775143
research-article2018

SLR0010.1177/0267658318775143Second Language ResearchGoss and Tamaoka

secondlanguageresearch

ArticleLexical accent perception
in highly-proficient L2
Japanese learners: The roles oflanguage-specific experienceand domain-general resources

Second Language Research
2019, Vol. 35(3) 351­–376© The Author(s) 2018
Article reuse guidelines:
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https://doi.org/10.1177/0267658318775143
DOI: 10.1177/0267658318775143
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Seth J Goss

Emory University, USA

Katsuo Tamaoka
Nagoya University, Japan

Abstract
This article reports empirical findings on the roles of domain-general resources and languagespecific experience in the second language (L2) acquisition of Japanese lexical pitch accent.
Sixty-one advanced-proficiency L2 Japanese learners from two first languages (L1s), Mandarin
Chinese and Korean, identified and categorized Japanese nouns embedded in short sentencesin two aurally-presented tasks. Mixed effects models showed that although the tonal-language
Chinese group outperformed non-tonal Korean speakers, L2 lexical knowledge, but not overallproficiency or learning experience, predicted performance on both perception tasks regardlessof L1, suggesting that long-term knowledge of L2 phonological structure facilitates perceptionof lexical-level prosody. Domain-general resources, however, played no predictive role inadvanced learners’ accent perception. A decision-tree analysis then revealed further divergencein perception accuracy by accent pattern, L1, and task type. Taken together, the results establish aclose connection between language learning experience and L2 speech perception at the advanced
level, and highlight the complexity inherent in the learning of non-native prosodic categories.

Keywords
cross-linguistic perception, individual differences, Japanese, lexical accent

Corresponding author:
Seth J Goss, Department of Russian and East Asian Languages and Cultures, Emory University, Modern
Languages Building, 532 Kilgo Circle, Atlanta, GA 30322, USA
Email: sethjgoss@emory.edu

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Second Language Research 35(3)I Introduction
Second language (L2) learners often struggle to accurately perceive non-native word-levelprosodic features such as pitch, tone, and stress (e.g. Strange and Shafer, 2008; Wong andPerrachione, 2007). In fact, low accuracy and a wide degree of individual variation in prosodic perception ability are commonly reported even among non-novice learners who areotherwise skilled listeners (Shibata and Hurtig, 2008; Taylor, 2011). One example of a perceptually challenging prosodic characteristic is the accentual system of Tokyo Japanese, inwhich individual words carry a pitch pattern as part of their phonological structure. Wordlevel accent is an important linguistic property in Japanese because it not only marks prominent syllables and delineates prosodic groupings (Beckman and Pierrehumbert, 1986), but isalso exploited by native (L1) listeners in the access and retrieval of words from the mental
lexicon (Ōtake and Cutler, 1999). Yet despite its phonological status, numerous studies haveshown that L2 Japanese learners find lexical pitch accent difficult both to perceive and produce, irrespective of their proficiency level, native language, or learning environment (e.g.
Lee et al., 2006; Shibata and Hurtig, 2008; Shport, 2016; Taylor, 2011).
Theories of L2 speech perception have focused heavily on segmental features, andattribute performance variation to perceptual biases resulting from the relative (dis)similarity in sound categories of the L1 and L2 (e.g. Best and Tyler, 2007; Strange, 2011).
That is, cross-linguistic perception models typically take specific sound categories as acentral focus of comparison, and often overlook non-phonetic sources of individual variation. While it is certainly true that such analyses have shed much light on how one’s L1
constrains perception of L2 sound categories (e.g. Bent et al., 2006; So and Best, 2010),
little work has looked at the role that individual differences play in attaining a high levelof prosodic perception ability.
L2 learners bring differences in both domain-general resources (e.g. Engel de Abreu
and Gathercole, 2012; Wong and Perrachione, 2007) and language-specific experience(e.g. Andringa et al. 2012; Martin and Ellis, 2012) to the task of language acquisition. Yet
the relative contribution of these differences to one’s ability to perceive L2 prosodic
categories is unclear. In the present study, we argue that differences in both learnerinternal resources and experience-based variables can account for some of the difficulty,
and wide degree of variation, reported in L2 Japanese learners’ perception of lexical
accent. To test this prediction, we identified four variables that potentially support lexical
accent perception: two domain-general capacities, phonological short-term memory(PM) and auditory processing ability; and two variables we classified as experiencebased, L2 lexical knowledge and L1 experience with lexical prosody. We first closely
matched two groups of advanced L2 Japanese learners, lexical-tone Mandarin Chinese
speakers and non-tone Korean speakers, on their Japanese proficiency and learning
experience. The four variables were subsequently used as predictors of learners’ performance on two lexical accent perception tasks. The following questions guided this study:
1.
2.

Do PM and auditory processing ability account for perceptual variation at theadvanced level?
What role does experience with L2 phonological patterning, as indexed by L2
lexical knowledge, play in learners’ perception of lexical accent?

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3.

353

Does L1 tone experience aid perception of lexical accent relative to a proficiencymatched non-tone L1 group?

II Background
1 Japanese lexical accent system
In Japanese, pitch accent is specified in the lexicon as a part of word form (Beckman
and Pierrehumbert, 1986). In acoustic terms, a fall in fundamental frequency (F0)on a given mora – the timing unit that determines many phonological processes inJapanese – serves to mark a word’s accent pattern as distinctive from other possible
patterns (Kubozono, 2008). Acoustic analyses indicate that accented words consistof a lexically-linked H(igh) pitch followed by a drop to L(ow) pitch (Sugito, 1982).
In accent-bearing words in Tokyo Japanese, this F0 fall does not vary with phonetic
context, as does pragmatic focus-marking in English. For example, a three-mora
Japanese noun can have n+1 pitch patterns, which are realized over the duration ofa word, with the H tone extending into the following postposition in the unaccented
pattern; see (1d) below. When presented in isolation, that is, without a postposition,
patterns 1c and 1d are identical because the pitch fall is not realized until the postposition in (1c) (accented moras in bold; examples from Tanaka and Kubozono,2012).
(1) a. megane ga (MEgane-ga; HLL-L) ‘glasses + SUBJECT marker’    b. tamago ga (taMAgo-ga; LHL-L) ‘egg + SUBJ’    c. otoko ga (oTOKO-ga; LHH-L) ‘man + SUBJ’    d. sakana ga (saKANA-GA; LHH-H) ‘fish + SUBJ’
Pitch is phonemic in Japanese, and it is estimated that between 10 to 14% of Japanesehomophones can be distinguished by accent pattern alone (Kitahara, 2001; Shibata andShibata, 1990). However, in this study, we are concerned with L2 learners’ use of pitch
as a perceptual cue to identify and categorize words by their accent pattern, rather thanits narrower role in differentiating words that contrast minimally in pitch (Kubozono,2008).
The lexically-linked status of pitch accent in Japanese has led researchers to assume
that accent patterns are processed and stored as part of a word’s phonological form in themental lexicon (e.g. Beckman and Pierrehumbert, 1986; Ōtake and Cutler, 1999). Withnouns, the location of an H to L fall in pitch, or the absence of such a drop in unaccented
words (1d), is unpredictable in that one cannot guess the pitch pattern with certainty
simply by knowing a word’s length or segmental structure.1 Because pitch-accent patterns are obligatory and accent location unpredictable in Japanese, felicitous pitch patterns aid word recognition in L1 Japanese speakers (Ōtake and Cutler, 1999; Sekiguchi
and Nakajima, 1999). In fact, findings from correctness judgment tasks support thishypothesis, with L1 listeners2 reporting accuracies greater than 90% (e.g. Goss andTamaoka, 2015; Sakamoto, 2010; Shibata and Hurtig, 2008), although wide variation has
been reported in pattern categorization tasks (Shport, 2015).

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Second Language Research 35(3)2 Accent acquisition by L2 Japanese learners
As their proficiency level increases, Japanese learners make steady gains in the perception of certain linguistic features, such as phonemic vowel and consonant length contrasts (e.g. Hardison and Saigo Motohashi Saigo, 2010; Shibata and Hurtig, 2008). Yet,
current findings indicate no such developmental trajectory for lexical accent perception(Ayusawa, 2003). For example, in a cross-sectional study of three proficiency groups of
L1 English-speaking Japanese learners in the United States, Shibata and Hurtig (2008)
found no difference between the groups on an accent perception task (Novice = 47%,
Intermediate = 49%, Advanced = 56%). In contrast, the same learners’ identification ofphonemic consonant length contrasts (e.g. kata ‘shoulder’ vs. katta ‘won’) clearly
improved by level (Novice = 45%, Intermediate = 62%, Advanced = 75%), despite thiscontrast not sharing phonemic status in the participants’ L1. Shibata and Hurtig examined L1 English speakers, but the difficulty in accent acquisition does not appear to belimited to speakers of a non-tonal language. For example, Lee et al. (2006) employed alongitudinal design to measure the accent production ability of three Cantonese speakers
over a two-year period. They found that although participants spent a year in Japan andattained a high level of Japanese proficiency, they showed no improvement in their production of lexical accent, even for high-frequency words.
Despite the reported difficulty in perceiving and producing lexical accent, some learners appear to acquire accent more easily than others. Shport (2011) noted a large degreeof individual variation among Japanese-naive L1 English speakers, in her study on theinitial state of accent perception. In an earlier study by Nishinuma et al. (1996), L1
English speakers who had studied Japanese for two years displayed wide variation on an
accent perception task, with the lower third of learners correctly identifying accent patterns at an average accuracy of 42%, while the top third averaged 73%. Finally, Taylor(2011) examined the production ability of Japanese lexical accent in two learner groups,
described as less versus more experienced in Japanese, and again found a high degree ofvariation, leading her to conclude that the acquisition of pitch accent by L1 English
speakers is essentially a random process. Although researchers (e.g. Shport, 2011;
Strange and Shafer, 2008) have suggested that factors beyond L1–L2 sound category
mismatches are a likely source of individual variation in prosodic acquisition, these havenot been explored systematically.

3 Learner variables in L2 speech perception
L2 speech perception involves an interplay of domain-general capacities (O’Brienet al., 2006; Wong and Perrachione, 2007), long-term knowledge of L2 phonologicalregularities (Speciale et al., 2004), and experience with L1 sound categories (So andBest, 2010). Although previous research has made a case for the involvement of eachof these factors in L2 perception, their relative contribution to the perception of lexical prosody is inadequately characterized. We next discuss our domain-general predictors: phonological short-term memory and auditory processing ability, along withthe higher-order, language-specific factors of L2 lexical knowledge and L1 tone
experience.

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355

a Phonological short-term memory. The phonological loop was proposed by Baddeley(1986; elaborated in Baddeley et al., 1998) as the component of working memory (WM)
responsible for the short-term storage and maintenance of verbal input in language processing. Its link to language learning is invoked by the fact that those efficient at thetemporary storage and processing of phonological input are better at ultimately transferring this input to long-term memory: a crucial step in mapping novel sounds to concepts(e.g. Cheung, 1996; Hummel, 2009; O’Brien et al., 2006; Speciale et al., 2004).
Although the link between the memory system and lexical prosody has not been
examined, previous research on the role of PM in L2 acquisition provides evidence of a
relationship. Speciale et al. (2004) found that PM capacity positively correlated withword learning in the early stages of L2 Spanish vocabulary acquisition, but as learning
progressed, long-term knowledge of sound regularities became the main factor facilitating word learning. Additionally, Martin and Ellis (2012) reported that PM, as measured
by non-word repetition performance, predicted 14% of the variance in learners’ ability toacquire vocabulary in an artificial language.
In the present study, we examined the role of PM as a mediator of lexical accent perception, and a potential source of individual variation in L2 speech perception. Despite
evidence for a link between PM capacity and L2 word learning ability, none of the languages examined thus far have featured pitch as a lexical property, and it remains to be
seen whether prosodic perception is mediated to any extent by a learner’s PM capacity.
Given that pitch is a lexical property in Japanese, it is plausible that the short-term store
is invoked in the brief retention of both pitch and segmental information in a perceptiontask that requires phonological form-based judgments on lexical accent. Specifically, we
assumed that PM would be implicated in a two-stage accent perception task, in whichlearners first judge accent correctness, then following a short pause, categorize spoken
words according to visual representations of pitch contours. Since listeners must categorize words based on a memory trace of the input – with the phonological store acting as
a buffer where this processing takes place – variation in performance on the delayed
categorization task in particular may be modulated by a listener’s PM capacity.
Alternately, given evidence that PM is intimately tied to language experience, in that it
may develop as a function of proficiency level (e.g. Andringa et al., 2012), it may be thecase that for the highly-experienced learners in the current study, this construct is inseparable from language-specific predictors like lexical knowledge.
b Auditory processing ability. Lexical accent in Japanese is realized through variations ina single acoustic parameter, fundamental frequency (F0), and thus shares this physicalproperty with non-linguistic or musical pitch. It has thus been argued that the same
domain-general auditory processing capacity involved in the perception of non-linguistic
pitch may also support the perception of lexical prosody (e.g. Asaridou and McQueen,
2013; Deutsch et al. 2009; Wayland et al., 2010; Wong and Perrachione, 2007). For
instance, Wong and Perrachione (2007) suggested that L2 learners acquire lexical prosody through a bottom-up process, which first implicates domain-general auditory
resources in the analysis of the phonetic properties of the target feature. They reported
that L1 English listeners who were initially more sensitive to non-linguistic pitch variations (i.e. trained musicians) were better at acquiring Mandarin Chinese tones

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Second Language Research 35(3)than non-musicians. In fact, initial pitch sensitivity predicted approximately half of thevariance in level of attainment after a series of training sessions. Somewhat surprisingly,
Goss and Tamaoka (2015) found that among L1 Japanese listeners, the ability to distinguish pairs of pure tones predicted performance on real-word accent perception tasks,
suggesting that even L1 speakers are relying on auditory processing resources to categorize words by their lexical accent pattern.
The above findings suggest a link between general auditory processing and the perception and learning of the prosodic features of language. However, this runs counter tothe notion that the mechanisms for auditory and speech processing are typically considered to be separate in the human perceptual system (Strange and Shafer, 2008). In general, speech perception models predict a dissociation of acoustic perception from therelative ease with which listeners perceive speech sounds in a known language (e.g. Bentet al., 2006). For example, Wayland et al. (2010) reported that although trained musicians
were initially more accurate at identifying Mandarin tones, non-musicians generally narrowed this gap following a series of training tasks, suggesting that the advantage forpitch-sensitive learners may decrease inversely with experience in the target language.
Yet, such conflicting findings – particularly those showing a relationship between
domain-general auditory processing and the perception of lexical accent in L1 listeners– warrant further investigation in the L2 context. In the current study, we assume that F0
discrimination involves domain-general auditory processing mechanisms, and given that
F0 information is relevant to lexical accent perception (e.g. Asaridou and McQueen,
2013; Wong and Perrachione, 2007), aim to measure the relationship between the two.
c L2 lexical knowledge. Lexical knowledge, or the accumulated representations of wordforms in the long-term store, has been shown to underpin L2 grammar learning (Williams and Lovatt, 2003), facilitate the automaticity of lexical decisions (Segalowitz et al.,1998), and provide a basis for further L2 lexical learning (Martin and Ellis, 2012). In
short, researchers widely acknowledge that L2 learners with larger vocabularies performbetter on a range of tasks (e.g. Meara, 1996; Nation, 2010).
As discussed earlier, pitch accent is a lexical property present in the speech input to
L2 Japanese learners, but also one that presents ongoing perceptual difficulty. If we consider the two abovementioned domain-general capacities as potentially supporting lexical processing, but still somewhat language-independent, then we can further posit thatthe long-term lexical store is involved in speech perception to an even greater extent. In
terms of Japanese phonology, L2 lexical knowledge encompasses an understanding of
the phonological regularities of the language, including segmental structure and lexical
accent patterns. Accordingly, accuracy on form-based judgments, such as determining
accent correctness, would entail that a word’s accent pattern has been acquired andretrieved from long-term memory. Thus, if we take a correctness judgment task to be anindicator of whether a word’s accent pattern has been established in long-term memory,then a measure of the size of the L2 lexicon should closely relate to the ability to judge
the correctness of individual words. Put differently, lexicon size, or ‘breadth’ of vocabulary knowledge, is predicted to relate to lexical accent judgments, which can be taken as
one measure of the ‘depth’ of lexical knowledge (Nation, 2010). As some have pointed
out, certain measures of lexical depth may only be possible for learners who possess aGoss and Tamaoka

357

substantial L2 lexicon (Pignot-Shahov, 2012). The advanced learners in the current study
are assumed to know the target words well in a general sense, but insufficient depth ofknowledge is likely to be found in judging and categorizing accent patterns, and incorporating L2 lexicon size as a predictor may help account for these gaps.
d L1 experience and accent perception. Learners of Japanese from both L1 Chinese andKorean backgrounds have been noted to attain a high level of Japanese proficiency in asimilar length of study (Tamaoka, 2014), and can thus be matched closely on their L2
proficiency. By doing so, we can compare the influence of the prosodic systems of theselanguages: Mandarin Chinese being a tonal language, while the standard dialect of
Korean (Seoul Korean) features a non-tonal, stress-like system of accentuation (Jun,
1998).
In both Mandarin and Japanese, tone and pitch, respectively, have phonemic status, as
is illustrated by the presence of minimally contrastive pairs.3 The frequent use of tone at
the syllable/word level in their L1 provides Mandarin speakers with rich experience
using lexical prosody, which they bring to the task of acquiring Japanese lexical accent.
Seoul Korean, on the other hand, is a non-tonal language, in that pitch is not used contrastively, nor are pitch variations alone used to mark prominent syllables at the wordlevel (Jun, 1998; see also Silva, 2016). Therefore, in the current study, we make thecrucial assumption that pitch is not represented in L1 Korean speakers’ mental lexicon asa part of phonological form in the same way as pitch accents are a part of L1 Japanese
speakers’ lexical knowledge. Namely, there is a difference in phonemic status of lexical
pitch between standard Japanese and Seoul Korean, and this difference may result in
decreased perceptual salience of word-level accent for Korean learners of Japanese,
resulting in greater difficulty in perceiving accent correctness and location.
In the current study, rather than comparing the influence of specific L1 prosodic categories with Japanese pitch patterns, we consider L1 experience with tone as a generalcontributor to the acquisition of Japanese lexical accent. The presence of phonemic tonein Mandarin may enhance learners’ attention to lexical accent in Japanese, making pitch
variations more salient to this group (So and Best, 2010), than to L1 Korean speakers.

III Method
1 Participants
Sixty-one advanced learners of Japanese, 31 L1 Mandarin Chinese speakers (Age: M =24.9 years, SD = 2.6) and 30 L1 Korean speakers (M = 25.6, SD = 3.8) participated in thisstudy. All participants were recruited from the undergraduate and graduate communities
at a large research university in Japan.
Participants’ Japanese learning background was controlled as follows. First, the L1
groups were matched for proficiency using tests of Japanese lexical and grammaticalknowledge (Miyaoka et al., 2011, 2014; details are provided below). No significant differences were found between the L1 groups on the measures of lexical (L1 Chinese: M =37.9, SD = 4.93; L1 Korean: M = 38.7, SD = 5.87; t(59) = 0.55, n.s.) or grammatical (L1Chinese: M = 31.9, SD = 3.05; L1 Korean: M = 32.6, SD = 2.67; t(59) = 0.90, n.s.)358

Second Language Research 35(3)knowledge. Questionnaire data indicated that the L1 Chinese group had an average
length of study (LoS) of 68 months, while the L1 Korean group’s LoS was 63 months.
Length of residence (LoR) was approximately the same as well, with Chinese participants having lived in Japan for an average of 27 months, and Korean participants for 28
months. Self-reported Japanese usage per week (hours spent speaking and listening) also
indicated comparability, with L1 Chinese reporting an average of 10.6 hours comparedto Koreans’ 10.4 hours. The proficiency tests and self-reported measures clearly indicated the similarity of the two groups’ Japanese learning experience.

2 Materials
The following tasks were presented to all participants in this study. The first three measures – F0 discrimination, serial non-word recognition, and L2 lexical knowledge – comprised the predictor variables. The accent pattern correctness judgment (PitchID),reaction time on these judgments (PitchID.RT), and the categorization (PitchCAT) task
were the dependent variables.
a F0 discrimination. An adaptive pitch test, which increased in difficulty based on performance, was used to measure participants’ just noticeable difference (JND) in the F0 of
paired tones. This task was web-based and similar in format to commonly used AX discrimination tasks, in that the first pitch stimulus (A) remained constant, while the second
stimulus (X) varied by F0 height. In this task specifically, the first tone of the two-item
pair was a 500 Hz pure tone, and the second tone differed by a predetermined interval ofhertz (96, 48, 24, 12, 6, 3, 1.5 Hz, etc.), with the between-stimulus Hz difference either
increasing or decreasing based on the accuracy of participants’ responses (see Mandel,2009). For example, at the 12 Hz interval, the first tone was 500 Hz and the second tone
512 Hz. Each tone was 250 ms in length and tones were separated by a 500 ms pause. The
resulting score represented the distance in Hz at which a listener could discriminate thepaired tones, with a lower score indicating a lower JND for pure-tone discrimination(Jongman et al., 2017).4 Stimuli were presented through headphones at a loudness ofapproximately 65 dB, a level comfortable for any normally hearing participant.
b Phonological short-term memory (PM). A serial non-word recognition (SNWR) task
was chosen as the measure of phonological memory capacity. The SNWR task closely
followed the design of the English syllable-based test used by O’Brien et al. (2006). In
contrast with the more common non-word repetition tasks measuring PM capacity, the
SNWR task requires no vocalization of the non-word stimuli, thereby removing the articulatory burden arising from speech production.
The task was presented aurally to participants and was composed of Japanese morabased non-words adhering to the Japanese phonotactic structure. PM measures featuring
non-words that are more word-like in the target language are purported to be better predictors of L2 vocabulary acquisition (Martin and Ellis, 2012). We selected an L2-based
memory measure primarily because our participants lacked a common L1, but also since
we assumed that our advanced-level participants were capable of handling an aurallypresented L2 memory task.

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359

Non-word stimuli were recorded by a speaker of Tokyo Japanese in a soundproof
recording booth at a sampling rate of 44.1 kHz. All of the non-words were spoken with a
Low-High pitch accent pattern, so that variability in pitch would not influence task performance. We recorded the stimuli with an existent accent pattern on the assumption that
pitch-carrying non-words sound more like speech than non-speech, and thus may better
predict real word task performance. Audio files for the PM task were then compiled insound-editing software as follows. For each trial, two lists consisting of the same numberof non-words were created with a 1,500 ms pause separating the lists; see (2) below. Nonword lists increased from 4 items in the practice phase to 5, 6, and 7 non-words for thetest phase. The inter-stimulus interval (ISI) between the non-words in each list was set to
750 ms. There were 8 trials at each of the 3 list lengths (i.e. 5, 6, and 7 non-words), yielding a total of 24 trials.
Two types of trials were then created from these lists, ‘same’ trials, meaning that the
non-words in both lists were presented in an identical order, and ‘different’ trials in which
the order of two of the non-words was switched. However, the first and last items of thelist were never switched. Half of the trials (n = 4) at each list length were same trials andthe other half different trials. The same/different sets were randomized within each set
length. Participants’ task was to decide if both lists were in the same or different order,
requiring them to keep track of the serial order of the non-words in order to make thisdecision. Note that participants were not required to use pitch accent to make judgmentson the non-word stimuli. A weighted scoring measure was used, so that the longer the set
length, the more points that were awarded for a correct response (see O’Brien et al.,2007).
(2) Two samples of 5 non-word-list length:    (Same)    gohe zuka imyo hezi baro <1.5 s> gohe zuka imyo hezi baro   (Different) tida ruge hami zare kebu <1.5 s> tida hami ruge zare kebuc Lexical and grammatical knowledge tests. Participants’ L2 lexical and grammaticalknowledge were measured with two multiple-choice tests, a 48-item lexical knowledge
test (Miyaoka et al., 2011) and a 36-item grammar test (Miyaoka et al., 2014). Both tests
were created with vocabulary and grammar items selected from the highest two levels of
the Japanese Language Proficiency Test (JLPT; Japan Foundation, 2002), and were validated in the two studies above, which used these tests to control for L2 proficiency. The
current study, likewise, used the tests first as a control for proficiency level, but more
importantly, used the L2 lexical knowledge test score as a predictor of lexical accent
perception.5
d Pitch accent identification and categorization. Forty-eight nouns were selected as stimuli,
and the length of the carrier sentences was set to 6–7 moras for 3-mora target nouns as in
八時に起きる / hatizi ni okiru / ‘(I) wake up at 8 o’clock’ and 7–8 moras for 4-mora targets as in 地下鉄に乗る / tikatetu ni noru / ‘(I) ride on the subway’ (M = 6.70 moras; SD= 0.52).6 Half of the 48 stimuli were 3-mora nouns, and the other half 4-mora nouns. These
were further divided into four accent patterns for both word lengths, yielding the following
patterns for 3-mora nouns (+L/H indicates the monomoraic particle that followed each

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Second Language Research 35(3)Figure 1. In the PitchCAT task, listeners categorized the noun-plus-postposition (shown in
dotted lines) into one of four pitch contours by selecting a visual representation of the pitch
pattern.

word): Pattern 1: HLL+L (initial-accent), Pattern 2: LHL+L (2nd-mora accent), Pattern 3:
LHH+L (ultimate accent), Pattern 4: LHH+H (unaccented); and likewise for 4-mora nouns:
HLLL+L, LHLL+L, LHHH+L, LHHH+H. We omitted the possible LHHL+L (penultimate) accent pattern from the tests to limit the 4-mora nouns to a total of four patterns.
Additionally, 24 of the noun stimuli were spoken with the correct pitch accent and 24 withan incorrect accent pattern. The number of correctly and incorrectly accented items wasbalanced to control for potential response bias. Stimuli were recorded by a male native
speaker of Tokyo Japanese with extensive training in accent production, and were sampled
at 44.1 kHz and presented to participants with no phonetic manipulation.
Stimuli were examined for frequency for each of the four accent patterns using the NTT
corpus of approximately 300 million words (Amano and Kondo, 2000). Given the participants’ high proficiency level and LoS (>5 years), we assumed that lexical frequency andfamiliarity effects would approximate those of L1 Japanese speakers (Crossley et al.,2014). Total mean normative frequency for all stimuli was 30.3 per million (SD = 46.3),
and by pattern as follows: Pattern 1 (M = 32.1, SD = 45.8), Pattern 2 (M = 14.4; SD = 13.1),
Pattern 3 (M = 37.3, SD = 59), Pattern 4 (M = 37.4; SD = 54.7). Word familiarity, a subjective measure which has been shown to influence both recognition accuracy and latency(e.g. Ueno et al., 2014), was also checked for each of the stimuli. Amano and Kondo (2000)
measured the combined lexical familiarity for a word’s spoken and written forms on a
Likert scale from 1 (not familiar at all) to 7 (very familiar). The mean familiarity rating forall stimuli was 6.19 (SD = 0.29), indicating the words were subjectively very familiar to L1
Japanese speakers. Refer to the Appendix 1 for a complete list of target words.
The PitchID and PitchCAT tasks were combined into one task using shared stimuli as
follows. First, participants had to judge the correctness of a target word’s accent patternon the PitchID task, where reaction time was measured. Then, for the correctly-accented
items only (n = 24), immediately following the PitchID judgment, participants categorized the noun-plus-postposition into one of four pitch contours by selecting a graphwhich corresponded to the pitch contour (Figure 1). Target sentences were only heard
once by the listener, and importantly, participants were never required to categorize aword that was spoken with the incorrect pitch accent.7

3 Procedure
Data for the PitchID task were structured as 2 word lengths (3 and 4-moras) × 4 accentpatterns × 6 words × 61 listeners (n = 2928), with half the number of words (i.e. correctly

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Goss and Tamaoka
Table 1. Descriptive statistics for all test scores for the L1 Chinese and Korean groups.
Variable

F0 discrimination
PM
Lexical test
Grammar test
PitchID
PitchID.RT
PitchCAT

L1 Chinese (n = 31)

L1 Korean (n = 30)

Max

M

SD

Max

M

SD

–
144
48
36
48
–
24

24.66
88.19
37.94
31.94
37.16
3,545
14.45

19.65
17.50
4.93
3.05
3.03
1,442
4.93

–
144
48
36
48
–
24

9.83
74.57
38.70
32.60
28.40
3,320
11.83

11.65
21.28
5.87
2.67
5.49
1,306
3.48

Notes. Max = maximum possible score; Pitch sensitivity is measured in Hz, lower mean score indicatesgreater sensitivity; PitchID.RT is measured in milliseconds.

accented items only) for PitchCAT (n = 1464). All tasks were presented to participants
on a laptop computer through headphones in a soundproofed room, except for the two
written proficiency tests. Stimuli for the PM task and the PitchID/PitchCAT perceptiontasks were presented in E-Prime, with correct or incorrect judgments made via a 2-button
response for the PM and PitchID tasks. Reaction time was measured for the PitchID task
only. The PitchCAT task employed a 4-button response in which each button corresponded to one of the pitch patterns in Figure 1. Experimental tasks were presented in thefollowing order, with a short break following the PM task: (1) F0 discrimination, (2) PM,(3) PitchID, and (4) PitchCAT. Each task began with a brief practice phase to familiarize
participants with the procedures. Due to the complexity of the PitchCAT task, participants were familiarized with sample pitch contour graphs similar to those in Figure 1
during the practice phase. All participants confirmed they were clear with the categorization procedure prior to starting the main experiment. Upon completing the tasks, the lexical and grammar knowledge tests were administered to all participants. All inclusive, thetask participation time was about 1 hour 15 minutes per individual. After the test completion, participants filled out a background questionnaire and received a small payment for
their participation.

IV Results
1 Comparison of L1 Chinese and Korean groups
Descriptive results for all variables are presented in Table 1. On the F0 discriminationtask, the L1 Korean speakers were significantly more sensitive than L1 Chinese speakers
to variations in F0 height (t(59) = 3.25, p < .01). However, L1 Chinese displayed agreater PM capacity than L1 Koreans (t(59) = 2.74, p < .01). As mentioned previously,
no difference was found between L1 Chinese and L1 Korean speakers on the L2 lexical
knowledge text. These three variables – F0 discrimination, PM, and L2 lexical knowledge – were subsequently examined, along with L1 background, as predictors of accent
perception ability.

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Second Language Research 35(3)Figure 2. Mean accuracy scores on PitchID and PitchCAT tasks by L1 group.
Notes. n = 61; * p < .05; *** p < .001; Bars indicate standard deviations.

Figure 2 displays the accuracy scores on the PitchID and PitchCAT perception tasks
separated by L1 group. Here we see a clear difference in accuracy rates between thegroups on both tasks. On the PitchID task, L1 Chinese attained a mean accuracy of
77.5%, while L1 Korean displayed a mean of 59.2% (t(59) = 7.74, p < .001). The same
trend was found on the PitchCAT task – in which learners matched the correctly-accented
stimuli from the PitchID task with visual representations of pitch contours – with the L1
Chinese performing at a 60.2% mean accuracy and L1 Koreans at 49.3% (t(59) = 2.39, p< .05). Bear in mind that the categorization task required a 1-out-of-4 answer choice, so
we consider the lower scores to be partially an outcome of task design.

2 Learner-variables and accent perception
In order to explore the relationship between our predictor variables and accent perception, we next examined correlation coefficients for the combined L1 groups (n = 61). We
found that Japanese lexical knowledge correlated positively with both PitchID (r = .32;
p < .05) and PitchCAT scores (r = .34; p < .01). Taking into account the differences inperception accuracy between the L1 groups – given that we also considered L1 knowledge an experienced-based predictor – these results suggest a relationship between theexperience-based predictors and lexical accent perception. By contrast, neither PM nor
F0 discrimination, the domain-general predictors, yielded significant correlations oneither of the accent perception tasks. Our analysis also revealed that listeners who could
accurately judge accent correctness on the PitchID task showed a strong tendency toperform well at categorization (r = .52; p < .01). Reaction time on the PitchID taskyielded the only significant correlation with PitchCAT accuracy (r = −.26; p < .05), indicating that listeners who made rapid decisions on accent correctness were also better atpattern categorization.
These results suggest that along with the apparent advantage for the L1 Chinese participants, who have rich experience using tone phonemically, the size of a learner’s L2
lexicon is also related to their ability to perceive lexical accent. To further explore therelation of the predictors to L2 accent perception, we next submitted the data for each of
the dependent variables (PitchID, PitchID.RT, and PitchCAT) to mixed-effects analysis
in R (version 3.2.0) using the lme4 package (Bates et al., 2015).

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Goss and Tamaoka
Table 2. Summary of mixed effects models for PitchID and PitchID.RT.

PitchID

PitchID.RT

Predictor

Est.

SE

z

(Intercept)
L1
Lexical knowledge
PM
F0 discrimination(Intercept)
L1
Lexical knowledge
PM
F0 discrimination

−5.24
−1.70
5.99
−1.63
0.03
4,449.02−183.44−1931.76
1,183.99−58.81

2.08
0.29
1.14
0.62
0.04
3,067.23
234.01
1,654.35
917.5267.02−2.52*−5.82***
5.21***−2.60**
0.80
1.45
−0.79
−1.15
1.29
−0.87

Notes. n = 61; ***p < .001; **p < .01; *p < .05; Model formula: PitchID ~ L1 + LexKnow + PM + F0 +(1 | Subject) + (L1 | Item).

First, a logistic mixed-effects regression model was conducted for the PitchID data
(Jaeger, 2008). The model included fixed-effect predictors for L1, Lexical knowledge,
PM, and F0 discrimination. Interactions between fixed effects were also entered into themodel (Barr et al., 2013). The random effects structure included random intercepts for
subjects and items, and by-item random slopes for L1. Significant main effects were
found for the predictors L1, Lexical knowledge, and PM. We then compared the fullmodel, with interactions included, to one without, and found that removing the interactions yielded a better fit, as assessed by Bayesian information criteria (full model: BIC =2,696, without interactions: BIC = 2,674; lower number indicates a preferred model). We
must note that because of the correlation between the two domain-general predictors, PM(r = .33) and F0 discrimination (r = .39), and L1, these fixed effects and their interactions
failed to account for meaningful variance in the model. The multicollinearity of the two
predictors may also explain why PM reached significance in the model: the higher PM
L1 Chinese outperformed the L1 Korean group on the PitchID task. Next, to determine
if the two experience-related predictors, L1 and Lexical knowledge, improved model fit,
we then compared the model with L1 removed to the full model, and found that its addition yielded a significantly better fit (χ2 (1) = 53.741, p < 0.001). This was repeated with
Lexical knowledge, the inclusion of which also improved the model fit (χ2 (1) = 22.843,
p < 0.001). Finally, marginal R2 was calculated for the combined predictors, which
accounted for 58.5% (R2 = .585) of variance in correctness judgment accuracy. The
results of the best-fitting model are shown in Table 2.
A second regression model was performed on the reaction times on the PitchID task
(Table 2), with an identical fixed and random effects structure as above. However, thismodel failed to reveal any significant predictors of learners’ decision latencies on thecorrectness judgments, suggesting the task’s processing demands were heavy, and thatreaction time was an unsuitable index of accent knowledge.
Finally, a logistic regression model was fitted to the PitchCAT data, with the same
fixed and random effects structure as in the PitchID model. Significant main effects were
found for the predictors L1 and Lexical knowledge. As with the PitchID model, the364Second Language Research 35(3)Table 3. Summary of mixed effects model for PitchCAT.
Predictor

Est.

SE

z

(Intercept)
L1
Lexical knowledge
PM
F0 discrimination

−7.04
−0.63
5.18
−0.43
−0.02

3.08
0.28
1.66
0.93
0.06

−2.28*−2.24**
3.10**−0.47−0.30

Notes. n = 61; ***p < .001; **p < .01; *p < .05; Model formula: PitchCAT ~ L1 + LexKnow + PM + F0 +(1 | Subject) + (L1 | Item).

inclusion of interactions did not improve model fit (full model: BIC = 1972, without
interactions: BIC = 1953), likely due to the intercorrelation of predictors stated earlier.
Comparisons again showed that models with L1 (χ2 (1) = 4.897, p < 0.05) and Lexical
knowledge (χ2 (1) = 9.086, p < 0.01) added significantly improved fit over the reduced
models without these respective predictors. Marginal R2 for the fixed effects accounted
for 22% (R2 = .221) of the variance in the categorization task. The final model is shownin Table 3. To summarize, both L1 and L2 experience were significant predictors in themodels that we fitted onto the accuracy data for L2 Japanese learners’ accent judgment
and categorization.

3 Analysis of individual accent patternsa PitchID. Considering that L1 was a significant language-experience predictor of
accuracy on both perception tasks, we next evaluated the response data by accent pattern,
to further examine if L1-related differences were reflected in the identification accuracyof individual patterns. Previous research has shown perception accuracy to differ widely
by accent pattern (e.g. Nishinuma et al., 1996; Toda, 2001), but cross-linguistic comparisons are lacking.
To this purpose, we conducted a decision-tree analysis (e.g. Tamaoka and Ikeda,
2010) to explore how PitchID responses diverged by both accent pattern and L1 group.
Although more common in business and decision science, decision tree analyses are
well-suited for analysing categorical response data and can be insightful in second language studies. In this analysis, we mapped participants’ response data onto a multi-tiered
chart, which is separated into branches, or nodes, according to the variables of interest.
Chi-squared tests of independence were used to divide the responses into these nodes,
with statistically-different variables (i.e. accent pattern or L1 group) being assigned toseparate nodes. It is important to note that variables which do not differ significantly are
combined into a single node in the tree, enabling easy visualization of where response
data diverged.
Figure 3 shows in the uppermost node (Node #0) the total number of correct andincorrect responses made by the combined groups (n = 61). The next tier down displays
responses by accent pattern in their order of accuracy, from left to right. Here we observethat the accent patterns were separated into three nodes, yielding the order: Pattern 4

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365

Figure 3. Decision tree analysis for the PitchID task by the L1 Chinese and Korean groups.

Notes. Pattern 1 = initially accented; Pattern 2 = 2nd-mora accent; Pattern 3 = ultimate accent; Pattern 4 =unaccented.

(82.79%) > Pattern 1 = Pattern 2 (72.40%) > Pattern 3 (46.17%). In instances where no
accuracy differences were detected between patterns, data were combined as in Patterns
1 and 2 (Node #2). In the lowest tier of branches, responses are further divided by L1
group. We can thus interpret the split below Pattern 4 (Nodes #4 and #5) as reflecting asignificant difference between Chinese (92.47% correct) and Korean speakers (72.78%
correct) on their judgments of unaccented words, as well as in their accuracy on Patterns
1 and 2 (84.27% vs. 60.14% correct) (Nodes #6 and #7). Finally, the lack of a split below
Node #3 reveals that Pattern 3 words were equally difficult for both groups.
b PitchCAT. The results of the 4-choice PitchCAT task for the L1 Chinese group arepresented as a confusion matrix in Table 4. The ‘Target’ column on the left indicates theactual lexical pitch pattern heard by listeners, while the ‘Response Percentage’ row at the
top displays the response listeners selected from among the four patterns. For example,
on Pattern 1 targets, L1 Chinese correctly categorized 69.35% of these as Pattern 1, while
only 10.22% were mistaken for Pattern 2. Contrast this with Pattern 3 targets, in which
listeners only selected the correct pattern on 42.47% of the targets.
Here we see a tendency for Chinese speakers to misidentify Pattern 3 as Pattern 4(42.47% vs. 40.32%), with a nearly equal percentage of responses made between theseaccent types when categorizing Pattern 3 stimuli. However, note that for Pattern 4 targets, this group was relatively accurate (69.89%). Table 5 displays L1 Korean speakers’
accent categorization results. Here we observe that this group was accurate in their366

Second Language Research 35(3)Table 4. Confusion matrix for accent categorizations by the L1 Chinese group (percentages).
TargetPattern 1

Response percentage
Pattern 1

Pattern 2

Pattern 3

Pattern 4

69.35

10.22

7.53

12.90

Pattern 2

5.38

59.14

19.35

16.13

Pattern 3

5.38

11.83

42.47

40.32

Pattern 4

6.99

6.99

16.13

69.89

Notes. n = 186 total responses per target. Shaded cells indicate correct responses.

Table 5. Confusion matrix for accent categorizations by the L1 Korean group (percentages).
TargetResponse percentage
Pattern 1

Pattern 2

Pattern 3

Pattern 4

Pattern 1

72.78

11.67

9.44

6.11

Pattern 2

8.33

52.22

26.67

12.78

Pattern 3

9.44

23.89

46.67

20.00

Pattern 4

17.22

21.67

34.44

26.67

Notes. n = 180 total responses per pattern. Shaded cells indicate correct responses.

categorization of Pattern 1 stimuli, but for the remainder of the accent patterns, there wasa gradual decrease in accuracy, with the lowest scores on Pattern 4.
We then created a decision tree for the categorization data from the combined
Chinese and Korean groups (Figure 4). The uppermost node (#0) indicates the total
correct and incorrect choices on this task. In the next tier down, the tree separates
accent patterns by accuracy rates, revealing an accuracy order of Pattern 1 (71.04%) >Pattern 2 (55.74%) > Pattern 3 = Pattern 4 (46.58%). The split in Nodes 4 and 5 showsthat the L1 groups differed only in their categorization of Patterns 3 and 4. The bottommost nodes (#6–9) then reveal that Chinese listeners displayed much higher accuracy
on Pattern 4 stimuli than Pattern 3, while Korean listeners were more accurate on
Pattern 3 stimuli than Pattern 4, with the latter group’s performance at nearly randomchance for Pattern 4 words (26.67%).

V Discussion
The present study examined how individual variation in two broad areas – domain-general resources and language-specific experience – influenced the L2 perception ofJapanese lexical accent. The primary motivation for this research was to account for thewide degree of hereto unexplained variability in accent perception ability across a rangeof L2 Japanese learners. Our findings paint a complex picture of the variables involved
in highly-proficient Japanese learners’ perception of lexically-accented speech, with fourGoss and Tamaoka

367

Figure 4. Decision tree analysis for the PitchCAT task by the L1 Chinese and Korean groups.

pertinent results emerging. First, Mandarin Chinese speakers’ experience with L1 tones
greatly facilitated the perception of lexical accent, leading to a large between-groupasymmetry in perception ability unrelated to Japanese proficiency measures. Second, L2
lexical knowledge predicted performance independently of L1. Next, we found that thedomain-general resources of F0 discrimination and phonological short-term memory
were not implicated in the perception of lexical accent in highly-proficient learners ofJapanese. Finally, decision tree analyses revealed that accuracy varied in relation to threefactors: accent pattern, L1 group, and task type.
The first finding was that L1 Chinese speakers were more accurate than L1 Korean
speakers at judging the correctness of accent patterns in Japanese noun-plus-postposition
units (PitchID: 77.5% vs. 59.2%). A parallel result was also obtained on the four-choicepattern categorization task (PitchCAT: 60.2% vs. 49.2%). Although the two L1 groups
were closely matched on language learning experience, L1 Chinese were significantly
more accurate on both perception tasks. We conjecture that this is an instance of experience with an L1 phonological property facilitating L2 perception ability. Mandarin

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Second Language Research 35(3)Chinese contains lexical tone contrasts that indicate phonemic variations; for example,
Tone 2 (rising) and Tone 4 (falling) have been noted to approximate the rising (L-H) andfalling (H-L) pitch contours found in Japanese (So and Best, 2010). Although these pitch
shapes are not phonetically identical in the two languages, and are used over different
timing units (i.e. single syllable in Mandarin versus two moras in Japanese), the phonemic
status of tone in Mandarin likely heightened awareness of word-level Japanese pitchaccent for these speakers. Additionally, the fact that Mandarin speakers focus on multiple
phonetic cues – pitch height and movement – when perceiving tones in their L1 potentially aided their perception of the pitch cues in Japanese (Gandour and Harshman, 1978).
Furthermore, most of this group’s errors were confined to their incorrect rejection andmiscategorization of a single accent type (Pattern 3). Pattern 3 words feature an H-L pitch
fall, as do Patterns 1 and 2, but it is located on the word-final mora, and its audibility isdependent on the presence of a following postposition. Although knowledge of a specificMandarin tone (Tone 4) may have facilitated perception of the accent fall in Pattern 1
words, in Tokyo Japanese, Patterns 2 and 3 feature both a pitch rise on the initial mora anda pitch fall later in the word. Considering this, familiarity with an L1 tone contour similar
to the Japanese H-L pattern alone may not have aided in the perception of Patterns 2 and3. Thus, because the presence of pattern-specific facilitation was unclear in the Chinesegroup, the most plausible account for their higher accuracy on non-Pattern 3 words is thatexperience with L1 lexical tone draws attention to the L2 accentual system in general,
which in turn facilitates acquisition of the contrasting accent patterns.8
Although the data indicated a marked advantage for the tonal-L1 group, we found thatL2 lexical knowledge was closely tied to perception accuracy in both L1 groups. The
mixed-effects regression models showed that L2 lexical knowledge significantly predicted learners’ ability to judge the correctness of and categorize lexical accent patterns(Tables 2 and 3). Importantly, even for Korean speakers, individuals estimated to possessa larger Japanese lexicon were more accurate on both accent perception tasks. In otherwords, individual variation in L2 lexical knowledge predicted accent perception ability
separate of the between-group factor L1 experience. If we consider that the two groups
displayed no differences in the Japanese experience measures (i.e. L2 lexical and grammatical knowledge, length of study, and length of residence in Japan), thereby ruling out
the possibility that performance was a function of general L2 proficiency and exposure,
we can conclude that accent perception ability is specifically tied to learners’ lexical
knowledge. Possessing a large L2 lexicon provides a wealth of exemplars of word form
with which to compare spoken input (Nation, 2010), and in Japanese in particular, ourresults suggest that this predictive relationship also holds for word-level prosodic perception, which in the current tasks can be construed as a measure of depth of lexical
knowledge (Meara, 1996). Furthermore, this finding ties in with theories suggesting aphonetic-to-lexical continuum of L2 acquisition, which propose that learners increasingly rely on lexical strategies, as opposed to basic perceptual resources, when processing speech input (e.g. Wong and Perrachione, 2007). This suggests that learners who
possess a large L2 lexicon are more likely to have internalized accent patterns, and arethus situated at the lexical end of the acquisition continuum.
This finding also echoes what SLA researchers have said about the role of longterm memory in lexical learning by non-beginner L2 learners. Namely, that stable

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369

phonological representations take a much more prominent role in language processing as
learners’ proficiency develops (e.g. Andringa et al., 2012; Martin and Ellis, 2012;
Speciale et al., 2004). Rather than reverting to domain-general resources such as auditory
processing or PM, the advanced learners in the present study likely utilized their explicit
knowledge of the Japanese accent system, in particular when categorizing words byaccent pattern. The results from this experiment support the prediction that learners who
possess a larger L2 lexicon, but who are not necessarily more experienced in the language, also have more robust representations of L2 accent categories, enabling greater
accuracy on measures of perception ability.
Yet in contrast with the language-specific variables, the domain-general resources of
F0 discrimination and PM were not significant predictors of perception ability.9 Withadvanced learners, auditory processing in a pure-tone discrimination task did not mediate performance on either accent perception task. In fact, most studies examining auditory processing ability and lexical perception have focused on target-language naive (i.e.
absolute beginners) or low proficiency learners, finding that sensitivity to pitch gained
from music training, for example, was related to perception accuracy on non-native pitch
and tone contrasts (Wayland et al., 2010; Wong and Perrachione, 2007; see also Goss andTamaoka, 2015). In these studies, listeners with no knowledge of the target language
perhaps had no recourse but to rely on basic perceptual resources when making decisions
on Mandarin tones, since these were probably perceived as non-speech. Moreover, thediscrimination task in the current study measured listeners’ acuity in distinguishing Hz
variations in pure tones, which although may have roughly approximated the F0 heightparameter in the bitonal (L-H/H-L) pitch patterns found in Japanese, undoubtedly tappedan acoustic, rather than linguistic, mode of perception (Strange and Shafer, 2008).
Furthermore, it is possible that the group difference on the F0 discrimination task (L1Chinese: 25 Hz vs. L1 Korean: 10 Hz) may not have been perceptually relevant to F0
perception in real words, since the relationship between perceptual scales for pitch (e.g.
mels) and Hz is non-linear above 500 Hz. Perhaps this can account for its failure to
explain any significant variance in the regression models. In short, auditory processing
ability as measured in this study was dissociated from lexical accent perception in bothtone and non-tone L1 participants.
The main observation regarding memory resources and accent perception is that in
highly-proficient learners, phonological short-term store capacity was no longer involvedin making judgments on short spoken stimuli. On the categorization task, in particular,
which was more metalinguistic in nature, we predicted that learners would rely on amemory trace of a spoken stimulus in order to compare it with schematized accent contours. However, it appears that learners were able to make these decisions by accessing
word forms from their mental lexicon, rather than invoking the phonological loop for
short-term maintenance of the stimuli. The fact that PM appeared to be negatively predictive in the model for correctness judgments was most likely a result of the imbalancein PM scores between the L1 groups, but may also suggest that this task measured aconstruct that is only indirectly related to language processing (Andringa et al., 2012).
PM is generally considered to be most involved in the early stages of L2 acquisition,
where it has been shown to be predictive of not only vocabulary learning, but also thedevelopment of grammar and speech fluency (e.g. Kaushanskaya, 2012; Martin and370

Second Language Research 35(3)Ellis, 2012; O’Brien et al., 2007). In future research on PM and L2 acquisition, training
studies or longitudinal designs would perhaps be more suitable in evaluating the relationship between the short-term phonological store and the emergent knowledge of L2 prosodic categories.
Finally, decision-tree analyses revealed that perception accuracy differed by accentpattern, L1 group and task type. On the PitchID task, we noted difficulty on Pattern 3
words, which was especially pronounced in the L1 Chinese group, who were quite accurate on the other three patterns. L1 Korean speakers also found Pattern 3 problematic,
although their errors were more diffusely spread throughout the other accent types. Why
the difficulty with Pattern 3? Pattern 3 words depend on the following postposition (seeSection II, Background) for their pitch fall to become audible: in isolation three-mora
Pattern 3 words carry the same (L-H-H) pitch contour as Pattern 4 (unaccented) words.
High accuracy on Pattern 4 has been previously reported (e.g. Nishinuma et al., 1996;
Toda, 2001), but only one other recent study has noted such a stark contrast between the
final-accented and unaccented patterns (Shport, 2016). Perceiving the difference between
these patterns requires listeners to attend to pitch beyond the word boundary, onto thefollowing postposition, with a failure to do so resulting in the inability to distinguishthese patterns.10
On the pattern categorization task, we again observed comparatively low accuracy on
Pattern 3 in both listener groups. Mandarin speakers were again more accurate on theother accent types, as shown in the confusion matrix in Table 4, while Korean speakers
made few errors with Pattern 1 categorizations, but showed a gradual decrease in accuracy across Patterns 2, 3, and 4 (Table 5). It appears that for Korean speakers, rather thana specific pattern causing categorization difficulty, any non-initially accented word
resulted in increased error rates. We conjecture that this is the result of an over-simplification of accent patterns by Korean speakers into an initial-accent versus non-initialaccent dichotomy. In other words, if a pitch fall was not detected on the first mora, then
the word was randomly categorized as one of the other three patterns, resulting in low
accuracy outside of Pattern 1.

VI Conclusions
Accurately perceiving L2 prosodic categories is often challenging for language learners
even into the advanced proficiency levels. The current study sheds light on why somelearners, despite very similar L2 learning experiences, are more successful than others at
attuning their perceptual system to Japanese lexical accent. Our results showed that L1
experience with lexical tone afforded Mandarin Chinese speakers a marked advantage
over proficiency-matched Korean speakers. When learning Japanese, Mandarin speakers
place greater focus on word-level pitch variations, and are therefore more likely to process this prosodic characteristic as an integral part of lexical form. Yet by no means do
we take this finding as indicative of the inability of Japanese learners from a non-tone L1
to acquire lexical accent. To the contrary, L2 lexical knowledge predicted learners’ ability to accurately perceive lexically-accented stimuli, regardless of the phonological status of tone in their L1. That is, the breadth of one’s L2 vocabulary knowledge, andpresumably the accompanying knowledge of accent patterns as well, proved to be a

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critical factor underpinning L2 prosodic perception in advanced-proficiency learners.
The fact that differences traceable to variation in lexical knowledge emerged in experienced learners’ prosodic perception ability underscores the importance of sustained
vocabulary instruction throughout the acquisition process.
Acknowledgements
We thank three anonymous reviewers for their helpful comments on earlier versions of this article.
We also thank Xiang Juan Chu, Kyoko Hayakawa, Sunju Park, and Kexin Xiong for assistancewith participant recruitment and data collection.

Declaration of Conflicting Interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship,
and/or publication of this article.

Funding
The author disclosed receipt of the following financial support for the research, authorship, and/or
publication of this article: The first author was supported by a Japan Foundation Dissertation
Fellowship while conducting this research.

Notes
1. With verbs and other inflecting forms, however, accent pattern is often predictable based on
the type of morphology attached to the verb, such as the negative suffix -nai (accented H-L).
2. These listeners were undergraduates from the Tōkai region, a standard dialect area in central
Japan (Shibatani, 1990).
3. It must be noted that this lexical-distinguishing function of tone is far more common in
Chinese, in which 71% of homophones are distinguished by tone (Shibata and Shibata, 1990).
4. This instrument does not generate data for each individual trial, but rather gives a single score
for the entire task. We acknowledge the imprecision of the measure, but at the same time feelthe need to expand the toolbox for complex research designs in L2 learning.
5. The test was designed as a measure of receptive vocabulary knowledge and can be considered as
an estimate of L2 lexicon size (Nation, 2010). Vocabulary was selected from all speech categories. Distribution of lexical accent patterns and word length of the test items were not controlled.
Words from the most difficult level (Level 1) of the JLPT, which constituted approximately
half of the items on the current lexical knowledge measure, are from a low frequency band.
Estimates suggest that learners capable of achieving a score above 70% (cut-off for passing) on
this test possess a lexicon of approximately 10,000 words (Ishizaki et al., 1999).
6. Considering the substantial amount of previously-reported variation in L1 perception of lexical accent, we must note that 32 of the 48 nouns were tested on L1 Japanese speakers in aprevious study by Goss and Tamaoka (2015). They found that L1 listeners judged correctness
with a mean accuracy of 93%, suggesting little perceptual variation for this set of nouns. The
16 nouns added to the current study were produced by the same talker and are of similar frequency and familiarity to those used in the previous study.
7. As a reviewer pointed out, the PitchID and PitchCAT tasks likely tap different mental processes. That is, the PitchID task is a type of lexical decision task that taps language-specificknowledge, while the PitchCAT task requires more of a metalinguistic judgment, and couldin theory be performed by a Japanese-naive listener.

372

Second Language Research 35(3)8. Although all participants reported familiarity with the accent system, L1 Chinese who studied
Japanese in their home country may have received more form-focused accent instruction than
Korean speakers, the importance of which is often emphasized in Japanese courses at Chinese
universities (personal communication, X. Chu).
9. We acknowledge that the variation between the L1 groups likely influenced the predictive
power of the two domain-general variables. Namely, L1 Chinese showed a much greatermean PM capacity, while L1 Koreans were more sensitive to non-linguistic pitch. This unexpected ‘noise’ from the participant sample needs to be accounted for in future studies on
individual differences.
10. As two reviewers pointed out, advanced learners may be sensitive to frequency distributionsof the pattern types themselves, as has been shown for L1 Japanese (see Ueno et al., 2014).
If we consider that at the 3- and 4-mora word lengths, Patterns 4 and 1 are the most frequent,then it appears that at least in light of accuracy orders on the PitchID task (Figure 3), this may
indeed be the case for advanced L2 speakers as well.

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Appendix 1. Target words and postpositions (underlined) with carrier sentences used in the
PitchID and PitchCAT tasks.
医学を学ぶ
めがねをかける
フォークで食べる
おもちゃで遊ぶ
八時に起きる
中身を見る
ななめにする
言葉にする
夜中に起きる
いなかに住む
ハガキを出す
手前に引く
来月に行く
湖でおよぐ
将来を考える
あさってまで待つ八月になる
オレンジを食べる
九日に行く
成績をつける
正月を楽しむ
地下鉄に乗る
専門を学ぶ

igaku o manabu
megane o kakeru
fooku de taberu
omotya de asobu
hatizi ni okiru
nakami o miru
naname ni suru
kotoba ni suru
yonaka ni okiru
inaka ni sumu
hagaki o dasu
temae ni hiku
raigetu ni iku
mizuumi de oyogu
syoorai o kangaeru
asatte made matu
hatigatsu ni naru
orenzi o taberu
kokonoka ni iku
seiseki o tukeru
syoogatu o tanosimu
tikatetu ni noru
senmon o manabu‘(I) study medicine’‘(I) put on glasses’‘(I) eat with a fork’‘(I) play with a toy’‘(I) wake up at 8 o’clock’‘(I) look at the contents’‘(I) turn it sideways’‘(I) put it into words’‘(I) wake up at midnight’‘(I) live in the countryside’‘(I) send a postcard’‘(I) pull it toward me’‘(I) go next month’‘(I) swim in the lake’‘(I) think of the future’‘(I) wait until the day after tomorrow’‘It becomes August’‘(I) eat an orange’‘(I) go on the 9th’‘(I) assign grades’‘(I) look forward to New Year’s Day’‘(I) ride the subway’‘(I) study for my major’(Continued)

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Second Language Research 35(3)Appendix 1. (Continued)
土曜日に行く
家内に話す技術をみがく
荷物を運ぶ
あなたがいる
ハサミがある
刺身を食べる
昼間に行く
娘がいる
男がいる
左に曲がる
昔に戻る
仕事を探す
太陽がのぼるタクシーに乗るマンションに住む
黒板に書く
飛行機に乗る
てぶくろを買う
弟がいる
妹がいる
二日目になる
六月になる
夕方になる
独身になる

doyoobi ni iku
kanai ni hanasu
gijutsu o migaku
nimotu o hakobu
anata ga iru
hasami ga aru
sasimi o taberu
hiruma ni iku
musume ga iru
otoko ga iru
hidari ni magaru
mukasi ni modoru
sigoto o sagasu
taiyoo ga noboru
takusii ni noru
mansyon ni sumu
kokuban ni kaku
hikooki ni noru
tebukuro o kau
otooto ga iru
imooto ga iru
hutukame ni naru
rokugatu ni naru
yuugata ni naru
dokusin ni naru‘(I) go on Saturday’‘(I) speak to my wife’‘(I) polish my skills’‘(I) carry luggage’‘You are there’‘There are scissors’‘(I) eat sashimi’‘(I) go in the afternoon’‘(I) have a daughter’‘There is a man’‘(I) turn left’‘(I) return to the past’‘(I) look for a job’
‘The sun rises’‘(I) get in a taxi’‘(I) live in an apartment’‘(I) write on the blackboard’‘(I) ride in an airplane’‘(I) buy gloves’‘(I) have a younger brother’‘(I) have a younger sister’‘It becomes the 2nd’
‘It becomes June’‘It becomes evening’‘(I) become single’