The connections between musical expertise, working memory (WM), and intelligence have been examined repeatedly in recent years with, to some extent, conflicting evidence. For this reason, more studies are needed that can clarify this matter. The present study investigated connections between musical expertise, WM, and intelligence. Fifty musicians and 50 nonmusicians solved tasks to measure WM (phonological and visuospatial) and intelligence (crystallized and fluid). The results provided further evidence that musical expertise is associated with a superior phonological WM, but not with a superior visuospatial WM or crystallized and fluid intelligence.

Music performance and music listening are processes that offer many starting points for the investigation of cognitive processes, e.g., memory processes. While music performance requires different sensory-motor skills depending on the specific instrument (e.g., singing vs. playing the piano), cognitive processing of music heard is a process that is relevant for all musicians as well as nonmusicians. For example, music heard is analyzed in terms of auditory features (melodic and rhythmic grouping), syntactic regularities, and several other features, and these analyses involve auditory sensory memory, WM, as well as long-term memory (Koelsch, 2011). These processes take place in both musicians and nonmusicians, but due to their long experience in instrumental practice which involves music-related perceptual and memory processes, musicians are assumed to be experts in music perception and memory as well (Münte et al., 2002).

According to Posner (1988), an expert is a person who consistently demonstrates outstanding achievement in an activity. Moreover, experts undergo long and intensive training (e.g., Talamini et al., 2022). Acquired domain knowledge—that is, explicit and implicit memory—has been the most prominent explanation for the superiority of expert performance (e.g., Jäncke, 2009; Lehmann & Gruber, 2006): musicians’ musical knowledge (theoretical and practical) “is represented in an elaborated format that allows quick access to relevant information and supports flexible reactions to domain-specific tasks” (Lehmann & Gruber, 2006, p. 463). If we understand musicians as experts in music performance as well as music perception and memory, as explained above, it is not surprising that musicians solve music-related tasks (e.g., pitch discrimination or detection of changes in auditory patterns) better and faster than nonmusicians (e.g., Kraus & Chandrasekaran, 2010; Tervaniemi et al., 2005). Beyond this, there is an extensive discussion in the literature about whether musical expertise is also related to more general cognitive abilities such as WM, or even intelligence. The present study focused on connections between musical expertise, WM (both phonological and visuospatial), and intelligence (crystallized and fluid). The most important findings as well as conflicting evidence on this question are summarized in the following sections.

WM is defined as a limited capacity store for retaining information for a short amount of time while performing mental operations on that information (see Baddeley, 2003). According to the Baddeley and Hitch WM model (Baddeley & Hitch, 1974), there are two subsystems that store and transform auditory and visuospatial information: the phonological loop and the visuospatial sketchpad. Baddeley added a further component later, the episodic buffer. It integrates multidimensional information and works as an interface between WM and long-term memory (Baddeley, 2000, 2003). A central executive is assumed to coordinate the subsystems, to focus attention, and to allocate the limited cognitive resources (Baddeley, 2003).

WM plays a crucial role in the processing of music because music unfolds in time. Working memory processes enable the listener to relate presently perceived sounds to those perceived before and thus, to perceive musical structures such as the key tone of a song or repetitions of musical phrases. WM involves the temporary storage and targeted processing of information (e.g., chunking), and the ability to adjust incoming information with information in memory stores (cf. Baddeley, 2003). The central executive is significant for music processing because of its function as an interface between WM and long-term memory. It comes into play when, for example, a melody is compared to another melody stored in long-term memory. While we chose the well-established Baddeley and Hitch (1974) WM model as the basis for our study, we acknowledge that the question of where music is processed in WM has not been finally resolved (see, for example, the hypothesis of a tonal loop, e.g., Berz, 1995). The present study, however, did not aim to examine the architecture of WM per se, but to analyze the relationship between musical expertise and WM.

In our presentation of existing evidence concerning the auditory subsystem of WM, we focus on auditory WM tasks in a broader sense; that is, on tasks involving the processing of auditory material which includes music as well as spoken words or digits. Musicians’ elaborate knowledge of musical rules (Koelsch et al., 1999) appears to be connected with an increased ability to organize and manipulate musical information within WM (e.g., Aleman et al., 2000; Vuvan et al., 2020). For example, George and Coch (2011) analyzed the relationship between music training and WM by investigating both neural and behavioral aspects of WM in musicians and nonmusicians. Behaviorally, musicians had higher scores on standardized subtests of what the authors called “auditory WM” (Digits Forward, Letter Forward). Neurally, musicians showed an increased sensitivity to the auditory standard/ deviant difference in an oddball task and faster updating of “auditory WM” (shorter latency of P300). These findings might indicate a connection between selective attention to auditory stimuli and music training (Kaganovich et al., 2013; Strait & Kraus, 2011; Vuvan et al., 2020). Talamini and colleagues (2022) suggested that the advantage of musicians for the processing of auditory material occurs in domains close to the trained domain. In the context of the Baddeley and Hitch (1974) WM model, where music as well as language are assumed to be processed in the phonological loop, the proximity of music and language is much closer than that of music and visuospatial information. Matching this idea, there is evidence that musicians have better memory skills compared to nonmusicians for musical memory tasks (e.g., Schulze et al., 2012), but also for verbal memory tasks (e.g., Hansen et al., 2013; Talamini et al., 2016).

However, there are a few studies that found no connection between music training and performance in auditory memory tasks (e.g., Williamson et al., 2010). For example, Suárez and colleagues (2016) did not find differences between musicians and nonmusicians in a forward digit span task and non-word recognition task. Nonetheless, in summary, most studies found that music training and performance in musical and verbal memory tasks are positively associated suggesting a superior phonological WM capacity in musicians compared to nonmusicians (Hansen et al., 2013; Koelsch et al., 2009; Vuvan et al., 2020).

Existing studies that investigated the relation of music training and visuospatial skills and their processing through the visuospatial sketchpad of WM provide conflicting evidence. Whereas some studies found better visuospatial memory in children with music training (e.g., Lee et al., 2007), others failed to find any effect (e.g., Rickard et al., 2012). The same is true among adults. For example, Jakobson and colleagues (2008) found that musicians performed significantly better than nonmusicians on a visual learning test. Suárez and colleagues (2016) could show that musicians outperformed nonmusicians in tasks related to visual-motor coordination, visual scanning ability, visual processing speed, and spatial memory suggesting that musicians promote these skills through their training (sight reading in particular). However, the majority of studies did not find a positive association of musical expertise and visuospatial processing (e.g., Brandler & Rammsayer, 2003; Hansen et al., 2013), suggesting that the connection between WM and music training may only exist in tasks for the auditory domain (e.g., Hansen et al., 2013; Talamini et al., 2022). This assumption was supported in the meta-analysis by Talamini and colleagues (2017), which showed that musicians’ WM advantage was large with tonal stimuli, moderate with verbal stimuli (larger when delivered auditorily suggesting an importance of the presentation modality), but small or null with visuospatial stimuli. That is, in summary, most studies found that music training and performance in visuospatial memory tasks are not positively associated.

In addition to WM, intelligence is also considered to be associated with musical expertise. According to Schellenberg (2004, 2006), music training could enhance individuals’ general intelligence, which promotes many cognitive and academic abilities (Deary et al., 2007). General intelligence (Spearman, 1904) is differentiated into crystallized and fluid intelligence (Cattell, 1963). Existing studies suggest that the association of musical expertise seems to mainly exist with fluid intelligence (e.g., Degé et al., 2011). Degé and colleagues (2011) found evidence for a positive association between music training and selective attention, set shifting, planning, and inhibition. They suggested that, to some extent, the association between music training and fluid intelligence might be explained by the positive influence music training has on executive functions (EFs). EFs are “a collection of top-down control processes used when going on automatic or relying on instinct or intuition would be ill-advised, insufficient, or impossible” (Diamond, 2013, p. 136) and include inhibition, WM, and cognitive flexibility (Miyake & Friedman, 2012). Please note that WM is considered as a subcomponent of EFs within EF research (Diamond, 2013), while the term central executive includes inhibitory control and cognitive flexibility into the model of WM (Baddeley & Hitch, 1994, cf. Diamond, 2013), a controversy that cannot be resolved in this paper. According to Friedman et al. (2006), EFs are mainly related to fluid intelligence and are considered in this context in the present paper. However, it has been discussed in the literature whether the association between music training and intelligence might be more general: Moreno and colleagues (2011), for example, reported links between music training and verbal (i.e., crystallized) intelligence. Children showed higher scores in a vocabulary knowledge test after 20 days of a short music training program. The authors assumed a possible overlap of music expertise and cognitive processes involved in language in general (Koelsch et al., 2009) and in other cognitive activities (Patel, 2009).

However, in all correlational relationships found, the question of causality remains open. Schellenberg (2019) argued that there have been “systematic interpretive biases in research on associations between music training and nonmusical variables” (p. 475) mainly because correlational evidence is often misinterpreted as a causal relationship. Genetic factors influence the link between musical training and general cognitive skills as well (e.g., Gobet, 2016; Mosing et al., 2016). For example, Swaminathan and colleagues (2017) found no association between music training and general intelligence after controlling for music aptitude. They suggest that individual differences in music aptitude could be responsible for associations between music training and intelligence. This means that pre-existing differences are assumed to play a role in the associations found (e.g., Schellenberg, 2019). The recent multilevel meta-analysis by Sala and Gobet (2020) supports this conclusion: they found no causal effect of music training on cognitive skills. In contrast to Sala and Gobet (2020), the meta-analysis by Cooper (2020) reported a moderate effect of music training on cognitive measures in school children; however, the effect became nonsignificant in laboratory settings. Bigand and Tillmann (2022) reanalyzed Sala and Gobet’s (2020) data and found a significant effect of music training on intelligence when differentiating between near and far transfer effects. They argue that meta-analyses, while statistically powerful, depend on the inclusion or exclusion of factors in the analyses, and in turn may underestimate or overestimate effects (Bigand & Tillmann, 2022).

A recent study by Vincenzi et al. (2022) added a factor that had so far been ignored, namely the differentiation between professional musicians and nonprofessionals with music training. They used tests for musical ability, intelligence, and personality among a large sample of professional musicians, nonprofessionals with music training, and people with no music training. Their somewhat surprising result was that professional musicians had the same average intelligence results as nonmusicians, while only nonprofessionals with music training had significantly better results. The authors propose that “individual differences in musical ability, personality, and cognitive ability, in combination with contextual factors […] jointly influence developmental trajectories of musical experience” (Vincenzi et al., 2022, p. 7).

To sum up, the findings regarding connections between musical expertise, WM, and intelligence yield conflicting evidence. There are studies speaking in favor and studies speaking against positive associations between musical expertise, WM, and intelligence. While meta-analyses are a very good tool for summarizing existing evidence, there is still a need for studies investigating the associations between all three variables with well-established testing procedures and with a sufficient sample size of musicians and nonmusicians to further clarify this matter.

Based on existing studies showing that musicians’ WM advantages were found to be large with tonal stimuli, moderate with verbal stimuli, and small or null with visuospatial stimuli (e.g., Talamini et al., 2017), we aimed to show that music training is positively associated with phonological but not with visuospatial WM performance. The Operation Span Task (OSPAN; Kane et al., 2004; Turner & Engle, 1989; Unsworth et al., 2005) for the phonological loop and the Symmetry Span Task (SSPAN; Kane et al., 2004; Shah & Miyake, 1996) for the visuospatial sketchpad were used. Both tasks were chosen because they are complex measures of WM with good statistical values for reliability and validity (see below), which is why they are often used in psychological research (e.g., Redick et al., 2012; Shipstead et al., 2016; Vuvan et al., 2020). Also, the present study tried to go beyond previous findings in WM research: the relationship between music training and intelligence (crystallized and fluid) was examined to incorporate an additional variable in the same study. The present study was a correlational study and therefore did not aim to prove causality.

Musicians and nonmusicians solved complex span tasks to capture the capacity of phonological and visuospatial WM as well as intelligence tests to capture crystallized and fluid intelligence. Based on previous studies (e.g., Degé et al., 2011; George & Coch, 2011; Sala & Gobet, 2017a, 2017b, 2020; Talamini et al., 2016; Talamini et al., 2017), we hypothesized:

  1. Musicians achieve higher scores in their performance of phonological WM compared to nonmusicians.

  2. Musicians and nonmusicians do not differ in their performance of visuospatial WM.

  3. Musicians and nonmusicians do not differ in their performance of crystallized intelligence.

  4. Since we worked with musicians with a high level of music training and the potential for a professional career, we assumed that musicians and nonmusicians do not differ in their performance of fluid intelligence.

Participants

Fifty-three musicians (26 females, age range = 17–55, M = 30.15 years, SD = 10.00 years) from various musical institutions and 51 nonmusicians (26 females, age range = 17–51, M = 27.33 years, SD = 7.70 years) initially participated in this experiment. Musicians and nonmusicians were matched in terms of age, gender, and years of formal education as well as regarding the distribution of academic degrees. All participants had the German Abitur. The distribution of college degrees (BA, MA, Diploma) was comparable between the groups, t(98) = 0.19, p = .85. All participants self-reported to have normal hearing and normal or corrected-to-normal vision. None of the musicians reported to have absolute pitch. All participants were given information about the general research question (“You are taking part in a study examining possible differences between musicians and nonmusicians”). A total of four participants (three musicians, one nonmusician) were excluded from the data analysis. Of the musicians, two did not meet the mentioned criteria we set to be considered “musical experts” (see below), and one participant was unable to fully perceive the presented auditory material. The nonmusician was excluded because she subsequently disclosed that she had learned a musical instrument and thus did not fulfill our nonmusician criteria. The final sample comprised 100 participants1: 50 musicians (25 females, age range = 17–55, M = 30.12 years, SD = 9.81 years) and 50 nonmusicians (25 females, age range = 17–51, M = 27.30 years, SD = 7.78 years). There was no significant difference in age, t(98) = 1.59, p = .11. Musicians represented a wide range of musical specializations: 5 musicians were drummers, 8 were pianists, 10 were guitarists, 15 were violinists, 6 played wind instruments (horn, saxophone), and the remaining 6 were singers. Thirty-eight had started music training before the age of 6, and the remaining 12 had started music training before the age of 10.

Musicians were categorized by a musical expertise questionnaire (based on the Ollen Musical Sophistication Index, OMSI; Ollen, 2006). The OMSI was chosen because it is widely used in music psychology literature to categorize musicians and nonmusicians (e.g., Zhang & Schubert, 2019), and because it is a time-saving instrument for the valid and reliable recording of musical expertise (e.g., Ollen, 2006; Zhang & Schubert, 2019). A musician was defined as an individual who met two or more of the following criteria: a) they were employed primarily as a musician, b) they had a minimum of ten years of music training (= musical practice duration [MPD]; range = 12–46 years; M = 21.02 years, SD = 9.85 years), or c) they averaged at least one to two hours of practice per day (= musical practice intensity [MPI]; range = 1.5–12 hours; M = 3.54 hours, SD = 2.00 hours). Nonmusicians (mostly students and employees from physics, computer sciences, and psychology) were defined as those who had never played a musical instrument and did not have any special musical education besides normal school education.

The experiment protocols were done in accordance with the Declaration of Helsinki (1964) and approved by the Ethical committee of the Department of Psychology, Humboldt-Universität zu Berlin. A written parental consent was required for underage participants. Participation was remunerated with either course credits for psychology students or monetary payment for all other participants.

Working Memory

Participants’ auditory and visuospatial WM was measured with complex span tasks of which we used computer-based versions: The Operation Span Task (OSPAN; Kane et al., 2004; Turner & Engle, 1989; Unsworth et al., 2005) for the phonological loop, and the Symmetry Span Task (SSPAN; Kane et al., 2004; Shah & Miyake, 1996) for the visuospatial sketchpad (see Baddeley’s WM model, e.g., Baddeley, 2003). Both tasks are complex measures of WM and frequently used (e.g., Redick et al., 2012; Shipstead et al., 2016). Both tests have also already been used when comparing musicians and nonmusicians (e.g., Vuvan et al., 2020).

Both the OSPAN and the SSPAN task were created to measure the trade-off between capacity and processing resources. They capture the ability to store, maintain, and retrieve isolated phonological and visuospatial stimuli in the face of interfering tasks (Kane et al., 2004). Both tests are characterized by good internal consistency (Cronbach’s alpha). It is between α = .75 and α =. 84 for the OSPAN (Redick et al., 2012), and between α = .63 and α =. 76 for the SSPAN (Redick et al., 2012). The retest reliability for the total score is r = .77 (OSPAN) and r = .62 (SSPAN), and the construct validity is r = .38 for both tests (Redick et al., 2012; Unsworth et al., 2005).

The OSPAN task requires participants to verify the correctness of mathematical operations while memorizing a set of unrelated letters. The operation-letter pairings were visually presented in sets of two to seven items. Participants were instructed to recall the letters in the presented order.

The SSPAN task is similarly structured: participants were required to judge visually presented matrix patterns for vertical symmetry while memorizing the sequential locations of red squares shown within an empty 4 x 4 matrix. The symmetry-square pairings were presented in sets of two to five items.

Absolute scores (number of correctly recalled sets) for musicians and nonmusicians serve as dependent variables.

Intelligence

To analyze the association between intelligence and musical expertise, the Mehrfachwahl-Wortschatz-Intelligenztest (MWT-B [Multiple-Choice Vocabulary Intelligence Test]; Lehrl, 2005) for crystallized intelligence and, following Jaeggi and colleagues (2010), the ten-minute version of the Bochumer Matrizentest (BOMAT [Bochum Matrices Test]; Hossiep et al., 1999) for fluid intelligence were used. Both tests are reliable measures of crystallized and fluid intelligence respectively and frequently used (e.g., Elmer et al., 2017; Jaeggi et al., 2010). We chose both tests to be able to test intelligence in an efficient way.

The average correlation coefficient between the MWT-B and other intelligence tests is r = .71 (Lehrl, 2005). The retest reliability after 14 months is r = .87 (Lehrl, 2005). The BOMAT is characterized by good internal consistency (Cronbach’s alpha) of α = .92 (Hossiep et al., 1999). The retest reliability is r = .73 (Hossiep et al., 1999).

The MWT-B is a vocabulary test that includes 37 items. Each item consists of five words: one real word and four pseudo-words. Participants were asked to find and mark the real word.

The BOMAT consists of visual analogy problems of increasing difficulty. Each problem presents a matrix of patterns in which one pattern is missing. The task is to find and mark the missing pattern among a set of given response alternatives.

The number of correctly answered matrices, as well as the number of correctly identified words for musicians and nonmusicians serve as dependent variables.

Procedure

The tests described in the present study took about 1–1.5 hours. They were part of a complex study that lasted about 3–4 hours and took place in the examination rooms of the Department of Cognitive Psychology of the Humboldt-Universität zu Berlin. The test procedure was as follows. Participants first answered the musical expertise questionnaire (approximately 3 min). Participants then took part in an EEG experiment.2 Afterwards, they were asked to complete the two WM tasks (randomized order; OSPAN approximately 35 min, SSPAN approximately 20 min). The two intelligence tests followed (also randomized order; MWT-B approximately 2–5 min, BOMAT 15 min [5 min practice + 10 min test]). To ensure that participants remained attentive and motivated, they were given an opportunity to rest and were given a snack break between single tasks.

Statistical Analyses

All statistical analyses were performed with the Statistical Package for the Social Sciences Version 22 software program (SPSS inc., Chicago, USA) and the JASP computer software (JASP Team, Version 0.14.1). The default JASP Cauchy priors were used. Independent sample t-tests and the Bayesian equivalents were calculated for the mean psychometric test scores in musicians versus nonmusicians. Additionally, Cohen’s d effect sizes were calculated. Pearson’s correlational analyses between musical expertise (musical practice duration [MPD] in years, musical practice intensity [MPI] in hours/day), scores on WM tasks (OSPAN, SSPAN), and scores on intelligence tests (MWT-B, BOMAT) were performed for musicians only. Alpha level was set at .05 for the correlational analyses and adjusted to .0125 for the four single independent sample t-tests.

Musicians (n = 50) and nonmusicians (n = 50) were compared in terms of their cognitive test performance (scores). Statistics of the cognitive tests are displayed in Table 1.

Table 1.

Scores of Cognitive Performance Tests

Cognitive performance testM (n = 50)NM (n = 50)t-valuesP valuesBF10Cohen’s d
OSPAN 54.90 (1.48) 40.02 (2.21) 5.60 < .001 62988.30 −1.12 
SSPAN 24.46 (1.35) 22.06 (1.20) 1.33 .19 .46 −.27 
MWT-B 80.05 (1.35) 79.08 (1.25) .53 .60 .24 −.11 
BOMAT 69.83 (2.42) 63.98 (2.45) 1.70 .09 .76 −.34 
Cognitive performance testM (n = 50)NM (n = 50)t-valuesP valuesBF10Cohen’s d
OSPAN 54.90 (1.48) 40.02 (2.21) 5.60 < .001 62988.30 −1.12 
SSPAN 24.46 (1.35) 22.06 (1.20) 1.33 .19 .46 −.27 
MWT-B 80.05 (1.35) 79.08 (1.25) .53 .60 .24 −.11 
BOMAT 69.83 (2.42) 63.98 (2.45) 1.70 .09 .76 −.34 

Note. OSPAN/ SSPAN in absolute scores = sum of correctly answered trials; BOMAT/ MWT-B in percent; SEM in parentheses in musicians (M) and nonmusicians (NM); n = participants, BF10 = Bayes factor.

In line with hypothesis 1, musicians displayed significantly better auditory WM (OSPAN) compared to nonmusicians. The Bayes factor3 shows extreme evidence for this association (BF10 > 100). Musicians correctly recalled more items than nonmusicians (see Figure 1). The group difference in OSPAN remained significant in an ANCOVA with fluid intelligence (BOMAT) held constant, F(1, 97) = 28.49, p < .001, partial η2=.227. In line with hypotheses 2 - 4, musicians and nonmusicians did not significantly differ in their visuospatial WM (SSPAN), crystallized intelligence (MWT-B), and fluid intelligence (BOMAT).

Figure 1.

Musician status in the OSPAN task (number of letters correctly recalled).

Figure 1.

Musician status in the OSPAN task (number of letters correctly recalled).

Close modal

Due to the operationalization of musical expertise chosen in the present study, we calculated two measures of musical expertise in the musician sample only: musical practice duration (MPD) in years and musical practice intensity (MPI) in hours/day. Pearson’s correlational analysis between musical expertise (MPD in years, MPI in hours/ day), scores on WM tasks (OSPAN, SSPAN), and scores on intelligence tests (MWT-B, BOMAT) were performed for musicians only (Table 2).

Table 2.

Pearson’s Correlations and the Bayesian Equivalents Between Major Variables of Interest in Musicians

VariablesMSEM123456
1 gender 1.5 .07        
2 MPD 21.02 1.39 .080      
   BF10 .205      
3 MPI 3.54 .28 −.152 .146     
   BF10 .302 .291     
4 OSPAN 54.90 1.48 −.056 .031 .086    
   BF10 .190 .180 .210    
5 SSPAN 24.46 1.35 −.151 −.274 .004 .291*   
   BF10 .299 1.068 .176 1.361   
6 MWT-B 80.05 1.35 −.051 .398** .086 .220 .057  
   BF10 .187 9.544 .209 .553 .190  
7 BOMAT 69.83 2.42 .004 −.254 −.347* .051 .185 .105 
   BF10 .176 .825 3.406 .187 .395 .228 
VariablesMSEM123456
1 gender 1.5 .07        
2 MPD 21.02 1.39 .080      
   BF10 .205      
3 MPI 3.54 .28 −.152 .146     
   BF10 .302 .291     
4 OSPAN 54.90 1.48 −.056 .031 .086    
   BF10 .190 .180 .210    
5 SSPAN 24.46 1.35 −.151 −.274 .004 .291*   
   BF10 .299 1.068 .176 1.361   
6 MWT-B 80.05 1.35 −.051 .398** .086 .220 .057  
   BF10 .187 9.544 .209 .553 .190  
7 BOMAT 69.83 2.42 .004 −.254 −.347* .051 .185 .105 
   BF10 .176 .825 3.406 .187 .395 .228 

Note. N = 50, MPD = musical practice duration (years active), MPI = musical practice intensity (hours/day), M = mean, SEM = standard error of the mean, BF10 = Bayes factor, **p < .01, *p < .05.

Only significant correlations relevant to content are reported here. MPD (in years) was positively correlated with crystallized intelligence (MWT-B; see Figure 2). MPI (hours/ day) was negatively correlated with fluid intelligence (BOMAT; see Figure 3). The Bayes factors show moderate evidence for both associations (BF10 > 3).

Figure 2.

Relationship between musical practice duration (MPD; in years) and MWT (Version B) performance (in percent) in musicians.

Figure 2.

Relationship between musical practice duration (MPD; in years) and MWT (Version B) performance (in percent) in musicians.

Close modal
Figure 3.

Relationship between musical practice intensity (MPI; in hours/day) and BOMAT performance (in percent) in musicians.

Figure 3.

Relationship between musical practice intensity (MPI; in hours/day) and BOMAT performance (in percent) in musicians.

Close modal

The present study aimed to show that music training is positively associated with phonological, but not with visuospatial WM performance, or crystallized and fluid intelligence (e.g., Degé et al., 2011; George & Coch, 2011; Sala & Gobet, 2017a, 2017b, 2020; Talamini et al., 2016; Talamini et al., 2017, Vincenzi et al., 2022). The associations between cognitive performance (WM; intelligence) and musical expertise were examined by means of a correlational study. Musicians and nonmusicians solved complex span tasks to capture the capacity of phonological and visuospatial WM as well as tests to capture crystallized and fluid intelligence. Regarding the hypotheses, the study yielded the following main results. First, musicians outperformed nonmusicians in their phonological WM (OSPAN). Second, musicians and nonmusicians did not significantly differ in their visuospatial WM (SSPAN), crystallized intelligence (MWT-B), and fluid intelligence (BOMAT).

As expected, musicians scored significantly higher on the OSPAN task compared to nonmusicians. This finding is a further indication that musical expertise is correlated with phonological WM performance (e.g., D’Souza et al., 2018). Vuvan and colleagues (2020) proposed that music training may be related to an increase in WM capacity. That is, higher scores on measures of auditory WM might indicate that musicians may be able to process more information in real time leading to a faster comprehension of the stimuli. Koelsch and colleagues (1999) showed that musicians compared to nonmusicians have superior pre-attentive auditory processing: musicians’ neural activity increased during enhanced sensory memory performance. It is argued that long-term music training strengthens cognitive functions, which in turn benefit auditory perception (e.g., Strait et al., 2010). Another line of argument is that musicians excel at WM tasks that require cognitive processes similar to those they have practiced through their training (e.g., Ericsson & Kintsch, 1995; Talamini et al., 2022). Hence, the cognitive benefit in musicians might be auditory-domain specific, and partially a consequence of music training (e.g., D’Souza et al., 2018; Koelsch et al., 1999). The present study focused on the relationship between musical expertise and auditory WM by means of the examination of the phonological loop. The phonological loop is relevant for the processing of musical as well as verbal information (e.g., Baddeley, 1986; Koelsch et al., 2009), which is what the OSPAN requires (e.g., D’Souza et al., 2018; Vuvan et al., 2020). We conclude that our results support previous findings: musicians show superior phonological WM performance compared to nonmusicians (e.g., George & Coch, 2011; Talamini et al., 2016, Talamini et al., 2017).

In line with our hypothesis, musicians and nonmusicians did not significantly differ in their visuospatial WM performance (SSPAN). Our results support the existing literature, which reported that musical expertise did not enhance visuospatial processing (e.g., Chan et et al., 1998; Sala & Gobet, 2017a, 2017b; Strait et al., 2010; Talamini et al., 2017). Music can be understood as a symbolic language and is read, stored, and retrieved as such. Musicians and nonmusicians might “translate”, that is, recode the visually presented musical symbols (Gaab & Schlaug, 2003) into (inner) sounds. This recoding is mediated by phonological, but not visuospatial WM. We conclude that while reading music notation might be considered as a process involving visuospatial processing, musical training does not seem to be connected to high visuospatial memory skills.

In line with our hypothesis, musicians and nonmusicians did not significantly differ in their crystallized (MWT-B) and fluid intelligence performance (BOMAT). In the MWT-B, both musicians and nonmusicians achieved high scores; that is, both groups had comparably high crystallized intelligence. The improvements in reading skills and verbal intelligence through music training reported by Moreno and colleagues (2011) were found in children, not in adults. In contrast to children, adults have acquired extensive declarative knowledge through long and in-depth learning, experience, and acculturation (e.g., Schneider & McGrew, 2018). Therefore, they can differentiate well between correct words and pseudo-words (as required in the MWT-B). This was also evident in the musicians and nonmusicians we examined.

The differences between musicians and nonmusicians in the BOMAT were not statistically significant (p = .09). We calculated Bayesian statistics in addition to the t-tests to test more precisely for group differences. The resulting Bayes factor was .76 and thus close to one providing neither evidence for the null hypothesis nor for the alternative hypothesis (e.g., Andraszewicz et al., 2015; Dienes, 2014). However, the results of the present study relate to the meta-analyses by Sala and Gobet (2017a, 2017b, 2020) who found that musical expertise has no influence on general intelligence.

By means of a further quantitative differentiation of musical expertise (musical practice duration in years and musical practice intensity in hours/day), its connection with WM and intelligence should be explored in more detail. Within the sample of musicians, correlational analyses between musical practice duration (MPD), musical practice intensity (MPI), scores on WM tasks, and scores on intelligence tests were performed. MPD (in years) was positively correlated with crystallized intelligence (MWT-B). That is, a longer practice experience seems to be related to musicians’ crystallized intelligence. Since crystallized intelligence primarily reflects world knowledge, this finding is not surprising: music training involves not only sensory-motor skills, but also declarative knowledge about music theory and composers. This means that music experience is also a way to accumulate more knowledge (e.g., Schneider & McGrew, 2018), which might be the reason for the correlation found here. MPI (hours/day) was negatively correlated with fluid intelligence (BOMAT). That is, a lower practice intensity seems to be related to higher fluid intelligence in musicians. This connection is a surprising finding that we did not expect. It could be an indication that musicians with higher fluid intelligence have better practice strategies that enable them to practice less to achieve a high quality of performance. That is, musicians with higher fluid intelligence might be more efficient. Such a connection has already been shown in the domain of mathematics, where mathematicians were more efficient the higher their mathematical expertise was (e.g., Bornemann et al., 2010). Neither MPD nor MPI correlated with WM (OSPAN, SSPAN). This might be an indication that genetic factors are involved in the development of WM as well as musical abilities (e.g., Schellenberg, 2019), but would need further investigation to strengthen this hypothesis.

The results of individual studies must always be considered in relation to the tests used and the samples tested. For this reason, the current findings are limited to the definition of musical expertise (musical practice duration in years, musical practice intensity in hours/day) and tasks used here. We did not differentiate between instrumental and vocal musicians, as effects of music training on cognition were previously found for both (e.g., Bialystok & DePape, 2009). In order to reduce the effects of possible confounding variables, we ensured that our musician and nonmusician groups contained the same number of males and females and were matched in age and years of formal education as well as regarding the distribution of qualifications. Nevertheless, nonmusicians came from different courses of study (i.e., they were heterogeneous in terms of specific knowledge and skills), which could have had an influence. Moreover, please note that we only present correlational evidence. No evidence can be provided for the directionality of the association between musical expertise and (superior) phonological WM performance. Possible explanations are: First, music training may affect phonological WM performance. Second, individuals with higher phonological WM abilities may be more willing and able to engage and persist in musical education (e.g., Elpus, 2013; Mosing et al., 2016). Third, music training may increase preexisting differences by drawing on the phonological WM abilities and thereby possibly improving them. The differences between musicians and nonmusicians reported here are likely the result of an interaction between training effects and genetic influences (e.g., Mosing et al., 2016). This interaction results in multifaceted individual differences in musicians and nonmusicians. Also, it is important to note that additional variables not considered in the present study, such as, for example, socioeconomic status, might also play an important role in pursuing music training. Longitudinal intervention studies and replications of studies are needed to answer these questions. Although exact replications of studies are not an easy task, it is important to double-check experimental results in order to increase their validity and reliability, and thus to strengthen confidence in scientific theories and empirical results (e.g., Frieler et al., 2013; Martin & Clarke, 2017). Moreover, negative results can be just as helpful (i.e., informative and valuable) as positive results (see Begley & Ellis, 2012) because science progresses by self-correction (Martin & Clarke, 2017).

The present study succeeded in confirming previous findings on superior phonological WM performance in musicians. That is, we added further evidence to the literature that musical expertise is associated with a superior phonological WM, but not with a superior visuospatial WM or crystallized and fluid intelligence (see also Sala & Gobet, 2017a, 2017b, 2020).

This research was funded by the Humboldt-Universität zu Berlin (CA and EvdM) and the Elsa-Neumann-Stipendium des Landes Berlin as a cooperation project with the Katholische Universität Eichstätt-Ingolstadt (KS). We are grateful to the participants for supporting our research. We thank Christina Rügen for her dedicated work with the participants. We thank Christina Reimer, Johannes Meixner, Antonia Papadakis, and Guido Kiecker for all their support. We thank Antonia Papadakis and Lindsay Flint for the language editing.

No potential conflict of interest was reported by the authors.

1

The original power analysis for the entire study using G*Power resulted in a sample size of 108 participants (medium effect f = .30; α err prob = .05; power 1-β = .92). Given our final sample of 100 participants, we calculated a post hoc power analysis using G*Power to adjust the power of our data (medium effect f = .30; α err prob = .05; power 1-β = .88).

2

Participants worked on an individual JND threshold task and on the Beat Perception and Melodic Memory tasks of the Gold-MSI (Müllensiefen et al., 2014), followed by a passive oddball paradigm. During the test period, an EEG was recorded in addition to behavioral parameters. The results of these tasks are reported elsewhere (see Arndt et al., 2020).

3

Andraszewicz and colleagues (2015) proposed the following interpretations of the Bayes factor (H1 compared to H0): 1 no evidence, 1–3 anecdotal evidence, 3–10 moderate evidence, 10–30 strong evidence, 30–100 very strong evidence, > 100 extreme evidence.

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