Received: May 25, 2022. Revised: November 21, 2022. Accepted: November 22, 2022
© The Author(s) 2022. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Cerebral Cortex, 2023, 1–13
https://doi.org/10.1093/cercor/bhad034
Original Article
Enhanced neural mechanisms of set shifting in
musically trained adolescents and young adults:
converging fMRI, EEG, and behavioral evidence
K. Saarikivi1,2,3,*, T.M.V. Chan4,5, M. Huotilainen2,6, M. Tervaniemi2,6, V. Putkinen2,7,8,9
1Department of Education, Faculty of Educational Sciences, University of Helsinki, Siltavuorenpenger 5, Helsinki, 00170, Finland,
2Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, Helsinki, 00290, Finland,
3Aalto NeuroImaging, Aalto University, Otakaari 5, Espoo, 02150, Finland,
4Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON M5S 3G3, Canada,
5Department of Psychology, University of Notre Dame, 390 Corbett Family Hall, Notre Dame, IN 46556, United States,
6Centre of Excellence in Music, Mind, Body, and Brain, Faculty of Educational Sciences, University of Helsinki, Siltavuorenpenger 5, Helsinki, 00170, Finland,
7Turku PET Center, University of Turku, Turku University Hospital, Kiinamyllynkatu 4-820520 Turku, Finland,
8Turku Institute for Advanced Studies, University of Turku, 20014 Turun yliopisto, Turku, Finland,
9Turku University Hospital, Turku, Finland
*Corresponding author: Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Haartmaninkatu 8,
00290, Helsinki, Finland. Email: katri.saarikivi@helsinki.fi
Musically trained individuals have been found to outperform untrained peers in various tasks for executive functions. Here, we present
longitudinal behavioral results and cross-sectional, event-related potential (ERP), and fMRI results on the maturation of executive
functions in musically trained and untrained children and adolescents. The results indicate that in school-age, the musically trained
children performed faster in a test for set shifting, but by late adolescence, these group differences had virtually disappeared. However,
in the fMRI experiment, the musically trained adolescents showed less activity in frontal, parietal, and occipital areas of the dorsal
attention network and the cerebellum during the set-shifting task than untrained peers. Also, the P3b responses of musically trained
participants to incongruent target stimuli in a task for set shifting showed a more posterior scalp distribution than control group
participants’ responses. Together these results suggest that the musician advantage in executive functions is more pronounced at an
earlier age than in late adolescence. However, it is still reflected as more efficient recruitment of neural resources in set-shifting tasks,
and distinct scalp topography of ERPs related to updating and working memory after childhood.
Key words: dorsal attention network; ERP; musical training; P3b; set shifting.
Introduction
Executive functions are control mechanisms that enable self-
regulation and higher order phenomena, such as complex
problem solving, goal-directed behavior, planning, and reasoning
(reviews: e.g. Stuss and Alexander 2000; Jurado and Rosselli 2007;
Diamond 2013). Behavioral studies have uncovered 3 correlated
but separable core components of executive functions: inhibition,
cognitive flexibility, and working memory (Miyake et al. 2000;
Lehto et al. 2003; Miyake and Friedman 2012; Diamond 2013;
Herd et al. 2014; Friedman and Miyake 2017; Karr et al. 2018).
Cognitive flexibility is further divided into fluency, or the ability
to relinquish inhibition and generate information from the mind,
and set shifting, or task-switching, the abilities to fluidly change
response strategies within a task, or fluidly change between 2
tasks. Executive functions rely on the prefrontal cortex and their
diverse connections to other brain regions (Alvarez and Emory
2006; Yuan and Raz 2014). These include the “dorsal attention
network,” consisting of the dorsolateral prefrontal cortex, frontal
eye fields, inferior precentral sulcus, and the superior parietal
lobule (Fox et al. 2005; Toro et al. 2008) and the “frontoparietal
control network” comprising the rostrolateral prefrontal cortex,
middle frontal gyrus, anterior insula, dorsal anterior cingulate
cortex, precuneus, and anterior inferior parietal lobule (Corbetta
and Shulman 2002; Badre and D’Esposito 2007; Vincent et al. 2008;
Spreng et al. 2010; Szczepanski et al. 2013). There is also evidence
of involvement of subcortical structures in executive functions,
including the cerebellum (Brissenden et al. 2016; Ramanoel et al.
2018; Habas 2021) and the basal ganglia (Hazy et al. 2007; Saban
et al. 2018).
The subcomponents of executive functions are distinguishable
on the neural level. fMRI studies have shown that the fron-
toparietal control network exhibits different patterns of connec-
tivity for working memory and inhibitory control (Harding et al.
2015). Cognitive flexibility and working memory have in turn
been connected to 2 distinct networks, the fronto-parietal and the
cingulo-opercular, respectively (Dosenbach et al. 2008; Cole et al.
2013; Power and Petersen 2013). Inhibition has been connected
to right-lateralized activation of the dorsolateral prefrontal cor-
tex (Zheng et al. 2008), the frontal inferior gyrus, and the pre-
supplementary motor area (Sharp et al. 2010), reflecting the
attention and motor processes involved in inhibiting prepotent
responses.
In EEG studies, P3b responses emerge to target stimuli in dif-
ferent tasks for executive functions, and have been found to
predict performance in these tasks. Recently, the amplitude of P3b
responses to to-be-remembered stimuli in aworkingmemory task
was connected to working memory capacity (Lenartowicz et al.
2021). In task switching experiments, larger P3b-like response
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amplitudes have been recorded to targets during switching than
updating trials, and to targets presented immediately after a
switching cue compared to repeating trials (Barcelo et al. 2018;
Brydges and Barceló 2018).
Some executive functions are already present at birth, and they
mature rapidly during preschool age (Garon et al. 2008). However,
the developmental trajectory of executive functions is prolonged,
extending to school-age and adolescence, and coinciding with
the development of particularly the prefrontal cortex but also
other brain areas associated with executive functions, such as
the anterior cingulate cortex and basal ganglia (Blakemore and
Choudhury 2006; Best andMiller 2010). Themechanisms of devel-
opment could include synaptic pruning, which takes place last in
prefrontal areas, myelination, and thickening or thinning of the
cortex (Best and Miller 2010).
Musically trained individuals have been found to outperform
untrained peers in tasks of inhibition and set shifting (Bialystok
and DePape 2009; Bugos and Mostafa 2011; Degé et al. 2011;
Moreno et al. 2011; Zuk et al. 2014; Saarikivi et al. 2016, 2019;
Strong and Mast 2019). Longitudinal studies with children under-
goingmusic training, and studies onmusic training interventions,
have found training-related improvements in working memory
(Roden et al. 2013; Nie et al. 2021; Frischen et al. 2021) and
inhibition (Moreno et al. 2011; Bugos and DeMarie 2017; Jaschke
et al. 2018; Frischen et al. 2019; Frischen et al. 2021), pointing
towards a causal role for music training in the enhancement
of executive functions (however, see Bialystok and DePape 2009;
Schellenberg 2011; Janus et al. 2016; Linnavalli et al. 2018). Music
training requires executive functions in many ways that are not
restricted to the auditory domain. For instance, inhibitory control
over distracting sounds is needed when attending to one’s own
instrument in joint playing, and working memory is required for
playing melodies by ear. Set shifting is an integral part of playing
from notes as the musician transforms visual stimuli into motor
responses, shifting between response strategies based on sharp
(), f lat (), and other symbols.
Neuroimaging and electrophysiological studies have also
reported differences between musically trained and untrained
individuals in indices of executive functions. For instance, adult
musicians show larger P3a responses than nonmusicians in
oddball paradigms (Trainor et al. 1999; Brattico et al. 2009; Vuust
et al. 2009; Virtala et al. 2018), indicating faster attention switching
towards sound changes. Adult musicians have also been found
to exhibit larger P3b responses than nonmusicians to deviant
sounds, signaling more efficient updating and working memory
processing (Trainor et al. 1999; Carrión and Bly 2008; Nikjeh et al.
2009). Longitudinal studies with children engaged in musical
activities have also reported enhancement of P3as recorded in an
oddball paradigm, signaling better deviance processing, smaller
P3as elicited by novel sounds, signaling decreased distractibility
(Putkinen et al. 2014, 2021), and larger P2 responses to no-go
targets during a go/no-go task, signaling better inhibitory control
(Moreno et al. 2014).
fMRI studies have revealed both increased and decreased acti-
vation in brain areas related to executive functions in musi-
cally trained individuals compared to untrained peers. Pallesen
et al. (2010) found stronger activation with increasing working
memory load in adult musicians relative to non-musicians in
various regions linked with executive functions including the
right frontal and parietal cortices, right insula, and the cingulate.
Similarly, Kausel et al. (2020) found that musically trained 10–13-
year-old children showed higher activation than their untrained
peers in the fronto-parietal control network during encoding in a
working memory task. In addition, better performance in the task
correlated with years of training and higher activity in the left
inferior frontal gyrus and the left supramarginal gyrus (SMG),
related to working memory. In a longitudinal study with older
adults (Guo et al. 2021), a 4-month music training intervention
increased activation in the right supplementary motor area and
left precuneus, but decreased activation in the bilateral posterior
cingulate gyrus as well as connectivity between the right posterior
cingulate gyrus and the left middle temporal gyrus, and between
the left putamen and the right superior temporal gyrus during the
forward and backward digit span tasks, used to assess working
memory task. Here, larger improvements in the working mem-
ory task were associated with larger reductions in connectivity
between the aforementioned regions. Similarly, in Alain et al.
(2018), adult musicians showed less activation in the prefrontal
cortex bilaterally than non-musicians when performing an audi-
tory N-back workingmemory task. Here, participants listened to a
stream of stimuli and were required to indicate whether an item
presented matched an item that had been presented a variable
number (n) of items back.
Differences between musically trained and untrained children
have also been uncovered in brain activations related to inhibition
and set shifting. In their study, Sachs et al. (2017) found that
children 8–9 years of age attending music training had stronger
bilateral activation of the pre-supplementarymotor area, anterior
cingulate cortex, the inferior frontal gyrus (IFG), and the insula
during a Stroop task when compared to control group partici-
pants, but not an active control group taking part in sports. In a
study with the same participants, 4 years later, these differences
were only present in the right IFG (Hennessy et al. 2019). A study
by Zuk et al. (2014) in turn found that 9–12-year-old musically
trained children recruited the supplemental motor area and ven-
trolateral prefrontal cortexmore strongly than untrained children
in a set-shifting task. However, the study was underpowered due
to the small sample size (total n=27 in groups of 15 musically
trained vs. 12 untrained children). Further, the above-mentioned
group differences were only seen with an uncorrected threshold,
while more subtle differences between the groups might have
been missed even with the liberal threshold. In sum, a view on
differences between musically trained children and adolescents
in brain activity related to particularly set shifting is still missing.
Together, behavioral and neuroimaging studies point towards
the potential of music training in augmenting the development of
executive functions. However, studies extending from childhood
to adolescence are rare, and consequently the age-related tra-
jectory of possible music-training-related enhancement of exec-
utive functions remains unclear. Furthermore, the neural mecha-
nisms of particularly the enhancement of set shifting inmusically
trained individuals are understudied. It is possible that in late
adolescence and early adulthood, the development of the most
demanding executive functions such as set shifting is at its peak,
and possible benefits gained from musical training in simple set-
shifting tasks will begin to dissipate when comparing behavior
between trained and untrained individuals.
In this study, we set out to fill these gaps by investigating
possible connections between music training and the maturation
of the mechanisms of set shifting during late adolescence
and early adulthood. To this end, we measured behavioral
performance, P3b event-related potentials (ERPs) to target stimuli,
and BOLD activations in a task for set shifting inmusically trained
and untrained adolescents aged 13–21 years. To build a better
understanding of how far into adolescence possible enhance-
ment of set shifting persists, we combined results from the
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Table 1.Means ages and numbers of participants in the music and control groups for the ERP, fMRI experiment, and
neuropsychological tests.
ERP fMRI Tests
Music Control Music Control Music Control
Mean age
(SD, range) 17.03 ± 2.36 17.22 ± 2.42 18.3 ± 1.66 18.3 ± 1.72 17 ± 2.32 17.1 ± 2.4
N 35 (9 male) 28 (16 male) 23 (7 male) 21 (11 male) 51 (20 male) 52 (27 male)
neuropsychological tests from 2 previous measurement rounds,
obtained 2–4 years prior to the brain measurements.We expected
more efficient recruitment of areas related to executive functions,
such as the dorsal attention network, in musically trained
individuals compared to untrained peers during set-shifting task
trials, and that P3b responses to incongruent stimuli in the EEG
version of the task would be larger in amplitude in the music
group than in the control group. Finally, we expected an age-
related decrease in differences in a task for set shifting.
Methods
Participants
The participants are from a 14-year longitudinal study called
the Early Auditory Skills project, conducted at the University
of Helsinki, mapping neurocognitive development in musically
trained and untrained children and adolescents. Seven-year-olds
were invited to participate in measurements every 2 years, and
a new group of children was recruited on each measurement
round. Therefore, the participants who were recruited earlier took
part in several measurements while those recruited later had
fewer measurements. The last measurements included in the
longitudinal study were conducted in 2017, and altogether, there
were 7 measurement rounds.
The longitudinal study first employed experiments recording
auditory ERPs. For the last 3 measurement rounds, neuropsycho-
logical tests for executive functions were also included, and on
the last round, the fMRI and ERP experiments reported here.
On average, the participants of this study were measured
with neuropsychological tests 2.43 times. The fMRI and EEG
data reported here were collected only once, during the last
measurement round of the longitudinal study.
The participants in the music group had played an instrument
starting at age 7 and attended a specialized public elementary
school that included classical music instruction in the regular
curriculum. The untrained participants were recruited from
regular elementary schools, have no formal music training, and
match the music group participants in age, IQ as measured
with the Vocabulary and Block design subtests from the fourth
edition of the Wechsler Intelligence Scale for Children (WISC-
IV), and socioeconomic status as measured by parental income
and education (Income scale: 1 =under a 1,000 Euros/month,
2 = 1,000–2,000 Euros/month, 3 = 2,000–3,000/month, 4 = 3,000–
4,000/month, 5 = 4,000–5,000 Euros/month, 6 =over 5,000/month;
Education scale: 1 = comprehensive school, 2 =upper secondary
school or vocational school, 3 = a higher degree than upper
secondary school or vocational school, 4 =Bachelor’s degree
or equivalent, 5 =Master’s degree or equivalent, 6 = licentiate
or doctoral-level degree) (Putkinen et al. 2014; Saarikivi et al.
2016). Most participants in both groups were from middle-class
and upper-middle-class families from the Helsinki metropolitan
area, and no participants had neurological, hearing, or sight
disturbances.
Forty-four participants (23 musically trained) aged 16–21 took
part in the fMRI experiment, 63 (35 musically trained), aged 13–21
in the EEG experiment, and 103 (51 musically trained) completed
the neuropsychological tests. Data from neuropsychological tests
from 2 previous measurement rounds were available for 24 par-
ticipants and from 1 previous measurement round for 20 partici-
pants.
Table 1 details the mean ages and number of participants in
each experiment.
Procedure
During the EEG experiments, the participants sat in an armchair
in an acoustically attenuated and electrically shielded room.
Visual stimuli were presented on a computer screen placed 1.5 m
in front of the participant. The neuropsychological set-shifting
task was administered in another quiet room by a psychology
major before the EEG experiment. An experimental session with
EEG and neuropsychological tests lasted for ∼2.5 h. Tests took
∼45 min, the EEG recording about 60 min, with 45 min reserved
for discussion with the participants about the procedure, breaks,
and for placing and removing the electrode cap. The MRI and
fMRI acquisitionswere conducted on a separate occasion after the
EEG and neuropsychological test at the AMI Centre of the Aalto
University School of Science.
The required safety screening measures were administered
prior to acquisition. Especially with the youngest participants,
here 16–17-year-olds, special care was taken to ensure comfort,
for instance bymonitoring the emotional state of the participants
and reacting with assurance to possible distress, and by ensuring
that participants had enough knowledge of what was going on to
create a sense of predictability and control.
Signed informed consent was obtained before measurements
from the parents of all underaged participants. A separate signed
consent was also obtained from 16 to 17-year-olds and oral con-
sent was obtained from participants of all ages before each mea-
surement session (EEG and fMRI). Separate information sheets
were designed for participants of different ages to ensure that oral
orwritten consentwas truly informed,and that the procedurewas
explained in an age-appropriate way. Before starting the EEG and
fMRI measurements, the participants performed a short practice
run, requiring the participant to respond to the arrow stimuli with
the appropriate responses, to ensure that the task was under-
stood before commencing the actual task. Measurements were
conducted by psychologists, or students of psychology or cognitive
science, who took special care to monitor the state of young
participants, inform the participants about what is going on, and
ensure that the measurements were as comfortable as possible.
The participants were awarded movie tickets for their efforts.
The experiment protocol for all measurements was approved by
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the Ethical Committee of the former Department of Psychology,
University of Helsinki, Finland.
EEG recording and MRI/fMRI acquisition
The BioSemi Active-Two system was used with a sampling rate
of 512 Hz, and 64 Ag–AgCl scalp electrodes mounted in a BioSemi
head cap according to the International 10–20 system.Three addi-
tional active electrodes were placed on the nose and on the right
and left mastoid to act as reference channels (only the mastoids
were used in analyses). Electro-oculogram for later removal of
blinks and eye movements was recorded with electrodes placed
above and at the outer canthus of the right eye.
The MRI/fMRI images were acquired with a 3-T MAGNETOM
Skyra whole-body scanner (Siemens Healthcare, Erlangen, Ger-
many)with a 20-channel head coil. Functional echo planar images
(EPI) were acquired with an imaging area consisting of 42 con-
tiguous oblique axial slices with the following parameters: TR
2,500 ms, TE 32 ms, flip angle 75◦, voxel matrix 64×64, field of
view 19.2 cm, in-plane resolution of 3×3 mm, and slice thickness
of 3 mm. Image acquisition was performed at a constant rate,
but it was asynchronized with stimulus onsets. Structural images
were acquired using a T1-weighted sequence (3D MP-RAGE) with
a resolution of 1 mm3, using a 256×256 voxel matrix.
Neuropsychological tests
The inhibition test from the NEPSY-II test battery (Korkman et al.
2008) was administered to investigate set shifting. Even though
the test is named inhibition it in fact contains 3 tasks: naming,
inhibition, and set shifting. In the first task, the participant sim-
ply names shapes (square/circle) and the directions of arrows
(up/down). The second task requires the participant to name
the opposite shape (“circle” if square; “square” if circle) and the
direction of the arrow (“up” if down; “down” if up). The third task,
set shifting, requires the participant to switch between naming
the shape or the direction of the arrow and naming the opposite
shape or direction, based on the color of the shape or the arrow (if
white, naming; if black, inhibition and naming the opposite). The
shapes and arrows are presented to participants from a booklet
placed on a table in front of the participant. The participant
may point to the shapes or arrows on the booklet while reciting
answers. The number of errors and completion time are used
as performance indexes. Only performance in the third task, set
shifting, was analyzed in this study.
The EEG and fMRI paradigms
fMRI and EEG versions of the set-shifting task of the inhibi-
tion test from NEPSY-II were created to acquire Blood Oxygen
Level-Dependent (BOLD) activations and ERPs related to task
performance. In transforming the task to suit neuroimaging set-
tings, and to allow the capture of shifting-related brain activity,
alterations were made. In comparison to the neuropsychological
set-shifting task, the fMRI and EEG versions required a motor
instead of verbal response to reduce movement artifacts caused
by speech. Also the pacing of the tasks used in neuroimaging
differed from the original task. In the fMRI and EEG versions,
obtaining event-related activations required a task where the
responses could be obtained within a specific timeframe of suffi-
cient duration, and not according to the participant’s own pace as
in the original task. Lastly, in the fMRI and EEG versions, reaction
times (RTs) were used as an index of performance instead of
total completion time of the task, as in the neuropsychological
set-shifting task.
The Arrows fMRI paradigm had 4 naming, 4 inhibition, and 8
set-shifting task blocks presented in random order. While in the
scanner, the participant viewed a white or black arrow at the
center of the screen pointing to the left or right and was asked
to press a button with the left or right hand according to the
instructions shown on the screen at the beginning of the block.
In baseline or naming blocks, the instruction was to press the
button in the same direction that the arrow was pointing, and
in the inhibition blocks, the task was to respond in the opposite
direction. In the set-shifting blocks, the participant was instructed
to respond with the congruent or incongruent hand depending
on the color of the arrow. This means that the participant had to
switch between response strategies (naming and inhibition) when
the color of the arrow changed. The rule was also changed so
that on different runs, the black color signified naming, and white
inhibition, and vice versa. Thus, there was no consistent map-
ping between the color of the arrow and the required response
throughout the experiment and the participant had to keep in
mind the rule that was presented on screen before the run. The
switch between rules during the task required shifting between
2 different response rules in mind, and was designed to elicit
brain activity specific to shifting. Fifty percent of the trials in
the set-shifting blocks required the incongruent response and the
other half the congruent response, requiring switching between
the congruent trials that correspond to the naming condition and
the incongruent trials that correspond to the inhibition condition.
Our main interest was to investigate how the additional shift-
ing and working memory demands in the set-shifting condition
would affect the contrast between the shifting-congruent vs.
baseline and shifting-incongruent vs. inhibition contrasts and
how brain activity revealed by these contrasts differ between the
groups.
The arrow stimuli in the Arrows fMRI paradigmwere presented
for 2,500, 5,000, or 7,500 ms. A black fixation cross on a white
background was presented between trials for 12.5 s. Forty eight
trials for each condition (baseline, inhibition, set shifting) were
presented, for a total of 192 trials, which were split over 2 runs.
The shifting rule, i.e. the color of the arrow indicating whether
to respond with a congruent or incongruent button press, was
switched between runs. Fig. 1 shows examples of the baseline,
inhibition, and set-shifting trials along with correct responses.
The Arrows EEG paradigm (Fig. 2) included only a set-
shifting condition, requiring the participant to switch between
a congruent and an incongruent response according to the color
(black or white) of the stimulus. The stimuli were presented for
1,000 ms, and the stimulus onset asynchrony was 1,500 ms. There
were altogether 100 stimulus presentations per run, and 2 runs.
Here too, the shifting rule changed between runs. The stimuli
were presented in pseudorandom order with no more than 2 of
the same stimuli in sequence. The total duration of the task was
2×2.5 min.
Data analysis
fMRI preprocessing and analyses
fMRI data preprocessing was carried out using the fMRIprep
pipeline (Esteban et al. 2019). Structural T1-weighted (T1w)
images were corrected with N4BiasFieldCorrection (Tustison
et al. 2010). Brain surfaces were reconstructed using recon-all
(FreeSurfer 6.0.1, Dale et al. 1999). Brain tissue segmentation
was performed on the brain-extracted T1w using fast (FSL 5.0.9,
Zhang et al. 2001). Functional data were slice-time corrected
using 3dTshift from AFNI 20160207 (Cox and Hyde 1997). The
BOLD time-series were resampled and corrected for head motion.
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Fig. 1. Illustration of the Arrows fMRIparadigm. A sequence of black and white arrows was presented with the task of pressing a button in the same or
in the opposite direction, either irrespective of (naming and inhibition) or depending on (set shifting) the color of the arrow, according to the instruction
of the set. The instruction for the set-shifting set was as depicted for 4 blocks and reversed (black, different direction) for the other 4 blocks. BOLD
responses to congruent and incongruent stimuli and response times were included in analyses.
Fig. 2. Illustration of the Arrows EEGparadigm. A sequence of black and white arrows was presented with the task of pressing a button in the same or
in the opposite direction, depending on the coloration of the target arrow. ERPs to congruent and incongruent stimuli and response times were included
in analyses.
The BOLD reference was co-registered to the T1w using bbregister
(FreeSurfer). Finally, functional data were spatially smoothedwith
a Gaussian kernel (9.0 mm FWHM).
Analyses of fMRI data were conducted with SPM12. To reveal
regions activated by naming, inhibition, and set shifting, a general
linear model (GLM) was fitted to the data where the design
matrix included regressors for the stimuli in each condition (i.e. 4
regressors altogether). The following contrast images were com-
puted: set-shifting trials> inhibition and naming trials, incon-
gruent set-shifting trials> inhibition trials, and congruent set-
shifting trials> inhibition trials. The effects of group were mod-
eled in the second-level analysis with age and sex as covariates.
Clusters surviving family-wise error (FWE) correction (P<0.05) are
reported.
EEG preprocessing
EEG data preprocessing and analyses were conducted in MATLAB
using the EEGLAB toolbox (v. 13.5.4b; Delorme and Makeig 2004).
The data were re-referenced to the average of the mastoid chan-
nels, and bad channels were excluded, and channel drift removed
with the clean_rawdata plugin. Flatline criterion was set to lack
of activity for 5 s or more, minimum acceptable correlation to
other channels 0.8, and a filter transition band of 0.25–0.75 Hzwas
used to remove channel drift. Independent component analysis
(ICA) was conducted and the ICLabel plugin was used to identify
and then remove artefactual components such as eye blinks. The
clean_rawdata plugin was then used to employ artifact subspace
reconstruction (ASR, Kothe and Makeig 2013) to clear the data of
any remaining artifacts.The standard deviation cutoff for removal
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Table 2. Results of the ANOVA on the effects of group and stimulus type (congruent vs. incongruent) on RTs in the Arrows EEG task
and the Arrows fMRI task.
Effects DFn F-value P-value Ges
Arrows EEG
Group 1 2.22 0.141 0.034
Stimulus 3 32.4 0.000∗∗∗ 0.023
Group × Stimulus 3 0.68 0.414 0.000
Arrows fMRI
Group 1 0.03 0.872 0.001
Stimulus 3 189.11 0.000∗∗∗ 0.455
Group × Stimulus 3 0.71 0.548 0.003
Ges=Generalized eta squared; DFn=Degrees of Freedom. ∗∗∗P≤0.001.
Table 3. Results of the ANOVA on the effects of group and stimulus type (congruent vs. incongruent) on the number of correct
responses in the Arrows EEG task and the Arrows fMRI task.
Effects DFn F-value P-value Ges
Arrows EEG
Group 1 1.58 0.214 0.022
Stimulus 3 2.69 0.106 0.005
Group × Stimulus 3 1.48 0.229 0.003
Arrows fMRI
Group 1 0. 54 0.468 0.007
Stimulus 3 25.12 0.000∗∗∗ 0.204
Group × Stimulus 3 0.04 0.988 0.000
Ges=Generalized eta squared. ∗∗∗P≤0.001.
of bursts (via ASR) was set to 20, and the parts of data where the
variance was larger were removed. The data were then low pass
filtered at 30 Hz, and epochs from 100 ms before to 1,500 ms after
stimulus onset were extracted. Bad channels were interpolated.
After this, epochs were averaged separately for congruent and
incongruent stimuli.
Behavioral performance and P3b responses in the Arrows
EEG task
Differences in performance as measured with RTs and the num-
ber of incorrect responses in the Arrows EEG and the Arrows
fMRI task were modeled with ANOVA (Group (Music vs. Con-
trol)×Stimulus (incongruent vs. congruent)).
To further study the effects of group, age, and channel location
on the P3b, mean amplitudes calculated over a 50-ms time
window centered at 350 ms at electrodes FC3, FCz, FC4, P3, Pz,
P4, Oz, and O2 were subjected to a repeated measures ANOVA
with factors Group (Music vs. Control)×Stimulus (incongruent
vs. congruent)×Left-Center-Right×Anterior–Posterior (frontal-
central-posterior). To quantify the connections between response
amplitude and task performance, correlations between the
response amplitude at POz (where the responsewasmaximal) and
RTs were computed separately for the incongruent and congruent
trials.
Longitudinal performance in the neuropsychological
set-shifting task
The effect of age and group membership on set-shifting test
performance across 3 measurement rounds was modeled with a
linear mixed model using the lmer function with Satterthwaite
approximation for degrees of freedom of the lme4 package in R
(Bates 2005; Bates et al. 2007). Age was mean-centered so that the
significant effect of Group indicates a group difference in the test
score at the average age of the participants. Linear mixed model-
ing was selected as the analysis since it allows different numbers
of data points across subjects and accounts for correlations of the
data within a subject.
Results
Behavioral results
In both the Arrows EEG and Arrows fMRI tasks, ANOVA revealed
longer RTs for the incongruent than congruent stimuli in both the
music and control groups (main effect of stimulus). No significant
group differences were found in RTs in either the EEG or the
fMRI versions. The average RT in the Arrows EEG in the music
group was 552 ms, SD 61 ms, and in the control group 532 ms, SD
47 ms. Also, no significant effects of group membership emerged
on the number of correct responses in either theArrows EEG or the
Arrows fMRI tasks. The results of the ANOVAs are summarized in
Tables 2 and 3.
The linear mixed model on the effects of age and group on per-
formance in the neuropsychological test for set shifting revealed
significant main effects of age and group, but no significant inter-
action, summarized in Table 4. Fig. 3 shows the test performance
in the 2 groups as a function of mean-centered age.
Even though there was no interaction between age and group,
we still tested whether data from only the last measurement
round with the oldest participants, aged 13–21, would show an
effect of music training, as the behavioral results of the Arrows
EEG task showed no effect of group. A Welch 2 sample t-test
revealed no group differences in the set-shifting task in this
subgroup (t=1.3031, df = 57.368, P=0.1978).
EEG results
Both the incongruent and congruent stimuli in the Arrows EEG
paradigm elicited a parietally maximal P3b-like response (Fig. 4a
and b). RTs in the task correlated negatively with the P3b ampli-
tude, i.e. the larger the response, the faster the RT, irrespective
of age or group membership (Fig. 4c). There were no significant
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K. Saarikivi et al. | 7
Table 4. Results of the linear mixed model analysis on the effects of age and group membership on test performance.
Effects Mean sq NumDF DenDF F-value P-value
Age 20054.1 1 211 156.97 0.000∗∗∗
Group 1885.1 1 100 14.76 0.000∗∗∗
Age × Group 53.0 1 209 0.42 0.520
NumDF=Numerator degrees of freedom; DenDF=Denominator degrees of freedom. ∗∗∗P≤0.001.
Table 5. Results of the ANOVA on the effects of age, group membership, stimulus, and location on response amplitude.
Effects DFn DFd F P
Group 1 61 0.8066537 0.3730
Stimulus 1 61 13.5613599 0.0005∗∗
Left-center-right 2 122 28.6353835 0.0000∗∗∗
Anterior/posterior 2 122 37.3381931 0.0000∗∗∗
Group × stimulus 1 61 0.6259287 0.4319
Group × left-center-right 2 122 0.3055774 0.7373
Group × anterior/posterior 2 122 3.9991336 0.0208∗
Stimulus × left-center-
right
2 122 3.6581879 0.0286∗
Stimulus × anterior/poste-
rior
2 122 3.7692873 0.0258∗
Lat × anterior/posterior 3 244 34.9482195 0.0000∗∗∗
Group × stimulus × left-
center-right
2 122 2.8375499 0.0624
Group × stimulus × ante-
rior/posterior
2 122 0.8888194 0.4138
Group × left-center-
right × anterior/posterior
4 244 1.8379597 0.1222
Stimulus × left-center-
right × anterior/posterior
4 244 1.7728853 0.1349
Group × stimulus × left-
center-
right × anterior/posterior
4 244 1.0818291 0.3661
∗P = <0.05 ∗∗P = <0.01, ∗∗∗P = <0.001.
Fig. 3. Set-shifting completion time as a function of mean-centered age.
A significant group (music vs. control) difference in set-shifting test
performance emerged.
correlations between response latency and RTs for the congruent
(r=0.108, P=0.399) and incongruent (r=0.118, P=0.357) stimuli.
ANOVA on group differences in topography indicated that
the P3b was more posteriorly distributed in the music group
(P=0.0208, with Greenhouse–Geisser correction P=0.040). Table 5
summarizes the results of the ANOVA.
fMRI results
The fMRI data indicated that the set-shifting condition (all set-
shifting trials> naming and inhibition conditions) activated areas
of the frontal, temporal and parietal cortices, the anterior insula,
and the cerebellum (Fig. 5).
With regard to group differences, the contrast between the
incongruent trials in the set-shifting condition and the inhibi-
tion trials (i.e. identical stimuli and stimulus–response mapping)
revealed stronger right-lateralized activity in the control group
than in the music group in areas of the dorsal attention network
(Fig. 6) including the right SMG and middle frontal gyrus/frontal
eye field, the superior parietal lobules, the angular gyrus, the
lingual gyrus, the cerebellum, as well as the occipital poles, and
inferior occipital gyri in the visual cortices. No other contrasts
revealed group differences, and no effects of musicians showing
stronger activations were found.
Discussion
In this study, we aimed at investigating possible associations
betweenmusic training and thematuration of themechanisms of
set shifting during late adolescence and early adulthood. Behav-
ioral performance, P3b responses to targets, and BOLD activations
were obtained in a task for set shifting in musically trained and
untrained adolescents aged 13–21 years. The results reveal that
the musically trained and untrained individuals showed no dif-
ferences in their performance in the set-shifting task conducted
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Fig. 4. P3b responses at POz (a) and scalp topographies of the P3b response (b) to congruent and incongruent stimuli, separately for the music and
control groups. Correlations between P3b amplitude and RTs in the Arrows EEG task (c). Time 0= stimulus onset.
Fig. 5. Activation during the set-shifting condition when contrasted with the naming and inhibition conditions. Activations in frontal (FO: frontal
operculum, MFG/FEF: middle frontal gyrus, frontal eye field, MCG: middle central gyrus), temporal (SMG: supramarginal gyrus, PrG: precentral gyrus),
and parietal (SPL: superior parietal lobule) cortices, the anterior insula (AINS), the thalamus (THA), and the cerebellum (CBM: cerebellum,CBV: cerebellar
vermis) across groups. The color bar indicates t-value.
during EEG or during the fMRI. Results from analyses including
behavioral test performance from previous measurement rounds
showed that the group differences in the neuropsychological set-
shifting task diminished with age.
Irrespective of music training, age had an effect on per-
formance in the neuropsychological set-shifting task, with
completion time becoming faster with increasing age. This
finding is in line with previous research on the development
of executive functions showing improvement in performance
in executive function tasks until early adulthood (Blakemore
and Choudhury 2006; Best and Miller 2010). This enhancement
of performance may stem from the maturation of brain
structures important for executive functioning (Best and Miller
2010).
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K. Saarikivi et al. | 9
Fig. 6. The control group showed higher activity (P< 0.05, corrected for multiple comparisons at the cluster level) than the music group for the set-
shifting incongruent inhibition contrast in areas of the dorsal attention network including the right SMG and middle frontal gyrus/the frontal eye field
(MFG/FEF), the superior parietal lobules (SPL), the angular gyrus (ANG), the lingual gyrus (LING) as well as the cerebellum (CBM), and in areas of the
visual cortex (OP: occipital poles, IOG: inferior occipital gyri). The color bar indicates t-value.
Differences in performance in the neuropsychological set-
shifting task between the musically trained and untrained
individuals were found on previous measurement rounds. In
the study by Saarikivi et al. (2016), the then 9–15-year-old
musically trained children exhibited better performance than
untrained peers in the set-shifting task. In the study by Putkinen
et al. (2021), with the participants aged 10–17, the difference
persisted. These differences were not explained by better general
cognitive abilities or background variables, because the music
and control groups were matched in IQ and SES. In line with
this, the current results on test performance pooled together
from the 3 measurement rounds, with participants aged 9–21,
show an overall group difference. However, in performance in
the EEG and the fMRI version of the set-shifting task, no group
differences emerged. The same was true when only investigating
performance differences in the neuropsychological set-shifting
task from the last measurement round, with participants aged
13–21. This would suggest that the group differences in the test
performance diminished with age. In other words, the music
group outperformed the control group in the set-shifting task
at younger ages, but the group difference disappeared as the
cohort aged.
The results suggest that music training is associated with an
advantage in set shifting during childhood but that this advantage
does not persist into adolescence and early adulthood. If this
were the case, we should not be able to see enhanced set-shifting
skills in musically trained adults. Indeed, some studies have not
found an association betweenmusical experience or aptitude and
set shifting in adults (Bialystok and DePape 2009; Slevc et al.
2016). In the study by Slevc et al. (2016), executive functions were
assessed with a wide array of tasks in both the visual and auditory
domains. Inhibition was investigated with the Simon task and
set shifting with a task where participants alternated between
categorizing numbers and letters based on the side of the screen
they appeared on.Musical ability or the amount of training did not
predict performance in either test but were connected to working
memory performance.
Some studies, however, have. In the study by Moradzadeh et al.
(2015), switchingwas investigatedwith the Quantity/Identity task.
Here, the respondent is asked to indicate the identity of digits
(the sum of digits presented, e.g. 1 3 =4) or quantity of digits
(1 3= 2) presented on a screen, or switch between indicating
one or the other according to a prompt. In this study, musically
trained individuals performed better than untrained individuals,
indicating more efficient set shifting.
Our results are more in line with those Slevc et al. (2016) and
Bialystok and DePape (2009) who found no differences in perfor-
mance between musically trained and untrained adults in inhibi-
tion and set-shifting ability, respectively. The differences between
our results and those of Moradzadeh et al. (2015) may in part be
explained by differences in the task used to measure set shifting.
The tasks used in studies reporting no differences in set shifting
are arguably simpler. For instance, the trail-making task used
by Bialystok and DePape requires participants to switch between
connecting digits and letters in alphabetical and numerical order.
In the task used by Moradzeh and colleagues, participants were
required to manipulate the presented information by performing
mental arithmetic. It is possible that with suchmore complex set-
shifting tasks requiringmore processing of the stimuli, differences
between musically trained and untrained participants might still
emerge in adulthood.
In contrast with behavioral results, the neuroimaging results
indicate slight differences in brain activity during the set-shifting
task. No significant amplitude differences at any single channel
were observed, but the scalp distribution of the P3b response
to incongruent trials was more posterior in musically trained
than untrained participants. The P3b is thought to reflect neural
mechanisms associated with workingmemory categorization and
updating of mental models (Polich 2003). Adult studies show a
posterior distribution for P3b responses (Volpe et al. 2007; Wronka
et al. 2012) and therefore the difference in scalp topographies
between musically trained and untrained participants signal a
more adult-like P3b response in the music group compared to
the control group. The result may indicate that musically trained
participants reached maturity of updating and working memory
functions related to target processing earlier than the musi-
cally untrained participants. The difference in maturity no longer
provided any benefit in performance in a relatively simple set-
shifting task but could still be seen in functional differences
related to target processing.
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The P3b amplitude correlated negatively with RTs to both
congruent and incongruent stimuli in the Arrows EEG task, i.e. the
shorter the RT, the larger the response. P3b response amplitudes
have previously been found to correlate with RTs (Provost et al.
2018).However, there is no consensus in literature onwhat exactly
the P3b response measured in active paradigms signals, with
explanations such as context updating process (Donchin 1981),
response selection (Verleger et al. 2005), and stimulus evaluation
and response selection (Pritchard et al. 1999). Our findings point
towards a link between P3b amplitude and faster processing of
targets during a set-shifting task.
Our fMRI results indicated in turn that the music group
engaged areas of the dorsal attention network less than the
control group on the incongruent trials of the set-shifting task.
In addition, visual areas showed less activity, most probably also
reflecting less need for cognitive effort in a visual task. Thus,
despite the lack of group differences at the behavioral level,
individuals in the music group were able to perform the task
with more efficient usage of neural resources, signaled by less
activation in areas required by the task.
These results are in line with the assertion that expert perfor-
mance requires less neural resources than that of novices, which
has a long history in cognitive neuroscience (Neubauer and Fink
2009). In the domain of executive functions, this “neural effi-
ciency” hypothesis is supported by studies that have linked lower
brain activity with experience-related enhancement in inhibition,
set shifting, and working memory (Jansma et al. 2001; Sayala et al.
2006; Manuel et al. 2010, 2013; Rodríguez-Pujadas et al. 2014).
Moreover, some studies indicate that maturation of executive
functions is accompanied by lower brain activity (Luna et al. 2010),
while aging has been associated with stronger recruitment of
frontal regions in executive functions tasks (Gold et al. 2013). It
is noteworthy that the effect in this study was observed for a non-
musical visual task, suggesting a domain-general enhancement
of the neural systems supporting executive functions in themusi-
cally trained adolescents. Furthermore, because the groups were
matched in terms of behavioral performance, this effect cannot
be attributed to processes like error monitoring but might reflect
reduced need for effortful, top-down control in the music group.
The group differences in brain activity were found for the
incongruent trials of the set-shifting condition when contrasted
with the inhibition condition, as well as with the naming
and inhibition conditions combined. Thus, the comparison
contrast was between conditions that were identical in terms of
stimuli and stimulus–response mapping but differed in cognitive
demands. Like many complex executive functions tasks, the
incongruent trials of the set-shifting condition tapped into
multiple subcomponents of executive functions. In addition to
set shifting, these trials required inhibition of the congruent
response while the condition as a whole required maintenance of
2 response–stimulus mappings in working memory. The finding
that these trials recruited areas of the dorsal attention network in
the control group concurs with previous studies that have found
activity in these regions in inhibition tasks that require working
memory updating, and task switching (Ravizza and Carter 2008;
Simmonds et al. 2008; Kim et al. 2012; Lemire-Rodger et al. 2019).
Limitations
According tomany authorities of the field, any study investigating
the effects of a formal or informal training (e.g. language or
music lessons or via a computer game) should be conducted in
a randomized controlled trial study design (Sala and Gobet 2017,
2020). In this design, the participants are randomly allocated
to a training or intervention program, thus ignoring their pref-
erence or motivation regarding it. In the current longitudinal
study, we adopted what we consider an ecologically more valid
approach and built the longitudinal study around the participants
who were committed to music training since age seven. By this
arrangement,we were able to follow up the neurocognitive neural
development of the participants for 10 years as revealed by the
mean age of the participants of this paper which was 17 years.
This could not have been achieved if the participants were given
their hobby based on randomization since even programs of 1 year
with RCT design suffer from participant drop-out (Tervaniemi
et al. 2021). Admittedly, our study outcomes therefore reveal
not only an outcome of music training. Instead, possible effects
of music training are mixed with factors that lead a child to
choose and persist in the musical hobby, such as support from
the family and personality factors (Corrigall et al. 2013; Corrigall
and Schellenberg 2015). However, in our view, since this is the best
approach when investigating the long-term effects of any training
as it occurs in real life, such a confound is not to be avoided
but to be accepted as part of the phenomenon we tackle when
investigating the neurocognitive effects of any type of training
spread over many years.
The differences between the neuropsychological and the EEG
and fMRI versions of the set-shifting tasks complicates our ability
to draw conclusions on group differences. The difference in trial
durations, response styles, and the measures of performance
between the neuropsychological, original set-shifting task and its
EEG and fMRI versionsmay also explain why no differences in per-
formance between musically trained and untrained participants
emerged in the latter 2. The original version of the set-shifting
task asks the participant to verbally respond to the stimuli and
proceed in a self-paced manner, but as quickly as possible. In the
EEG and fMRI versions, in turn, the response happens via a button
press, and the stimulus presentation rate is constant, and slower
than in the original version. Both the trial length and response
style in EEG and fMRI versions may have made these versions of
the task less prone to elicit mistakes, and therefore less sensitive
in bringing out differences in performance. Also, it is possible self-
paced responding gives more information about processing speed
and thereby the efficiency of set shifting than RTs, making the
neuropsychological version better at bringing out differences in
performance. Irrespective of the differences between the tasks
used in this study, we feel they still measure the same set-
shifting function, as all tasks require changing responses accord-
ing to rules and stimulus characteristics. Furthermore, musically
trained individuals are not more or less accustomed than their
untrained peers in responding verbally or pressing buttons during
tasks. This means that the presence of lack of group differences is
most probably not explained by differences in how the tasks were
administered.
Summary and conclusions
The literature is somewhat mixed with regard to which subcom-
ponents of executive functions differentiate musicians and non-
musicians, at which age these differences are seen, and whether
they are accompanied by a decrease or increase in brain activity.
Our behavioral results from a neuropsychological test for set
shifting indicated that during school-age and early adolescence
the music group outperformed the control group, but that this
group difference had diminished by late adolescence and early
adulthood. One explanation for this finding is that during these
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K. Saarikivi et al. | 11
“cognitive prime years”when executive functions reach fullmatu-
rity, the musician advantage disappears or requires more difficult
tasks to be detected (Moradzadeh et al. 2015). The fMRI results
showed that the music group recruited areas of the dorsal atten-
tion network less strongly than the control group. This suggests
that—despite the lack of group differences at the behavioral
level—the musically trained adolescents needed fewer resources
to successfully complete the task. Moreover, musically trained
adolescents exhibited a more adult-like scalp topography of the
P3b response during target processing in the set-shifting task
than musically untrained peers. These neural differences were
evident in a visual and completely non-musical task suggesting
that the enhancement of executive functions in the music group
was domain-general.
In sum, our multimethodological results elucidate the neural
underpinnings of individual differences in executive functions
and are in line with the notion that music training might benefit
the maturation of these functions in childhood and early adoles-
cence.
CRediT authors statement
Katri Saarikivi (Conceptualization,Data curation, Formal analysis,
Investigation, Methodology, Visualization,Writing—original draft,
Writing—review & editing); Vanessa Chan (Data curation, Formal
analysis, Investigation,Writing—original draft,Writing—review&
editing); Minna Huotilainen (Conceptualization, Funding acqui-
sition, Project administration, Resources, Supervision); Mari Ter-
vaniemi (Conceptualization, Funding acquisition, Project admin-
istration, Supervision, Writing—original draft, Writing—review
and editing); Vesa Putkinen (Conceptualization, Data curation,
Formal analysis, Investigation, Methodology, Project administra-
tion, Software, Supervision, Visualization,Writing—original draft,
Writing—review and editing)
Data availability
The data are available from the corresponding author on reason-
able request.
Funding
This study was funded by the Academy of Finland and the Jenny
and Antti Wihuri Foundation.
Conflict of interest statement: The authors declare no conflicts of
interest.
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