Music is traditionally studied in historical contexts as part of the cultural practice, but also regarding its social and cultural contexts (see among others, Kramer, Reference Kramer1990, Reference Kramer2001). A fundamental question tapping into diverse fields of research is how music affects the listener. This has been examined from a historic and socio-cultural perspective (e.g., Kassabian, Reference Kassabian2013), but gained particular interest in psychology and biology (Prince, Reference Prince1972). Three distinct underlying dimensions for listening to music have been proposed: to achieve self-awareness, to express social relatedness, and to regulate mood and arousal (Schäfer, Sedlmeier, Städtler, & Huron, Reference Schäfer, Sedlmeier, Städtler and Huron2013). In particular, the latter requires biological investigations, which, however, are still sparse.

A well-known example of the effects music can have on the human mind, behavior, and brain are the soothing effects of lullabies, which are known and used across human cultures and historical ages. In their target articles, Savage et al. and Mehr et al. discuss lullabies for infants as examples for music that especially fosters bonding, showing evidence for both a one-by-one level bonding promoting group cohesion, and evidence that this effect can be found at a cross cultural level. Musical features in lullabies throughout centuries show cross-culturally consistent aspects fostering the feeling of being socially secure, which goes together with soothing effects for the babies, and they even work in the case of unfamiliar foreign lullabies (see e.g., Bainbridge & Bertolo et al. Reference Bainbridge, Bertolo, Youngers, Atwood, Yurdum, Simson and Mehr2021; Sands & Sekaquaptewa, Reference Sands and Sekaquaptewa1978; Spitz, Reference Spitz1979; Trehub, Unyk, & Trainor, Reference Trehub, Unyk and Trainor1993). This suggests that musical effects likely involve a strong biological component. In fact, lullabies can be seen as particularly suited to be studied as examples for musical effects on the biological level: their intentional aim is a change in the physiological state of the recipient, from wakefulness to sleep.

Importantly, lullabies work for infants as well as for adults – the market for relaxation/relax music/sounds, sleep (aid) music, or deep/easy/healing sleep music has again come into focus and is commercially growing with the COVID-19 crisis in 2020, being reviewed regularly also in major newspapers (see e.g., Chow, Reference Chow2020; Times Staff, 2020). From a practical research perspective, the effects of lullabies can thus conveniently be studied in the most easily available research population, namely young adults.

That music can indeed exert “somnogenic” effects is backed up by a body of empirical research. A number of studies have demonstrated therapeutic effects of music in cases of sleep disorders (for a review and meta-analysis see Feng et al., Reference Feng, Zhang, Hou, Jiyai, Jiang, Li, Zhao and Li2018; Jespersen, Otto, Kringelbach, Someren, & Vuust, Reference Jespersen, Otto, Kringelbach, Someren and Vuust2019; Wang, Sun, & Zang, Reference Wang, Sun and Zang2014). Also in healthy volunteers, music has been shown to affect sleep quality as assessed by questionnaires (e.g., Chang, Lai, Chen, Hsieh, & Lee, Reference Chang, Lai, Chen, Hsieh and Lee2012; Field, Reference Field1999; Harmat, Takács, & Bódizs, Reference Harmat, Takács and Bódizs2007; Johnson, Reference Johnson2003; Lai & Good, Reference Lai and Good2005; Lamboley, Reference Lamboley1998; Trahan, Durrant, Müllensiefen, & Williamson, Reference Trahan, Durrant, Müllensiefen and Williamson2018).

As a more objective measure, several studies in recent years have used polysomnography to test the effects of music on the brain (e.g., Chen et al., Reference Chen, Pei, Chen, Huang, Chou, Wu and Wu2014; Cordi, Ackermann, & Rasch, Reference Cordi, Ackermann and Rasch2019; DuRousseau, Mindlin, Insler, & Levin, Reference DuRousseau, Mindlin, Insler and Levin2011; Loewy, Hallan, Friedman, & Martinez, Reference Loewy, Hallan, Friedman and Martinez2005). Sleep is a physiologically exceptionally well characterized state in which even subtle changes can be robustly detected, in the absence of motor artifacts that would be difficult to avoid in awake music listeners. Oscillatory patterns in the sleep electroencephalogram (EEG) such as an increase in theta activity and a disappearance of alpha activity can be used as objective markers of the process of falling asleep, whereas slow wave activity can be used as an objectively quantifiable indicator of sleep depth (Chen et al., Reference Chen, Pei, Chen, Huang, Chou, Wu and Wu2014; Cordi et al., Reference Cordi, Ackermann and Rasch2019; Lazic & Ogilvie, Reference Lazic and Ogilvie2007). Beyond the effects of music before sleep and during transition phases, different kinds of acoustic stimulation are increasingly used during sleep to enhance sleep depth (Ngo, Martinez, Born, & Mölle, Reference Ngo, Martinez, Born and Mölle2013) or cognitive functions of sleep (Hu, Cheng, Chiu, & Paller, Reference Hu, Cheng, Chiu and Paller2020).

Beyond these advantages of lullabies as test cases for the neurobiological effects of music, two obstacles have to be noted. First, classical music repertoire includes an abundance of lullabies, and the contemporary music market provides an even broader plethora of soothing music. The choice for specific lullaby candidates is thus often based on heuristics rather than systematic evaluation (Cordi et al., Reference Cordi, Ackermann and Rasch2019; Loewy, Reference Loewy2020; but see Trahan et al., Reference Trahan, Durrant, Müllensiefen and Williamson2018). Second, full polysomnography in the sleep laboratory as the gold standard of sleep research is effortful and costly, thus restricting the scope of a more systematic investigation. However, recent advances in sleep technology and computer science provide promising options to overcome these limitations: A growing market of sleep wearables including convenient sleep EEG headbands allow for the large-scale, low-cost acquisition of longitudinal sleep data with considerable samples sizes (Depner et al., Reference Depner, Cheng, Devine, Khosla, de Zambotti, Robillard and Drummond2020; Scott, Lack, & Lovato, Reference Scott, Lack and Lovato2020). Resulting big sleep data sets can, in turn, be processed with (semi-)automatic machine-learning-based analysis pipelines (see e.g., sleeptrip.org). These possibilities enable novel interdisciplinary approaches to the study of music: Historically, sociologically, and psychologically informed sets of candidate lullabies can be tested in large populations using wearable sleep recording technology. The resulting large-scale data sets can be analyzed with machine-learning approaches to extract particularly effective somnogenic musical features, which, in turn, can be fed back into traditional musicological analysis. Overall, lullabies can thus be considered an ideal musical genre to elucidate the associations and differences between neurobiological and cultural aspects of music.