Wong&Wong2013 AP ms - HKU - Faculty of Educationweb.edu.hku.hk/f/acadstaff/3661/Wong&Wong2013_AP_… · · 2016-10-27of the Associated Board of the Royal Schools of Music (ABRSM)

Absolute Pitch Memory 1

Absolute pitch memory: Its prevalence among musicians and

dependence on the testing context

Yetta Kwailing Wong1* & Alan C.-N. Wong2*

Department of Applied Social Studies, City University of Hong Kong, Kowloon Tong,

Hong Kong1

Department of Psychology, The Chinese University of Hong Kong, Shatin, Hong Kong2

Citation: Wong, Y. K. & Wong, A. C.-‐N. (2014). Absolute pitch: Its prevalence among musicians and

dependence on the testing context. Psychonomic Bulletin & Review, 21(2), 534-‐542

Word count: 3990

Keywords: pitch processing, auditory, music training, context, multimodal, expertise

*Corresponding Authors: (1) Yetta Kwailing Wong Y7414, Academic Building I, Department of Applied Social Studies City University of Hong Kong Tat Chee Avenue, Kowloon, Hong Kong Email: [email protected] Phone number: +852-3442-7073 (2) Alan C.-N. Wong 344 Sino Building Department of Psychology The Chinese University of Hong Kong, Shatin, Hong Kong Email: [email protected] Phone number: +852-3943-6505


Abstract

Absolute pitch (AP) is widely believed to be a rare ability possessed by only a small

group of gifted and special individuals (‘AP possessors’). While AP has fascinated

psychologists, neuroscientists and musicians for more than a century, no theory can

satisfactorily explain why this ability is so rare and difficult to learn. Here we show that

AP ability appears rare because of the methodological issues of the standard pitch-

naming test. Specifically, the standard test unnecessarily poses a high decisional demand

on AP judgments and uses a highly inconsistent testing context to one’s musical training.

These extra cognitive challenges are not central to AP memory per se, and have thus led

to consistent underestimation of AP ability in the population. Using the standard test, we

replicated the typical findings that the accuracy for general violinists was low (12.38%;

chance level = 0%). With identical stimuli, scoring criteria and participants, violinists

attained 25% accuracy in a pitch-verification test in which the decisional demand of AP

judgment was reduced. When the testing context was increasingly similar to their musical

experience, verification accuracy improved further and reached 39%, three times higher

than that for the standard test. Results were replicated with a separate group of pianists.

Our findings challenge current theories about AP, and suggest that the prevalence of AP

among musicians has been highly underestimated in prior work. A multimodal

framework is proposed to better explain AP memory.


Introduction

To the majority of people, labeling the pitch of an isolated tone is difficult

(Takeuchi & Hulse, 1993), unless they are given an external pitch reference beforehand

(Ward, 1999). Years of explicit musical training do not make this task any easier for

professional musicians (Athos et al., 2007; Levitin & Rogers, 2005; Zatorre, 2003). A

small group of people, however, can label or produce isolated tones accurately and

effortlessly. They are typically called ‘absolute pitch (AP) possessors’, and conventional

estimates suggest that only 1 out of 10000 people have this ability (Takeuchi & Hulse,

1993). This remarkably rare ability has been considered a special talent and musical

endowment for gifted musicians (Deutsch, 2002; Ward, 1999; but see Miyazaki, 1993),

and has fascinated musicians, psychologists and neuroscientists for more than a century

(Deutsch, 2002; Levitin & Rogers, 2005; Takeuchi & Hulse, 1993; Ward, 1999).

Different theories have been proposed to explain the differences between ‘AP

possessors’ and ‘non-possessors’. One theory suggests that, in adulthood, pitch memory

is organized along a free-floating helix, so it is impossible for general musicians to name

a pitch without external reference (Ward, 1999). Only for ‘AP possessors’, the helix is

somehow well anchored with a permanent pitch label, which explains their ease in

absolute pitch labeling (Ward, 1999). However, this account does not explain why the

general population has highly precise pitch memory for familiar songs and recordings of

popular television shows (Halpern, 1989; Levitin, 1994; Schellenberg & Trehub, 2003).

In contrast, a widely accepted working hypothesis suggests that general listeners

have AP memory to a considerable extent (Halpern, 1989; Levitin, 1994; Schellenberg &

Trehub, 2003), while only the ‘AP possessors’ can associate the represented pitches with


verbal names (Brancucci et al., 2009; Deutsch, 2002; Levitin & Rogers, 2005;

Schellenberg & Trehub, 2003; Vanzella & Schellenberg, 2010). Nonetheless, the puzzle

remains as to why general listeners encounter such a great difficulty associating

represented pitches with names. Associating names with established concepts should be a

trainable skill, as demonstrated by children learning to name familiar objects in new

languages (Gathercole & Baddeley, 1990), and adults learning to name novel objects with

non-words within hours (Wong, Palmeri, & Gauthier, 2009; Wong, Folstein, & Gauthier,

2011). Why, then, do general musicians, after having spent years of explicit training in

associating pitches with labels, still fail to name the pitches that are represented in

auditory memory? Why did most of the intensive AP training in adulthood result in

limited success (Brady, 1970; Cuddy, 1970; Takeuchi & Hulse, 1993; Ward, 1999)? Is

the low AP performance of general musicians really an issue of naming, or does it stem

from other causes?

To address this question, let us consider how AP ability is typically measured. To

express one’s AP ability, one can label a sounded note verbally (e.g., this tone is a ‘C’),

produce a specific pitch by singing, reproduce a sounded note on an instrument, etc.

(Takeuchi & Hulse, 1993; Zatorre, 2003). Among these, the most common and standard

way to assess AP ability is by the pitch-naming test, in which observers name the pitch of

tones (mostly sine wave tones) presented in isolation (Athos et al., 2007; Takeuchi &

Hulse, 1993; Zatorre, 2003).

There are at least two reasons why this standard pitch-naming test may be sub-

optimal. Firstly, to name a tone, listeners have to choose a name out of twelve

possibilities (the twelve semitones in the Western music scale). However, it has been


shown in other cognitive tasks that, with the stimulus set held constant, handling more

alternatives places a higher decisional demand on the listeners, leading to lowered

performance (Churchland, Kiani, & Shadlen, 2008; Niwa & Ditterich, 2008). Secondly,

the testing context is highly inconsistent with musicians’ experience. Context here does

not mean an external reference (e.g., another tone and its pitch label as in relative pitch

tasks); instead it refers to the circumstances involved in the presentation of a tone. For

example, with extensive training, musicians represent musical notes as multimodal

objects by automatically integrating information from visual, auditory, somatosensory

and motor modalities (Wong & Gauthier, 2010; Zatorre & Beckett, 1989; Zatorre, Chen,

& Penhune, 2007). However, the standard test eliminates much of the important

information that is integrated into the pitch concept, such as the timbre, the playing

posture, the fingering associated with the tone, and the visual image of the fingering and

the instrument. Such a difference in context between learning and testing impairs

performance in memory tasks (Godden & Baddeley, 1975; Smith & Vela, 2001). Even

for ‘AP possessors’, performance are impaired with contextual inconsistencies during

testing, such as the use of different timbres and pitch registers (Levitin & Rogers, 2005;

Takeuchi & Hulse, 1993). If we accept AP as simply the ability to identify the pitch of an

isolated tone (Athos et al., 2007; Deutsch, 2002; Levitin & Rogers, 2005; Schellenberg &

Trehub, 2003; Takeuchi & Hulse, 1993; Vanzella & Schellenberg, 2010; Ward, 1999;

Zatorre, 2003), the high decisional demand and the contextual inconsistencies between

testing and one’s musical experience seem extraneous and may have unnecessarily

prevented general musicians from expressing their AP memory.


To test this hypothesis, we devised a pitch verification test for assessing AP

ability. Participants judged if an isolated tone matched with a given pitch label. The pitch

label was either the same as the presented tone, or differed by one or three semitones.

This verification test used the most stringent scoring standard for AP ability (Takeuchi &

Hulse, 1993; Zatorre, 2003). Firstly, it used isolated tones without any external pitch

reference. Secondly, we calculated the pitch verification accuracy with only the most

difficult trials, those with one semitone difference between the label and the tone.

Failures to discriminate between neighboring semitones (e.g. treating a ‘C’ as a ‘C#’)

were regarded as errors, the most stringent scoring standard used in the literature

(Takeuchi & Hulse, 1993; Zatorre, 2003; Deutsch et al., 2006; Lee & Lee, 2010). The

major advantage of the verification test is that the number of possible answers is reduced

to two (match or non-match), thus reducing the decisional demand on the participants.

All participants also performed the standard test and were scored with an identical

standard for assessing how the difference in decisional demand affects AP performance.

To examine whether contextual consistency affects AP judgment in general

musicians, we manipulated testing contexts during verification. In Experiment 1, 36

violinists took the test at four levels of increasing contextual similarity to musical

experience. The basic level used sine wave tones identical to those used in pitch naming.

The second level used tones in violin timbre. At the third level, participants viewed video

clips showing another person playing the labeled pitch, which either matched with the

testing violin tones or not. At the fourth level, participants held the playing posture and

correct fingering themselves without producing any sound according to the labeled pitch,

which either matched with the testing violin tones or not. It was predicted that, with


identical sine wave tones, performance of general musicians (‘non-AP possessors’ using

conventional definition) should be better in the verification test compared with the

standard naming test because of the relief in decisional demand. Their performance

should further improve when the testing context becomes more similar to one’s musical

experience. In Experiment 2, we replicated the experiment with a separate group of 34

pianists using similar procedures, except that the video clip context was not introduced.

Experiment 1

Methods

Participants

Thirty-six violinists (13 males, 23 females; Mage = 21.6, s.d. = 3.0) were recruited

in Hong Kong. On average, the violinists started violin lessons at 8 years old and had

12.8 years of playing experience. All had passed the Grade Seven examination or above

of the Associated Board of the Royal Schools of Music (ABRSM). All participants

reported normal or corrected-to-normal vision and hearing. They gave informed consent

according to the ethics guidelines of the Chinese University of Hong Kong, and were

remunerated for their participation.

Materials and Stimuli

The experiment was conducted on Mac Minis using Matlab (Natick, MA) with

the PsychToolbox extension (Brainard, 1997; Pelli, 1997). Eighteen violin tones from D4

to G5 played by a volunteer violinist were videotaped in a soundproof room. For each

tone, the playing posture was identical, and the fingering and the movement of the bow


could be seen clearly. A microphone was put at about 8cm from the violin and directly

connected to a PC, which recorded the violin tones in Audacity 1.3. The sampling rate of

the tones was 44100Hz. No quantization was performed as the precision of the tones was

checked during recording by a tuner. Sine wave tones in the same pitch range and

identical to those in prior AP tests (Bermudez, Lerch, Evans, & Zatorre, 2009) were used.

All clips were edited in Audacity such that they lasted for 1 second with a 0.1-second

linear onset and 0.1-second linear offset and were of the same magnitude.

Procedure

Pitch Verification Test. Participants first took a pitch verification test. In

each trial, a pitch name was presented on a computer screen. When the participant was

ready, the experimenter pressed a key to start the tone. Participants judged if the tone

matched with the pitch name within 6 seconds. The pitch name matched with the tone for

half of the trials. For the other half, the pitch names were either -3, -1, +1 or +3 semitones

from the presented tones with equal probabilities. Ten practice trials with sine wave tones

were provided with feedback before the test. There was no feedback during the test.

The violinists took the verification test in four levels of contextual similarity to

music training experience. The first level used sine wave tones (‘sine’). The second level

used tones in violin timbre (‘timbre’). For the third level (‘video’), a pitch label was

presented with a silent video clip showing another violinist playing the labeled pitch on a

violin. The fingering of the violinists could be clearly seen and always matched with that

required for the pitch label. A violin tone was presented simultaneously with the video

and the label. Participants had to verify whether the violin tone they heard was the same


or different from that indicated by the pitch label and the video. For the fourth level

(‘posture’), when the pitch name was presented, participants held the correct fingering

and playing posture corresponding to the pitch label without the bow touching the string

and producing any sound. When the posture was ready, the experimenter played a violin

tone and participants judged whether the tone matched with the pitch name or not. The

condition order was counterbalanced across participants. Participants brought their own

violin to the experimental session. There were 144 trials for each condition and thus 576

trials in total.

Standard Pitching-Naming Test We created an abridged version of the

standard pitch-naming test (Takeuchi & Hulse, 1993; Zatorre, 2003). Sine wave tones

from B3 to A#5 were used since this pitch range was the most familiar for violinists. In

each trial, a tone was presented, followed by a picture showing the 12 possible pitch

names coupled with 12 letter keys. Participants named the pitches by key presses with no

time limit. No feedback was provided. Each pitch was tested twice and there were 48

trials in total, presented in a randomized order.

Analyses

The most stringent scoring standard was used in both verification and pitch-

naming, in which failures of discriminating between neighboring semitones (e.g. treating

a ‘C’ as a ‘C#’) are regarded as errors (Takeuchi & Hulse, 1993; Zatorre, 2003). To

match the difficulty level between the two tests, only trials with the pitch name deviating


from the presented tones at -1, 0, or +1 semitones were used for the calculation of

accuracy for pitch verification.

Performance was measured using two dependent variables: (i) accuracy corrected

for guessing and (ii) sensitivity according to the signal detection theory.

First, accuracy corrected for guessing was included for comparing the naming and

verification tests since the chance-level performance for the two tasks was different. For

accuracy in pitch naming, chance performance was 8.33% (12 alternatives), and

correction was conducted using Percent Correct = [(Raw Percent Correct – 8.333)/(100

– 8.333)]×100. For accuracy in verification, chance level was 50% (match/mismatch),

and thus the following formula was used: Percent Correct = [(Raw Percent Correct –

50)/(100 – 50)]×100. After correction for guessing, the measure offered the same metric

for comparing performance in naming and verification tests (i.e., 0% and 100%

representing chance and perfect performance respectively).

A potential shortcoming of the use of percent correct is that it is unknown if the

effects were driven by differences in sensitivity or in bias. Therefore sensitivity was also

used to compare between different context conditions (note that there was no A’ for the

naming test). We used A’ as a non-parametric measure of sensitivity according to the

signal detection theory without the assumption of normality or that of equal variance

(Stanislaw & Todorov, 1999, Wong et al., 2011, 2012). It is calculated as:

€

A'= .5 + sign(H − F)(H − F)2+ |H − F |4max(H,F) − 4HF

#

$ %

&

' (

where H and F represent hit rate and false alarm rate respectively.


Results

Using the standard naming test, we replicated the typical observation in the

literature. Seven violinists were identified as typical ‘AP possessors’ with 70% accuracy

or above (for prior work adopting similar accuracy level for identifying ‘AP possessors’

without correction for guessing, see Athos et al., 2007; Bermudez & Zatorre, 2009;

Levitin & Rogers, 2005; Takeuchi & Hulse, 1993). As shown in Figure 1A, the

remaining 29 violinists had a low accuracy in naming sine wave tones, with a median of

6.82% and a mean of 12.38% (Fig. 1A), consistent with past observations that only a

small group of individuals perform well in the standard pitch naming test (Athos et al.,

2007; Takeuchi & Hulse, 1993).

We excluded data from the seven AP possessors due to the obvious ceiling issue

and analyzed the data from the remaining 29 violinists. Accuracy was reported in terms

of both percent correct and A’, except when comparisons with the standard naming test

were involved (as there was no A’ for the naming task).

------------------------

Insert Figure 1 here

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The verification test showed much better performance than the standard naming

test. Accuracy improved drastically from 12.38% in pitch-naming to 24.65% in

verification of sine-wave tones, [t(28)=3.48, p=.0017, d=.64], indicating that simply

reducing the decisional demand allowed violinists to better express their AP ability.

Further analyses revealed the importance of a musical testing context in

measuring AP ability. Accuracy for ‘posture’ was better than that for ‘sine’ [% correct:


t(28)=4.28, p=.0002, d=1.39; A’: t(28)=3.90, p=.0005., d=.72], ‘timbre’ [% correct:

t(28)=3.38, p=.0021, d=.62; A’: t(28)=3.21, p=.0032., d=.59] and ‘video’ [% correct:

t(28)=2.33, p=.0270, d=.43; A’: t(28)=3.37, p=.0022, d=.62]. Although performances for

the latter three conditions were not significantly different (% correct: ps > .12; A’:

ps>.62), a trend analysis showed that verification performance improved linearly when

the testing context was increasingly similar to their own musical experience, from ‘sine’,

‘timbre’, ‘video’ to ‘posture’ [% correct: 24.65%, 26.69%, 31.35%, 39.08%, t(28)=4.23,

p=.0002; A’: .680, .683, .694, .760, t(28)=3.37, p=.0021].

As a group, the violinists performed better than chance level in all conditions

(ps<.0001). Notably, for the best-performing third of the violinists in ‘posture’, accuracy

averaged 66.11%, approaching the conventional definition of the ‘AP possessors’ (Athos

et al., 2007; Bermudez et al., 2009; Levitin & Rogers, 2005; Takeuchi & Hulse, 1993).

The standard test was not sensitive enough to detect the AP ability possessed by

some of the violinists. In the standard pitch-naming test, the accuracy of the lower-

performing third of the violinists was not different from chance (mean = 0.25%, p=.681).

Yet in the verification test, the accuracy of the same participants became significantly

better than chance with identical sine wave tones (mean = 9.05%, p=.001), and rose to

22.43% in the posture context. In other words, their AP ability, which is reliably above

chance, can be observed with appropriate testing contexts, but not with the most

commonly used standard AP test.

In sum, simply reducing the decisional demand allows general musicians to better

express their AP ability, and performance further improves with musical testing contexts.

This is in stark contrast to the typical observation that only a small group of individuals


possesses AP (Athos et al., 2007; Takeuchi & Hulse, 1993). Next, we examined if these

results could be replicated with a separate group of pianists.

Experiment 2

Method

Thirty-four pianists (4 males, 30 females; Mage = 20.0, s.d. = 1.84) were recruited

in Hong Kong. On average, they started learning piano at 6.4 years old and had 12.5

years of playing experience. All had passed the Grade Eight examination or above of the

ABRSM. The testing methods and procedures were identical to that of Experiment 1

except for the following. First, 18 piano tones of the same pitches as the violin tones

recorded with an electric keyboard (Yamaha S31) were used. Second, the pianists

performed in the sine, timbre and posture conditions (with the video condition dropped)

with an electric keyboard (Yamaha S31). There were 432 trials in total in the verification

test.

Results

Results in Experiment 1 were replicated with pianists. First, using the standard

pitch-naming test, we identified three pianists as ‘AP possessors’ with 70% accuracy or

above (Athos et al., 2007; Bermudez et al., 2009; Levitin & Rogers, 2005; Takeuchi &

Hulse, 1993). As shown in Figure 1B, the remaining 31 pianists showed a typical low

accuracy, with a median of 4.55% and a mean of 11.07%. We excluded data from the

three AP possessors due to the obvious ceiling issue and analyzed the data from the

remaining 31 pianists. Accuracy was again reported in terms of both percent correct and


A’, except when comparisons with the standard naming test were involved (as there was

no A’ for the naming task).

Performance in the verification test for sine wave tones was 21.27%, significantly

higher than that in the standard naming test [t(30)=2.93, p=.0063, d=.52], indicating the

importance of reducing the decisional demand of the AP test.

Furthermore, accuracy for ‘posture’ was higher than that for ‘sine’ [% correct:

t(30)=3.04, p=.0048, d=.54; A’: t(30)=3.35, p=.0022, d=.60], and marginally higher than

that for ‘timbre’ [% correct: t(30)=1.84, p=.0755, d=.33; A’: t(30)=2.94, p=.0967, d=.30].

Trend analysis indicated that verification accuracy increased linearly from ‘sine’,

‘timbre’ to ‘posture’ [% correct: 21.27%, 27.60%, 31.96%, t(30)=3.27, p=.0026; A’:

.657, .696, .728, t(30)=3.19, p=.0033].

As a group, the pianist performed with above-chance accuracy in all conditions

(ps<.005). For the best-performing third of the pianists in ‘posture’, accuracy averaged

57.41%, again getting close to the conventional definition of ‘AP possessors’ (Athos et

al., 2007; Bermudez et al., 2009; Levitin & Rogers, 2005; Takeuchi & Hulse, 1993).

The standard test was again insensitive to reliable AP abilities of some of the

pianists. In the standard pitch-naming test, the lower-performing two-thirds of pianists

did not perform above chance (mean = 0.65%, p=.540). However, their accuracy rose

above chance in the verification test with identical sine wave tones (mean = 16.84%,

p<.0001), and improved to 26.81% in ‘posture’.

Overall, we replicated findings in Experiment 1 with a separate group of pianists,

including the substantial improvement on AP performance by reducing the decisional

demand and by providing a familiar musical context during testing.


Combined Analyses of Violinists and Pianists

To know if the context effect was robust across musicians with different levels of

AP memory, we examined the sensitivity (A’) data for participants in both experiments,

including the “AP possessors”. We first collapsed the violinists and pianists, and divided

them into four groups (‘AP possessors’, upper third, middle third, and lower third) using

their standard naming test performance. Then for each group we compared A’ in the

different context conditions (“sine”, “timbre”, and “play”). Figure 2 shows the

performance of these groups.

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Insert Figure 2 here

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All groups in general showed an increase in A’ as the context became richer,

except for the ‘AP possessors’ whose performance was at ceiling. An analysis of variance

(ANOVA) showed only significant main effects of context [F(2,132)=8.58, p=.0003,

ηp=.104] and group [F(3,66)=32.48, p<.0001, ηp=.59] but not their interaction

[F(6,132)=1.69, p=.126]. However, separate ANOVAs for the groups showed a context

effect in the upper third [F(2,42)=4.68, p=.014, ηp=.18], the middle third [F(2,40)=5.86,

p=.005, ηp=.22], and the lower third [F(2,32)=4.37, p=.020, ηp=.21], but not in the ‘AP

possessors’ [F(2,18)=1.81, p=.190]. Similarly, trend analyses also showed that A’

increased linearly with a richer context in the upper third [t(21)=2.67, p=.014], the middle

third [t(20)=3.23, p=.003], and the lower third [t(16)=2.41, p=.027], but not in the ‘AP


possessors’ [t(9)=1.30, p=.224]. While a context effect was not found among the ‘AP

possessors’, it remains to be seen if the AP possessor would also show a context effect

given a larger sample size (10 in our current sample) and performance away from ceiling.

For others, the context effect seems to hold irrespective of their level of performance.

General Discussion

This study asked why AP has been found to be a special and rare ability possessed

only by a few individuals but not by the general musicians. Using a pitch verification test

with reduced cognitive demands, we demonstrated that general musicians have a much

better AP memory than previously estimated by the standard pitch-naming test. With

identical stimuli, our sample of musicians attained 22.9% accuracy in verification, two

times as high as that in pitch-naming (11.7%). Performance was further enhanced when

the testing context became increasingly similar to musical experience. Accuracy

increased linearly to 35.40% in the posture condition, a three-fold increase of that in

pitch-naming. All these differences were obtained in the same participants with the same

scoring strengency.

These findings challenge current theories about AP. First, AP memory is much

more prevalent than previously estimated, leading to questions about how rare and

special AP is (Athos et al., 2007; Levitin & Rogers, 2005; Takeuchi & Hulse, 1993;

Ward, 1999; Zatorre, 2003). Second, the findings disagree with the theory proposing that

general musicians cannot associate absolute pitches with verbal names (Brancucci et al.,

2009; Deutsch, 2002; Levitin & Rogers, 2005; Schellenberg & Trehub, 2003; Vanzella &

Schellenberg, 2010). During verification, participants were required to match the tones


with a pitch name. A failure in retrieving the names of the tones should have resulted in

chance performances for all conditions.

Note that our goal was not so much to determine the exact prevalence of AP in the

population as to point out how AP has been consistently underestimated with the use of

the standard test, which may have led to a misinterpretation of the prevalence of AP, at

least among musicians. The prevalence of AP in our posture condition cannot be

explained by the fact that our Asian participants speak tonal languages (Cantonese),

which has been associated with exceptionally high AP occurrence rate (Deutsch et al.,

2006; Lee & Lee, 2010). First, the effect of different tests and musical contexts were

revealed with a within-subjects design, therefore these results cannot be explained by the

specific demographic backgrounds of the participants. Moreover, the rate of AP

occurrence was low compared with prior reports with tonal language speakers.

Specifically, with similar onset age of musical training and AP criterion (at least 70%

accuracy in the pitch-naming test), only 14% of our 70 musicians were considered ‘AP

possessors’, which was more comparable with that in the American population (about

<12%), but far lower than the 40%-70% in tonal language speakers reported in prior

studies (Deutsch et al., 2006; Lee & Lee, 2010). These confirmed that our results cannot

be explained by the especially high AP occurrence rate among tonal speakers.

Is the context-dependent AP ‘true AP’?

While there is no consensus regarding the importance of context on AP judgment

in the literature (e.g., Ward, 1999; Zatorre & Beckett, 1989), most researchers adopt a

simple definition for AP, i.e., the ability to identify or produce the pitch of a tone without


external reference (Athos et al., 2007; Deutsch, 2002; Levitin & Rogers, 2005;

Schellenberg & Trehub, 2003; Takeuchi & Hulse, 1993; Vanzella & Schellenberg, 2010;

Ward, 1999; Zatorre, 2003). Under this definition, AP ability does not preclude the use of

contextual cues, e.g., context-dependent AP has been defined as one of the forms of AP

(e.g. AP only for the timbre of one’s instrument; Levitin & Rogers, 2005).

Regardless of what definition of ‘true AP’ one adopts, the importance of

contextual cues on AP memory should be recognized. While the idea that context

influences AP performance is not new, all previous studies have focused on the effects on

‘AP possessors’ (Levitin & Rogers, 2005; Takeuchi & Hulse, 1993). Our findings

demonstrate that acknowledging the contextual influence on AP memory among general

musicians reveals a very different picture about the prevalence of AP in the population.

The multimodal framework for understanding AP

We believe that AP memory representation can be better understood if one takes

into account the multimodal nature of musical experience (e.g., Zatorre & Beckett, 1989).

For musicians, the concept of pitch is not only tied to the frequency of the sound, but also

highly associated with multimodal information, including other dimensions of the

auditory input (e.g. timbre), somatosensory and motor input (e.g. fingering, arm positions

or lip vibration), visual input (e.g. musical notation or the instrument), and other specific

contexts (e.g. the melody and arrangement of a familiar song; Halpern, 1989; Levitin,

1994; Schellenberg & Trehub, 2003; Bermudez et al., 2009; Wong & Gauthier, 2010;

Zatorre & Beckett, 1989). In this multidimensional representation of pitch, all other

associations may serve as cues to specify the pitch information, as well as specifying


information in other modalities. For example, motor prediction in music playing may

incorporate online sensory information (e.g., the auditory tone, visual musical notation

and somatosensory vibration) to adjust and refine the motor plan and execution in the

feedforward-feedback loop of information flow (Desmurget et al., 2000). Such

associations are largely shaped by music training and experience. Under this framework,

AP memory representation refers to the multimodal representation of pitch when external

pitch references are absent. Therefore, performance in the standard pitch-naming test

depends on how well one can extract pitch information from this multimodal pitch

representation, which is constrained by at least two factors. One is the quality of this

multimodal pitch representation. The other is how well one can dissociate pitch from its

multimodal associations.

This framework predicts that AP memory is best expressed with an ideal musical

context, e.g., when musicians are allowed to play the instrument as normal, such that

musicians can naturally compare the testing tone with the multimodal pitch

representation. In this case, the variability of AP performance is mainly determined by

the quality of multimodal pitch representation.

However, typical AP tests have eliminated at least part of the musical context. For

example, the use of sine wave tones almost completely deprive one of the musical

context, while our ‘posture’ condition excluded the arm movement of the bow and the

string vibration during sound production. In these cases, for a given quality of

multimodal pitch representation, performance is modulated by the ability of dissociating

pitch from the multimodal context. Those who find it easy can excel at the AP tests


regardless of impoverished or rich contexts, while others are impaired to different

degrees when musical cues are eliminated.

In this framework, the ‘AP possessors’ are those who have great quality of

multimodal pitch representation and a high capability of extracting pitch from its musical

context. Such extraction is seldom perfect, therefore contextual influences on their AP

judgment can still be observed (Levitin & Rogers, 2005; Takeuchi & Hulse, 1993). The

‘non-AP possessors’ are those who have limited ability in dissociating pitch from the

context in general. Thus they perform poorly at the standard pitch-naming test. However,

with a rich musical context, their AP judgment becomes above-chance or even highly

accurate according to each individual’s quality of the multimodal pitch representation.

The ability to dissociate pitch from its context may also explain the higher occurrence of

AP among individuals with Autism or with Williams Syndrome (Sacks, 1995), since both

disorders have been associated with the preference for processing local features while

ignoring contextualized meanings and relationships (Bellugi et al., 2000; Happe, 1999).

This framework generates new questions regarding the mechanisms of AP. For

example, AP ability is associated with an early onset of musical training (Takeuchi &

Hulse, 1993). One should clarify whether the benefit of early musical training is about

establishing AP memory of better quality, developing pitch memory independent of the

musical context, or both. One should also clarify the nature of the shift of pitch

processing from an absolute to relative basis during development (Stalinski &

Schellenberg, 2010; Takeuchi & Hulse, 1993) since musicians maintain both types of

abilities in adulthood, as suggested by the prevalence of AP. Besides, while AP ability is

associated with hemispheric asymmetry of the platnum temporale in terms of size


(Keenan, Thangaraj, Halpern & Schlaug, 2001; Schlaug, Jancke, Huang, & Steinmetz,

1995), it is unclear whether such platnum temporale asymmetry supports better AP

memory, context-independent retrieval of pitch, or both. And while widespread

multimodal brain regions are engaged by visual presentation of musical notes (Wong &

Gauthier, 2010), it remains to be seen if stronger connectivity can be observed among

these regions for those with better AP memory. Last but not least, our finding that all

musicians carry AP memory to a certain degree seems to be at odd with the prior findings

that associated AP ability with very few or even a single genetic locus (Drayna, 2007).

Further studies should clarify the contribution of carrying the AP-related genes to AP

memory, e.g., leading to better AP memory, or context-independent retrieval of pitch

information, or both. These are important questions for understanding the genesis of AP.

Finally, this framework raises concerns about using the standard test for

measuring AP memory. Our results demonstrate how the standard test consistently

underestimates AP ability compared with the verification test using identical sine wave

stimuli, because the standard test includes extraneous decisional demand that is non-

central to AP memory. Unless researchers would like to specifically study one’s ability in

the standard test (i.e., one’s AP ability under high decisional demand), one should

consider minimizing the extraneous decisional demand of the standard test when

assessing AP memory in future.


Acknowledgement

This research was supported by grants from the Chinese University of Hong Kong

(Direct Grant 2021110) to Alan Wong. The authors declare no conflict of interest. We

thank Helene Wong Hoi Shan for her help in violin tone production, Crystal Yuen for her

help in data collection, and Patrick Bermudez for providing the sine wave tones used in

the standard AP test.


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Figure 1. Box plots showing accuracy in the standard AP test and the verification test for violinists (A) and pianists (B), after excluding participants defined as “AP possessors” by their performance in the standard naming test. Y-axes show accuracy after correction for guessing (see Methods), with 0% and 100% representing chance and perfect performance respectively. Limits of the boxes represent 25th and 75th percentiles, the cross and solid line inside the box represent the mean and median respectively, ends of whiskers represent one standard deviation above and below the mean.


Figure 2. Verification performance for three context conditions. The y-axis shows sensitivity (see Methods), with chance performance at 0 and perfect performance at 1. The violinists and pianists were collapsed and then divided into four groups based on their performance in the standard naming test.

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