1 
This is a preprint version of the following chapter: 
 
Okada, B. M., & Slevc, L. R. (In Press). Musical training: Contributions to executive function. In M. 
Bunting, J. Novick, M. Dougherty, & R. W. Engle (Eds.), An Integrative Approach to Cognitive and 
Working Memory Training: Perspectives from Psychology, Neuroscience, and Human 
Development. New York, NY: Oxford University Press. 
 
 
Musical Training: Contributions to Executive Function 
 
 
Brooke M. Okada and L. Robert Slevc 
University of Maryland, College Park 
 
Imagine you are the first violinist in a string quartet. Each time you and your 
colleagues rehearse, you engage multiple cognitive processes. While playing, you must 
continuously listen and attend to multiple auditory streams of music (i.e., your own 
playing as well as the music from your violinist, violist, and cellist) to coordinate your 
group’s sound. You must also constantly adjust your tempo, volume, and timbre to 
match that of your group to highlight certain melodies and coordinate nuances within the 
music. Simultaneously, you are also reading your music filled with notes and extra 
markings, and these items must be held in mind until played. These processes likely 
require a variety of cognitive processes, such as the cognitive flexibility to switch 
between auditory streams and the ability to update working memory when reading from 
the score. Both are part of a larger set of cognitive processes termed executive 
functions (EFs). 
 
EFs, also known as cognitive control, are a set of top-down processes involved in 
the planning and regulation of cognition and behavior (e.g., Diamond, 2013; Diamond & 
Ling, 2017; Miyake & Friedman, 2012). Most models of EF posit three core related but 
separable components: inhibitory control, cognitive flexibility (also known as switching 
or shifting), and working memory updating (Diamond, 2013; Lehto, Juujärvi, Kooistra, & 
Pulkkinen, 2003; Logue & Gould, 2014; Miyake et al., 2000; Miyake & Friedman, 2012). 
Inhibitory control requires the control of one’s thoughts or behavior to override a 
prepotent response. Cognitive flexibility requires one to change perspectives or switch 
between task demands. Lastly, working memory updating requires one to maintain, add, 
and delete items from memory. These EFs develop through adolescence and can be 
improved with practice (Diamond, Barnett, Thomas, & Munro, 2007; for a review, see 
Diamond, 2013). 
 
Because EFs are such a critical aspect of cognition (e.g., EFs are important 
predictors of success in school and mental health; Diamond, 2013), there is 
considerable interest in predictors of EF abilities and how one might improve EFs (see 
Diamond & Ling, 2017). Accordingly, a variety of EF training paradigms have been 
developed (e.g., Chein & Morrison, 2010; Jaeggi, Buschkuehl, Jonides, & Perrig, 2008), 
although it is debatable whether these programs lead to improvements extending 
beyond the specific EF skill trained (particularly regarding fluid intelligence; e.g., Au et 
al., 2015; Dougherty, Hamovitz, & Tidwell, 2015; Owen et al., 2010). However, it is clear 
that specific EF processes can be improved from even relatively short experiences 
2 
(Harrison et al., 2013). That is, there is good evidence for process-specific “near 
transfer,” where improvements reflect the specific abilities taxed by a training task. 
Thus, regular engagement in tasks that rely on specific EF abilities could benefit those 
specific aspects of EF.  
 
While carefully controlled EF training paradigms provide essential tools to 
understand cognitive training and transfer effects, one limitation with many of these 
paradigms is that the training tasks are not particularly interesting or rewarding on their 
own. Even if intense training on, for example, a dual N-back task can lead to 
improvements in EFs or other cognitive abilities (Jaeggi et al., 2008), it seems unlikely 
that many people would like to spend hours practicing their N-back skills. Some informal 
evidence for this comes from the fact that most cognitive training studies pay 
participants relatively well, yet still have relatively high rates of attrition. For example, 
Redick et al. (2013) reported an approximately 20% attrition rate despite paying $352 to 
participants who completed all parts of the study. Many commercial (and 
noncommercial) cognitive training programs address this problem of engagement by 
making training tasks game-like (e.g., the web-based packages Cogmed® and 
Lumosity®). However, an alternative possibility is to target other tasks that draw heavily 
on specific EF abilities but that have their own intrinsic rewards. One such complex, yet 
commonly pursued activity is music training (e.g., Hannon & Trainor, 2007; Moreno & 
Farzan, 2015; Slevc & Okada, 2015). 
 
The idea that musical experience might lead to benefits to cognitive processes 
(as well as social outcomes) is both longstanding and widely held. For example, Jose 
Antonio Abreu, the founder of El Sistema (a music program targeting children in need) 
has said: 
 
“Children engaged in the programme [El Sistema] attain above-average results in 
school and show a tremendous capacity for collective community action. The 
orchestra and the choirs, the heart of the programme, help create a sense of 
solidarity. Involvement becomes a weapon against poverty and inequality, 
violence and drug abuse.” (Burton-Hill, 2012, p. 1) 
 
Note that Abreu’s suggestion is that active engagement in a music program, not 
passive exposure to music, will affect cognitive processes. Although passive music 
listening can affect a range of behaviors, including shopping behavior (Andersson, 
Kristensson, Wästlund, & Gustafsson, 2012; Milliman, 1982), worker productivity and 
attention (Shih, Huang, & Chiang, 2012), and exercise (Karageorghis & Priest, 2012), 
there is only limited evidence for effects of passive musical exposure on cognitive 
functioning (i.e., there is little evidence for the “Mozart Effect”; Pietschnig, Voracek, & 
Formann, 2010). Instead, cognitive effects of passive musical exposure likely reflect 
underlying effects on emotion and arousal rather than transfer effects per se 
(Thompson, Schellenberg, & Husain, 2001; Slevc & Okada, 2014). 
 
 In contrast, there is growing evidence that active music training is associated with 
a variety of nonmusical cognitive abilities (e.g., the special edition of Music Perception 
on “Music Training and Nonmusical Abilities” [Schellenberg & Winner, 2011]). As 
mentioned above, the idea that music training might produce collateral benefits for EFs 
is particularly interesting in part because many people find learning to play an 
3 
instrument (or sing) intrinsically enjoyable in contrast to computerized EF training 
paradigms, for example. Another benefit of music training as an EF training paradigm is 
that it naturally involves multiple cognitive processes since it is a multifaceted activity. 
Playing an instrument requires that one learn to read music, translate printed notes into 
planned motor sequences, acquire fine motor coordination, and hold many notes and 
pieces in memory (Peretz & Zatorre, 2005). Indeed, Diamond (2014; Diamond & Ling, 
2017) has posited that activities requiring progressively challenging EF skills, and in 
which individuals find enjoyment and emotional investment, should be the best at 
improving EFs.    
 
In addition, there is good reason to think that music training can lead to both 
neural and cognitive changes. Much of the evidence for this focuses on consequences 
for motor and auditory processing. For example, compared to a matched control group, 
those who took 15 months of private keyboard lessons showed structural brain changes 
(based on deformation-based morphometry) in motor and auditory areas involved in 
learning music (Hyde et al., 2009). Similarly, professional keyboard players have 
greater gray matter density in motor areas (left Heschl’s gyrus and left inferior frontal 
gyrus) in comparison to amateur musicians and nonmusicians (Gaser & Schlaug, 2003). 
In addition to structural brain changes, music training has also been associated with 
behavioral advantages in a wide range of cognitive processes. Music lessons have 
been found to correlate with multiple cognitive processes including, but not limited to: 
verbal memory (Chan, Ho, & Cheung, 1998; Ho, Cheung, & Chan, 2003; Jakobson, 
Cuddy, & Kilgour, 2003; Jakobson, Lewycky, Kilgour, & Stoesz, 2008); reading ability 
(Lamb & Gregory, 1993; Butzlaff, 2000); mathematical ability (Vaughn, 2000); spatial 
skills (Bilhartz, Bruhn, & Olson, 2000); IQ and academic ability (Schellenberg, 2006, but 
see Sala & Gobet, 2017; Schellenberg & Moreno, 2009); selective auditory attention 
(Strait & Kraus, 2011); auditory working memory (Pallesen et al., 2010); and processing 
speed (Bugos & Mostafa, 2011). 
 
 Of particular interest here is whether musical experience can specifically affect 
EF abilities. There is at least some evidence for EF-relevant neural effects of music 
training. For example, playing an instrument is related to an increased rate of cortical 
thickness maturation not only in motor areas recruited for planning and producing 
movement, but also in the dorsolateral prefrontal cortex and orbitofrontal cortex, areas 
often involved in executive functioning (Hudziak et al., 2014). However, effects of 
musical experience on EF abilities are only predicted for those aspects of EF on which 
music training relies (see Principle #2 in Diamond & Ling, 2017). Thus, a first step to 
understand the relationship of music training and EFs is to consider how music training 
draws on EF abilities. Below, we detail evidence that music processing involves and 
taxes different aspects of EF as well as correlational evidence that music training is 
associated with advantages in EF abilities. We then describe the small, but growing, 
literature examining the effects of longitudinal music training programs on EF abilities.   
 
Musical Training and Cognitive Flexibility  
 
Music is often played in ensembles (e.g., symphonic orchestras, string quartets, 
or choirs), which requires that musicians play in coordination with each other (Palmer, 
2013). Such coordination requires performers to switch flexibly between multiple 
4 
auditory streams (Loehr, Kourtis, Vesper, Sebanz, & Knoblich, 2013) and to adjust to 
other members of the group (e.g., Loehr & Palmer, 2011; Moore & Chen, 2010). This 
may result in relatively general advantages in cognitive flexibility; indeed, musicians 
outperform nonmusicians on the Trail-Making Test (a standard switching task; Hanna-
Pladdy & MacKay, 2011; see also Bugos, Perlstein, McCrae, Brophy, & Bedenbaugh, 
2007) and in cued task-switching (Moradzadeh, Blumenthal, & Wiseheart, 2015). 
Musically trained children also show greater BOLD (blood oxygenation level dependent) 
responses in a task-switching paradigm (to bivalent versus univalent stimuli) in bilateral 
ventrolateral prefrontal cortex and supplementary motor area (Zuk, Benjamin, Kenyon, 
& Gaab, 2014), which are regions linked to executive functioning (Nachev, Kennard, & 
Husain, 2008; Nee et al., 2013). 
 
Musical Training and Inhibitory Control 
 
Adjusting one’s own playing to that of other performers not only requires the 
shifting of attention, but also exercising inhibitory control to monitor for conflict and 
control one’s own performance (Jentzsch, Mkrtchian, & Kansal, 2014; Palmer, 2013). In 
addition, listening to or tapping complex polyrhythms (e.g., tapping a main meter with 
one hand and a counter meter with the other) also requires inhibitory control; for 
example, tapping “4 against 3” is associated with activation in Broadmann Area 47 and 
the anterior cingulate cortex (ACC) (Vuust, Roepstorff, Wallentin, Mouridsen, & 
Østergaard, 2006; Vuust, Wallentin, Mouridsen, Østergaard, & Roepstorff, 2011). 
Because the ACC is associated with aspects of inhibitory control (particularly with 
conflict monitoring; Botvinick, Braver, Barch, Carter, & Cohen, 2001), polyrhythmic 
processing likely involves inhibitory control processes. Thus, music training may lead to 
more general advantages in inhibitory control. Indeed, adult musicians show smaller 
interference effects in both a pitch-based auditory Stroop task (where the words “high” 
or “low” are sung in either a high or low pitch) and in a visual Simon Arrows task (where 
left- or right-pointing arrows appear on the left or right side of the screen) (Bialystok & 
DePape, 2009). Musicians have also been shown to outperform nonmusicians on a 
stop-signal task (Strait, Kraus, Parbery-Clark, Ashley, 2010; see also Moreno, 
Wodniecka, Tays, Alain, & Bialystok, 2014), suggesting an advantage in motor, as well 
as cognitive, inhibition.  
 
Musical Training and Working Memory Updating 
 
Sight-reading music, or playing unpracticed from a score, requires looking ahead 
in the music to prepare one’s performance, and good sight-readers typically look about 
four notes ahead of where they are playing (Furneaux & Land, 1999; cf. Drake & 
Palmer, 2000; Goolsby, 1994). Thus, sight-readers are constantly updating the contents 
of working memory (WM): they must keep in mind which notes are being played and 
which are yet-to-be played. Correspondingly, sight-reading ability is related to 
nonmusical measures of WM capacity, as indexed by an average of scores for 
operation span, reading span, rotation span, and matrix span (Meinz & Hambrick, 
2010). In addition, musicians outperform nonmusicians on N-back tasks (Oechslin, Van 
De Ville, Lazeyras, Hauert, & James, 2013; Pallesen et al., 2010; Slevc, Davey, 
Buschkuehl, & Jaeggi, 2016) and show enhanced BOLD responses in the prefrontal 
5 
cortex for N-back difficulty (Pallesen et al., 2010). Finally, music training predicts overall 
performance on measures of WM updating (i.e., combined performance on spatial N-
back, letter memory, and keep track tasks, Okada, 2016), and months of music lessons 
in children correlate with performance on tasks tapping inhibition, updating, and shifting 
(Degé, Wehrum, Stark, & Schwarzer, 2011; but see Schellenberg, 2011). 
 
Randomized Controlled Trials: Musical Training and EF 
 
There is considerable evidence that music training is associated with advantages 
in all three domains of EF. However, the studies described above are correlational in 
nature, and have contrasted musicians and nonmusicians by recruiting participants who 
had already undergone various amounts and types of music training. Consequently, it is 
entirely possible that musicians’ advantages in EF tasks do not result from training per 
se, but instead reflect pre-existing differences that lead some people with relatively high 
EF abilities to pursue music training (cf. Schellenberg, 2015).  
 
Although the possibility of pre-existing differences and selection bias preclude 
claims of causation from these studies, promising results have still been found while 
trying to control for these factors. Therefore, these correlational findings are best 
interpreted as suggesting appropriate avenues to explore in experimental studies that 
(ideally) randomly assign participants to music training or a well-matched control 
training regimen. Although the few longitudinal, randomized studies that have been 
conducted used different tasks that span a wide range of cognitive abilities (e.g., WM, 
reading ability, and verbal intelligence; Moreno et al., 2011) and employed music 
training regimens of varying types, duration, and intensity, intriguing results have been 
found suggesting that taking music lessons may indeed enhance EF performance. The 
small body of experimental studies of music training and EFs are summarized in Table 
1. 
In the first training study to specially evaluate EFs, Bugos et al. (2007) randomly 
assigned 31 older adults (aged 60 to 85 years) to receive individualized piano lessons 
or to a no-treatment control group. The weekly piano lessons included lessons about 
music theory, practice with etudes, and songs from a lesson book. After six months of 
lessons, those who received music training showed improved cognitive functioning on 
the Trail-Making Test, Part B, in which subjects connect dots alternating between 
sequential numbers and letters in alphabetical order (i.e., A, 1, B, 2, C, 3, etc.). Given 
that the comparison performance was with a no-treatment control group, these results 
must be taken with caution (Shadish, Cook, & Campbell, 2002). Nonetheless, these 
data still suggest that music lessons may improve performance on nonmusical aspects 
of cognitive flexibility. 
 
While it is difficult to draw conclusions from studies with a no-treatment control 
group given the possibility of cognitive “placebo” effects (cf. Boot, Simons, Stothart, & 
Stutts, 2013), more definitive conclusions can be drawn when there is a comparable 
active control group. One such randomized study looked at the effect of a computerized 
music program on the EF component of inhibitory control. Moreno et al. (2011) 
assigned four- to six-year-old children to either a computerized music training program, 
in which they learned about basic musical concepts like pitch and rhythm, or a 
computerized visual arts training program, in which they learned about concepts like 
6 
shape or color. (Note that, in contrast to most studies investigating transfer from music 
training, the tasks used by Moreno et al. were all perceptual, and children did not learn 
how to play an instrument.) After four weeks of daily training, the children who received 
music training showed greater gains in inhibitory control than the children who received 
visual arts training, as shown in larger P2 peak amplitudes in no-go trials in a go/no-go 
task (see Moreno & Farzan, 2015, for discussion). 
 
Two other studies have focused on the EF component of working memory, 
albeit using different tasks to measure this construct. Roden, Grube, Bongard, and 
Kreutz (2014) assigned 7–8-year-old children to either 18 months of weekly music 
lessons on an instrument of their choosing or a natural science training program. They 
found that those in the music group performed better on a counting span test and a 
complex span test (Roden et al., 2014). Somewhat more equivocal evidence comes 
from Mehr, Schachner, Katz, and Spelke (2013), who randomly assigned 4-year-old 
children to either music or visual arts training, both of which were designed to cover 
many content standards in the National Standards for Arts Education and to encourage 
“parent-child play” (Mehr et al., 2013, Exp. 1). After six weeks of weekly lessons, those 
who had music lessons performed better on a map use/navigation task, which involves 
holding a 2-D map with landmarks (i.e., circles that represented barrels from an aerial 
view) in working memory and manipulating it to navigate a 3-D world (i.e., real barrels in 
the room). Both tasks necessitate the storing and processing of information, and can be 
considered tasks tapping working memory ability. However, Mehr et al. (2013) did not 
find differences on an omnibus test of all four tasks assessed between the music and 
visual arts group but found this difference only when analyzing data from the two spatial 
tasks described here. Moreover, in a follow-up study comparing a new group of children 
randomly assigned to either music lessons or to a no-treatment control, this effect was 
not found (Mehr et al., 2013, Exp. 2). 
 
Lastly, Schellenberg (2004) reported evidence that music lessons can impact 
measures of general intelligence, which likely involve multiple aspects of EFs (e.g., 
reasoning and problem solving). Six-year-old children were randomly assigned to 
standard keyboard lessons, Kodály voice lessons, drama lessons, or no lessons. After 
36 weeks of training, those who received keyboard and voice lessons had greater 
increases in full-scale IQ than those taking drama lessons or no lessons. There is some 
debate as to whether these effects on general intelligence reflect underlying EF 
advantages. In a subsequent correlational study, Schellenberg (2011) found no link 
between music lessons and most measures of EF assessed (Phonological Fluency, 
Sun-Moon Stroop, Tower of Hanoi, and Wisconsin Card Sorting Test). However, these 
tasks may not have recruited EFs given the age group (9-12 year olds, Bialystok, 2011), 
and other evidence from Degé, Kubicek, and Schwarzer (2011) suggests that EFs do 
indeed mediate the relation between music lessons and intelligence when using 
different EF tasks (from the NEPSY-II; Korkman, Kirk, & Kemp, 2007). Given that these 
studies use different categorizations of musicians versus nonmusicians, as well as 
different EF tasks, it remains unclear whether increases in IQ are mediated by 
improvements of lower-level EFs.  
 
Despite these promising results, other evidence suggests that music lessons 
may not benefit cognitive processing. As mentioned earlier, results from Mehr et al.’s 
7 
(2013) randomized control experiments were equivocal overall. Furthermore, Rickard, 
Bambrick, and Gill (2012) found no differences between 10–13-year-old children who 
took 6.5 months of classroom-based music lessons and children who instead took 6.5 
months of drama or art lessons on tests of verbal memory as well as IQ (Rickard et al., 
2012). However, these studies examined only classroom-based group music lessons, 
which may place different demands on EFs than private music lessons.  
 
In sum, the small body of extant randomized control studies on EFs and music 
training have found somewhat mixed results. Because of the disparity in the EF 
measures used, EF components studied, and the length, intensity, and type of training 
administered, it is difficult to compare and draw general conclusions about these results. 
Additional work is thus clearly needed. Nevertheless, these few studies combined with a 
larger body of correlational evidence (see above) suggests that music training might 
indeed show transfer to tasks of EFs. And because EFs are predictive of multiple 
positive outcomes (e.g., success in school, quality of life, and mental health; see 
Diamond, 2013), it would be tremendously valuable to know if, and how, musical 
experiences could function as a kind of EF training. Below, we consider several relevant 
questions posed by Diamond and Ling’s challenge chapter (2017) from the perspective 
of music training and EFs. It is our hope that these considerations can help guide future 
research on if and how music training might serve not only as an intrinsically valuable 
pursuit, but also as an enjoyable “naturalistic” EF training program.  
 
Who benefits most from what? Does the answer differ by age, gender, or cultural 
group of the participants, or another variable? For example, are different methods 
for improving EFs more beneficial at different ages? 
 
 Given the limited populations investigated in previous randomized studies of 
music training, it is not yet clear if certain age groups benefit more from music training. 
All but one of the randomized studies listed in Table 1 looked at young children (age 
range of 4-7 years) because EFs are thought to be more malleable during childhood 
and adolescence. However, one study on older adults aged 60 to 85 did find EF 
improvements after piano lessons as well (albeit without an active control group for 
comparison; Bugos et al., 2007). There is also correlational evidence that music training 
may help prevent perceptual cognitive decline in the aging brain (Kraus & White-
Schwoch, 2014), which suggests that music training may be beneficial for the older 
adult population as well. A likely, but so far untested, possibility is that beneficial effects 
of musical experience will be more pronounced in populations with relatively lower EF 
abilities, such as children, elderly adults, or neuropsychological patients, as has been 
found regarding “bilingual advantages” in inhibitory control (cf. Bialystok, Craik, & Luk, 
2012). 
 
 Differences related to gender and culture are yet unknown: no study examining 
music training has reported differential EF benefits by gender, and all studies to date of 
EF and music training teach music grounded in the Western tonal system. Presumably, 
different cultures would benefit more from training in their own musical system, but it is 
not necessarily the case that different musical systems place the same demands on 
EFs. 
 
8 
 
If a real-life activity improves EFs (be it theater, martial arts, soccer, a school 
curriculum, or something else), what aspect(s) of the program are responsible for 
that? Why did the program improve EFs? 
 
As stated above, future studies should begin to investigate the effect of different 
aspects of music training (e.g., sight-reading and ensemble playing) on EFs. By using 
well-controlled, randomized studies to examine various components of music training, 
we will be able to determine which components are responsible for improving EFs. 
Thus, we will have a better understanding of how music training, as a whole, taxes and 
affects EFs. 
 
While it is of utmost importance to perform well-controlled longitudinal studies in 
the lab (to determine causal relations), it is equally vital to perform quasi-experimental 
or observational studies of music training in the field. Even though these types of 
studies will suffer from selection bias, it is still important to understand the types of 
people who choose to study music and how this training affects them. Specifically, 
these more “naturalistic” music training programs should also produce insights into the 
effects of real-life music training on EFs. Furthermore, because taking music lessons is 
associated not only with cognitive benefits, but also with various social effects (e.g., 
music lessons are associated with increased quality of school life [Eerola & Eerola, 
2014] and joint music making leads to prosocial behavior [Kirschner & Tomasello, 
2010]), examination of a range of noncognitive abilities affected by music training is also 
needed. 
 
What makes the best control condition? 
 
As has been discussed in other domains of training and transfer (e.g., video 
games: Boot et al., 2013), a proper control condition needs to be equally engaging as 
music training (i.e., a convincing placebo control). Additionally, if the control condition 
does not account for all differential expectations, it will confound the experiment, and no 
causal claims can be made about the music training group (Boot et al., 2013). 
Therefore, a proper control condition should also incorporate as many nonmusical 
aspects of the music training program as possible (e.g., motor training, engaging 
lessons, self-efficacy, any social aspects, etc.) so that any differences in the outcome 
variables can be attributed solely to the music lessons. (Of course, potential EF benefits 
from these other aspects of music training are also worthy of investigation, even if not 
specific to the musical aspect of training.) Early work on music training did not use 
active control groups and so offers only suggestive evidence that music training 
enhances aspects of cognitive ability in children (Bilhartz et al., 2000; Costa-Giomi, 
1999; 2004) and older adults (Bugos et al., 2007). However, more recent studies have 
used drama lessons, natural science lessons, and visual arts lessons for active control 
groups (Schellenberg, 2006; Roden et al., 2014 Moreno et al., 2011; Mehr et al., 2013), 
and, in an ongoing large longitudinal study, sports training (Habibi et al., 2014; Habibi, 
Cahn, Damasio, & Damasio, 2016). The variety in these training programs is 
advantageous as they are likely to be effective controls for different nonmusical aspects 
of music training.  
 
9 
What are the best doses, frequencies, and durations for different programs 
intended to improve EFs? Does the answer differ by type of program, EF 
component (e.g., WM or response inhibition), the age, gender, or cultural group of 
the participants, or any other variable? For example, does the optimal dose or 
frequency differ by age?  
 
Because of the shortage of longitudinal, randomized studies with music training 
programs, there is a lack of converging evidence for effects of different types of music 
training on each of the three EF components. Furthermore, the length and duration of 
training regimens greatly differs in the few randomized studies that have been 
conducted (see Table 1). Extant studies have used training programs ranging from only 
4.5 hours over six weeks (Mehr et al., 2013) to 45 minutes weekly over 1.5 years 
(Roden et al., 2014). Improved performance on a switching task was found after six 
months of lessons (Bugos et al. 2007), and increased performance on an inhibition task 
was found after only 28 days of more intense daily training (Moreno et al., 2011). 
Furthermore, effects such as increased full-scale IQ have been seen in musicians after 
as little as a total of 28 hours (over 36 weeks) of music lessons (Schellenberg, 2004). 
However, Mehr et al. (2013) failed to find any effects on tasks of receptive vocabulary, 
numerical quantity discrimination, and spatial reasoning after 4.5 hours of lessons over 
six weeks (see Table 1 for summary), which suggests that music training programs may 
need to exceed this low dosage to see transfer effects.  
 
Taken together, these findings suggest that training studies should employ at 
least 13 hours of lessons (as in Bugos et al., 2007) and should ideally allow for practice 
daily (Moreno et al., 2011) or weekly (Bugos et al., 2007). Because many correlations 
have been found between cognitive processes and real-world music lessons, which 
typically occur once a week, and because it is important to maximize the external 
validity of these studies, it seems reasonable to recommend that a music training 
program should administer lessons at least once per week.  
   
What factors affect how long benefits last? Does it matter which method is used 
to try to sustain benefits (e.g., booster sessions or embed in daily activities)? If 
refresher or booster sessions are used, at what intervals should they be given 
and for what duration? Do the answers to these questions differ by type of 
program, EF component (e.g., WM or response inhibition), the age, gender, or 
cultural group of the participants, or other variables? 
 
Unfortunately, very little evidence exists regarding the degree to which musically-
induced EF benefits persist (Costa-Giomi, 2014). To our knowledge, the only 
longitudinal music training study using delayed testing was Bugos et al. (2007), who 
found that only improvements on the digit symbol task, a test of processing speed, 
remained three months after training had stopped (without a booster session). However, 
improvements on other tasks improved by training (specifically, the Trail-Making Test, 
an EF task) did not persist after the three-month delay. Other evidence from a 
correlational study showed that adults who had taken music lessons during childhood 
had more robust brain stem responses to speech in noise than adults who had not 
taken music lessons as children (Skoe & Kraus, 2012), suggesting that neural changes 
10 
may persist for several years after music training has been discontinued. However, 
more work is clearly necessary to examine the longevity of these effects. 
 
What about training people in strategies to minimize the need for EFs, so that 
people do not have to expend so much effort trying to exercise EFs? 
 
Although previous work has not explicitly investigated this topic, evidence that 
music training leads to improvements in EF task performance could also be interpreted 
as music training minimizing the need for EFs in those tasks. For example, developing 
skill in reading and playing music may allow musicians to do these tasks with less 
cognitive effort (e.g., via expertise-dependent strategies for storing and processing 
musical material; e.g., Barry & Hallam, 2003; Chaffin & Imreh, 1997; 2002; Nielsen, 
2001), which is interesting from the perspective of musical skill acquisition. In addition, 
this raises the possibility that developing strategies that minimize reliance on EFs in 
musical contexts might transfer to some other sorts of EF-demanding tasks, which 
would be a quite different explanation for transfer from the EF-enhancement account 
discussed here. 
 
Will the type of program end up mattering more or the way it is done? 
 
The type of program and the way it is enacted are likely equally important. To 
see benefits, any training program should be done in a way that constantly taxes and 
strengthens EFs and continues to increase in difficulty (Diamond, 2013). In terms of a 
music training program, then, music lessons should incorporate skills that build upon 
one another, with gradual increases in complexity (Bugos et al., 2007). In addition, one 
presumed advantage of music training interventions is the intrinsic value of musical 
experiences. That is, people engage in playing instruments or singing because it is an 
enjoyable activity. For this reason, it is important that music training programs teach 
self-efficacy and motivate participants to practice (Bugos et al., 2007).  
 
If we expect to see near-transfer from music lessons to specific components of 
EF, then a music training program should be one that contains lessons that tax each 
specific underlying component of EF. For example, to produce transfer from music 
training to working memory updating, music lessons might usefully include practice 
reading music and sight-reading. As discussed above, these processes require one to 
visually scan ahead in the musical score (Goolsby, 1994) and hold the representations 
of these notes in mind while simultaneously updating the notes that have already been 
and are yet to be played. If, indeed, practice reading music and sight-reading can 
strengthen working memory updating, one should expect to see transfer effects to tasks 
of working memory updating.  
 
Similarly, to investigate transfer effects to cognitive flexibility, a music training 
program might include practice playing in an ensemble, which may tap cognitive 
flexibility (e.g., Loehr & Palmer, 2011; Moore & Chen, 2010). Lastly, a music training 
program targeting inhibitory control might usefully include practice with complex 
polyrhythms (e.g., Vuust et al., 2006; 2011). 
 
 
11 
Conclusion 
 
Because many aspects of music training (e.g., reading music, practicing, playing 
in ensembles) are likely to engage EFs, and because many people enjoy music lessons 
solely for their intrinsic value, music training can be considered a relatively easy to 
implement “naturalistic” EF training program. Given the pervasive role of EFs that may 
underlie learning to play an instrument, future research should consider the many 
different types of processes engaged in music training programs as well as the various 
EF components each process might affect. To use music training as a “naturalistic” EF 
training program, we must first understand which specific aspects of music lessons are 
causing the benefits in specific components of EF. Fortunately, there is growing interest 
in the cognitive underpinnings of musical tasks and in the potential for nonmusical 
effects of music training. This blossoming area of research will likely paint a clearer 
picture of the exact associations between music training and EFs. 
  
12 
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(S
ub
te
st
s	f
ro
m
	W
PP
SI
-II
I)
PP
VT
-II
I
Nu
m
er
ica
l	D
isc
rim
in
at
io
n
PP
VT
-II
I
Nu
m
er
ica
l	D
isc
rim
in
at
io
n
On
e-
sy
lla
bl
e	
W
or
d	
Sp
an
No
n-
w
or
d	
Re
ca
ll	
Te
st
Co
rs
i	B
lo
ck
	T
es
t
M
at
rix
	S
pa
n
Co
un
tin
g	
Sp
an
K-
TE
A
BA
SC
Tr
ai
l	M
ak
in
g	
Te
st
	-	
Pa
rt
	A
Fi
ng
er
	T
ap
pi
ng
	T
es
t
Th
e	
Gr
oo
ve
d	
Pe
gb
oa
rd
Bl
oc
k	
De
sig
n
Di
gi
t	S
pa
n	
Fo
rw
ar
d	
&
	B
ac
kw
ar
ds
Sp
at
ia
l	S
pa
n	
Fo
rw
ar
d	
&
	B
ac
kw
ar
ds
Sy
m
bo
l	D
ig
it	
M
od
al
iti
es
	T
es
t
Fo
rm
al
	Le
xic
al
	T
es
t
Tr
ai
ni
ng
	Le
ng
th
	
(T
ot
al
	h
ou
rs
/D
ur
at
io
n)
13
	h
ou
rs
	/	
6	
m
on
th
s
40
	h
ou
rs
	/	
4	
w
ee
ks
4.
5	
ho
ur
s	/
	6
	w
ee
ks
4.
5	
ho
ur
s	/
	6
	w
ee
ks
58
.5
	h
ou
rs
	/	
1.
5	
ye
ar
s
28
	h
ou
rs
	/	
36
	w
ee
ks
24
	h
ou
rs
	/	
4	
m
on
th
s
M
us
ic	
Cu
rr
icu
lu
m
W
ee
kl
y	
In
di
vi
du
al
ize
d	
Pi
an
o	
In
st
ru
ct
io
n	
(T
he
or
y,
	E
tu
de
s,	
Le
ss
on
);	
"b
ro
ad
-b
as
ed
	m
us
ic	
ed
uc
at
io
n	
pr
og
ra
m
"
Co
m
pu
te
riz
ed
	m
us
ic	
pr
og
ra
m
:	p
rim
ar
ily
	
lis
te
ni
ng
	a
ct
iv
iti
es
	
"P
ar
en
t-c
hi
ld
	p
la
y"
	m
od
el
ed
	a
fte
r	
Ea
st
m
an
	C
om
m
un
ity
	M
us
ic	
Sc
ho
ol
's	
Ea
rly
	C
hi
ld
ho
od
	M
us
ic	
Pr
og
ra
m
"P
ar
en
t-c
hi
ld
	p
la
y"
	m
od
el
ed
	
af
te
r	E
as
tm
an
	C
om
m
un
ity
	
M
us
ic	
Sc
ho
ol
's	
Ea
rly
	
Ch
ild
ho
od
	M
us
ic	
Pr
og
ra
m
W
ee
kl
y	
le
ss
on
s	o
n	
m
us
ica
l	
in
st
ru
m
en
t	o
f	t
he
ir	
ch
oi
ce
;	
di
ffe
re
nt
	in
st
ru
ct
io
n	
ba
se
d	
on
	a
ge
Ei
th
er
	st
an
da
rd
	k
ey
bo
ar
d	
le
ss
on
s	o
r	K
od
ál
y	
vo
ice
	
le
ss
on
s
Gr
ou
p	
pi
an
o	
le
ss
on
s	i
nc
or
po
ra
tin
g	
m
us
ic	
th
eo
ry
	a
nd
	p
ra
ct
ice
	p
la
yi
ng
Co
nt
ro
l	G
ro
up
No
	tr
ea
tm
en
t	c
on
tr
ol
Co
m
pu
te
riz
ed
	v
isu
al
	a
rt
s	
tr
ai
ni
ng
:	d
ev
el
op
m
en
t	o
f	
vi
su
os
pa
tia
l	s
ki
lls
Vi
su
al
	a
rt
s	t
ra
in
in
g:
	"p
ar
en
t-c
hi
ld
	
pl
ay
"	i
n	
th
e	
st
yl
e	
of
	R
eg
gi
o	
Em
ili
a
No
	tr
ea
tm
en
t	c
on
tr
ol
Na
tu
ra
l	s
cie
nc
e	
tr
ai
ni
ng
	
pr
og
ra
m
Ei
th
er
	d
ra
m
a	
le
ss
on
s	o
r	
no
	le
ss
on
s
Ot
he
r	l
ei
su
re
	a
ct
iv
iti
es
	(e
.g
.,	
ph
ys
ica
l	a
ct
iv
ity
,	p
ai
nt
in
g	
le
ss
on
s,	
ph
ilo
so
ph
y	
le
ss
on
s,	
co
m
pu
te
r	
le
ss
on
s)
	
Ag
e	
(S
D)
	o
f	P
ar
tic
ip
an
ts
	(y
ea
rs
)
M
us
ic	
Gr
ou
p	
/	C
on
tr
ol
	G
ro
up
69
.6
	(4
.7
)	/
	7
1.
4	
(6
.4
)
5.
32
	/	
5.
31
b
4.
86
	(.
30
7)
	/	
4.
64
	(.
26
8)
4.
71
	(.
26
0)
	/	
4.
72
	(.
35
3)
7.
36
	(0
.5
7)
	/	
7.
72
	(0
.6
8)
Ke
yb
oa
rd
:	6
.2
0	
(.2
1)
Vo
ice
:	6
.2
8	
(.2
1)
	
Dr
am
a:
	6
.2
0	
(.2
3)
No
	Le
ss
on
s:	
6.
31
	(.
22
)
69
.3
0	
(2
.0
3)
	/	
69
.5
6	
(1
.4
3)
In
iti
al
	/	
Fi
na
l	S
am
pl
e	
Si
ze
39
	/	
31
71
	/	
48
32
	/	
29
46
	/	
45
50
14
4	
/	1
32
41
	/	
29
*	
Ci
ta
tio
n	
Co
un
t	a
s	o
f	J
un
e	
27
,	2
01
6	
-	G
oo
gl
e
a	
Di
ffe
re
nc
e	
ef
fe
ct
s	n
ot
	re
po
rt
ed
b	
St
an
da
rd
	d
ev
ia
tio
ns
	fo
r	a
ge
	n
ot
	re
po
rt
ed