Learning to play a musical instrument in the middle school is associated with superior audiovisual working memory and fluid intelligence: A cross-sectional behavioral study

Music training, in all its forms, is known to have an impact on behavior both in childhood and even in aging. In the delicate life period of transition from childhood to adulthood, music training might have a special role for behavioral and cognitive maturation. Among the several kinds of music training programs implemented in the educational communities, we focused on instrumental training incorporated in the public middle school curriculum in Italy that includes both individual, group and collective (orchestral) lessons several times a week. At three middle schools, we tested 285 preadolescent children (aged 10–14 years) with a test and questionnaire battery including adaptive tests for visuo-spatial working memory skills (with the Jack and Jill test), fluid intelligence (with a matrix reasoning test) and music-related perceptual and memory abilities (with listening tests). Of these children, 163 belonged to a music curriculum within the school and 122 to a standard curriculum. Significant differences between students of the music and standard curricula were found in both perceptual and cognitive domains, even when controlling for pre-existing individual differences in musical sophistication. The music children attending the third and last grade of middle school had better performance and showed the largest advantage compared to the control group on both audiovisual working memory and fluid intelligence. Furthermore, some gender differences were found for several tests and across groups in favor of females. The present results indicate that learning to play a musical instrument as part of the middle school curriculum represents a resource for preadolescent education. Even though the current evidence is not sufficient to establish the causality of the found effects, it can still guide future research evaluation with longitudinal data.

Introduction

In recent years, several studies with continuous variables (e.g., duration of music training) have been carried out exhibiting the relationship between music training and overall cognitive ability both in childhood and in adulthood, making this area one of the most investigated within the field of music psychology (Schellenberg, 2006; Wetter et al., 2009; Degé et al., 2011; Corrigall et al., 2013; Swaminathan et al., 2017). Previous studies focusing on children (Schellenberg, 2004, 2006, 2011a) confirmed the hypothesis of a positive correlation of intelligence with the duration of musical courses, while reporting a long-term association between formal exposure to music in childhood and both IQ and academic performance.

Some studies showed a stronger relation of cognitive abilities with music lessons than with other leisure activities: in an intervention trial, participants allocated to music lessons showed greater increases in full-scale IQ points than students assigned to theater education or to a passive control group (Schellenberg, 2004). In a longitudinal study by Degé et al. (2011) with preschoolers, participants who took part in the phonological skills program or the music program significantly improved their phonological awareness, but this improvement was not seen in the sports group. Moreover, in some studies music training is associated with improvements in cognitive skills (Schlaug et al., 2005; Sachs et al., 2017; Guo et al., 2022). It is know that individuals with more music training have a significant advantage in tasks that require perceiving and discriminating between pitches, rhythms, and melodies (Wallentin et al., 2010; Law and Zentner, 2012; Zarate et al., 2012; Ireland et al., 2018). This means that a substantial correlation exists between music training and the cognitive abilities known as “near abilities,” which are promoted in music lessons; on the contrary, the concept of “far abilities” implies the transfer of the learner’s knowledge and skills from the taught context to another dissimilar context.

Correlational research revealed that individual differences in music lesson duration were associated with various “far” intellectual domains (Schellenberg, 2006). Music training involves procedural memory and long-term memory and, as it requires hours of practice, it includes the integration of auditory and visual information in a temporal framework; a study by Bugos and Mostafa (2011) showed a positive correlation between music training and information processing speed in the visual and auditory domains in adolescents with years of active music training compared to non-musicians. Further analyses on the relationship between music and cognitive abilities confirmed that children who receive music training have better performances in visuo-spatial abilities, language processing, verbal memory and reading ability (Jaschke et al., 2018; Linnavalli et al., 2018; James et al., 2020). In another study by Swaminathan and Schellenberg (2020), musical ability predicted language ability. Also, it has been found that learning to play an instrument as a child may predict academic performance and IQ in adulthood (Okada and Slevc, 2018).

Finding associations between music training and mental abilities like linguistic aptitude, visuo-spatial thinking, fluid reasoning and processing speed is fascinating and poses challenging issues concerning the underlying causes and developmental mechanisms. However, although there are favorable correlations between music training and cognitive skills, most of the relevant evidence is based on correlational studies, i.e., observational data, excluding causal inferences or only making it possible under certain conditions (Silas et al., 2022). Consequently, the very idea of non-musical benefits of music training is still questioned and debated in the scientific community.

According to some authors, the cause of improvements in cognitive abilities cannot be attributed solely to music training since transfer between very far abilities is, in human cognition, a rare effect (Gordon et al., 2015; Sala and Gobet, 2017, 2020). Moreover, most research on the relationship between learning music and improving cognitive ability is correlational, thus looking at whether continuous variables increase or decrease together. Hence, inference of causal effects from correlational studies can be confounded by pre-existing individual differences in social background, cognitive ability, personality, and genetic repertoire (Schellenberg, 2019; Vincenzi et al., 2022). Further issues, such as small sample size and no random assignment, together with the lack of an active control group, preclude the possibility of clearly determining causation between music training and non-musical abilities (Tierney and Kraus, 2013; McPherson, 2016; Schellenberg, 2020a).

In the study by Swaminathan and Schellenberg (2020), the link between music and language appeared to arise primarily from pre-existing factors and not from formal training in music. Moreover, literature showing brain structural differences between musicians and non-musicians has not yet ultimately established whether such differences result from pre-existing traits, musical training, or an interaction between the two (Norton et al., 2005; Vollmann et al., 2014; Olszewska et al., 2021). Hence, these findings impede one to make any definite inferences of causality in relation to cognitive transfer of music training. However, compelling proof of a causal link between musical experience and cognitive abilities comes from longitudinal experimental investigations.

The mentioned study by Degé et al. (2011), together with a study by Roden et al. (2014b) where results showed better performance in processing speed tasks in a group of children who followed music training for 1 year compared to children who followed a science training, are examples of longitudinal studies with active control groups. Even longitudinal studies with random assignment obtained enhanced reading skills and phonological awareness in musicians compared with controls (Cogo-Moreira et al., 2013; Slater et al., 2014; Rautenberg, 2015); this latter type of studies is actually the most effective way to address the issues regarding correlational studies (Tierney and Kraus, 2013). This also applies to the mentioned correlational literature investigating the relationship between music training and intelligence, unable to provide firm evidence on the effects of music training. Although, more recent studies on this topic suggest that the link between music training and IQ has an inverse causal relationship: children with higher IQ are rather more inclined to take music lessons than children with lower IQ (Schellenberg, 2019, 2020b; Kragness et al., 2021).

To achieve an “intelligent”, goal-oriented behavior (Arffa, 2007), individuals must be able to control their attention, inhibit their impulses, automatic responses and emotions, focus, maintain and protect attention from distraction, generate information and manipulate it in memory making it possible for us to engage in complex cognitive tasks, and adapt readily to changing circumstances. All these abilities correspond to the psychological concept of executive functions (EF; Arffa, 2007). In some studies, IQ has been found to be associated with executive functions although this remains a controversial topic (Friedman et al., 2006; Garcia-Molina et al., 2010; Ardila, 2018). Authors found some overlap between IQ and EFs. Specifically, EF refers to a group of skills that are often subdivided into the components of cognitive flexibility, working memory (WM) and inhibitory control (Stuss, 2011; Diamond, 2013; Marzocchi et al., 2020). The cognitive flexibility component comprises fluency, the ability to relinquish inhibition and generate information from mind and set shifting, also known as task switching, the ability to quickly shift between two activities or quickly switch response methods within a task. WM, according to the original definition by Baddeley and Hitch (1974), includes the ability to temporarily hold information in mind (maintenance) and manipulate it (updating), which relies on several interconnected operations: flexibility, mental manipulation and inhibition of distractors including visuo-spatial skills (Baddeley, 1986, 2000). Finally, inhibitory control is the suppression of inputs and behavioral reactions that are unrelated to the aim. Current developmental classifications distinguish between the ability to suppress interference from distracting stimuli (attentional inhibition) and the ability to block a preeminent motor response (response inhibition) (Tiego et al., 2018).

Each component of EF is known to mature at a distinct age and to develop at its own rate during childhood and adolescence; cognitive flexibility prolongs its development into young adulthood and, together with its subcomponents (set shifting and task switching), is the last to develop. This applies also to WM and inhibitory control which become distinct cognitive functions only after 9–10 years of age, although the basilar characteristics of both first appear about 6 months of age (Mata et al., 2017; Camos and Barrouillet, 2018; Tiego et al., 2018). The prolonged development of WM throughout adolescence is linked to brain changes, notably the prefrontal cortex’s maturation (Ferguson et al., 2021). Such rapid changes lead to an imbalance between the cognitive control system, relying on prefrontal brain structures and supporting the decision-making process through impulse inhibition, and the socioemotional system, relying on ventral striatum and orbitofrontal cortex (Donati et al., 2014; Meisel et al., 2019). This makes adolescents prone to risky behavioral decisions, and therefore it is important at this stage to possess a developed self-control. Being able to intentionally suppress or control behavioral reactions incompatible with one’s goals, is a sign of good self-regulation (Hofmann et al., 2012). As seen, inhibitory control and WM are two related but separate constructs; also, WM is supported by the same neural networks as cognitive flexibility which in turn relies on mental processes analogous to fluid intelligence (Stevens et al., 2002; Lambek and Shevlin, 2011; Barbey et al., 2014).

Fluid intelligence has been conceptualized as a discrete factor of general intelligence related to problem-solving and in contrast to crystallized intelligence which comes through learning (Cattell, 1963; Kent, 2017). Spearman’s (1927) theory hypothesized the presence of a universal factor that influences all cognitive processes called general intelligence (g); thus, g and lower-order skills like fluid intelligence are also correlated with auditory discrimination skills, since they share general and specialized genetic factors (Mosing et al., 2014; Silvia et al., 2016).

Notably, WM and fluid intelligence are closely associated functions and complementary measures of cognitive processes supporting complex cognition (Unsworth et al., 2014). WM capacity, allowing humans to flexibly create task-relevant connections between disparate bits of knowledge, hence, represents a key cognitive process underlying fluid intelligence together with focus of attention (Shipstead et al., 2016; Gray et al., 2017). Moreover, both fluid intelligence and WM are crucial for an adequate healthy development. On one hand, fluid intelligence allows for handling new problems without having prior knowledge of the task, and it is predictive of outcomes like educational success; also, it was found to be strongly associated with logical problem-solving and social adaptation (Huepe et al., 2011; Fuhrmann et al., 2020). On the other hand, disorders in WM functional maturation are linked to the emergence of neurodevelopmental disorders (Andre et al., 2016). The importance of a good cognitive and executive functioning in preadolescence and adolescence is, therefore, unquestioned; indeed, from childhood on, self-regulation is an important predictor of academic achievement and mental health (Woodward et al., 2017).

Due to the relatively gradual maturity of EFs, their course of development may be influenced by early activities through a variety of programs (Blair et al., 2008) including music training. Some of the most striking examples in the literature highlight the positive effect of music training on both neural and behavioral development: previous studies have shown that extra-curricular music training helps improve EF in children (Putkinen et al., 2015; Sachs et al., 2017; Shen et al., 2019; Frischen et al., 2021). Also, event-related potential and neuroimaging studies found evidence for improved EFs in musically trained children and adolescents (Zuk et al., 2014; Moreno et al., 2015; Kausel et al., 2020; Putkinen et al., 2021; Saarikivi, 2022) in addition to a more efficient use of neural systems supporting those functions (Putkinen and Saarikivi, 2018). Furthermore, in a longitudinal study of children from underprivileged backgrounds, those who took music lessons after school exhibited an enhanced ability to delay gratification compared to their counterparts who took sports or no after-school program; the music group also improved from 2 to 3 years on a test of response inhibition (Hennessy et al., 2019). In another study, Okada and Slevc (2018) found that actively participating in a music course requires the ability to consistently manipulate, remove and add information. In two recent experiments carried out by Chen et al. (2022), the level of music training was found to be positively correlated with response inhibition and WM abilities, implying that music training is linked to improved EF abilities in childhood. These studies assume that the intensity and length of practice are related to the degree of structural and functional adaptation in the brain (Habibi et al., 2018).

Some other studies focusing on the effects of musical education in childhood assume that music lessons can improve EFs, hence, boosting general cognitive advantages (Schellenberg and Peretz, 2008; Degé et al., 2011; Schellenberg and Winner, 2011; Holochwost et al., 2017). However, it remains unclear whether EFs play a role in the link between music lessons and general cognitive performance. In some cases, for example, the association between music training and IQ appears to be completely mediated by EFs while, in other cases, music training is correlated with IQ but not with EFs, except for WM (Swaminathan and Schellenberg, 2016). The effects on cognitive development may be influenced by the onset of music lessons tied to specific developmental stages and by a number of other relevant variables such as motivation, reward and contextual social factors.

In the specific case of WM, retention, processing and integration of complicated pitch and time sequences are all required in music training; consequently, music training and WM are clearly able to activate analogous mechanisms (Yurgil et al., 2020). When playing an instrument, WM integrates sound events, retrieves data from memory systems, connects sounds to meaning and memories, and aids in the development of emotional responses (Burunat et al., 2018; Criscuolo et al., 2019). The involvement of a WM network in musical tasks has been reported in several studies with higher brain activation in musicians than in non-musicians (Schulze et al., 2011b; Schulze and Koelsch, 2012; Burunat et al., 2014). Moreover, several positive effects on WM were found when comparing musicians and non-musicians. In previous studies, musicians outperformed non-musicians on n-back tasks showing that music training leads to improvements in WM tasks (Pallesen et al., 2010; Oechslin et al., 2013; Slevc et al., 2016; Ding et al., 2018). More recently, in Criscuolo et al. (2019) adult musicians showed higher general intelligence, verbal intelligence, WM and attention skills than non-musicians, while amateur musicians scored in between.

Despite this evidence, the findings of some studies of music training and WM in children and preadolescents are mixed. In a longitudinal study by Saarikivi et al. (2019), the results suggest that music training, particularly in late childhood, is significantly linked with improved WM capacity and maintenance but not with improved WM updating. Furthermore, some cross-sectional studies showed that musicians have an edge in auditory WM but not in visual WM (Strait et al., 2012). By contrast, in another study, Sachs et al. (2017) did not find any effect of music training on WM, although they did identify changes in neural activity in brain areas involved with cognitive functions between children who had received music training and those who had not (Sachs et al., 2017).

However, in a recent meta-analysis Lorenzen and Brattico (2020) provided a synthesized estimate of the effect of music training on a combined measure of short-term memory and WM in children, demonstrating that music training can increase these abilities. In another study by Bergman Nutley et al. (2014), the favorable effects of music practice on WM development, even controlling for parental education and other leisure activities, provide evidence for the significance of practice for WM growth during childhood and adolescence. Finally, in a study by Kausel et al. (2020), across attention conditions, preadolescent musicians performed better on bimodal (auditory/visual) attention tasks adapted by adding the memory retrieval task and showed, at the same time, more activity in fronto-parietal control network areas than controls.

Working memory is frequently studied separately for each modality, such as for visual and auditory WM. Auditory WM is the process of recalling sounds in mind for short periods of time when the sounds are no longer present in the environment; particularly, the expression “musical WM” specifically refers to the temporary storage and manipulation of musical inputs, like notations or musical sounds, useful for the completion of musical tasks (Silas et al., 2022). Auditory WM was also found to have a specific relationship with music sophistication (Lin et al., 2021; Lad et al., 2022; Silas et al., 2022), a multifaceted construct applicable to the general population since it focuses on multiple aspects of an individual’s musicality (improvisation, having a strong sense of pitch and rhythm, musical comprehension, appreciation, judgment, and communication abilities) which do not necessarily require mastering an instrument (Ollen, 2006; Müllensiefen et al., 2014; Zhang et al., 2020; Porflitt and Rosas, 2022). While several studies clearly indicate advantages of musicians in auditory WM (Schulze et al., 2011b; Kraus et al., 2012; Zuk et al., 2014; Slater and Kraus, 2016; Slevc et al., 2016; Alain et al., 2018; Guo et al., 2018; Nie et al., 2021), the evidence is controversial for visual WM.

The WM visuo-spatial sketchpad consists of two subsystems: a temporary visual store (“visual cache”) for visual shape and color, and a temporary spatial store (“inner scribe”) for movement sequences and planning (Logie, 1986, 2011). In a study by Hansen et al. (2013), significant effects were found in verbal WM but not in the visuo-spatial domain. These results converge with a more recent study involving adult participants where Talamini et al. (2017) found that musicians’ memory advantage is large for musical stimuli, medium for verbal stimuli (digits and words) and small for visual and/or spatial stimuli. Nevertheless, a particular relation has been found between music training and visuo-spatial sequence learning in the study by Anaya et al. (2017), showing that musicians may exhibit this domain enhanced due to fundamental differences in their visual-spatial abilities because of their extensive training and involvement in musical activities: encoding and recognition of visuo-temporal patterns is a common process during instrumental playing; think of reading a score or following the hands while playing (Janata and Grafton, 2003; Proverbio and Orlandi, 2016; Proverbio and Bellini, 2018). Also, a convergent finding by Rodrigues et al. (2014) suggests that musicians may score better on the visual memory test due to their enhanced sensorimotor connection. A further study conducted with children by Roden et al. (2014a) suggest an enhanced visual memory for the musicians, although without finding significant effect in those tests addressing the visuo-spatial sketchpad. In an additional longitudinal study with children, participants belonging to the music group exhibited significant improvements in the post-test compared to the sport group participants in visuo-spatial WM; however, no significant group x time interactions were found (Frischen et al., 2019).

As seen, previous research has been inconsistent on whether musical aptitude affects performance on complex non-musical tasks, such as tests of WM and intelligence, especially in preadolescence; thus, more investigation is needed on the topic. In the present study, we chose to focus on the two interrelated cognitive constructs of WM and fluid intelligence because they are central for the development of self-regulation abilities during preadolescence, preparing for adolescence and adulthood. Preadolescence is a period characterized by specific evolutionary tasks and milestones necessary for development, and the inattention to needs in this age range can have consequences for the functioning of adolescents and adults (Moore and Halle, 2001; Moore and Theokas, 2008; Fasano et al., 2019a). Hence, preadolescence can be thought of as a pivotal stage to understand the development of WM and its role together with fluid intelligence in academic achievement and psychosocial adaptation and, thus, a sensitive period for prevention.

Instrumental education in music middle schools

In the educational communities, various forms of music training exist, and initial evidence suggests that even the type of training might affect transfer outcomes, with formal instrumental music training being more efficient than less structured programs (James et al., 2020). Hence, the complexity of musical development as well as the type of music training received need to be taken into account (Ilari et al., 2016). The present study focuses on a music training program carried out in Italian music middle schools, whose impact has only been little examined. Musical middle schools can be considered one of the largest and most original investments of the Italian public school, allowing tens of thousands of students to study a musical instrument for free within the curricular study pathway. The program was born in 1975 in some Italian middle schools following the traditional didactic model of the Conservatory until they became regulated starting from the academic year 2000/01. Characteristic elements of the regulation process were:

1) Emancipation from the traditional Conservatory teaching and the expansion of the range of possible activities: from individual lessons to group lessons, to the practice of ensemble music, to increasingly original forms of learning musical notation.

2) A strong link with curricular music education and with the didactic and educational programming of schools.

3) The ordinary and extended use of organizational and didactic autonomy for all schools.

4) The presence in schools of teachers having a Conservatory Master’s degree and specialized in the arrangement/composition of pieces suitable for students in that age group.

In musical middle schools it is possible for students to be enrolled in the standard curriculum as well: in fact, students enrolled in the music curriculum and in the standard curriculum attend their courses within the same school building. Both groups of students receive the same number and type of lessons in the morning; in the afternoon, only students enrolled in the music curriculum attend the instrument lessons two or three times a week. In some cases, separate course sections for students of the musical curriculum on one side and standard curriculum on the other side are set up within the same school building; otherwise, students of the music and standard curriculum are classmates. The morning lessons’ contents comply with the directives active throughout the national territory of the Italian Ministry of Education for lower secondary schools and include Italian, English and second community language, History, Geography, Mathematics, Science, Music, Art, Physical Education and Technology. Recently, the teaching of Citizenship and Constitution was added to these disciplines.¹ In addition to the standard curriculum, numerous extra projects are offered, within the PON (National Operational Plan of the Italian Ministry of Education, funded by the European Social Fund and the European Regional Development Fund)² to all students during the school year for creating an additional high quality, effective and fair education and training system. PON projects range from courses for strengthening basic school subjects to foreign language courses, computer labs, sport and artistic programs. One PON learning module lasts 30 h and, if necessary, can be repeated several times. Furthermore, these kinds of activities are also carried out during the summer months thanks to the “Summer Plan” of the Italian Ministry of Public Education.

Admission to the musical curriculum at the first middle school grade takes place through music aptitude tests at the beginning of the school year. Tests are regulated by law and consist of several steps: rhythmic and melodic abilities are evaluated through imitation tests where the candidate has to reproduce some musical phrases proposed by the teacher. Then, a pitch recognition test is followed by a first approach to the instruments (see footnote 1). After having tried several musical instruments, the candidate is asked to prioritize them to avoid the assignment of an instrument that is not particularly appreciated. Each student is assigned a numerical score by the examining board; it is necessary to obtain a minimum score in order to be admitted to the musical curriculum. At the end of the aptitude tests, a ranking of the admitted students is drawn up. Music middle schools curriculum, regulated by law by the Italian Ministry of Public Education, includes 2 h per week in the afternoon for 1st grade students and 3 h per week for 2nd and 3rd grade students. Classes are held in the afternoon and are organized as follows: 1 h of individual instrument class and/or in small groups; 1 h of collective class of music theory, music reading and group music; 1 h of group music class (orchestra and instrumental ensemble). Concerts are also performed during the school year.

Thus far, only one behavioral longitudinal study (Carioti et al., 2019) has been conducted on the peculiar kind of music instrument training offered at the Italian music middle schools, namely at the Negri-Calasanzio Middle School of Milan, involving a sample of 128 pre-adolescents: 72 students belonged to the music curriculum (30 with previous music experience and 42 without), and 56 belonged to the standard curriculum (44 with prior music experience and 12 without). Group differences in both musical and auditory cognitive skills were found, as well as in language processing and general cognitive abilities. Moreover, the music children showed superiority of memory, visuo-spatial, numerical and reading skills than the non-music children. However, no significant curriculum-by-time interactions were found. The authors, hence, concluded that pre-existing group differences might have a strong role in the observed behavioral findings; however, the study does not exclude that the school environment may bring benefits, and this needs to be investigated in future studies.

Altogether, we believe that the music middle school is a relevant context for investigating transfer effects of music training for several reasons: First, it offers the possibility of investigating music training carried out as a curricular, and thus mandatory, publicly-funded activity, while so far research has focused on extracurricular music activities conducted on a voluntary (typically self-financed) basis. If, on the one hand, extra-curricular activities have an impact on intrinsic motivational engagement in music and academic achievement (Pitts, 2007; Metsäpelto and Pulkkinen, 2012; Guo et al., 2022), on the other hand, including the instrumental training in the school curriculum may mean an increase of accuracy when studying and of the number of hours spent in music practice, which, in turn, may likely affect other skills in a different way, as well as provide an initial strong extrinsic motivation due to the evaluation of the instrumental course as one of the curricular subjects. Moreover, curricular training can reach students with various socio-economic backgrounds since materials and musical instruments are often given on loan for use to less well-off children.

Furthermore, studying music middle schools allows us to investigate the effects of this peculiar music training on the preadolescent population, and also how the peculiar environment and the daily opportunities to carry out music-related activities, together with group activities and other leisure activities, can contribute to balance the evolutionary trajectories and to foster a healthy development up to adolescence. Indeed, while several researchers consider primary school as an optimal environment for using music training as an aid to cognitive maturation, middle school targets the preadolescence phase in which music increases in importance, and musical identities start forming.

The present study

This study is part of the MiddleMusic project, which was set up with the aim of collecting observational data on cognitive, emotional and performative skills, as well as on the sense of self-efficacy and general quality of life. Apart from the specific interest in musical middle schools, a particular focus of this project is on the preadolescent period and on the importance of self-regulation at this stage of development. Thus, this study aims to test the effects of middle school music training on two crucial cognitive constructs related to self-regulation, namely WM and fluid intelligence in preadolescence.

Three schools from three very different metropolitan areas participated. One of them was located in the city center, while the remaining two were located in suburban municipalities. According to the latest data provided by Istat (Italian National Institute of Statistics)³ for the year 2020 and similarly to previous years, an important gap can be found in the Apulian province of Bari between the city center and most of the municipalities in terms of per capita income and economic vulnerability. Hence, differently from the previous study by Carioti et al. (2019) focusing on a single school, the social contexts are very different according to the location, and therefore the socio-cultural level of the participants is extremely varied. Some studies in the past already focused on the effect on academic achievement and on the increase of cognitive abilities in musically-trained children at risk or with socio-cultural disadvantage: a 3-year longitudinal study by Rauscher and Hinton (2011) was conducted to determine whether music lessons could improve mathematics test and visuo-spatial test scores of economically disadvantaged elementary school children. Children at risk as well as middle-class children were assigned to music lessons and compared to control groups (computer lessons or no lessons). The results showed better performance by the musically-trained children only at the end of the third year of the study in those participants who had started music training before the age of 7. Furthermore, two longitudinal studies by Slater et al. (2014) demonstrate how music training can provide auditory and cognitive enrichment, helping children in critical developmental phase to improve their literacy skills and proceed with their academic progress. Also, a few recent investigations focused on collective music training, such as El Sistema offered for children coming from under-privileged areas (Alemán et al., 2017; Fasano et al., 2019b,in press) and showed a significant improvement of inhibitory control in the music group that was not found in the control group. Moreover, those children who did not follow any intense music program had a significant increase of hyperactivity-impulsivity from the pre- to the post-test, while the music group did not show a significant difference (Fasano et al., 2019b). However, more studies are required to account for the diversity of music training programs to scientifically inform future music education policies.

In the current study, we chose to focus on visuo-spatial and auditory WM. We did not test inhibitory control, although EFs often share correlates, and WM and inhibition in particular are closely related; indeed, inhibition tends to cohere more with WM measures than with measures of other types of inhibition (Diamond, 2013). The visuo-spatial aspect is crucial for the proper functioning of different cognitive skills and consequently for academic achievement, especially in the case of students with attention problems and ADHD (Gropper and Tannock, 2009; Giofrè et al., 2013, 2018; Mawjee et al., 2015). The Jack and Jill WM test is aimed specifically to measure the component of the visuo-spatial sketchpad in WM. It demonstrated strong correlations with other well-known tests of WM (Backward Digit Span, Memory Updating Figural, Corsi Block-tapping test), spatial tasks (Paper Folding, Shape Rotation, Mechanical Reasoning, Pattern Assembly), and a non-verbal intelligence test (Raven’s Progressive Matrices) (Tsigeman et al., 2022). This correlation is important as these tests are among the most used so far in the studies of WM and music (Talamini et al., 2017; Lorenzen and Brattico, 2020). Moreover, in this MiddleMusic project we used performance tests that were both used to measure music perception skills and musical sophistication (along with extensive questionnaires), but also served to assess auditory WM skills (Mas-Herrero et al., 2013, 2021; Müllensiefen et al., 2014). Hence, in addition to visuo-spatial skills we aimed to test auditory WM to provide a complete picture of the individual abilities that allow preadolescent children to explore multimodal world environments, in line with the increasing interest of researchers in multimodal cognition (Setti et al., 2021, 2022; Xie et al., 2021). The second scope of the study was to identify training-related changes in fluid intelligence, as assessed with a test based on Raven’s Progressive Matrices, currently one of the most accurate tests to evaluate both the ability to infer abstract relations and the ability to handle a broad range of problem-solving tasks in a dynamic way, which are the distinctive features of the g factor (Jensen, 1998; Holyoak, 2012; Martinez, 2013). In fact, the tests of fluid intelligence, which include creative problem-solving, are often the best ways to evaluate g (Carroll, 1993).

Moreover, we decided to investigate the gender variable. Examining gender variations in cognitive processes has garnered increasing attention in recent publications; particularly, the idea that each gender processes information using distinct cognitive strategies seems to be widely spread. According to prior literature, women would be inclined to elaborate information material in more depth, whereas males are more likely to be motivated by schemas or overarching information themes (Guillem and Mograss, 2005; Seinstra et al., 2015). In addition, females have been found to outperform males in word recalling, language and memory tests (Kaushanskaya et al., 2013; Loprinzi and Frith, 2018; Theofilidis et al., 2020) while males generally showed better performance in spatial abilities, especially in mental rotation (Levine et al., 2016; Lauer et al., 2019). However, the results are not always clear-cut, and they are often more related to the socio-cultural context and to the type of task (Wai et al., 2010; Nazareth et al., 2013; Miller and Halpern, 2014). Mostly, it was shown that the promotion or inhibition of cognitive abilities, such as spatial or verbal abilities, is highly mediated by gender-role. Boys and girls are encouraged by parents to engage in either stereotypically masculine or feminine activities appropriate to their gender, as “masculine” typed activities promote the development of spatial abilities while “feminine” typed activities pursuits instead strengthen of reading and language abilities (Reilly and Neumann, 2013). Despite all the evidence on gender differences in cognitive abilities, the relationship between EF performance in musicians compared to non-musicians and potential gender disparities have not yet been systematically studied (but see Jansen et al., 2022). Hence, it is pivotal to determine whether gender, especially in preadolescent age, can be a primary factor in the performance of cognitive tests.

The present study has a quasi-experimental research design, where cross-sectional analyses have been carried out. Moreover, the MiddleMusic project is still ongoing, and longitudinal data will be collected from the same children over the coming years. From the literature on the topic, it can be deduced that higher levels of musical ability are favorable for cognition performance, and according to previous studies musical ability can explain part of some cognitive performance factors, such as WM and fluid intelligence (Silvia et al., 2016; Slevc et al., 2016; Baker et al., 2018). As suggested by correlational evidence, we expected a replication of the association between following a music training program within the school curriculum and the performance on tests of cognitive and musical abilities (possibly due to pre-existing differences and selection). We also expected older students to perform better on the cognitive and musical tests (due to maturation). Moreover, we expected to find a stronger growth in abilities for the music group than for the control group across age groups (due to the effects of music training). Overall, we expected to find differences in favor of musicians beyond all the possible confounders and covariates. Finally, considering the literature on gender differences, we expected to find significantly better performance in females due to the developmental differences.

Materials and methods

Ethics

The present study is part of a larger project (“MiddleMusic”) which involves a longitudinal analysis with the same middle schools over an extended time period and involves parents through the administration of three questionnaires in paper format. In addition to the sample of children analyzed for the present study, samples of adult participants were included in the MiddleMusic project with the aim of carrying out the Italian validation of some instruments. The research protocol has been approved by the Ethics Committee of the Department of Education, Psychology and Communication of the University of Bari “Aldo Moro” (reference: ET-21-15).

Participants

The sample includes 324 preadolescents (range 10–14 years; mean age = 12.3 SD = 0.94) selected from 21 classes of three music-focused middle schools in the Bari area (Apulia, Italy): De Amicis-Dizonno (Triggiano), Alighieri-Tanzi (Mola di Bari), and Massari-Galilei (Bari). Children (47.2% females and 52.8% males) belonged either to the musical curriculum (“musicians” N = 194) or to the standard curriculum (non-musicians N = 130).

The musicians were compared with their schoolmates or classmates enrolled in the standard curriculum. This was a necessary choice since in this kind of community-based studies choosing whether a child should participate in the music curriculum or the conventional curriculum at school is neither logistically possible nor ethically acceptable. For practical reasons especially due to COVID-19, we were unable to collect more specific information on prior musical expertise and general background of participants through preliminary interviews as in the case of the study conducted by Carioti et al. (2019). Instead, we administered extensive questionnaires (Gold-MSI, detailed below) in order to obtain information on their level of expertise, start age and amount of music training. Moreover, we aimed to evaluate the children’s musical background at the start of the study in order to determine the impact of music training on audiovisual WM and fluid intelligence.

The music training program of the music curriculum consists of 2 h of general musical lessons per week; the standard curriculum does not include additional afternoon lessons. For this reason, standard curriculum students tend to have more free time and have more chances to attend the extra above-mentioned PON projects within their own schools. In any case, the spending of free time should be investigated more in depth, as no data in this respect are available in the present study. Such projects were also running during the measurement periods and addressed also to the classes involved in the study. For example, within the “Summer Plan” 2022, among the projects presented by the schools, the most popular themes were the following: strengthening of functional alphabetic competence (19%) learning modules to enhance language skills (17%), strengthening of “STEM” subjects - Science, Technology, Engineering and Mathematical Subjects (14%), Digital Strengthening (10%), Physical Education (10%) art theater creative writing (9%), cultural awareness and expression (9%), music and singing (5%). And, moreover, in the general plan creative and artisanal workshops, education for active citizenship and education for legality and human rights projects are foreseen (see text footnote 1).

For example, some of the activities carried out in the last year at the three participating schools consisted in: entertaining logical puzzles (gamification), digital competence modules, “Edugreen” workshops on eco-sustainability, modules for the strengthening of English and French languages, creative writing and finally, multi-disciplinary group artistic workshops.⁴

Data were collected for the most part in December 2021; only a small group attending 2nd and 3rd years of middle school was tested in January 2022.

Tests and questionnaires

The MiddleMusic project shares several objectives with the LongGold project: LongGold investigates and follows groups of secondary school students for several years in order to demonstrate how longitudinal research may give a solid empirical foundation for understanding the origins and impacts of musical engagement (Müllensiefen and Harrison, 2020). Students are assessed once a year and complete a test battery of musical and non-musical tasks as well as questionnaires asking about music and other free-time activities, self-concepts, personal strengths and difficulties, personality and other person-related aspects. The battery runs in a standard internet browser, and pupils are tested in their classroom. LongGold researchers use school computers or can supply tablets and headphones to perform the test sessions, depending on the school facilities.⁵

One of the main differences between the present study and the study by Carioti et al. (2019) as well as other studies is our use of fairly entertaining and motivating ability and performance tests to keep motivation high: in fact, tasks such as the Digit Span task often employed in this type of studies can be perceived as boring and repeating to children, and this could negatively affect the performance. All cognitive and performance tests use an adaptive procedure which homes in on the participant’s individual ability level as quickly as possible and therefore saves a lot of testing time. The adaptive testing methods and underlying statistical theory of the musical ability tests employed in this study are explained in Harrison et al. (2017), Harrison and Müllensiefen (2018), and Larrouy-Maestri et al. (2019). The method of computerized adaptive testing (CAT) involves selecting test items using an algorithm based on the test-taker’s previous replies. The goal of item selection is often to maximize the amount of information each item conveys regarding the test-taker’s actual aptitude. This gives a considerable advantage in improving the testing efficiency and the test reliability, as adaptive testing aims to give the most informative items possible at each testing point.

To administer the tests, we employed the psychTestR online testing framework (Harrison, 2020). Several adaptive ability tests have been developed in this framework, including the melody discrimination test (MDT; Harrison et al., 2017), the beat perception test (BAT; Harrison and Müllensiefen, 2018), the mistuning perception test (MPT; Larrouy-Maestri et al., 2019), the visuospatial WM test (JAJ; Tsigeman et al., 2022), and the rhythmic ability test (RAT; MacGregor et al., 2022). In this study we used a selection of tests and questionnaires as follows, summarized also in Table 1. To measure cognitive abilities the Jack and Jill and the Matrix Reasoning were used.

TABLE 1

Table 1. LongGold tests employed in the MiddleMusic project.

The Jack&Jill WM test (JAJ) measures visuo-spatial WM capacity employing a dual task paradigm (Tsigeman et al., 2022). The task is adaptive and based on an explanatory item response theory (IRT) model. The final IRT scores of the test show a bell-shaped distribution that is centered around 0. Negative scores indicate a performance worse than the average of the calibration sample, and positive scores indicate a better than average performance. The test is implemented in the open-source R package JAJ.⁶ The test length on the LongGold project was set to eight trials. In the present study, the participants completed a total of 7 trials for the JAJ since in the original study the authors found that reliability and validity for this test are acceptable after 7 or 8 trials (5–6 mins), with diminishing gains from more trials (Tsigeman et al., 2022).

Many of the classic tests used for WM require keeping the information while working on a second task, which can cause interference, and the Jack and Jill test is also based on this dual-task paradigm. The JAJ task presents participants with pictures of a young female on the left (“Jill”) and a young male on the right (“Jack”) of the screen on a white background. Before the task, the participant is instructed on the procedures through a tutorial. The task is divided into trials of different lengths increasing over time, totaling 14 trials in one testing session.

While Jack rotates about his axis on each stimulus presentation and can grasp a ball in either of his right or left hand, Jill always remains in the same position while clutching a blue ball in her right hand. Additionally, Jack’s ball moves, randomly taking one of the six indicated spots on the screen (orange dots). Participants must complete two tasks for each stimulus presentation: (1) determining whether Jack is holding the ball in the same hand as Jill, and (2) memorizing the location of the ball at that moment. Participants are required to click on the designated spots in the proper order at the conclusion of each trial in order to recall the order of the ball positions.

Correct answers are represented by cursor positions. Responses from participants are evaluated as correct (1) or wrong (0) depending on whether the entirety of a trial can be successfully repeated. The duration of the sequence of ball placements that a participant can recall serves as an indicator of their visuo-spatial WM span. Because the JAJ task needs participants to alternate between memory encoding (memorizing the position of the ball) and mental rotation with a subsequent choice on visual input, which adds additional cognitive strain, it can be classified as a complex span task.

Matrix Reasoning (MIQ) is a test that contains stimuli that are similar to those used in Raven’s Progressive Matrices (Condon and Revelle, 2014; Chan and Kosinski, 2015). The stimuli are 3 × 3 arrays of geometric shapes with one of the nine shapes missing. Participants are instructed to identify which of the six geometric shapes presented as response choices will best complete the stimuli. In each round the stimulus disappears after 120 s, leaving only the response options visible for the participant.

The Computerized Adaptive Beat Alignment Test (CA-BAT) is a test that probes a listener’s beat perception ability using the beat alignment paradigm where the listener tries to detect misalignment between a metronome and a musical extract (Harrison and Müllensiefen, 2018). This paradigm comprises a series of two alternative forced choice trials. Each task presents the participant with two versions of a music track, both overlaid on a metronome-like beep track. In one version, the target beep track is exactly in time with the locations of the musical beats. In the other version, the beep track is “off” from the main musical beat. The participant’s task is to identify whether the track is “on” or “off” the beat. The CA-BAT is an adaptive test. Therefore, the item selection adapts to the candidate’s previous responses. Typically, the goal is to maximize the amount of information each subsequent item provides about the candidate’s true skill level, which in practice means that poor performers receive easier items and better performers receive more difficult items.

Melody Discrimination Test (MDT) is an adaptive test characterized by a three-alternative forced-choice (3-AFC) melodic discrimination paradigm (Harrison et al., 2017). In the 3-AFC paradigm, each melody version is compared with every other version, producing three similarity comparisons in total; in addition, item difficulty is determined by manipulating the melody’s length. The participant performs a decision-making process to determine which melody was the odd-one-out, on the basis of these similarity judgments. In each trial, the participant is presented with three versions of the same melody. Two of these versions have the same interval structure and are called lures, but one version has exactly one altered note and is called odd. These three versions can occur in any order, and the participant’s job is to identify which version is the odd one. Two of these melody pairs must be different, and one must be the same. The participant’s task is therefore to identify the most similar pair, and then the odd-one-out must be the melody not contained within this pair. The duration of the test may vary according to the specific needs of the research; in the case of our study, the test consisted of 18 items.

Mistuning Perception Test (MPT) is a test that uses a two-alternative forced choice (2-AFC) paradigm in which each trial comprises two versions of the same musical extract, one “in tune” and the other “out of tune” (Larrouy-Maestri et al., 2019). The stimulus material consists of short excerpts (6–12 s in length) from pop music performances (obtained from MedleyDB; Bittner et al., 2017) for which the vocal track was pitch-shifted relative to the instrumental tracks. Each musical extract has a vocalist singing the main melodic line and an instrumental accompaniment. Out-of-tune extracts are produced by adding a constant pitch shift to the vocal line. The listener’s task is then to identify which of the pair was the out-of-tune version.

Rhythm Ability Test (RAT) is an adaptive test in which participants have to select the correct image that corresponds to the rhythm heard (MacGregor et al., 2022). The rhythms are made up of high-pitched and low-pitched sounds. High-pitched and low-pitched sounds are represented in each picture with two rows of squares of two different colors placed at the top row (to indicate high-pitched sounds) and at the bottom row (to indicate low-pitched sounds). The tasks are presented according to a progressive increasing difficulty, and the stimuli are made up of 4, 8, and 16 sounds (quarter, eights, sixteenths notes).

Emotion Discrimination Test (EDT) is an adaptive test and uses a two-alternative forced choice (2-AFC) format: two played or sung fragments of the same melody are presented for each task (MacGregor and Müllensiefen, 2019). The participant’s task is to identify which of the two fragments corresponds to the expression of a specific emotion (anger, happiness, sadness, and tenderness). Excerpts differ between trials in terms of musical features such as length, instrument, melody, target emotion and comparison emotion, and item difficulty is assessed with regard to the contribution of these features. The key features of angry excerpts are high amplitude, fast tempo and greater roughness, happy excerpts are similar to angry excerpts though without high roughness, sad excerpts exhibit slow tempos and low amplitude, and tender excerpts have similar acoustic properties as those conveying sadness.

In addition to the cognitive and music perception tests, the pre-adolescent participants were asked to complete a demographic questionnaire and the Gold-MSI, a 39-item self-report scale that comprises five subscales (Active Musical Engagement, Perceptual Abilities, Music Training, Singing Abilities, and Emotional Engagement with Music) and one general factor (General Musical Sophistication) (Müllensiefen et al., 2014).

The Active Engagement factor includes a variety of active musical engagement behaviors, including the conscious devoting of time and money to musical activities; the Perceptual Abilities factor represents the self-assessment of various musical abilities, most of which are related to music listening skills; the music training factor concerns one’s level of self-assessed musicianship, as well as the amount of music training and practice; the Singing Abilities factor reflects different skills and activities related to singing; the Emotions factor covers different and mainly active behaviors related to emotional responses to music; and finally, the General Musical Sophistication factor incorporates aspects from all above mentioned five sub-scales.

Initially developed for use with adults, the factor structure and internal reliability of the Gold-MSI have been replicated and validated for the use with secondary school pupils in a large German sample of 11–19-year-olds (Schaal et al., 2014; Fiedler and Müllensiefen, 2015). All the perceptive test instructions were translated into Italian from English. The Gold-MSI was translated with the method of the back translation (similar to the method described in Lin et al., 2021): Two parallel translations were carried out and the two versions of the same test, translated from English to Italian and from Italian to English, were secondly compared with a native speaker.

Procedures

The data collection took place in the schools’ IT rooms, and the whole process was guided by the experimenters. The tests were administered during class hours for a total duration of 2 h after prior agreements with principals and teachers and after having received informed consent signed by parents. The tests were administered on a specific online platform implemented by the LongGold group which can be accessed through a link (a demo version of the LongGold tests can be found here: https://longgold.org/tests/). In addition, unique numerical identification codes were created for each participant. The battery of tests was split into two parts and administered in two sessions on two different days to avoid cognitive overload and not to compromise the performance. The order of tasks was balanced by type and required level of cognitive load; the cognitive tasks were placed at the very beginning of the battery, the perceptual tests and questionnaires were presented alternately to keep the participant’s attention high.

The JAJ test was administered within the second session. Due to the non-attendance at school of a group of students on the second day session, the participants in the JAJ test appear to be fewer in number than the participants who completed the remaining tests. Of the children invited and who consented to participate, 29 were not at school due to COVID reasons on the second day of measurement, and 10 more missed to complete only the first part of the battery. For this reason, the final sample obtained includes 285 participants instead of 324 (47.7% females and 52.3% males) belonging to a music class (N = 163) or a class standard (N = 122).

As reported in Table 2, the sample of students involved was equally distributed among the three grades; in each group the age of the participants ranged between 10 and 14 years. In the original sample, 14 participants had learning disabilities or impairments, but they were not excluded from the present analysis as they presented mild cognitive disabilities. In the final sample, only 10 of them remained (six musicians and four non-musicians) and their scores appeared not to be dissimilar to those obtained by the rest of the participants. None of the participants reported a salient hearing impairment, and no differences were found in the scores of the only participant who declared to have an increased hearing sensitivity than normal. Finally, some data were missing for the Rhythm Ability Test (n = 1), the Mistuning Perception Test (n = 3) and the Start Age sub-scale of the Gold-MSI (n = 27) from non-musicians only.

TABLE 2

Table 2. Sample descriptives.

Data analyses

Statistical analyses were performed using the Jamovi software (version 2.2, 2021, retrieved from https://www.jamovi.org/; R Core Team, 2021). As a first step, an independent sample t-test was performed to detect whether there were significant differences in general music sophistication in the 1st grade. Subsequently, a series of two-factor ANOVAs were performed to verify the two main basic hypotheses and to detect whether there were significant differences in cognitive and musical skills for age and group. Therefore, two-factor ANOVAs were performed using the two independent variables Grade (1/2/3) and Group (Musicians/Non-musicians) on the following dependent variables:

1. Working Memory (JAJ: Jack&Jill Test);

2. IQ (MIQ: Matrix Reasoning Test);

3. Beat Perception (BAT: Beat Alignment Perception Test);

4. Melody Discrimination (MDT: Melodic Discrimination Test);

5. Mistuning Perception (MPT: Mistuning Discrimination Test);

6. Emotion Discrimination (EDT: Emotion Discrimination Test);

7. Rhythmic Ability (RAT: Rhythm Ability Test).

Subsequently, gender was also included as a further independent variable in the analyses. For the same variables, a series of ANCOVAs were performed treating the Gold-MSI General Index score as a covariate to account for probable pre-existing differences, and also due to lack of subjects’ random assignment. Moreover, general musical sophistication was found to correlate with self-reported music training (Müllensiefen et al., 2014). As the students of the musical curriculum are chosen through aptitude tests based on imitation and recognition of rhythm, melody and pitch, we aimed to statistically control for pre-existing individual differences in musicality to better see the effect of music training, similarly to other studies where some Gold-MSI subscales were used as covariates (Kang and Williamson, 2012; Farrugia et al., 2015; Ma et al., 2021; Wallmark et al., 2021; Krause et al., 2022).

It is important to note that the Gold-MSI has a hierarchical factor structure, and the General value is the mean of an assorted set of items across all the other subscales. In the Gold-MSI original study by Müllensiefen et al. (2014), 18 items were drawn from all five Gold-MSI subscales to index musical sophistication in general, with a clear preponderance of items from the Musical Training and the Singing Abilities subscales. In addition, for removing outliers we applied a filter in the analysis of MIQ scores that was based on the estimated amount of measurement error, rejecting all observations with a standard error of the mean (SEM) value < 1.5 with the aim to eliminate performance scores which were estimated in the presence of a high amount of measurement error, thereby eliminating nine more participant’s scores from the dataset. For the JAJ no outliers were found.

Results

The scores of the adaptive tests were given on a z-scale and had a population mean around 0 and ranged from −4 to 4. JAJ and MIQ score ranges, mean and standard deviations for each grade and group are summarized in Table 3, likewise the SEM ranges for both JAJ and MIQ.

TABLE 3

Table 3. Jack&Jill (JAJ) and matrix reasoning (MIQ) descriptives for grades and groups and gender.

Overall, JAJ scores ranged [−1.80, 2.83] with a mean of 0.016 (SD = 0.89) while MIQ scores ranged [−3.59, 1.71] with a mean of −1.87 (SD = 0.93). For the JAJ, the lowest ability value (−1.89) would correspond to the ability of remembering not more than one position consistently (Tsigeman et al., 2022). Considering this, in the present study 47.4% of the participants obtained a score below zero for the JAJ: of them, 48.1% are musicians and 51.8 non-musicians. Moreover, the 21.7% of participants who obtained scores below 0 attend the 1st grade. Regarding gender, no significant differences were found.

A comparison of estimated marginal means showed the following: For the music group 1st Grade MIQ scores ranged [−2.31, −1.82] while for the 1st Grade non-musicians scores ranged [−2.58, −2.08]; 3rd Grades musicians scores ranged [−1.83, −1.34] and 3rd Grade non-musicians scores ranged [−2.42, −1.85]. For JAJ, 1st Grade musicians scores ranged [−0.35, 0.10] and 1st Grade non-musicians scores ranged [−0.58, −0.10]; 3rd Grade musicians scores ranged [0.06, 0.51] while 3rd Grade non-musicians scores ranged [−0.50, 0.03].

Comparing the estimated marginal means for the perceptive tests showed similar results. Descriptive statistics for all the perceptual tests can be also found in Table 4: BAT (M = −1; SD = 1.23); MDT (M = −1.14; SD = 1.08); MPT (M = −0.7; SD = 1.15); EDT (M = 0.6; SD = 0.9) RAT (M = 0.6; SD = 0.9). For all the three grades, higher mean scores can be always found for the musicians compared to non-musicians, and overall, both groups tend to improve their scores in 3rd grade compared to 1st.

TABLE 4

Table 4. Descriptive statistics for all the perceptive tests.

Table 5 reports descriptive statistics for the GOLD-MSI. Musicians show better scores than non-musicians in all sub-scales except for Starting Age; however, the data missing from 27 non-musicians may have affected the results. Another exception was found in the sub-scale Emotion for the 2nd Grade, where musicians (M = 5.14; SD = 0.99) and non-musicians (M = 5.14; SD = 1.20) have the same mean scores.

TABLE 5

Table 5. Gold-MSI descriptives for all subscales.

As we aimed to test whether there are pre-existing differences in musical sophistication and expertise between the two groups when they enter middle school, we decided to perform a t-test as the first step. The results of the independent sample t-test indeed showed a significant difference between the groups of participants enrolled in the 1st grade of middle school (Table 6).

TABLE 6

Table 6. Independent samples t-test for 1st grade groups using general music sophistication as a dependent variable.

The 56 1st grade musicians (M = 4.28; SD = 0.765) compared to the 51 participants in the control group (M = 3.69; SD = 0.712) demonstrated better scores: t(105) = 4.11; p < 0.001; d = 0.795. Welch’s test results also suggest a significant difference between the two groups: t(105) = 4.12; p < 0.001. Also, Mann-Whitney test reported similar results: as the p value obtained from the Mann-Whitney U test is significant (U = 777, p < 0.001), we conclude that the general level of musical sophistication of the two groups is significantly different. Moreover, the general Gold-MSI score was found to be distributed normally, as verified by the Shapiro-Wilk test (p = 0.648).

The subsequent series of 7 two-factor ANOVAs showed a significant difference for both of the cognitive tests, namely JAJ and MIQ, with higher scores for musicians than non-musicians (JAJ: df = 1; F = 6.101; p = 0.025; ηp² = 0.018; MIQ: df = 1; F = 8.462; p = 0.004; ηp² = 0.030). The analysis also showed a robust difference between the groups for both JAJ and MIQ especially in 3rd grade. This and the results between groups and across age for all the remaining perceptive tests are summarized in Figure 1. Significantly better performances of musicians were found on the BAT, MDT, and MPT. Significant differences were also found between year groups on the same cognitive and perceptive tests; moreover, an additional significant difference between groups was found for the RAT and between year groups for the EDT. Based on these ANOVA models, post hoc multiple comparisons using Bonferroni correction revealed significant differences between groups for all the tests except for EDT. Significant differences for grades were also found for all the tests except for JAJ and EDT. Significant differences for grades x group interaction were found throughout all the variables except for EDT.

FIGURE 1

Figure 1. Mean ability values for the seven performance tests by grade and quasi-experimental group. Error bars indicate standard error of mean. BAT, Beat Alignment Test; EDT, Emotion Discrimination Test; JAJ, Jack&Jill Working Memory Test; MDT, Melody Discrimination Test; MIQ, matrix reasoning; MPT, Mistuning Perception Test; RAT, Rhythm Ability Test.

As significant differences were found in the starting t-test for the General Music Sophistication Index in 1st grade, a series of seven ANCOVAs were also employed treating the GOLD-MSI General Index score as a covariate. Significant differences between musicians and non-musicians remained significant both for JAJ (df = 1; F = 6.008; p = 0.015; ηp² = 0.022) and MIQ (df = 1; F = 9.735; p = 0.002; ηp² = 0.035) and as well as for year groups. Regarding the perceptive tests, the results for BAT and MPT barely changed after controlling for GMSI and they remained significant for the music groups. For the MDT results remained significant for both groups and years, while for the RAT no changes were found. Gender was also added as a variable, and the results are summarized in Table 7. Significant differences were found for EDT and MPT in favor of females. Predicted abilities showing the predictions of the final model with gender as covariate are summarized in Figure 2.

TABLE 7

Table 7. ANCOVAs results for gender after Bonferroni correction and controlling for general music sophistication.

FIGURE 2

Figure 2. Predicted ability values for the seven performance tests by class (X-axis) and gender (red = females, blue = males) for the three levels of General Music Sophistication. BAT, Beat Alignment Test; EDT, Emotion Discrimination Test; JAJ, Jack&Jill Working Memory Test; MDT, Melody Discrimination Test; MIQ, Matrix Reasoning; MPT, Mistuning Perception Test; RAT, Rhythm Ability Test.

Finally, post hoc multiple comparisons using Bonferroni correction based on the ANCOVA models showed significant differences between groups for all the tests except for EDT (Table 8); across age, significant differences were also found for all the tests but not for JAJ and EDT. Several significant interaction effects grades x groups were found except for EDT. In both post hoc analysis significant interaction effects were mostly found for comparisons between 1st Grade non-musicians and 2nd/3rd Grades musicians. The only exception was found for MDT where significant grades x groups interactions were found between 2nd Grade non-musicians - 3rd Grade musicians and 3rd Grade musicians - 3rd Grade non-musicians.

TABLE 8

Table 8. ANCOVA’s post hoc comparison (Bonferroni correction) for grade, group and grade × group interaction.

Discussion

The present study was carried out with the aim of evaluating the effects of music training on audiovisual WM and fluid intelligence in preadolescence. In particular, the focus was on instrumental training carried out in musical middle schools. For this purpose, a series of cognitive and perceptual tests were employed to check whether there is any difference between the two groups of musicians and non-musicians and over the years. Fluid intelligence, visuo-spatial WM, beat perception, melodic discrimination, mistuning perception, rhythmic ability, emotional discrimination and general music sophistication were assessed.

First of all, from analyzing the descriptive data for the JAJ test we found a lower percentage of below-zero results for the musicians, which seems significant considering that the participants belonging to the experimental group outnumber the participants of the control group. Furthermore, differences between the two groups in music perceptual tests could be traced back to pre-existing dissimilarities. In fact, a significant difference emerged in general music sophistication between the musical and non-musical group in the 1st grade, probably due to the fact that children in the musical middle schools are selected through aptitude tests to be enrolled in the music curriculum, and therefore the general musical abilities appear to be more enhanced in the musical group from the beginning.

Taken together, the results allow us to reject the null hypotheses, which predicts the absence of significant differences between the musical and non-musical group and over the years; in fact, significant results in musicians and for years, with a considerable variance between groups, were found. We would state that at least two of the three alternative hypotheses of the present study can be accepted: in one of them, we predicted the association between music training and superior audiovisual WM and fluid intelligence and, actually, the group of musicians showed a better performance compared to non-musicians in both domains. The present results converge with prior studies where music training was associated with superior cognitive abilities (Schellenberg, 2006, 2011b; Degé et al., 2011; Slevc et al., 2016; Baker et al., 2018; Meyer et al., 2020; Porflitt and Rosas, 2022).

In line with the first hypothesis, results also showed a significantly better performance by musicians in most of the perceptual tests. Since the music perceptual LongGold tests contain memory retention among the cognitive processes evaluated, similar results in these tests suggest that music training is associated with auditory memory, although, as already mentioned, this may possibly be due to pre-existing differences and selection. In fact, in previous studies, connections with potentially pre-existing auditory cognitive abilities that may help to explain the discrepancies between musicians and non-musicians were discussed (Mankel and Bidelman, 2018; Bidelman and Mankel, 2019). Note that significant differences remained even after controlling for general music sophistication for all the perceptive tests except for EDT, suggesting a convergence with the studies that found correlations between music training and auditory WM due to the near transfer effect (Kraus et al., 2012; Zuk et al., 2014; Slater and Kraus, 2016; Slevc et al., 2016; Alain et al., 2018; Nie et al., 2021).

Furthermore, the second alternative hypothesis that predicted a better performance from older groups due to maturation can partially be accepted; having found clear differences between the means of the three age groups suggests a maturation effect. However, the relative measurement error for the younger age group provides some uncertainty around these results. The high measurement error might be due to some younger students not obtaining a very good understanding of the task, but this can be verified with subsequent longitudinal data. Something similar was found for the perceptive tests: the tendency to improve the performance over the years seems to be an effect of the general maturation. These results converge with those of Rauscher and Hinton (2011) where children showed improvements in the third year of study, although in that case it was a longitudinal analysis unlike the present study. Here, an exception is represented by JAJ and EDT. For the MIQ results, on the other hand, growth is rather gradual over the years.

In the specific case of gender, previous studies suggested that boys may experience a window of increased sensitivity to risk-taking throughout adolescence that is broader in magnitude and longer in duration than females (Bennett et al., 2005; Shulman et al., 2015). This could be due to a difference in maturation between the two genders that would lead girls to show a better self-control and, by consequence, a better academic achievement (Matthews et al., 2009; Duckworth et al., 2019). Particularly, for WM a slight tendency in favor of females was found as well because of the existence of gender-specific strategies produced by different neurodevelopment trajectories, more specifically by hemispheric differences in the neural substrate. In fact, females tend to perform more accurately at the cost of longer reaction times (Grissom and Reyes, 2019). Although, in the specific domain of visuo-spatial WM, males seem to have an advantage (Voyer et al., 2017). In spite of this evidence suggesting differences between genders in cognitive maturation, no significant differences were found for the visuo-spatial WM nor for intelligence. However, significant results were found for MPT and EDT. In addition, the latter results clearly indicate a higher emotional maturation of females at this stage of development.

Finally, for the third and last hypothesis the question is more controversial: comparing the estimated marginal means after controlling for music sophistication showed a difference from the start between groups, both for MIQ and JAJ as well as the perceptive tests, and also a difference between 1st and 3rd grades consistently with a better performance by the group of musicians. Moreover, after Bonferroni correction some significant interaction effects were found for grades and groups. However, this would be not a definite proof yet of the positive effects of music training. Certainly, the starting difference between groups is clear-cut, so the contribution of other different factors to these results cannot be excluded, as the developmental trajectories of musical experience may be influenced by individual variations in musical ability, personality and cognitive capacity as well as environmental factors, such as socioeconomic background (Vincenzi et al., 2022). However, it can certainly result in a concrete hypothesis for further studies where further information may be provided by exploring these variables.

Similarly to the study carried out by Carioti et al. (2019), in the present study we aimed to demonstrate how the positive association between music training and better performance in visuo-spatial WM and fluid intelligence increase with increasing years of training, despite individual differences and other variables. In addition, we found improvements in auditory memory. But differently from the Carioti study, the present study is a cross-sectional analysis across the different ages and the three grades of students of the Italian musical middle schools that would serve as a hypothesis for future longitudinal analyses. Another difference and a strength of our study lies in having tested a larger sample size, recruited in three different schools located in three different parts of Bari’s metropolitan area and characterized by different social contexts. Moreover, a further difference to the Carioti study consists in a clearer distinction in terms of musical experience between the groups within the sample to reduce the risk of spuriousness due to the presence of music-skilled subjects in the control group.

In sum, the contributions of the present study certainly allow to add evidence to previous studies where music training had been correlated to better performance in visuo-spatial and auditory WM (Pallesen et al., 2010; George and Coch, 2011; Schulze et al., 2011a; Suárez et al., 2016; Anaya et al., 2017). As regards the specific domain of visuo-spatial WM, our results highlight the connection between instrumental training and implicit sequence learning. Similarly, to the study by Anaya et al. (2017), we found better performance by the group of musicians in the JAJ; in their study, participants were shown a sequence of black squares that were individually illuminated to form a sequence, and they were asked to reproduce it. The sequence was scored as correct if the participant was able to reproduce the entire sequence without error. In our study, one of the two tasks of the JAJ concerns precisely remembering increasingly long sequences of ball positions according to Jack’s rotation. Therefore, the JAJ task deals with memory encoding, which is one of the critical processes, together with storage and retrieval, involved in sequential memory (Liang et al., 2020), so, it is plausible find a better performance in musicians engaged in formal music training with this type of task.

It is certain that playing an instrument requires a lot of discipline, effort and patience. It requires also rote work and many hours of practicing and involving procedural memory and sequence processing. Sequences generally seem to be learned implicitly, in which case there is no relationship with WM. On the contrary, sequence learning tasks show WM differences if the learning is explicit or purposeful, as WM actively learns to direct attentional focus and cognitive control (Janacsek and Nemeth, 2013). It therefore seems conceivable that music training, together with other influencing factors, is able to affect sequence learning. Our findings seem to match the findings by Pau et al. (2013), who discovered a relationship between musicians’ functional brain alterations and memorizing visual sequences; however, such outcome may be a reflection of music students’ background in combination with their formal education, activities and years spent playing an instrument that are not present here.

Furthermore, we found a wider gap in 3rd grade scores in favor of musicians compared to the remaining school grades, but no Grade × Group interaction was found except for melody discrimination. Thus, it is not an inferential result and further studies are needed in order to enrich the literature on the possible effects of reiterated music training. Swaminathan et al. (2017) showed that music abilities, rather than the amount of music training, predicted far transfer effects in a sample of adults. This finding supports the idea that the correlation between far transfer effects and engagement in music is mediated by the level of music skills rather than the duration.

On the other hand, Criscuolo et al. (2019) showed significant positive relationships between WM and the duration of musical practice, even after controlling for intelligence and background variables, such as personality traits. It may be that the extent of the training influences the persistence of the effect after termination of the training. According to these studies it may be assumed that music training would be able to prepare a basis for a range of skills and thus promote cognitive development (Miendlarzewska and Trost, 2014). In addition, music training over time can be made progressively more challenging, and this is a unique strength to consider in future research when advocating for music training as a cognitive growth intervention (Cooper, 2020).

Overall, the contributions of this study concern the use of new instruments suitable for preadolescents to assess these domains for the first time with Italian children; the exploration of a new environment of study together with curricular instrumental training and the providing of new evidence to preadolescents, cognitive abilities and music training literature (Schellenberg, 2011a; Corrigall et al., 2013; Bergman Nutley et al., 2014; Putkinen et al., 2015; Carioti et al., 2019). Finally, our study provided new evidence for gender differences regarding the improvement of auditory skills in favor of females, with a particularly strong effect for the musical emotion recognition task.

In sum, music middle schools can be considered a resource for preadolescent education, a delicate stage of development in which it is necessary to offer the child the possibility of an overall maturation and to carry out group activities for a good transition to adulthood. Hence, in this sense, music middle schools certainly constitute an excellent public and free resource for the community. Even if the present results are not sufficient to direct research on WM and cognitive characteristics related to auditory stimulation and middle schools’ music training toward solid evidence, the promising lines of this study may be considered surely encouraging.

Limitations and future research

Despite the contributions to the field, the present study is subject to several limitations. First of all, the cross-sectional setting does not allow to comment strongly on the influence of music training, since without longitudinal data it is not possible to establish a true cause and effect relationship; in fact, longitudinal experimental investigations are the most reliable proof that reading and musical experience are causally related ability (Tierney and Kraus, 2013).

Thus, the present study could be considered a cross-sectional examination of variations in musical ability and visuo-spatial WM comparing people with and without music training in three distinct age groups. However, cross-sectional studies can estimate prevalence of outcome of interest and are useful for the generation of hypotheses (Levin, 2006). Furthermore, as previously mentioned, the MiddleMusic project which this study is part of is still under progress and, throughout the following years, longitudinal data from the same children will be gathered.

Ideally, measurements should be conducted before training, immediately following training, and after training to see if any training-related advantages persists. Moreover, the control group should be active to leave out the possibility that trained children are performing better than control children merely because they are getting a treatment that the other group is not (Tierney and Kraus, 2013). In this regard, another important limitation of this study consists in the lack of an active control group together with the pre-existing differences between the two groups, as other factors such as receiving teachers’ increased attention, more interaction with peers or more time spent at school may influence results; although, it might be logistically challenging to find an active control training program that matches music training in the same level of intensity and enthusiasm. Additionally, even if several extra-curricular projects addressed to all students are put on throughout the school year, the specific activities of our sample were not investigated. As regards pre-existing differences, they may determine the desire or ability that led some of the children to study music and, by consequence, the differences between groups; in this regard, the initial choice of school curriculum alone seems not to be sufficient to isolate the influence of other potential factors. In fact, a future musician may decide to enroll in an educational program with intensive music training due to his predispositions, making it difficult to determine whether the cognitive advantage seen in musically trained children is a genuine effect of the training, whether it is a specific one, or whether it is a spurious effect. According to the latter approach, Schellenberg’s argument that music training is better suited for analyzing pre-existing differences in brain and cognitive development than plasticity brought on by training would be confirmed (Schellenberg, 2011a).

The risk of the high impact of pre-existing differences on differences between groups occurs especially when children are not randomly allocated to either a music training group or a control group. Since the current study is quasi-experimental research, random assignment is another crucial topic to deepen. According to Barrett et al. (2013), it can be difficult to conduct a study on the effects of music training with randomization. As a result, there is frequently a compromise between the ecological validity of musical education and the degree to which study designs can satisfy these requirements. The results of such investigations will be challenging to adapt to actual music learning environments, but using the experimenter’s training programs can enable more rigorously controlled trials (Barrett et al., 2013). On the other hand, researchers might increase the relevance of their findings to educators at the expense of certain study design restrictions by looking at current programs that have been shown effective in teaching adults and children musical abilities (Fasano et al., 2020). The method used to establish group equivalency is crucial when proper randomization is not realistically feasible.

Also, due to lack of a second-level interaction, here and in the previous study by Carioti et al. (2019) it might be concluded that 1 year of intensive music training alone is not sufficient to strongly affect cognitive maturation. However, cognitive and emotional abilities and mutual acceptance among children can be further benefited, regardless of the relatively advanced stage of children’s emotional maturation, although further research is required on the topic. Hence, overall, our findings allow us to join the chorus of other researchers in suggesting that primary school could be exploited as a privileged environment and should include music training as a significant tool to aid cognitive maturation (Rauscher and Hinton, 2011; Hallam, 2015; Altenmüller, 2016; Carioti et al., 2019; Peretz, 2019; Kraus and White-Schwoch, 2020). In fact, starting music training in primary school and persevering with it also during pre-adolescence might more likely produce significant results on cognitive maturation.

These findings stand upon neural basis; as already mentioned, increasing neuroscientific research demonstrates its positive effects on brain development. For example, musical expertise is associated with the efficiency of a right-lateralized frontoparietal network activated by the EF tasks, meaning that musically trained children are able to engage neural resources that are inaccessible to untrained children, perhaps because of earlier maturation, stronger task engagement, or the use of different cognitive strategies (Putkinen and Saarikivi, 2018).

Again, the question is if these effects are the product of intensive music training rather than of other factors, such as pre-existing differences or biological traits of musicality. The anatomical evidence reveals that musicians have specific brain areas that appear different compared to the starting age of music training; (e.g., a study with functional magnetic resonance imaging identified the left superior temporal gyrus as the region that is linked with music training, in terms of cumulative practice hours (Ellis et al., 2013). Diffusion tensor imaging studies, on the other hand, show greater structural connectivity in other areas (Schlaug, 2015; Møller et al., 2021; Criscuolo et al., 2022). A further longitudinal study with pre-adolescents showed an improvement in some perceptual and cognitive abilities after a 6-month individual instrumental training carried out at a public music school and also a neuroplastic change of the frontotemporal networks of brain connectivity, measured with neuroimaging techniques (Cantou et al., 2018; Brattico, 2019; Fasano et al., 2020). Then, according to these studies, it is reasonable to assume that music training can induce brain changes not attributable to pre-existing differences or biological traits.

Recent research has also shown that the child’s agreeableness and the parents’ openness to new experiences are the best indicators of how music lessons would affect the youngster (Corrigall and Schellenberg, 2015). In this respect, individual variables, such as personality or socioeconomic status that could affect outcomes, are important to explore in further studies; as these factors have not been analyzed in the present study, this correlation should be verified by evaluating the personality types of the parents and the children. Mostly, to contrast the limitations of this study, more longitudinal and random assigned trials on pre-adolescent musicians are needed when feasible. In addition, the presence of the active control condition is prominent and, importantly, both experimental and active control groups should be tested for far transfer (Bigand and Tillmann, 2022). This would allow to make equal and fair comparisons and to have a clearer view on the possible causal effects of music training; therefore, it is essential to move in this direction for future research.

Data availability statement

The dataset analyzed for this study can be found in the Zenodo digital repository at doi: 10.5281/zenodo.6958523.

Ethics statement

The studies involving human participants were reviewed and approved the Ethics Committee, Department of Education, Psychology, Communication University of Bari “Aldo Moro”, Italy. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.

Author contributions

ML: design, coordination, recruitment, data acquisition, data management, data analysis and interpretation, and manuscript writing. EB: conceptualization, design, recruitment, data analysis and interpretation, and manuscript revision. DM: design, data management, data analysis and interpretation, and manuscript revision. KF: design, data management, figures preparation, and manuscript revision. BM: data acquisition, data interpretation, and manuscript revision. PV: design, funding, and manuscript revision. RC: design and manuscript revision. All authors contributed to the study and to the final revision of the manuscript.

Funding

Center for Music in the Brain (MIB) covered the costs of consumables and the publication fee of this study. MIB was funded by the Danish National Research Foundation (project number: 117).

Acknowledgments

We would like to thank María Ángeles Bermell Corral and José González Such from University of Valencia for support during the early stages of the project. Moreover, we are grateful to the directors, teachers, students, and their families of the schools De Amicis-Dizonno (Triggiano), Alighieri-Tanzi (Mola di Bari), and Massari-Galilei (Bari) for collaborating on this project. The project also profited from the precious help provided at different stages of data collection and analysis by the following Bachelor’s students from University of Bari Aldo Moro: Gaia Fratepietro, Giuseppe Fuggiano, Alessia Bianco, Roberta Capuano, Luigi Di Blasio, Adriana Dagnello, Giorgia Mazzone, and Alessandro Liguigli.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Footnotes

1. ^ www.miur.gov.it
2. ^ https://ec.europa.eu/
3. ^ https://www.istat.it/
4. ^ https://www.istruzione.it/pon/index.html
5. ^ https://longgold.org/
6. ^ https://github.com/klausfrieler/JAJ

References

Alain, C., Khatamian, Y., He, Y., Lee, Y., Moreno, S., Leung, A. W., et al. (2018). Different neural activities support auditory working memory in musicians and bilinguals. Ann. N.Y. Acad. Sci. 1423, 435–446. doi: 10.1111/nyas.13717

PubMed Abstract | CrossRef Full Text | Google Scholar

Alemán, X., Duryea, S., Guerra, N. G., McEwan, P. J., Muñoz, R., Stampini, M., et al. (2017). The effects of musical training on child development: a randomized trial of El Sistema in Venezuela. Prevent. Sci. 18, 865–878. doi: 10.1007/s11121-016-0727-3

PubMed Abstract | CrossRef Full Text | Google Scholar

Altenmüller, E. (2016). Empowering musicians: teaching, transforming, living: promoting health and wellbeing when making music: a holistic approach in music education. Am. Music Teach. 65, 50–53.

Google Scholar

Anaya, E. M., Pisoni, D. B., and Kronenberger, W. G. (2017). Visual-spatial sequence learning and memory in trained musicians. Psychol. Music 45, 5–21. doi: 10.1177/0305735616638942

PubMed Abstract | CrossRef Full Text | Google Scholar

Andre, J., Picchioni, M., Zhang, R., and Toulopoulou, T. (2016). Working memory circuit as a function of increasing age in healthy adolescence: a systematic review and meta-analyses. NeuroImage Clin. 12, 940–948. doi: 10.1016/j.nicl.2015.12.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Ardila, A. (2018). Is intelligence equivalent to executive functions? Psicothema 30, 159–164.

Google Scholar

Arffa, S. (2007). The relationship of intelligence to executive function and non-executive function measures in a sample of average, above average, and gifted youth. Arch. Clin. Neuropsychol. 22, 969–978. doi: 10.1016/j.acn.2007.08.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Baddeley, A. D. (1986). Working Memory. Oxford: Oxford University Press.

Google Scholar

Baddeley, A. D. (2000). The episodic buffer: a new component of working memory? Trends Cogn. Sci. 4:417423. doi: 10.1016/S1364-6613(00)01538-2

CrossRef Full Text | Google Scholar

Baddeley, A. D., and Hitch, G. J. (1974). “Working memory,” in The Psychology of Learning and Motivation, ed. G. H. Bower (New York, NY: Academic Press). doi: 10.1016/S0079-7421(08)60452-1

CrossRef Full Text | Google Scholar

Baker, D. J., Elliott, E. M., Shanahan, D., Ventura, J., Monzingo, E., Ritter, B., et al. (2018). “Explaining objective and subjective aspects of musical sophistication: insights from general fluid intelligence and working memory,” in Proceedings of the 15th International Conference on Music Perception and Cognition (ICMPC15), Graz.

Google Scholar

Barbey, A. K., Colom, R., Paul, E. J., and Grafman, J. (2014). Architecture of fluid intelligence and working memory revealed by lesion mapping. Brain Struct. Funct. 219, 485–494. doi: 10.1007/s00429-013-0512-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Barrett, K. C., Ashley, R., Strait, D. L., and Kraus, N. (2013). Art and science: how musical training shapes the brain. Front. Psychol. 4:713. doi: 10.3389/fpsyg.2013.00713

PubMed Abstract | CrossRef Full Text | Google Scholar

Bennett, S., Farrington, D. P., and Huesmann, L. R. (2005). Explaining gender differences in crime and violence: the importance of social cognitive skills. Aggress. Violent Behav. 10, 263–288. doi: 10.1016/j.avb.2004.07.001

CrossRef Full Text | Google Scholar

Bergman Nutley, S., Darki, F., and Klingberg, T. (2014). Music practice is associated with development of working memory during childhood and adolescence. Front. Hum. Neurosci. 7:926. doi: 10.3389/fnhum.2013.00926

PubMed Abstract | CrossRef Full Text | Google Scholar

Bidelman, G. M., and Mankel, K. (2019). Reply to Schellenberg: is there more to auditory plasticity than meets the ear? Proc. Natl. Acad. Sci. U.S.A. 116, 2785–2786. doi: 10.1073/pnas.1900068116

PubMed Abstract | CrossRef Full Text | Google Scholar

Bigand, E., and Tillmann, B. (2022). Near and far transfer: is music special? Mem. Cogn. 50, 339–347. doi: 10.3758/s13421-021-01226-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Bittner, R. M., McFee, B., Salamon, J., Li, P., and Bello, J. P. (2017). “Deep salience representations for F0 estimation in polyphonic music,” in Proceedings of the 18th international society for music information retrieval conference, ISMIR 2017, eds S. J. Cunningham, Z. Duan, X. Hu, and D. Turnbull (Suzhou: International Society for Music Information), 63–70.

Google Scholar

Blair, C., Knipe, H., and Gamson, D. (2008). Is there a role for executive functions in the development of mathematics ability? Mind Brain Educ. 2, 80–89. doi: 10.1111/j.1751-228X.2008.00036.x

CrossRef Full Text | Google Scholar

Brattico, E. (2019). “The neuroaesthetics of music: a research agenda coming of age,” in The Oxford handbook of music and the brain, eds M. H. Thaut and D. A. Hodges (Oxford: Oxford University Press), 364–390. doi: 10.1093/oxfordhb/9780198804123.013.15

CrossRef Full Text | Google Scholar

Bugos, J., and Mostafa, W. (2011). Musical training enhances information processing speed. Bull. Council Res. Music Educ. 187, 7–18.

Google Scholar

Burunat, I., Alluri, V., Toiviainen, P., Numminen, J., and Brattico, E. (2014). Dynamics of brain activity underlying working memory for music in a naturalistic condition. Cortex 57, 254–269. doi: 10.1016/j.cortex.2014.04.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Burunat, I., Brattico, E., Hartmann, M., Vuust, P., Särkämö, T., and Toiviainen, P. (2018). Musical training predicts cerebello-hippocampal coupling during music listening. Psychomusicol. Music Mind Brain 28:152. doi: 10.1037/pmu0000215

CrossRef Full Text | Google Scholar

Camos, V., and Barrouillet, P. (2018). Working Memory in Development. London: Routledge. doi: 10.4324/9781315660851

PubMed Abstract | CrossRef Full Text | Google Scholar

Cantou, P., Platel, H., Desgranges, B., and Groussard, M. (2018). How motor, cognitive and musical expertise shapes the brain: focus on fMRI and EEG resting-state functional connectivity. J. Chem. Neuroanat. 89, 60–68. doi: 10.1016/j.jchemneu.2017.08.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Carioti, D., Danelli, L., Guasti, M. T., Gallucci, M., Perugini, M., Steca, P., et al. (2019). Music education at school: too little and too late? Evidence from a longitudinal study on music training in preadolescents. Front. Psychol. 10:2704. doi: 10.3389/fpsyg.2019.02704

PubMed Abstract | CrossRef Full Text | Google Scholar

Carroll, J. B. (1993). Human Cognitive Abilities: A Survey of Factor-Analytic Studies (No. 1). Cambridge, MA: Cambridge University Press. doi: 10.1017/CBO9780511571312

CrossRef Full Text | Google Scholar

Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: a critical experiment. J. Educ. Psychol. 54:1. doi: 10.1037/h0046743

CrossRef Full Text | Google Scholar

Chan, Y. W. F., and Kosinski, M. (2015). ICAR Project Wiki. Evanston, IL: International Cognitive Ability Resource (ICAR).

Google Scholar

Chen, J., Scheller, M., Wu, C., Hu, B., Peng, R., Liu, C., et al. (2022). The relationship between early musical training and executive functions: validation of effects of the sensitive period. Psychol. Music 50, 86–99. doi: 10.1177/0305735620978690

CrossRef Full Text | Google Scholar

Cogo-Moreira, H., de Avila, C. R. B., Ploubidis, G. B., and Mari, J. D. J. (2013). Effectiveness of music education for the improvement of reading skills and academic achievement in young poor readers: a pragmatic cluster-randomized, controlled clinical trial. PLoS One 8:e59984. doi: 10.1371/journal.pone.0059984

PubMed Abstract | CrossRef Full Text | Google Scholar

Condon, D. M., and Revelle, W. (2014). The international cognitive ability resource: development and initial validation of a public-domain measure. Intelligence 43, 52–64. doi: 10.1016/j.intell.2014.01.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Cooper, P. K. (2020). It’s all in your head: a meta-analysis on the effects of music training on cognitive measures in schoolchildren. Int. J. Music Educ. 38, 321–336. doi: 10.1177/0255761419881495

CrossRef Full Text | Google Scholar

Corrigall, K. A., and Schellenberg, E. G. (2015). Predicting who takes music lessons: parent and child characteristics. Front. Psychol. 6:282. doi: 10.3389/fpsyg.2015.00282

PubMed Abstract | CrossRef Full Text | Google Scholar

Corrigall, K. A., Schellenberg, E. G., and Misura, N. M. (2013). Music training, cognition, and personality. Front. Psychol. 4:222. doi: 10.3389/fpsyg.2013.00222

PubMed Abstract | CrossRef Full Text | Google Scholar

Criscuolo, A., Bonetti, L., Särkämö, T., Kliuchko, M., and Brattico, E. (2019). On the association between musical training, intelligence and executive functions in adulthood. Front. Psychol. 10:1704. doi: 10.3389/fpsyg.2019.01704

PubMed Abstract | CrossRef Full Text | Google Scholar

Criscuolo, A., Pando-Naude, V., Bonetti, L., Vuust, P., and Brattico, E. (2022). An ALE meta-analytic review of musical expertise. Sci. Rep. 12, 1–17. doi: 10.1038/s41598-022-14959-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Degé, F., Kubicek, C., and Schwarzer, G. (2011). Music lessons and intelligence: a relation mediated by executive functions. Music Percept. 29, 195–201. doi: 10.1525/mp.2011.29.2.195

CrossRef Full Text | Google Scholar

Diamond, A. (2013). Executive functions. Annu. Rev. Psychol. 64:135. doi: 10.1146/annurev-psych-113011-143750

PubMed Abstract | CrossRef Full Text | Google Scholar

Ding, Y., Gray, K., Forrence, A., Wang, X., and Huang, J. (2018). A behavioral study on tonal working memory in musicians and non-musicians. PLoS One 13:e0201765. doi: 10.1371/journal.pone.0201765

PubMed Abstract | CrossRef Full Text | Google Scholar

Donati, M. A., Panno, A., Chiesi, F., and Primi, C. (2014). A mediation model to explain decision making under conditions of risk among adolescents: the role of fluid intelligence and probabilistic reasoning. J. Clin. Exp. Neuropsychol. 36, 588–595. doi: 10.1080/13803395.2014.918091

PubMed Abstract | CrossRef Full Text | Google Scholar

Duckworth, A. L., Taxer, J. L., Eskreis-Winkler, L., Galla, B. M., and Gross, J. J. (2019). Self-control and academic achievement. Annu. Rev. Psychol. 70, 373–399. doi: 10.1146/annurev-psych-010418-103230

PubMed Abstract | CrossRef Full Text | Google Scholar

Ellis, R. J., Bruijn, B., Norton, A. C., Winner, E., and Schlaug, G. (2013). Training-mediated leftward asymmetries during music processing: a cross-sectional and longitudinal fMRI analysis. NeuroImage 75, 97–107. doi: 10.1016/j.neuroimage.2013.02.045

PubMed Abstract | CrossRef Full Text | Google Scholar

Farrugia, N., Jakubowski, K., Cusack, R., and Stewart, L. (2015). Tunes stuck in your brain: the frequency and affective evaluation of involuntary musical imagery correlate with cortical structure. Conscious. Cogn. 35, 66–77. doi: 10.1016/j.concog.2015.04.020

PubMed Abstract | CrossRef Full Text | Google Scholar

Fasano, M. C., Brattico, E., Siemens, I., Gargiulo, A., Kringelbach, M. L., Semeraro, C., et al. (in press). “The power of orchestral music training in children: Pedagogical and psychological insights,” in Arts and mindfulness education for human flourishing, eds T. Chemi, E. Brattico, L. Fjordback, and L. Harmat (London: Routledge).

Google Scholar

Fasano, M. C., Cabral, J., Stevner, A., Vuust, P., Cantou, P., Brattico, E., et al. (2019a). The early adolescent brain on music: analysis of functional dynamics reveals engagement of orbitofrontal cortex reward system. biorxiv [Preprint]. doi: 10.1101/2020.06.18.148072

CrossRef Full Text | Google Scholar

Fasano, M. C., Semeraro, C., Cassibba, R., Kringelbach, M. L., Monacis, L., De Palo, V., et al. (2019b). Short-term orchestral music training modulates hyperactivity and inhibitory control in school-age children: a longitudinal behavioural study. Front. Psychol. 10:750. doi: 10.3389/fpsyg.2019.00750

PubMed Abstract | CrossRef Full Text | Google Scholar

Fasano, M. C., Glerean, E., Gold, B. P., Sheng, D., Sams, M., Vuust, P., et al. (2020). Intersubject similarity of brain activity in expert musicians after multimodal learning: a behavioral and neuroimaging study on learning to play a piano sonata. Neuroscience 441, 102–116. doi: 10.1016/j.neuroscience.2020.06.015

PubMed Abstract | CrossRef Full Text | Google Scholar

Ferguson, H. J., Brunsdon, V. E., and Bradford, E. E. (2021). The developmental trajectories of executive function from adolescence to old age. Sci. Rep. 11, 1–17. doi: 10.1038/s41598-020-80866-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Fiedler, D., and Müllensiefen, D. (2015). Validation of the Gold-MSI questionnaire to measure musical sophistication of German students at secondary education schools. Res. Music Educ. 36, 199–219.

Google Scholar

Friedman, N. P., Miyake, A., Corley, R. P., Young, S. E., DeFries, J. C., and Hewitt, J. K. (2006). Not all executive functions are related to intelligence. Psychol. Sci. 17, 172–179. doi: 10.1111/j.1467-9280.2006.01681.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Frischen, U., Schwarzer, G., and Degé, F. (2019). Comparing the effects of rhythm-based music training and pitch-based music training on executive functions in preschoolers. Front. Integr. Neurosci. 13:41. doi: 10.3389/fnint.2019.00041

PubMed Abstract | CrossRef Full Text | Google Scholar

Frischen, U., Schwarzer, G., and Degé, F. (2021). Music lessons enhance executive functions in 6-to 7-year-old children. Learn. Instr. 74:101442. doi: 10.1016/j.learninstruc.2021.101442

CrossRef Full Text | Google Scholar

Fuhrmann, D., Simpson-Kent, I. L., Bathelt, J., Calm Team, and Kievit, R. A. (2020). A hierarchical watershed model of fluid intelligence in childhood and adolescence. Cerebr. Cortex 30, 339–352. doi: 10.1093/cercor/bhz091

PubMed Abstract | CrossRef Full Text | Google Scholar

Garcia-Molina, A., Tirapu-Ustarroz, J., Luna-Lario, P., Ibáñez, J., and Duque, P. (2010). Are intelligence and executive functions the same thing? Rev. Neurol. 50, 738–746. doi: 10.33588/rn.5012.2009713

CrossRef Full Text | Google Scholar

George, E. M., and Coch, D. (2011). Music training and working memory: an ERP study. Neuropsychologia 49, 1083–1094. doi: 10.1016/j.neuropsychologia.2011.02.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Giofrè, D., Donolato, E., and Mammarella, I. C. (2018). The differential role of verbal and visuospatial working memory in mathematics and reading. Trends Neurosci. Educ. 12, 1–6. doi: 10.1016/j.tine.2018.07.001

CrossRef Full Text | Google Scholar

Giofrè, D., Mammarella, I. C., Ronconi, L., and Cornoldi, C. (2013). Visuospatial working memory in intuitive geometry, and in academic achievement in geometry. Learn. Individ. Diff. 23, 114–122. doi: 10.1016/j.lindif.2012.09.012

CrossRef Full Text | Google Scholar

Gordon, R. L., Fehd, H. M., and McCandliss, B. D. (2015). Does music training enhance literacy skills? A meta-analysis. Front. Psychol. 6:1777. doi: 10.3389/fpsyg.2015.01777

PubMed Abstract | CrossRef Full Text | Google Scholar

Gray, S., Green, S., Alt, M., Hogan, T., Kuo, T., Brinkley, S., et al. (2017). The structure of working memory in young children and its relation to intelligence. J. Mem. Lang. 92, 183–201. doi: 10.1016/j.jml.2016.06.004

PubMed Abstract | CrossRef Full Text | Google Scholar

Grissom, N. M., and Reyes, T. M. (2019). Let’s call the whole thing off: evaluating gender and sex differences in executive function. Neuropsychopharmacology 44, 86–96. doi: 10.1038/s41386-018-0179-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Gropper, R. J., and Tannock, R. (2009). A pilot study of working memory and academic achievement in college students with ADHD. J. Attent. Disord. 12, 574–581. doi: 10.1177/1087054708320390

PubMed Abstract | CrossRef Full Text | Google Scholar

Guillem, F., and Mograss, M. (2005). Gender differences in memory processing: evidence from event-related potentials to faces. Brain Cogn. 57, 84–92. doi: 10.1016/j.bandc.2004.08.026

PubMed Abstract | CrossRef Full Text | Google Scholar

Guo, H., Yuan, W., Fung, C. V., Chen, F., and Li, Y. (2022). The relationship between extracurricular music activity participation and music and Chinese language academic achievements of primary school students in China. Psychol. Music 50, 742–755. doi: 10.1177/03057356211027642

CrossRef Full Text | Google Scholar

Guo, X., Ohsawa, C., Suzuki, A., and Sekiyama, K. (2018). Improved Digit Span in children after a 6-week intervention of playing a musical instrument: an exploratory randomized controlled trial. Front. Psychol. 8:2303. doi: 10.3389/fpsyg.2017.02303

PubMed Abstract | CrossRef Full Text | Google Scholar

Habibi, A., Damasio, A., Ilari, B., Veiga, R., Joshi, A. A., Leahy, R. M., et al. (2018). Childhood music training induces change in micro and macroscopic brain structure: results from a longitudinal study. Cerebr. Cortex 28, 4336–4347. doi: 10.1093/cercor/bhx286

PubMed Abstract | CrossRef Full Text | Google Scholar

Hallam, S. (2015). The Power of Music. London: International Music Education Research Centre (iMERC) Press.

Google Scholar

Hansen, M., Wallentin, M., and Vuust, P. (2013). Working memory and musical competence of musicians and non-musicians. Psychol. Music 41, 779–793. doi: 10.1177/0305735612452186

CrossRef Full Text | Google Scholar

Harrison, P. (2020). psychTestR: an R package for designing and conducting behavioural psychological experiments. J. Open Source Softw. 5:2088. doi: 10.21105/joss.02088

CrossRef Full Text | Google Scholar

Harrison, P., Collins, T., and Müllensiefen, D. (2017). Applying modern psychometric techniques to melodic discrimination testing: item response theory, computerised adaptive testing, and automatic item generation. Sci. Rep. 7, 1–18. doi: 10.1038/s41598-017-03586-z

PubMed Abstract | CrossRef Full Text | Google Scholar

Harrison, P., and Müllensiefen, D. (2018). Development and validation of the computerised adaptive beat alignment test (CA-BAT). Sci. Rep. 8, 1–19. doi: 10.1038/s41598-018-30318-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Hennessy, S. L., Sachs, M. E., Ilari, B., and Habibi, A. (2019). Effects of music training on inhibitory control and associated neural networks in school-aged children: a longitudinal study. Front. Neurosci. 13:1080. doi: 10.3389/fnins.2019.01080

PubMed Abstract | CrossRef Full Text | Google Scholar

Hofmann, W., Schmeichel, B. J., and Baddeley, A. D. (2012). Executive functions and self-regulation. Trends Cogn. Sci. 16, 174–180. doi: 10.1016/j.tics.2012.01.006

PubMed Abstract | CrossRef Full Text | Google Scholar

Holochwost, S. J., Propper, C. B., Wolf, D. P., Willoughby, M. T., Fisher, K. R., Kolacz, J., et al. (2017). Music education, academic achievement, and executive functions. Psychol. Aesthet. Creat. Arts 11:147. doi: 10.1037/aca0000112

CrossRef Full Text | Google Scholar

Holyoak, K. J. (2012). “Analogy and relational reasoning,” in The Oxford Handbook of Thinking and Reasoning, eds K. J. Holyoak and R. G. Morrison (Oxford: Oxford University Press), 234–259. doi: 10.1093/oxfordhb/9780199734689.013.0013

CrossRef Full Text | Google Scholar

Huepe, D., Roca, M., Salas, N., Canales-Johnson, A., Rivera-Rei, ÁA., Zamorano, L., et al. (2011). Fluid intelligence and psychosocial outcome: from logical problem solving to social adaptation. PLoS One 6:e24858. doi: 10.1371/journal.pone.0024858

PubMed Abstract | CrossRef Full Text | Google Scholar

Ilari, B. S., Keller, P., Damasio, H., and Habibi, A. (2016). The development of musical skills of underprivileged children over the course of 1 year: a study in the context of an El Sistema-inspired program. Front. Psychol. 7:62. doi: 10.3389/fpsyg.2016.00062

PubMed Abstract | CrossRef Full Text | Google Scholar

Ireland, K., Parker, A., Foster, N., and Penhune, V. (2018). Rhythm and melody tasks for school-aged children with and without musical training: age-equivalent scores and reliability. Front. Psychol. 9:426. doi: 10.3389/fpsyg.2018.00426

PubMed Abstract | CrossRef Full Text | Google Scholar

James, C. E., Zuber, S., Dupuis-Lozeron, E., Abdili, L., Gervaise, D., and Kliegel, M. (2020). Formal string instrument training in a class setting enhances cognitive and sensorimotor development of primary school children. Front. Neurosci. 14:567. doi: 10.3389/fnins.2020.00567

PubMed Abstract | CrossRef Full Text | Google Scholar

Janacsek, K., and Nemeth, D. (2013). Implicit sequence learning and working memory: correlated or complicated? Cortex 49, 2001–2006. doi: 10.1016/j.cortex.2013.02.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Janata, P., and Grafton, S. T. (2003). Swinging in the brain: Shared neural substrates for behaviors related to sequencing and music. Nat. Neurosci. 6, 682–687.

Google Scholar

Jansen, P., Hoja, S., and Jost, L. (2022). Are there gender differences in executive functions in musicians and non-musicians? J. Individ. Diff. 43:20. doi: 10.1027/1614-0001/a000350

PubMed Abstract | CrossRef Full Text | Google Scholar

Jaschke, A. C., Honing, H., and Scherder, E. J. (2018). Longitudinal analysis of music education on executive functions in primary school children. Front. Neurosci. 12:103. doi: 10.3389/fnins.2018.00103

PubMed Abstract | CrossRef Full Text | Google Scholar

Jensen, A. R. (1998). “The g factor and the design of education,” in Intelligence, Instruction, and Assessment: Theory into Practice, eds R. J. Sternberg and W. M. Williams (Mahwah, NJ: Lawrence Erlbaum Associates Publishers), 111–131.

Google Scholar

Kang, H. J., and Williamson, V. J. (2012). “The effect of background music on second language learning,” in Proceedings of the 12th International Conference on Music Perception and Cognition and the 8th Triennial Conference of the European Society for the Cognitive Sciences of Music, Blantyre, 516–518.

Google Scholar

Kausel, L., Zamorano, F., Billeke, P., Sutherland, M. E., Larrain-Valenzuela, J., Stecher, X., et al. (2020). Neural dynamics of improved bimodal attention and working memory in musically trained children. Front. Neurosci. 14:554731. doi: 10.3389/fnins.2020.554731

PubMed Abstract | CrossRef Full Text | Google Scholar

Kaushanskaya, M., Gross, M., and Buac, M. (2013). Gender differences in child word learning. Learn. Individ. Diff. 27, 82–89. doi: 10.1016/j.lindif.2013.07.002

PubMed Abstract | CrossRef Full Text | Google Scholar

Kent, P. (2017). Fluid intelligence: a brief history. Appl. Neuropsychol. Child 6, 193–203. doi: 10.1080/21622965.2017.1317480

PubMed Abstract | CrossRef Full Text | Google Scholar

Kragness, H. E., Swaminathan, S., Cirelli, L. K., and Schellenberg, E. G. (2021). Individual differences in musical ability are stable over time in childhood. Dev. Sci. 24:e13081. doi: 10.1111/desc.13081

PubMed Abstract | CrossRef Full Text | Google Scholar

Kraus, N., Strait, D. L., and Parbery-Clark, A. (2012). Cognitive factors shape brain networks for auditory skills: spotlight on auditory working memory. Ann. N.Y. Acad. Sci. 1252, 100–107. doi: 10.1111/j.1749-6632.2012.06463.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Kraus, N., and White-Schwoch, T. (2020). The argument for music education. Am. Sci. 108, 210–214. doi: 10.1511/2020.108.4.210

CrossRef Full Text | Google Scholar

Krause, A. E., Forbes, M., and Lowe-Brown, X. (2022). Does reality television-style singing influence singing self-concept? J. Voice [Epub ahead of print]. doi: 10.1016/j.jvoice.2022.06.024

PubMed Abstract | CrossRef Full Text | Google Scholar

Lad, M., Billig, A. J., Kumar, S., and Griffiths, T. D. (2022). A specific relationship between musical sophistication and auditory working memory. Sci. Rep. 12, 1–10. doi: 10.1038/s41598-022-07568-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Lambek, R., and Shevlin, M. (2011). Working memory and response inhibition in children and adolescents: age and organization issues. Scand. J. Psychol. 52, 427–432. doi: 10.1111/j.1467-9450.2011.00899.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Larrouy-Maestri, P., Harrison, P., and Müllensiefen, D. (2019). The mistuning perception test: a new measurement instrument. Behav. Res. Methods 51, 663–675. doi: 10.3758/s13428-019-01225-1

PubMed Abstract | CrossRef Full Text | Google Scholar

Lauer, J. E., Yhang, E., and Lourenco, S. F. (2019). The development of gender differences in spatial reasoning: a meta-analytic review. Psychol. Bull. 145:537. doi: 10.1037/bul0000191

PubMed Abstract | CrossRef Full Text | Google Scholar

Law, L. N., and Zentner, M. (2012). Assessing musical abilities objectively: construction and validation of the profile of music perception skills. PLoS One 7:e52508. doi: 10.1371/journal.pone.0052508

PubMed Abstract | CrossRef Full Text | Google Scholar

Levin, K. A. (2006). Study design III: cross-sectional studies. Evid. Based Dentist. 7, 24–25. doi: 10.1038/sj.ebd.6400375

PubMed Abstract | CrossRef Full Text | Google Scholar

Levine, S. C., Foley, A., Lourenco, S., Ehrlich, S., and Ratliff, K. (2016). Sex differences in spatial cognition: advancing the conversation. Wiley Interdiscip. Rev. Cogn. Sci. 7, 127–155. doi: 10.1002/wcs.1380

PubMed Abstract | CrossRef Full Text | Google Scholar

Liang, Q., Zeng, Y., and Xu, B. (2020). Temporal-sequential learning with a brain-inspired spiking neural network and its application to musical memory. Front. Comput. Neurosci. 14:51. doi: 10.3389/fncom.2020.00051

PubMed Abstract | CrossRef Full Text | Google Scholar

Lin, H. R., Kopiez, R., Müllensiefen, D., and Wolf, A. (2021). The Chinese version of the Gold-MSI: adaptation and validation of an inventory for the measurement of musical sophistication in a Taiwanese sample. Musicae Sci. 25, 226–251. doi: 10.1177/1029864919871987

CrossRef Full Text | Google Scholar

Linnavalli, T., Putkinen, V., Lipsanen, J., Huotilainen, M., and Tervaniemi, M. (2018). Music playschool enhances children’s linguistic skills. Sci. Rep. 8, 1–10. doi: 10.1038/s41598-018-27126-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Logie, R. H. (1986). Visuo-spatial processing in working memory. Q. J. Exp. Psychol. A Hum. Exp. Psychol. 38A, 229–247. doi: 10.1080/14640748608401596

PubMed Abstract | CrossRef Full Text | Google Scholar

Logie, R. H. (2011). “The visual and the spatial of a multicomponent working memory,” in Spatial Working Memory, eds A. Vandierendonck and A. Szmalec (Hove: Psychology Press), 19–45.

Google Scholar

Loprinzi, P. D., and Frith, E. (2018). The role of sex in memory function: considerations and recommendations in the context of exercise. J. Clin. Med. 7:132. doi: 10.3390/jcm7060132

PubMed Abstract | CrossRef Full Text | Google Scholar

Lorenzen, I., and Brattico, E. (2020). “Learning-the impact of instrumental music training on children’s memory,” in MIB Annual Report 2019, Fællestrykkeriet SUN-TRYK, eds P. Vuust and H. Kastbjerg (Aarhus: Aarhus University), 34–37.

Google Scholar

Ma, Y., Baker, D. J., Vukovics, K., Davis, C., and Elliott, E. M. (2021). Generalizing the effect of lyrics on emotion rating. PsyArXiv [Preprint]. doi: 10.31234/osf.io/5ku43

CrossRef Full Text | Google Scholar

MacGregor, C., Andrade, P. E., Forth, J., Frieler, K., and Müllensiefen, D. (2022). The rhythm ability test (RAT): A new test of rhythm memory in children and adults (Manuscript in preparation).

Google Scholar

MacGregor, C., and Müllensiefen, D. (2019). The musical emotion discrimination task: a new measure for assessing the ability to discriminate emotions in music. Front. Psychol. 10:1955. doi: 10.3389/fpsyg.2019.01955

PubMed Abstract | CrossRef Full Text | Google Scholar

Mankel, K., and Bidelman, G. M. (2018). Inherent auditory skills rather than formal music training shape the neural encoding of speech. Proc. Natl. Acad. Sci. U.S.A. 115, 13129–13134. doi: 10.1073/pnas.1811793115

PubMed Abstract | CrossRef Full Text | Google Scholar

Martinez, M. E. (2013). Future Bright: A Transforming Vision of Human Intelligence. Oxford: Oxford University Press. doi: 10.1093/acprof:osobl/9780199781843.001.0001

CrossRef Full Text | Google Scholar

Marzocchi, G. M., Usai, M. C., and Howard, S. J. (2020). Training and enhancing executive function. Front. Psychol. 11:2031. doi: 10.3389/fpsyg.2020.02031

PubMed Abstract | CrossRef Full Text | Google Scholar

Mas-Herrero, E., Dagher, A., Farrés-Franch, M., and Zatorre, R. J. (2021). Unraveling the temporal dynamics of reward signals in music-induced pleasure with TMS. J. Neurosci. 41, 3889–3899. doi: 10.1523/JNEUROSCI.0727-20.2020

PubMed Abstract | CrossRef Full Text | Google Scholar

Mas-Herrero, E., Marco-Pallares, J., Lorenzo-Seva, U., Zatorre, R. J., and Rodriguez-Fornells, A. (2013). Barcelona Music Reward Questionnaire. Washington, DC: APA. doi: 10.1037/t31533-000

CrossRef Full Text | Google Scholar

Mata, S., Gómez-Pérez, M. M., Molinero, C., and Calero, M. D. (2017). Interpersonal problem-solving skills, executive function and learning potential in preadolescents with high/low family risk. Span. J. Psychol. 20:E56. doi: 10.1017/sjp.2017.54

PubMed Abstract | CrossRef Full Text | Google Scholar

Matthews, J. S., Ponitz, C. C., and Morrison, F. J. (2009). Early gender differences in self-regulation and academic achievement. J. Educ. Psychol. 101:689. doi: 10.1037/a0014240

CrossRef Full Text | Google Scholar

Mawjee, K., Woltering, S., and Tannock, R. (2015). Working memory training in post-secondary students with ADHD: a randomized controlled study. PLoS One 10:e0137173. doi: 10.1371/journal.pone.0137173

PubMed Abstract | CrossRef Full Text | Google Scholar

McPherson, G. (ed.) (2016). The Child As Musician: A Handbook of Musical Development. Oxford: Oxford University Press. doi: 10.1093/acprof:oso/9780198744443.001.0001

PubMed Abstract | CrossRef Full Text | Google Scholar

Meisel, S. N., Fosco, W. D., Hawk, L. W., and Colder, C. R. (2019). Mind the gap: a review and recommendations for statistically evaluating Dual Systems models of adolescent risk behavior. Dev. Cogn. Neurosci. 39:100681. doi: 10.1016/j.dcn.2019.100681

PubMed Abstract | CrossRef Full Text | Google Scholar

Metsäpelto, R. L., and Pulkkinen, L. (2012). Socioemotional behavior and school achievement in relation to extracurricular activity participation in middle childhood. Scand. J. Educ. Res. 56, 167–182. doi: 10.1080/00313831.2011.581681

CrossRef Full Text | Google Scholar

Meyer, J., Oguz, P. G., and Moore, K. S. (2020). Superior fluid cognition in trained musicians. Psychol. Music 48, 434–447. doi: 10.1177/0305735618808089

CrossRef Full Text | Google Scholar

Miendlarzewska, E. A., and Trost, W. J. (2014). How musical training affects cognitive development: rhythm, reward and other modulating variables. Front. Neurosci. 7:279. doi: 10.3389/fnins.2013.00279

PubMed Abstract | CrossRef Full Text | Google Scholar

Miller, D. I., and Halpern, D. F. (2014). The new science of cognitive sex differences. Trends Cogn. Sci. 18, 37–45. doi: 10.1016/j.tics.2013.10.011

PubMed Abstract | CrossRef Full Text | Google Scholar

Møller, C., Garza-Villarreal, E. A., Hansen, N. C., Højlund, A., Bærentsen, K. B., Chakravarty, M. M., et al. (2021). Audiovisual structural connectivity in musicians and non-musicians: a cortical thickness and diffusion tensor imaging study. Sci. Rep. 11, 1–14. doi: 10.1038/s41598-021-83135-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Moore, K., and Halle, T. (2001). Preventing problems vs. promoting the positive: what do we want for our children? Adv. Life Course Res. 6, 141–170. doi: 10.1016/S1040-2608(01)80009-5

CrossRef Full Text | Google Scholar

Moore, K. A., and Theokas, C. (2008). Conceptualizing a monitoring system for indicators in middle childhood. Child Indic. Res. 1, 109–128. doi: 10.1007/s12187-008-9011-9

CrossRef Full Text | Google Scholar

Moreno, S., Lee, Y., Janus, M., and Bialystok, E. (2015). Short-term second language and music training induces lasting functional brain changes in early childhood. Child Dev. 86, 394–406. doi: 10.1111/cdev.12297

PubMed Abstract | CrossRef Full Text | Google Scholar

Mosing, M. A., Pedersen, N. L., Madison, G., and Ullén, F. (2014). Genetic pleiotropy explains associations between musical auditory discrimination and intelligence. PLoS One 9:e113874. doi: 10.1371/journal.pone.0113874

PubMed Abstract | CrossRef Full Text | Google Scholar

Müllensiefen, D., Gingras, B., Musil, J., and Stewart, L. (2014). Measuring the facets of musicality: the goldsmiths musical sophistication index (Gold-MSI). Pers. Individ. Diff. 60:S35. doi: 10.1016/j.paid.2013.07.081

PubMed Abstract | CrossRef Full Text | Google Scholar

Müllensiefen, D., and Harrison, P. (2020). “The impact of music on adolescents’ cognitive and socio-emotional learning,” in The ‘BrainCanDo’Handbook of Teaching and Learning, eds J. Harrington, J. Beale, A. Fancourt, and C. Lutz (London: David Fulton Publishers), 222–239. doi: 10.4324/9780429197741-11

PubMed Abstract | CrossRef Full Text | Google Scholar

Nazareth, A., Herrera, A., and Pruden, S. M. (2013). Explaining sex differences in mental rotation: role of spatial activity experience. Cogn. Process. 14, 201–204. doi: 10.1007/s10339-013-0542-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Nie, P., Wang, C., Rong, G., Du, B., Lu, J., Li, S., et al. (2021). Effects of music training on the auditory working memory of chinese-speaking school-aged children: a longitudinal intervention study. Front. Psychol. 12:770425. doi: 10.3389/fpsyg.2021.770425

PubMed Abstract | CrossRef Full Text | Google Scholar

Norton, A., Winner, E., Cronin, K., Overy, K., Lee, D. J., and Schlaug, G. (2005). Are there pre-existing neural, cognitive, or motoric markers for musical ability? Brain Cogn. 59, 124–134. doi: 10.1016/j.bandc.2005.05.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Oechslin, M. S., Van De Ville, D., Lazeyras, F., Hauert, C. A., and James, C. E. (2013). Degree of musical expertise modulates higher order brain functioning. Cerebr. Cortex 23, 2213–2224. doi: 10.1093/cercor/bhs206

PubMed Abstract | CrossRef Full Text | Google Scholar

Okada, B. M., and Slevc, L. R. (2018). Individual differences in musical training and executive functions: a latent variable approach. Mem. Cogn. 46, 1076–1092. doi: 10.3758/s13421-018-0822-8

PubMed Abstract | CrossRef Full Text | Google Scholar

Ollen, J. E. (2006). A Criterion-Related Validity Test of Selected Indicators of Musical Sophistication Using Expert Ratings. Doctoral dissertation. Columbus, OH: The Ohio State University.

Google Scholar

Olszewska, A. M., Gaca, M., Herman, A. M., Jednoróg, K., and Marchewka, A. (2021). How musical training shapes the adult brain: predispositions and neuroplasticity. Front. Neurosci. 15:630829. doi: 10.3389/fnins.2021.630829

PubMed Abstract | CrossRef Full Text | Google Scholar

Pallesen, K. J., Brattico, E., Bailey, C. J., Korvenoja, A., Koivisto, J., Gjedde, A., et al. (2010). Cognitive control in auditory working memory is enhanced in musicians. PLoS One 5:e11120. doi: 10.1371/journal.pone.0011120

PubMed Abstract | CrossRef Full Text | Google Scholar

Pau, S., Jahn, G., Sakreida, K., Domin, M., and Lotze, M. (2013). Encoding and recall of finger sequences in experienced pianists compared with musically naive controls: a combined behavioral and functional imaging study. Neuroimage 64, 379–387. doi: 10.1016/j.neuroimage.2012.09.012

PubMed Abstract | CrossRef Full Text | Google Scholar

Peretz, I. (2019). How Music Sculpts Our Brain. Paris: Odile Jacob.

Google Scholar

Pitts, S. E. (2007). Anything goes: a case study of extra-curricular musical participation in an English secondary school. Music Educ. Res. 9, 145–165. doi: 10.1080/14613800601127627

CrossRef Full Text | Google Scholar

Porflitt, F., and Rosas, R. (2022). Musical sophistication explains a good deal of cognitive performance. A cross-sectional study of musicians and non-musicians. Números 24, 147–167. doi: 10.7764/res.2020.47.9

CrossRef Full Text | Google Scholar

Proverbio, A. M., and Bellini, E. (2018). How the degree of instrumental practice in music increases perceptual sensitivity. Brain Res. 1691, 15–25. doi: 10.1016/j.brainres.2018.04.020

PubMed Abstract | CrossRef Full Text | Google Scholar

Proverbio, A. M., and Orlandi, A. (2016). Instrument-specific effects of musical expertise on audiovisual processing (clarinet vs. violin). Music Percept. Interdiscip. J. 33, 446–456. doi: 10.1525/mp.2016.33.4.446

CrossRef Full Text | Google Scholar

Putkinen, V., and Saarikivi, K. (2018). Neural correlates of enhanced executive functions: is less more? Ann. N.Y. Acad. Sci. 1423, 117–125. doi: 10.1111/nyas.13645

PubMed Abstract | CrossRef Full Text | Google Scholar

Putkinen, V., Saarikivi, K., Chan, T. M. V., and Tervaniemi, M. (2021). Faster maturation of selective attention in musically trained children and adolescents: converging behavioral and event-related potential evidence. Eur. J. Neurosci. 54, 4246–4257. doi: 10.1111/ejn.15262

PubMed Abstract | CrossRef Full Text | Google Scholar

Putkinen, V., Tervaniemi, M., Saarikivi, K., and Huotilainen, M. (2015). Promises of formal and informal musical activities in advancing neurocognitive development throughout childhood. Ann. N.Y. Acad. Sci. 1337, 153–162. doi: 10.1111/nyas.12656

PubMed Abstract | CrossRef Full Text | Google Scholar

R Core Team (2021). R: A Language and Environment for Statistical Computing. (Version 4.0).

Google Scholar

Rauscher, F. H., and Hinton, S. C. (2011). Music instruction and its diverse extra-musical benefits. Music Percept. 29, 215–226. doi: 10.1525/mp.2011.29.2.215

CrossRef Full Text | Google Scholar

Rautenberg, I. (2015). The effects of musical training on the decoding skills of German-speaking primary school children. J. Res. Read. 38, 1–17. doi: 10.1111/jrir.12010

CrossRef Full Text | Google Scholar

Reilly, D., and Neumann, D. L. (2013). Gender-role differences in spatial ability: a meta-analytic review. Sex Roles 68, 521–535. doi: 10.1007/s11199-013-0269-0

CrossRef Full Text | Google Scholar

Roden, I., Könen, T., Bongard, S., Frankenberg, E., Friedrich, E. K., and Kreutz, G. (2014b). Effects of music training on attention, processing speed and cognitive music abilities—findings from a longitudinal study. Appl. Cogn. Psychol. 28, 545–557. doi: 10.1002/acp.3034

CrossRef Full Text | Google Scholar

Roden, I., Grube, D., Bongard, S., and Kreutz, G. (2014a). Does music training enhance working memory performance? Findings from a quasi-experimental longitudinal study. Psychol. Music 42, 284–298. doi: 10.1177/0305735612471239

CrossRef Full Text | Google Scholar

Rodrigues, A. C., Loureiro, M., and Caramelli, P. (2014). Visual memory in musicians and non-musicians. Front. Hum. Neurosci. 8:424. doi: 10.3389/fnhum.2014.00424

PubMed Abstract | CrossRef Full Text | Google Scholar

Saarikivi, K. (2022). Augmented Maturation of Executive Functions in Musically Trained Children and Adolescents. Helsinki: University of Helsinki.

Google Scholar

Saarikivi, K. A., Huotilainen, M., Tervaniemi, M., and Putkinen, V. (2019). Selectively enhanced development of working memory in musically trained children and adolescents. Front. Integr. Neurosci. 13:62. doi: 10.3389/fnint.2019.00062

PubMed Abstract | CrossRef Full Text | Google Scholar

Sachs, M., Kaplan, J., Der Sarkissian, A., and Habibi, A. (2017). Increased engagement of the cognitive control network associated with music training in children during an fMRI Stroop task. PLoS One 12:e0187254. doi: 10.1371/journal.pone.0187254

PubMed Abstract | CrossRef Full Text | Google Scholar

Sala, G., and Gobet, F. (2017). Does far transfer exist? Negative evidence from chess, music, and working memory training. Curr. Direct. Psychol. Sci. 26, 515–520. doi: 10.1177/0963721417712760

PubMed Abstract | CrossRef Full Text | Google Scholar

Sala, G., and Gobet, F. (2020). Cognitive and academic benefits of music training with children: a multilevel meta-analysis. Mem. Cogn. 48, 1429–1441. doi: 10.3758/s13421-020-01060-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Schaal, N. K., Bauer, A. K. R., and Müllensiefen, D. (2014). Der Gold-MSI: replikation und validierung eines fragebogens instrumentes zur messung musikalischer erfahrenheit anhand einer deutschen stichprobe. Musicae Sci. 18, 423–447. doi: 10.1177/1029864914541851

CrossRef Full Text | Google Scholar

Schellenberg, E. G. (2004). Music lessons enhance IQ. Psychol. Sci. 15, 511–514. doi: 10.1111/j.0956-7976.2004.00711.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Schellenberg, E. G. (2006). Long-term positive associations between music lessons and IQ. J. Educ. Psychol. 98:457. doi: 10.1037/0022-0663.98.2.457

CrossRef Full Text | Google Scholar

Schellenberg, E. G. (2011a). Examining the association between music lessons and intelligence. Br. J. Psychol. 102, 283–302. doi: 10.1111/j.2044-8295.2010.02000.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Schellenberg, E. G. (2011b). Music lessons, emotional intelligence, and IQ. Music Percept. 29, 185–194. doi: 10.1525/mp.2011.29.2.185

PubMed Abstract | CrossRef Full Text | Google Scholar

Schellenberg, E. G. (2019). Music training, music aptitude, and speech perception. Proc. Natl. Acad. Sci. U.S.A. 116, 2783–2784. doi: 10.1073/pnas.1821109116

PubMed Abstract | CrossRef Full Text | Google Scholar

Schellenberg, E. G. (2020a). Correlation= causation? Music training, psychology, and neuroscience. Psychol. Aesthet. Creat. Arts 14:475. doi: 10.1037/aca0000263

CrossRef Full Text | Google Scholar

Schellenberg, E. G. (2020b). “Music training, individual differences, and plasticity,” in Educational Neuroscience: Development Across the Life Span, eds M. S. C. Thomas, D. Mareschal, and I. Dumontheil (London: Routledge), 415–441. doi: 10.4324/9781003016830-21

PubMed Abstract | CrossRef Full Text | Google Scholar

Schellenberg, E. G., and Peretz, I. (2008). Music, language and cognition: unresolved issues. Trends Cogn. Sci. 12, 45–46. doi: 10.1016/j.tics.2007.11.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Schellenberg, E. G., and Winner, E. (2011). Music training and nonmusical abilities: introduction. Music Percept. 29, 129–132. doi: 10.1525/mp.2011.29.2.129

PubMed Abstract | CrossRef Full Text | Google Scholar

Schlaug, G. (2015). Musicians and music making as a model for the study of brain plasticity. Prog. Brain Res. 217, 37–55. doi: 10.1016/bs.pbr.2014.11.020

PubMed Abstract | CrossRef Full Text | Google Scholar

Schlaug, G., Norton, A., Overy, K., and Winner, E. (2005). Effects of music training on the child’s brain and cognitive development. Ann. N. Y. Acad. Sci. 1060, 219–230.

Google Scholar

Schulze, K., Jay Dowling, W., and Tillmann, B. (2011a). Working memory for tonal and atonal sequences during a forward and a backward recognition task. Music Percept. 29, 255–267. doi: 10.1525/mp.2012.29.3.255

CrossRef Full Text | Google Scholar

Schulze, K., Müller, K., and Koelsch, S. (2011b). Neural correlates of strategy use during auditory working memory in musicians and non-musicians. Eur. J. Neurosci. 33, 189–196. doi: 10.1111/j.1460-9568.2010.07470.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Schulze, K., and Koelsch, S. (2012). Working memory for speech and music. Ann. N.Y. Acad. Sci. 1252, 229–236. doi: 10.1111/j.1749-6632.2012.06447.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Seinstra, M., Grzymek, K., and Kalenscher, T. (2015). Gender-specific differences in the relationship between autobiographical memory and intertemporal choice in older adults. PLoS One 10:e0137061. doi: 10.1371/journal.pone.0137061

PubMed Abstract | CrossRef Full Text | Google Scholar

Setti, W., Cuturi, L. F., Engel, I., Picinali, L., and Gori, M. (2022). The influence of early visual deprivation on audio-spatial working memory. Neuropsychology 36:55. doi: 10.1037/neu0000776

PubMed Abstract | CrossRef Full Text | Google Scholar

Setti, W., Cuturi, L. F., Sandini, G., and Gori, M. (2021). Changes in audio-spatial working memory abilities during childhood: the role of spatial and phonological development. PLoS One 16:e0260700. doi: 10.1371/journal.pone.0260700

PubMed Abstract | CrossRef Full Text | Google Scholar

Shen, Y., Lin, Y., Liu, S., Fang, L., and Liu, G. (2019). Sustained effect of music training on the enhancement of executive function in preschool children. Front. Psychol. 10:1910. doi: 10.3389/fpsyg.2019.01910

PubMed Abstract | CrossRef Full Text | Google Scholar

Shipstead, Z., Harrison, T. L., and Engle, R. W. (2016). Working memory capacity and fluid intelligence: maintenance and disengagement. Perspect. Psychol. Sci. 11, 771–799. doi: 10.1177/1745691616650647

PubMed Abstract | CrossRef Full Text | Google Scholar

Shulman, E. P., Harden, K. P., Chein, J. M., and Steinberg, L. (2015). Sex differences in the developmental trajectories of impulse control and sensation-seeking from early adolescence to early adulthood. J. Youth Adolesc. 44, 1–17. doi: 10.1007/s10964-014-0116-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Silas, S., Müllensiefen, D., Gelding, R., Frieler, K., and Harrison, P. (2022). The associations between music training, musical working memory, and visuospatial working memory: an opportunity for causal modelling. Music Percept. 39, 401–420. doi: 10.1525/mp.2022.39.4.401

CrossRef Full Text | Google Scholar

Silvia, P. J., Thomas, K. S., Nusbaum, E. C., Beaty, R. E., and Hodges, D. A. (2016). How does music training predict cognitive abilities? A bifactor approach to musical expertise and intelligence. Psychol. Aesthet. Creat. Arts 10:184. doi: 10.1037/aca0000058

CrossRef Full Text | Google Scholar

Slater, J., and Kraus, N. (2016). The role of rhythm in perceiving speech in noise: a comparison of percussionists, vocalists and non-musicians. Cogn. Process. 17, 79–87. doi: 10.1007/s10339-015-0740-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Slater, J., Strait, D. L., Skoe, E., O’Connell, S., Thompson, E., and Kraus, N. (2014). Longitudinal effects of group music instruction on literacy skills in low-income children. PLoS One 9:e113383. doi: 10.1371/journal.pone.0113383

PubMed Abstract | CrossRef Full Text | Google Scholar

Slevc, L. R., Davey, N. S., Buschkuehl, M., and Jaeggi, S. M. (2016). Tuning the mind: exploring the connections between musical ability and executive functions. Cognition 152, 199–211. doi: 10.1016/j.cognition.2016.03.017

PubMed Abstract | CrossRef Full Text | Google Scholar

Spearman, C. (1927). The measurement of intelligence. Nature 120, 577–578. doi: 10.1038/120577a0

CrossRef Full Text | Google Scholar

Stevens, J., Quittner, A. L., Zuckerman, J. B., and Moore, S. (2002). Behavioral inhibition, self-regulation of motivation, and working memory in children with attention deficit hyperactivity disorder. Dev. Neuropsychol. 21, 117–139. doi: 10.1207/S15326942DN2102_1

CrossRef Full Text | Google Scholar

Strait, D. L., Parbery-Clark, A., Hittner, E., and Kraus, N. (2012). Musical training during early childhood enhances the neural encoding of speech in noise. Brain Lang. 123, 191–201. doi: 10.1016/j.bandl.2012.09.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Stuss, D. T. (2011). Functions of the frontal lobes: relation to executive functions. J. Int. Neuropsychol. Soc. 17, 759–765. doi: 10.1017/S1355617711000695

PubMed Abstract | CrossRef Full Text | Google Scholar

Suárez, L., Elangovan, S., and Au, A. (2016). Cross-sectional study on the relationship between music training and working memory in adults. Austral. J. Psychol. 68, 38–46. doi: 10.1111/ajpy.12087

PubMed Abstract | CrossRef Full Text | Google Scholar

Swaminathan, S., and Schellenberg, E. G. (2016). “Music training,” in Cognitive Training: An Overview of Features and Applications, eds T. Strobach and J. Karbach (Cham: Springer), 137–144. doi: 10.1007/978-3-319-42662-4_13

CrossRef Full Text | Google Scholar

Swaminathan, S., and Schellenberg, E. G. (2020). Musical ability, music training, and language ability in childhood. J. Exp. Psychol. Learn. Mem. Cogn. 46:2340. doi: 10.1037/xlm0000798

PubMed Abstract | CrossRef Full Text | Google Scholar

Swaminathan, S., Schellenberg, E. G., and Khalil, S. (2017). Revisiting the association between music lessons and intelligence: training effects or music aptitude? Intelligence 62, 119–124. doi: 10.1016/j.intell.2017.03.005

CrossRef Full Text | Google Scholar

Talamini, F., Altoè, G., Carretti, B., and Grassi, M. (2017). Musicians have better memory than nonmusicians: a meta-analysis. PLoS One 12:e0186773. doi: 10.1371/journal.pone.0186773

PubMed Abstract | CrossRef Full Text | Google Scholar

Theofilidis, A., Karakasi, M. V., Kevrekidis, D. P., Pavlidis, P., Sofologi, M., Trypsiannis, G., et al. (2020). Gender differences in short-term memory related to music genres. Neuroscience 448, 266–271. doi: 10.1016/j.neuroscience.2020.08.035

PubMed Abstract | CrossRef Full Text | Google Scholar

Tiego, J., Testa, R., Bellgrove, M. A., Pantelis, C., and Whittle, S. (2018). A hierarchical model of inhibitory control. Front. Psychol. 9:1339. doi: 10.3389/fpsyg.2018.01339

PubMed Abstract | CrossRef Full Text | Google Scholar

Tierney, A., and Kraus, N. (2013). Music training for the development of reading skills. Prog. Brain Res. 207, 209–241. doi: 10.1016/B978-0-444-63327-9.00008-4

PubMed Abstract | CrossRef Full Text | Google Scholar

Tsigeman, E., Silas, S., Frieler, K., Likhanov, M., Gelding, R., Kovas, Y., et al. (2022). The jack and jill adaptive working memory task: construction, calibration and validation. PLoS One 17:e0262200. doi: 10.1371/journal.pone.0262200

PubMed Abstract | CrossRef Full Text | Google Scholar

Unsworth, N., Fukuda, K., Awh, E., and Vogel, E. K. (2014). Working memory and fluid intelligence: capacity, attention control, and secondary memory retrieval. Cogn. Psychol. 71, 1–26. doi: 10.1016/j.cogpsych.2014.01.003

PubMed Abstract | CrossRef Full Text | Google Scholar

Vincenzi, M., Correia, A. I., Vanzella, P., Pinheiro, A. P., Lima, C. F., and Schellenberg, E. G. (2022). Associations between music training and cognitive abilities: the special case of professional musicians. Psychol. Aesthet. Creat. Arts [Epub ahead of print]. doi: 10.1037/aca0000481

CrossRef Full Text | Google Scholar

Vollmann, H., Ragert, P., Conde, V., Villringer, A., Classen, J., Witte, O. W., et al. (2014). Instrument specific use-dependent plasticity shapes the anatomical properties of the corpus callosum: a comparison between musicians and non-musicians. Front. Behav. Neurosci. 8:245. doi: 10.3389/fnbeh.2014.00245

PubMed Abstract | CrossRef Full Text | Google Scholar

Voyer, D., Voyer, S. D., and Saint-Aubin, J. (2017). Sex differences in visual-spatial working memory: a meta-analysis. Psychon. Bull. Rev. 24, 307–334. doi: 10.3758/s13423-016-1085-7

PubMed Abstract | CrossRef Full Text | Google Scholar

Wai, J., Cacchio, M., Putallaz, M., and Makel, M. C. (2010). Sex differences in the right tail of cognitive abilities: a 30 year examination. Intelligence 38, 412–423. doi: 10.1016/j.intell.2010.04.006

CrossRef Full Text | Google Scholar

Wallentin, M., Nielsen, A. H., Friis-Olivarius, M., Vuust, C., and Vuust, P. (2010). The Musical Ear Test, a new reliable test for measuring musical competence. Learn. Individ. Diff. 20, 188–196. doi: 10.1016/j.lindif.2010.02.004

CrossRef Full Text | Google Scholar

Wallmark, Z., Nghiem, L., and Marks, L. E. (2021). Does timbre modulate visual perception? Exploring crossmodal interactions. Music Percept. Interdiscip. J. 39, 1–20. doi: 10.1525/mp.2021.39.1.1

CrossRef Full Text | Google Scholar

Wetter, O. E., Koerner, F., and Schwaninger, A. (2009). Does musical training improve school performance? Instruct. Sci. 37, 365–374. doi: 10.1007/s11251-008-9052-y

CrossRef Full Text | Google Scholar

Woodward, L. J., Lu, Z., Morris, A. R., and Healey, D. M. (2017). Preschool self regulation predicts later mental health and educational achievement in very preterm and typically developing children. Clin. Neuropsychol. 31, 404–422. doi: 10.1080/13854046.2016.1251614

PubMed Abstract | CrossRef Full Text | Google Scholar

Xie, Y., Li, Y., Duan, H., Xu, X., Zhang, W., and Fang, P. (2021). Theta oscillations and source connectivity during complex audiovisual object encoding in working memory. Front. Hum. Neurosci. 15:614950. doi: 10.3389/fnhum.2021.614950

PubMed Abstract | CrossRef Full Text | Google Scholar

Yurgil, K. A., Velasquez, M. A., Winston, J. L., Reichman, N. B., and Colombo, P. J. (2020). Music training, working memory, and neural oscillations: a review. Front. Psychol. 11:266. doi: 10.3389/fpsyg.2020.00266

PubMed Abstract | CrossRef Full Text | Google Scholar

Zarate, J. M., Ritson, C. R., and Poeppel, D. (2012). Pitch-interval discrimination and musical expertise: is the semitone a perceptual boundary? J. Acoust. Soc. Am. 132, 984–993. doi: 10.1121/1.4733535

PubMed Abstract | CrossRef Full Text | Google Scholar

Zhang, J. D., Schubert, E., and McPherson, G. E. (2020). Aspects of music performance that are most highly related to musical sophistication. Psychomusicol. Music Mind Brain 30:64. doi: 10.1037/pmu0000252

CrossRef Full Text | Google Scholar

Zuk, J., Benjamin, C., Kenyon, A., and Gaab, N. (2014). Behavioral and neural correlates of executive functioning in musicians and non-musicians. PLoS One 9:e99868. doi: 10.1371/journal.pone.0099868

PubMed Abstract | CrossRef Full Text | Google Scholar