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Japanese Psychological Research2015, Volume 57, No. 2, 143–154

doi: 10.1111/jpr.12076

Predicting lexical accent perception in native Japanese
speakers: An investigation of acoustic pitch sensitivity
and working memory1
SETH GOSS2*

Ohio State University

KATSUO TAMAOKA

Nagoya University

Abstract: Spoken language perception may be constrained by a listener’s cognitive
resources, including verbal working memory (WM) capacity and basic auditory perception mechanisms. For Japanese listeners, it is unknown how, or even if, theseresources are involved in the processing of pitch accent at the word level. The present
study examined the extent to which native Japanese speakers could make correctness judgments on and categorize spoken Japanese words by pitch accent pattern,
and how verbal WM capacity and acoustic pitch sensitivity related to perception
ability. Results showed that Japanese listeners were highly accurate at judging pitchaccent correctness (M = 93%), but that the more cognitively demanding accent
categorization task yielded notably lower performance (M = 61%). Of chief interest
was the finding that acoustic pitch sensitivity significantly predicted accuracy scoreson both perception tasks, while verbal WM had a predictive role only for the categorization of a specific accent pattern. These results indicate first, that task demands
greatly influence accuracy and second, that basic cognitive capacities continue tosupport perception of lexical prosody even in adult listeners.
Key words: speech perception, lexical accent, pitch sensitivity, verbal working
memory.

Perception of spoken language is a complex taskthat may be supported by a listener’s cognitive
resources. Two such resources purportedly
involved in the processing of speech input are
nonlinguistic, or acoustic, pitch sensitivity andverbal working memory (WM).The assumption
is that those who are more sensitive to subtle
acoustic variations (e.g., Deutsch, Dooley,
Henthorn, & Head, 2009; Wayland, Herrera, &Kaan, 2010) or who possess a greater capacity totemporarily maintain aural input in WM (e.g.,
Baddeley, Gathercole, & Papagno, 1998;
Kaushanskaya & Yoo, 2013) are capable of utilizing these abilities when processing spoken
language as well. In other words, measures ofbasic, nonlinguistic capacities may predict how
accurate one is in processing certain features of
spoken language. In the case of Japanese, which
is a lexically accented language, these cognitive
abilities may continue to play a role in native*Correspondence concerning this article should be sent to: Seth Goss, Department of East Asian Languages
and Literatures, Ohio State University, Columbus, OH 43210, USA. (Email: goss.33@osu.edu)
1
The first author was supported financially by a Japan Foundation Dissertation Fellowship while conducting
this research.
2
We are grateful to Nobuhiro Saito for recording test stimuli and Sachiko Kiyama for suggesting the adaptive
pitch test. We also thank Kyoko Hayakawa and Rinus Verdonschot for their assistance with participant
recruitment and software expertise.

© 2014 Japanese Psychological Association. Published by Wiley Publishing Asia Pty Ltd.

144

S. Goss and K. Tamaoka(L1) listeners’ ability to perceive spoken wordlevel accents. Despite the general assumptionthat speech perception in the L1 is robust, automatic, and places little demand on cognitive
resources (e.g., Strange & Shafer, 2008), variation in lexical accent perception ability among
Tokyo-standard Japanese speakers has beenreported in previous research (e.g., HiranoCook, 2011; Shport, 2008). Some of this variation is likely a consequence of the use of experimental tasks that vary in the demands placed on
the listener and the presence of low-frequency
stimuli, both of which have been noted to influence speech perception (Strange & Shafer,2008). However, differences in individual listeners’ cognitive capacities may underlie a significant portion of the variation in perception
accuracy. However, to our knowledge no link
has yet been established in the literature
between basic cognitive resources and the perception of lexically accented speech. To address
this gap, the present study first compared L1
Japanese listeners’ performance on a two-phaselexical accent perception measure (a correct orincorrect judgment followed by a categorizationtask) neither of which have been measured
together in the same listener population. Furthermore, lexical frequency was controlled inorder to minimize perception difficulty arisingfrom low-frequency stimuli. We then measured
participants’ verbal WM and acoustic pitch sensitivity to explore how basic cognitive abilities
relate to perception ability on the two accent
perception tasks.
In Japanese, pitch accent is specified in thelexicon as a part of word form (Beckman &Pierrehumbert, 1986). Accent location ismarked by a single phonetic cue, which acoustic
models of pitch accent indicate is a lexicallylinked High (H) tone followed by a drop to
Low (L) pitch (Venditti, 2005). For example, athree-mora Japanese noun can have n + 1 pitch
patterns, as shown in the words below(examples from Vance, 2008).33

Mora pronounced with a high pitch are indicatedin capital letters. Accent location is perceived by
Japanese speakers on the high-pitched mora immediately preceding the fall to low pitch.

© Japanese Psychological Association 2014.

1
2
3
4

makura wa (MAkura-wa) “pillow + TOPIC
marker”
tamago wa (taMAgo-wa) “egg + TOPIC
marker”
takara wa (taKARA-wa) “treasure +TOPIC marker”
sakana wa (saKANA-WA) “fish + TOPIC
marker”

These lexically linked accents are thought tobe stored in a native speaker’s mental lexicon
in combination with a word’s segmental form
(e.g., Beckman & Pierrehumbert, 1986; Otake& Cutler, 1999). Accordingly, when native
Japanese speakers process spoken language,
pitch accents are likely activated simultaneously with lexical form. It follows then that L1
speakers should be able to accurately perceive
whether aurally presented words were spoken
with the correct or incorrect accent pattern inthe same way that they can make decisions on
features such as vowels and consonants. In
fact, Shibata and Hurtig (2008) reported that
adult L1 Japanese speakers made correctness
judgments on high-frequency spoken words
with a 97% accuracy rate. Ueno et al. (2014)
also found that L1 Japanese could perceive,then imitate aloud, spoken words containing
both correct and incorrect accents with nearceiling accuracy. However, perception tasks
that require listeners to identify accent location or categorize words by accent type haveproduced notable performance variation. For
instance, Shport (2008) noted that Japaneselisteners only attained a mean accuracy of
59% in identifying pitch accent location on an
AXY perception task. The task required participants to listen to a set of three words, oneof which (either X or Y) differed in pitchaccent from the other two, and decide if the
odd-one-out was the X or Y stimulus. In asimilar type of prominence identification task,
Hirano-Cook (2011) also showed a lower than
expected accuracy rate of 70% in L1 listeners’ability to mark the accent location of spoken
words on Japanese-script transcriptions.
Despite both researchers acknowledging theinherent task difficulty, this poor performanceon categorization-type tasks led them toLexical accent perception in L1 Japanese

conclude that pitch accent is a relatively minor
aspect of lexical form.
Methodologically, both the studies by Shport(2008) and Hirano-Cook (2011) used lowfrequency words, and their perception tasks,
which required listeners to identify accent location or compare two or more pitch patterns,
were likely more cognitively demanding than acorrectness judgment or repetition task. Strange
and Shafer (2008) noted that experimental variables greatly influence perception performance,in that L1 listeners can make correctness judgments based on existing knowledge of phonological categories, while categorization tasks,
which require explicit acoustic comparisonor metalinguistic knowledge of the features
being tested, may involve additional processing
resources.Therefore, in the present study both acorrectness judgment and a categorization task
were conducted with the same group of participants, with the aim of first exploring how cognitive resources are implicated in these disparate
tasks types, and second how this two-step decision process influences accuracy.
Recent research has attempted to shed lighton basic cognitive abilities that support spoken
language processing (e.g., Wayland et al., 2010;
Wong & Perrachione, 2007). Next, we will elaborate on two such abilities that may be involved in
the processing of lexical pitch accent: verbal
WM and acoustic pitch sensitivity.
Verbal WM and its correlate the phonologicalloop have been shown to be implicated in arange of language tasks including vocabularyacquisition (Gathercole, Willis, Emslie, &Baddeley, 1992), reading (Jincho, Namiki, &Mazuka, 2008), and spoken language comprehension (Adams, Bourke, & Willis, 1999). The
phonological loop is the subcomponent of WM
that is responsible for the short term storageand maintenance of phonological information
derived from both spoken and visual (i.e., text or
namable images) input (Baddeley et al., 1998).
One fundamental role is to therefore maintainspeech input for a short duration, so that morein-depth processing can be performed. In thecase of Japanese, this entails that pitch accent
information is held in the short-term phonological store, which may be implicated in lexical

145

processing tasks. Furthermore, listeners with alarge store capacity are likely capable of accurately retaining more sound information when
making subsequent lexical judgments involving
pitch accent.While much research has examined
the role of verbal WM in the processing of
vocabulary and syntax, to our knowledge nostudies have sought to demonstrate the possible
involvement of WM in the maintenance oflexical-level accent. Despite the absence of
research, Chan, Ho, and Cheung (1998) demonstrated that pitch sensitivity gained throughmusical training improved adult Chinese speakers’ verbal memory for native vocabulary. In
their study, participants with music training
scored 16% higher than nonmusician controls
on a lexical recall (i.e., verbal memory) task
featuring orally presented words. Their finding suggests shared memory resources fornonlinguistic pitch and tone-carrying words
in a tone language. Semal, Demany, Ueda, andHalle (1996) found that on a speech memorytask, listeners processed pitch variations inspeech and nonspeech sounds in a similarmanner, and concluded that both types of soundare handled similarly by the short-term memory
system.
In Japanese, lexical pitch accent may play afacilitative role in both word and nonword
retention in verbal WM. On a verbal WM task,
Yuzawa and Saito (2006) reported that 3- to
4-year-old Japanese children showed superior
recall ability for nonwords that were spoken
with an existing pitch pattern as opposed tothose produced without pitch variation. It is
important to note here that all words inTokyo-standard Japanese feature some type ofpitch variation, even the so-called unaccented
pattern (example 4, above). Prosody has also
been shown to facilitate recall in real-word
stimuli in young children, but not in older
children (e.g., Roy & Chiat, 2004; Yuzawa,
2002).
Although previous research has suggested afacilitative effect of lexical accent on recall
in memory tasks, the opposite connection,
namely, verbal WM supporting the processingof accented words, has yet to be established.
Considering the earlier discussion of pitch© Japanese Psychological Association 2014.

146

S. Goss and K. Tamaoka

accent as an inherent part of word form in Japanese, in the present study we assumed thatverbal WM capacity will be predictive of performance on a cognitively demanding perception task such as lexical categorization, which
requires an explicit phonetic analysis of thetarget while maintaining pitch information
in memory. In addition, the use of lexicallyaccented nonwords in the WM task is likely
a critical factor if we are to compare serial
recall capacity with performance on real-wordperception tasks, as the recall of accented
nonwords may relate to the perception ofaccented speech (Yuzawa & Saito, 2006).
The second cognitive capacity of interest is
acoustic pitch perception. Research from thefield of cognitive science has suggested a link
between a listener’s sensitivity to acoustic
pitch and the capacity to accurately perceive
speech sounds by adult L1 listeners. Liu, Patel,
Fourcin, and Stewart (2010) have reported
evidence of shared structures for music andlanguage perception in the brain. In theirstudy, native speakers of English who claimed
to be “tone-deaf” (congenital amusica) lagged
behind matched normal participants in theirability to correctly identify intonation patterns
in their native language, despite all participants
reporting normal communicative ability in their
L1. Nan, Sun, and Peretz (2010) added support
to these findings by demonstrating that evennative speakers of a tone language (Mandarin
Chinese) who reported tone deafness also displayed impaired processing of L1 tone patterns,
again in the presence of normal production
ability. Further, in a normal adult population,
Bent, Bradlow, and Wright (2006) demonstrated that L1 Chinese speakers consistently
made perception errors on nonspeech pitch
contours that were phonetically similar to L1
tone patterns, indicating that these listeners
processed speech and nonspeech pitch withshared perceptual resources.
However, a contrary viewpoint posits thatperception for speech and nonspeech features
is separated very early in life, as a result of thetuning-in to one’s native language sound categories, which in turn facilitates acquisitionof L1 phonological regularities (e.g., Werker &© Japanese Psychological Association 2014.

Curtin, 2005). Models of speech perception
such as the native language magnet (NLM)
theory state that as we gain experience with our
first language, our perceptual space is molded
to the sound categories of that language(Iverson & Kuhl, 1996). Accordingly, this would
predict dissociation between the processing ofnonspeech sounds and performance on L1
speech perception tasks by adult listeners. In
other words, listeners may perceive unknown
phonological categories like nonspeech or
non-native tones acoustically, while perceiving
speech sounds linguistically. Yet the relationship between the type of acoustic sensitivity
measure and the speech feature under investigation (i.e., lexical pitch) may hinge on the phonetic similarity between the stimuli in bothtasks. Namely, an acoustic sensitivity measure
resembling the bitonal (Low-High/High-Low)
pitch accent structure of Japanese may be predictive of performance on word perceptiontasks. Therefore, our third aim is to examine therelationship between acoustic pitch sensitivity
and the processing of real-word stimuli by L1
Japanese listeners.
In sum, the present study aims to investigate
the relationship between basic cognitive abilities and the perception of lexically accented
words in two speech perception tasks by adult
L1 Japanese speakers. We first predicted that
adult listeners can reliably use pitch accent tojudge the correctness of spoken word accents,
but that accent categorization, which involves
greater processing demands, will yield moreindividual variation. Secondly, as Japanese pitchaccent fundamentally consists of contrastinghigh and low tones, listeners who possess
greater acoustic pitch sensitivity will performbetter on both lexical perception tasks. Lastly,
verbal WM span will be implicated in thememory-resource demanding accent categorization task, but not in the speeded correctness
judgment task.

MethodParticipants
Thirty native Japanese speakers (19 female
and 11 male) with a mean age of 19.2 years

Lexical accent perception in L1 Japanese(SD = 0.78) at a research university in Japan
participated in the experiment. The participants
indicated on a questionnaire that they werefrom the Tokai region in central Japan, which isclassified as a Tokyo-standard accent region(Shibatani, 1990). Participants reported normal
hearing, and none had an extended length ofmusical training (≤3 years) or fluency in a tone
language. All participants were volunteers andwere compensated for their participation.
Materials
Verbal WM. The serial nonword recognition (SNWR) task was first used by O’Brien,
Segalowitz, Collentine and Freed (2006) as ameasure of phonological short-term memory,and provides an alternative to the conventional
nonword repetition (NWR) tasks that require
vocalization of nonword stimuli. Eliminating
the production aspect may enable a moreaccurate measure of verbal WM capacity. In
contrast with the English-based nonwords used
by O’Brien et al. (2006), in the current study
stimuli for the SNWR task were nonwords
based on the Japanese mora system, of thestructure /(C)VCV/. Nonword stimuli wererecorded by a speaker of Tokyo-standard
Japanese and were spoken with an unaccented
Low-High pitch pattern (see example 4 inIntroduction). We chose to record the nonword
stimuli with an existent accent pattern because
our aim was to measure the relationshipbetween WM capacity and the processing oflexically accented words, on the assumptionthat pitch-carrying nonwords will better predict
real-word task performance. The task design is
as follows. Two sets consisting of the same
number of nonwords were played to participants with a 1500 ms pause between sets. The
sets increased from four items in the practicephase to five, six, and seven nonwords for thetest phase. The interstimulus interval (ISI)
between the nonwords in each list was 750 ms.
Half of the sets at each length consisted of“same” trials and the other half of “different”
trials. In the different trials, the order of two of
the nonwords was switched. However, the firstand last items of the list were never switched.

147

The participants’ task was to decide if both sets
were in the same or different order, requiringthem to keep track of the serial order of the
nonwords in order to make this decision.
Sample nonword sets (5 nonwords in length).
Switched items are in italics.
(Same)(Different)gohe zuka imyo heji baro<pause>gohe zuka imyo heji baro
chida ruge hami jare kebu<pause>chida hami ruge jare kebu

Pitch sensitivity. An adaptive pitch test that
increased in difficulty based on participants’performance was used as the measure of acoustic pitch sensitivity. This task is web-based
and is similar in format to commonly used
AX discrimination tasks, in that the first pitch
stimulus (A) remained constant, while thesecond stimulus (X) varied by a predeterminedparameter (Mandel, 2009). In this task specifically, the first tone of the two-item pair was a
500 Hz pure tone, and the second tone differedby set intervals of hertz (96, 48, 24, 12, 6, 3, 1.5 Hz,etc.), with the between-stimulus pitch difference
either increasing or decreasing based on participants’ responses. For example, at the 12 Hz
interval,the first tone was 500 Hz and the second
tone 512 Hz. Each tone was 500 ms in length andtones were separated by a 250 ms pause. Loudness was adjusted to an adequate level foreach participant prior to the task. The resultingscore represented the degree to which a listener
could reliably distinguish the paired tones, withgreater sensitivity indicated by a lower score.
Pitch accent perception. Two perceptiontasks using Japanese sentences were the dependent variables. First, participants judged if anoun followed by a postposition was spoken
with the correct or incorrect accent pattern(below, PitchID). Second, for the correct
items only, they categorized the noun-pluspostposition into one of four pitch contours ina four-alternative forced choice (AFC) task
(PitchCAT). A total of 32 test sentences werecreated, all of which were of the structure© Japanese Psychological Association 2014.

148

S. Goss and K. Tamaoka(N + postposition + V, e.g., 一人で行く, Hitori deiku, “I go alone”). Half of these stimuli (N = 16)
were spoken with the correct accent and theother half with an incorrect accent. Stimuli
were composed of a mixture of three- and fourmora nouns featuring one of four accentpatterns (for 3-mora words: HLL-L, LHL-L,
LHH-L and LHH-H; 4-mora words: HLLL-L,
LHLL-L, LHHH-L, LHHH-H).4 Words were
controlled by frequency across each of the four
accent types based on the NTT database of
approximately 300 million words (Amano &Kondo, 2000). Total mean normative frequency
for all stimuli was 36.6 per million (SD = 52.6)
and by pattern as follows: Pattern 1 (M = 35.7,
SD = 54.0), Pattern 2 (M = 15.1; SD = 15.1),
Pattern 3 (M = 47.7, SD = 67.5), Pattern 4(M = 46.3; SD = 61.4). Although the normative
frequencies appeared to vary between patterns, this difference was not significant, F(3,28) = 0.633, p = .600. Accent validity ratings
were also checked in the same database, which
revealed that three words failed to reach 100%inter-rater agreement among Tokyo-dialect
speakers, indicating the presence of a variant
accent pattern for each of these items (Amano& Kondo, 1999). We therefore removed thesewords from our analysis of the PitchCAT
task, yielding 13 items, as listeners may havecategorized targets based not on the accent of1

the spoken stimuli, but on their own lexical
representation.5 Lastly, all carrier sentenceswere controlled to a mean length of 6.7 mora
(SD = 0.52). Refer to Appendix for a complete
list of test words, including the excluded items.
Stimuli were sampled at 44 100 Hz by anative speaker of Tokyo-standard Japanesewith extensive training in accent production.
No acoustic modification was made to thetest stimuli, although, pitch waveforms werechecked in Praat sound-analysis software toconfirm that accent location matched theintended pattern (Boersma & Weenink, 2008).
Participants were instructed to focus on thesentence-initial (N + postposition) in making
their decisions. After a correctly accented word
was presented and the correctness judgment
made, four pitch graphs corresponding to thefour possible pitch contours were immediately
displayed onscreen. Figure 1 shows an exampleof the categorization graphs. The participants’task was to select the pitch graph that they felt
most closely fit the accent pattern of the spoken
target.
Procedure
Participants completed four tasks in the following order: (a) acoustic pitch sensitivity test, (b)
SNWR task (verbal WM), (c) the combined
lexical accent correctness judgment (PitchID),

2

3

4

仕 事を（探す）
Figure 1 In the PitchCAT task, listeners categorized the noun-plus-postposition (/shigoto o sagasu/ “(I)search for a job”) into one of four pitch contours by selecting a visual representation of the pitch pattern. Only
the target noun-plus-preposition shigoto o was presented visually.

4

Note that for 4-mora words, a fifth pattern (LHHL-L)
exists in Tokyo-standard Japanese, but was not
used in order to maintain the structure of the
4-response PitchCAT task.

© Japanese Psychological Association 2014.

5

We thank an anonymous reviewer for confirming
the accent validity ratings. Note that all 32 words
were retained for the PitchID task, as their removal
did not affect perception accuracy.

Lexical accent perception in L1 Japanese

Table 1

149

Descriptive statistics for all variables

Variable

Max

M

SD

Pitch sensitivity
Verbal working memory
PitchID
PitchCAT
PitchID reaction time (ms)

–
144
32
13
–

2.49
84.07
29.77
7.93
2,215.00

1.44
17.34
1.63
2.97
233.00

Range
5.00
50.00
26.00
3.00
1816.00−0.91
117.0032.00
13.00
2683.00

Note. Three items were removed from the original 16 words in the PitchCAT analysis.

and (d) categorization (PitchCAT) tasks.
Besides the web-based pitch sensitivity test, thestimuli were presented in E-Prime 2.0 experimental software. Responses were made via a
response box, with the WM and PitchID tasks
requiring a two-button Yes/No answer, and the
PitchCAT task using a four-button response.
Reaction time (RT) was measured on the
PitchID task, and participants were instructed
to respond as rapidly as possible after the
spoken stimulus ended. Tasks were presented
aurally with headphones in a quiet room. All
tasks were preceded by a short practice phaseto familiarize participants with the procedures,with further explanation provided by the
researcher if participants were unclear about
the task procedures. Due to the complexity of
the PitchCAT task in particular, participants
were familiarized with sample pitch contour
graphs (Figure 1) similar to those in the maincategorization task during the practice phase.
All participants stated that they were clear
with the categorization process prior to beginning the main experiment. Participants tookapproximately 20 min to complete all experimental tasks.

Results
Descriptive results for all tasks in the order ofpresentation are shown in Table 1. Pitch sensitivity scores reflected the average threshold at
which participants were able to distinguish
two pure tones. Thus, the lower the numericalscore, the more sensitive the listener was topitch variations. Scores were log2-converted foranalysis in order to regularize the Hz intervals
and obtain a more normal distribution. Verbal
WM was scored by a weighted measurement,

assigning points to correct responses based on
the length of the sets, with the longer sets (i.e.,
those containing more nonwords) yielding ahigher point total (see O’Brien et al., 2006 forthe scoring procedure).
Participants successfully judged 93% of
the spoken noun-plus-postposition targets as
having either the correct or incorrect accentpattern (PitchID). However, only 61% of thecorrectly accented words were accurately categorized by visual pattern on the four-choicecategorization task (PitchCAT). The RT in milliseconds from the onset of the audio in the
PitchID task was also measured as a predictor
for PitchCAT performance.
Correlation coefficients for the predictor
variables and the PitchID and PitchCAT tasks
are presented in Table 2. Significant negative
correlations were found between pitch sensitivity in both the PitchID (r = −.42, p < .05) andPitchCAT tasks (r = −.42, p < .05), indicatingthat those with a lower acoustic sensitivity
threshold were more accurate on the spoken
word perception tasks. However, no significant
correlations were found for verbal WM capacity on either task. The RT for PitchID was also
negatively correlated with participants’ performance on the PitchCAT task, suggesting that
listeners who made faster correctness judgments were in turn more accurate at categorizing accent patterns. Note also that PitchID
accuracy was not correlated with PitchCATscore, a finding which will be discussed later.
A multiple regression analysis was next conducted with pitch sensitivity and verbal WM
entered as predictors for PitchID performance.
As shown in Table 3, these two variables
accounted for 27% of the variance in participants’ accent correctness judgments, with pitch© Japanese Psychological Association 2014.

150

S. Goss and K. Tamaoka

Table 2

Correlation matrix for all test variables

Variable

1

2

3

4

5

1. Verbal working memory
2. Pitch sensitivity
3. PitchID
4. PitchCAT
5. PitchID reaction time

–
.28
.18
−.11
.04

–
−.42*
−.42*
−.20

–
.01
−.06

–
−.39*

–

*p < .05.

Table 3

Multiple regression results for the PitchID and
PitchCAT tasks

Task

Predictors

PitchID

R

R2

.52

.27*

Pitch sensitivity
Verbal working memory
PitchCAT.53
Pitch sensitivity
PitchID reaction time
Verbal working memory

β

t

−.51
.32

−2.99**
1.87

−.35
−.33
.03

−1.99*
−1.91
0.16

.28*

**p < .01. *p < .05.

sensitivity being identified as a highly significant predictor (β = −.51, p < .001), and verbal
WM capacity approaching significance (β = .32,
p = .073). Likewise, we created a separate
regression model with three predictors for
PitchCAT: pitch sensitivity, verbal WM, andPitchID RT. Table 3 shows that these three variables accounted for 28% of the variance inaccent categorization, with participants’ pitch
sensitivity score again a significant predictor(β = −.35, p < .05) and RT approaching significance (β = −.33, p = .067). However, verbal
WM failed to attain significance as a predictor(β = .03, p = .875).
We next wished to explore listeners’ error
patterns on the perception tasks, as specificaccent types may have influenced overall performance. Table 4 provides accuracy scoresby accent pattern for both the PitchID and
PitchCAT tasks. On the PitchID task, accuracy
scores on Pattern 3 words (82.5%) were markedly lower than for the other three accent patterns. For the four-choice categorization task,
initial-mora accented Pattern 1 (HLL-L) words
were categorized with 72.5% accuracy, but
noticeably dropped on the remaining patterns.
© Japanese Psychological Association 2014.

Although the predicted correlation between
verbal WM capacity and the more demanding
PitchCAT task was not found, we next wanted
to determine whether WM related to accuracy
on any specific accent pattern in this task. When
analyzed by pattern, a significant correlation(r = .476, p < .01) was found only for Pattern 4(unaccented, or LHH-H) words, suggesting thatmemory span for unaccented nonwords related
to categorization ability of real words of thesame pitch pattern. To further explore performance on the unaccented stimuli in the
PitchCAT and PitchID tasks, we then separated
participants into three even groups (n = 10)
according to the highest (M = 102.9, SD = 9.43),
middle (M = 83.8; SD = 5.26), and lowest(M = 65.5; SD = 8.68) verbal WM scores,
F(2, 27) = 54.61, p < .001. A one-way ANOVA
revealed that the WM groups differed significantly in their categorization of Pattern 4
words, F(2, 27) = 4.049, p < .05, with the Highspan group (p < .05) and Mid-span group(p = .064) outperforming the Low-span group.
By contrast, no group difference was foundfor Pattern 4 words on the PitchID task,
F(2, 27) = 1.00, ns, likely due to a performanceLexical accent perception in L1 Japanese151

Table 4 Overall responses by accent pattern on both accent
perception tasks
Task

TargetCorrect responses

Incorrect responses

(n)

%

(n)

%

Total

PitchID

Pattern 1
Pattern 2
Pattern 3
Pattern 4

220
235
198
239

91.67
97.92
82.50
99.58

20
5
42
1

8.33
2.08
17.50
0.42

240
240
240
240

PitchCAT

Pattern 1
Pattern 2
Pattern 3
Pattern 4

87
49
59
67

72.50
54.44
61.66
55.83

33
41
61
53

27.50
45.55
38.33
44.17

120
90
60
120

Note. PitchID consisted of 120 correctly accented and 120 incorrectly
accented items. PitchCAT featured only the correctly accented items,
yielding 120 total items. One Pattern 2 item and two Pattern 3 items
were removed from the analysis.

ceiling effect for this pattern (99.5% mean
accuracy). Thus, the group analysis suggested
that serial recognition of unaccented nonwords
on the WM task was predictive of categorization performance for Pattern 4 words only,
which were spoken with the same Low-High
unaccented pitch pattern.
To summarize the results overall, L1 Japanese speakers were highly accurate at making
correctness judgments on the accent patterns of
spoken words, while greater individual variation was observed in the categorization task.
Of particular interest was that listeners withgreater sensitivity to nonlinguistic pitch contrasts on the adaptive pitch test were superior
at both judging and categorizing lexical accents.
Although verbal WM was not predictive of
overall accuracy, when stimuli were analyzed
by pattern, listeners with a higher verbal WM
span for unaccented nonwords were better at
categorizing real words with the same accentpattern (i.e., Pattern 4).

Discussion
The present study examined three predictions
regarding Japanese speakers’ ability to perceive
lexical pitch accent. We first considered theeffect of task type on perception accuracy
scores for lexically accented words. Then wemeasured acoustic pitch sensitivity and verbal
WM capacity as two possible predictors of perception task accuracy.
First, native Japanese speakers were accurate (M = 93%) at determining whether highfrequency noun-plus-postposition targets werespoken with the correct or incorrect pitchaccent (PitchID). This finding corroborates
with previous perception research which indicates that pitch and tone are accessed alongwith word form by native listeners (Otake &Cutler, 1999; Shibata & Hurtig, 2008). Contrast
this with the categorization task (PitchCAT),on which listeners were only able to accurately
categorize 61% of the targets. Despite participants reporting familiarity with task instructions, accuracy rates were similar to thosereported in previous studies using accentpattern comparison or categorization tasks(e.g., Hirano-Cook, 2011; Shport, 2008).
Although the task design makes direct comparison of the results problematic (1-of-2 correctness judgment vs. 1-of-4 categorization), we
can interpret the performance discrepancy andresulting lack of correlation between the twotasks as suggestive of the differences in processing demands placed on L1 listeners by thetwo tasks, despite having well-developed native
lexical stores. In other words, accent correctness judgments were intuitively possible for© Japanese Psychological Association 2014.

152

S. Goss and K. Tamaoka

Japanese speakers, but categorization, in which
participants’ held a spoken stimulus in WM
while selecting a visual representation of theaccent pattern, likely placed a high processing
load even on native listeners. By comparison,
Ueno et al. (2014) recently found that L1 Japanese speakers could repeat aloud single, lexically accented words at near-perfect accuracy
on an immediate imitation task, echoing ourparticipants’ high scores on the speeded
PitchID task. Thus, the present findings suggestthat the low categorization accuracy and wide
variation (ranging from 23 to 100%) werepotentially a byproduct of this two-step decision process itself, rather than a reflection ofparticipants’ reduced perceptual ability per se.
Accordingly, conclusions that pitch accent is
not a salient feature in spoken Japanese based
solely on categorization tasks are unwarranted.
Second, the multiple regression analysis
showed that acoustic pitch sensitivity significantly predicted accuracy for the PitchID and
PitchCAT tasks. In other words, listeners who
were highly sensitive to subtle, nonlinguistic
pitch variations performed better on bothlexical accent perception measures, regardlessof the differences in task demands pointed out
above. The fact that a nonlinguistic capacity
predicted lexical perception ability is in linewith speech perception models which suggestthat speech is processed in a bottom-up fashion,
whereby spoken words are processed by listeners starting with an acoustic analysis of the
input, prior to the availability of higher level
lexical or contextual information (e.g., Norris,
McQueen, & Cutler, 2000; Warrier & Zatorre,
2004). This finding also provides evidence
for shared perceptual resources for pitch andtone in both speech and nonspeech sounds.
Although all participants reported normal
hearing and no extensive music training, bothof which may relate to pitch perception (e.g.,
Deutsch et al., 2009), a significant degree ofvariation was found that could be explained bydifferences in general acoustic sensitivity. Thisfinding runs contrary to models which assume
that adult perception of native language phonological categories is the product of early-age
L1 experience and unrelated to basic auditory© Japanese Psychological Association 2014.

capacities (e.g., Iverson & Kuhl, 1996; Strange& Shafer, 2008). Although we do not disputethat language experience in infancy is theprimary source of adult perceptual acuity in the
L1, in the case of lexical accent, the phonetic
similarity between the tones in the acoustic sensitivity measure and accent perception tasks inthe present study may have contributed to thepredictive power of this nonlinguistic capacity.
Further, because pitch accent is considered tohave a relatively low psychoacoustic salience,in that its phonetic cue of pitch height is not asprominent as, for example, the length contrasts
found in Japanese vowels and consonants, anonlinguistic capacity may enhance perceptionof this relatively subtle feature (Bohn, 1995).
Lastly, the role of verbal WM in accent perception was negligible in terms of overalltask performance. That our serial recognition
task required participants to focus on thelinear order of the nonword stimuli, rather
than explicitly on their sound properties, may
account for this finding. Yet, we did find that
WM capacity significantly predicted performance on Pattern 4 (LHH-H pitch) words,
which were identical in accent pattern with thatused in the nonword recognition task. Interestingly, this echoes the connection between pitchaccent contributing to recall ability that has
been noted in previous research (e.g., Yuzawa& Saito, 2006), albeit in the direction thatmemory for nonwords is predictive of word perception only for targets of the same accentpattern, suggesting a possible pattern-specificeffect for pitch memory. Future studies should
employ verbal WM measures featuring avariety of accent patterns to confirm if indeed
pitch memory is selectively predictive of lexical
accent pattern. In addition, further explorationof the role of verbal WM in tone languages like
Mandarin, in the form of tasks which specifically target pitch memory, is needed to shedlight on this issue.
In conclusion, the present study showed that
L1 Japanese listeners can reliably make correctness decisions on lexically accented spoken
stimuli. However, categorizing words by theiraccent pattern in a task requiring phonetic comparison with visual pitch contours proved to beLexical accent perception in L1 Japanesemore difficult. Importantly, acoustic pitch sensitivity, a general cognitive ability, significantly
predicted performance variation on bothexperimental tasks. In addition, verbal WM
capacity predicted perception ability only in an
accent pattern-specific manner on the memoryintensive categorization task. This pattern offindings suggests that even among adult L1
Japanese listeners, task demands contributeto performance variation, and domain-general
cognitive resources continue to support the perception of speech sounds involving lexical-level
accent.

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Appendix
Target words (N = 32) and postpositions with carrier sentences used in the PitchID and
PitchCAT tasks
医学を学ぶ igaku o manabu “(I) study medicine”
めがねをかける megane o kakeru “(I) put on glasses”
フォークで食べる fooku de taberu “(I) eat with a fork”
おもちゃで遊ぶ omocha de asobu “(I) play with a toy”
八時に起きる hachiji ni okiru ‘(I) wake up at 8 o‘clock’
中身を見る nakami o miru “(I) look at the contents”
ななめにする naname ni suru “(I) turn it sideways”
言葉にする kotoba ni suru “(I) put it into words”
夜中に起きる yonaka ni okiru “(I) wake up at midnight”
いなかに住む inaka ni sumu “(I) live in the countryside”
ハガキを出す hagaki o dasu “(I) send a postcard”
手前に引く temae ni hiku “(I) pull it toward me”
来月に行く raigetsu ni iku “(I) go next month”
あさってまで待つ asatte made matsu “(I) wait until

家内に話す kanai ni hanasu “(I) speak to my wife”
技術をみがく gijutsu o migaku “(I) polish my skills”
荷物を運ぶ nimotsu o hakobu “(I) carry luggage”
あなたがいる anata ga iru “You are there”* ハサミがある hasami ga aru “There are scissors”* 刺身を食べる sashimi o taberu “(I) eat sashimi”
昼間に行く hiruma ni iku “(I) go in the afternoon”
娘がいる musume ga iru “(I) have a daughter”
男がいる otoko ga iru “There is a man”
左に曲がる hidari ni magaru “(I) turn left”
昔に戻る mukashi ni modoru “(I) return to the past”
仕事を探す shigoto o sagasu “(I) search for a job”
タクシーに乗る takushii ni noru “(I) get in a taxi”
飛行機に乗る hikooki ni noru “(I) ride in an airplane”

the day after tomorrow”* 九日に行く kokonoka ni iku “(I) go on the 9th”
夕方になる yuugata ni naru “It becomes evening”

妹がいる imooto ga iru “(I) have a younger sister”
地下鉄に乗る chikatetsu ni noru “(I) ride the subway”

Note. Items preceded by an asterisk were removed from the PitchCAT analysis.

© Japanese Psychological Association 2014.