Subjective frequency norms for 100 Japanese verb-verb compounds:
The first step toward constructing a behavioral database for Japanesecompound words
Yijia Yao（Graduate school of Languages and Linguistics Sophia University）

Abstract
Lexical frequency has been commonly used in previous studies to investigate how compound words
are processed in the mental lexicon. Since the work of Balota et al., (2007), a growing number of
databases have been constructed using normative and behavioral data of compound words (for English,
see Balota, Yap, Hutchison, Cortese, Kessler, Loftis, Neely, Nelson, Simpson, & Treiman, 2007; for
French, see Ferrand, New, Brysbaert, Keuleers, Bonin, Méot, Augustinova & Pallier, 2010; forChinese, see Sun, Hendrix, Ma, & Baayen, 2018; for Dutch, see Keuleers, Diependaele, & Brysbaert,
2010), which make it possible to investigate and compare the mechanism of compound processing
across languages. However, despite the fact that compound words are prevalently used in Japanese, tothe best of our knowledge, such database have not previously been constructed for Japanese compoundwords. This study reported two subjective ratings and six corpus-based objective frequency estimatesfor 100 Japanese verb-verb compounds: familiarity, age-of-acquisition (AoA), whole-word surface
frequency, whole-word lemma frequency, first-constituent surface frequency, first-constituent lemma
frequency, second-constituent surface frequency, second-constituent lemma frequency. The
relationship among these frequency measures was examined by using correlational and hierarchicalregression analyses. Negative correlation was obtained between the familiarity and AoA ratings,
indicating that more familiar compounds tended to be rated as being of earlier-acquired words bynative speakers of Japanese. Moreover, the whole-word lemma frequency was the best single predictorof both familiarity and AoA ratings, revealing that when native speakers of Japanese are asked to rate
a Japanese verb-verb compound on a particular variable, the verb-verb compound is processed as awhole unit. These normative data should be beneficial to researchers who are interested in selecting
stimuli for psycholinguistic experiments, and it will also help us to gradually construct a normativeand behavioral database for Japanese compound words.
1.

Introduction
Lexical processing is tuned to lexical frequency. Generally, high-frequency words are processedmore quickly and accurately than low-frequency words. With respect to compound words, which arecomposed of two or more monomorphemic words (e.g., watermelon), the past four decades have seena simmering debate as to whether compound words are processed via a holistic route or via an analyticroute in the mental lexicon (for review, see Libben, Gagné, & Dressler, 2020), and this debate has
centered largely around the question of which frequency variable is the best predictor for compoundprocessing. Specifically, it is assumed that if the processing efficiency of a compound word varies
according to the frequency of the whole-word form but not according to the frequencies of its
corresponding constituents, then the compound word might be processed as a whole unit in the mental
lexical (Full-listing models, Butterworth, 1983; Bybee, 1995). On the other hand, if the processing
efficiency of a compound word is modulated by the constituent frequencies but not by the whole-wordfrequency, then the compound word might be processed as separate components in the mental lexicon

(Full-parsing models, Libben, Derwing, & de Almeida, 1999; Taft & Forster, 1976). In addition,
jointly influences of the whole-word and constituent frequencies are interpreted as results of dualroute processing (for Sub-lexical model, see Rastle, Davis, & New, 2004; for Supra-lexical model, see
Giraudo & Grainger, 2000; for Parallel dual-route model, see Schreuder & Baayen, 1995).
Because of its critical importance as a diagnostic factor in investigating the mechanism ofcompound processing, the problem of how to select the most appropriate indicator of lexical frequency
for compound words have increasingly attracted the attention of psycholinguistic researchers. Previous
studies have showed that different types of frequency measures may tap into different underlying
constructs (for French, see Bonin, Laroche, & Méot, 2021; for English, see Juhasz, Lai, & Woodcock,2011; for Chinese, see Su, Li, & Li, 2023). This means that using different frequency measures may
yield somewhat different experimental results, and without solid validation of the measurements thatare used to gather the frequency information, comparisons across studies and languages might bemisleading. Therefore, to attempt to better understand the essential frequency effects on compoundprocessing, this paper reported normative data for 100 Japanese verb-verb compounds and statisticallyexamined the relationships among the different frequency measures. To the best of our knowledge,
this work is the first normative study on Japanese verb-verb compounds.
2.

Previous studies

2.1 Corpus-based measures of objective frequency
In psycholinguistic studies, the most widely used measure to estimate lexical frequency is the
corpus-based frequency measure. Generally, two types of frequency estimates can be derived from a
corpus: Surface frequency and lemma frequency. Surface frequency refers to the frequency of theparticular word form. In the case of compound words, such as watermelon, three types of surface
frequency can be distinguished: Whole-word surface frequency (the surface frequency of watermelon),
first-constituent surface frequency (the surface frequency of water), and second-constituent surface
frequency (the surface frequency of melon). On the other hand, lemma frequency refers to the summed
frequency of a word form and its inflectional variant. For example, for a compound verb, such as
double-check, three types of lemma frequency can be derived: Whole-word lemma frequency (thesummed frequency of double-check, double-checked, double-checking, double-checks), firstconstituent lemma frequency (the summed frequency of double, doubled, doubling, doubles), andsecond-constituent lemma frequency (the summed frequency of check, checked, checking, checks).
These corpus-based estimates serve as important indicators of compound processing in
psycholinguistic studies. For example, most studies showed that the visual recognition of compoundwords involves access to both whole-word and constituent representations, as evidenced by aninteractive effect of whole-word and constituent frequencies on visual lexical decision times (forEnglish, see Schmidtke, Gagné, Kuperman, Spalding & Tucker, 2018; for German, see Günther,
Marelli, & Bolte, 2020; for Spanish, see Güemes, Gattei, & Wainselboim, 2019). However, this is notalways the case when data from Asian languages is used. It was shown that direct access to the wholeword representation of compound words is more preferable than morpheme-based access in Asian
languages, such as Chinese (Myers, Huang, & Wang, 2006; Tse, Yap, Chan, Sze, Shaoul, & Lin, 2017),
Japanese (姚 2020), and Vietnamese (Pham & Baayen, 2015), as evidenced by the predominant effectof whole-word frequency. In other words, there seems to exist a typological distinction in compoundprocessing. However, before we delve into this linguistic typological puzzle, the first thing we wantto know is whether surface and lemma frequency measures orient to the same aspects of mental
representations of compound words. This is important because differences in measurements mightmake previous results incomparable with each other.
Although consensus has not been reached yet, previous studies dealing with this ongoing problem
reported two important findings: Firstly, the predictive power of surface and lemma frequency
measures on lexical decision times is different. Surface frequency is demonstrated to be less powerfulto predict the effect of whole-word frequency on lexical decision times, as compared to lemma
frequency (Ji, Gagné, & Spalding, 2011; Juhasz, Lai, & Woodcock, 2015; Keuleers, Brysbaert, & New,
2010). For example, by collecting both constituent-based surface and lemma frequency informationon the same stimuli, Ji, Gagné, & Spalding (2011) found that whereas lemma frequency is the mostimportant variable to predict lexical decision times, no influence of surface frequency on lexicaldecision times was observed. In addition, Keuleers, Brysbaert, & New (2010) reported that adding
compound-based lemma frequency to the stepwise multiple regression model increases the explained
variance in lexical decision times by 10% points for compound words. Secondly, there is a nonlinear
relationship between surface frequency and lexical decision times. The predictive power of surface
frequency for words in the high (i.e., above 100 per million) and low (i.e., below 1 per million)
frequency bands were weaker than those in the medium frequency band (Balota, Cortese, SergentMarshall, Spieler, & Yap, 2004; Kuperman & Van Dyke, 2013). Moreover, surface frequency tends tobe less reliable in predicting of proficient readers’ (i.e., readers have more reading experience or larger
vocabulary size) task performance (Falkauskas & Kuperman, 2015; Kuperman & Van Dyke, 2013).
For example, Kuperman & Van Dyke (2013) reported that proficient readers are equally faster inprocessing both high- and low-frequency words. These results indicate that although both surface andlemma frequencies are well-accepted diagnostic factors in previous studies, they may represent somerelated but distinct constructs, and they might not be reliable indicators of individual differences inlanguage experience.
2.2 Rating-based measures of subjective frequency
In order to better understanding the effects of experience-driven individual differences oncompound processing, two rating-based frequency measures are increasingly used: Familiarity andAoA. Familiarity refers to the degree of the mental impression with a word. This estimate is typicallyobtained by asking participants to evaluate how often they encounter or produce a given word in daily
communication, by using a Likert scale. For example, Tagalakis & Keane (2006) asked their
participants to judge whether they had encountered the compound word before and how frequently
using a 7-point Likert scale (1 = very unfamiliar, 7 = very familiar). Momenian, Cham, Amini, Radman,& Weekes (2021) asked their participants to rate the compound word and its corresponding
constituents by using a 7-point Likert scale familiarity questionnaire (1 = very unfamiliar, 7 = very
familiar). AoA refers to the age at which people learn a particular word and are often obtained byasking subjects to indicate their own age at which they learned the words. For instance, Juhasz, Lai,
& Woodcock (2015) used a 7-point scale with 2-year age bands to collect the age at which compoundwords were acquired (7 = the word was learned at age 13 or order). Although it is known that rated
AoA is less reliable for the words which are learned before the age of 4 and after the age 15 (Brysbaert,2017), compound words that were judged to be earlier-acquired were shown to be recognized andproduced faster and more accurately than those judged to be later-acquired (Juhasz, 2018; Song & Li,2021).
By collecting these two rating-based frequency estimates on the same compound word dataset,previous studies reported several interesting findings regarding their relationship: Firstly, familiarity
and AoA are associated with each other, as evidenced by a negative correlation between familiarity
and AoA rating scores (Bonin, Laroche, & Méot, 2021; Juhasz, Lai, & Woodcock, 2015; Su, Li, & Li,2023). Familiar compound words are more likely to be rated as earlier-acquired compounds and viceversa. Secondly, both of them are reliable predictors of lexical decision times (Bonin, Laroche, & Méot,2021; Juhasz, Lai, & Woodcock, 2015). Thirdly, adding familiarity and AoA into the statistical model
produced a significant increase in the amount of variance explained compared to the baseline model
which only includes corpus-based frequency estimates (Juhasz, Lai, & Woodcock, 2015). This meansthat rating-based subjective frequency measures were able to capture some important and uniquefeatures of compound words, which were not reflected by corpus-based objective frequency measures.
However, despite the increasingly widespread use of these two rating-based frequency measures in
psycholinguistic studies, there are two major mysteries still remain unsolved. Firstly, whether
familiarity and AoA reflect the same underlying constructs? With respect to this question, inspectionof the literature reveals that, compared to AoA, familiarity is more strongly related to orthographic
properties of compound words, such as number of strokes (Su, Li, & Li, 2023). On the other hand,
AoA is more strongly associated with semantic properties of compound words, such as imageability
and sensory experience (Bonin, Laroche, & Méot, 2021; Juhasz, Lai, & Woodcock, 2015). Secondly,
how they are related to corpus-based measures? Although scarce, previous studies showed thatfamiliarity were strongly associated with whole-word frequency rather than constituent frequencies,
whereas AoA were weakly associated with both whole-word and constituent frequencies (Bonin,
Laroche, & Méot, 2021; Juhasz, Lai, & Woodcock, 2015; Su, Li, & Li, 2023). These results indicatethat although both familiarity and AoA have proven to be effective measures of personal linguistic
experience, they may tap in some different aspects of word features.
2.3 Why Japanese verb-verb compounds?
Japanese is rich in verb-verb compounds. According to Compound Verb Lexicon (National Institutefor Japanese Language and Linguistics, 2015), which is an online database for Japanese verb-verb
compounds, at least 2700 verb-verb compounds are commonly used in contemporary Japanese.
Besides this quantitative feature, Japanese verb-verb compounds also possess some peculiar structuralfeatures which might play special roles in processing.
Firstly, Japanese verb-verb compounds are all concatenated words. Specifically, no orthographicalsegmentation cues, such as spaces or hyphens, can be inserted between the two constituents ofJapanese verb-verb compounds. This is distinctly different from English compound words, which canbe presented either with or without a space or hyphen. In addition, unlike Greek and Polish, in which
compound words are generally formed by inserting a linking vowel between their corresponding
constituents, Japanese verb-verb compounds are usually formed by melting two monomorphemic
words together (i.e., by eliminating the verbal ending of their corresponding first constituents). For
example, to form a Japanese verb-verb compound 食べ始める “start eating”, we need to transform
its first constituent 食べる “eat” into the continuous form 食べ by eliminating the verbal ending る.
In other words, compound verbs are orthographically far more like monomorphemic words inJapanese than in other languages.
Secondly, although Japanese verb-verb compounds are similar in orthographic structure, there are
two distinct association patterns between the first and second constituents. One pattern, the so-calledsyntactic verb-verb compound, is highly productive because principally it allows flexible combinationof all monomorphemic verbs, the other pattern, the so-called lexical verb-verb compound, is highly
restricted because it is based on the transitivity of the constituents (影山 1993). In case of lexical verbverb compounds, for example, a transitive verb 拭く “wipe” can be combined with another transitive
verb 消す “to make something disappear”, but can never be combined with an intransitive verb, suchas 消える “disappear”. In contrast, with respect to syntactic verb-verb compounds, a transitive verb
拭く “wipe” can be combined with either a transitive verb 始める “start” or an intransitive verb 始
まる “started”. In other words, regular word-formation rules and exceptions co-exist in Japaneseverb-verb compounds.
Thirdly, in Japanese verb-verb compounds, the orthographic-semantic associations are not alwayssystematic. Most of the Japanese verb-verb compounds are polysemous words. For example, a verbverb compound 仰ぎ見る may refer either to “look up” or “look up to someone as”. In addition,
many Japanese verb-verb compounds are composed of homonymic constituent verbs. For example, amonomorphemic verb つく may refers either to “attached” (i.e., in 追いつく “catch up”) or “toimagine something” (i.e., in 思いつく “come up with”). In other words, the transparency of thesemantic relationship between the Japanese verb-verb compound and its corresponding constituents is
relatively low.
To sum up, the aforementioned quantitative and structural features make Japanese verb-verb
compounds to be an ideal testing ground for exploring the relationship between different frequency
measures, since the morphological productivity of the compound words (Frost, Kugler, Deutsch, &Forster, 2005; Velan & Frost, 2011), the orthographic segmentation cues (Rastle, Davis, & New, 2004),
and the homonymy of the constituents (Bertram, Laine, Baayen, Schreuder, & Hyönä, 2000; Bertram,
Schreuder & Baayen, 2000) are assumed to be influential factors of compound processing. However,to the best of our knowledge, this relationship has not yet been investigated in Japanese verb-verb
compounds and no normative data are available for Japanese verb-verb compounds. Thus, the current
study aims to fill these gaps by providing normative data for 100 Japanese verb-verb compounds andinvestigates how different frequency measures are related to each other.
3. Method
3.1 Participants
A total of forty-five native speakers of Japanese participated in our study (14 males and 31 females;mean age, 24.27 years; range, 18-54 years). All participants had normal or corrected-to-normal vision.
They received ¥4,500 as compensation for their participation. Participants performed familiarity andAoA ratings individually. This study involving human participants was reviewed and approved by theSophia University Ethics Committee for Research on Human Subjects.
3.2 Materials
A total of 100 Japanese verb-verb compounds were selected for familiarity and AoA rating tasks
from the database BCCWJ (Balanced Corpus of Contemporary Written Japanese; National Institutefor Japanese Language and Linguistics, 2012). The selection was performed by the author.
To be included in the stimulus list, a word had to have a “monomorphemic verb + monomorphemicverb” structure. The verb-verb compounds ranged in word length from four to six characters (meanword length = 4.46, SD = 0.54) and syllable length from four to seven (mean syllable length = 5.26,
SD = 0.8). The word lengths of first constituents ranged from two to four characters (mean word length= 2.34, SD = 0.5), and the word lengths of second constituents ranged from two to five characters(mean word length = 2.78, SD = 0.79). The syllable lengths of first constituents ranged from two to
five (mean syllable length = 2.78, SD = 0.79), and the syllable lengths of second constituents ranged
from two to four (mean syllable length = 2.8, SD = 0.64).
Two types of corpus-based objective frequency estimates were derived from the BCCWJ corpus byusing Chunagon interface (about 104 million words). Surface frequency was operationalized as thetoken frequency per million with which the particular word-form appears in the corpus (spellingvariants were not included). Surface frequency ranged from 2 to 1 per million for verb-verb
compounds (mean surface frequency = 0.37, SD = 0.43), ranged from 9 to 1 per million for first
constituent (mean surface frequency = 1.89, SD = 1.71), and ranged from 501 to 1 per million forsecond constituents (mean surface frequency = 37.49, SD = 62.93).
Lemma frequency was operationalized as the summed frequency of all inflectional and spellingvariants of a particular word. For instance, lemma frequency of a verb-verb compound 食べ始める“start eating” was the summed frequency of its inflectional (e.g., 食べ始める “start eating”, 食べ始めた “started eating”, 食べ始めよう “let’s start eating”, 食べ始めたら “once (I) started
eating”) and spelling variants (e.g., 食べはじめる, たべはじめる, たべ始める, 食べはじめた).
Lemma frequency ranged from 17 to 1 per million for verb-verb compounds (mean lemma frequency= 2.07, SD = 2.58), ranged from 98 to 1 per million for first constituents (mean lemma frequency =10.93, SD = 13.48), and ranged from 820 to 1 per million for second constituents (mean lemma
frequency = 166, SD = 229.28).
Two types of rating-based subjective frequency estimates of verb-verb compounds were collectedby using questionnaires. Familiarity was operationalized in terms of ratings of how often a word isused or heard in daily communication. Familiarity of verb-verb compounds was collected by using a
7-point Likert scale. Response scales ranged from very familiar with the verb-verb compound (1) tovery unfamiliar with the verb-verb compound (7). AoA was operationalized as the age at which theparticular compound verbs were acquired. AoA of verb-verb compounds was collected by using a 12point scale with 0.5-year age bands. Response scales ranged from word acquired at 1-year-old (1) toword acquired after 7-year-old (7). For each rating task, a form was created in which the order of word
presentation was randomized across participants.
3.3 Procedure
Participants first gave their consent. They were instructed to rate their level of familiarity or age ofacquisition to each verb-verb compound intuitively. The familiarity rating task was provided first foreach item. At the top of the form, participants were provided with an instruction consisting of the mainquestion and a description of the Likert scale categories. In the header of each page, a brief statement
reiterating the scale was also provided. Target words were presented on the left-hand side of the page
and participants made their ratings by marking the point with a circle. Secondly, the AoA rating task
was provided and the procedure was identical to the familiarity rating task.
3.4 Statistical analysis
All statistical analyses were conducted using SPSS version 24.0 (SPSS Inc., Chiago, IL, USA). The
Pearson correlation analysis was used for assessing the correlations between different frequency
estimates. The item-level hierarchical multiple and multiple regression analyses were used forevaluating the predictive power of objective frequency estimates on subjective rating scores. For all
analyses, a value of p < .05 was considered significant. For each corpus-based objective frequency
estimate, we added 1 to the raw frequency value, then calculated the frequency per million, and finallycalculated the log10 of the frequency of the per million value.
4.

Results
The mean ratings and standard derivations, and ranges for each of the rating-based subjective
frequency estimates are presented in Table 1.
Table 1 Descriptive statistics for the two rating-based subjective frequency estimates
Estimate
Minimum
Maximum
Mean
SD
Familiarity
6.87
2.22
5.58
1.16
AoA
6.96
4.48
6.24
0.62
SD = standard deviation. AoA = age of acquisition. All ratings are on a 1 to 7 scale.
4.1 Correlation analyses
Table 2 presents the correlations between the two rating-based subjective and the six corpus-based
objective frequency estimates. Familiarity was positively correlated with whole-word surface

frequency [r (100) = .491, p = .000] and whole-word lemma frequency [r (100) = .664, p = .000], andweakly correlated with first-constituent surface frequency [r (100) = .252, p = .011] as well as firstconstituent lemma frequency [r (100) = .267, p = .007]. However, it was not found to be significantly
correlated either with second-constituent surface frequency [r (100) = .057, p = .576] or with second
constituent-lemma frequency [r (100) = .043, p = .669]. These results suggest that verb-verb
compounds that with higher whole-word frequency or with more frequent first-constituents tended to
be rated as being more familiar by native speakers of Japanese.
On the other hand, AoA was marginally negatively correlated with whole-word surface frequency[r (100) = -.258, p = .01] and whole-word lemma frequency [r (100) = -.412, p = .000]. A significant
but weak negative correlation was observed between AoA and first-constituent surface frequency [r(100) = -.212, p = .034], as well as between AoA and first-constituent lemma frequency [r (100) =-.269, p = .007]. However, no significant correlation was found either between AoA with secondconstituent surface frequency [r (100) = -.043, p = .674], or between AoA and second-constituent
lemma frequency [r (100) = -.043, p = .673]. These results suggest that verb-verb compounds that withhigher whole-word frequency or with more frequent first-constituents tended to be rated as being ofearlier-acquired words by native speakers of Japanese.
Finally, AoA were strongly negatively correlated with familiarity [r (100) = -.599, p = .000],
indicating that verb-verb compounds learned early in life tended to be rated as more familiar by native
speakers of Japanese.
Table 2 Correlation between rating-based and corpus-based (log10) frequency estimates
Fam
AoA
WW1st
1st
2nd
2nd
surfacelemma
surface lemma
surface lemma
Fam1-.559*** .491***.664***.252*.267**
.057
.043
AoA
1
-.258**-.412***-.212*-.269**
-.043
-.043
***
**
***
W-surface
1
.795
.280
.178
.390
.357***
W-lemma
1.352***.325***
.172
.155
***
1st-surface
1
.794
.134
.159
1st-lemma
1
.070
.094
2nd-surface
1.975***
2nd-lemma1
Fam = whole-word familiarity. AoA = whole-word age of acquisition. W-surface = whole-word surface
frequency. W-lemma = whole-word lemma frequency. 1st-surface = first-constituent surface frequency. 1stlemma = first-constituent lemma frequency. 2nd-surface = second-constituent surface frequency. 2ndlemma = second-constituent lemma frequency. Frequency is the log-transformed per million frequency
from the BCCWJ corpus (National Institute for Japanese Language and Linguistics, 2012). * p < .05. ** p
< .01. *** p < .001.
4.2 Regression analyses
To further examine whether the characteristics of the verb-verb compounds and their corresponding
constituents influence the familiarity and AoA rating scores, firstly we conduct three-step hierarchicalitem-level multiple regression analyses for both familiarity and AoA ratings, with rating scores as
dependent measures. Length variables (Whole-word length, First-constituent word length, Secondconstituent word length, Whole-word syllable length, First-constituent syllable length, Secondconstituent syllable length) were entered in Step 1, Constituent-based frequency variables (Firstconstituent surface frequency, First-constituent lemma frequency, Second-constituent surface
frequency, Second-constituent lemma frequency) were entered in Step 2, and Compound-basedfrequency variables (Whole-word surface frequency, Whole-word lemma frequency) were entered in
Step 3. The results of these hierarchical regression analyses are shown in Table 3.
Table 3 Standardized regression coefficients (β) and significance from Step 1 to 3 of the regression
analyses for familiarity and AoA ratings (Hierarchical regression)
Predictors

Familiarity ratings

Step 1: Length variables
R-square.123
Step 2: Constituent-based frequency variables
R-square.203
ΔR2 = .081
Step 3: Compound-based frequency variables
R-square.427***
ΔR2 = .293

AoA ratings
-.009
.045
ΔR2 = .089
.156**
ΔR2 = .117

Length variables = Whole-word length, First-constituent word length, Second-constituent word length,
Whole-word syllable length, First-constituent syllable length, Second-constituent syllable length.
Constituent-based frequency variables = First-constituent surface frequency, First-constituent lemma
frequency, Second-constituent surface frequency, Second-constituent lemma frequency. Compound-basedfrequency variables = Whole-word surface frequency, Whole-word lemma frequency. Frequency is the logtransformed per million frequency from the BCCWJ corpus (National Institute for Japanese Language andLinguistics, 2012). * p < .05. ** p < .01. *** p < .001.

As shown in Table 3, Length variables did not account for significant variance across the ratingscores in both familiarity and AoA ratings. Likewise, Frequency variables at the constituent level also
did not account for significant variance in both familiarity and AoA rating scores. However, Frequency
variables at the whole-word level accounted for significant additional unique variance in the two
rating-based estimates (29.3% and 11.7% for rating scores in familiarity and AoA rating respectively).
These results indicate that compound-based frequency variables play determinant and exclusive rolein subjective ratings.
For further investigate the influence of corpus-based objective frequencies on subjective ratings, we
conduct multiple regression analyses for both familiarity ratings and AoA ratings, with rating scoresas dependent measures, and including the following predictors: Whole-word surface frequency,
Whole-word lemma frequency, First-constituent surface frequency, First-constituent lemma frequency,
Second-constituent surface frequency, Second-constituent lemma frequency. The results of these
multiple regression analyses are shown in Table 4.

Table 4 Standardized regression coefficients (β) and significance from models predicting
familiarity and AoA ratings (Multiple regression)
Predictors
2Adjusted R
Whole-word surface frequency
Whole-word lemma frequency
First-constituent surface frequency
First-constituent lemma frequency
Second-constituent surface frequency
Second-constituent lemma frequency

Familiarity ratings

AoA ratings

.414
-.057
.701***
-.037
.085
.073
-.118

.150
.125
-.498**
.076
-.194
-.003
-.012

AoA = age of acquisition. Frequency is the per million frequency from the BCCWJ corpus (National

Institute for Japanese Language and Linguistics, 2012). * p < .05. ** p < .01. *** p < .001.

As shown in Table 4, with approximately 70% of explained variance, familiarity rating scores werepredicted to a large extent by the whole-word lemma frequency, with verb-verb compounds rated as
more familiar as the whole-word lemma frequency becomes higher. However, familiarity rating scores
were not significantly predicted by either whole-word surface frequency or constituent-based
surface/lemma frequencies. Likewise, whole-word lemma frequency was also a significant predictorof AoA rating scores. The higher whole-word lemma frequency the verb-verb compounds with, themore they were rated as early acquired. Similar to what occurred for familiarity, AoA rating scores
were not significantly predicted by either whole-word surface frequency or constituent-based
surface/lemma frequencies. In other words, the whole-word lemma frequency is the single best
determinant for both familiarity and AoA rating scores.
5.

Discussion
As familiarity and AoA become widely used factors in psycholinguistic studies, there has been an
intense surge of interest in the investigation of their construct validity (Bonin, Laroche, & Méot, 2022;
Juhasz, Lai, & Woodcock, 2015; Song & Li, 2021). However, due to data limitations, the relationshipbetween familiarity and AoA was not yet validated in Japanese verb-verb compounds. In this article,
we provided the first normative dataset of Japanese verb-verb compounds. This dataset contains two
types of subjective frequency measures (familiarity, AoA) for 100 Japanese verb-verb compounds aswell as two types of objective frequency measures (surface frequency, lemma frequency) for 100
Japanese verb-verb compounds and their corresponding constituents. Familiarity ratings were madeon a scale ranging from 1 = very familiar to 7 = very unfamiliar (7-point Likert scale) and AoA ratings
were made on a scale ranging from 1 = 1-year-old to 7 = after 6-year-old (12-point scale with 0.5-year
age bands) by 45 native speakers of Japanese. Surface and lemma frequencies were calculated on thebasis of a corpus of approximately 104 million words gathered from modern Japanese texts. To findout whether these two frequency measures related to one another we further performed correlation andregression analyses on the aforementioned dataset. The major findings were as follows.
With respect to correlation analyses, first of all, we found that the two rating-based subjective
frequency measures were strongly correlated with each other. Compound words that were judged to
be familiar in daily communication were estimated to be acquired earlier in life. Secondly, we further
found that the two rating-based subjective frequency measures have similar but not identical patternsof intercorrelations with corpus-based objective frequency measures. For familiarity, rating scores
were strongly associated with both whole-word surface and whole-word lemma frequencies, andweakly associated with first-constituent surface and first-constituent lemma frequencies. Likewise, for
AoA, rating scores were weakly related to whole-word surface frequency and first-constituent
frequencies, and more closely associated with whole-word lemma frequency. However, neither ofthem was associated with second-constituent frequencies. On the other hand, the major findings ofregression analyses shed light on whether and to what extent objective frequency variables influence
subjective frequency ratings: We found that whole-word lemma frequency was the best single
predictor of familiarity and AoA rating scores. This result indicates that the total probability ofencountering a compound word rather than the probability of encountering a particular word-form
determines native speakers’ impression of the compound word.
The predominant effect of whole-word lemma frequency on subjective frequency ratings in thepresent study challenges the adequacy of existing Full-parsing models (e.g., Libben, Derwing, & deAlmeida, 1999; Taft & Forster, 1976) of compound processing, which assume obligatory
morphological decomposition during compound processing. On the basis of the coefficient patterns
induced in familiarity and AoA ratings, we would claim that native speakers of Japanese tended toprocess verb-verb compounds as a whole unit when they were asked to rate the lexical properties of
those words explicitly in un-speeded task settings. This is consistent with the predictions of Full-listingaccounts which hypothesize that compound words are activated directly via their whole-word
representations during processing (e.g., Butterworth, 1983; Bybee, 1995).
The lack of effects of constituent frequencies on lexical ratings could be attributed to theorthographic and semantic features of Japanese verb-verb compounds. As mentioned in the review
section, Japanese verb-verb compounds are words with low orthographic decomposability and lowtransparency of orthographic-semantic association. Both of these two features were assumed toinfluence readers’ perception of the constituents of the compound words (Gagné, Spalding, Spicer,
Wong, Rubio, & Cruz, 2020). For example, spaced compound words are more likely to be processed
as separate units than closed compound words (Ji, Gagné & Spalding, 2011), and semantically opaquecompounds were more likely to be processed as a whole unit compared to semantically transparent
compounds (Marelli & Luzzatti, 2012). In other words, it might be the degree of salience of theconstituents affects whether the compound word will be processed as a whole unit or as separate units,
and for compound words with less salient constituents, such as Japanese verb-verb compounds,
holistic processing route rather than analytic processing route became the preferable route toprocessing.
Another interesting result of the present study is the absence of effect of whole-word surface
frequency on the subjective ratings. This result leads to the conclusion that Japanese verb-verb
compounds possess their own lemma in the mental lexicon, and inconsistent with the previous results
reported in English and French compound nouns, in which the subjective rating scores of compoundwords were co-determined by whole-word and constituent surface frequencies (Bonin, Laroche, &Méot, 2022; Juhasz, Lai, & Woodcock, 2015). This discrepancy may be due to the fact that unlike
noun-noun compounds, verb-verb compounds are rarely used in their original form, and instead arecommonly used in inflected forms, such as past-tense or passive forms. Thus, for compound verbs,
whole-word surface frequency might not be powerful enough to predict or explain lexical behaviors,
and instead, whole-word lemma frequency might be a better indicator of lexical processing.
6.

Conclusion
Taken as a whole, over the past two decades, there has been an explosion of interest in using
databases of behavioral performance measures to investigate questions concerning mechanisms ofcompound processing (for English, see Balota, Yap, Hutchison, Cortese, Kessler, Loftis, Neely, Nelson,
Simpson, & Treiman, 2007; for French, see Ferrand, New, Brysbaert, Keuleers, Bonin, Méot,
Augustinova & Pallier, 2010; for Chinese, see Sun, Hendrix, Ma, & Baayen, 2018; for Dutch, see
Keuleers, Diependaele, & Brysbaert, 2010). Those databases allow a more complete picture of how
compound words are processed in the mental lexicon and serve as a useful tool for cross-language
comparisons.
However, the majority of available behavioral data are established for Indo-European languages,
and there is a lack of normative and behavioral data for Japanese. In this study, we established two
psycholinguistic norms for 100 Japanese verb-verb compounds derived from native speakers ofJapanese. By examining whether characteristics of the verb-verb compounds and their corresponding
constituents influence ratings of familiarity and AoA of verb-verb compounds, we found that wholeword lemma frequency is the best single predictor of the rating scores, and there was only a weakassociation between first-constituent frequencies and subjective rating scores. These findings support
the holistic accounts of compound processing (e.g., Butterworth, 1983; Bybee, 1995). Ourinterpretation of these findings is that the low degree of salience of the constituents inhibits themorphological decomposition and facilitates the lexical storage.
As a first step toward constructing a new database of behavioral performance measures for Japanesecompound words, this study develops our understanding of compound processing and the underlying
organization of mental lexicon. However, further work is required to evaluate whether familiarity andAoA are powerful predictors of behavioral performance data (i.e., lexical decision times, word namingtimes) of Japanese verb-verb compounds, and to improve and expand the current dataset to incorporate
other lexical (i.e., morphological family size, morphological family frequency) and psycholinguistic
variables (i.e., semantic transparency, concreteness, imageability).
Acknowledgements
This research was supported by JSPS KAKENHI Grant number 22K20032.
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