Read Writ (2013) 26:205–223
DOI 10.1007/s11145-012-9363-x

The effects of visual complexity for Japanese kanji
processing with high and low frequencies
Katsuo Tamaoka · Sachiko Kiyama

Published online: 22 March 2012
© Springer Science+Business Media B.V. 2012

Abstract The present study investigated the effects of visual complexity for kanji
processing by selecting target kanji from different stroke ranges of visually simple(2–6 strokes), medium (8–12 strokes), and complex (14–20 strokes) kanji with highand low frequencies. A kanji lexical decision task in Experiment 1 and a kanji
naming task in Experiment 2 were administered to native Japanese speakers. Resultsof both experiments showed that visual complexity inhibited the processing of lowfrequency kanji, whereas such consistent, inhibitory effects of visual complexity
were not observed in the processing of high-frequency kanji. Kanji with medium
complexity were processed faster than simple and complex kanji in high frequency.
Keywords Japanese kanji · Kanji frequency · Strokes · 13th stroke boundary ·Visual complexity

Introduction
Unlike English which uses a sequence of simple alphabetic characters, Japanese
kanji vary in complexity from simple like the kanji 王 (king) to complex like thekanji 題 (title). The visual complexity of kanji seems to affect speed and accuracy of
their processing. However, the facilitation/inhibition direction of effects regarding
visual complexity on cognitive processing of kanji is not clear. For many years ofkanji experiments, visual complexity has been treated as one characteristic of kanji
which ought to be controlled prior to running experiments without knowing its
K. Tamaoka (&) · S. Kiyama
Graduate School of Languages and Cultures, Nagoya University, Furo-cho, Chikusa-ku,
Nagoya-shi, Aichi-ken 464-8601, Japane-mail: ktamaoka@lang.nagoya-u.ac.jp; ktamaoka@gc4.so-net.ne.jp
S. Kiyamae-mail: zua04776@nifty.com

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actual effects (e.g., Hino & Lupker, 1998; Hino, Lupker, Ogawa, & Sears, 2003;
Ogawa & Saito, 2006; Tamaoka, 2005, 2007; Tamaoka & Hatsuzuka, 1998;
Tamaoka & Taft, 2010). The question, then, remains whether visual complexity
facilitates or inhibits kanji processing.

Three assumptions on kanji visual complexity
Visual complexity is fundamentally understood as the bottom-up processing from
simple visual features to a single kanji, further to a two-kanji compound, and finallyto access the meanings of kanji or compounds (e.g., Taft, 1991; Taft & Zhu, 1997;
Tamaoka & Hatsuzuka, 1998). There are three possible assumptions on the effectsof visual complexity.
The first assumption is that visual complexity facilitates kanji processing. An
early kanji study by Kawai (1966) measured kanji visual complexity by counting
lines and points. For instance, a kanji 二 (two) is counted as 2, consisting of twolines, while a kanji 人 (human) is counted as 3 having two lines and one crossing
point. Using this measuring approach, Kawai found a high correlation of r = .76(n = 50, p \ .001) between kanji printed-frequency (hear after, kanji frequency)
and visual complexity among 50 kanji randomly selected from a list of 1,965. The
same 50 kanji, however, showed an even higher correlation of r = .93 (n = 50,
p \ .001) between kanji frequency and traditional stroke numbers (the way inwhich kanji are drawn by a Japanese traditional writing brush). Despite Kawai’s
efforts, stroke count had a stronger correlation with kanji frequency than the lineand-point measure. Furthermore, Kawai tested accuracy of kanji pronunciations
among 80 mature native Japanese speakers, and found that the more complex kanji
were read more accurately than the less complex ones when kanji frequency is held
constant. Consequently, Kawai (1966) concluded that visual complexity facilitatedkanji processing from orthography to phonology.
The second assumption is that the difficulty of kanji processing increases up to acertain point of visual complexity while it decreases after the critical point. As
depicted in Fig. 1, the token frequency of kanji in the former list of 1,945
commonly-used kanji (Cabinet Announcement by the Japanese Government, 1981)
increases with stroke count up to 12 strokes. At 13 strokes, however, the number
gradually decreases until the maximum 23 strokes. Taking both visual complexity
measured by kanji strokes and the number of existing kanji into consideration,
Kaiho (1979) put forward his assumption that difficulty in kanji processing increases
proportionally up to 12 strokes, but after the 13th stroke, the number of strokes
facilitates kanji processing. In this assumption, Kaiho (1979) disagreed with Kawai(1966) in the way that the difficulty of kanji processing increases up to the 13th
stroke, but Kaiho agreed with Kawai’s finding that stroke-count facilitate kanji
processing after the 13th stroke. Despite the conflicting argument, this assumptionof a 13 stroke boundary has not been supported by clear experimental evidence.
The third assumption is that visual complexity inhibits processing of kanji
compounds when they are low frequency. Tamaoka and Takahashi (1999)
investigated latencies for kanji writing initiation using visually complex/simple

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207

Medium220
200

Number of kanji

180
160
140

SimpleComplex

120
100
80
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Number of strokes
Fig. 1 Stroke distribution of the 1,945 commonly-used Japanese kanji

two-kanji compound words with high and low frequencies. Initiation times for writing
behavior indicated kanji visual complexity (number of strokes) inhibited retrieving
orthographic representations of two kanji from the mental lexicon when stimuli were
low frequency. This tendency was not observed in high frequency kanji. Likewise,
Leong, Cheng, and Mulcahy (1987) found that three major factors of simple/complex
orthography, high/low printed frequency, and skilled/less skilled reading ability
influence the processing of Chinese characters. Although Leong et al. (1987) used
Chinese characters, those with less visual complexity (i.e., simple orthography) were
processed faster than those with greater visual complexity (i.e., complex orthography)
regardless of printed-frequency and skilled/less skilled reading ability. Complex
characters with low frequency by less skilled readers were especially slow inprocessing. As such, Leong et al. (1987) found inhibitory effects of visual complexity
on Chinese characters with high frequency as well as with low frequency.
It is well-depicted by various studies (e.g., Hino & Lupker, 1998; Tamaoka &Hatsuzuka, 1995 for Japanese kanji, and Taft, Huang & Zhu, 1994; Taft & Zhu, 1995;
Wu, Chou, & Liu, 1994; Zhou & Marslen-Wilson, 1994 for Chinese characters) thatkanji frequency per se strongly affects kanji processing. However, neither the first
assumption of facilitation effects (Kawai, 1966) nor the second assumption of the 13th
stroke boundary (Kaiho, 1979) took word frequency into consideration. Therefore, thepresent study set high and low word frequencies up as an experimental condition toinvestigate the effects of visual complexity on Japanese kanji processing.
The sense-determinative and sense-discriminative functionsfor kanji processing
The theoretical framework for the effects of visual complexity was put forward forinterpreting the processing of the Chinese characters from the linguistic perspective
by Wang (1973, 1981) and from the psycholinguistic perspective by Leong (1986).

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They explicated scriptural double functions of Chinese characters as sensedeterminative and sense-discriminative functions. The sense-determinative function
works within a single character, which enables a reader to identify its shape by
visual features. Putting this function into psycholinguistic terms, visual features of
the target character are decoded to activate its orthographic representation whichfurther activates its conceptual and/or phonological representation. In contrast, thesense-discriminative function works among multiple characters, distinguishing thetarget character from others with similar components.
The first assumption of facilitation effects will be supported when only the sensedeterminative function works in all stroke ranges. The more kanji become visuallycomplex, the easier kanji are processed. In contrast, if both sense-determinative andsense-discriminative functions work supplementarily, the second assumption of the
13th stroke boundary (Kaiho, 1979) can be explained as follows. Up to 12 strokes,
the sense-discriminative function will help to distinguish the target kanji from otherswith similar visual complexity while the sense-determinative function will assist to
identify the target kanji as visual complexity increases. After the 13th stroke, thenumber of kanji decreases and visual complexity gets greater. The assumption of the
13th stroke boundary suggests that kanji in the range of 1 to 12 strokes gradually
become difficult to identify due to the steady-increase of the number of kanji up to
the 12th stroke, but it become easier after the 13th stroke since the number of kanji
steadily decreases and since kanji with visual complexity will support the sensedeterminative function to work well. Despite the fact that both the first and thesecond assumptions expect facilitation effects, the third assumption of inhibitory
effects depicts quite a different picture. The sense-determinative function does nothelp speed up kanji processing, but rather slows it down. This inhibitory tendency
could be caused by the increase of decoding burden for visual features. The sensediscriminative function, on the other hand, may assist processing kanji in a certain
stroke range where various kanji exist.
The 1,945 commonly-used kanji shown in Fig. 1 are normally distributed, having
an average of 10.84 strokes with a 3.76 standard deviation (Tamaoka, Kirsner,
Yanase, Miyaoka, & Kawakami, 2002). Considering stroke count as an index ofvisual complexity, utilizing two tasks of kanji lexical decision in Experiment 1 andkanji naming in Experiment 2, the present study investigated the effects of visualcomplexity on kanji processing by selecting target kanji from three different stroke
ranges of visually simple (2–6 strokes), medium (8–12 strokes) and complex (14–20
strokes). Since kanji frequency seems to affect their processing (e.g., Hino &Lupker, 1998; Taft et al., 1994; Taft & Zhu, 1995; Tamaoka & Hatsuzuka, 1995;
Wu et al., 1994; Zhou & Marslen-Wilson, 1994), the present study added kanji withhigh and low frequencies as an experimental condition.

Experiment 1: Kanji lexical decision task
Experiment 1 investigated the effects of visual complexity on kanji processing usinga kanji lexical decision task which asked native Japanese speakers to judge whether
or not a presented kanji exists in Japanese.

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209

ParticipantsParticipants in Experiment 1 comprised 42 undergraduate and graduate students (22females and 20 males) at Nagoya University in Japan, all native speakers ofJapanese. Ages ranged from 18 years and 7 months to 38 years and 9 months. The
average age was 21 years and 1 month with a standard deviation of 3 years and8 months on the respective day of testing.
Kanji stimuli
As target stimuli, 24 kanji consisting of 12 high and 12 low printed-frequencies
were selected from each of three kanji stroke ranges of 2–6, 8–12, and 14–20(72 kanji in total) out of the 1,945 commonly-used basic kanji taught from the firstto the ninth grades of formal schooling to assure that all the participants would beacquainted with them. A lexical decision task (determining whether or not a kanji iscorrect by pressing a ‘Yes’ or a ‘No’ key) was administered using these 72 target
kanji for correct ‘Yes’ responses with another 72 pseudo-kanji for correct ‘No’
responses. All stimuli are listed in “Appendix 1” for real kanji and “Appendix 2” forpseudo-kanji.
As shown in Table 1, eight different features controlled 72 target kanji. The
experimental condition of visual complexity as measured by the number of kanji
strokes (#1 in Table 1) was controlled by selecting kanji from the three different
specific ranges. This feature showed a significant main effect of three visualcomplexity groups of simple, medium, and complex [F(2, 69) = 368.907,
p \ .001]. Another experimental condition of high and low kanji frequency was
manipulated by three different indexes (#2–4). First, Kokuritsu Kokugo Kenkyujyo(National Institute for Japanese Language and Linguistics) in 1976 calculated kanji
frequencies of appearance for 1,000 kanji (#2) from three major newspapers, Asashi,
Yomiuri, and Mainichi, during the year 1966, showing a significant difference
between two groups of high and low kanji frequencies [tL(35.683) = 8.926,
p \ .0011]. Second, Yokoyama, Sasahara, Nozaki, and Long (1998) published kanji
frequency data based on all the kanji in the Tokyo edition of the Asahi newspaper
printed in 1993 (#3), again showing a significant difference between high and low
kanji frequency groups [tL(36.009) = 8.771, p \ .001]. Third, CD-ROM versions of
the Tokyo edition of the Asahi newspaper in 1993 (#4) also showed a significant
difference between high and low kanji frequency groups [tL(36.009) = 8.357,
p \ .001]. Three different kanji frequency indexes showed a great difference in theexperimental condition of kanji frequency.
There were four control conditions (#5–#8). The school grade in which the target
kanji are taught was also taken into consideration. The assignment of the first 1,006
basic kanji to each school grade follows the Japanese language curriculum as
outlined by the Japanese Ministry of Education in 1989. Since the remaining
1

Levene’s test rejected a homogeneous variance assumption in two kanji high and low frequency groups,
so that a t test with no assumption of homogeneity of variance was used to analyze the data. This t test ismarked by L and hereafter the same.

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Table 1 Features of kanji with visual complexity of simple, medium, and complex#

Kanji features

Simple (n = 24)

Medium (n = 24)

Complex (n = 24)

M

M

M

SD

SD

SD

1

Number of strokes

4.79

1.06

9.67

1.31

15.71

1.73

2

Kanji frequency in 1976

0.45

0.69

0.73

0.87

0.60

0.77

3

Kanji frequency in 1993

6,421

11,201

14,89016,74911,55815,883

4

Kanji frequency in CD 1993

10,14018,18021,93725,97815,916

21,8495School grades

5.08

2.10

5.46

1.72

5.58

1.67

6

JLPT-test

1.63

0.71

1.61

0.72

1.64

0.66

7

Number of homophones

17.92

17.00

16.17

15.25

11.04

8.13

8

Radical frequency

25.75

30.29

40.83

34.38

43.25

32.39

M means, SD standard deviations

939 basic kanji are taught in Grades 7–9; these are all recorded with the numeral ‘7’.
Using these figures, the averages of school grades were calculated (#5). Visualcomplexity did not show a main effect [F(2, 69) = 0.480, p = .621, ns]. Thus, thethree visual complexity groups of target kanji have no differences among the gradesin which they are taught at school. In contrast, the difference between the high andlow kanji frequency groups showed a significant difference [tL(64.598) = 8.870,
p \ .001], indicating that high frequency kanji were taught at higher grades thanwere low frequency kanji. Kanji assignment to the four levels of the former versionof the Japanese Language Proficiency Test (#6) was used to control three visualcomplexity conditions, with no main effect apparent [F(2, 69) = 0.009, p = .991,ns], but showing a significant difference between high and low frequency[tL(52.900) = 9.410, p \ .001]. The number of kanji On- and Kun-reading
homophones (#7) was counted according to how many kanji out of the 1,945 basic
kanji shared the same sound (Tamaoka, Kirsner, Yanase, Miyaoka, & Kawakami,
2002). There was no significant main effect of visual complexity [F(2, 69) = 1.564,
p = .217, ns], and no significant difference between high and low frequency[t(70) = 0.058, p = .954, ns.]. Radical frequency (#8) counted how many of the
1,945 basic kanji share the same radical (Tamaoka, et al., 2002). Again, no
significant main effect of visual complexity was found [F(2, 69) = 2.057, p = .136,ns], and there was no significant difference between high and low frequency[t(70) = 1.011, p = .315, ns.]. Overall, visual complexity was well-controlled inthese four kanji features. However, kanji frequency was associated with the otherfeatures of school grade and the Japanese language proficiency (JLP) test level.
Basically, high frequency kanji were allocated at the earlier school grades and theeasier level of the JLP test.
Procedure
All real and pseudo kanji were randomly presented one-by-one to participants in thecenter of a computer screen 600 ms after the appearance of an eye fixation point
marked by ‘*’. Stimulus randomization was operated with each participant, so that

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211

each was presented with a different order of stimulus items. The participants were
instructed to respond as quickly and as accurately as possible in deciding whether or
not the kanji exists in Japanese, by pressing the ‘Yes’ or ‘No’ response key. Afterthe response, the participant pressed the space key to initiate the next presentation.
With an interval of 600 ms, the eye fixation point ‘*’ appeared for 600 ms, after
which a new kanji stimulus was shown to the participant. Participants repeated thisprocess until all the real kanji and pseudo kanji were presented in the font of MS
Gothic. Each response (i.e., reaction) time and its result of correct or incorrect wereautomatically recorded by computer. Sixteen practice trials were given toparticipants before the commencement of actual testing.
Analysis and results
Prior to the analysis of reaction times for both correct ‘Yes’ and ‘No’ responses,
extremes among kanji lexical decision times (responses slower than 300 ms or
longer than 3,000 ms) were recorded as missing values. Responses outside of 2.5
standard deviations at both high and low ranges were replaced by boundaries
indicated by 2.5 standard deviations from the individual means of participants ineach category. Only correct ‘Yes’ and ‘No’ responses were used for the analysis ofreaction times.
The means of correct ‘Yes’ reaction times and error rates for kanji lexicaldecisions are reported in Table 2. The data were analyzed using a linear mixed
effects (LME) methodology (Baayen, 2008; Baayen, Davidson, & Bates, 2008). A
two-way 3 (visual complexity; simple, medium, or complex) 9 2 (kanji frequency;
high or low) ANOVA was conducted on reaction times. The main effects of visualcomplexity [F(2, 141.261) = 31.546, p \ .001] and kanji frequency [F(1,1,368.486) = 307.566, p \ .001] were significant. Their interaction [F(2,3,065.211) = 60.381, p \ .001] was also significant.
Further analyses were conducted on high and low kanji frequency separately. For kanji
with high frequency, a LME one-way ANOVA for visual complexity showed asignificant main effect [F(2, 1,578.199) = 6.324, p \ .01]. Multiple comparisonsby the LSD method showed the significances of medium (M = 482 ms) \ simple
Table 2 Reaction times and error rates for kanji lexical decision of correct items
Kanji frequency

High

Low

Visual complexity

Reaction time (ms)Error rate (%)

M

M

SD

SD

Simple

493

89

2.72

16.27

Medium

482

80

1.45

11.96

Complex

498

94

1.27

11.20

Simple

502

81

2.17

14.60

Medium

564

115

9.96

29.98

Complex

582

126

12.68

33.31

n = 42
M means, SD standard deviations

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K. Tamaoka, S. Kiyama

600

***

SimpleMediumComplex

*

***

16%

*

14%

*

***

SimpleMediumComplex

***

12%

500

Error rates

Reaction times (ms)

700

400
300
200

10%
8%
6%
4%

100

2%
0%
High frequency

Low frequency

Kanji frequency

High frequency

Low frequency

Kanji frequency

Fig. 2 Lexical decision for correct kanji (correct ‘Yes’ responses) in function of high/low kanji
frequencies and visual complexity. * p \ .05. ** p \ .01. *** p \ .001. Bars indicate SE(M = 493 ms) = complex (M = 498 ms). Likewise, the same one-way ANOVA for kanji
with low frequency also showed the significant main effect [F(2, 2,235.100) = 58.945,
p \ .001]. Multiple comparisons by the LSD method indicated simple (M =502 ms) \ medium (M = 564 ms) \ complex (M = 582 ms)]. Results of reaction times
are depicted in Fig. 2.
Likewise, the same LME two-way ANOVA was conducted on error rates. Main
effects of visual complexity [F(2, 3,306.000) = 13.128, p \ .001] and frequency[F(1, 3,306.000) = 75.300, p \ .001], and their interaction [F(2, 3,306.000) =23.388, p \ .001] were all significant. Further analyses were conducted on high andlow kanji frequency separately. For kanji with high frequency, a LME one-way
ANOVA for visual complexity showed no significant main effect [F(2,1,653.113) = 1.936, p = .145, ns.]. The same multiple comparisons by the LSD
method naturally showed no significant difference among complex (M = 1.27 %),medium (M = 1.45 %) and simple (M = 2.72 %) in high frequency. For kanji withlow frequency, the same LME one-way ANOVA for visual complexity showed asignificant main effect [F(2, 3,031.121) = 3.627, p \ .05]. Again, the same
multiple comparisons by the LSD method were conducted to three groups of visualcomplexity, indicating simple (M = 2.17 %) \ medium (M = 9.96 %) = complex(M = 12.68 %). The results of error rates are depicted in Fig. 2.
The means of reaction times and error rates for correct ‘No’ responses of kanji
lexical decision are reported in Table 3. Only correct responses were used for the
analysis of reaction times. The main effect of visual complexity on reaction times
analyzed by a LME one-way ANOVA was significant [F(2, 3,031.121) = 3.627,
p \ .05]. The LSD Multiple comparisons indicated significances of medium(M = 544 ms) \ simple (M = 557 ms), but complex (M = 550 ms) fell between
them. However, error rates did not show a significant main effect of visualcomplexity [F(2, 429.080) = 2.258, p = .106, ns.].
Discussion
The results of kanji lexical decision in Experiment 1 indicated that the effects ofvisual complexity changes depending on high/low kanji frequency. Kanji with low

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Table 3 Reaction times and error rates for kanji lexical decision for incorrect items
Visual complexity

Reaction time (ms)Error rate (%)

M

SD

M

SD

Simple

557

108

0.08

0.27

Medium

544

108

0.06

0.23

Complex

550

125

0.08

0.27

n = 42
M means, SD standard deviations

frequency showed a clear inhibitory trend from simple to complex in both reaction
times and error rates. Thus, Experiment 1 did not support the first assumption offacilitation effects (Kawai, 1966). On the other hand, kanji with high frequency
showed a different trend. For the reaction time data, kanji with the medium range
from 8 to 12 strokes were faster to process than those with the simple and complex
stroke ranges. In contrast, the error rate data for kanji with low frequency showed aflat trend from simple to complex. Despite the second assumption of inhibitory
effects in the medium range (Kaiho, 1979), the reversal prediction of facilitation
effects was observed in Experiment 1. The third assumption of inhibitory effects
proposed by Tamaoka and Takahashi (1999) and Leong et al. (1987) was supported
by reaction times and error rates of kanji with low frequency. However, as observedby initiation latency times for writing behavior (Tamaoka & Takahashi, 1999), kanji
with high frequency showed a different trend from those with low frequency; kanji
in the medium range complexity were more quickly processed than simple andcomplex ones.

Experiment 2: Kanji naming task
Experiment 2 investigated the effects of visual complexity on kanji processing usinga kanji naming task asking native Japanese speakers to pronounce kanji presented tothem.
ParticipantsParticipants in Experiment 2 comprised 40 undergraduate and graduate students(21 females and 19 males) at Nagoya University in Japan, all native speakers ofJapanese. Ages ranged from 18 years and 4 months to 34 years and 6 months. The
average age was 20 years and 11 months with a standard deviation of 3 years and4 months on the respective day of testing.
Kanji stimuli
Since pseudo kanji cannot be pronounced, Experiment 2 used only the real kanji(kanji items for correct ‘Yes’ responses) in Experiment 1.

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Procedure
Real kanji were randomly presented to participants in the center of a computer
screen 600 ms after the appearance of an eye fixation point marked by ‘*’. Stimulus
randomization was operated with each participant, so that each was presented with adifferent order of stimulus items. Participants were instructed to pronounce the kanji
presented to them as quickly and as accurately as possible. After the response, anexperimenter pressed the space key to initiate the next presentation. With an intervalof 600 ms, the eye fixation point ‘*’ appeared for 600 ms, after which a new kanji
stimulus was shown to the participant. Participants repeated this process until all thereal kanji were presented. The naming latency of each kanji pronunciation wasrecorded by computer, with correct or incorrect response results input by theexperimenter. Ten practice trials were given to participants before the commencement of actual testing.
Analysis and results
Prior to the analysis of reaction times, extremes among kanji naming times(responses slower than 300 ms or longer than 3,000 ms) were recorded as missing
values. Responses outside of 2.5 standard deviations at both high and low ranges
were replaced by boundaries indicated by 2.5 standard deviations from theindividual means of participants in each category. The means of correct naming
latencies and error rates was reported in Table 4. Only correct responses were used
for the analysis of reaction times.
The data were analyzed using a linear mixed effects methodology. A two-way, 3(visual complexity; simple, medium, or complex) 9 2 (kanji frequency; high or low)
ANOVA was conducted on naming latencies for kanji naming. Both the main
effects of visual complexity [F(2, 318.527) = 7.633, p \ .001] and frequency [F(1,1,988.158) = 204.905, p \ .001] were significant. Their interaction [F(2,2,488.992) = 18.987, p \ .001] was also significant.
Further analyses were conducted on high and low kanji frequency separately. For
kanji with high frequency, a LME one-way ANOVA for visual complexity showed
Table 4 Naming latencies and error rates for simple, medium, and complex kanji
Kanji frequency

High

Low

Visual complexity

Error rate (%)

M

M

SD

SD

Simple

602

166

0.00

0.00

Medium

572

127

0.00

0.00

Complex

603

199

0.23

4.76

Simple

648

255

0.68

8.24

Medium

722

276

1.00

9.97

Complex

736

274

0.82

9.00

n = 42
M means, SD standard deviations

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Naming latency (ms)The effects of visual complexity

900

215

***

SimpleMediumNaming Latency (ms)

800
700

***

Complex

**

**

600
500
400
300
200
100
0
High frequency

Low frequency

Kanji frequency
Fig. 3 Naming latancies for kanji in function of high/low kanji frequencies and visual complexity.
* p \ .05. ** p \ .01. *** p \ .001. Bars indicate SEa significant main effect [F(2, 316.833) = 7.453, p \ .001]. Multiple comparisonsby the LSD method showed the significances of medium (M = 572 ms) \ simple(M = 602 ms) = complex (M = 603 ms) in high frequency. For kanji with low
frequency, a LME one-way ANOVA for visual complexity showed the significant
main effect [F(2, 1,157.797) = 19.829, p \ .001]. Multiple comparisons by the
LSD method showed that simple (M = 648 ms) \ medium (M = 722 ms) = complex(M = 736 ms) in low frequency. The results are depicted in Fig. 3.
The same ANOVA was conducted on error rates. The main effect of visualcomplexity [F(2, 2,723.380) = 0.072, p = .931, ns.] was not significant while themain effect of frequency [F(1, 2,576.575) = 7.847, p \ .01] was. However, theirinteraction was not significant [F(2, 2,539.696) = 0.237, p = .789, ns]. Thus, theresults simply indicate that high frequency kanji were named more accurately thanwere low frequency ones.
Discussion
The results of kanji naming in Experiment 2 indicated a similar trend as observed inExperiment 1, suggesting that the effects of visual complexity change depending on
kanji frequency. Kanji with low frequency showed a clear inhibitory trend from
simple to complex in naming latencies, but not in error rates. However, for highfrequency, kanji in the medium stroke range were faster to process for naming thanthose in the simple and complex stroke ranges. Despite the prediction of inhibition
by Kaiho (1979), kanji in the medium range of 8–12 strokes showed the reversal
result of facilitation. Neither the first assumption nor the second assumption was
supported by the results of Experiment 2, which fits to the third assumption of
inhibitory effects depending on kanji frequency (Tamaoka & Takahashi, 1999).

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General discussion
Without clarifying the effects of visual complexity, it is usually treated as one
characteristic of kanji which ought to be controlled prior to running experiments (e.g.,
Hino & Lupker, 1998; Hino et al., 2003; Ogawa & Saito, 2006; Tamaoka, 2005, 2007;
Tamaoka & Hatsuzuka, 1998; Tamaoka & Taft, 2010). The present study, therefore,
conducted two experiments of kanji lexical decision in Experiment 1 and kanji
naming in Experiment 2 on the kanji processing in function of simple/medium/complex visual complexity and high/low frequency. As suggested by previous studies(e.g., Hino & Lupker, 1998; Tamaoka & Hatsuzuka, 1995 for Japanese kanji, and andTaft et al., 1994; Taft & Zhu, 1995; Wu et al., 1994; Zhou & Marslen-Wilson, 1994 for
Chinese characters), kanji frequency played a major function for kanji processing.
Similar to the findings of Tamaoka and Takahashi (1999) on the initiation times forkanji writing behavior, the results of both experiments in the present study supported
the third assumption of visual complexity effects depending on kanji frequency.

Results of the present study related to the three assumptions
A clear incline was observed among kanji with low frequency. Visual complexity
affected speed and accuracy of kanji processing in the inhibitory direction; theheavier its complexity becomes, the slower kanji is processed. The sensedeterminative function seems not to work well among kanji with low frequency;
rather, the present result among low frequency kanji is simply explained by the ideathat decoding from complex features takes longer than simpler ones. This clear
inhibitory trend from simple to complex was supported by the study on Chinesecharacters by Leong et al. (1987) as well as Japanese kanji by Tamaoka and
Takahashi (1999). Thus, the results of the present study rejected the first assumptionof the linear facilitation trend of visual complexity proposed by Kawai (1966).
Kanji with high frequency showed that kanji in the medium complexity of the
8–12 stroke range were more quickly processed than those in the simple 2–6 and thecomplex 14–20 stroke-range complexity. The 13th stroke boundary of the second
assumption (Kaiho, 1979) predicted the kanji in the medium stroke range to bedifficult to process due to the need to distinguish the target kanji from many otherkanji in the same stroke range, but after the 13th stroke, kanji processing becomeseasier with the help of the sense-determinative function generated by visuallycomplex kanji features. However, despite this expectation, the present study among
kanji with high frequency indicated a different pattern: that is, kanji in the medium
range were processed faster than kanji in the simple range of below 6 strokes andkanji in the complex range of over 14 strokes, but the ranges of simple and complex
did not differ from one another. Indeed, the 13th stroke boundary showed a clear
difference in processing of kanji with high frequency, but its trends of processing
speed indicated the opposite direction. As such, the results of the experiments
refuted the assumption of the 13th stroke boundary put forward by Kaiho (1979).
It is rather unanticipated that kanji of the medium stroke range with highfrequency were processed more quickly than the simple and the complex items with

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217

high frequency. The question rose what caused the discrepancy in the findings withhigh-frequency kanji between the medium, and the simple/complex items.
The present study found no difference for kanji lexical decision and naming
between the simple and complex kanji in high frequency. Instead of an explanation
using the functions of sense-discrimination and sense-determination (Leong, 1986;
Wang, 1973, 1981), the straight-forward and simple explanation for this trend is thatfrequently-used kanji are processed holistically as a whole single unit, which
eliminates the influence of kanji visual complexity. In contrast, kanji with low
frequency required bottom-up processing from small figures to kanji via constructing units, resulting in inhibitory effects depending on visual complexity. Yet, thequestion still remains why kanji with the medium 8–12 stroke range were processed
faster than simple and complex kanji in high frequency.

Explanation for the facilitation effects on processing of kanji
in the 8–12 medium range strokes
Although the results in the present study did not support Kaiho’s original 13th
stroke boundary, a distinct difference in kanji processing seems to be created aroundthis boundary. One of potential characteristics of kanji visual complexity in themedium range influencing kanji processing could be a smaller unit which constructs
a whole kanji. Visual units of radicals are used for classifying kanji in a kanji
dictionary. In fact, radicals produce various kanji especially in the medium 8–12
stroke range. For example, a frequently-used radical 木 (tree) constructs 8 stroke
kanji of 板 (board), 杯 (cup) and 林 (grove), 9 stoke kanji of 柱 (post), 枯 (blight)
and 柿 (persimmon), 10 stroke kanji of 桜 (cherry blossom), 株 (stock), and 桃(peach), 11 stroke kanji of 梓 (Japanese cherry birch), 梢 (treetop), and 梗 (hardbranch), and 12 stroke kanji of 検 (examine), 棟 (ridge) and 棒 (bar). In the present
study, both Experiments 1 and 2 indicated that kanji in the medium stroke range
facilitated processing.
Regarding kanji radicals, two possible frequencies may be related to kanji
processing. First, the radical (token) frequency indicates how often each radical
occurs in written materials. Second, radical neighborhood size refers to how
frequently a specific radical constructs kanji. If kanji in the medium stroke range arelikely to be characterized as those with high radical frequency and highneighborhood size, it should be assumed that high radical frequency and highradical neighborhood size should facilitate kanji processing according to the resultsof the present study. Two studies on Chinese characters (Feldman & Siok, 1997;
Taft & Zhu, 1997), not Japanese kanji, indicated Chinese characters with lower
radical neighborhood size showed higher error rates and slower processing speed
than those with higher radical neighborhood size. Taft and Zhu (1997) explainedthat in the case of high radical neighborhood size, a specific radical constructing atarget character received higher activations from multiple characters embedded in
the same radical, which strengthens the connection with the target character andresults in the facilitation effects. Although the experiments by Feldman and Siok
(1997) and Taft and Zhu (1997) were conducted on Chinese characters, they can

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K. Tamaoka, S. Kiyama

provide an explanation; high-frequency kanji in the medium stroke range are likely
to have higher radical neighborhood size, which produces the facilitation effects forkanji processing.
Masuda and Saito (1999), conducting experiments on Japanese kanji, reported
the reversal direction of inhibitory effects regarding kanji radical frequencies andtheir neighborhood size. They conducted two experiments using side-by-side kanji(e.g., 日 + 寺 = 時, and likewise, 討, 秩, 牧, 特 etc.). In Experiment 1, after
presenting two kanji (e.g., 研 and 秒) within 40 ms, participants were asked to writethese radicals and then whole kanji. The result indicated that kanji with high radical
frequency were more accurately written than those with low radical frequency.
Furthermore, another experiment under the same experimental condition as the firstwas conducted by manipulating radical neighborhood size controlling frequenciesof radicals (all of which appear in the left side of the kanji) and kanji. The results
showed that kanji with low neighborhood size were more accurately written thankanji with high neighborhood size. They explain that kanji with larger neighborhood
size will activate multiple kanji sharing the same radical. Native Japanese speakers
have to suppress these kanji to identify the target kanji which creates the inhibitory
effects for kanji processing. As such, both facilitation and inhibition were observedin processing the logographic script of Chinese characters and Japanese kanji
regarding radical neighborhood size.

Conclusion
The present study suggests that the effects of kanji visual complexity changes
depending on kanji frequency. In low frequency, visual complexity shows inhibitory
effects throughout the whole kanji stroke range: The more visually complex kanji
become, the longer processing times are required. In high frequency, kanji could beprocessed holistically, which removes visual inhibitory effects. However, kanji inthe medium complexity 8–12 stroke range seem to have additional factors whichfacilitate processing speed of these kanji. A possible candidate will be radical
frequency and radical neighborhood size. However, previous studies provide bothpredictions of facilitation (Feldman & Siok, 1997; Taft & Zhu, 1997) and inhibition(Masuda & Saito, 1999). Consequently, the reason why kanji in the medium 8–12
stroke range were processed faster than simple and complex kanji in high frequency
still remains as an unanswered question.

Appendix 1
See Table 5.

123

The effects of visual complexity

219

Table 5 Target kanji list for correct ‘Yes’ responses
Targetkanji

Strokes Kanji
frequency
in 1976Kanji
frequency
in 1993Kanji
frequency in
CD 1993

SchoolgradesJLPTtestlevel

Number of Radical
homophones frequency

High frequency kanji
2–6 stroke range of simple kanji
士

3

0.833

12,812

18,686

4

1

45

5

王

4

0.500

5,126

6,900

1

2

12

11

区

4

2.529

28,39647,382

3

2

4

4

刊

5

0.251

3,482

5,235

5

2

44

30

令

5

0.336

3,546

5,196

4

2

12

99

巨

5

0.279

3,730

4,875

7

2

9

2

央

5

0.553

6,193

9,028

3

2

12

15

号

5

0.694

6,374

10,745

3

2

5

59

刑

6

0.218

3,796

6,081

7

1

26

30

宅

6

0.693

10,205

16,699

6

2

7

35

件

6

0.896

18,77829,821

5

2

29

99

百

6

2.465

47,83477,0921

4

1

5

8–12 stroke range of medium kanji
官

8

1.116

23,00627,898

4

2

44

35

協

8

1.204

29,38940,125

4

2

26

11

制

8

1.268

38,664

49,142

5

2

31

30

信

9

1.259

22,48030,898

4

2

28

99

派

9

0.876

22,52429,087

6

1

5

109

界

9

1.411

25,87333,592

3

3

21

17

県

9

1.281

29,51567,055

3

2

29

15

約

9

1.634

37,694

63,182

4

2

5

61

院

10

1.083

24,30433,743

3

3

10

28

員

10

3.133

57,25688,150

3

3

10

59

第

11

2.404

23,49034,077

3

2

5

22

14–20 stroke range of complex kanji
税

12

0.672

22,14527,932

5

2

1

22

算

14

0.761

12,811

18,190

2

2

12

22

銀

14

0.982

13,73820,006

3

3

2

29

演

14

1.159

16,89521,805

5

2

14

109

領

14

0.747

20,68624,221

5

2

13

17

総

14

1.529

30,736

41,544

5

2

33

61

談

15

0.829

13,397

17,917

3

2

8

61

億

15

0.663

18,87429,211

4

2

3

99

論

15

0.965

19,53725,340

6

2

1

61

輸

16

0.618

13,682

19,522

5

2

6

15

職

18

0.761

15,83526,932

5

2

9

5

題

18

2.039

32,31541,310

3

3

5

17

議

20

3.049

67,80994,398

4

2

11

61

123

220

K. Tamaoka, S. Kiyama

Table 5 continued
Targetkanji

Strokes Kanji
frequency
in 1976Kanji
frequency
in 1993Kanji
frequency in
CD 1993

SchoolgradesJLPTtestlevel

Number of Radical
homophones frequency

Low frequency kanji
2–6 stroke range of simple kanji
寸

3

0.041

363

539

6

1

1

12

才

3

0.356

644

837

2

2

21

79

厄

4

0.000

67

98

7

1

5

4

孔

4

0.019

102

151

7

1

60

9

冗

4

0.020

212

292

7

1

20

3

尺

4

0.013

219

292

6

1

6

14

凶

4

0.021

499

743

7

1

26

4

皿

5

0.029

486

692

3

2

0

8

芋

6

0.000

47

67

7

1

0

36

迅

6

0.000

132

177

7

1

8

49

壮

6

0.053

417

600

7

1

33

5

缶

6

0.028

654

1,138

7

2

44

1

8–12 stroke range of medium kanji
奔

8

0.012

156

215

7

1

3

15

侯

9

0.000

66

84

7

–

60

99

姻

9

0.000

91

119

7

1

10

32

幽

9

0.012

95

146

7

1

14

4

峠

9

0.020

107

208

7

1

0

13

倹

10

0.000

36

51

7

1

29

99

蚊

10

0.018

81

112

7

1

0

7

殉

10

0.010

81

131

7

1

11

5

栓

10

0.000

81

140

7

1

26

75

惰

12

0.000

46

68

7

1

5

70

蛮

12

0.010

74

112

7

1

4

7

塀

12

0.000

114

215

7

1

11

46
35

14–20 stroke range of complex kanji
寡

14

0.000

84

102

7

1

31

嫡

14

0.000

98

125

7

1

2

32

概

14

0.071

230

327

7

1

8

75

遵

15

0.000

6

7

7

1

11

49

墾

16

0.000

21

36

7

1

11

46

儒

16

0.000

48

65

7

1

6

99

嬢

16

0.034

108

151

7

1

20

32

錠

16

0.291

125

303

7

1

20

29

隷

16

0.000

126

144

7

–

12

1

爵

17

0.000

65

69

7

–

6

1

擬

17

0.012

121

215

7

1

11

79

韻

19

0.000

48

55

7

1

10

3

123

The effects of visual complexity

221

Appendix 2
See Table 6.
Table 6 Pseudo kanji list for correct ‘No’ responses
2-6 stroke range(simple)Pseudokanji

8-12 stroke range(medium)

Strokes

Pseudokanji

14-20 stroke range(complex)

Strokes

Pseudokanji

Strokes

4

8

14

4

8

14

4

9

14

4

9

14

4

9

14

5

9

15

5

9

15

5

9

15

5

10

15

5

10

15

5

10

15

5

10

15

6

10

16

6

10

16

6

10

16

6

10

16

6

10

17

6

11

17

6

11

17

6

11

18

6

11

18

6

11

19

6

12

19

6

12

20

123

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K. Tamaoka, S. Kiyama

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