Semantic radicals in Japanese 1

Running head: SEMANTIC RADICALS IN JAPANESE

Semantic radicals in Japanese two-character word recognition

Koji Miwa
Department of Linguistics, University of Alberta, Edmonton, Canada
Gary Libben
Department of Linguistics, University of Calgary, Calgary, Canada
R. Harald Baayen
Department of Linguistics, University of Alberta, Edmonton, Canada

Address for correspondence:
Koji Miwa
4-32 Assiniboia Hall, Department of Linguistics
University of Alberta, Edmonton, Alberta, T6G2E7 Canada
E-mail: kmiwa@ualberta.ca,

Phone: +1(780)492-3434,

FAX: +1(780)492-0806

Semantic radicals in Japanese 2

Abstract

Extending previous studies on sub-lexical character constituent activation in Japaneseand Chinese, the present regression study investigates whether recognition of
two-character words in Japanese involves activation of semantic radicals and whether
the semantic radicals’ contribution is orthographic or semantic in nature. A
mixed-effects model complemented with a random forest analysis provided support for
the importance of the semantic radical, as witnessed by two orthogonal sets of semantic
radical properties: the radicals’ semantic transparency and usefulness, as well as thesemantic radicals’ combinability and token frequency. The two frequency effects were
facilitatory for the head of the compound, but inhibitory for the modifier, possibly due
to incompatibility of the semantic class marked by the modifier’s radical with that of thecompound as a whole. Thus, semantic radicals emerge as not just orthographic
components but as fully-fledged purely orthographic morphemes.

Semantic radicals in Japanese 3

Semantic radicals in Japanese two-character word recognition

Research on morphological processing suggests that complex words are notrecognized simply by full-form-to-meaning matching nor are they recognized only
through feed-forward combinatorial computations (Bertram, Baayen, & Schreuder,2000; Frost, Grainger, & Carreiras, 2008; Frost, Grainger, & Rastle, 2005; Libben,
1998; Rastle, Davis, & New, 2004; Taft & Kougious, 2004). Instead, bothcomputational efficiency and storage efficiency seem to be optimized simultaneously(e.g., Kuperman, Schreuder, Bertram, & Baayen, 2009; Libben, 2006).
This optimization of storage and computation should also apply to reading ofJapanese and Chinese, languages with a morphographic writing system. In theselanguages, a large majority of words is represented orthographically by means of twocomplex characters. There is clear evidence for character activation in the recognition of
two-character words (see Joyce 2002; Kawakami, 2002; Tamaoka & Hatsuzuka, 1995,
1998 for Japanese, and Huang, Lee, Tsai, Hung, & Tzeng, 2006; Ji & Gagné, 2007;
Zhou, Marslen-Wilson, Taft, & Shu, 1999 for Chinese). Tamaoka and Hatsuzuka (1995)
reported that, independently of whole word frequency, the right character’s frequency
speeds up responses in a two-character word lexical decision. Effects of characters’Semantic radicals in Japanese 4

meanings (Tamaoka & Hatsuzuka, 1998) and an effect of the conceptual relation
governing the interpretation of two character compounds (Ji & Gagné, 2007) suggest
that, as expected, the characters in a two-character compound mediate lexical
processing. However, no study on two-character word recognition has assessed thecontribution of sub-morphemic components, the orthographic morphemes unique tomorphographic orthography known as semantic radicals.
A majority of characters are composed of two radicals: a semantic radical, asemantic constituent encoding a basic category meaning, and a phonetic radical, aphonological constituent. Phonetic radical activation was witnessed in studies
addressing the processing of single-characters (Hsu, Tsai, Lee, & Tzeng, 2009; Lee,Tsai, Huang, Hung, & Tzeng, 2006). The present study focuses on the role of semantic
radicals. Semantic radicals function as entries in character dictionaries. They also
provide useful classification cues in learning the 1,006 characters taught in primaryeducation (Ministry of Education, Culture, Sports, Science and Technology, 2009).
Experimental evidence for semantic radical activation comes fromsingle-character decision (Feldman & Siok, 1997 on Chinese), single-character decision
with priming (Feldman & Siok, 1999 on Chinese), speeded single-character
semantic-categorization (Flores d’Arcais & Saito, 1993 on Japanese), andSemantic radicals in Japanese 5

single-character word naming (Flores d’Arcais, Saito, & Kawakami, 1995 on Japanese).
Previous research has shown that characters with a semantic radical occurring in manyother characters are read faster. Taft and Zhu (1997) and Saito (1997) assume thatsemantic radicals play a similar role in the reading of two-character words. As we shallsee this assumption is only partially validated by our study, which addresses the role
played by semantic radicals in the ecologically important context of two-characterwords. More specifically, the present study seeks to clarify whether the effects ofsemantic radicals depend on their position in the left (modifier) versus the right (head)character. We also aim to clarify the role of the semantic transparency of semantic
radicals, and to establish the extent to which radical type frequency effects are
independent of age of acquisition (AoA, see, e.g., Juhasz, 2005). Finally, we investigate
how the commonly-used partial priming manipulation affects complex word
recognition.
We implemented an analysis of covariance design, combining several numericalpredictors with a factorial treatment, overt priming, as most studies addressing lexical
processing in Japanese and Chinese using behavioral measures made use of priming
manipulations (see Feldman & Siok, 1999; Joyce, 2002). For the statistical analysis, wemade use of a mixed-effects model complemented by random forests (a conditional

Semantic radicals in Japanese 6

inference tree-based ensemble method, see, e.g., Strobl, Boulesteix, Kneib, Augustin, &Zeileis, 2008; Strobl, Malley, & Tutz, 2009). This combined approach allows us toevaluate both significance and magnitude of semantic radicals’ contribution to response
speed relative to the contribution of character and whole word properties.

MethodParticipants
Thirty native speakers of Japanese (23 females, mean age = 28.5, SD = 7.9) wererecruited as paid participants at the University of Alberta and neighboring cities inAlberta, Canada.
Apparatus
The experiment was run with PsyScope (Cohen, MacWhinney, Flatt, & Provost,1993) using a Macintosh iMac computer and an iBook computer operating under OS
9.2. The S and L keys on a Macintosh keyboard were used for lexical decision
responses.
Materials
Forty-six prime-target pairs of two-kanji words were constructed. Prime andtarget pairs shared the semantic radical of their right character, as previous research has

Semantic radicals in Japanese 7

shown that the right character, the head, co-determines lexical decision latencies to agreater extent than the left character (Libben, Gibson, Yoon, & Sandra, 2003; Tamaoka& Hatsuzuka, 1995). Forty-six two-character pseudo-homophonous nonwords wereprepared by replacing the first character of existing two-character words by anotherexisting homophonic character. We divided the 46 critical word pairs into two sets (Aand B) of 23 word pairs each. One group of participants was presented with the wordsof set A paired with primes sharing the semantic radical, with the words from set B
paired with control primes that did not share the radical. A second group of participants
was presented with the words from set B paired with primes sharing the right semantic
radical and the words from set A paired with non-matching control primes. During theexperiment, therefore, participants encountered 92 stimuli in total: 23 primed pairs, 23
control pairs, and 46 nonwords (See Table 1). A given word was presented only once toa given participant. Mean semantic similarity, gauged by LSA scores (Landauer, Foltz,& Laham, 1998) for the translated prime-target pairs, was 0.07 for both primed andunprimed conditions [t(45) = 0.12, p = 0.91].

(Table 1 about here)

Semantic radicals in Japanese 8

Procedure
A trial consisted of a fixation point (*) presented at the centre of the display for
1,000 ms, followed by a 230 ms prime, followed by a backward mask (##) of 200 ms to
avoid visual-overlap effects, after which the target word appeared and remained on thescreen until participants responded by pressing one of two keys on the keyboard.
Participants were instructed to decide, as quickly and accurately as possible, whether thesecond word (i.e. the target) is an existing word in Japanese by pressing the L key forwords and S key for nonwords. The prime word was always an existing two-character
word. Fixation point and masks were presented in Times New Roman 48, and words in
Mincho 48.

Results
Statistical analyses in this study were carried out by using R version 2.9.2 (RDevelopment Core Team, 2009). One participant with 43% error rate was excluded
from the data analysis. Stimuli that elicited RTs shorter than 300 ms or longer than
3,000 ms were also excluded (8 data points). Furthermore, four target words thatelicited more than 30% error rate (113 target data points, 8.5% of the data) were
excluded from the analysis. The mean error rate for the remaining 1,213 target

Semantic radicals in Japanese 9

responses was 8% (1,121 correct, 92 incorrect). For these 1,213 data points, thequantiles of the target error rates were 0% (minimum), 0% (1st quartile), 5% (median),
14% (3rd quartile), and 28% (maximum). The corresponding quantiles of the subject
error rates were 0% (minimum), 5% (1st quartile), 5% (median), 12% (3rd quartile), and
26% (maximum). A reciprocal transformation was applied to RTs (-1000/RT) to
remove the skew characterizing the distribution of the raw RTs. Only correct responses,
1,121 data points, were considered for the response time analyses. All predictors with a
noticeably skewed distribution were logarithmically transformed.

(Table 2 about here)

Table 2 lists the predictors in our model, ordered by linguistic levels. At the levelof radicals, the level of our primary interest, previous studies have shown that radicals
used across many characters are processed faster (Feldman & Siok, 1997, 1999; Taft &Zhu, 1997). Following the terminology used by Feldman and Siok (1997, 1999), radical
combinability counts (LogLeftKanjiRadicalCombinability,
LogRightKanjiRadicalCombinability) represent the type count of basic kanji characterssharing a given semantic radical with any of the other 1,945 basic kanji characters.
Radical token frequency measures (LogLeftKanjiRadicalTokenFreq, LogRightKanjiSemantic radicals in Japanese 10

RadicalTokenFreq) represent the cumulative token frequency of the kanji characterssharing a given semantic radical (Tamaoka, Kirsner, Yanase, Miyaoka, & Kawakami,
2002; Yokoyama, Sasahara, Nozaki and Long, 1998).
Three further radical measures were considered: the position of the semantic
radical in the right character (RightKanjiRadicalPosition, left vs. other) and type
frequency of the non-semantic radical in the right character(LogRightKanjiOtherRadicalFreq, Saito, Kawakami, & Masuda, 1995, 1997). Finally,
we considered a factor distinguishing between prime-target pairs sharing the semantic
radical in the same position and those in which the semantic radical appeared at
different positions (PrimeTargetRadicalPositionConsistency). The positional measures
did not reach or even approach significance in our analysis (see also Figure 2), andhence will not be reported below.
We obtained two semantic measures for the right kanji radicals.
RightKanjiRadicalTransparency is a measure based on twenty-one native Japanesereaders’ evaluation on a seven-point scale of the congruity between the meaning of thecharacter and the meaning of the component radical. RightKanjiRadicalUsefulness
gauges how useful a given semantic radical is in predicting the character meaning.
Thirty native Japanese readers were given a sheet of paper with single-characters with

Semantic radicals in Japanese 11

their semantic radical portion visible and all other portions ink-blobbed. This task
measured the independent meaningfulness of semantic radicals. The rating scores in
RightKanjiRadicalTransparency and RightKanjiRadicalUsefulness were significantly
correlated (r = 0.53, p < 0.01). Since the reliability of the semantic information provided
by a given semantic radical varies across the characters of the language, we expect thatsemantic radicals with greater independent meaningfulness play a more substantial role
in reading.
As predicted at the level of features, we encoded constituent complexity using
stroke counts (PrimeRightKanjiStrokes, LeftKanjiStrokes, LeftKanjiRadicalStrokes,
RightKanjiStrokes, RightKanjiRadicalStrokes). At the character level, we consideredwritten token frequency (Amano & Kondo, 2003) for the left and right characters oftarget words (LogRightKanjiTokenFreq, LogLeftKanjiTokenFreq) and for the rightcharacter of the prime words (LogPrimeRightKanjiTokenFreq) whose semantic radical
is the target of the present the priming manipulation. The character neighbour measures(LogPrimeRightKanjiNeighbour, LogLeftKanjiNeighbour, LogRightKanjiNeighbour)
represent a given character’s position-specific morphographic family size (Joyce &Ohta, 2002). Our age of acquisition (AoA) measures (PrimeRightKanjiAoA,
LeftKanjiAoA, RightKanjiAoA) represents the school grades at which the characters inSemantic radicals in Japanese 12

our prime and target words are first taught. They are an objective measure for AoA.
Lastly, we considered the token frequency of the whole word
(LogWholeWordTokenFreq, Amano & Kondo, 2003).
Given substantial multicollinearity, we made use of two statistical techniques: theparametric technique of mixed effects modeling with orthogonalization of predictors
through a principal component analysis (PCA, Belsley, Kuh, & Welch, 2004) and a
non-parametric technique, random forests.

Principal components regression analysis
Principal component orthogonalization was applied separately to the eighteen
target word properties and to the five prime word properties. We then selected thosePCs that accounted for at least 5% of the variance. Among target PCs, the top seven
PCs cumulatively accounting for 84.1% of the variance (See Table 2). For the primePCs, the three accounted for 88.4% of the variance. These PCs were entered as
predictors in a mixed-effects analysis of covariance (Baayen, Davidson, & Bates, 2008;
Bates, Maechler, & Dai, 2007). A backward stepwise variable selection procedure
identified five target PCs as significant: PC1, PC3, PC4, PC5, and PC7.
Table 3 lists the estimate, standard error, upper and lower limits of Highest

Semantic radicals in Japanese 13

Posterior Density (HPD) confidence interval, as well as t-values and p-values based on
Markov chain Monte Carlo (MCMC) sampling for these five PCs. Each PC’scontribution is visualized in Figure 1. In order to clarify the nature of the PCs, we
inspected the loadings of the original lexical predictors on these PCs. Table 2 presents
the predictors with the largest loadings in bold (the predictors with high loadings
exceeding 0.30 or smaller than -0.30 are shown in bold). In our discussion, we highlight
only those predictors that have the greatest loadings and carry the main trends. We willcomplement this discussion with a random forests analysis that evaluates thecontribution of individual predictors in a non-parametric way.

(Table 3 about here)(Figure 1 about here)

The inhibitory predictor PC1 (Figure 1 panel a), effect size (range) 218 ms,
quantifies primarily the combinability and token frequency of the right character’s
semantic radical (LogRightKanjiRadicalCombinability and
LogRightKanjiRadicalTokenFreq). Since these predictors correlate negatively with an
inhibitory PC, their effect on RTs is facilitatory. Right characters that contain aSemantic radicals in Japanese 14

semantic radical with high combinability or high token frequency are read faster. In
contrast to the above facilitatory predictors, RightKanjiAoA loaded positively on PC1
and hence their effects on RTs are inhibitory. Words with a character that has beentaught at a later age are read less quickly, as expected.
PC3 is a facilitatory predictor (panel b, effect size 111 ms). Both
LogWholeWordTokenFreq and LogRightKanjiTokenFreq loaded positively on PC3 (seeTable 2) and hence indicate facilitation. LeftKanjiRadicalStroke has a large negative
loading on PC3, indicating increasing RTs with increased orthographic complexity ofthe semantic radical of the left character.
PC4 is an inhibitory predictor (panel c, effect size 254 ms), and it is characterized
by LeftKanjiStrokes and LogLeftKanjiRadicalCombinability, which have the largest
positive loadings, indicating inhibition. The inhibitory effect ofLogLeftKanjiRadicalCombinability on the RTs contrasts with the facilitatory
contribution of the right character’s semantic radicals as witnessed by the effects of
LogRightKanjiRadicalCombinability and LogRightKanjiRadicalTokenFreq loading on
PC1.
As shown in panel d, PC5 speeded up responses in the primed condition (effectsize 38 ms) but slowed down responses in the unprimed control condition (effect size 61

Semantic radicals in Japanese 15ms). PC5 is characterized by large negative loadings of the semantic radical properties,
RightKanjiRadicalTransparency and RightKanjiRadicalUsefulness. In the unprimed
control condition, characters with a semantically more transparent and more usefulradical elicited shorter response latencies. This pattern reverses when a related prime ispresented. In other words, target words with radicals with low transparency andusefulness benefit more from the priming manipulation.
We note here that a main effects model with just the priming manipulation as
predictor yielded a facilitatory effect of priming (mean RT control 718 ms, mean RT
primed 703 ms, effect size 15 ms) that just failed to reach significance at α = 0.05 (p =0.0570 using Markov chain Monte Carlo sampling, p = 0.0501 using the upper boundfor the degrees of freedom for the t-test). Importantly, our analysis of covarianceallowed us to clarify that the priming effect is indeed a semantic effect, as expected forsemantic radicals. Furthermore, the analysis of covariance also clarified that the priming
effect increased for decreasing transparency of semantic radicals. Finally, the analysis
of covariance also allowed us to bring the priming effect into perspective with respect toother distributional predictors: compared to the other predictors in our model, the effectsize of the priming manipulation is modest.
The effect of PC7 was inhibitory (effect size 187 ms). Since the loadings on PC7

Semantic radicals in Japanese 16

are dominated by LogRightKanjiOtherRadicaTypeFreq (0.64), PC7 represents aninhibitory effect of type frequency of the non-semantic radical component. This
inhibitory effect of the non-semantic radical contrasts with the facilitation observed forthe frequency of the semantic radical represented by PC1(LogRightKanjiRadicalCombinability and LogRightKanjiRadicalTokenFreq havenegative loadings on PC1). This result supports theories that distinguish between
semantic and non-semantic radicals (e.g., Feldman & Siok, 1997, 1999).
The position of the semantic radical in the right character(RightKanjiRadicalPosition) was not predictive. Similarly, the factor specifying
whether the radical occupied the same position across prime and target(PrimeTargetRadicalPositionConsistency) failed to reach significance as well. This
allows us to conclude that the priming effect is semantic in nature and was not driven bypositional overlap.
Prime PCs, representing the properties of the prime words, as well as participants’characteristics (i.e., age, sex, months of stay in Canada), did not emerge as significant
predictors. We also investigated whether words with different
ON(Chinese-origin)-KUN(Japanese-origin) pronunciations affected the results.
Removal of the relevant words and re-analyses did not change the results.

Semantic radicals in Japanese 17

Random forests analysis
Our regression model does not inform us about the specifics of the relative
importance of the individual lexical variables that loaded onto various PC. For example,
RightKanjiRadicalTransparency and RightKanjiRadicalUsefulness have very similarloadings on PC5, and their individual contributions cannot be teased apart. Furthermore,
LogWholeWordTokenFrequency has high loadings on three PCs (PC3, PC4, and PC7)
and hence its contribution is larger than one would expect on the basis of inspection ofindividual PCs. In order to obtain better insight into the relative importance of theindividual variables, we made use of a random forests recursive partitioning method, atechnique particularly useful for assessing a large number of predictors with small
samples (small n large p problem, see Strobl, Malley, & Tutz, 2009). Conditional
inference trees are grown for subsets of observations and subsets of predictors.
Predictions are obtained by an ensemble method in which the votes of individual trees
are collected. Variable importance is gauged by evaluating reduction in prediction
accuracy when a given predictor is not considered (Breiman, 2001; Strobl, Boulesteix,
Zeileis, & Hothorn, 2007).
Variable importance rankings (using the conditional permutation scheme

Semantic radicals in Japanese 18

proposed by Strobl, Boulesteix, Kneib, Augustin, & Zeileis, 2008, implemented in the
cforest function in the party package of Hothorn, Buehlmann, Dudoit, Molinaro,& Van Der Laan, 2006; Strobl, Boulesteix, Kneib, Augustin, & Zeileis, 2008; Strobl,
Boulesteix, Zeileis, & Hothorn, 2007) for the random forests fitted separately to theprimed and the control conditions are shown in Figure 2. Basically, the variable
importance plotted on the horizontal axes is a measure of the drop in prediction
accuracy when the predictor is withheld from the model specification. For important
predictors, failure to include them results in a large loss of prediction accuracy. For
irrelevant factors, on the other hand, it does not matter when they are not included in themodel specification.

(Figure 2 about here)

Figure 2 reveals a pattern consistent with the PCA regression analysis.
LogWholeWordTokenFreq is identified, as the most important variable, which dovetails
well with the observation that LogWholeWordTokenFreq has high loadings on several
different PCs. The next most important variable is the token frequency of the rightcharacter (LogRightKanjiTokenFreq) in both primed and control conditions. The rightcharacter’s importance in lexical decision is consistent with the result of Tamaoka andSemantic radicals in Japanese 19

Hatsuzuka (1995). While the PCA did not distinguish between contributions ofsemantic radical combinability and cumulative token frequency, the random forests
analysis suggests that token frequency (LogRightKanjiRadicalTokenFreq) is more
important than combinability (LogRightKanjiRadicalCombinability) in the unprimed
condition. Note that RightKanjiRadicalTransparency and RightKanjiRadicalUsefulness
are ranked higher in the primed condition, further confirming that, in the primed
condition, the properties of the semantic radical afforded a processing advantage. Note
that, as in the regression analysis, the positional predictors RightKanjiRadicalPosition
and PrimeTargetRadicalPositionConsistency were irrelevant. Finally,
LogRightKanjiOtherRadicaTypeFreq ranks among the top half of the importanceranked predictors.

General discussion
We identified several graded effects of semantic radical properties in
two-character kanji word recognition in addition to the effects of character frequency
and whole word frequency. Interestingly, the effect of a semantic radical’s type andtoken frequencies depends on its position in the two-character word: inhibitory inmodifier position and facilitatory in head position. This suggests that the facilitation

Semantic radicals in Japanese 20

observed in single-character studies does not generalize straightforwardly to themodifier position in two-character words. This asymmetrical effect of radical frequency
was not modulated by the priming manipulation, which emerged only in interaction
with RightKanjiRadicalTransparency and RightKanjiRadicalUsefulness. The
facilitation characterizing heads and the inhibition observed for modifiers may be due tothe semantic radical functioning as a kind of classifier. The semantic class indicated by
the semantic radical of the head is congruent with that of the compound as a whole. For
the modifier, by contrast, the semantic class indicated by its semantic radical is at odds
with that of the compound as a whole.
Turning to this interaction, which expressed itself on PC5 in our PCA regression
model, we observed facilitation for words with a right semantic radical with lower
values on either semantic measure. This suggests that semantic radicals are not mere
orthographic units but orthographic morphemes combining form and meaning
properties. Random forests clarified that RightKanjiRadicalTransparency is morepredictive than RightKanjiRadicalUsefulness, suggesting that the compositional
part-whole relation between a semantic radical and its character is more influential in avisual lexical decision task than the intrinsic semantic richness of this radical.
In the PCA regression, AoA loaded on the same principal component as semantic

Semantic radicals in Japanese 21

radical type and token frequencies. Random forests analysis indicated that
LogRightKanjiTokenFreq and LogLeftKanjiTokenFreq outperformed RightKanjiAoA
and LeftKanjiAoA respectively in both priming conditions. Nevertheless, random forests
analysis indicates that AoA is a robust predictor. Note that the random forest, using ameasure of AoA which is not based on human ratings or performance but on the age at
which characters are learned in school, replicates the finding that the frequency effectcannot be reduced to AoA (cf. Brysbaert, Lange, & Wijnendaele, 2000; Morrison &Ellis, 2000).
The present results indicate that describing the reading of two-character words as
analogous to alphabetic compound word processing still underestimates theorthography-specific morphographic complexity of reading in Japanese. Kanjicharacters are themselves morphologically complex. The semantic radical can beviewed as a purely orthographic morpheme, combining a visual form with richsemantics, without support from a phonological/acoustic form. The experimental
fingerprint of the semantic radical emerging from our study resembles in many ways theexperimental fingerprint of standard morphemes as observed for many European
languages, with position-dependent effects, with graded effects of semantictransparency, with effects of AoA, and with greater combinability affording fasterSemantic radicals in Japanese 22

processing (see, e.g., Moscoso del Prado Martín et al., 2004). This suggests that inJapanese, the orthography provides an additional layer of morphological complexity to
the already complex classificatory system provided by the spoken language.

Semantic radicals in Japanese 23

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Semantic radicals in Japanese 31

Table 1
Types of prime-target pairs used in the present study

Condition

Prime
Word

Primed

時計

Control

救急

Nonword

印刷

TargetTranslation

Radicaltoke‘clock’kinkyu‘emergency’

言gomben心kokoroinsatsu‘fireplace’

刂
ritto

Word
書記
書記
渋症

Translation

Radicalshoki‘scribeshoki‘scribe言
gomben言
gomben

jusho
‘N/A’

疒
yamaidare

SharedYes
No
No

Semantic radicals in Japanese 32

Table 2Eighteen original predictors of target words and their loadings on the seven target PCs.
The * mark represents significantly influential predictors in the regression modelsummarized in Table 3. The bolded values represent relatively large loadings(exceeding 0.30 or smaller than -0.30).

Type

Predictors

PC1* PC2

PC3* PC4* PC5* PC6

PC7*

StrokeStrokeStrokeStrokeRadical
Radical
Radical
Radical
Radical
Radical
RadicalCharacter
Character
Character
Character
Character
Character
Word

LeftKanjiStrokes
LeftKanjiRadicalStrokes
RightKanjiStrokes
RightKanjiRadicalStrokes
LogLeftKanjiRadicalCombinability
LogLeftKanjiRadicalTokenFreq
LogRightKanjiRadicalCombinability
LogRightKanjiRadicalTokenFreq
LogRightKanjiOtherRadicalFreq
RightKanjiRadicalTransparency
RightKanjiRadicalUsefulness
LogLeftKanjiNeighbour
LogLeftKanjiTokenFreq
LeftKanjiAoA
LogRightKanjiNeighbour
LogRightKanjiTokenFreq
RightKanjiAoA
LogWholeWordTokenFreq

0.05
-0.03
0.28
0.30
0.06
-0.04
-0.38
-0.38
0.11
0.03
0.23
-0.19
-0.22
0.30
-0.29
-0.29
0.33
-0.09

-0.23
-0.24
0.09
0.09
0.22
0.41
-0.01
-0.10
0.13
0.17
0.21
0.41
0.43
-0.34
-0.21
-0.14
0.10
0.10

-0.13
-0.36
0.04
0.24
0.27
0.16
-0.11
-0.03
0.30
-0.06
0.08
-0.22
-0.09
0.23
0.24
0.44
-0.30
0.37

0.42
-0.01
-0.33
-0.17
0.47
0.33
0.17
0.08
0.14
0.31
0.14
-0.15
-0.17
0.10
0.01
-0.11
-0.03
-0.33

0.04
-0.19
-0.25
-0.29
0.21
0.24
-0.22
-0.20
-0.19
-0.49
-0.50
-0.08
0.10
0.10
-0.23
-0.07
0.11
0.11

-0.39
-0.44
-0.12
-0.24
-0.04
-0.13
0.23
0.31
-0.20
0.25
0.12
-0.20
-0.14
0.21
-0.17
-0.13
0.31
0.20

-0.33
-0.16
-0.36
-0.07
-0.27
-0.10
-0.13
0.00
0.64
-0.07
-0.12
0.13
-0.14
-0.01
-0.12
-0.08
-0.13
-0.37

0.23
0.23

0.16
0.39

0.13
0.52

0.11
0.63

0.09
0.72

0.07
0.79

0.05
0.84

Variance accounted for by each PC
Cumulative variance accounted forSemantic radicals in Japanese 33

Table 3
Influential principal components (PCs) with their estimate, standard error, upper andlower limits of HPD confidence interval, t-value and p-value based on 10,000 Markov
chain Monte Carlo (MCMC) samples from the posterior distributions of the parameters.

(Intercept)
TargetPC1
TargetPC3
TargetPC4
TargetPC5
Condition (primed)
TargetPC7
TargetPC5 by
Condition (primed)

Estimate Std.Error HPD95lower HPD95upper

t-value pMCMC-1.3898
0.0428-0.0355
0.07890.0227-0.03220.06430.03890.01040.01380.01490.01760.01530.0217-1.4550
0.0242-0.01100.1052
0.0539-0.0627
0.0265-1.3247
0.0613-0.0603
0.05180.0092-0.00200.1033-35.77
4.10
-2.57
5.30
1.29
-2.10
2.97

0.00010.00010.00640.00010.14820.03560.0014-0.0376

0.0130-0.0134-0.0642-2.90

0.0034

Semantic radicals in Japanese 34

Figure Captions

Figure 1. Partial effects of four influential principal components (PCs) on response
latencies in the primed lexical decision. PC1: Larger right character semantic radical
combinability/ token frequency (negative loadings on PC1) indicate shorter RTs (panela); PC3: Larger word/ right character frequency (positive loadings on PC3) indicate
shorter RTs (panel b); PC4: Larger left character semantic radical combinability/ token
frequency (positive loadings on PC4) slow responses (panel c); PC5: Larger rightcharacter radical transparency/ usefulness (negative loadings on PC5) indicate shorter
RTs in the control condition but longer RTs in the primed condition (panel d). PC7:
Larger right character non-semantic radical type frequency (positive loadings on PC7)
indicates longer RTs.

Figure 2. A random forest’s variable importance ranking for the primed and the control
conditions. Variable importance is assessed in terms of mean decrease in accuracy.

Semantic radicals in Japanese 35

Figure 1

Semantic radicals in Japanese 36

Figure 2

Semantic radicals in Japanese 37

APPENDIX

A list of word pairs used in the present study. Items marked with * were excluded fromthe final analyses based on error rate.

RadicalShared CriticalPrime CriticalTarget RadicalShared ControlPrime ControlTarget
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

豆粒
救急
犠牲
反転
時計
旅館
道路
睡眠
筆箱
積荷
満点
自爆
釣針
結婚
空港
家財
薄味
教授
学校
車掌
心霊
潔癖
苦悩
物価
皆勤
決闘
樹脂

妖精
得意
放牧
五輪
書記
*綿飴
跳躍
明瞭
縦笛
*国花
高熱
*土煙
手鏡
年始
血液
盗賊
合唱
薬指
球根
攻撃
落雷
治療
習慣
同僚
援助
玄関
頭脳

No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No

薄味
家財
学校
犠牲
救急
教授
空港
苦悩
潔癖
自爆
車掌
心霊
睡眠
積荷
釣針
道路
時計
反転
筆箱
豆粒
満点
旅館
結婚
海図
角度
皆勤
壁紙

妖精
得意
放牧
五輪
書記
*綿飴
跳躍
明瞭
縦笛
*国花
高熱
*土煙
手鏡
年始
血液
盗賊
合唱
薬指
球根
攻撃
落雷
治療
習慣
同僚
援助
玄関
頭脳

Semantic radicals in Japanese 38

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

新鮮
海図
壁紙
監獄
悲鳴
工場
破裂
砂嵐
乗客
進展
角度
下駄
骨盤
補聴
木造
洗剤
宅配
景観
卓越

捕鯨
楽園
電線
密猟
*折鶴
食塩
変装
断崖
合宿
質屋
売店
実験
連盟
就職
交通
短剣
発酵
両親
隆起

No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No

景観
下駄
骨盤
決闘
補聴
監獄
工場
樹脂
新鮮
乗客
進展
砂嵐
洗剤
卓越
破裂
宅配
悲鳴
物価
木造

捕鯨
楽園
電線
密猟
*折鶴
食塩
変装
断崖
合宿
質屋
売店
実験
連盟
就職
交通
短剣
発酵
両親
隆起

Semantic radicals in Japanese 39

Acknowledgment

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). The authors are indebted to Laurie Feldman andHisashi Masuda for constructive discussion.
This is an electronic postprint of an article published in Language and Cognitive
Processes. The published article is available online at:
http://www.tandf.co.uk/journals/pp/01690965.html