Professional Music Training and Novel Word Learning: - Am MIT spezialisierte KI-Forschung

Professional Music Training and Novel Word Learning:
From Faster Semantic Encoding to Longer-lasting
Word Representations

Eva Dittinger1, Mylène Barbaroux1, Mariapaola D’Imperio1, Lutz Jäncke2,
Stefan Elmer2*, and Mireille Besson1*

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Abstrakt

■ On the basis of previous results showing that music training
positively influences different aspects of speech perception and
Erkenntnis, the aim of this series of experiments was to test the
hypothesis that adult professional musicians would learn the
meaning of novel words through picture–word associations
more efficiently than controls without music training (d.h., fewer
errors and faster RTs). We also expected musicians to show fas-
ter changes in brain electrical activity than controls, insbesondere
regarding the N400 component that develops with word learn-
ing. In line with these hypotheses, musicians outperformed
controls in the most difficult semantic task. Darüber hinaus, although
a frontally distributed N400 component developed in both
groups of participants after only a few minutes of novel word
learning, in musicians this frontal distribution rapidly shifted

to parietal scalp sites, as typically found for the N400 elicited
by known words. Endlich, musicians showed evidence for better
long-term memory for novel words 5 months after the main ex-
perimental session. Results are discussed in terms of cascading
effects from enhanced perception to memory as well as in
terms of multifaceted improvements of cognitive processing
due to music training. To our knowledge, this is the first report
showing that music training influences semantic aspects of lan-
guage processing in adults. These results open new perspec-
tives for education in showing that early music training can
facilitate later foreign language learning. Darüber hinaus, the design
used in the present experiment can help to specify the stages of
word learning that are impaired in children and adults with
word learning difficulties. ■

EINFÜHRUNG

The aim of the present experiment was to examine the
influence of music training on word learning using both
behavioral and electrophysiological measures. On the ba-
sis of the evidence reviewed below, we tested the hy-
pothesis that musicians would be more efficient at
word learning than nonmusicians and that the differ-
ences would be reflected in their pattern of brain waves.
There is strong evidence from previous cross-sectional
studies comparing adult musicians and nonmusicians
that long-term music training promotes brain plasticity
(Münte, Altenmüller, & Jäncke, 2002) in modifying the
functional (Schneider et al., 2002; Pantev et al., 1998)
and structural (Elmer, Hänggi, Meyer, & Jäncke, 2013;
Gaser & Schlaug, 2003; Schneider et al., 2002) architec-
ture of the auditory pathway. Results of longitudinal stud-
ies, mostly in children, showed that music training can be
the cause of the observed effects (François, Chobert,
Besson, & Schön, 2013; Strait, Parbery-Clark, O’Connell,
& Kraus, 2013; Chobert, François, Velay, & Besson, 2012;
Moreno et al., 2011; Hyde et al., 2009; Moreno et al.,
2009). Most importantly for the present purposes, Dort

1CNRS and Université Aix Marseille, 2University of Zurich
*Shared last authorship.

is also evidence that music training improves different
aspects of speech processing (for review, see Asaridou &
McQueen, 2013; Besson, Chobert, & Marie, 2011; Kraus &
Chandrasekaran, 2010). These transfer effects possibly
arise because speech and music are auditory signals rely-
ing on similar acoustic cues (d.h., Dauer, frequency, In-
tensity, and timbre) and because they share, at least in
Teil, common neuronal substrates for auditory perception
(Peretz, Vuvan, Lagrois, & Armony, 2015; Jäncke, 2009)
and for higher-order cognitive processing (Rogalsky, Rong,
Saberi, & Hickok, 2011; Patel, 2008; Maess, Kölsch,
Gunter, & Friederici, 2001). Zum Beispiel, music training fa-
cilitates the processing of a variety of segmental (Bidelmann,
Weiss, Moreno, & Alain, 2014; Kühnis, Elmer, & Jäncke,
2014; Elmer, Meyer, & Jäncke, 2012; Chobert, Marie,
François, Schön, & Besson, 2011; Musacchia, Sams, Skoe,
& Kraus, 2007) and suprasegmental speech attributes (Marie,
Delogu, Lampis, Olivetti Belardinelli, & Besson, 2011; Wong
& Perrachione, 2007) within native (Schön, Magne, &
Besson, 2004) and nonnative languages (Marques, Moreno,
Castro, & Besson, 2007). Darüber hinaus, both musically trained
Kinder (Jentschke & Kölsch, 2009) and adults (Fitzroy &
Sanders, 2013) are more sensitive to violations of linguistic
and music syntax than participants without music training.
Perhaps most importantly, recent results also showed that

Zeitschrift für kognitive Neurowissenschaften 28:10, S. 1584–1602
doi:10.1162/jocn_a_00997

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long-term music training positively improves cognitive func-
tions such as auditory attention (Strait, Slater, O’Connell, &
Kraus, 2015), visual attention ( Wang, Ossher, & Reuter-
Lorenz, 2015), working and verbal memory (George &
Coch, 2011; Ho, Cheung, & Chan, 2003), executive func-
tionen (Zuk, Benjamin, Kenyon, & Gaab, 2014; Moreno
et al., 2011; Pallesen et al., 2010), and general intelligence
(Schellenberg, 2004). These findings are not surprising in-
sofar as playing an instrument at a professional level is a
multidimensional task that, together with specific motor
abilities, requires acute auditory perception and focused at-
Aufmerksamkeit, code switching between the visual information on
the score and the corresponding sounds, as well as the abil-
ity to maintain auditory information in short- and long-term
Erinnerung. Taken together, these results are in line with dy-
namic models of human cognition (Friederici & Singer,
2015; Hagoort, 2014) positing that language—and possibly
music—are processed in interaction with other cognitive
functions.

Similar to playing music, word learning is also a multi-
dimensional task requiring both perceptive and higher-
order cognitive abilities. Let us take the example of Thai.
Thai is a tonal and a quantitative language that mainly
comprises monosyllabic words (as Mandarin Chinese
and other tonal languages) and in which both tonal (d.h.,
five tones) and vowel length contrasts are linguistically rel-
evant for understanding word meaning (z.B., /pa1/ low
tone with a short vowel means “to find” and /pa:1/ niedrig
tone with a long vowel means “forest”; Gandour et al.,
2002). Daher, when it comes to learn novel words in Thai,
the learner has to focus attention on the acoustic stream
to discriminate spectral and temporal phonetic contrasts
and to build new phonological representations that can
then be associated with lexical meaning by recruiting
working, short-term, episodic, and semantic memory pro-
Prozesse. Daher, if music skills translate into improved audi-
tory perception and attention together with enhanced
working and verbal memory, it should be easier for musi-
cians to learn a language such as Thai.

The ERP method is one of the most eligible methods
to capture the fast temporal dynamics of word learning
and to examine brain plasticity, as reflected by changes
in the amplitude and/or latency of ERP components dur-
ing learning. Previous results in adults have shown that
the N400, a negative-going component that typically de-
velops between 300 Und 600 msec poststimulus onset
(Kutas & Hillyard, 1980), increases in amplitude when
meaningless items acquired meaning. Speziell, results
showed N400 enhancements in native English speakers
nach 14 hr of learning the meaning of novel French
Wörter (McLaughlin, Osterhout, & Kim, 2004) and after
45 min of learning the meaning of rare words (z.B., “clow-
der”; Perfetti, Wlotko, & Hart, 2005). Darüber hinaus, if a novel
word (Borovsky, Elman, & Kutas, 2012; Borovsky, Kutas,
& Elman, 2010; Mestres-Missé, Rodriguez-Fornells, &
Münte, 2007) or pseudoword (Batterink & Neville,
2011) is presented in a strongly constrained and mean-

ingful context, even a single exposure can be sufficient
to build up initial word representations, an effect referred
to as “fast mapping” (Carey, 1978). An incubation-like pe-
riod and further exposures are then required for consol-
idation and integration into existing lexical networks
(Dumay & Gaskell, 2007). Daher, the N400 is taken as a
reliable index of word learning, reflecting the formation
of semantic representations.

Note though that the N400 component at the core of
the above-mentioned experiments clearly showed a more
frontal scalp distribution (Borovsky et al., 2010; Mestres-
Missé et al., 2007) than the centroparietal N400 typically
elicited by already known words (Kutas, Van Petten, &
Besson, 1988). This frontal N400 distribution is compati-
ble with results showing that prefrontal and temporal
brain regions are associated with the maintenance of novel
information in working or short-term memory and the for-
mation of new associations (Hagoort, 2014) and/or with
the initial building-up of word representations in episodic
Erinnerung (Rodriguez-Fornells, Cunillera, Mestres-Missé, &
De Diego-Balaguer, 2009; Wagner et al., 1998).

Wie oben erwähnt, most studies of music-to-language
transfer effects have focused on segmental, suprasegmen-
tal, and syntactic processing levels. On the basis of the re-
sults on word learning reviewed above, this study aimed at
going a step further so as to determine whether profes-
sional music training would also influence the semantic
level of processing, most often considered as language-
specific (but see Koelsch et al., 2004) by facilitating the
learning process of novel word meaning. The general hy-
pothesis was that the optimization of perceptual and cog-
nitive functions in professional musicians would positively
influence the speed and quality of word learning as re-
flected by a behavioral advantage for musicians (d.h., lower
error rates [ERRs] and faster RTs). Darüber hinaus, basierend auf
ERPs and word learning literature (Borovsky et al., 2010,
2012; Batterink & Neville, 2011; Mestres-Missé et al.,
2007; Perfetti et al., 2005; McLaughlin et al., 2004), we ex-
pected a frontally distributed N400 component to develop
in all participants during the early stages of novel word
learning. Jedoch, if the perceptual and cognitive compu-
tations involved in word learning were facilitated in musi-
cians, the development of the N400 component should be
faster in musicians than in controls in the learning phase.
Im Gegensatz, we expected the N400 to show a centroparietal
distribution when novel word learning was consolidated.

To test these general hypotheses, we used an ecolog-
ically valid experimental design inspired by Wong and
Perrachione (2007) and based on a series of four experi-
ments that comprised several tasks performed during
the main experimental session (see Figure 1A–E). Erste, Zu
further test the hypothesis of improved auditory speech
discrimination in musicians compared with controls, Par-
ticipants performed a phonological categorization task at
the beginning and at the end of the main experimental
session (see Figure 1A and E). On the basis of previous
results showing that musicians are advantaged when

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Figur 1. Experimentelles Design. Participants performed a series of tasks in the main experimental session (A–E): Erste, in the phonological categorization
Aufgabe (A), nine natural Thai monosyllabic words had to be categorized based on voicing (Task 1), vowel length (Task 2), pitch (Task 3), or aspiration
contrasts (Task 4). Zweite, in the word learning phase (B), each word was paired with its respective picture. This phase included two separate blocks of
Versuche. Dritte, in the matching task (C), the words were presented with one of the pictures, either matching or mismatching the previously learned
associations. This task included two separate blocks of trials. Vierte, in the semantic task (D), the words were presented with novel pictures that were
either semantically related or unrelated to the novel words. Wieder, this task included two separate blocks of trials. Fünfte, participants did again the four
tasks of the phonological categorization task (E). Endlich, participants came back 5 months after the main session to perform again the matching and
semantic tasks (F).

the discrimination is most difficult (Diamond, 2013; Schön
et al., 2004), we expected musicians to outperform controls
in identifying phonemic contrasts that are not relevant for
lexical discrimination in French. Darüber hinaus, based on previ-
ous literature reporting that the N100 component reflects
encoding of auditory cues in the auditory-related cortex
(Kühnis et al., 2014) and is influenced by auditory attention
and perceptual learning (Seppänen, Hämäläinen, Pesonen,
& Tervaniemi, 2012; Woldorff & Hillyard, 1991), we ex-
pected this behavioral advantage to be accompanied by
an increased N100 amplitude in musicians.

Zweite, participants were asked to learn the meaning
of the novel words through picture–word associations
(see Figure 1B), a design that has often been used in word
learning experiments in children (Friedrich & Friederici,
2008; Torkildsen et al., 2008) and in adults (Dobel,
Lagemann, & Zwitserlood, 2009). No behavioral response
was required during this word learning phase, but ERPs
were recorded to test the main hypothesis that frontally
distributed N400 components would develop in both
groups of participants (François et al., 2013; Borovsky
et al., 2010; Rodriguez-Fornells et al., 2009) but with faster
temporal dynamics in musicians than in controls.

Dritte, to test for the efficacy of the learning phase, Par-
ticipants performed a matching task and were asked to
decide whether a picture–word pair matched or mis-
matched the previously learned pairs (see Figure 1C).
Vierte, an important aspect was to determine whether
word learning was specific to the picture–word pairs
learned during the word learning phase or whether the
meaning of the newly learned words was already integrated
into semantic networks so that priming effects generalized
to new pictures. Zu diesem Zweck, participants performed a se-
mantic task during which novel pictures that had not been
seen in the previous tasks were presented. They were
asked to decide whether the picture and the word were
semantically related or unrelated (see Figure 1D). In both
the matching and the semantic tasks and in both groups
of participants, we predicted that N400 amplitudes would
be larger for mismatch and semantically unrelated words
than for match and semantically related words (d.h., the typ-
ical N400 effect; Kutas & Hillyard, 1980), thereby showing
that participants had learned the meaning of the novel
Wörter. Darüber hinaus, if the novel words’ meanings were
already integrated into existing semantic networks at the
end of the word learning phase (Borovsky et al., 2012;

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Volumen 28, Nummer 10

Batterink & Neville, 2011), we expected the N400 effect
(mismatching–matching and unrelated–related words) In
the matching and semantic tasks to show the centroparie-
tal distribution typically found for already known words
(Kutas et al., 1988). Of main interest was to specify the spa-
tiotemporal dynamics of the N400 effect in musicians and
in nonmusicians. Endlich, if we were to find that music train-
ing influenced word learning, then we expected musical
ability to be positively correlated with word learning effi-
cacy, as reflected by behavioral measures and/or the
N400 effect in the matching and semantic tasks.

Endlich, a subset of the participants was behaviorally re-
tested after 5 months (see Figure 1F) in the matching and
semantic tasks to evaluate the maintenance of novel
words in long-term memory. It was of interest to deter-
mine whether the behavioral advantages of musicians in
a variety of cognitive domains, as reviewed above, extend
to long-term memory. To the best of our knowledge, Das
aspect has not yet been investigated.

Zusammenfassend, this experimental design is relevant for
specifying whether music expertise influences the se-
mantic level of speech processing, an issue that, to our
Wissen, has not been addressed before. By analyzing
ERPs, we aimed at better understanding the dynamics of
word learning, how fast semantic processes develop, Und
whether and how the N400 is influenced by music train-
ing. Showing that long-term music training with an early
start (as it is most often the case in professional musi-
cians) may facilitate foreign language learning later in life
should add evidence to the claim that music training has
important societal consequences for education (Besson
et al., 2011; Kraus & Chandrasekaran, 2010). Endlich, Das
experimental design is of potential interest for clinical re-
suchen: using several different tasks that call upon several
perceptual and cognitive functions (phonological catego-
rization, formation of picture–word associations, main-
taining these associations in short-term and long-term
memory and generalization of learning effects) innerhalb
the same patient may help specify the processing stages
that are deficient in adults or children with language
learning disorders.

METHODEN

Teilnehmer

A total of 30 participants with 15 professional musicians
(MUS, acht Frauen) Und 15 controls without formal mu-
sic training (nonmusicians, NM, acht Frauen) aber in-
volved in a regular leisure activity (z.B., sports, tanzen,
theater) were paid to participate in the experimental ses-
sion lasting for 2.5 Std (including the application of the
Electrocap, psychometric measurements, and experimen-
tal tasks). The two groups did not differ in age (MUS:
Durchschnittsalter = 25.1 Jahre, age range = 19–30, SD = 3.9;
NM: Durchschnittsalter = 25.7 Jahre, age range = 19–33, SD =
4.8; F(1, 28) = 0.02, p = .68). All participants were native

French speakers, had comparable education levels (uni-
versity degree) and socioeconomic background (Kriterien
of the National Institute of Statistics and Economic Stud-
ies; MUS: 4.4; NM: 4.9; F(1, 28) = 1.45, p = .24), and re-
ported no past or current audiological or neurological
deficits. MUS practiced their instruments for an average
von 17 Jahre (range = 11–24, SD = 4.1) and the musician
group included three pianists, two accordionists, four vio-
linists, one cellist, two guitarists, one hornist, one tubist,
and one flautist. None of the participants was bilingual,
but all spoke English as a second language and most par-
ticipants (except for 1 MUS and 3 NM) had a rudimentary
knowledge of a third language that was neither tonal nor
quantitative. The study was conducted in accordance with
the Helsinki declaration, and all participants gave their in-
formed consent before enrolling in the experiment.

Screening Measures

Cognitive Ability

Standardized psychometric tests were used to examine
short-term and working memory (forward and reverse
Digit Span, WISC-IV; Wechsler, 2003), visual attention
(NEPSY from Korkman, Kirk, & Kemp, 1998) and non-
verbal general intelligence (progressive matrices, PM47;
Raven, Corporation, & Lewis, 1962).

Musical Aptitude

Participants performed two musicality tests (adapted
from the MBEA battery; Peretz, Champod, & Hyde,
2003) consisting in judging whether pairs of piano melo-
dies were same or different, based either on melodic or
on rhythmic information.

Experimental Stimuli

Auditory Stimuli

Nine natural Thai monosyllabic words were selected for
the experiment: /ba1/, /pa1/, /pha1/, /ba:1/, /pa:1/, /pha:1/,
/ba:0/, /pa:0/, /pha:0/.1 These words varied in vowel dura-
tion, with short (/ba1/, /pa1/ and /pha1/; 261 msec on
average) and long vowels (/ba:1/, /pa:1/, /pha:1/, /ba:0/,
/pa:0/ and /p ha:0/; 531 msec on average), und in
fundamental frequency, with low-tone (/ ba1/, /pa1/,
/pha1/, /ba:1/, /pa:1/ and /pha:1/; F0 = 175 Hz on average)
and midtone vowels (/ba:0/, /pa:0/ and /pha:0/; F0 = 218 Hz
on average). Außerdem, words contained voicing
contrasts (/ba1/, /ba:1/ and /ba:0/, VOT = −144 msec vs.
/pa1/, /pa:1/ and /pa:0/, VOT = 3 ms) as well as aspiration
contrasts (/pa1/, /pa:1/ and /pa:0/, VOT = 3 msec vs. /pha1/,
/pha:1/ and /pha:0/, VOT = 77 ms).2 Stimuli were recorded
by a female Thai–French bilingual, ensuring that all words
were produced naturally. For each word, five versions were
digitally recorded to reproduce natural speech variability.
Sound pressure level was normalized across all words to

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a mean level of 70 dB by using Praat software (Boersma
& Weenink, 2011).

than four successive same responses. Task order and re-
sponse side were counterbalanced across participants.

Visual Stimuli

For the learning phase, nine pictures representing famil-
iar objects (d.h., bear, flower, key, chair, bell, eye, strawberry,
train, glass) were selected based on the standardized set of
260 pictures (that are matched for name and image agree-
ment, Vertrautheit, and visual complexity) built by Snodgrass
and Vanderwart (1980).3 The same pictures as in the learn-
ing phase were then presented in the matching task. Für
the semantic task, 54 new pictures that the participants
had not seen before in the experiment and that were se-
mantically related or unrelated to the meaning of the newly
learned words were chosen from the Internet by two of the
authors (ED and MB). Students from our university (n =
60; age range = 19–25 years) were asked to rate the seman-
tic relatedness between new and old pictures (das ist, those
previously presented during the word learning phase). Half
of the presented pairs were semantically related and the
other half were semantically unrelated, and this was con-
firmed by the students’ ratings.

Experimental Tasks

Participants were tested individually in a quiet experimen-
tal room (d.h., Faraday cage), where they sat in a comfort-
able chair at about 1 m from a computer screen. Auditiv
stimuli were presented through HiFi headphones (HD590,
Sennheiser Electronic GmBH, Wedemark, Deutschland) bei
70-dB sound pressure level. Visual and auditory stimuli
presentation as well as the collection of behavioral data
were controlled by Presentation software ( Version 11.0,
Neurobehavioral Systems, Berkeley CA).

Main Experimental Session (See Figure 1A–E)
Phonological categorization task. Am Anfang
and at the end of the experiment, participants performed
four different phonological tasks that lasted for 2.3 min
jede. All nine Thai monosyllabic words were presented
in each task, but participants were asked to categorize
them based upon different features in each task: (1) Die
voicing contrast (z.B., /ba1/ vs. /pa1/), (2) the vowel length
(z.B., short: /ba1/ vs. long /ba:1/), (3) pitch (z.B., niedrig: /pa:1/
vs. hoch: /ba:0/), Und (4) the aspiration contrast (z.B., /pa1/
vs. /pha1/; see Figure 1A and E). For each task, the contrast
was visually represented on the left (z.B., “short” with a
short line) and right (z.B., “long” with a long line) half of
the screen and participants had to press one of two re-
sponse buttons according to the correct side (z.B., links
one for short and right one for long vowels), as quickly and
accurately as possible. Each word was presented 10 mal
in a pseudorandomized order with the constraints of no
immediate repetition of the same word and no more

Word learning phase. Participants were asked to learn
the meaning of each word previously presented in the
phonological categorization task using picture–word as-
sociations. Zum Beispiel, a drawing of a bear was followed
by the auditory presentation of the word /ba1/, and thus,
/ba1/ was the word for bear in our “foreign” language
(see Figure 1B). Each of the nine picture–word pairs
was presented 20 mal, ergebend 180 trials that were
pseudorandomly presented (d.h., no immediate repeti-
tion of the same association) in two blocks of 3 min each.
The picture was presented first and then followed after
750 msec by one of the nine words. Total trial duration
War 2000 ms. Two different lists were built, so that
across participants different pictures were associated
with different words. No behavioral response was re-
quired from the participants but they were told that sub-
sequent tests would evaluate whether they learned the
meaning of the novel words.

Matching task. One of the nine pictures was presented,
followed after 750 msec by an auditory word that
matched or mismatched the associations previously
learned in the word learning phase. Zum Beispiel, Wo-
as the drawing of a bear followed by /ba1/ (d.h., bear) War
a match, the drawing of a strawberry followed by /ba1/
was a mismatch (see Figure 1C). Participants were asked
to press one of two response keys accordingly, as quickly
and accurately as possible. Response hand was counter-
balanced across participants. At the end of the trial, a row
of XXXX appeared on the screen, and participants were
asked to blink during this time period (1000 ms; total
trial duration: 3750 ms) to minimize eye movement
artifacts during word presentation. Each word was pre-
gesendet 20 mal, half in match condition and half in mis-
match condition. The total of 180 trials was pseudorandomly
vorgeführt (d.h., no immediate repetition of the same asso-
ciation and no more than four successive same responses)
within two blocks of 5.6 min each.

Semantic task. One of the new pictures was presented,
followed after 1500 msec by an auditory word that was
semantically related or unrelated. Zum Beispiel, although
the picture of a lock was semantically related to the pre-
viously learned word /pa:1/ (d.h., “key”), the picture of a
strawberry cake was semantically unrelated to /pa:1/ (sehen
Figure 1D). Participants were asked to press one of two
response keys accordingly, as quickly and accurately as
möglich. A familiarization task including four trials was
administrated before starting the task. Response hand
was counterbalanced across participants. At the end of
the trial, a row of XXXX appeared on the screen, and par-
ticipants were asked to blink during this time period
(1000 ms; total trial duration = 4500 ms). Each word
was presented 12 mal, but none of the new pictures

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Zeitschrift für kognitive Neurowissenschaften

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were repeated, so that on each trial the word was associated
with a different related or unrelated picture. Half of the
picture–word pairs were semantically related, and half
were semantically unrelated. A total of 108 trials was pre-
sented pseudorandomly (d.h., no immediate repetition of
the same association and no more than four successive
same responses) within two blocks of 4 min each.

Long-term Memory Session (See Figure 1F)

To test for long-term memory effects on behavior (d.h.,
ERRs and RTs), participants performed again the matching
(always administered first) and semantic tasks 5 months af-
ter the main experimental session (no ERPs were recorded).
Because of a dropout rate of 33%, nur 10 Teilnehmer
were retested in each group. In the matching task, a total
von 270 trials were presented within three blocks. Im
semantic task, insgesamt 216 trials were presented in two
blocks (with a short pause within each block).

EEG Data Acquisition

The EEG was continuously recorded at a sampling rate of
512 Hz with a band-pass filter of 0–102.4 Hz by using a
Biosemi amplifier system (BioSemi Active 2, Amsterdam,
Die Niederlande) mit 32 active Ag/Cl electrodes (Biosemi
Pintype) located at standard positions according to the
International 10/20 System (Jasper, 1958). The EOG was re-
corded from flat-type active electrodes placed 1 cm to the
left and right of the external canthi and from an electrode
beneath the right eye. Two additional electrodes were
placed on the left and right mastoids. Electrode imped-
ance was kept below 5 kΩ. EEG data were analyzed using
Brain Vision Analyzer software ( Version 1.05.0005 &
Version 2.1.0; Brain Products, München, Deutschland). Alle
data were re-referenced offline to the averaged left and
right mastoids, filtered with a bandpass filter from 1 Zu
30 Hz (slope of 24 dB/oct), and independent component
analysis and inverse independent component analysis
were used to identify and remove components associated
with vertical and horizontal ocular movements. Endlich,
DC-detrend and removal of artifacts above a gradient cri-
terion of 10 μV/msec or a max–min criterion of 100 μV
over the entire epoch were applied automatically. Für
each participant, ERPs were time-locked to word onset,
segmented into 2700 msec epochs, including a 200-msec
baseline and averaged within each condition. Individuell
averages were then averaged together to obtain the grand
average across all participants.

task that was performed both at the beginning and at the
end of the experiment, Order (preexperiment vs. postex-
periment) was included as a within-subject factor together
with Task ( Voicing vs. Vowel length vs. Pitch vs. Aspira-
tion). The matching and semantic tasks were only per-
formed once but each experiment was divided into two
blocks of trials so that factors were Block (1 vs. 2) and Con-
dition (match vs. mismatch or related vs. unrelated). To
further increase clarity, factors were again specified at the
beginning of each task in the Results section. On the basis
of ERRs, four outliers (2 MUS and 2 NM, ±2 SD away from
der Mittelwert) were excluded from further analyses.

For ERPs, we analyzed the early stages of auditory pro-
cessing in the phonological categorization task using N100
peak amplitude measures. Im Gegensatz, during the word
learning phase, as well as in the matching and semantic
tasks, we focused on semantic processing and we analyzed
the mean amplitude of the N400 component. Effects on
the N200 were also analyzed using mean amplitudes. Sei-
cause ERPs were only analyzed for correct responses and
because the ERP traces of the four outliers that were elim-
inated from behavioral analyses were similar to the grand
average in each group, all participants (d.h., 15 MUS and
15 NM) were included in the ERP analyses. ANOVAs always
included Group (MUS vs. NM) as a between-subject factor
and Laterality (links: F3, C3, P3; midline: Fz, Cz, Pz; Rechts: F4,
C4, P4) and Anterior/Posterior (frontal: F3, Fz, F4; zentral:
C3, Cz, C4; parietal: P3, Pz, P4) as within-subject factors, Zu-
gether with specific factors for each task. As for behavior,
for the phonological categorization task, these factors were
Order (pre vs. post) and Task (Voicing vs. Vowel length vs.
Pitch vs. Aspiration). For the matching and semantic tasks,
factors were Block (1 vs. 2) and Condition (match vs. mis-
match or related vs. unrelated). Post hoc Tukey tests (Re-
ducing the probability of Type I errors) were used to
determine the origin of significant main effects and interac-
tionen. To simplify results presentation, we only report sig-
nificant results related to our hypotheses (full statistical
results can be seen in Table 1). Endlich, correlation analyses
(Pearson’s coefficient) were computed between error rates
in the musicality task with error rates or N400 effects in the
semantic task. General linear models (including Group as a
categorical factor, error rates in the musicality task as a con-
tinuous factor, and error rates or N400 effects in the seman-
tic task as a dependent factor) were used to test whether
the differences between the slopes and intercepts of the
two groups were significant.

ERGEBNISSE

Statistical Analyses

ANOVAs were computed using Statistica software
( Version 12.0, StatSoft, Inc., Tulsa, OK). For ERRs and
RTs, ANOVAs always included Group (MUS vs. NM) als
between-subject factor as well as specific factors for each
Aufgabe. As the phonological categorization task was the only

Results are presented first for the screening measures of
cognitive ability and musical aptitude and second for the ex-
perimental tasks. For each experimental task, behavioral da-
ta are presented first followed by the ERPs data, except for
the word learning phase in which no behavioral data were
recorded and for the long-term memory tasks in which no
ERPs were recorded. Endlich, for ERPs data (except in the

Dittinger et al.

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Tisch 1. Results of ANOVAs on the ERPs Data in Different Tasks of the Main Experimental Session

Word Learning Phase

Matching Task

Semantic Task

ALL

MUS

ALL

MUS

ALL

MUS

N400 (340–550 msec)

G × B

G × L

G × R

G × B × L

G × B × R

G × L × R

2.72 .11

0.12 .73

0.41 .67

0.13 .88

4.65 .01

1.87 .16

0.67 .62

G × B × L × R

0.82 .52

G × C

G × B × C

G × C × L

G × C × R

G × B × C × L

G × B × C × R

G × C × L × R

G × B × C × L × R

–

3.85

3.23

0.21

0.75

0.07

2.40

1.27

0.40

1.61

0.42

1.25

3.14

0.83

1.63

1.82

1.97

.06

.08

.82

.48

.93

.10

.29

.81

.21

.52

.29

.05

.44

.21

.13

.43

–

4.25 .05

1.57

.23

2.72 .12

3.13

.09 <.001 7.05 .002 16.38 <.001 0.10 .91 0.52 1.32 .27 0.42 .80 3.29 0.68 .05 .61 0.12 .89 1.46 0.54 .71 1.06 .60 .24 .38 .95 .29 0.28 0.08 0.84 0.53 0.07 0.01 1.17 9.72 <.001 5.36 B B × L B × R B × L × R C C × B C × L C × R C × B × L C × B × R C × B × L × R N200 (230–340 msec) G G × B G × L G × R G × B × L G × B × R G × L × R – – – – – – – – – – – – – – 1.24 .28 0.13 .73 0.60 .55 0.04 .96 1.05 .36 0.70 .50 0.99 .42 G × B × L × R 0.68 .61 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – p F p F p – – – – – – – – – – – – – – – – .99 .76 .93 .50 .48 .79 .01 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 7.68 .02 0.31 .74 5.08 .01 0.54 .71 1.38 .26 2.06 .17 0.93 0.02 0.21 3.14 0.37 0.01 2.23 0.60 1.06 0.84 0.21 0.04 0.01 0.15 1.19 0.52 1.69 0.81 0.36 0.65 0.10 1.46 5.58 .009 1.41 .34 .89 .81 .05 .69 .99 .07 .67 .31 .37 .81 .96 .99 .86 .32 .72 .20 .45 .70 .63 .75 .24 .25 F – – – – – – – – – – – – – – – – p – – – – – – – – – – – – – – – – F – – – – – – – – – – – – – – – – p – – – – – – – – – – – – – – – – 0.63 0.25 0.10 0.71 0.65 0.04 0.19 .44 .78 .90 .59 .43 .85 .83 1.11 0.91 0.32 0.51 0.42 2.43 2.25 .31 .42 .73 .73 .53 .14 .12 27.80 <.001 28.34 <.001 6.38 .005 19.18 <.001 8.98 <.001 10.22 <.001 0.47 2.43 1.29 5.56 0.04 0.27 0.87 0.03 0.69 1.55 0.20 .63 .10 .28 .03 .84 .77 .43 .97 .51 .19 .94 1.02 2.99 1.11 .38 .07 .36 0.39 .68 0.09 .91 1.15 .34 0.45 1.12 0.59 .64 .33 .67 0.43 1.36 0.57 .65 .27 .69 0.13 0.18 0.54 .88 .84 .71 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 1.49 7.26 2.41 2.56 0.29 0.85 1.76 0.69 .23 .01 .10 .09 .75 .43 .14 .60 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 1590 Journal of Cognitive Neuroscience Volume 28, Number 10 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / t . f u s e r o n 1 7 M a y 2 0 2 1 Table 1. (continued ) Word Learning Phase Matching Task Semantic Task ALL MUS NM ALL MUS NM ALL MUS NM G × C G × B × C G × C × L G × C × R G × B × C × L G × B × C × R G × C × L × R G × B × C × L × R B B × L B × R B × L × R C C × B C × L C × R C × B × L C × B × R C × B × L × R F – – – – – – – – p – – – – – – – – F – – – – – – – – p – – – – – – – – F – – – – – – – – p – – – – – – – – F 2.19 0.28 1.85 1.41 0.14 0.31 1.51 1.99 11.61 .002 6.50 4.44 .02 0.93 .40 0.51 .73 4.14 1.25 0.26 .02 .03 .30 .91 5.11 .04 2.28 0.82 .45 1.62 0.47 .63 0.30 1.33 .27 0.71 p .15 .60 .17 .25 .87 .74 .21 .10 .14 .21 .74 .58 F – – – – – – – – p – – – – – – – – F – – – – – – – – p – – – – – – – – 0.81 0.77 0.20 0.37 .38 .47 .82 .83 1.55 .23 0.90 .42 1.33 .28 0.62 .65 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 16.53 <.001 15.22 .002 3.38 .09 0.72 3.02 .40 .06 0.93 4.04 .35 .03 0.05 .82 1.18 .32 20.06 <.001 15.12 <.001 6.12 .006 6.83 .002 5.05 0.21 1.34 .81 .27 0.20 1.13 .82 .34 0.16 .85 0.26 .77 4.25 .003 3.98 .007 1.88 .13 0.28 5.25 1.53 .76 0.57 .008 4.97 .20 1.24 F 1.49 0.004 0.64 0.88 0.37 0.50 2.19 0.69 0.19 1.24 0.26 0.41 4.16 4.32 1.59 p .23 .95 .53 .42 .70 .61 .08 .60 .67 .30 .77 .80 .05 .05 .21 F – – – – – – – – 5.36 0.78 0.58 1.40 4.87 2.00 1.60 p – – – – – – – – .04 .47 .57 .25 .05 .18 .22 .01 .57 .01 .30 F – – – – – – – – 2.35 0.75 0.52 0.09 0.37 2.33 0.22 1.89 0.10 1.17 0.94 p – – – – – – – – .15 .48 .60 .99 .55 .15 .80 .17 .90 .33 .45 Although the Condition × Anterior/posterior interactions are significant in musicians (MUS) and in nonmusicians (NM) for the N400 component, the effects are reversed in both groups (typical N400 effect over parietal sites in MUS, inversed N400 effect over frontal sites in NM). Significant effects are printed in italics, and exact levels of significance are indicated except when the p values are inferior to .001 (<.001). G = Group; B = Block; C = Condition; L = Laterality; R = Anterior/ Posterior. phonological categorization task where the N100 compo- nent is of main interest), analysis of the N400 component is presented first, followed by analyses of the N200. Screening Measures Cognitive Ability Psychometric data were evaluated by means of univariate ANOVAs. Results showed no significant Group differences regarding general reasoning abilities (i.e., progressive matrices, PM47; F(1, 28) = 1.37, p = .25), verbal working memory (reverse digit span; F(1, 28) = 2.88, p = .10), nor visual attention (F(1, 28) = 3.17, p = .09). By con- trast, MUS (mean = 7.6, SD = 0.30) showed better short-term memory abilities than NM (mean = 6.7, SD = 0.30; direct digit span; F(1, 28) = 5.53, p = .03). Musical Aptitude A 2 × 2 ANOVA (i.e., 2 Groups × 2 Tasks) showed that MUS made fewer errors (6.7%, SD = 2.0) than NM (17.6%, SD = 2.0; main effect of Group: F(1, 28) = 14.71, p < .001), and all participants performed better on the rhythmic (9.8%, SD = 1.6) than on the melodic task (14.4%, SD = 2.0; main effect of Task: F(1, 28) = 4.19, p = .05) with no Group × Task interaction. Experimental Tasks Phonological Categorization Task Behavioral data. Results of 2 × 2 × 4 ANOVAs [i.e., 2 Groups (MUS vs. NM) × 2 Orders (pre vs. post) × 4 Tasks ( Voicing vs. Vowel length vs. Pitch vs. Aspiration)] showed that MUS (6.6%, SD = 2.2) made overall fewer errors compared with NM (19.1%, SD = 2.2; main effect of Group: F(1, 24) = 16.29, p < .001; Figure 2A). The influence of music training was largest in the pitch (MUS: 4.1%, SD = 4.8; NM: 25.8%, SD = 4.9; Tukey, p < .001) and aspiration tasks (MUS: 7.6%, SD = 5.5; NM: 28.7%, SD = 5.5; Tukey, p < .001; Group × Task interaction: F(3, 72) = 11.82, p < .001). Finally, only NM improved their level of performance from pre to post in the pitch task (Group × Task × Order interaction: F(3, 72) = 3.31, Dittinger et al. 1591 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j f t . / u s e r o n 1 7 M a y 2 0 2 1 p = .03; NM: pre: 30.3%, SD = 4.1, post: 21.3%, SD = 3.3, Tukey, p = .02; MUS: pre: 4.5%, SD = 4.1, post: 3.7%, SD = 3.3, Tukey, p = .99). Analyses of RTs did not reveal sig- nificant Group differences (Figure 2B). Electrophysiological data. N100 amplitudes were eval- uated by means of a 2 × 2 × 4 × 3 × 3 ANOVA (i.e., 2 Groups × 2 Orders × 4 Tasks × 3 Laterality positions [left vs. midline vs. right] × 3 Anterior/Posterior posi- tions [frontal vs. central vs. parietal]). Results revealed a significant Group × Task × Laterality interaction effect (F(6, 168) = 3.19, p = .005). Separate ANOVAs for each group showed that only MUS were characterized by a larger N100 in the aspiration task (MUS: −6.50 μV, SD = 2.59 and NM: −5.57 μV, SD = 1.81) compared with the other three tasks over the left hemisphere and at midline electrodes (Task × Laterality interaction: MUS: −5.47 μV, SD = 1.78; F(6, 84) = 3.13, p = .008, Tukey, both ps < .001; NM: −5.01 μV, SD = 1.74; F < 1; Fig- ure 3A and B). Word Learning Phase Electrophysiological data. The N400 as well as the N200 were evaluated by means of 2 × 2 × 3 × 3 ANOVAs (i.e., 2 Groups × 2 Blocks [1 vs. 2] × 3 Laterality × 3 Anterior/Posterior positions). For all participants and in line with previous results, the N400 component was larger over frontal (−2.63 μV, SD = 0.87) and central (−2.52 μV, SD = 0.76) sites compared with parietal sites (−1.29 μV, SD = 0.66; Tukey, both ps < .001; main effect of Anterior/Posterior: F(2, 56) = 25.25, p < .001). In addition, the Group × Block × Later- ality interaction effect was significant (F(2, 56) = 4.65, D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / . f t u s e r o n 1 7 M a y 2 0 2 1 Figure 2. Percentages of errors (ERRs) and RTs in the different tasks are shown for musicians (MUS) in red and for nonmusician controls (NM) in black. For the phonological categorization task (A and B), results are illustrated for the premeasurement (beginning) and postmeasurement (end of main experimental session), separately for each task (voicing, vowel length, pitch, and aspiration). For the matching task (C and D), results for Match (solid lines) and Mismatch (MM: dotted lines) words are illustrated in the two blocks of the main experimental session and in the three blocks of the long-term memory session. For the semantic task (E and F), results for semantically Related (solid lines) and Unrelated (dotted lines) words are illustrated in the two blocks of the main experimental session and of the long-term memory session. 1592 Journal of Cognitive Neuroscience Volume 28, Number 10 Figure 3. Phonological categorization. (A) N100 components at the Central (Cz) electrode are compared between tasks for musicians (MUS) and for nonmusician controls (NM). In this and subsequent figures, time in milliseconds is in abscissa and the amplitude of the effects in microvolt is in ordinate. Time zero corresponds to word onset and negativity is plotted upwards. Latency windows for statistical analyses are indicated with gray dotted lines and the level of significance is represented by stars with *p < .05, **p < .01, and ***p < .001 (red stars for MUS and black stars for NM). (B) Topographic voltage distribution maps illustrate the N100s to the words separately for each task and for MUS and NM. Voltage values are scaled from −8 to +8 μV. p = .01). Separate group analyses showed that only MUS showed significantly increased amplitudes from Block 1 to Block 2. This effect was localized over the left hemisphere and midline electrodes (MUS: Block 1: −1.54 μV, SD = 0.76 and Block 2: −2.16 μV, SD = 0.79; Block × Laterality interaction: F(2, 28) = 16.38, p < .001; Tukey, both ps < .001 and NM: Block 1: −2.34 μV, SD = 1.36 and Block 2: −2.91 μV, SD = 1.38; main effect of Block: F(1, 14) = 2.72, p = .12; Fig- ure 4A and B). Analyses of the N200 component did not reveal signif- icant Group differences (main effect of Group: F(1, 28) = 1.24, p = .28), but all participants showed significantly increased amplitudes from Block 1 (−1.13 μV, SD = 1.22) to Block 2 (−1.79 μV, SD = 0.99; main effect of Block: F(1, 28) = 11.61, p = .002; Figure 4A and B). Matching Task Behavioral data. Results of three-way ANOVAs (i.e., 2 Groups × 2 Blocks × 2 Conditions [match vs. mismatch]) showed that ERRs did not significantly differ between the two groups (main effect of Group: F(1, 24) = 2.19, p = .15). However, all participants committed overall fewer er- rors for match (14.2%, SD = 2.6) compared with mismatch words (19.4%, SD = 2.2) and fewer errors in Block 2 (15.2%, SD = 2.1) than in Block 1 (18.5%, SD = 2.2; main effect of Condition: F(1, 24) = 7.68, p = .01; main effect of Block: F(1, 24) = 9.27 p = .006; Figure 2C). In line with ERRs, analyses of RTs did not reveal between-group differ- ences (main effect of Group: F < 1), but overall faster RTs for match (1041 msec, SD = 64) than for mismatch words (1080 msec, SD = 70; main effect of Condition: F(1, 24) = 5.90, p = .02) and faster RTs in Block 2 (994 msec, SD = 61) than in Block 1 (1128 msec, SD = 73; main effect of Block: F(1, 24) = 60.45, p < .001; Figure 2D). Electrophysiological data. The N400 as well as the N200 component were evaluated by means of 2 × 2 × 2 × 3 × 3 ANOVAs (2 Groups × 2 Blocks × 2 Conditions × 3 Later- ality × 3 Anterior/Posterior positions). Analysis of the N400 revealed a significant Group × Con- dition × Anterior/Posterior interaction effect (F(2, 56) = 3.14, p = .05). Results of separate group analyses showed larger N400 amplitudes in MUS for mismatch (−0.10 μV, SD = 1.82) compared with match words over centroparietal regions (0.67 μV, SD = 1.58; Condition × Anterior/Posterior interaction: F(2, 28) = 28.34, p < .001; Tukey, central: p = .02; parietal: p < .001). The opposite pattern was found in NM with larger N400 for match (−1.34 μV, SD = 1.42) than for Dittinger et al. 1593 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j . t / f u s e r o n 1 7 M a y 2 0 2 1 mismatch words over frontocentral sites (−0.89 μV, SD = 1.53; Condition × Anterior/Posterior interaction: F(2, 28) = 6.38, p = .005; Tukey, frontal: p = .001; central: p = .03; Figure 5A and B). The N200 amplitude was overall smaller in MUS (−0.15 μV, SD = 0.60) compared with NM (−2.14 μV, SD = 0.60; main effect of Group: F(1, 28) = 5.56, p = .03) and the N200 effect (i.e., mismatch minus match words) was more widely distributed in MUS compared with NM (Group × Block × Condition × Laterality × Anterior/Posterior interaction: F(4, 112) = 1.99, p = .10; Figure 5A and B). MUS showed larger N200 amplitudes for mismatch (P4: −1.98 μV, SD = 0.76) than for match words (P4: 0.01 μV, SD = 0.68) over centro- parietal scalp sites with largest differences over midline and right hemisphere (Condition × Anterior/Posterior interac- tion: F(2, 28) = 15.12, p < .001; Condition × Laterality in- teraction: F(2, 28) = 4.04, p = .03). In addition, the N200 effect was larger in Block 2 (P4: −2.22 μV, SD = 0.50) than in Block 1 (P4: −1.66 μV, SD = 0.63) over midline and right centroparietal sites (Condition × Block × Laterality × Anterior/Posterior interaction: F(4, 56) = 3.98, p = .007). NM also showed an N200 effect that was localized over pa- rietal sites (−1.06 μV, SD = 1.41; Condition × Anterior/ Posterior interaction: F(2, 28) = 6.12, p = .006). D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j t f . / u s e r o n 1 7 M a y 2 0 2 1 Figure 4. Word learning phase. (A) ERPs recorded at left parietal (P3), central (Cz), and right frontal sites (F4) in Block 1 (solid line) and Block 2 (dotted line) are overlapped for all participants (ALL: black lines, top), and separately below for musicians (MUS: red lines) and nonmusician controls (NM: black lines). (B) Topographic voltage distribution maps of the differences between the two blocks (Block 2 minus Block 1) are illustrated for the ERP components of interest (N200, N400), separately for MUS and for NM. Voltage values are scaled from −1.0 to +1.0 μV. 1594 Journal of Cognitive Neuroscience Volume 28, Number 10 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j t f . / u s e r o n 1 7 M a y 2 0 2 1 Figure 5. Matching task. (A) Left: ERPs recorded at frontal (Fz) and parietal (Pz) sites are overlapped for Match (solid lines) and Mismatch (dotted lines) words for all participants across the two blocks of trials (ALL: black lines). Central and right: ERPs are presented separately for Block 1 and Block 2 and for musicians (MUS: red lines) and nonmusician controls (NM: black lines). (B) Difference waves (Mismatch minus Match) are overlapped for MUS (red) and NM (black) separately for Block 1 and for Block 2 at Fz and Pz. Topographic voltage distribution maps of the Mismatch minus Match differences are illustrated for N200 and N400 components separately and for MUS and NM in Block 1 and Block 2. Voltage values are scaled from −2.5 to +1.5 μV. Semantic Task Behavioral data. Results of three-way ANOVAs (i.e., 2 Groups × 2 Blocks × 2 Conditions [related vs. unrelated]) showed that MUS (23.6%, SD = 2.0) made overall fewer errors than NM (30.5%, SD = 2.0; main effect of Group: F(1, 24) = 5.82, p = .02), and all participants made fewer errors for unrelated (22.6%, SD = 2.6) than for related words (31.5%, SD = 2.9; main effect of Condition: F(1, 24) = 11.24, p = .003; Figure 2E). Moreover, all par- ticipants made fewer errors in Block 2 (30.2%, SD = 2.3) than in Block 1 (23.9%, SD = 2.4; main effect of Block: F(1, 24) = 12.37, p = .002). RTs were faster for related (1210 msec, SD = 72) than for unrelated words (1342 msec, SD = 71; main effect of Condition: F(1, 24) = 41.32, p < .001) and faster in Block 2 (1159 msec, SD = 70) than in Block 1 (1393 msec, SD = 75; main effect of Block: F(1, 24) = 88.92, p < .001), with no between-group differences (main effect of Group: F < 1; Figure 2F). Electrophysiological data. The N400 as well as the N200 component were evaluated by means of 2 × 2 × 2 × 3 × 3 ANOVAs (i.e., 2 Groups × 2 Blocks × 2 Con- ditions × 3 Laterality × 3 Anterior/Posterior positions). N400 analyses revealed a significant Group × Anterior/ Posterior interaction effect (F(2, 56) = 3.14, p = .05). As typically reported in the literature (Kutas et al., 1988), the N400 was larger for semantically unrelated (2.17 μV, SD = 1.93) compared with related words (3.29 μV, SD = 1.66) over parietal sites in MUS (Condition × Anterior/Posterior interaction: F(2, 28) = 8.98, p < .001; Tukey, parietal: p < .001). By contrast, a reversed N400 effect was found in NM with larger N400 for related (−2.09 μV, SD = 1.60) than for unrelated words (−1.19 μV, SD = 1.06) over frontal sites (Condition × Anterior/Posterior; F(2, 28) = 10.22, p < .001; Tukey, frontal: NM: p = .002, Figure 6A and B). The N200 amplitude was larger in Block 2 than in Block 1 in MUS only (Group × Block: (F(1, 28) = 7.26, p = .01; MUS: Block 1: 1.58 μV, SD = 2.62 and Block 2: 0.89 μV, SD = 2.68; F(1, 14) = 5.35, p = .04 and NM: Block 1: −0.11 μV, SD = 3.15 and Block 2: 0.39 μV, SD = 2.58; F(1, 14) = 2.35, p = .15; Figure 6A and B). In addition, the N200 was also larger for unrelated than for related words in MUS but not in NM (main effect of Dittinger et al. 1595 D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / f . t Figure 6. Semantic task. (A) Left: ERPs recorded at frontal (Fz) and parietal sites (Pz) are overlapped for semantically Related (solid lines) and Unrelated (dotted lines) words for all participants across the two blocks of trials (ALL: black lines). Central and right panels: ERPs are presented separately for Block 1 and Block 2 and for musicians (MUS: red lines) and nonmusician controls (NM: black lines). (B) Difference waves (Unrelated minus Related) are overlapped for MUS (red) and NM (black) separately for Block 1 and Block 2 at Fz and Pz. Topographic voltage distribution maps of the Unrelated minus Related differences are illustrated for N200 and N400 components separately and for MUS and NM in Block 1 and Block 2. Voltage values are scaled from −2.5 to +1.0 μV. Condition: MUS: Related: 1.88 μV, SD = 2.67 and Unre- lated: 0.59 μV, SD = 3.03; F(1, 14) = 4.87, p = .05 and NM: Related: 0.30 μV, SD = 3.63 and Unrelated: −0.02 μV, SD = 2.23; F < 1; Group × Condition × Laterality × Anterior/Posterior interaction: F(4, 112) = 2.19, p = .08), and this effect was larger over central and parietal sites (Condition × Anterior/Posterior interaction: F(2, 28) = 5.05, p = .01; Tukey, both ps < .001). Figure 7. Correlation analyses. Correlations between the percentages of error in the musicality test (ERRs Musicality) and the sizes of the N400 effect (Unrelated minus Related) in the semantic task are illustrated for musicians (MUS: red) and for nonmusician controls (NM: black). Dotted lines represent the 95% confidence interval of the correlation line. u s e r o n 1 7 M a y 2 0 2 1 1596 Journal of Cognitive Neuroscience Volume 28, Number 10 Long-term Memory: Matching Task (Behavior) Results of three-way ANOVAs (i.e., 2 Groups × 3 Blocks × 2 Conditions [match vs. mismatch]) showed that MUS (20.3%, SD = 2.8) made fewer errors compared with NM (28.6%, SD = 2.9; main effect of Group: F(1, 19) = 4.19, p = .05; Figure 2C). Moreover, all participants improved their level of performance from Block 1 (31.4%, SD = 2.6) to Blocks 2 and 3 (22.2%, SD = 3.4 and 19.8%, SD = 3.6, respectively; main effect of Block: F(2, 38) = 26.40, p < .001). No significant between-group differences were found on RTs but in both groups, RTs were faster in Block 3 (965 msec, SD = 67) than in Block 2 (1049 msec, SD = 72) and slowest in Block 1 (1168 msec, SD = 82; main ef- fect of Block: F(2, 38) = 36.48, p < .001; Figure 2D). Long-term Memory: Semantic Task (Behavior) Results of three-way ANOVAs (i.e., 2 Groups × 2 Blocks × 2 Conditions [related vs. unrelated]) showed that between- group differences were not significant on ERRs nor on RTs. However, all participants made fewer errors for un- related (21.5%, SD = 6.0) than for related words (29.7%, SD = 6.3; main effect of Condition; F(1, 19) = 6.60, p = .02) and were faster for related (1055 msec, SD = 106) than for unrelated words (1151 msec, SD = 119; main effect of Condition; F(1, 19) = 14.54, p = .001; Figure 2E and F). Relationships between Musical Aptitude, Behavioral Data, and Brain Activity Error rates in the musicality test were correlated with the size of the N400 effects in the semantic task for MUS (r = .74, p = .01) but not for NM (r = .01, p = .97; Figure 7A and B). Moreover, the slopes of the correla- tions were significantly different for the two groups (main effect of Group: F(1, 26) = 7.36, p = .01) as well as the cor- relation intercepts (Group × Musicality interaction: F(2, 26) = 6.52, p = .005). DISCUSSION Summary of Results By using an ecologic valid experimental design we tested the general hypothesis that professional musicians would learn the meaning of novel words more efficiently than control participants. Overall, both behavioral and electro- physiological data support this hypothesis. Behaviorally, musicians performed better than controls in the musicality and phonological categorization tasks. In addition, al- though all participants performed similarly in the matching task, musicians made significantly fewer errors in the se- mantic task. Finally, after 5 months, musicians remem- bered more words than controls as reflected by lower error rates in the matching task. The electrophysiological markers of word learning also clearly differed between the two groups. Although con- trol participants showed similar N100 amplitudes in all phonological categorization tasks, musicians showed an increase in N100 amplitude when categorizing the most difficult aspiration contrast. Most importantly and in line with the word learning literature, both groups showed enhanced N400 amplitudes over frontal scalp sites when learning the meaning of novel words. However, only musicians were additionally characterized by larger left- lateralized N400s in the second block compared with the first block of the word learning phase. Finally, only musicians showed the typical centroparietal distribution of the N400 effect in the matching and semantic tasks. By contrast, nonmusicians showed reversed N400 effects in both tasks over frontal sites. These findings are dis- cussed in detail below. It is, however, important to note that cross-sectional studies, as the one reported here, are necessary to first demonstrate differences between musically trained and untrained participants before de- signing longitudinal experiments to test for the causality of the reported effects. Spatiotemporal Dynamics in the Learning Phase Results showed clear evidence for fast brain plasticity, as reflected by the rapid development of the N400 in both groups of participants after only 3 min of novel word learning (Block 1), that is after 10 repetitions of each picture–word association (see Figure 4). This finding extends previous results on word learning showing N400 enhancements when learning a second language (McLaughlin et al., 2004), the meaning of rare words (Perfetti et al., 2005), and when learning the meaning of novel words or pseudowords from highly constrained sentence contexts (Borovsky et al., 2010, 2012; Batterink & Neville, 2011; Mestres-Missé et al., 2007). Importantly, and in line with previous work in adults (Borovsky et al., 2012; Mestres-Missé et al., 2007) and in children (François et al., 2013; Friedrich & Friederici, 2008), in both musi- cians and controls the N400 component to novel words was larger frontocentrally than parietally. These results are compatible with previous findings, suggesting that pre- frontal and temporal brain regions are associated with the maintenance of novel information in working memory (Hagoort, 2014) and the acquisition of word meaning (Rodriguez-Fornells et al., 2009). Importantly, however, the N400 increase from the first to the second block of novel word learning was only sig- nificant in musicians (see Figure 4A). In line with our hy- pothesis, this is taken to suggest faster encoding of novel word meaning in musicians than in controls. In addition, word learning in musicians was accompanied by a rapid shift of the N400 component distribution from frontal to centroparietal sites. This shift in distribution is in line with the hypothesis that musicians have already integrated novel words representations in semantic memory (Batterink & Dittinger et al. 1597 D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / t f . u s e r o n 1 7 M a y 2 0 2 1 Neville, 2011) and that the N400 centroparietal distribution in musicians reflects access to semantic memory (Kutas & Federmeier, 2011). By contrast, in control participants, the N400 component remained larger frontally throughout the entire learning phase, suggesting that nonmusicians had not yet integrated the novel words’ meaning into estab- lished semantic networks. This interpretation can be di- rectly tested in future experiments by increasing the number of repetitions of picture–word associations. Under such conditions, a frontal to centroparietal shift in N400 distribution should also be found in nonmusicians. Evidence for Rapidly Established Representations of Novel Words in the Matching and Semantic Tasks As expected, all participants, whether musicians or con- trols, were able to learn the nine picture–word associa- tions, as reflected by error rates below chance level (i.e., 50% errors) in both the matching (mean = 17%) and se- mantic tasks (mean = 27%; see Figure 2C and E). To follow the dynamics of word learning within each task, results were compared between the first and the second block of trials. Interestingly, the level of performance increased with repetition in both tasks, indicating a still ongoing learning effect. Moreover, both musicians and controls showed clear matching effects with lower error rates and fas- ter RTs for match compared with mismatch words (Boddy & Weinberg, 1981). However, and in contrast to typical se- mantic priming effects (Meyer & Schvaneveldt, 1971), both groups of participants made more errors for semantically related words than for semantically unrelated words. Al- though unexpected, this result may reflect a response bias towards rejection (i.e., considering the word as unrelated to the picture) as a consequence of task difficulty generat- ing higher uncertainty (Gigerenzer, 1991). In other words, when participants were not certain whether the pictures and the words were semantically related (e.g., “honey” and “bear”), they tended to respond that they were unre- lated. By contrast, results for RTs conform to the literature (i.e., faster RTs for semantically related than for unrelated words; Meyer & Schvaneveldt, 1971). Although the pres- ence of a speed–accuracy trade-off limits the interpretation of this priming effect, faster RTs for semantically related words are indicative that new pictures that had not been seen before in the experiment did activate the representa- tions of semantically related newly learned words. Turning to the influence of music training, musicians and nonmusician controls performed similarly in the matching task but musicians outperformed controls in the semantic task. This contrastive pattern of results sug- gests that the two tasks tap into different memory sys- tems. To decide whether the newly learned word matched the picture in the matching task, participants had to retrieve the specific picture–word associations that were stored in episodic memory during the word learning phase. By contrast, in the semantic task partici- pants had to retrieve general information from semantic memory because the novel pictures that were presented before the newly learned words had not been seen be- fore in the experiment. In line with the centroparietal shift in N400 distribution observed at the end of the word learning phase, the finding that musicians outperformed nonmusicians in the semantic task is taken as evidence that musicians had already integrated the novel words’ meanings into semantic memory so that priming effects generalized to new pictures. ERPs in the matching and semantic tasks also add sup- port to this interpretation. In musicians and for both tasks, the N400 over centroparietal sites was larger for unexpected (mismatch/unrelated) than for expected words (match/related; Figures 5 and 6). This sensitivity to word characteristics and this scalp distribution corre- spond to the N400 component, typically considered as the electrophysiological marker of the integration of novel words’ meanings into semantic memory (Borovsky et al., 2012; Batterink & Neville, 2011; Mestres-Missé et al., 2007) and “as reflecting the activity in a multimodal long-term memory system that is induced by a given input stimulus during a delimited time window as meaning is dynamically constructed” (Kutas & Federmeier, 2011, p. 22; see also Steinbeis & Koelsch, 2008, for N400s elicited by single chords incongruous with the preceding context). By con- trast, in nonmusician controls, the N400 effect was re- versed over frontocentral sites in both tasks, with larger N400 for expected (match/related) than for unexpected words (mismatch/unrelated). This finding was surprising based on previous results showing that the N400 is larger for unrelated than for related words in both lexical deci- sion tasks (Borovsky et al., 2012) and semantic priming ex- periments (Mestres-Missé et al., 2007). Because the amount of music training was not controlled in these ex- periments, a possible explanation is that some of the par- ticipants had musical skills, hence influencing the results. However, stimulus and task differences are more likely to account for this discrepancy. Most experiments on ERPs and word learning in adults used similar designs with novel words embedded in sentence contexts. Here and as in ex- periments conducted with children (Friedrich & Friederici, 2008; Torkildsen et al., 2008) and with adults (Dobel et al., 2009), participants learned novel words through picture– word associations. It is thus possible that this learning mode was more demanding than learning the meaning of novel words from sentence contexts. Along these lines, we interpret the reversed N400 effect in nonmusicians in the matching and semantic tasks as showing that nonmu- sicians had not yet fully learned the picture–word associa- tions (as reflected by the frontal distribution of the N400 in these tasks that is similar to the frontal N400 found during word learning) and that they were still building up new ep- isodic memory traces based on the correct information provided by the matching words (as reflected by larger N400 increase from the first to the second block for match than for mismatch words; see Figure 5A). As mentioned 1598 Journal of Cognitive Neuroscience Volume 28, Number 10 D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j . f t / u s e r o n 1 7 M a y 2 0 2 1 above, these interpretations can be tested by increasing the number of trials in the learning phase as well as in the matching and semantic tasks and by comparing different word learning designs (e.g., novel words in sentence con- texts vs. picture–word associations). Finally, although the main focus of this experiment was on the modulations of N400 amplitude with novel word learning, results also revealed interesting effects on the N200 component. In both groups, the N200 components in the matching task were larger for mismatching than for matching words over parietal sites (see Figure 5). Insofar as the N200 has been associated with categorization pro- cesses (Friedrich & Friederici, 2008), mismatching words possibly required more effortful categorization processes than matching words. However, there is also evidence that the N200 component reflects early contextual influ- ences (van den Brink, Brown, & Hagoort, 2001) and pho- nological processing (Connolly & Phillips, 1994). Thus, mismatching words possibly elicited larger N200 than matching words because they were unexpected based on the picture context and/or at the phonological level. In the semantic task, the increase in N200 amplitude for semantically unrelated compared with related words was only found in musicians, again suggesting that word categorization was possibly more efficient in musicians and/or that musicians were more sensitive to the context (i.e., the picture) or to the phonological characteristics of novel words than nonmusicians. Evidence for Long-lasting Representations of Novel Words To our knowledge, this is the first experiment comparing long-term memory (after 5 months) for novel words in musicians and controls. Clearly, results need to be con- sidered with caution because of the high dropout rate. Nevertheless, they point to improved long-term memory in musicians compared with controls in the matching task (lower error rates) that was always performed first. The memory traces of the words that have been learned 5 months before therefore seem stronger in musicians than in nonmusicians. By contrast, no between-group dif- ferences were found in the semantic task possibly be- cause both groups of participants similarly benefited from the reactivation of memory traces during the match- ing task. Taken together, these results point to long- lasting effects of rapidly established word representations during word learning, and they open new perspectives to further test for the influence of music training on long-term memory. Evidence for Transfer Effects from Music Training to Word Learning In summary, behavioral and electrophysiological data showed that music training improved several aspects of word learning. How can we account for the influence of music training on novel word learning? Cascading Effects from Perception to Word Learning The first interpretation, in terms of cascading effects, is that enhanced auditory perception and auditory atten- tion (Strait et al., 2015) in musicians drive the facilitation observed in word learning through different subsequent steps (i.e., building up new phonological representations and attaching meaning to them, storing this new informa- tion in short- and long-term memory). In support of this interpretation, the error rate in the musicality test was cor- related with the size of the N400 effect in the semantic task in musicians but not in controls (Figure 7), thereby clearly pointing to a relationship between auditory perception/ attention and word learning. Moreover, in line with the hypothesis of improved speech discrimination in musicians when the task is most difficult (Diamond, 2013; Schön et al., 2004), musicians outperformed control participants in the phonological categorization of tonal and aspiration contrasts, but both groups performed equally well for simple voicing con- trasts (/Ba/ vs. /Pa/). Thus, music training was most useful to discriminate phonemes that are contrastive in Thai but that do not belong to the French phonemic repertoire (Figure 2A). It should be noted in this respect that, al- though vowel length is not contrastive in French, controls nevertheless performed as well as musicians in this task, possibly because the natural difference in vowel duration was large enough (270 msec) to be easily perceived by non- musician controls. Taken together, these findings add sup- port to the hypothesis of transfer effects between music and speech (Asaridou & McQueen, 2013; Besson et al., 2011), with near transfer for acoustic cues common to mu- sic and speech such as frequency and with far transfer to unfamiliar linguistic cues such as aspiration. Note also that the N100 amplitude in musicians was en- hanced for the nonnative aspiration contrast (see Figure 3). Because this task was more difficult for French native speakers than the other tasks, as revealed by behavioral data, the increased N100 may reflect increased focused attention (Strait et al., 2015) and mobilization of neuro- nal resources in musicians. An alternative but com- plementary interpretation is that this increase in N100 amplitude reflected increased neural synchronicity and structural connectivity in musicians, who are typically more sensitive to the acoustic-phonetic properties of speech sounds than nonmusicians who showed similar N100 com- ponents in all tasks (Bidelman et al., 2014; Elmer et al., 2013; Chobert et al., 2012; Musacchia et al., 2007; Wong & Perrachione, 2007). Finally, and in line with the cascade hypothesis, it may be that the very nature of the stimuli (monosyllabic Thai words) is at least partly responsible of the musician’s ad- vantage in the semantic task. Because musicians are more sensitive than nonmusicians to variations in pitch, Dittinger et al. 1599 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 8 / 1 2 0 8 / 1 1 5 0 8 / 4 1 1 5 9 8 5 4 1 / 3 1 5 2 7 8 o 5 c 4 n 9 _ 6 a / _ j 0 o 0 c 9 n 9 7 _ a p _ d 0 0 b 9 y 9 g 7 u . e p s t d o f n b 0 y 7 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j t . f / u s e r o n 1 7 M a y 2 0 2 1 duration, and VOT, this possibly helped them build pre- cise phonological representations that were then more easily associated to a novel word meaning. One way to test for this interpretation would be to use a similar de- sign but with written instead of spoken words thereby potentially reducing the musicians’ acoustic-phonetic advantage. Multidimensional Facilitation Effects The second interpretation, that is by no means contradic- tory but rather complementary to the cascading interpre- tation, is that the multidimensional nature of music training independently improved the several functions that are necessary for word learning. There is already some evidence in the literature for enhanced working and verbal memory (George & Coch, 2011; Ho et al., 2003) and executive functions (Zuk et al., 2014; Moreno et al., 2011; Rogalsky et al., 2011; Pallesen et al., 2010) with musicianship. In line with these results, musicians showed enhanced auditory short-term memory (digit span; George & Coch, 2011). Moreover, as reported in previous experiments, we also found significant be- tween-group differences in the ERP component related to semantic integration and memory (N400; Mestres- Missé et al., 2007; Perfetti et al., 2005; McLaughlin et al., 2004). However, in contrast to previous results, we found no evidence for increased working memory (reversed digit span; George & Coch, 2011) or for im- proved nonverbal intelligence (PM47) in adult musicians. Note that the assessment of general cognitive abilities was quite limited in the present experiment because of time constraints. It is thus possible that general cognitive abilities other than the ones tested here could have influ- enced the observed between-group differences in word learning. Future experiments will aim at including more tests targeting at elucidating different cognitive functions (selective and sustained attention, working, short-term, and long-term memory, executive functions). Finally, it could also be argued that musicians performed better than nonmusicians because they were generally more motivated (Corrigall, Schellenberg, & Misura, 2013). Al- though this is difficult to control for, it is important to note that participants were not informed about the aim of the experiment until the end of the session. Only that we were interested in language, music, and the brain. Thus, as they discovered task by task what the experi- mental session was about, it is unlikely that general motivation accounted for the present behavioral and electrophysiological effects. Conclusions In summary, by recording ERPs during both learning and test phases (matching and semantic tasks) of novel word learning, results revealed fast changes in brain activity after only a few minutes of exposure to picture–word associa- tions. Moreover, these changes were stronger in musi- cians than in controls. Specifically, the frontoparietal shift of the N400 in the word learning phase (i.e., without motor responses) only developed in musicians, which we interpret as an electrophysiological signature of “fast map- ping” (Carey, 1978). To our knowledge, this is the first re- port showing that music training influences semantic processing. As a future step, we plan to use a longitudinal approach with nonmusician controls trained with music so as to test for the causal influence of music training on word learning. Finally, these results also open new per- spectives to further investigate the influence of music training on long-term memory for applications of music training in the domain of native and second language learning (Moreno, Lee, Janus, & Bialystok, 2015; Chobert & Besson, 2013) and for using this type of experimental design in clinical research to specify the stages of word learning that are most deficient. Acknowledgments We would like to thank all the participants, Chotiga Pattamadilok for registering the auditory stimuli, and Benjamin Furnari for his help with data acquisition and analyses. The work of E. D. M. Ba., M. D. I., and M. B., carried out within the Labex BLRI (ANR-11-LABX-0036), has benefited from support from the French government, managed by the French National Agency for Research (ANR), under the program “Investissements d’Avenir” (ANR-11-IDEX-0001-02). E. D. was supported by a doctoral fellowship from the BLRI and M. Ba. by a doctoral fellowship from the French Ministry of Research and Education. Reprint requests should be sent to Eva Dittinger, Laboratoire de Neuroscience Cognitives, Université Aix Marseille, Centre Saint Charles, 3 Place Victor Hugo, Marseille, France, 13331, or via e-mail: eva.dittinger@blri.fr. Notes 1. Following phonetic transcription in Thai, 1 refers to low- tone, 0 to midtone, ph to aspirated voicing, and the colon to long vowel duration. 2. Voice Onset Time (VOT) is defined as the interval between the noise burst produced at consonant release and the wave- form periodicity associated with vocal cord vibrations (Lisker & Abramson, 1967) . 3. 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