Listening to classical music during a lecture and again as you sleep can help improve test performance


College students who listened to classical music by Beethoven and Chopin during a computer-interactive lecture on microeconomics – and heard the music played again that night – did better on a test the next day than did peers who were in the same lecture, but instead slept that evening with white noise in the background.

Over the long haul – when students took a similar test nine months later – the boost did not last.

Scores dropped to floor levels, with everyone failing and performance averaging less than 25% percent for both groups.

However, targeted memory reactivation (TMR) may aid during deep sleep, when memories are theorized to be reactivated and moved from temporary storage in one part of the brain to more permanent storage in other parts, researchers said.

The study, supported by the National Science Foundation and conducted by Baylor’s Sleep Neuroscience and Cognition Laboratory (SNAC), is published in the journal Neurobiology of Learning and Memory.

“All educators want to teach students how to integrate concepts, not just memorize details, but that’s notoriously difficult to do,” said Michael K. Scullin, Ph.D., director of Baylor’s sleep lab and assistant professor of psychology and neuroscience.

“What we found was that by experimentally priming these concepts during sleep, we increased performance on integration questions by 18% on the test the next day. What student wouldn’t want a boost or two to their letter grade?

The effects were particularly enhanced in participants who showed heightened frontal lobe activity in the brain during slow wave sleep, which is deep sleep.”

He noted that the effects emerged when using gold standard procedures: neither participants nor experimenters knew who received a particular treatment, sleep was measured using EEG in a laboratory setting, and the learning materials matched those that would actually be used in a college classroom, in this case an undergraduate microeconomics lecture.

Poor sleep is widespread in college students, with 60 percent habitually sleeping fewer than the recommended seven hours on 50 to 65 percent of nights.

While students may be more concerned about immediate test results — and TMR may help them cram for an exam — learning by rote (item memory) does not normally benefit grasping and retaining a concept.

For the study, researchers recruited 50 college students ages 18 to 33 for a learning task with a self-paced, computer-interactive lecture; and for two overnight polysomnography sessions, with the first night an adaptation to the lab and screening for sleep disorders, and the second done the evening of the lecture.

During the lecture, soft background selections were played from a computer: the first movement of Beethoven’s “Moonlight” Piano Sonata, the first movement of Vivaldi’s “Spring” Violin Concerto and Chopin’s Nocturne in E-flat major, Op. 9, No. 2.

That night in Baylor’s sleep lab, research personnel applied electrodes and used computers to monitor sleep patterns of both test and control groups.

Once technicians observed a person was in deep sleep, they played either the classical music or the white noise — depending on whether the individual was in the test or control group — for about 15 minutes.

“Deep slow wave sleep won’t last super long before shifting back to light sleep, so we couldn’t play them endlessly,” Scullin said. “If we played it during light sleep, the music probably would have awoken participants. The first slow wave cycle is the deepest and longest.”

The music choice was important, researchers said.

“We ruled out jazz because it’s too sporadic and would probably cause people to wake,” Scullin said. “We ruled out popular music because lyrical music disrupts initial studying.

You can’t read words and sing lyrics — just try it.

We also ruled out ocean waves and ambient music because it’s very easy to ignore. You’re going to have a heck of a time forming a strong association between some learning material and a bland song or ambient noise.

“That left us with classical music, which many students already listen to while studying,” he said.

“The songs can be very distinctive and therefore pair well with learning material.”

In the microeconomics exam the next day, the TMR of classical music more than doubled the likelihood of passing the test when compared with the control condition of white noise.

Scullin cautioned against confusing the Baylor study’s findings with the so-called “Mozart Effect” — the finding that having students listen to Mozart pieces led to better scores on intelligence tests.

Subsequent tests of the “Mozart Effect” found that it either did not replicate or that boosts were strictly due to increased arousal when listening to energetic music.

“Mozart doesn’t make memories,” Scullin said.

Previous researchers have found that memories associated with sensory cues — such as an odor or song — are re-activated when the same cue is received later.

When that happens during deep sleep, the corresponding memories are activated and strengthened, said co-researcher Chenlu Gao, a doctoral candidate of psychology and neuroscience at Baylor.

Early experimenters also played audio tapes during sleep to test whether individuals can learn new knowledge while sleeping. But while those experiments failed to create new memories, “our study suggests it is possible to reactivate and strengthen existing memories of lecture materials during sleep,” Gao said.

“Our next step is to implement this technique in classrooms — or in online lectures while students complete their education at home due to COVID-19 social distancing measures — so we can help college students ‘re-study’ their class materials during sleep.”

“We think it is possible there could be long-term benefits of using TMR but that you might have to repeat the music across multiple nights,” Scullin added.

“After all, you wouldn’t just study material a single time and then expect to remember it months later for a final exam. The best learning is repeated at spaced-out intervals — and, of course, while maintaining good sleep habits.”

Funding: The study was supported by the National Science Foundation. Paul Fillmore, assistant professor of communication sciences and disorders in Baylor’s Robbins College of Health and Human Sciences, also was a co-researcher.

Background music refers to any music that is played while the listener’s primary attention is focused on another task or activity (Radocy and Boyle, 1988). This background music effect differs from the so-called Mozart effect (Rauscher et al., 1993), which refers to the changes in cognitive abilities following listening to music.

Despite the above difference, there is consensus on these effects to operate on common mechanisms (Schellenberg and Weiss, 2013). The present study primarily focused on the background music effect.

Some studies on the effect of background music on performance in cognitive tasks have shown improvements in episodic memory (Ferreri et al., 2013), IQ scores (Cockerton et al., 1997), verbal and visual processing speed (Angel et al., 2010), arithmetic skill (Hallam and Price, 1998), reading (Oliver, 1997), and second languages learning (Kang and Williamson, 2013).

However, there is also evidence of reduced performance when background music is present (see Kämpfe et al., 2010 for a review).

According to the “arousal and mood hypothesis” (Thompson et al., 2001), the positive effect of music on human behavior is considered to be a consequence of the impact of music on mood and arousal. In particular, listening to music affects arousal (degree of physiological activation), mood (long lasting emotions), and listener’s enjoyment, which in turn influence cognitive performance (Hallam et al., 2002).

The impact of music on arousal and on mood of listeners seems to be determined by the tempo (fast vs. slow) and the mode (major vs. minus) of the music itself, respectively (Gabrielsson and Lindström, 2010). In particular, as reported in the context of the Mozart effect, fast tempo and major mode music tend to induce a positive/happy mood and higher arousal levels, whereas slow tempo and minor mode music induce a more negative/sad mood and lower arousal levels (e.g., Husain et al., 2002; Hunter and Schellenberg, 2010). Moreover, the effects of these different levels of mood and arousal seem to vary depending on the cognitive abilities considered.

In particular, several studies investigating the Mozart effect reported benefits primarily using tasks tapping processing speed and visuo-spatial abilities but only when the music had a fast tempo and a major mode (e.g., Thompson et al., 2001; Husain et al., 2002; Schellenberg et al., 2007).

Conversely, disturbing and interfering effects of background music have been reported for multimedia learning (Moreno and Mayer, 2000), surgeons learning of new procedures (Miskovic et al., 2008), mathematics (Bloor, 2009), and reading (Madsen, 1987).

These negative findings could be explained by the “cognitive-capacity hypothesis” (Kahneman, 1973), positing that a limited pool of resources is available for cognitive processing at any given moment (Baddeley, 2003), thus background music can disrupt cognitive tasks when there is a potential for interference (e.g., Polzella and Schoeling, 2004) due to an overtax of resources (Norman and Bobrow, 1975).

In particular, detrimental effects related to background music seem to be modulated by task complexity: the more complex and demanding the task, the stronger is the detrimental effect of music (Furnham and Bradley, 1997; Furnham and Allass, 1999).

In summary, with respect to the background music effect there are conflicting results as well as conflicting theoretical approaches that may, in principle, provide a unified account of the effect (or lack of it) on the basis of task complexity. When task complexity surpasses some critical threshold, then performance is impaired.

Conversely, below a certain level of task complexity the arousal and mood hypothesis may account for some beneficial effects of background music on task performance. While this theoretical stance may be appealing, it is not clear why background music, below certain levels of task complexity, it is not simply neutral, but it is indeed beneficial.

An interesting way to test the potential merit of the above hypotheses consists of assessing the background music effect on older adults. Given that normal aging is particularly associated with deficits in inhibiting irrelevant information and with deficits in tasks performed under divided attention (e.g., Parks, 2007), background music should negatively affect performance in cognitive tasks in older adults.

Hence, if background music does provide a beneficial effect to performance in cognitive tasks in older adults, then the validity of the “cognitive capacity hypothesis” would be weakened. Therefore, the present study intended to assess the impact of background music on the performance of older adults in cognitive tasks.

To the best of our knowledge, only three studies had been conducted on normal aging (Thompson et al., 2005; Mammarella et al., 2007; Ferreri et al., 2014).

These compared the effects of listening to music excerpt with high tempo and major mode vs. no-music on word fluency (Thompson et al., 2005; Mammarella et al., 2007), on working memory (Mammarella et al., 2007), and on recognition memory (Ferreri et al., 2014).

Interestingly, they all reported a specific positive effect of the background music that was able to enhance performance on the cognitive abilities examined. However, these studies in failing to include a negative emotional-valence background music (low tempo in minor mode), did not provide a thorough assessment of the impact of background music on cognitive tasks.

Hence, in the present study we included two different types of background music. We selected Mozart’s Eine Kleine Nachtmusik (positive background music with fast tempo and major mode) and Mahler’s 5th Symphony Adagietto (negative background music with slow tempo and minor mode), on the basis that these two pieces of music have been shown to induce happy and sad moods, and high and low arousal levels, respectively (Niedenthal and Setterlund, 1994; Storbeck and Clore, 2005; Riener et al., 2011).

Furthermore, we also used two control conditions: a no-music and a white noise control conditions to assess whether music improves (impairs) performance over baseline conditions. In particular, the white noise refers to a special type of environmental stimulation consisting in the exposure to a continuous auditory signal.

Previous evidences on white noise have produced mixed findings, reporting some instances of disturbing effects due to a competition for cognitive resources (e.g., Hygge et al., 2003; Boman et al., 2005) and others demonstrating that it was able to promote learning in those subjects with attentional deficits thanks to an increase of arousal levels (e.g., Söderlund et al., 20072010).

In order to evaluate the effects of background music on different cognitive abilities, we used tests tapping processing speed and declarative memory. Our decision was driven by three main reasons.

Firstly, processing speed is one of those abilities sensitive to the tempo and the mode of the music in those studies involving students (e.g., Schellenberg et al., 2007; Angel et al., 2010), thus it could represent a clear probe of the possible different effects of positive and negative background music in older adults.

Second, the effect of background music on memory is rather controversial in the literature on young adults, with evidences of both beneficial effects (e.g., Ferreri et al., 2013) and detrimental effects (e.g., Moreno and Mayer, 2000; Miskovic et al., 2008). Hence, we intended to assess the impact of different types of background music on tests tapping what are nominally called episodic memory (free recall) and semantic memory (phonemic fluency).

Third, both processing speed and memory are cognitive abilities mostly affected by aging (see Salthouse, 2004), thus it is of interest to assess whether background music may have a negative or positive effects on these tasks among older adults.

Because of the above theoretical considerations, some speculations could be put forward. On one hand, if older adults are sensitive to the “arousal and mood” effect of music (Thompson et al., 2001), their performance should be enhanced by background music in comparison to the two control conditions (no-music and white noise) with different effects between the positive and the negative condition.

With respect to processing speed, performance should improve while listening to the fast tempo and major background music compared to a slow tempo and minor mode background music (e.g., Schellenberg et al., 2007). With respect to memory, prior research suggested that fast tempo and major mode background music should improve performance in the elderly (Mammarella et al., 2007; Ferreri et al., 2014). To the best of our knowledge the effect of a slow tempo and minor mode background music on memory among older adults has not been investigated.

On the other hand, according to the “cognitive-capacity hypothesis” (Kahneman, 1973), we should expect that in the tasks used performance among older adults when exposed to no-music will be significantly greater than in the other experimental conditions.

Baylor University


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