Diamond (2013) provided a comprehensive review of EFs including a description of the core EFs, possible commonalities and differences between the subcomponents, as well as representative psychological tasks to assess these. The three most widely researched EFs (Diamond, 2013; Miyake et al., 2000) are inhibition, working memory, and cognitive flexibility, which we introduce with reference to music making:

Working memory (WM) refers to processes of maintaining information in mind and mentally updating this information (Diamond, 2013). WM is important for stimuli that unfold over time (Diamond, 2013), such as language or music, because WM enables us to relate later passages of music to earlier ones and keep track of regularities and variations. A typical task for measuring WM in experiments is the backward digit span task, in which digits previously presented must be reported in reverse order. Another WM task is the complex span task (Conway et al., 2005) in which participants must maintain information (e.g., a series of letters) while performing another task interspersed to letter encoding (e.g., judging the correctness of an equation, see Table S1 in the supplemental material for a graphical depiction of the tasks used).

Inhibition or inhibitory control subsumes the control of attention, the inhibition of unwanted thoughts or behavior, or the delay of gratification (Diamond, 2013). An example for inhibitory control of attention in music is that we can choose voluntarily to ignore particular stimuli (such as the rhythm of a specific instrument) and attend to other stimuli (such as the rhythm of one’s own instrument) based on our goals (Diamond, 2013). An experimental task for measuring inhibition is the Stroop task, in which participants must name the font color in which words are written. This involves inhibitory control as all presented words are color words, i.e., the meaning of the word (e.g., “red”) must be ignored when asked to focus only on classifying the font color (e.g., green).

Cognitive flexibility builds on inhibition and WM (Diamond, 2013) and involves the ability to adjust to changing demands in thoughts or actions. An instrumentalist, for example, must adjust the fingering of a note when an accidental precedes it (i.e., changing an F to an F#), or when the clef has been changed from bass clef to treble clef (Slama et al., 2017). In experiments, cognitive flexibility is often measured with task-switching requirements (see Kiesel et al., 2010, for a review). A typical switching task involves an instruction on changing task demands, i.e. alternating between two types of stimuli as in the trail making test. In this task, letters and digits must be connected in ascending order, thereby constantly switching between considering letters and digits.

In their unity and diversity approach, Friedman and Miyake (2017) argue that the three above mentioned core EFs are commonly tested separately by experimental procedures but are also correlated with one another on the level of latent variables that extract shared variance (Miyake et al., 2000), thereby motivating to treat them as unique yet related set of cognitive functions (Aryanto et al., 2026). The question of how to model individual differences in EF components is still debated in cognitive psychology (Friedman & Miyake, 2017) and will not be discussed in detail here. Given that distinct task requirements exist (in experimental procedures as well as in the examples given for the music domain) we decided to first examine the three core EFs in a joint analysis across all three components and then analyze each component separately, taking moderators into account. These separate analyses provide a more fine-grained picture of the impact of music training on particular cognitive functions whereas the question remains to be answered whether executive functions as an overarching construct (like the g-factor in intelligence) really exists (Aryanto et al., 2026; Friedman & Miyake, 2017; Rey-Mermet et al., 2019).

Although this paper does not test the causal assumption of a transfer effect, it is nevertheless important to note that EFs can be improved at any age throughout the lifetime, while it is still unclear which specific conditions lead to long-term effects (Diamond, 2013; Valian, 2015).

While there is little doubt that EFs are required for both music listening and music making (Degé & Frischen, 2022), there is ongoing research on the question of potential relationships between music training and EFs. Since EFs can generally be trained in children (Diamond & Lee, 2011), the question arises as to whether music training (which often starts during childhood) can lead to improved EFs (i.e., a transfer effect, Jaeggi et al., 2011). The reason for any transfer effects could be that making music is a complex activity that builds on a wide range of cognitive abilities (Schellenberg & Lima, 2024). Musicians practice translating complex musical notation into sound, often in groups, so that social aspects must be considered in addition to cognitive aspects (Degé & Frischen, 2022). Degé and Frischen (2022) argue that playing music combines the aspects identified by Diamond and Lee (2011) for successful training of EFs: it is engaging for one's own interests, it often takes place in social groups, and it is challenging. On the other hand, it is also possible that better EFs lead to successful music training. The question of possible transfer effects can only be answered conclusively by experimental studies, while correlations between tasks used to measure EFs and music training have also been investigated by individual differences studies comparing participants with varying levels of music training. There are a few review papers on EFs and music training which are summarized below.

Degé and Frischen (2022) presented a systematic review on longitudinal studies with children below the age of 11 years. They reported 21 samples, 20 of which had a positive effect of music training on at least one EF (no mean effect sizes were reported in this systematic review). On the other hand, a large meta-analysis by Sala and Gobet (2020) on cognitive and academic benefits of music training in children aged 3 to 16 years found an overall null effect (g = -0.021–0.064) for studies using active controls or implementing random assignment of participants, regardless of the type of outcome measure (i.e., no positive effect was found for any EF).

Román-Caballero et al. (2018) suggested that age-related cognitive decline which often becomes visible around the age of 60 might be reduced in persons with a history of music training. They presented a systematic review and meta-analysis on nine correlational and four experimental studies on cognitive function in adults above the age of 58 years. Correlational studies identified positive effect sizes in favor of the musically trained groups on all cognitive functions (standardized mean difference [SMD] = 0.316 for processing speed, SMD = 0.441 for attention, SMD = 1.766 for inhibition, SMDs between 0.180 and 1.660 for more complex functions such as verbal memory), including a few samples on EFs. The experimental studies included short-term music training between four to six months and reported a positive effect size for flexibility (SMD = 0.93).

Reviewing studies with adult samples, Talamini et al. (2017) provided a meta-analysis of 29 memory studies including long-term memory, short-term memory, and WM. The meta-analysis reports a positive effect of music training on WM (g = 0.56). The musicians’ advantage in WM measures was large with tonal stimuli (g = 1.04), moderate with verbal stimuli (g = 0.59), and null with visuospatial stimuli (g = 0.01).

A paper that comes close to our own objective is that of Pérez-Eizaguirre et al. (2023). They reviewed data from 16 papers on EFs, memory, and intelligence, which included altogether 92 independent samples. Among these, 37 samples deal with EFs. The authors found a positive mean effect size of g = 0.37 for EFs and a moderating effect of the duration of music training, with larger effect sizes being associated with a shorter duration of music training. This somewhat odd result is explained as reflecting an effect of age when starting the training as young children have least training but show strongest effects (Pérez-Eizaguirre et al., 2023).

Another paper that comes close to our own objective is the recent systematic review and meta-analysis by Aryanto et al. (2026) on the associations between musical expertise and EFs. Their analysis included 47 studies published up to 2020 with adult participants and they report a mean effect size of g = 0.43 for associations between musical expertise and EFs as latent construct, as well as significant associations between musical expertise and inhibition (g = 0.31), musical expertise and shifting (g = 0.22), and musical expertise and WM (g = 0.49). Among several potential moderator variables, the authors found that only risk of bias was a significant moderator of the association between musical expertise and inhibition, while for all other moderators, the evidence was non-significant or ambiguous (Aryanto et al., 2026).

In sum, the available studies for child samples show a transfer effect of music training on executive functions in longitudinal studies (Degé & Frischen, 2022), but no group differences between children with and without music training (Sala & Gobet, 2020). In older adults, both correlative and causal effects on EFs have been demonstrated (Román-Caballero et al., 2018), although the effects in older adults were smaller than in younger adults in the analysis of Pérez-Eizaguirre et al. (2023). The analysis by Aryanto et al. (2026) underlines the importance of differentiating between types of EF and reports a slightly higher effect size than Pérez-Eizaguirre et al. (2023) for the non-differentiated view on EFs. In our own analysis, we aim to provide additional evidence on the association between music training and EFs on potential moderating variables for this association by expanding the selection of studies to include gray literature as well as studies up to 2025. The following section explains our selection process and statistical analyses.

We based the search for and selection of articles on the PRISMA guidelines for reporting systematic reviews (Page et al., 2021; see also https://www.prisma-statement.org).

Our final sample for the meta-analysis included 72 studies published between 2003 and 2025. Descriptive statistics of all studies included in the meta-analyses, including information on moderating variables and risk of bias variables, can be found on OSF.

The aim of our study was to identify the magnitude of any relationships between music training and EFs by summarizing existing studies in a meta-analysis. In our meta-analysis we opted for a more detailed picture of associations between music training and EFs in adults by considering both the shared variance of the construct as well as the classic tripartite structure of EFs, namely WM, cognitive flexibility, and inhibition (Diamond, 2013; Miyake et al., 2000). For all facets we identified comparisons between different groups of participants that received music training and those who did not or to a much lesser degree. Additionally, we analyzed studies assessing music training as continuous variable and covariate for performance in EFs. Besides calculating mean effects for the respective group differences and correlations, we considered the mean age, the duration of music training, the modality of the cognitive task, the type of cognitive task, and a risk of bias score as potential moderating variables in our analyses.

The analysis across all three facets of EFs revealed a significant mean effect for both the group comparisons (g = 0.59) and the correlational studies (g = 0.16). The group effect is larger than the findings by Pérez-Eizaguirre et al. (2023, g = 0.37) and Aryanto et al. (2026, g = 0.43), while there is no respective result for correlational studies in prior work. A possible explanation for the smaller effect size in the correlational studies might be that those samples mostly reflect university populations and were less selective regarding musical training. The results of the separate meta-analyses per facet confirm the positive associations between music training and all three facets of EFs. At the same time, the effect sizes of the group comparison studies differ between the three EF facets and were highest in the studies testing for WM. WM is also the most widely researched EF facet. The overall estimated effect size for WM studies of g = 0.50 (without the study by Schulze et al., 2012) is of a similar magnitude as findings by Talamini et al. (2017, g = 0.56) and by Aryanto et al. (2026, g = 0.49), while our estimated effect sizes for inhibition and for cognitive flexibility are larger than those found by Aryanto et al. (2026). The reason for this difference could be due to the larger number of included studies in our analyses (n = 72 vs. n = 47). Nevertheless, it must be noted that the results of our meta-analyses are in line with all other reported meta-analyses in reporting a positive association between music training and EFs. This association can be explained by the demands of music-making as a complex activity: Firstly, music-making places high demands on WM, as it involves, for example, maintaining auditory information to assess one’s own playing in relation to the desired sounds, while at the same time coordinating the execution of playing movements. Also, the representation of music in long-term memory is updated in WM to make it available for the actual performance. Secondly, cognitive flexibility is required as musicians need to adapt to changing musical parameters such as key or time signature while simultaneously performing multiple tasks such as listening, remembering, reading music, and translating musical ideas into movement. Thirdly, inhibition is required, for example, by the fact that music is most likely not performed in isolation but in close relationship to other musicians some of which require different melodic, rhythmic and temporal structure to be performed at the same time. Thus, to perform music together with other musicians, it is necessary to focus closely on one’s own performance while remaining relaxed and (to a certain degree) uninfluenced by the input of others. This has some similarities with engaging in a Stroop task in which the semantic meaning of the color word must be ignored to successfully name the ink color.

The analyses of moderating variables revealed no effects of age for any facet of EFs. This means that samples with older adults resulted in similar group differences between musically trained and untrained groups as samples with younger adults or students, a finding that makes sense as all samples included healthy participants whose intensive music training had taken place during childhood or adolescence. The result suggests that the association between music training and EF is long-lasting and, if explained as a transfer effect (Jaeggi et al., 2011), does not “wear off” with age. There was also no effect of the duration of music training as moderating variable. If the relationship between music training and EFs was explained by a transfer effect (Jaeggi et al., 2011), the expectation would be larger effects in groups with longer music training. Our data do not support this assumption, but we would like to point out that the assessment of music training is limited to self-reported years of practice in many studies and thus not very precise (see below for a discussion of the risk of bias variables). The third moderator, task modality, affected the effect sizes for inhibition and WM; both were larger in samples with tasks using auditory stimuli than with visual stimulus material. For WM, this mirrors the result of Talamini et al. (2017) and could be interpreted as a larger near transfer effect (from music training to auditory WM) and smaller far transfer effect (from music training to visual WM). Task type was identified as a significant moderator for group comparisons in cognitive flexibility, the effects were larger in the studies employing frequently used tasks. Those tasks often yield effects in the expected direction and are therefore considered to be the “Gold Standard” for one function (Miyake et al., 2000) and frequently included in custom made batteries for cognitive assessment.

The risk of bias assessment showed that there is a substantial risk of bias in the assessment of music training in many studies. In the cross-sectional studies used in our meta-analysis, music training is most often self-reported in years of music instruction or years of instrumental activity while practice intensity (e.g., a weekly amount of practice hours) is rarely reported. This results in a lack of clarity in the operationalization of music training. Although the cut-offs used to differentiate between musicians and non-musicians are comparable in many studies, they also seem arbitrary given the lack of information on correlations between training intensity and effects. Only a minority of studies used standardized tests or professional musicians in the musically trained group. In contrast, the risk of bias was low throughout all studies for the cognitive assessment which was mostly done by standard procedures of cognitive psychology. While the sampling of participant groups is well documented in most studies, a minority of studies included potential confounding variables into their analyses. Consequently, studies differed in their overall risk of bias scores, while at the same time, this resulted in a significant moderating influence in the meta-analyses of inhibition and cognitive flexibility which had also been identified by Aryanto et al. (2026) for inhibition.

While we argue that music making has some obvious similarities with tasks used to measure EFs, we also point out that our meta-analysis cannot draw any conclusions about causal relationships. Given the use of only cross-sectional samples and correlational data, the meta-analysis is not suited to properly answer the question whether music training supports EFs or whether those with excellent EFs master music training most successfully. This question can only be answered in controlled experimental studies. Furthermore, the relationship between taking up music training, succeeding in music making, and cognitive capabilities is not controlled for in the cross-sectional and correlational studies reported. Possibly, persons with high cognitive capabilities might be more inclined to engage in further cognitively stimulating training such as music whereas those struggling with academic tasks might be more likely to drop out earlier in training before achieving a certain level (Valian, 2015). This explanation mirrors the “pre-existing dispositions hypothesis” (Silas et al., 2022) meaning that a disposition as a third variable could be the underlying factor influencing both cognitive and music tasks. Consequently, the result of our meta-analysis confirms a group difference between individuals with and without music training but leaves open the question of causality.

We would like to mention some further caveats. The first is that most included data came from published studies, and the analyses showed some indication of publication bias. In sum, there is the risk of publication bias while usually peer-reviewed studies should provide higher quality than unpublished work which is more likely to remain hidden in local university repositories. Additionally, not all studies provided the necessary raw data, and some requests were not answered, which is a common problem for meta-analyses. Next, different criteria (i.e., questionnaire scores, self-report) were used to quantify music training and sort participants into the musician vs. non-musician group. The effect sizes obtained for correlational studies suggest that more fine-grained measures (i.e., continuous scales) would lead to smaller yet in sum significant effects.

Overall, cross-sectional studies with adult participants who were either musically trained or not showed moderate positive associations between music training and EFs, which is especially true for the facet WM, and to a slightly lesser degree for inhibition and cognitive flexibility. Tasks assessed mostly represented well-established standard tasks for those constructs, and effect sizes were smaller yet still positive when continuous measures for music training were used, proving a robust association between music training and EFs.