of Japanese two-kanji-character words
investigated the order in which a whole two-character word and its morphographic
constituents are activated in the course of lexical access, the relative contributions of
the left and the right characters in lexical decision, the depth to which semantic
radicals are processed, and how non-linguistic factors affect lexical processes.
Mixed-effects regression analyses of response times and subgaze durations (i.e.,
first-pass fixation time spent on each the two characters) revealed joint
contributions of morphographic units at all levels of the linguistic structure with themagnitude and the direction of the lexical effects modulated by readers’ locus ofattention in a left-to-right preferred processing path. During the early time frame,character effects were larger in magnitude and more robust than radical and wholeword effects, regardless of the font size and the type of nonwords. Extending
previous radical-based and character-based models, we propose atask/decision-sensitive character-driven processing model with a level-skipping
assumption: Connections from the feature level by-pass the lower radical level andlink up directly to the higher character level.

Key words: visual word recognition; morphological processing; Japanese; lexicaldecision; eye movement!

Word count: 179 (abstract) + 12,546 (body) + 3,387 (reference) = 16,112

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Quarterly Journal of Experimental Psychology

Japanese morphographic word recognition 3
The time-course of lexical activation in Japanese morphographic word recognition:
Evidence for a character-driven processing modelStudies on the recognition of complex entities, irrespective of whether theseare scenes, objects, or human faces, need to consider how the whole and its parts
contribute to our recognition of the input as a coherent meaningful unit (Beck, 1966;
Biederman, Mezzanotte, & Rabinowitz, 1982; Greene & Oliva, 2009; Joseph &Tanaka, 2003; Kahneman, Treisman, & Gibbs, 1992; Navon, 1977; Tanaka, Kiefer,& Bukach, 2004; Treisman & Gelade, 1980; Wachsmuth, Oram, & Perrett, 1994).
Word recognition is no exception in this respect. Some researchers have argued thatmorphologically complex words are represented and processed as wholes(Aitchison, 1987; Butterworth, 1983; Caramazza, Laudanna, & Romani, 1988;
Janssen, Bi, & Caramazza, 2008). In the word-based supralexical model of Giraudo
and Grainger (2001), the activation of the whole word precedes the activation of theconstituent parts.
Many others believe that there is a rapid and automatic morphologicaldecomposition process in recognition and production. In this view, word recognition
is not a simple process matching whole word forms to whole word meanings:
Sublexical units are posited to exist and also play a role in recognition. Thereremains, however, an on-going debate over how and at what point in time sublexicalunits contribute to lexical access (see Frost, Grainger, & Carreiras, 2008; Frost,
Grainger, & Rastle, 2005, for overviews). Strict morpheme-based theories of lexicalaccess in reading claim that complex words are decomposed into their constituentsand subsequently recombined into a whole word representation (Taft, 2004; Taft &URL: http:/mc.manuscriptcentral.com/pqjeQuarterly Journal of Experimental Psychology

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Japanese morphographic word recognition 4
Forster, 1975; Taft & Nguyen-Hoan, 2010). Although interactive activation models
allow top-down feedback, bottom-up combinatorial processing is a dominantcharacteristic of these models as well (McClelland & Rumelhart, 1981; Taft, 1994).
Yet other models proceed on the assumption that the whole and its parts areaccessed in parallel (Baayen, Dijkstra, & Schreuder, 1997; Diependaele, Duñabeitia,
Morris, & Keuleers, 2011; Frauenfelder & Schreuder, 1992; Kuperman, Schreuder,
Bertram, & Baayen, 2009; Pollatsek, Hyönä, & Bertram, 2000). Although efficiency
in lexical processing has often been discussed in terms of the dichotomy ofcomputational efficiency and storage efficiency (McClelland & Patterson, 2002a,
2002b; Pinker & Ullman, 2002a, 2002b), it has also been argued that it is efficient
to redundantly represent and activate all constituent morphemes, as well as wholeword units, thus maximizing opportunities for word identification (Libben, 2006).
Previous eye-tracking studies provided partial support for such parallel-routearchitecture. Pollatsek et al. (2000) tracked eye-movements when Finnish
compounds were read in sentences. Although a complete decompositional model
predicts a whole compound frequency effect to appear later than an effect of thesecond constituent frequency, the study found that whole compound frequency
effect appears at least as early as the second constituent frequency effect, indicatinga race between a decompositonal route to activate the constituents and a direct routeto activate the whole compound. Kuperman et al. (2009) more recently combined
lexical decision with eye-tracking and observed simultaneous contributions ofwhole word frequency and morphological constituent frequency already at the first
fixation, before the entire word had been scanned. These results challenge stricthierarchical processing models but are compatible with both non-hierarchical

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Japanese morphographic word recognition 5
multiple route models and with hierarchical models that allow lower level units toconnect with higher level units while skipping intermediate levels.

Morphographic word recognition
The writing systems of Chinese and Japanese add various layers ofcomplexities to the current theories developed for English and other related
languages. Morphographic orthographies make use of very large numbers ofsymbols. The minimal basic set of characters taught in Japanese compulsory
education comprises 1,945 distinct characters (Japanese Ministry of Education,
Culture, Sports, Science and Technology, 2009). The Japanese industrial standard(JIS) list of characters for computers includes 6,353 characters, and ordinary
Japanese and Chinese morphographic character dictionaries contain well over
10,000 characters (Coulmas, 2003; Kess & Miyamoto, 1999). Unlike alphabetic
letter symbols, Japanese morphographic characters directly encode meaning (e.g.,! /ki/ ‘wood’).
Although kanji characters have often been compared to morphemes inalphabetic languages, the majority of characters are themselves decomposable into
smaller units. The character " /kai/ ‘sea’, for example, consists of a semantic
radical 氵 and a phonetic radical #. Among 2,965 Japanese Industrial Standard
kanji characters, 83% of the characters consist of either left and right radicals or top
and bottom radicals (Saito, Kawakami, & Masuda, 1995, 1997). Semantic radicals
encode a general basic category meaning. The radical 氵 ‘water’, for example, isshared by characters whose meaning is associated with ‘water’ (e.g., " ‘sea’, $‘liquid’, and % ‘liquor’), although the contribution of the semantic radical to the

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Japanese morphographic word recognition 6
whole character is not always transparent (e.g., & ‘law’ is not related to ‘water’).
Phonetic radicals, on the other hand, encode approximate information about the
pronunciation of the character (e.g., " and ' are both pronounced /kai/). The
different functions of semantic and phonetic radicals are explicitly taught in primaryschool.
When they encounter unfamiliar words, readers of Japanese can rely on theradicals. For example, an unfamiliar two-character word such as () ‘winteryellowtail’, which appeared only once in 14 years of newspaper texts (Amano &Kondo, 2003), is relatively well-interpretable thanks to the right character’s
semantic radical * ‘fish’ and the left character ( ‘cold’, even though the reader
may not know what the phonological form of the Japanese word is (/kanburi/; the/n/ denotes a moraic nasal). A large majority of Japanese words are written with two
kanji characters (70% as estimated by Yokosawa & Umeda, 1988). A question
addressed in this study is how readers process radical and character information incomprehending relatively familiar two-character words.1
Several experimental studies suggest that the characters in two-characterwords are accessed in reading. Hirose (1992), observing a stronger priming effect of
the left character over that of the right character in primed lexical decision,
proposed that two-character words are represented in clusters centered around theshared left character, and that they are processed from left to right, with the leftcharacter functioning as the retrieval cue. While this perspective appears to be inline with importance of the initial constituent reported by Taft and Forster (1976)
for English and Yan et al (2006) for Chinese, Tamaoka and Hatsuzuka (1995) andZhang and Peng (1992), in contrast, reported that the frequency of the right

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Japanese morphographic word recognition 7
character facilitates two-character lexical decision responses more than the leftcharacter in Chinese and Japanese respectively. Kawakami (2002) reported
facilitation from the type frequency of characters in two-character word lexicaldecision.2 In addition to character frequency effects, Tamaoka (2005) observed thatlarger numbers of homophones associated with the left character lead to longer
response times in lexical decision and naming. Tamaoka and Hatsuzuka (1998,
lexical decision and naming) further reported that semantic/conceptual properties ofcharacters co-determine word recognition responses (cf. Ji & Gagné, 2007,
sense-nonsense judgment with English compounds).
A separate series of studies has addressed the role of radicals insingle-character words. Taft and Zhu (1997) reported that higher type frequency of
the right radical speeds up character decision. Feldman and Siok (1997) similarly
reported facilitatory effects of radical type frequency, but they considered thefunction of radicals (i.e., semantic vs. phonetic), rather than their positions. Theyobserved that a greater type frequency of the semantic radical facilitated character
decision when the radical is located in the left position of the character. Feldman
and Siok (1999) further argued, from primed character decision data, that themeaning of the semantic radical is co-activated. A contribution of radicals also has
been reported in speeded semantic categorization (Flores d’Arcais & Saito, 1993)
and in word naming (Flores d’Arcais, Saito, & Kawakami, 1995).
---------------------------------------------------------(Insert Figure 1 around here)!
---------------------------------------------------------In the present study, we primarily test the predictions of the two hierarchical

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Japanese morphographic word recognition 8models of morphographic two-character word recognition shown in Figure 1. The
character-based model (left, Tamaoka & Hatsuzuka, 1998) claims that characters
are the basic lexical units, whereas the radical-based model (right, Ding, Peng, &Taft, 2004; Saito, 1997; Saito, Masuda, & Kawakami, 1998; Taft & Zhu, 1997; Taft,
Zhu, & Peng, 1999) assumes that radicals mediate between strokes and characters.
Both models presuppose left-to-right scanning of the visual input (Taft & Zhu,
1997; Tamaoka & Hatsuzuka, 1995), and both assume that a higher level unit can
only be activated once its lower level constituent units are activated.
The two models diverge with respect to the role of radicals. Taft et al. (1999)
and Saito (1997) argue that morphographic characters are initially decomposed into
radicals. In models that distinguish characters and radicals, an issue at stake iswhether semantic radicals are semantically interpreted as soon as they are activated.
Taft et al. (1999) assume that characters form the first level in the hierarchy thatprovides access to meaning. In other words, in this model, radicals function as
purely orthographic access codes. However, there is some experimental evidence
suggesting that semantic radicals are interpreted semantically as soon as they havebeen activated. (Feldman & Siok, 1997, 1999; Miwa, Libben, & Baayen, 2012). The
evidence for the two models in Figure 1 comes from two distinct streams ofresearch. Evidence for characters as processing units was obtained with experiments
using two-character words, while evidence for radicals as processing units was
obtained using single-character words. Miwa et al. (2012) performed the first study
addressing the role of semantic radicals in the processing of two-character words. In
their lexical decision study with partial repetition priming of the semantic radical in
the right character, a significant interaction was observed between the priming

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Japanese morphographic word recognition 9
manipulation and the semantic properties of the semantic radicals, suggesting thateven in two-character words, an effect of semantic radicals can be detected.

Goals of this study
The studies reviewed in the previous section involved 15 lexical decisionexperiments, all based on only 30 to 90 target words (M = 51, SD = 17.8) matchedon a limited number of experimental variables. As Cutler (1981) pointed out three
decades ago, it is a “confounded nuisance” to pre-experimentally control for thegrowing number of all potentially important variables, and we will be lost for words.
For example, Yan et al., (2006) manipulated frequencies of words and characters ina 2*2*2 design with strokes and radical frequencies controlled; each of the eight
conditions consequently contained only six words. If radical frequencies were also
to be manipulated, in theory, 32 conditions would be necessary. While Tamaoka(2005, 2007) carefully controlled for a relatively large number of 11 and 18
potentially important variables, all the other studies controlled for a much smaller
number of variables. Pre-experimental matching on numerical covariates may leadto substantial loss of statistical power (Baayen, 2010; Cohen, 1983; MacCallum,
Zhang, Preacher, & Rucker, 2002), and may negatively affect the representativenessof the sampled items. We therefore opted for a regression design analyzed with
mixed-effects models (Baayen, 2008; Baayen, Davidson, & Bates, 2008; Baayen &Milin, 2010), assessing subject, item, and task effects jointly to obtain a morecomprehensive picture of Japanese visual word recognition with 24 lexical variables,using 708 target words.
All previously mentioned studies relied on chronometric measures. In order to

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obtain more insight into the microstructure of information processing in lexicaldecision, we conducted an eye-tracking experiment combined with lexical decision.
Previous studies (Hyönä & Pollatsek, 1998; Kuperman et al., 2008, 2009; Pollatsek
et al., 2000) suggest that morphological processes can be investigated througheye-movements (but see Andrews, Miller, & Rayner, 2004, for lack of such stronglink). Using a regression design with over 500 two-character words, we tested
several questions in parallel. First, what is the time course of activation of strokes,
radicals, characters, and words? Hierarchical models predict higher level units tobecome active only once their lower level constituent units have been activated.
Hence, these models predict stroke effects to precede radical effects in theeye-movement record, radical effects to precede character effects, and character
effects to precede whole word effects. The magnitude of the effects is also expected
to vary with time. For instance, radical frequency is expected to have a large effecton initial fixation durations but little or no effect on later fixations. Of special
interest here, given the early compound frequency effect observed in Kuperman etal. (2009), is the moment in time at which the effect of compound frequency firstemerges.
Second, what is the relative importance of the left and the right characters in
two-character word recognition? Does the left character have a privileged statuscompared to the right character, as argued by Hirose (1992)? If so, does an initial
fixation on the right character have a catastrophic effect on comprehension? If,
however, the right character is important, as suggested by Tamaoka and Hatsuzuka(1995) and Zhang and Peng (1992), it is worth considering whether the rightcharacter’s privilege is due to a left-to-right scan process (Tamaoka & Hatsuzuka,

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Japanese morphographic word recognition 11
1995) or due to the fact that the right character is the main morpheme that should beprocessed first, at least in reading modifier-head compounds (Zhang & Peng, 1992).
If a left-to-right scanning is preferred for Japanese, as for alphabetic languages(Hyönä & Pollatsek, 1998; Pollatsek et al., 2000), early and late time frames, as
determined by eye fixations, should reflect the left and the right characters’
contributions respectively.
Third, are semantic radicals interpreted semantically or do they function justas orthographic access codes? In the former case, we expect that the degree to which
the semantic radicals contribute to the meaning of the character, as gauged bysemantic transparency ratings (Feldman & Siok, 1999; Miwa et al., 2012), should
co-determine fixation durations and/or lexical decision speed. If a semantic radical
is interpreted semantically, then a next question would be whether a semantictransparency effect appears early, indicating early morpho-semantic processing(Diependaele et al., 2005, 2011; Feldman, O'Connor, & Moscoso del Prado Martín,2009) or late, indicating that early morphological processing is semantically blind(Davis & Rastle, 2010; Longtin & Meunier, 2005; Longtin, Segui, & Halle, 2003;
McCormick, Rastle, & Davis, 2008; McCormick, Rastle, & Davis, 2009). If an
early semantic involvement in morphological processing is a must, then radical andcharacter semantic transparencies should show facilitation in the earliest time frame.
Fourth, to what extent is the uptake of visual information co-determined by
non-linguistic factors? We manipulated the readers’ attention by varying the
fixation point, which was positioned on the left character, on the right character, orin between the two characters. Kajii, Nazir, and Osaka (2001) report that fixations
tend to fall onto the left character in sentential reading. However, the position of

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fixations seems to be more flexible (left or centre) in Chinese (Yan, Kliegl, Richter,
Nuthmann, & Shu, 2010). Furthermore, if the right character is the main morpheme(Zhang & Peng, 1992), then an initial fixation on the right character may be more
beneficial. Most previous isolated word reading studies directed the readers’attention to the word centre, which limits generalizability of the results. However,by shifting attention to other positions in the word, the consequences of
dis-preferred initial fixation positions can be evaluated.

Predictors
In our study, we made use of a regression design with subjects and items as
crossed random-effect factors. This section introduces the fixed-effect factors andcovariates that we considered. Unless noted otherwise, we used lexical
distributional data as available in the web-accessible database for Japanesecharacters constructed by Tamaoka et al. (2002) and Tamaoka and Makioka (2004).
Table 1 summarizes the lexical distributional properties considered in the present
study, grouped by different levels of linguistic structure posited by the hierarchicalmodels as developed by Taft et al. (1999) and Saito (1997).
---------------------------------------------------------Insert Table 1 around here---------------------------------------------------------Feature-level predictors
At the feature level, LeftKanjiStrokes and RightKanjiStrokes quantify the
number of strokes in a character. The stroke count measure is designed to capture
what word length captures for alphabetic languages: the complexity of the visual

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input. Word length generally has an inhibitory effect in chronometric andeye-tracking studies (Balota et al., 2004; Vitu, O’Regan, & Mittau, 1990), although
there is some evidence for non-linearity for shorter word lengths (Baayen, 2005;
New, Ferrand, Pallier, & Brysbaert, 2006). Similarly, previous studies on Japaneseand Chinese suggest that characters with many strokes are processed slower thanthose with few strokes (Leong, Cheng, & Mulcahy, 1987; Liu, Shu, & Li, 2007).
Note, however, that feature level complexity in Japanese manifests itself in the formof the density of visual information within a highly restricted fixed word region. As
a consequence, the visual acuity limitation relevant for scanning extended strings ofletters in alphabetic languages will not contribute to the visual complexity effects inJapanese.

Radical-level predictors
At the level of radicals, LeftKanjiRadicalCombinability andRightKanjiRadicalCombinability are the log-transformed type frequency of thesemantic radicals, representing how many basic Japanese characters share a given
semantic radical. LeftKanjiRadicalTokenFreq and RightKanjiRadicalTokenFreq are
the log-transformed cumulative token frequency of all characters (in the 1,945 basic
kanji list) sharing a given semantic radical, calculated from Amano and Kondo(2000). Previous studies (Feldman & Siok, 1997, 1999; Miwa et al., 2012; Taft &Zhu, 1997) suggest that we may expect facilitatory contributions from these type
and token frequency measures. The present study considers only semantic radicals
because all characters, regardless of their complexity, contain a semantic radical
without exception whereas characters need not contain a phonetic radical.

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Character-level predictors
At the level of characters, we considered log-transformed character token
frequency (LeftKanjiTokenFreq, RightKanjiTokenFreq) and log-transformed
position-dependent character neighbourhood size in two-character words(LeftKanjiNeighbour and RightKanjiNeighbour). Independent effects of constituent
frequency and neighbourhood size in two-character word recognition have beenreported by Tamaoka and Hatsuzuka (1995) and Kawakami (2002) respectively.

Word-level predictors
At the whole word level, we considered log-transformed written frequency(WholeWordFreq), based on newspapers published in the 14-year period from 1985
to 1998 in the lexical database of Amano and Kondo (2003) covering 341,771
words. We complemented this frequency measure with the log-transformed Google
document frequency as of November 29, 2008. This dispersion measure provides anestimate of the range of different documents (genres, registers) in which a word isused. Contextual diversity of words has been reported as a powerful measure insome recent studies (e.g., Adelman, Brown, & Quesada, 2006; Brysbaert & New,
2009), and we expected this Google dispersion frequency to have an additive effecton top of the standard word frequency effect (see Ji & Gagné, 2007 and Myers,
Huang, & Wang, 2006 for previous studies using Google document frequency).

Phonological predictors
In order to assess phonological ambiguity and its effect on reading (Ferrand &URL: http:/mc.manuscriptcentral.com/pqje

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Grainger, 2003; Pexman, Lupker, & Jared, 2001; Tamaoka, 2005), we made use of
the log-transformed number of homophonous characters (LeftKanjiHomophones
and RightKanjiHomophones). Tamaoka (2005) reported that words with a leftcharacter with many homophonic characters, relative to few, elicited longer
response times in lexical decision and naming.3

Semantic predictors
Given the possibility of a processing advantage for semantically transparent
compounds (Libben, 1998; Libben, Gibson, Yoon, & Sandra, 2003), we also
included two measures for the semantic transparency of the characters in thecompound. Although character activation in compound reading has been argued to
be orthographic (Kawakami, 2002; Saito, 1997), other studies suggest that meaningsof characters are co-activated (Tamaoka & Hatsuzuka, 1998; Ji & Gagné, 2007).
LeftKanjiTransparency and RightKanjiTransparency gauge the semantic congruity
between the meaning of the character and the meaning of the whole word. Bothmeasures are based on mean ratings elicited from six native Japanese readers, using
a seven-point scale (Cronbach’s alpha > 0.99, M = 6.0, SD = 1.1 for
LeftKanjiTransparency; Cronbach’s alpha > 0.99, M = 6.0, SD = 1.0 for
RightKanjiTransparency, using the psy package for R by Falissard, 2007). For
example, + ‘halberd’ and , ‘shield’ in +, ‘contradiction’ are relatively
opaque with transparency ratings of 2 for both characters, while 空 ‘air’ and 港‘port’ in 空港 ‘airport’ are relatively transparency with transparency ratings of 6
for both characters.
Furthermore, in order to test whether semantic radicals are mere orthographic

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access units or meaningful “orthographic morphemes”, we included two measuresof semantic radical transparency (LeftKanjiRadicalTransparency andRightKanjiRadicalTransparency). These measures represent the degree of semantic
congruity between the meaning of the character and the meaning of the radical.
Eight native Japanese readers rated similarity in meaning between characters andtheir semantic radical on a seven-point scale (M = 3.9, SD = 1.7, Cronbach’s alpha >0.99). In the analyses below, we used the mean ratings. For example, the semantic
radical - ‘fire’ in . ‘cook’ is relatively transparent (transparency rating = 6)
while 氵 ‘water’ in 法 ‘law’ is opaque (transparency rating = 1).

Multicollinearity among lexical predictors !
The present set of lexical distributional predictors is characterized by serious
multicollinearity. We removed most of this collinearity by residualization ofcorrelated predictors, following Kuperman et al. (2009). For example, because
WholeWordFreq is highly correlated with GoogleDocFreq (r = 0.59, p < 0.01), we
regressed the latter on the former and used the resulting residuals,
GoogleDocFreqResid, as a new predictor gauging the Google document frequency
uncontaminated by the written newspaper-based frequency. We followed the sameprocedure for other pairs of predictors that are highly correlated:
RightKanjiNeighbourResid was orthogonalized with respect toRightKanjiTokenFreq (r = 0.88 for the correlation between
RightKanjiNeighbourResid and RightKanjiNeighbour),
RightKanjiRadicalTokenFreqResid was residualized on
RightKanjiRadicalCombinability (r = 0.48), and RightKanjiStrokesResid was

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residualized on RightKanjiNeighbour (r = 0.92). Because the pattern ofmulticollinearity among lexical predictors was identical for characters at the left
position, the same procedure was followed for computing residualized predictors.
As a result, all pairwise correlations among the given lexical properties became lessthan 0.30, except that between LeftKanjiTransparency and RightKanjiTransparency(r = 0.59). As for these two predictors, we tested one predictor at a time in a given
analysis. As we shall see below, one predictor always outperformed the other, so
this correlation was not a problem (see Appendix A for a correlation matrix for all
the numerical predictors considered in this study).

Individual differences and task-related predictors
Although the readers we tested in the present study were all native Japanesereaders, they differed in the extent to which they are using Japanese in Canada. As ameasure of language proficiency, we included their log-transformed
LengthOfStayCanada in months as a predictor. This measure correlated positively
with age (r = 0.47, p = 0.03) and negatively with log-transformed self-ratings ofdaily exposure to Japanese (r = -0.52, p = 0.01) and the 100-Rakan Japanese kanji
reading ability scores (Kondo & Amano, 2001, r = -0.54, p = 0.01).
LengthOfStayCanada did not correlate significantly with vocabulary size inJapanese (Amano, Kondo, & Kataoka, 2005) for the readers we tested. Vocabulary
size in Japanese, however, correlated positively with 100-Rakan reading ability
scores (r = 0.46, p = 0.04, cf., r = 0.70, N = 1000; Amano, 2007), which also
correlated with LengthOfStayCanada. Given this multicollinearity, we opted forLengthOfStayCanada as the predictor reflecting various types of individual

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differences and language proficiency for our statistical analyses, leaving the specificadvantages and disadvantages of the other related measures to future research.
Consistency in human behaviour often leads to auto-correlated time series of
response times and fixation durations (Baayen & Milin, 2010; de Vaan, Schreuder,& Baayen, 2007; Kuperman et al, 2009; Perea & Carreiras, 2003). We removed theauto-correlation from the errors by including three control predictors: PreviousRT,
the response time at the previous trial, PreviousTrialCorrect, a factor encoding thecorrectness of the response at the previous trial (levels Correct and Incorrect), andTrial, the rank of the item in the experimental list.
A further predictor was Fixation, a factor specifying whether the initial
fixation was directed to the Left character, the Central position between the two
characters, or the Right character.
In the eye-movement analyses, we considered PreviousSubgazeDuration, thesubgaze duration at the previously fixated region, and EyePosition, a factor
encoding the current eye position (levels Left and Right character regions).

Experiment 1: Lexical decision with eye-tracking!!
MethodParticipants. Twenty-one native Japanese speakers (18 female, 3 males; mean
age = 21.2 years old, SD = 2.9) were recruited at the University of Alberta. All
participants had normal or corrected-to-normal vision, and their mean score on the
100-Rakan kanji word reading test was 48.7 out of 100 (SD = 19.9), which iscomparable to the larger population mean (M = 49.6, SD = 19.6, N = 1000; Amano,2007). The participants had been in Canada for 25.9 months on average (SD = 26.9,

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range 0 to 76 months).
Apparatus. An SR Research EyeLink !! head-mounted eye-tracker was used
to track participants’ eye-movements. The pupil-only mode was used to track eye
movement with a sampling rate of 250 Hz. Words were presented on a 20-inch
display controlled by SR Research Experiment Builder.
Materials. Target words in this lexical decision experiment were randomly
sampled from a subset of the NTT lexical database (Amano & Kondo, 2003). This
subset was compiled from the database by imposing the following restrictions. First,
the words should occur at least 100 times in the newspaper corpus. Second, only
common nouns were selected. Third, the words with homophonous neighbors were
excluded. Fourth, the words should not contain a duplicated character (e.g. oriori/0 ‘occasional’ where 0 indicates that the left character is repeated) nor a kanji
numeral (e.g. hachinin 12 ‘eight people’). Fifth, the words should not be
restricted in their use to fixed or idiomatic phrases (e.g., katabo 34 ‘a bar’normally occurs in an idiom katabo wo katsugu ‘take part in’). Sixth, relatively
unfamiliar two-character words that are not listed in Kojien Japanese Dictionary(Nimura, 2002) were excluded as well (e.g., konkaku 56 ‘mass capturing’).
From the resulting subset, we randomly sampled 708 two-character words.
We also prepared 708 nonwords falling into four different types: (1) 60
nonwords were created by switching the order of two characters, (2) 60 nonwords
were created by replacing the first constituent with another homophonous character,(3) 60 nonwords were created by replacing the second constituent with anotherhomophonous character, (4) the remaining 528 nonwords were created by randomly
combining characters. The first three sets of nonwords were included as part of a

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separate study not reported here.
Procedure. The experiment consisted of three sessions conducted on different
days. Each session lasted for approximately 90 minutes, except for the first sessionthat lasted for 120 minutes. At the beginning of the first session, participants
completed the 100-Rakan test and the vocabulary size estimation test.
In the lexical decision experiment, participants were asked to indicate whether
the presented word is a legitimate Japanese two-character word or not by pressing
buttons on a Microsoft SideWinder game pad with their left (= No) and right (=Yes) index fingers. Their eye-movements were tracked by an EyeLink !!
head-mounted eye-tracker. For each trial, a fixation point (an asterisk * in 60 point
Verdana bold font), which was also used for drift correction, was presented for atleast 500 ms, followed by a target two-character word in white Mincho font, size
130, on a black background. With a viewing distance of 70 cm from the screen, thevisual angle was 5.3° for each character. The word remained on the screen until theparticipant responded. A drift correction was performed at every trial; a target word
did not appear until participants had fixated on the fixation point. The location of
the fixation point was varied across different sessions such that participants were
presented with a fixation either at the central position of the screen, at a position
slightly towards the left (i.e., where a left character was presented), or at a position
slightly towards the right (i.e., where a right character was presented). The order ofsessions with different fixation points was counter-balanced within subjects.
The lexical decision experiment started with 12 practice trials in each session,
followed by 472 experimental trials ((708 + 708)/3) containing two breaks. Afterthe practice trials and at each break point, participants were given feedback as to

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how fast (ms) and accurately (correct %) they had been responding so far.
Throughout the entire experiment, the left eye was tracked for the half of theparticipants and the right eye was tracked for the rest of the participants. The words
were presented in a different randomized order to each subject.

Results
Statistical analyses were carried out using R version 2.13.2 (R Development
Core Team, 2011). Data from two participants were excluded from the subsequent
RT and eye-movement analyses due to high error rates (exceeding 35%). All
predictors with a skewed distribution (i.e., frequency-based predictors and thereaders’ length of stay in Canada) were logarithmically transformed.
As dependent variables, we considered response times (RTs), as well as firstand second subgaze durations. Total fixation durations were virtually identical toresponse times and are not analyzed separately. Subgaze duration was defined as the
cumulative first-pass fixation duration that fell into one character before the eye
departed to another character. The onset of the first subgaze period on a target word
began from the onset of the target word presentation. We opted for the subgaze
duration based on character regions, as visual inspection of the on-line
eye-movements and density plots for fixations suggested that the eye-movements
were character-based and not radical-based. In trials with two and three fixations,
70% of the eye-movements moved to the other character region (71.3%, 65.3%, and73.3% for the left, central, and right fixation conditions respectively).

Response time analysis. For the response time analysis, data points with

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response time shorter than 300 ms or longer than 3,000 ms were excluded from thedataset. In addition, all data points of those words that elicited over 40% incorrect
responses were removed. Furthermore, remaining individual data points with anincorrect response were excluded as well. The analysis was restricted to those
two-character words for which the lexical distributional properties were available
for both the left and right characters. This resulted in a dataset with 9,228 data
points for 555 different words. Because the distribution of RTs was highly skewed
with a long right tail, a reciprocal transformation (-1000/RT) was applied to the RTs.
Using a linear mixed-effects model with subject and word as crossed random-effectfactors (Baayen, 2008; Baayen et al., 2008; Bates, Maechler, & Dai, 2007), we first
fitted a simple main effects model with lexical properties at all levels of thehierarchy listed in Table 1.4 We then considered interactions with respect toFixation, PreviousTrialCorrect, and LengthOfStayCanada. After removing
non-significant predictors to obtain the most parsimonious yet adequate model, we
removed as potentially harmful outliers data points with standardized residuals
exceeding 2.5 standard deviation units, and then refitted the model. The random
effect structure of the final model comprised random intercepts for item (SD = 0.12)
and subject (SD = 0.21), by-subject random slopes for centralized Trial (SD = 0.01),
for centralized PreviousRT (SD = 0.07), and for GoogleDocFreqResid (SD = 0.01).
Other random slopes were tested, and none were significant. The standard deviationof the residual error was 0.26. Table 2 summarizes the coefficients of this modeland Figure 2 visualizes the interactions. Predictors that did not reach significance atthe 5% level are not listed in Table 2.
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Insert Table 2 and Figure 2 around here---------------------------------------------------------Feature-level effects. Lexical distributional properties at all levels of thehierarchy emerged as significant predictors of the response times. Words withgreater left character feature complexity (LeftKanjiStrokesResid) elicited longer
response times (effect size = 101 ms). The absence of a significant effect ofRightKanjiStrokesResid is consistent with theories that assume processing toproceed from left to right (Hirose, 1992; Taft & Zhu, 1997; Tamaoka & Hatsuzuka,
1995).
Character-level effects. The effect of RightKanjiTokenFreq was facilitatory,
particularly when the response at the previous trial was incorrect (Figure 2, Panel a).
We suspect that after readers make an error, they pay special attention to the headcharacter, as this will help them to make a correct lexicality decision: In order toreject a stimulus such as cloudchair, the readers have to assess whether cloudchairis an existing kind of chair. If this interpretation is correct, the effect ofRightKanjiTokenFreq is a late, conceptual, effect.
Word-level effects. WholeWordFreq and GoogleDocFreqResid bothfacilitated responses (effect sizes = -180 ms and -180 ms). The presence of theadditive effect of GoogleDocFreqResid suggests a need to consider contextual
diversity of words as an important factor in understanding how words areentrenched in memory (Adelman et al., 2006; Brysbaert & New, 2009). Adelman etal. (2006) reported for English that when frequency is residualized on contextual
diversity, it is no longer a significant predictor. For the present data, this did not
hold: Both residualized frequency and GoogleDocFreq contribute independently to

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the model, both p < 0.0001).
Phonological effects. The number of homophones of the right character
slowed down responses as well (effect size = 53 ms), as expected. This finding
contrasts with Tamaoka’s (2005) observation of an inhibitory morphemic
homophony effect for the left character only. This difference might be due to theway nonwords were constructed. In Tamaoka’s (2005) study, nonwords were
pseudo-homophones with homophonic left characters only. In the present study, thepseudo-homophones appeared in both positions, while in addition many nonwords
were random combinations of characters. As a consequence, the role of the rightconstituent as the head is more important in the present study. This morphemic
homophony effect may reflect a rebounding effect of phonology to orthography(Pexman et al., 2001; Tamaoka, 2005, 2007). Alternatively, it may reflectcompetition between different meanings associated with homophonic alternatives.
We will return to the homophone effect below when discussing the second subgaze
durations.
Semantic effects. The semantic transparency of the right character speeded up
responses as the experiment went by (Figure 2, Panel b), suggesting that the criteria
for discriminating between words and nonwords were adjusted in the course of theexperiment. In this task, it is not trivial to discriminate real transparent compounds
such as handbag from nonwords such as toebag. In the course of the experiment,
the reader becomes more proficient at discriminating the words from the nonwords,
apparently by relying more on the presence of a transparent semantic relation
between the head and the modifier in memory, which is not available for nonwords.
As a consequence, the expected facilitation from the head transparency emerges

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later in the experiment. These effects of the character transparency emerged only thereaction time analysis and were absent in the analyses of subgaze durations. Thissuggests that the effect occurs late, after the eye has completed extracting
information from the individual characters.
Individual differences. Finally, individual differences were present (Figure 2,
Panel c), notably for trials with the fixation mark placed at the central position. As
can be seen in Panel c, the central fixation position elicited faster response times,suggesting that this central position is the optimal viewing position for isolated
compound reading. For readers who have stayed longer in Canada, however, theadvantage of this optimal viewing position became increasingly smaller. Recall that
LengthOfStayCanda is correlated with other predictors (e.g., the amount of
exposure to Japanese, age, and reading ability), hence a precise interpretation of thiseffect requires further research (cf. Goral et al., 2008, for dissociation of age andlinguistic effects in lexical attrition). Table 2 also lists the contribution of
LeftKanjiNeighbourResid: Response times decreased (effect size = -41 ms) withincreasing LeftKanjiNeighbourResid. We discuss the interpretation of this effectbelow in the analyses of the subgaze durations.

First subgaze duration analysis. Only items and subjects analyzed in the
response time analysis were considered for eye movement analyses. The number offixations elicited varied from 1 to 15 per trial, with the mode at 3 fixations (3,203
trials), followed by 2 fixations (2,772 trials) and 4 fixations (1,348 trials). A small
minority of 428 trials elicited only one fixation. In the subsequent analyses, we
focused on subgaze durations. Subgaze counts varied from one to eight fixations

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with the mode at two subgazes. In the subsequent subgaze duration analyses, we
focus on the trials with exactly two subgazes, which represent the large majority ofdata points (72% of the subgazes).4
For the analysis of the first subgaze durations (3,711 data points), initial
fixations shorter than 100 ms were removed. In a quantile-quantile plot of the firstsubgaze durations, these short fixations patterned differently from the remaining
durations. Trials that elicited incorrect responses for the lexical decision and trials
with a blink were also excluded. The remaining durations were subsequently
log-transformed to adjust for non-normality. The quantiles of raw first subgaze
durations are 113 ms (minimum), 237 ms (1st quartile), 319 ms (median), 409 ms(3rd quartile), and 1080 ms (maximum).

---------------------------------------------------------Insert Table 3 and Figure 3 around here---------------------------------------------------------We fitted a mixed-effects model with subjects and items as crossed random
effect factors to the first subgaze durations. We considered all pairwise interactions
and removed unsupported coefficients from the model specification. To safeguard
against adverse effects of outliers, data points with absolute standardized residuals
exceeding 2.5 were removed and the model was refitted. The coefficients of thismodel are summarized in Table 3, and the interactions are visualized in Figure 3.
The random effect structure of this model comprised random intercepts for item (SD= 0.07) and subject (SD = 0.18), by-subject random slopes for Trial (SD = 0.0003),

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and by-subject random contrasts for EyePosition (SD = 0.37). The random contrasts
for EyePosition capture the heteroscedasticity characterizing the two eye positions,with greater variance when the eye is fixating on the right character. The standard
deviation of the residual error was 0.25.
Feature-level effects. As expected, feature-level complexity contributed
substantially to the first subgaze durations. Character stroke complexity interactedwith the location of the fixation (EyePosition) illustrated for LeftKanjiStrokesResidin Panel a and RightKanjiStrokesResid in Panel b. More complex characters elicited
longer subgaze durations when the character was currently fixated on, but shorter
subgaze durations when the character was not fixated on. This pattern resembles
parafoveal-on-foveal effects as reported in sentence reading, with complexity anddifficulty in the parafoveal region attracting attention and shortening the time the
eye remains on the current constituent (Hyönä & Bertram, 2004; Kennedy & Pynte,
2005; Kliegl, Nuthmann, & Engbert, 2006; Pynte, Kennedy, & Ducrot, 2004). The
processing of the non-fixated information unit indicates that the strict eye-mind
assumption is too restrictive.
Radical-level effects. The type frequency of the characters’ radicals,
LeftKanjiRadicalCombinability and RightKanjiRadicalCombinability, wasinhibitory for the left character (effect size = 24 ms) and facilitatory for the rightcharacter (effect size = -24 ms). The asymmetrical contributions of the left and theright radicals arose possibly because the semantic class marked by the modifier’s
radical was incompatible with that of the whole word (see also Miwa et al., 2012,
for asymmetrical contribution of the left and the right radicals). In addition,
RightKanjiRadicalTokenFreqResid co-determined the first subgaze durations but in

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an attention-dependent manner (Panel c): Its inhibitory contribution was evident
only when the eye was on the right character. Note that although radical properties
co-determined the first subgaze durations, the magnitudes of their effects were small
or only EyePosition-specific.
Character-level effects. An effect of LeftKanjiTokenFreq was present in aninteraction with EyePosition and LeftKanjiNeighbourResid, the type count of thenumber of two-character words sharing the left character. When the eye was
fixating on the left character (Panel d), regardless of the number of the left kanji’sneighbours, LeftKanjiTokenFreq speeded up recognition. When the eye was fixatingon the right character, a cross-over interaction was observed (Panel e). Words withfew LeftKanjiNeighbourResid showed facilitation from the left character’sfrequency. As the number of completions increased, this facilitation disappeared
and reversed into inhibition. Panels (d) and (e) together illustrate a generalpreference for processing the left character regardless of the initial eye position.
LeftKanjiTokenFreq therefore shows an expected facilitatory effect when thecharacter is attended (Panel d).
In addition to the effect of LeftKanjiTokenFreq, an effect ofRightKanjiTokenFreq was present but only in an interaction with
LeftKanjiNeighbourResid (Panel f): When there are few possible completions on theright (low LeftKanjiNeighbourResid), facilitation by the right character’s frequency
was observed. However, in the presence of greater uncertainty about the identity of
the right character in a dense neighbourhood, readers cannot utilize
RightKanjiTokenFreq. This is in line with Hyönä, Bertram, and Pollatsek’s (2004)
report that the second constituent is processed more deeply when it is more

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constrained. In their sentential reading study with an eye-movement–contingent
display change technique, the change effect associated with the second constituent
was stronger for words with a first constituent with low frequency and small family
size. The effect of RightKanjiTokenFreq for both eye positions is consistent withthe previously discussed effect of parafoveal preprocessing of feature properties(Panels a and b).
Word-level effects. More frequent compounds elicited shorter first subgaze
durations, as reflected by the negative coefficients of GoogleDocFreqResid (-26ms), although WholeWordFreq was not significant. Such an early contribution ofwhole word frequency was also reported by Kuperman et al. (2009) for Dutch. As
we shall see below, the effect of compound frequency became stronger at thesecond subgaze.
Phonological effects. Character phonology, LeftKanjiHomophones andRightKanjiHomophones, did not co-determine the first subgaze duration. This isconsistent with the hypothesis that homophonic effects in visual word recognition
are due to rebounding activation from phonology to orthography (Tamaoka, 2005;
Pexman, Lupker, & Jared, 2001). If this line of reasoning is correct, we should be
able to observe phonological effects at the second subgaze duration (see below).
Semantic effects. Furthermore, there was an inhibitory effect ofRightKanjiRadicalTransparency (12 ms). If the radical is more transparent, it ismore effective in activating its own typically more general meaning (e.g., 7 ‘bodypart’ in 8 ‘brain’), which will compete with the meaning denoted by its character.
Unlike in the analysis of response times, LeftKanjiTransparency andRightKanjiTransparency, both of which evaluate the semantic contribution of the

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character to the meaning of the two-character compound, did not reach significancefor the first subgaze duration. Apparently, at the first subgaze, it is a local semantic
relation, transparency of the radical and its character, that is available forprocessing.

Second subgaze duration analysis. 3,731 data points for the second subgaze
durations in the trials with two subgazes were analyzed in a mixed-effects modelwith subjects and items as crossed random effect factors. A square roottransformation was used to adjust non-normality in the distribution of the subgaze
durations. The quantiles of raw second subgaze durations are 28 ms (minimum),180 ms (1st quartile), 288 ms (median), 404 ms (3rd quartile), and 1196 ms(maximum).
The random effect structure of the final model comprised random intercepts
for item (SD = 0.93) and subject (SD = 1.35), by-subject random slopes forcentralized Trial (SD = 0.002), centralized PreviousRT (SD = 0.47), centralized
PreviousSubgazeDuration (SD = 1.84), and by-subject random contrasts for
EyePosition (SD = 2.18). The standard deviation of the residual error was 2.94.
Table 4 lists the coefficients of the model and Figure 4 visualizes the interactions.
---------------------------------------------------------Insert Table 5 and Figure 4 around here---------------------------------------------------------Feature-level effects. As can be seen in Figure 4, Panels a and b, the effects
of character stroke complexity, LeftKanjiStrokesResid and RightKanjiStrokesResid,
depended on the location of the eye fixation. The general patterns of these

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interactions are comparable to those observed for the first subgaze duration (Figure
3, Panels a and b). However, at this second subgaze, if the eye fixated on the leftcharacter, LeftKanjiStrokesResid greatly slowed down the second subgaze (the solidline, Figure 4, Panel a), while if the eye fixated on the right character, the effect of
LeftKanjiStrokesResid was muted. The effects of RightKanjiStrokesResid showed areversed pattern (Panel b). Interestingly, the effects of the two character stroke
complexities are small when the eye rests on the right character, but large when theeye rests on the left character. This difference may be due to the preferential
processing path from left to right. If the reader starts at the left, the second subgaze
duration concerns the right character. At this point, a substantial amount of
information is already available from the first character, smoothing the way forreading the second character. However, if the reader starts from the right character,then the second subgaze duration concerns the left character, the normal starting
position for reading, and therefore inviting more in-depth processing of the leftcharacter.
Character-level effects. The contributions of RightKanjiTokenFreq (-52 ms)
and RightKanjiNeighbourResid (-52 ms) are comparable to the corresponding
effects of the left character at the first subgaze. Whereas LeftKanjiTokenFreq and
LeftKanjiNeighbourResid contributed at the first subgaze, they did not reachsignificance at the second subgaze. This suggests that the weight of importanceshifts from the left character to the right character in this later time frame.
Word-level effects. As expected, the effects of frequency and contextual
diversity of the whole word, WholeWordFreq and GoogleDocFreqResid, became
larger at the second subgaze (-69 ms and -82 ms respectively). As will be discussed

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below, WholeWordFreq interacted with LeftKanjiRadicalTransparency.
Phonological effects. Significant contributions of the numbers of homophoniccharacters were present for both the left and the right characters(LeftKanjiHomophones and RightKanjiHomophones, -29 ms and 56 ms
respectively). Consistent with the analysis of response times (Table 2, 53 ms),
RightKanjiHomophones was inhibitory. Furthermore, there was a smaller
facilitatory effect of LeftKanjiHomophones, which contrasted with the inhibitory
effect of LeftKanjiHomophones reported in Tamaoka’s (2005) lexical decision study.
This difference may be due to the different kinds of nonwords that we used, which
included two-character words with illegal left characters. The late emergence of
these homophone effects is consistent with the hypothesis that homophoniccharacters are activated only after the target character’s phonological representation
has been activated (rebounding activation; Tamaoka, 2005).
Semantic effects. The semantic congruity between the characters and theirsemantic radical, LeftKanjiRadicalTransparency co-determined the second subgaze
durations (Figure 4, Panel c). The processing advantage for words with semanticallytransparent constituents is consistent with the results of Libben et al. (2003).
However, facilitation was restricted to higher frequency words and disappeared for
low frequency words. LeftKanjiRadicalTransparency facilitates the recognition only
when WholeWordFreq is high. Conversely, the effect of WholeWordFreq wasstrongest for words with high LeftKanjiRadicalTransparency. This interaction
suggests that whole word frequency effect is at least in part a semantic effect.
The kinds of the effects observed at the second subgaze are qualitatively
similar to those observed for the lexical decision response times. Interestingly,

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however, not only the second but also the first subgaze durations correlated with theRTs (r = 0.34, p < 0.0001, for the first subgaze duration; r = 0.51, p < 0.0001 for thesecond subgaze duration) with comparable ß in the regression analysis (ß = 0.14 andß = 0.16 respectively).

Discussion
Overall, the analysis of the first gaze durations identified contributions oflexical distributional properties at all levels of the morphographic structural
hierarchy shown in Table 1. Although whole word frequency, character frequency,
and radical frequency all co-determined first subgaze durations, the magnitude of
their contributions differed. Properties of characters contributed robustly to a larger
extent than properties of radicals and properties of whole word units, as diagnosedby their feature complexity, frequency, or transparency. The large contributions ofcharacters suggest that the characters, rather than radicals, are the dominant
recognition units for two-character words. Importantly, the above effects wereobserved across all subjects because we carefully checked for random-effect slopes
for subject for our predictors. The present findings are more consistent with thecharacter-based models of two-character word recognition (Tamaoka & Hatsuzuka,
1998; Joyce, 2004). However, the presence of both whole word frequency andradical effects at the first subgaze indicates that models positing that lexical accesswould proceed by first accessing the character and only then accessing the radical
and the whole word representation are too restrictive.
With regard to the relative importance of the left and the right constituents,
the properties of the left character contributed more than those of the right character

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at the first subgaze. This suggests that it is more effective to parse two-characterwords from left to right, although when read from right to left, the properties of theright character come into play as well, albeit to a lesser extent.
Thus far, we have interpreted the second subgaze in the same way as the firstsubgaze duration. However, in trials with more than one subgaze, the last subgaze
was interrupted by the button press, which terminated the trials. This raises thequestion of to what extent the second subgaze is interpretable as a measure of
information extraction and lexical access. The response time and the second
subgaze duration incorporate the time required for motor response planning andresponse execution, estimated to be on the order of magnitude of 200 ms by
Schmidt (1982). Given that the mean lexical decision response time in trials with
two subgazes was 653 ms, it is estimated that the lexical decision was finalizedaround 653 – 200 = 453 ms post stimulus onset, i.e., after the first subgaze (M = 323ms) but well before the end of the second subgaze. Assuming that the response
execution time is constant, apart from random execution noise, and independent
from lexical properties, then only the intercept of the regression model for thesecond subgaze is affected.5
The larger contributions of character properties compared to radical properties,
particularly during the early processing stages, indicate that two-character words areprocessed in a character-driven manner, rather than by strictly combinatorial
processes. However, joint contributions of morphographic units at all levels of thelinguistic structure suggest that the character-based model is not sufficient to fully
capture the complexity of morphographic word recognition at its current state. With
respect to relative importance of the left and the right characters, eye-tracking

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highlighted their contributions at early and late processing stages respectively.
Although the right character contributes more prominently to lexical decision
responses, this was not because the right character is the primary access unit but
because it contributes late when lexical decisions are made. Furthermore, semantictransparency effects for radicals indicate that radicals are not mere orthographic
components.
Finally, it was also notable that the magnitude and the direction of lexical
effects were modulated by readers’ locus of attention in a left-to-right preferred
processing path such that lexical properties of the fixated and non-fixated charactersshowed asymmetrical joint contributions.
It might, however, be argued that the character-driven processes we observed
were induced by the large inter-character space that goes hand in hand with therelatively large character font size. Similarly, the small whole word frequency effectobserved during the early time frame might be merely due to visual acuity limitation.
Bertram and Hyönä (2003) investigated an effect of word length on morphologicalprocesses in Finnish and suggested that a decompositional route dominates over a
direct route when processing long compounds. If a direct route to the compound
representation also exists in Japanese, a smaller font size may trigger a substantiallylarger whole compound frequency effect at the early stage.
In addition to the font size, it might also be argued that the small contributionsof radicals during the early stage of lexical processing in Experiment 1 were due tothe nature of the nonwords. The nonwords in Experiments 1 were random
combinations of characters. Hence, readers would not have to zoom in on radicals to
distinguish words from nonwords. We evaluated the font size and nonword type

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accounts in Experiment 2.
!
Experiment 2: Evaluation of the font size and nonword type accounts
In Experiment 2, we tested whether the pattern of lexical activation we
observed in Experiment 1 generalizes to words presented in the more commonlyused 40-sized fonts (visual angle = 1.64º). The 40-size font represents a typical fontsize used in previous isolated word lexical decision studies (e.g., 1.38º in Feldman& Siok, 1999; 1.6º in Miwa et al., 2012; 1.23º in Myers et al., 2006; 2.05º in Shen &Forster, 1999; 1.6º in Taft & Zhu, 1997; 2.78º in Zhou, Marslen-Wilson, Taft, &Shu, 1999, where that the viewing distance was assumed to be 70 cm unless
reported otherwise). In Experiment 2, we also used nonwords containing a
non-existing character, with the aim of forcing readers to pay closer attention to
intra-character components. Under those circumstances, the effect of radicals may
emerge more prominently. However, if reading Japanese two-character compounds
is fundamentally character-driven, then this manipulation of the nonwords should
not affect the main patterns of results.

MethodParticipants. Twenty-one native Japanese readers (17 females, mean age =23.3 years old, SD = 5.9) participated at the University of Alberta, Canada.
Materials. Two hundred words were sampled randomly from the set of words
used in Experiment 1, equally across ten frequency-ordered bins. An equal numberof nonwords were prepared by replacing either the left or the right character’s
intra-character component with an existing constituent to make a non-existing

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character. Half the nonwords contained a non-existing left character, and the otherhalf contained a non-existing right character.
Procedure. The procedure was identical to that in Experiment 1, but words
were presented in smaller 40-size font (visual angle for each character = 1.64º).

Results
Response time analysis. The data were trimmed, and the response times weretransformed in the same way as in Experiment 1. A mixed-effects model was fitted
to inversely transformed response times for 192 words (3,559 data points). In our
final model, the random effect structure comprised random intercepts for item (SD =0.10) and subject (SD = 0.18), and by-subject random slopes for centralized Trial(SD = 0.05) and PreviousRT (SD = 0.07). The standard deviation of the residual
error was 0.25.
As fixed effects, we identified WholeWordFreq (p < 0.0001, effect size = -94ms) and GoogleDocFreqResid (p < 0.0001, effect size = -153 ms) as dominantlexical effects. The left and the right characters contributed to a comparable extent:
LeftKanjiTokenFreq (p < 0.0342, effect size = -33 ms) and RightKanjiTokenFreq (p< 0.0185, effect size = -40 ms). Importantly, although the task forced the readers toattend to the intra-character structure, only a Trial-dependent small effect of
LeftKanjiRadicalTransparency was observed (effect size changed from -14 ms to 30
ms, as the experiment went by). LengthOfStayCanada did not have a significant
main effect, as in Experiment 1 (see Appendix B for the full summary of thesignificant fixed effects).!
First fixation duration analysis. For analyses of eye movements, data points

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excluded for the response time analysis were excluded here as well. Words were
scanned with two fixations most of the time (10% for a single fixation, 65% for two
fixations, 20% for three fixations, and 3% for four fixations), and fixation counts
ranged from 1 to 6 (M = 2.2, SD = 0.7). Since two fixations constituted the majorityof the trials, we analyzed first and second fixation durations in trials with exactly
two fixations.6
As in Experiment 1, only the trials with a correct response that elicited two
fixations were analyzed (192 words, 2,272 data points). Initial fixations shorter than
100 ms and longer than 800 ms were removed (5 data point). The quantiles of raw
first fixation durations are 120 ms (minimum), 296 ms (1st quartile), 348 ms(median), 412 ms (3rd quartile), and 792 ms (maximum).
In our final model fitted to the log-transformed first fixation durations, therandom effect structure comprised random intercepts for item (SD = 0.07) andsubject (SD = 0.10). The standard deviation of the residual error was 0.17. The fixed
effect structure comprised a small yet significant effect of GoogleDocFreqResid (p= 0.0023, effect size = -44 ms) and large contributions of the left character
properties, such as LeftKanjiTokenFreq (p < 0.0001, effect size = -123 ms).
Importantly for the purpose of Experiment 2, radical properties did not contributeprominently: the observed radical effect of LeftKanjiRadicalCombinability was
small and inhibitory (p = 0.0047, effect size = 24 ms) and is comparable to its effectobserved in the first subgaze duration analysis in Experiment 1. In Experiment 2,
the right characters’ properties contributed more prominently than Experiment 1.
Interestingly, as in Experiment 1, the left character effects and the right character
effects were asymmetrical, and the magnitudes of effects for the former were larger:

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For example, LeftKanjiStrokesResid inhibited (p = 0.0001, effect size = 129 ms)
while RightKanjiStrokesResid facilitated (p = 0.0001, effect size = -60 ms), and
LeftKanjiTokenFreq facilitated (p = 0.0001, effect size = -123 ms) while
RightKanjiTokenFreq inhibited (p = 0.0022, effect size = 39 ms). All of these effects
replicated the findings in Experiment 1 (see Appendix B for the full summary of thesignificant fixed effects). When subgazes were analyzed, the character-driven
processing pattern was still replicated.!
Second fixation duration analysis. We fitted a mixed-effects model to thesquare-root-transformed second fixation durations in the subset of trials analyzed
above. The quantiles of raw second fixation durations are 24 ms (minimum), 144
ms (1st quartile), 216 ms (median), 288 ms (3rd quartile), and 732 ms (maximum).
In our final model, the random effect structure comprised random intercepts foritem (SD = 0.66) and subject (SD = 1.20), and by-subject random slopes forcentralized Trial (SD = 0.36). The standard deviation of the residual error was 2.25.
As fixed effects, as in Experiment 1, properties of the right character and thewhole compound unit dominated: WholeWordFreq (p = 0.0001, effect size = -43ms), GoogleDocFreqResid (p = 0.0001, effect size = -88 ms), RightKanjiTokenFreq(p = 0.0001, effect size = -49 ms). Left character frequency effects did not reachsignificance. Note that, at this later fixation, whole word effects are large inmagnitude, and RightKanjiTokenFreq shows a standard facilitatory frequency effect.
Interestingly, this later time frame was also co-determined by the Trial-dependent
effect of LeftKanjiRadicalTransparency, as seen in the response time analysis (SeeAppendix B for the summary of all significant predictors).

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Discussion
Experiment 2 largely replicates the main findings of Experiment 1. Even
when words are presented in smaller-size font and together with different nonwords,
the effects of character properties were more prominent than those of radicals
properties during the early processing stages. Experiment 2 also replicates that awhole word frequency effect emerges already in the early time frame with small yet
significant effects, and contributes more strongly in the later time frame. The small
effect of the frequency of the whole word unit at the first fixation in Experiment 2
suggests that the small effect size associated with the early whole word frequency
effect in Experiment 1 was not due to a visual acuity constraint, but is an essential
characteristic of morphological processing in Japanese observable across all
subjects (i.e., random slopes for subjects were not justified for a whole word
frequency effect). Importantly, when the subset of data in Experiment 1 with the left
fixation position was analyzed (185 words for each subject), the pattern of results
remained unchanged, suggesting that the fixation position and statistical power did
not contaminate the comparison between the two experiments. With respect torelationship between non-linguistic task demand and lexical processing, the above
results are in line with Kaakinen and Hyönä (2010)’s eye-tracking sentential reading
study with a manipulation of task demands. In their study, depending on whether
the task was comprehension or proof-reading, readers adjusted eye movements
already at the first fixation according to the given task demand, with regard to thelanding position and the fixation duration. However, lexical effects were notmodulated by the task demands during this early time frame, while they were in thelater time frame proved by the gaze duration analysis. Experiment 2 of the present

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study similarly demonstrated that, even when the task requires attention to
intra-character radical components and the font size motivates fewer eye
movements, character-driven processing remains unaffected.

General discussion
In this visual lexical decision with eye-tracking study, we tested several
hypotheses in parallel: namely, whether the processing of morphographic
two-character words proceeds strictly from the smallest units to large units in abottom-up combinatorial manner, whether the right character is quantitatively andqualitatively more important than the left character, whether semantic radicals areprocessed semantically, and how non-linguistic variables affect lexical processes.
First, we studied the temporal order in which a two-character word and its
constituent characters and radicals are activated in the course of lexical access.
During the earliest time frame, both in Experiment 1 and 2, we observed a larger
effect of left character frequency than those of radical combinability and wholeword frequency. The early emergence of a whole word frequency effect replicatesthe previous findings for Dutch and Finnish (Kuperman, Bertram, & Baayen, 2008;
Kuperman et al., 2009). During the later time frame, the effect of the frequency ofthe left character disappeared and was replaced by a large effect of the frequency of
the right character. The magnitude of the whole word frequency effect increased inthis later time frame. The early large effects of character frequency in combination
with a small effect of whole word frequency, as well as later predominant effects ofright character and whole word frequency effects, were replicated when words were
presented in smaller fonts and presented with different types of nonwords. This

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indicates that the present character-driven processing signature does not depend on
font sizes nor nonword-induced task demand in lexical decision.
Second, we studied the different contributions of the left and the rightcharacters to the lexical decision responses. On the basis of the lexical decision
response times alone, using frequency as a diagnostic for access to lexical
representations, one would have to conclude that the right character is more
important than the left (Experiment 1) or both are equally important (Experiment 2).
Interestingly, the eye-tracking record revealed clear and strong frequency effects of
the left character in the early time frame and those of the right character in the latertime frame. The early left character advantage is consistent with the Yan et al.
(2006) study, in which fixation durations on target words were co-determined moreby the left character than by the right character. This indicates that the rightcharacter advantage reflected in the response times arises not because the rightcharacter is the main morpheme to be processed first. Instead, response times
predominantly reflect later processes (i.e., later information uptake and subsequentdecision processes; cf., Tamaoka & Hatsuzuka, 1995).
The time-course of the left-then-right constituent activation observed in thepresent study is comparable to that in eye-tracking studies on alphabetic compoundprocessing (Hyönä & Pollatsek, 1998; Kuperman et al., 2008; Pollatsek et al.,2000). It should be noted, however, that the two constituents do not simply facilitateprocessing at different points in time; We observed that one inhibited processing
while the other was facilitatory in nature (see also Vergara-Martínez, Duñabeitia,
Laka, & Carreiras, 2009, for qualitatively different EEG signatures between left andright constituents in Basque compound word reading).

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Third, we were interested in the depth to which semantic radicals areprocessed. Slight yet significant contributions of semantic radical transparency wereobserved in both eye movement and response time analyses, providing further
support for Feldman and Siok’s (1997, 1999) and Miwa et al.’s (2012) claim thatsemantic radicals contribute to the semantic interpretation of words. An issue thatshould be considered in parallel is whether initial morphological decomposition ismorpho-orthographic (Davis & Rastle, 2010; Longtin, Meunier, 2005; Longtin,
Segui, & Halle, 2003; McCormick, Rastle, & Davis, 2008; McCormick, Rastle, &Davis, 2009; Rastle, Davis, Marslen-Wilson, & Tyler, 2000; Taft & Nguyen-Hoan,
2010) or morpho-semantic in nature (Diependaele et al., 2011; Diependaele, Sandra,& Grainger, 2005; Feldman, O'Connor, & Moscoso del Prado Martín, 2009). The
early radical transparency effect observed in the earliest time frame in Experiment 1
indicates that a semantic effect may co-determine the early morphological process.
However, the early radical transparency effect we observed was not facilitatory but
inhibitory, suggesting that the processing of semantic radicals was not in harmony
with normal comprehension. Moreover, the effect was not observed in Experiment 2.
This indicates that the effect is only conditional in nature. Indeed, the subset
analyses confirmed that the radical transparency effect reached significance whenthe eye was on the left character (ß = 0.005, p = 0.1) but not when the eye was on
the right character (ß = 0.024, p = 0.0075). The results indicate that an earlysemantic involvement is not a must.
Fourth, by manipulating the location of the fixation point and tracking eye
movements, for the first time as an isolated word reading study, we found effects ofa locus of attention on lexical processing. Strong parafoveal-on-foveal effects

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emerged, with the sign and the magnitude of stroke complexity effects modulatedby the fixation location. When the eye attends to one character first, it is attracted tothe other character when that character is highly complex, indicating the need forallocating processing resources to the other character (Kliegl et al., 2006; Pynte etal., 2004; Hyönä & Bertram, 2004). As a consequence, the greater the complexity of
the unfixated character, the shorter the eye rests on the fixated character. Font size
and task demand manipulations left the above pattern unchanged. It should be noted,
however, in addition to the perceptual parafoveal-on-foveal interpretation, that alexical interpretation is also possible in the case of compound processing. That is,
activation of the first character activates the second character in the lexicon,
regardless of the perceptual information in the parafoveal region.
In what follows, we assess how well current models of morphologicalprocessing explain the temporal order and the magnitude of effects of whole word,
character, and radical activation. The supra-lexical model of Giraudo and Grainger(2001) predicts the whole word to be activated before its constituents (i.e., strong
effects of whole word frequency, weaker effects of character frequency, and theweakest effects of radicals in the earliest time frame). However, the time-course ofactivation that emerges from our data is one in which the character is activated first,
followed by the activation of the whole word on one hand and the activation ofradicals on the other: In the early time frame, strong character frequency effects pairwith a small whole word frequency effect and small radical combinability effects,
indicating an initial access to character representations and subsequent spreading
activations to radical and whole word representations.
The multilevel interactive activation model proposed for Japanese by

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Tamaoka and Hatsuzuka (1998) correctly predicts, in the earliest time frame, thatwhole word effects should be smaller than character effects. It also correctlypredicts rebounding phonological effects, which appeared late in our data. However,in this interactive activation model, semantic radicals are not represented byseparate nodes. Given that combinability and transparency of semantic radicals
affect lexical processes, albeit with small effect sizes, nodes for semantic radicals
need to be incorporated in the model architecture.!
Adding radical nodes to the model of Tamaoka and Hatsuzuka (1998) leads tothe interactive activation architecture proposed for Chinese by Taft and Zhu (1997)
and Taft et al. (1999) and for Japanese by Saito (1997). These models predict a
time-course of activation that is exactly the opposite of the time-course predicted by
the supra-lexical model. Now, radicals are supposed to be activated before
characters, and characters before whole words. This architecture, however, ischallenged by our eye-tracking data in that, in the earliest processing stages, effects
of characters dominate over those of radicals.
Within the general interactive activation approach to lexical processing inJapanese, our data suggest a modification of both the model of Tamaoka and
Hatsuzuka (1998) on one hand and that of Saito (1997) on the other. The
compromise presented in Figure 5 incorporates nodes for radicals, characters, andwords as in the model of Saito (1997) but, unlike this model, it includes connections
from the feature level that link up directly to the character level, by-passing theradical nodes. Consequently, radicals can be activated, either by receiving
rebounding activation from the character level or by receiving activation from thefeature level (the dotted line in Figure 5). Our current data do not allow us to

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estimate the relative importance of these two routes for activation of radicals.
However, given that radical effects are not modulated by frequency of the charactersnor by word frequency, processing of radicals proceeds independently, withcharacter activation taking precedence at least in early processing stages. By
including level-skipping links from features to characters, the model accounts forthe fact the characters are the most prominent units from the earliest time frame
onward: Characters receive more bottom-up support than radicals.
---------------------------------------------------------Insert Figure 5 around here---------------------------------------------------------Interestingly, this level-skipping assumption we propose for Japanese iscomparable to direct whole word activation routes assumed to function in parallel tosequential decompositional routes in recent morphological processing models foralphabetic languages (Diependaele et al., 2005, 2011; Kuperman et al., 2009;
Pollatsek et al., 2000). Diependaele et al. (2005, 2011) observed facilitatory
semantic transparency effects in masked priming. In order to account for thisarguably early morpho-semantic processing, Diependaele et al. (2011) reason that
direct whole word access routes should be assumed, although they do not claim thatwhole word representations are the primary processing units. The results in thepresent study indicate that characters are the dominant processing units, at least in
the early processing stages. The character-driven processing model provides a morestraightforward interpretation of the results than strictly bottom-up models.
However, this does not totally preclude the obligatory radical-based recognition
models for Japanese and Chinese, not to mention obligatory morpheme-based

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models for alphabetic languages. To arrive at a fair conclusion, evidence should beaccumulated with respect to what different experimental techniques measure (e.g.,priming, eye-tracking), what language-general morphemes are, and what different
statistical techniques do (i.e., the issue of statistical power).
The question remains why character representations emerge as primary accessunits. Our hypothesis is that characters carry the greatest amount of information fora word's intended meaning, compared to radicals and compounds. Radicals occur inany positions (i.e., top, bottom, left, right). Furthermore, semantic radicals may or
may not denote general semantic categories, and phonetic radicals similarly may or
may not provide information about a character's pronunciation. As a consequence,
they are unreliable cues to a word's meaning (e.g., the semantic radical ! ‘wood’is not a helpful cue for 9 ‘extreme’, and occur at the bottom : or top ;positions). Conversely, many two-character compounds are semantically at least
partially transparent and compositional. The greater their compositionality, the more
the burden of interpretation rests with the characters. In other words, characters may
be the most important cues to meaning, compared to radicals (which are ambiguousand less useful cues) and compared to whole words (due to compositionality). We
leave the validation of this hypothesis, for instance within the naive discrimination
learning framework proposed by Baayen et al. (2011) to future research.
In addition to the level-skipping assumption, there are two other differences
between the architecture proposed in Figure 5 and the models proposed in theliterature. First, we take semantic radicals to be the smallest meaningful units in theidentification system; in other words, we consider semantic radicals to beorthographic morphemes. In Figure 5, semantic radicals therefore have out-going

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connections that link up to the semantic representations. These links are motivatedby the significant radical transparency effect observed in our data, consistent withthe results of Feldman and Siok (1997, 1999) and Miwa et al. (2012). Althoughradical morphemes, unlike morphemes in alphabetic languages, do not function as
primary recognition units, they nevertheless contribute to a word’s meaning percept.
Second, Figure 5 makes it explicit that task demands and decision making
strategies co-determine responses and potentially affect lexical processing at later
processing stages. In Experiment 1, the effect of the accuracy on the previous trial,in interaction with right character frequency, on the RTs indicates changes in local
response criteria, while the interaction between trial and right character transparency
is indicative of changes in global response criteria. These interactions involving
lexical distributional predictors indicate that the two systems are not strictly staged
but function in parallel at least at later processing stages. This assumption of lateinvolvement of the non-linguistic system is based on that in the Bilingual Interactive
Activation (BIA+) model (Dijkstra & van Heuven, 2002), which makes it explicit
that bilinguals cannot suppress activation of two languages even when activation of
one language is sufficient for completion of a given task.
With regard to lexical predictors to be considered for the visual recognition ofJapanese morphographic words, we are fully aware that the present study
considered only 18 lexical variables and that it remains important to extend therange of predictors to include, for instance, imageability (e.g., Balota et al., 2004;
McMullen & Bryden, 1987), visuoperceptual features and geometrical complexity(Grainger, Rey, & Dufau, 2008; Huang & Wang, 1992), collocational N-gramfrequency (Arnon & Snider, 2010; Tremblay, Derwing, Libben, & Westbury, 2011),

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and whether a compound is endocentric (and right-headed) or exocentric (see Joyce,
2002 for consideration of compound formation principles).
The experimental design requires consideration as well. The purpose of thepresent study was primarily to extend previous isolated word reading studies andtest existing models of isolated word reading, rather than making a claim regarding
what readers do in sentential reading. To this end, the present study examined eye
movements simply as the means to infer cognitive processes, as in Kuperman et al.
(2009). Although isolated word reading and sentential reading lead to a comparable
processing architecture in Kuperman et al. (2008, 2009), it should yet to be tested
whether this is the case for Japanese and Chinese, as sentential reading involves
extra complexities (e.g., parafovel preview before fixating on a target word). For
example, our isolated word reading study did not identify interaction between
character and word frequencies reported in Yan et al.’s (2006) sentential reading
study.7
Future research should also assess potential effects of individual differenceson lexical access (see Andrews & Lo, 2011; Kuperman & Van Dyke, 2011; Yap,
Balota, Sibley, & Ratcliff, 2011). Because we carefully checked for random-effectslopes for subject for our predictors, the main effects reported in the present study
are very unlikely to be due to individual differences. Furthermore, our models canbe used to extrapolate to domestic Japanese readers by setting the value ofLengthOfStayCanada slightly below zero, in order to predict their expected
response times. As the effects of LengthOfStayCanada are small, no majordifferences for domestic readers are anticipated. We leave it to future research todisentangle the precise contributions of length of stay, age, daily exposure to the

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language, and reading ability.
In conclusion, the present study documents processing consequences from alllevels of morphographic structure, namely the radicals, the character, and the wholeword. Eye-movements revealed that two-character words in Japanese are
preferentially processed from the left character to the right character, with wholeword frequency exerting an effect already from the earliest time frame. Importantly,
the effects of character properties were robust and larger in magnitude than those ofradicals and whole word properties at early processing stages. The patterns observedin all data combined led us to propose a character-driven architecture with alevel-skipping assumption: Connections from the feature level by-pass the lower
radical level and link up directly to the higher character level, allowing character
effects to dominate early processing stages irrespective of font sizes and task
demands.

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Acknowledgements

This research was supported by a Major Collaborative Research Initiative Grant(#412-2001-1009) from the Social Sciences and Humanities Research Council of
Canada to Gary Libben (Director), Gonia Jarema, Eva Kehayia, Bruce Derwing, andLori Buchanan (Co-investigators) and the Izaak Walton Killam pre-doctoral grantfrom the Killam Trusts to the first author. We are indebted to Shigeaki Amano,
Raymond Bertram, and Rob Schreuder for discussion, to Sally Andrews, Jon
Andoni Duñabeitia, Barbara Juhasz, Sachiko Kinoshita, Reinhold Kliegl, and Kevin
Paterson for their constructive feedback on an earlier version of the manuscript.

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Japanese morphographic word recognition 68
Appendix A.
A correlation matrix among numerical predictors considered in this study.
Predictors with the superscript R were end-products of a residualization procedure.
The significant correlations at the 0.01 level are bolded.!
Predictors
1. LeftKanjiStrokes
1R. LeftKanjiStrokesResid
2. RightKanjiStrokes
2R. RightKanjiStrokesResid
3. LeftKanjiRadicalCombinability
4. RightKanjiRadicalCombinability
5. LeftKanjiRadicalTokenFreq
5R. LeftKanjiRadicalTokenFreqResid
6. RightKanjiRadicalTokenFreq
6R. RightKanjiRadicalTokenFreqResid
7. LeftKanjiNeighbour
7R. LeftKanjiNeighbourResid
8. RightKanjiNeighbour
8R. RightKanjiNeighbourResid
9. LeftKanjiTokenFreq
10. RightKanjiTokenFreq
11. WholeWordFreq
12. GoogleDocFreq
12R. GoogleDocFreqResid
13. LeftKanjiHomophones
14. RightKanjiHomophones
15. LeftKanjiRadicalTransparency
16. RightKanjiRadicalTransparency
17. LeftKanjiTransparency
18. RightKanjiTransparency

1
1.00

1R
0.95
1.00

2
0.00
0.00
1.00

2R
-0.01
-0.01
0.92
1.00

3
4
0.16 -0.02
0.14 -0.03
0.04 0.09
0.04 0.04
1.00 -0.04
1.00

5
-0.01
0.01
0.04
0.05
0.85
-0.02
1.00

5R
-0.15
-0.16
-0.03
-0.02
0.00
0.02
0.47
1.00

6
-0.03
-0.04
-0.03
-0.05
-0.05
0.84
-0.01
0.06
1.00

6R
-0.02
-0.03
-0.09
-0.14
-0.04
0.00
0.00
0.07
0.48
1.00

Predictors
1. LeftKanjiStrokes
1R. LeftKanjiStrokesResid
2. RightKanjiStrokes
2R. RightKanjiStrokesResid
3. LeftKanjiRadicalCombinability
4. RightKanjiRadicalCombinability
5. LeftKanjiRadicalTokenFreq
5R. LeftKanjiRadicalTokenFreqResid
6. RightKanjiRadicalTokenFreq
6R. RightKanjiRadicalTokenFreqResid
7. LeftKanjiNeighbour
7R. LeftKanjiNeighbourResid
8. RightKanjiNeighbour
8R. RightKanjiNeighbourResid
9. LeftKanjiTokenFreq
10. RightKanjiTokenFreq
11. WholeWordFreq

7
-0.31
0.00
0.01
0.00
-0.09
-0.03
0.05
-0.01
-0.03
-0.01
1.00

7R
-0.17
0.09
-0.05
-0.05
-0.01
-0.05
-0.02
-0.01
-0.06
-0.02
0.85
1.00

8
-0.03
-0.03
-0.38
0.00
0.00
-0.15
0.00
0.02
-0.06
-0.09
-0.01
0.01
1.00

8R
-0.03
-0.03
-0.30
0.04
0.01
-0.10
-0.01
0.01
-0.14
-0.11
-0.02
0.04
0.88
1.00

9
-0.31
-0.15
0.09
0.08
-0.15
0.01
0.12
0.00
0.03
0.01
0.53
0.00
-0.04
-0.09
1.00

11
-0.01
-0.01
-0.04
-0.04
-0.01
-0.04
0.04
-0.01
0.04
0.02
0.00
-0.14
0.01
-0.13
0.23
0.26
1.00

12
-0.04
-0.03
-0.03
-0.03
-0.07
-0.03
-0.03
-0.04
0.02
0.02
0.02
-0.08
0.03
-0.04
0.17
0.13
0.59

12R
-0.04
-0.04
-0.01
0.00
-0.07
0.00
-0.07
-0.04
0.00
0.01
0.03
0.00
0.02
0.05
0.04
-0.03
0.00

13
0.00
-0.05
-0.06
-0.05
-0.06
0.04
-0.04
0.03
0.03
0.00
-0.15
-0.18
0.04
0.07
0.00
-0.03
0.03

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-0.01
-0.01
-0.26
-0.08
0.00
-0.13
0.04
0.03
0.13
0.00
0.01
-0.04
0.48
0.00
0.09
1.00

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Quarterly Journal of Experimental Psychology

Japanese morphographic word recognition 69
12. GoogleDocFreq
12R. GoogleDocFreqResid
13. LeftKanjiHomophones
14. RightKanjiHomophones
15. LeftKanjiRadicalTransparency
16. RightKanjiRadicalTransparency
17. LeftKanjiTransparency
18. RightKanjiTransparency
Predictors
1. LeftKanjiStrokes
1R. LeftKanjiStrokesResid
2. RightKanjiStrokes
2R. RightKanjiStrokesResid
3. LeftKanjiRadicalCombinability
4. RightKanjiRadicalCombinability
5. LeftKanjiRadicalTokenFreq
5R. LeftKanjiRadicalTokenFreqResid
6. RightKanjiRadicalTokenFreq
6R. RightKanjiRadicalTokenFreqResid
7. LeftKanjiNeighbour
7R. LeftKanjiNeighbourResid
8. RightKanjiNeighbour
8R. RightKanjiNeighbourResid
9. LeftKanjiTokenFreq
10. RightKanjiTokenFreq
11. WholeWordFreq
12. GoogleDocFreq
12R. GoogleDocFreqResid
13. LeftKanjiHomophones
14. RightKanjiHomophones
15. LeftKanjiRadicalTransparency
16. RightKanjiRadicalTransparency
17. LeftKanjiTransparency
18. RightKanjiTransparency

1.00

14
-0.07
-0.06
0.02
0.01
0.01
0.04
0.01
0.03
0.07
0.03
0.05
0.09
-0.02
-0.06
-0.05
0.07
-0.04
-0.09
-0.08
-0.16
1.00

15
-0.19
-0.12
-0.08
-0.07
-0.03
-0.04
-0.07
-0.07
-0.07
-0.01
0.27
0.34
0.04
0.10
-0.03
-0.10
-0.13
-0.06
0.02
-0.07
0.05
1.00

16
0.02
-0.01
-0.20
-0.12
-0.05
0.07
-0.07
-0.03
0.01
-0.11
-0.09
-0.07
0.23
0.26
-0.06
0.01
-0.05
-0.01
0.03
-0.01
0.05
0.04
1.00

17
0.08
0.07
0.02
0.04
0.07
-0.02
0.08
-0.01
-0.02
-0.05
-0.03
-0.09
0.05
0.01
0.08
0.08
0.03
0.03
0.02
0.07
-0.09
-0.10
-0.01
1.00

18
0.07
0.07
-0.01
0.01
0.08
0.03
0.09
0.02
0.03
-0.03
-0.04
-0.05
0.05
0.01
0.02
0.09
0.02
-0.03
-0.06
0.04
-0.14
-0.09
0.07
0.51
1.00

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0.81
1.00

0.02
0.00
1.00

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Japanese morphographic word recognition 70
Appendix B.
Estimate, standard error, t-value, p-value, and effect size of influential predictors forthe response times, first fixation durations, and second fixation durations for trials
with two fixations in Experiment 2.
Response timeType

(Intercept)
PreviousRT
Trial
PreviousTrialCorrect (Incorrect)
LeftKanjiStrokesResid
LeftKanjiTokenFreq
LeftKanjiNeighbourResid
RightKanjiTokenFreq
WholeWordFreq
GoogleDocFreqResid
LeftKanjiHomophones
LeftKanjiRadicalTransparency
LeftKanjiRadicalTransparency
x TrialFirst fixation duration

Task
Task
Task
FeatureCharacter
Character
Character
Word
Word
Phonology
Semantics
Semantics
x TaskType(Intercept)
LeftKanjiStrokesResid
RightKanjiStrokesResid
LeftKanjiRadicalCombinability
LeftKanjiTokenFreq
LeftKanjiNeighbourResid
RightKanjiTokenFreq
RightKanjiNeighbourResid
GoogleDocFreqResid
Second fixation duration

FeatureFeatureRadicalCharacter
Character
Character
Character
Word
Type

(Intercept)
PreviousFixationDuration
PreviousRT
Trial
PreviousTrialCorrect (Incorrect)
RightKanjiTokenFreq
WholeWordFreq
GoogleDocFreqResid
LeftKanjiHomophones
LeftKanjiRadicalTransparency
LeftKanjiRadicalTransparency
x Trial

Task
Task
Task
Task
Character
Word
Word
Phonology
Sematics
Semantics
x TrialEstimate

Std.Error

t-value-1.1220.125-0.0820.086
0.007-0.012-0.025-0.014-0.044-0.059-0.024
0.006

0.101
0.022
0.015
0.016
0.002
0.006
0.010
0.006
0.007
0.007
0.010
0.005

-11.12
5.80
-5.42
5.42
3.25
-2.12
-2.48
-2.36
-6.39
-8.45
-2.25
1.06

< 0.0001< 0.0001< 0.0001< 0.0001
0.00120.03420.01330.0185< 0.0001< 0.0001
0.02440.28740.006
Estimate

0.002
Std.Error

2.79
t-value0.0053p-value

6.076
0.019
-0.0090.017-0.037-0.0270.013
0.019-0.016
Estimate

0.072
0.002
0.002
0.006
0.004
0.007
0.004
0.007
0.005
Std.Error

84.08
11.19
-4.37
2.83
-8.42
-3.81
3.07
2.85
-3.05
t-value< 0.0001< 0.0001< 0.0001
0.0047< 0.0001
0.00010.00220.00440.0023p-value

75.148-9.3910.807-0.5850.682-0.192-0.219-0.382-0.228
0.040

1.545
0.234
0.152
0.134
0.184
0.047
0.053
0.057
0.084
0.041

48.64
-40.18
5.31
-4.37
3.70
-4.03
-4.12
-6.68
-2.73
0.98

< 0.0001< 0.0001< 0.0001< 0.0001
0.00020.0001< 0.0001< 0.0001
0.00640.3274

-872
66
-49
20
-49
-43
-88
-28
8

0.053

0.025

2.13

0.0336-12: 23

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p-value

Effect size
(ms)
119
-84
30
44
-33
-40
-40
-94
-153
-32
11
-14: 30
Effect size
(ms)
129
-60
24
-123
-45
39
36
-44
Effect size
(ms)

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Japanese morphographic word recognition 71

Footnotes
1

With respect to two-character compounds, Japanese morphology has beenargued to be predominantly right-headed (Kageyama, 2010), although exocentriccompounds such as voyage (‘ship’ + ‘sea’) seem to occur more often than inEnglish or Dutch.
2

Although Kawakami interpreted this type count as a measure of orthographic

neighbourhood density (cf., Coltheart, Davelaar, Janasson, & Besner, 1977; Forster& Taft, 1994), it can also be viewed as a measure of morphological family size
(Bertram, Baayen, & Schreuder, 2000; Joyce & Ohta, 2002; Moscoso del Prado
Martín et al., 2004; Schreuder & Baayen, 1997).
3

In Japanese, characters may have two kinds of pronunciations: ku(On-Reading, Chinese origin) and sora (Kun-Reading, Japanese origin). In thecontext of kuko => ‘airport’, the On-Reading is applied, while in the context ofsorairo =? ‘sky blue’, the Kun-Reading is applied. Given that visual lexicaldecisions are based to a larger extent on orthographic and semantic properties ofwords, as well as that On-Kun status is finalized only after the whole word isactivated, the effect of On-Kun distinction is expected to be small or null in thepresent study. This was indeed the case in the present study. Hence, this predictor is
not mentioned in this paper.
4

The analysis of the subgaze counts indicates that this subset is biased slightly

towards words preceded by trials with a short response latency, words responded toby readers who had only recently left Japan, words with fewer strokes, and words
with higher frequencies.

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Japanese morphographic word recognition 72
5

The assumption that response planning and execution time is constant anddoes not vary with lexical properties may involve a simplification. For instance,
Abrams and Balota (1991) observed that word frequency affects not only the timing
but also the force with which the response is executed. As we asked our participants
to keep their fingers on the response buttons during the experiment, theconsequences of the differences in the force with which lexical decisions may havebeen executed for the estimates of the lexical decision speed and second subgaze
durations are negligible.
6

The analysis of the fixation counts indicates that this subset is biased slightly

towards words preceded by trials with a short response latency, words presented
later in the experiment, words with fewer strokes, and words with higher wholeword and right character frequencies.
7

The difference could be due to the task factor but also to a statistical aspect.

That is, the latter study used matching, and only six words were studied in each of
the eight conditions. As in any studies with matching followed by a fixed-effects
model, it is not certain whether the effects are generalizable to all words beyond
these specific stimuli.

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Japanese morphographic word recognition 73

Table 1
Lexical predictors, individual differences, and task effects considered in this study
Type
FeaturePredictors% LeftKanjiStrokesResid% RightKanjiStrokesResid

Radical% LeftKanjiRadicalCombinability% RightKanjiRadicalCombinability( 氵 )% LeftKanjiRadicalTokenFreqResid% RightKanjiRadicalTokenFreqResid

Character% LeftKanjiNeighbourResid% RightKanjiNeighbourResid( > )% LeftKanjiTokenFreq% RightKanjiTokenFreq

Word% WholeWordFreq% GoogleDocFreqResid

Phonology% LeftKanjiHomophones% RightKanjiHomophones

Semantics% LeftKanjiRadicalTransparency% RightKanjiRadicalTransparency% LeftKanjiTransparency% RightKanjiTransparency(@@!)( => )

Individual% LengthOfStayCanada

Task% PreviousRT% PreviousTrialCorrect% PreviousSubgazeDuration% Trial% EyePosition

URL: http:/mc.manuscriptcentral.com/pqje% Fixation

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Japanese morphographic word recognition 74
Table 2Estimate, standard error, t-value, p-value, and effect size of influential predictors forthe lexical decision response times.

Type(Intercept)
PreviousRT
Trial
Fixation (Left)
Fixation (Right)
PreviousTrialCorrect (Incorrect)
LengthOfStayCanada
LeftKanjiStrokesResid
LeftKanjiNeighbourResid
RightKanjiTokenFreq
WholeWordFreq
GoogleDocFreqResid
RightKanjiHomophones
RightKanjiTransparency
RightKanjiTokenFreq
x PreviousTrialCorrect (Incorrect)
RightKanjiTransparency
x TaskLengthOfStayCanadax Fixation (Left)
LengthOfStayCanadax Fixation (Right)

Task
Task
Task
Task
Task
Individual
FeatureCharacter
Character
Word
Word
Phonology
Semantics
Characterx TaskSemantics
x TaskIndividualx TaskIndividualx TaskEstimate

Std.Error

t-valuep-value-1.0650.140-0.010
0.083
0.099
0.189
0.058
0.010
-0.015-0.009-0.057-0.0520.026-0.009

0.128
0.018
0.005
0.022
0.020
0.055
0.039
0.002
0.006
0.004
0.004
0.006
0.007
0.006

-8.29
7.88
-2.01
3.79
4.86
3.43
1.47
6.11
-2.61
-2.16
-13.28
-8.69
3.64
-1.59

< 0.0001< 0.0001
0.04450.0002< 0.0001
0.00060.1408< 0.0001
0.00920.0305< 0.0001< 0.0001
0.00030.1116

180
-156
3
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5
125
101
-41
-36
-180
-180
53
-28

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0.005

-3.59

0.0003Figure 2 (a)-0.002

0.001

-2.69

0.0072

Figure 2 (b)-0.027

0.008

-3.57

0.0004

Figure 2 (c)-0.032

0.007

-4.65

< 0.0001

Figure 2 (c)URL: http:/mc.manuscriptcentral.com/pqje

Effect size
(ms)

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Quarterly Journal of Experimental Psychology

Japanese morphographic word recognition 75
Table 3
Estimate, standard error, t-value, p-value, and effect size of influential predictors forthe first subgaze durations for trials with two subgazes.

Type(Intercept)
Trial
PreviousRT
EyePosition (Right)
LeftKanjiStrokesResid
RightKanjiStrokesResid
LeftKanjiRadicalCombinability
RightKanjiRadicalCombinability
RightKanjiRadicalTokenFreqResid
LeftKanjiTokenFreq
LeftKanjiNeighbourResid
RightKanjiTokenFreq
GoogleDocFreqResid
RightKanjiRadicalTransparency
LeftKanjiStrokesResidx EyePosition (Right)
RightKanjiStrokesResidx EyePosition (Right)
RightKanjiRadicalTokenFreqResidx EyePosition (Right)
LeftKanjiNeighbourResidx LeftKanjiTokenFreq
LeftKanjiNeighbourResidx EyePosition (Right)
LeftKanjiTokenFreq
x EyePosition (Right)
LeftKanjiNeighbourResidx LeftKanjiTokenFreq
x EyePosition (Right)
LeftKanjiNeighbourResidx RightKanjiTokenFreq

Task
Task
Task
FeatureFeatureRadical
Radical
RadicalCharacter
Character
Character
Word
Semantics
Featurex TaskFeaturex TaskRadicalx TaskCharacterx Character
Characterx TaskCharacterx TaskCharacterx Characterx TaskCharacterx Character

Estimate

Std.Error

t-valuep-value

6.196
0.000
0.043
-0.8820.019-0.0090.019-0.0190.002-0.038-0.1390.004-0.012
0.006

0.079
0.000
0.013
0.140
0.002
0.002
0.005
0.005
0.010
0.004
0.055
0.004
0.005
0.003

78.49
-2.00
3.37
-6.30
12.11
-5.55
3.91
-3.72
0.25
-9.42
-2.50
1.00
-2.45
2.14

< 0.0001
0.04540.0007< 0.0001< 0.0001< 0.0001
0.00010.00020.8021< 0.0001
0.01240.31760.01420.0328

-63
39
-92
128
-57
24
-24
3
-114
-53
14
-26
12

-0.0450.004-11.18< 0.0001

Figure 3 (a)

0.024

0.004

5.54

< 0.0001

Figure 3 (b)

0.073

0.028

2.64

0.0083Figure 3 (c)

0.000

0.004

0.11

0.9130Figure 3 (d e)-0.247

0.110

-2.24

0.0249Figure 3 (d e)

0.045

0.010

4.56

< 0.0001

Figure 3 (d e)

0.030

0.010

2.98

0.0029Figure 3 (d e)

0.009

0.003

2.73

0.0064

Figure 3 (f)URL: http:/mc.manuscriptcentral.com/pqje

Effect Size(ms)

Quarterly Journal of Experimental Psychology

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Page 76 of 93

Japanese morphographic word recognition 76
Table 4
Estimate, standard error, t-value, p-value, and effect size of influential predictors forthe second subgaze durations for trials with two subgazes.

Type(Intercept)
PreviousSubgazeDuration
PreviousRT
Trial
EyePosition (Right)
LeftKanjiStrokesResid
RightKanjiStrokesResid
RightKanjiTokenFreq
RightKanjiNeighbourResid
WholeWordFreq
GoogleDocFreqResid
LeftKanjiHomophones
RightKanjiHomophones
LeftKanjiRadicalTransparency
LeftKanjiStrokesResidx EyePosition (Right)
RightKanjiStrokesResidx EyePosition (Right)
LeftKanjiRadicalTransparency
x WholeWordFreq

Task
Task
Task
Task
FeatureFeatureCharacter
Character
Word
Word
Phonology
Phonology
Semantics
Featurex TaskFeaturex TaskSemantics
x WordEstimate

Std.Error

t-valuep-value

19.921-5.4720.967
0.000-0.1550.146-0.089-0.190-0.273-0.101-0.345-0.207
0.404
0.365

0.934
0.455
0.190
0.001
0.549
0.042
0.046
0.046
0.067
0.104
0.057
0.077
0.079
0.170

21.33
-12.02
5.10
-0.42
-0.28
3.44
-1.95
-4.11
-4.11
-0.97
-6.05
-2.69
5.13
2.15

< 0.0001< 0.0001< 0.0001
0.67480.77810.0006
0.0516< 0.0001< 0.0001
0.3320< 0.0001
0.0072< 0.0001
0.0314

-1052
92
-11
-1
102
-59
-52
-52
-69
-82
-29
56
8

-0.184

0.044

-4.20

< 0.0001

Figure 4 (a)

0.156

0.047

3.32

0.0009Figure 4 (b)-0.060

0.025

-2.45

0.0143Figure 4 (c)URL: http:/mc.manuscriptcentral.com/pqje

Effect size
(ms)

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Quarterly Journal of Experimental Psychology

Japanese morphographic word recognition 77

Figure Captions

Figure 1

Radical-based and character-based multilevel models of morphographic

word recognition, summarizing representations and links proposed by Taft, Zhu,
and Peng (1999), Saito (1997), and Tamaoka and Hatsuzuka (1998). Activation ofneighboring words and characters are not depicted in the figures. Lemma
representations in Tamaoka and Hatsuzuka’s (1998) model are not shown in thecharacter-based model depicted here (left).

Figure 2

Interactions co-determining the lexical decision response times and thenumber of subgazes

Figure 3

Interactions co-determining the first subgaze durations in trials with

two subgazes

Figure 4

Interactions co-determining the second subgaze durations in trials with

two subgazes

Figure 5

A character-driven processing model of Japanese two-character word

recognition with semantic radicals as orthographic morphemes. The activations ofmorphographic neighbours, phonological neighbours, and semantic associates are
not specified in the figure.

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Japanese morphographic word recognition 78
Figure 1

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Quarterly Journal of Experimental Psychology

Japanese morphographic word recognition 79
Figure 2

URL: http:/mc.manuscriptcentral.com/pqjeQuarterly Journal of Experimental Psychology

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Japanese morphographic word recognition 80
Figure 3

URL: http:/mc.manuscriptcentral.com/pqje

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Quarterly Journal of Experimental Psychology

Japanese morphographic word recognition 81
Figure 4!
!

URL: http:/mc.manuscriptcentral.com/pqjeQuarterly Journal of Experimental Psychology

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Japanese morphographic word recognition 82

Figure 5

URL: http:/mc.manuscriptcentral.com/pqje

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