Why do some people learn music more quickly than others?


Intelligence could play a role, according to a Michigan State University study that investigated the early stages of learning to play piano.

Published in the journal Intelligence, the study may be the first to examine the relationship between intelligence, music aptitude and growth mindset in beginner pianists.

Growth mindset refers to whether students believe they can improve basic abilities, like piano ability.

“The strongest predictor of skill acquisition was intelligence, followed by music aptitude,” said Alexander Burgoyne, a doctoral candidate in cognition and cognitive neuroscience.

“By contrast, the correlation between growth mindset and piano performance was about as close to zero as possible.”

In the study, 161 undergraduates were taught how to play “Happy Birthday” on the piano with the help of a video guide.

After practice, the students performed the 25-note song multiple times.

Three MSU graduate students judged the performances based on their melodic and rhythmic accuracy.

There were striking differences in the students’ skill acquisition trajectories. Some learned quickly, earning perfect marks within six minutes of practice.

Others performed poorly at first but improved substantially later.

By comparison, some seemed to fade as if they had lost their motivation and others never figured it out, performing poorly throughout the study.

So why did some students fail while others succeeded?

To find out, the researchers gave the students tests of cognitive ability that measured things like problem-solving skills and processing speed, and tests of music aptitude that measured, for example, the ability to differentiate between similar rhythms.

They also surveyed their growth mindset.

“The results were surprising, because people have claimed that mindset plays an important role when students are confronted with challenges, like trying to learn a new musical instrument,” Burgoyne said. “And yet, it didn’t predict skill acquisition.”

It follows a recent review of mindset research that found a weak relationship between growth mindset and academic achievement.

Perhaps more concerning, that study found interventions designed to boost achievement by encouraging children to believe they can improve their basic abilities may be fruitless.

That said, results will likely differ for those with greater skill.

“Our study examined one of the earliest stages of skill acquisition,” Burgoyne said.

“Early experiences can be formative, but I would caution against drawing conclusions about skilled musicians based on our study of beginners.”

But applied generally, the study’s findings may be helpful in education.

It follows a recent review of mindset research that found a weak relationship between growth mindset and academic achievement.

Perhaps more concerning, that study found interventions designed to boost achievement by encouraging children to believe they can improve their basic abilities may be fruitless.

That is, when those interventions successfully altered students’ mindsets, there wasn’t a significant effect on academic achievement.

ntroduction and Theoretical Background

Music has become much more readily available to the public in the past decades. One influencing factor was the increasing availability of music: whilst in the past one was in need of CDs or tapes and an according player, nowadays music can be played digitally on many different devices such as computers, mobile phones or iPods. Furthermore, the choice of available songs is almost endless due to music portals. This makes it possible to select suitable songs for different situations, such as relaxing songs for a cozy evening or activating songs before going out. Due to these advances in music technology, learning with background music has received more and more attention over the last decade (e.g., Schwartz et al., 2017).

For some situations it seems intuitive to think that music would help to enhance our experience – but how do music and learning fit together? At present the effects of background music while learning and the mechanisms behind this are unclear. On the one side, music seems to have a positive (Mozart effect; Rauscher et al., 1993) and stimulating effect (arousal-mood-hypothesis; Husain et al., 2002), which could improve learning. On the other side, background music could lead to an additional burden on working memory (seductive detail effect; e.g., Rey, 2012), thus hindering learning. To be able to simultaneously deal with the learning material and the background music, the learner’s working memory capacity is a crucial factor to consider.

Background Music

In this study we define background music as music that plays in the background while studying, i.e., when reading a text. Learners are intended to listen to this music but there is no relation between the music itself and the main task, namely learning the text.

Results of studies investigating the relationship between background music and learning outcomes are varied. While some studies found no effect of background music (e.g., Moreno and Mayer, 2000Jäncke and Sandmann, 2010) others found that it negatively impacted learning outcomes [e.g., Furnham and Bradley, 1997Randsell and Gilroy, 2001Hallam et al., 2002 (study 2)]. Further studies report that it has a positive impact [e.g., Hallam et al., 2002 (study 1); de Groot, 2006], especially on students with learning disabilities (Savan, 1999) or poor spelling skills (Scheree et al., 2000).

Thompson et al. (2011) gave a first hint as to why previous results were so mixed. They revealed that music characteristics like tempo and intensity have an influence on learning outcomes: only soft fast music had a positive influence, whilst loud fast as well as soft slow or loud slow music hindered learning. In addition, instrumental music disturbs learners less than music with lyrics (Perham and Currie, 2014). As each study used their own music and did not control for the characteristics of their music choice, this is one possible explanation for the heterogeneous study results mentioned above. Moreover, it seems plausible that learner’s characteristics such as their musical expertise (Wallace, 1994) or their familiarity with the presented music could also impact their learning.

Importantly, it is not the characteristics of a song per se, but their effects on the learner which influence learning outcomes. These effects on the learner have been explained by different theoretical approaches. These can be grouped into approaches positing either a potentially positive or negative influence on learning outcomes.

The first theoretical perspective explains why background music could positively influence learning and cognitive abilities. Probably the most well-known approach in this field is the so-called Mozart effect (Rauscher et al., 1993). In this study, before completing a task that measured spatial abilities, some participants listened to a Mozart sonata, while others did not listen to any music. Participants in the Mozart condition outperformed the other group. The authors found a direct, positive influence of listening to Mozart sonatas on spatial abilities. They explain these better test results though priming effects. Even though in the experiment the exposition to music took place in advance of the task, the results are transferrable to listening to music while learning. Priming effects should be even stronger during the exposition to the stimulus and decay over time (e.g., Foss, 1982).

This priming explanation, however, was criticized by Husain et al. (2002). They formulated the arousal-mood-hypothesis. It states, that listening to background music does not have a direct influence on cognitive abilities, but affects it through the mediators of arousal and mood. The prerequisite for this assumed mediation is that background music has an impact on arousal and mood, which in turn impact learning outcomes. Moreover, the authors go one step further and postulate that this mediation effect should not only influence spatial abilities, but also cognitive performance.

When considering arousal, Husain et al. (2002) follow Sloboda and Juslin’s (2001) definition, that arousal describes physical activation.

The influence of listening to background music on arousal (for an overview, see Pelletier, 2004) is well-established: Music can increase or decrease arousal, mostly influenced by the tempo of a song (Husain et al., 2002). In addition, there is broad evidence of the impact of arousal on learning (e.g., Kleinsmith and Kaplan, 1963Eysenck, 1976Heuer and Reisberg, 2014). The Yerkes–Dodson law (Yerkes and Dodson, 1908) describes optimal arousal in a learning situation following an inverted U-shaped pattern.

While learners with little arousal are not engaged enough to really invest in the learning process, too much arousal can cause distractive feelings like anxiety. Thus, a medium level of arousal is optimal for learning. In conclusion, a mediation effect of background music over arousal on learning seems probable, as there seems to be an influence of background music on arousal as well as an impact of arousal on learning.

When considering mood, the arousal-mood-hypothesis defines mood as referring to emotions (Sloboda and Juslin, 2001). Several studies have found background music to influence mood (e.g., Juslin and O’Neill, 2001Sloboda and Juslin, 2001Schmidt and Trainor, 2010). Background music leads to different emotions dependent on whether they are composed in a major or minor mode (Husain et al., 2002). Moreover, several theoretical approaches and studies state that mood influences learning (Ilsen, 1984Pekrun, 2006Goetz and Hall, 2013Heuer and Reisberg, 2014Pekrun et al., 2017). In general, positive mood is associated with better learning outcomes (Isen, 2002) while negative mood or boredom hinders learning (O’Hanlon, 1981Pekrun, 2006). Based on this, a mediation effect of mood also seems plausible.

To conclude, Husain et al. (2002) state that besides these two mediation effects (mood and arousal mediating the influence of background music on learning) and in contrast to the Mozart effect, music does not directly influence learning. The authors underpinned this statement by referring to a study by Nantais and Schellenberg (1999). In this study participants listened to a Mozart sonata and to a short story and completed a spatial task after each. Participants were also asked if they liked the sonata or the story better. In general, participants performed better after listening to the stimulus (sonata or story) they preferred. Thus, Husain et al. (2002) reasoned that better cognitive performance when listening to background music is due to the exposure to a pleasant stimulus.

In sum, both the Mozart effect and the arousal-mood-hypothesis state that listening to background music can foster learning, while the arousal-mood-hypothesis also takes characteristics of the melody into account. A piece of music needs to be in the right tempo and mode to be able to evoke the appropriate arousal and mood in the learner.

When investigating arousal and mood evocation, it is not enough to simply measure arousal and mood after learning, but measurements need to be taken before and after learning. Only in this way is it possible to calculate the change in arousal and mood during the learning phase.

Another completely contradictory theoretical perspective describes why background music can also have a negative impact on learning. When learning with background music, the learners have to divide their attention between the learning task and the music. Thus, they have to invest cognitive resources to process the background music in addition to the learning task, as auditive information always gets processed first (Salamé and Baddeley, 1989) and cannot be ignored (Mayer, 2001).

Background music is not related to the task, but can attract the learner’s attention and therefore can be defined as a seductive detail (Rey, 2012). Such information distracts the learner from the main task, i.e., the learning task, and therefore hinders learning.

Hence, it is not surprising that a meta-analysis of the influence of background music that involved many types of music (including different tempi and modes) (Kämpfe et al., 2010) revealed an overall negative impact on learning. Music becomes an unnecessary burden on working memory, which is a crucial point when regarding the limitations of working memory capacity (Miller, 1994Cowan, 2001).

Working Memory Capacity

The importance of working memory and its capacity in a learning situation is due to the fact that all information within a learning situation (including learning material, learning task, and context factors) needs to be processed within working memory. There is an ongoing debate about the structure of working memory. Baddeley (1986) and Cowan (1999) published probably the two most prominent working memory models. As the experimental group in this study has to deal with visual (reading a text) as well as auditive information (listening to background music) we will especially focus on how this information gets processed according to Baddeley’s (1986) and Cowan’s (1999) models.

Baddeley (1986) assumes working memory to be a system with a hierarchical structure: the central executive controls the two subsystems which are phonological loop and visuospatial sketchpad. He postulates that working memory is separated to long-term memory, even though long-term memory can have an influence on processes within working memory.

For example, prior knowledge activated in long-term memory can facilitate the processing and integration of new information in working memory.

Due to different independent subsystems, which work in parallel and all involve their own independent capacity, it is easier to process information of different modalities.

A visual text is processed with the phonological loop after being recoded through subvocal processes. Background music is phonological information as well as it is presented auditory, and thus might overload the phonological loop. However, there is evidence that musical information gets processed in a slightly different way to verbal auditive information (Salamé and Baddeley, 1989).

Different authors assume an additional, subsystem to be responsible for processing background music, which is partly independent from the phonological loop (Deutsch, 1970Rowe et al., 1974Paivio et al., 1975Rowe, 2013).

Referring to this, there is more capacity available while processing music in addition to a visual text as two different subsystems are utilized, compared to the processing of auditive text in addition to a visual text processed in the same subsystem. As such, background music would still interfere with reading, but not as severely as, for example, when verbal auditive information is processed by the same subsystem.

Another approach to working memory was put forward by Cowan (1999) who proposed the embedded-processes model. Working memory in this model is the activated part of long-term memory, without differentiating between the processing of different modalities.

Cowan argues, that the similarity of information has an influence on how much information can be processed simultaneously: the less similar the content and modality of the information is, the easier it is to process them simultaneously.

Concerning instrumental background music and reading a text at the same time, this would mean that instrumental music would be less disruptive compared to music with lyrics or a classical auditive text because of the added verbal aspect. However, processing background music still relies on the same cognitive capacity, thus, hindering learning.

Independent of which model describes working memory better, they both assert that listening to background music while learning requires additional cognitive capacity that could otherwise be invested into the learning process. This is especially important, as working memory capacity is limited.

Working memory capacity can be defined as the number of separate concepts that can be dealt with at the same time in working memory (Cowan, 2012). Cowan (2001) states that 3–4 chunks of information can be stored and manipulated at the same time. A wide variety of studies show an advantage in learning situations for learners with a higher working memory capacity [e.g., Daneman and Carpenter, 1983King and Just, 1991Whitney et al., 1991Rosen and Engle, 1998 (Experiment 1); Alloy and Alloy, 2010]: the more information an individual can deal with simultaneously, the more efficient the learning process. However, listening to background music reduces the available memory capacity for the learning process. How then do background music and working memory capacity interact?

Interaction between Background Music and Working Memory Capacity on Learning

Salamé and Baddeley (1989) postulate firstly, that it is impossible not to process auditive information and secondly, that auditive information is always processed first. Thus, only if working memory capacity is high enough do learners have sufficient capacity to invest in the learning task after processing the auditive information.

In this case, appropriate background music could be of benefit to learners by influencing their mood and arousal level to an optimal state, thereby fostering the learning process. However, even for those learners melodies should be chosen that only pose a small burden on working memory. Comparing instrumental music with songs with lyrics, it seems plausible that when lyrics are present they would need to be additionally processed.

According to Baddeley’s (1986) model, these lyrics are auditive texts that burden the phonological loop, leading to a larger decrease in learning performance compared to an instrumental song.

The same is true for Cowan’s (1999) model, where the lyrics are too similar to the visual text and therefore lead to interferences during learning.

Therefore, when attempting to foster learning for high-capacity learners by improving mood and arousal, one should use a music without lyrics. In this case learners may be able to process the learning material as well as the song.

Therefore, sufficient working memory capacity may compensate for the additional cognitive burden, so that the potential positive effect of the music may benefit the learner.

This is comparable to the ability-as-compensator effect (Mayer and Sims, 1994), where a learner’s ability (in this study: sufficient working memory capacity), is required to deal with a specific element of the instructional design (in this study: Background music).

When learners with low working memory capacity have to process background music there is not enough capacity left to invest in the learning task. Even if the learners were in a perfect learning condition concerning arousal and mood, they would not be able to learn as they simply would not be able to process the information in the learning material in addition to the music.

To our knowledge, there is no empirical evidence of the interaction between background music and working memory capacity on learning outcomes which could support these theoretical assumptions. As we defined background music as a seductive detail, we argue that research on other seductive details in interaction with working memory capacity might be transferrable. Sanchez and Wiley (2006) found, that learners with low working memory capacity were hindered in their learning if learning materials included seductive pictures in addition to the text.

Interestingly, learners with higher working memory capacity were not affected by these pictures, however, their performance did not increased either.

As the pictures used in Sanchez and Wiley’s (2006) experiment were normed to not influence arousal or mood as our experiment does, this result is not contradictory to our assumptions.

A study by Fenesi et al. (2016) found similar results: Learners with low working memory capacity perform worse when presented with irrelevant pictures in addition to learning material.

The cut-off between a working memory capacity that is “too small” and “high enough” depends on the characteristics of the learning material. Highly complex or poorly designed learning tasks burden working memory capacity more than content which is less complex or better designed (Sweller, 2010Sweller et al., 2011).

This indicated that background music should only be considered when the learning material itself is not too demanding. A similar effect is was found in a study by Park et al. (2011) where pictures were used as a seductive detail. The researchers varied the complexity of the main task and found that pictures hindered learning less when the main task was not very demanding, whereas the seductive details effect was revealed with highly demanding tasks.

Learning Outcomes

Besides the complexity of the learning material, the level of learning outcomes could also play an important role. So far, we have discussed learning outcomes in general. However, one can differentiate between different levels of learning outcomes, like recall or comprehension (e.g., Bloom, 1956). For exams it is typically necessary to remember and understand the learning content. Thus, the post-test of this study differentiates between both of these learning outcomes.

To our knowledge no studies as yet differentiate between the influence of background music on recall and comprehension, so we can only establish assumptions on a theoretical basis and turn to results of comparable studies for comparisons. As cited above, in a study by Park et al. (2011) the seductive detail effect depended on task difficulty with easy tasks not affected by seductive details.

Transferring these results to learning with background music and to different levels of learning outcomes, i.e., recall and comprehension, one would expect background music to influence comprehension outcomes but not recall. Easier recall tasks are a smaller burden in working memory so that a learner may be able to process background music simultaneously.

In addition, working memory capacity does not play an important role, as the learner does not need a high capacity. This is also why also the interaction between both factors should not influence recall performance.

However, comprehension tasks are more demanding and are bigger cognitive burdens. In this case, background music should affect comprehension outcomes, as well as working memory capacity. Moreover, we should witness an interaction between both factors in the way described above.

Research Questions and Hypothesis

To sum up, the influence of background music on learning is not clear: while the Mozart effect (Rauscher et al., 1993) implies a direct, positive effect, the arousal-mood-hypothesis (Husain et al., 2002) postulates a mediation effect over arousal and mood. Furthermore, the seductive detail effect indicates that background music has a direct negative effect on learning. In addition, the level of learning outcomes could also play an important role.

On this basis, we pose the following research questions: Does listening to background music influence learning directly or is this association mediated by arousal or mood? And which role does the learner’s working memory capacity have and how does it interact with background music?

All three theoretical assumptions (Mozart effect, arousal-mood-hypothesis and seductive detail effect) have theoretical and empirical justifications. As we are the first to compare all three of these, we formulate the following in parts competing hypotheses: Background music does not influence recall (H1.1), but comprehension (H1.2):

  • simple H1.2a: Due to the Mozart effect, comprehension will be influenced positively and directly by background music.
  • simple H1.2b: Due to the arousal-mood-hypothesis, we hypothesis that arousal and mood will be related to music and learning outcomes. As we chose music that was intended to induce positive mood and learning enhancing arousal, we expect background music to influence mood positively, thus fostering comprehension. Secondly, we expect that background music to have a positive impact on arousal, with arousal improving comprehension.
  • simple H1.2c: On the basis of the seductive detail effect, we hypothesize that there will be a direct negative influence of background music on comprehension.

Several studies cited above found better learning outcomes for learners with higher working memory capacity. As we think that a higher working memory capacity is only necessary for more demanding tasks, we hypothesize that there will be no main effect of working memory capacity on (H2.1) recall but on (H2.2) comprehension, with better comprehension scores recorded for learners with higher working memory capacity.

There is a lack of research investigating the interaction between listening to background music and working memory capacity.

Theoretically, we assume that learners with low working memory capacity will be overburdened by processing both the learning material and the background music. Nevertheless, learners with sufficiently high working memory capacity could benefit from the potential positive effect of background music which compensates for the additional cognitive burden (see Mayer, 2001).

However, this should only be relevant for comprehension tasks which are highly demanding. Based on these theoretical assumptions and the results of transferrable studies, we hypothesize that there will be (H3.1) no interaction effect between background music and working memory capacity on recall.

However, we hypothesis that (H3.2) this interaction effect will be present in the case of comprehension. More specifically, we hypothesise that there will be (H3.2a) better comprehension outcomes for learners with low working memory scores while not listening to background music. Learners with high working memory capacity, (H3.2b) will have better comprehension outcomes when listening to background music while learning.

Michigan State University
Media Contacts:
Kristen Parker – Michigan State University
Image Source:
The image is in the public domain.

Original Research: Open access
“Predicting piano skill acquisition in beginners: The role of general intelligence, music aptitude, and mindset”. Alexander P. Burgoyne, Lauren Julius Harris, David Z. Hambrick.
Intelligence doi:10.1016/j.intell.2019.101383.


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