Examining Links Between Music-Induced Physiological Response,Active Engagement with Music,and Musical Competence

Abstract

The present study investigated the links between music-induced physiological response and active engagement with music to understand individual differences in musical competence. Building on McPherson and Williamon's differentiated model of musical giftedness and talent as the theoretical foundation, the study treated individuals’ music-induced physiological response as an indicator of natural abilities. Active engagement with music represented the developmental process, while musical competence reflected individuals’ ability to perceive, remember, and distinguish musical rhythms and melodies. The sample included 203 adults. Music-induced physiological response was measured using event-related skin conductance responses. Active engagement was evaluated through self-reports of musical behaviors, including music listening, music instrument playing, and music training. Musical competence was assessed using computerized adaptive tests of musical listening. Correlation and path analyses revealed a link between music-induced physiological response and music listening, but not with music instrument playing or music training. In addition, the self-reported musical behaviors were interconnected. Finally, music instrument playing and music training were linked to musical competence. The findings suggested that individuals’ music-induced physiological response can influence music-listening behaviors, which in turn are related to music instrument playing and music training. Moreover, the results indicated that playing a musical instrument and receiving music training contribute to higher musical competence.

Keywords

Music-induced physiological response active engagement with music musical competence path analysis

Researchers in evolution, neuroscience, psychology, and the sociobiology of music generally agree that music is a fundamental and universal component of humankind and that Homo sapiens, as a species, has the capacity for musical development (for a review, see, e.g., Harvey, 2017; Honing, 2018; Levitin, 2006; Morley, 2013; Patel, 2008). At the same time, considerable empirical evidence shows that observable musical competencies and developmental potential vary across individuals (Burgoyne et al., 2019; Cohrdes et al., 2019; Correia et al., 2023; Müllensiefen et al., 2022). The sources of these differences remain a topic of ongoing debate within the nature-versus-nurture discussion of musical development (McPherson & Hallam, 2016; see also Habibi et al., 2018; Howe et al., 1998; Mosing et al., 2014; Putkinen et al., 2014; Slevc et al., 2016; Swaminathan & Schellenberg, 2017, 2018; Tan et al., 2014). In the present study, we aimed to contribute to this debate by examining links among individuals’ music-induced physiological response, active engagement with music, and musical competence. Drawing on McPherson and Williamon’s (2006, 2015) differentiated model of musical giftedness and talent, we treated music-induced physiological response, measured through event-related skin conductance responses (SCR; see, e.g., Hodges, 2016; Khalfa et al., 2002), as a proxy for individuals’ predispositions that can be mapped to natural abilities. In addition, we measured the model-implied developmental process using self-reported musical behaviors (i.e., music listening, music instrument playing, and music training) to operationalize individuals’ active engagement with music (Chin & Rickard, 2012). Subsequently, we captured musical competence through a set of computerized adaptive tests of musical listening (Harrison et al., 2017; Harrison & Müllensiefen, 2018; Larrouy-Maestri et al., 2019) to describe individuals’ “ability to perceive, remember, and discriminate musical melodies and rhythms” (Swaminathan & Schellenberg, 2018, p. 1). Finally, we systematically explored the links and directional relationships among these measures, as suggested by various theoretical and empirical assumptions.

Individual Differences in Active Engagement with Music and Musical Competence

Music psychology and music education research generally agree that musical development, like skill and expertise acquisition in other fields (for a review, see, e.g., Kaufman & Duckworth, 2017), is a complex, dynamic, and nonlinear process influenced by the interplay of nature and nurture (for a review, see, e.g., Bamberger, 2005; Hallam, 2015; see also Howe et al., 1998; Mosing et al., 2014; Slevc et al., 2016; Swaminathan & Schellenberg, 2017, 2018). Therefore, musical development encompasses numerous interrelated personal and environmental factors (Gembris, 2006; Hargreaves & Lamont, 2017; McPherson & Hallam, 2016; Putkinen et al., 2014). In their differentiated model of musical giftedness and talent, McPherson and Williamon (2006, 2015) outlined several factors that may foster the development of observable musical competencies (see also Hargreaves & Lamont, 2017; McPherson & Hallam, 2016). Their model is based on Gagné's (2004, 2013) general talent development framework, which holds that natural abilities, along with environmental (e.g., milieu, individuals, and resources) and intrapersonal (e.g., physical, mental, awareness, motivation, and volition) catalysts, influence the developmental process, which in turn shapes individuals’ musical competencies. In this context, natural abilities, defined as the innate potential to achieve, encompass six specific domains, similar to those identified by Gagné (2004, 2013): four mental domains (intellectual, creative, social, and perceptual) and two physical domains (muscular and motor control). Regarding the developmental process, McPherson and Williamon (2006, 2015) described activities (such as access, content, and formal), progress (including stages, locations, and turning points), and investments (like time, money, and energy). Lastly, among musical competencies, they identified eight specific activities: performing, improvising, composing, arranging, analyzing, appraising, conducting, and music teaching (see also McPherson & Hallam, 2016).

In the present study, McPherson and Williamon's (2006, 2015) model provides a theoretical framework for how natural abilities (nature) and developmental processes (nurture) lead to individual differences in observable musical competencies (Howe et al., 1998; Mosing et al., 2014; Slevc et al., 2016; Swaminathan & Schellenberg, 2017, 2018). Furthermore, it reflects both gene-environment correlations (i.e., people seek out environments that match their genetic predispositions) and gene-environment interactions (i.e., the effects of developmental processes are moderated by genetic predispositions; Swaminathan & Schellenberg, 2018, p. 2). With additional theoretical and empirical assumptions (Schellenberg, 2015; Swaminathan & Schellenberg, 2018; Ullén et al., 2015), it identifies positive links and directional relationships among the measures.

The focus and added value of our study lie in integrating music-induced physiological response, measured via event-related SCR to musical stimuli (Khalfa et al., 2002), as an individual predisposition within the mental aspect of natural abilities, particularly in the perceptual domain of McPherson and Williamon's model (2006, 2015; see also Koelsch & Jäncke, 2015; Krumhansl, 1997). Furthermore, we suggested that active engagement with music aligns with the model-implied developmental process and can be reflected in self-reports of musical behaviors (i.e., music listening, music instrument playing, and music training), which, according to Chin and Rickard (2012), “depict engagement as the presence of certain types of actions or performances” (p. 430) in behavioral terms. Lastly, we aimed to predict individuals’ musical competence, measured using a set of computerized adaptive tests of musical listening, including a mistuning perception test (Larrouy-Maestri et al., 2019), a beat alignment test (Harrison & Müllensiefen, 2018), and a melodic discrimination test (Harrison et al., 2017). In accordance with Swaminathan and Schellenberg's (2018) definition of musical competence, these computerized adaptive tests measured individuals’ “ability to perceive, remember, and discriminate musical melodies and rhythms” (p. 1; see also Pausch et al., 2022; Liu et al., 2023) and can be mapped to the analytical component of individuals’ musical competencies within McPherson and Williamon's (2006, 2015) model.

Notably, to our knowledge, the existing empirical literature on musical development has not yet explicitly considered music-induced physiological response and active engagement with music to explain individual differences in musical competence. Therefore, the links and directional relationships among these measures have not yet been empirically examined.

Emotional Responsiveness and Preference for Music as Innate Natural Abilities

According to McPherson and Hallam (2016), music is an “aural art form.” Therefore, “much of the literature has concentrated on the ‘perceptual’ domain to describe an underlying ‘trait’ of musical potential which might form an integral component of success in all forms of musical talent” (pp. 439–440). In this context, researchers have employed various conceptions to define and explain innate natural abilities: Gardner (1983) noted sensitivity to the physical and emotional aspects of sound, while Gordon (1993, 2007) referred to “audiation” (musical memory) – the ability to comprehend sound internally. Furthermore, Mainwaring (1941, 1947) emphasized the ability to “think in sound.” These earlier conceptions of innate natural abilities have been expanded by Brodsky (2004) based on Papoušek's (1996) findings. Brodsky (2004) speculated about the extent to which the processing of complex musical structures might be an innate predisposition in humans. In addition, Winner and Martino (2000) suggested that the core ability of musically gifted individuals, especially children, “is a sensitivity to the structure of music – tonality, key, harmony, and rhythm, and the ability to hear the expressive properties of music” (p. 102; see also Kirnarskaya, 2009; Kirnarskaya & Winner, 1997). Because of this sensitivity to musical structure, individuals may be able to remember music and play it back with ease, either with an instrument or vocally, and transpose, improvise, and even invent music (Winner & Martino, 2000, p. 102). Moreover, Csikszentmihalyi (1998) expanded these conceptions by asserting that individuals “whose neurological makeup makes them particularly sensitive to sounds will be motivated to pay attention to aural stimulation, be self-confident in listening and singing, and likely to seek out training in music […]” (p. 411). In this context, Brodsky (2004) suggested that the innate natural potential for processing music develops as “children become more aware of sound and start to identify and associate with music according to their own ‘auditory style’” (McPherson & Hallam, 2016, p. 440; see also, e.g., Schäfer et al., 2013; Shifriss et al., 2015; Reybrouck & Eerola, 2017; Zentner & Kagan, 1998). In other words, for Brodsky (2004), the innate natural potential for processing music (i.e., individuals’ predisposition) involves a fusion of emotional responsiveness and a preference for music, which is linked to individuals’ awareness of this potential. According to McPherson and Hallam (2016), “these concepts of responsiveness and preference are associated with motivation and interest […]” (p. 440) and, therefore, they may be linked to individuals’ active engagement with music.

Regarding emotional responsiveness and preference for music, the literature generally holds that emotional responses typically consist of three components: experience, expression, and physiology (for a review, see, e.g., Buck, 1994; Ekman, 1993; Izard, 1977, 1989; Lang, 1995; Levenson, 1994; Leventhal, 1984; Plutchik, 1994). Building on these ideas, the key proposition of this study is that individuals’ emotional responsiveness and preference for music may be fundamentally reflected in their physiological responses to music (for a review, see Pace-Schott et al., 2019). These responses can be measured using modern psychophysiological methods that record autonomic nervous system responses to various stimulation modalities (for a review, see Hodges, 2016), including event-related SCR to musical stimuli (Khalfa et al., 2002) to operationalize individuals’ music-induced physiological response.

Music-Induced Physiological Response

Conceptualizing SCR

We focused on measuring galvanic skin response (GSR), also known as electrodermal activity (EDA; e.g., Andreassi, 2007). EDA “reflects the output of integrated attentional and affective and motivational processes within the central nervous system acting on the body” (Critchley & Nagai, 2013, p. 666) and “is a measure of neurally mediated effects on sweat gland permeability, observed as changes in the resistance of the skin to a small electrical current, or as differences in the electrical potential between different parts of the skin” (p. 666). EDA measures skin conductance (SC), derived from microscopic changes in perspiration on the skin's surface (Boucsein, 2012). Sweat secretion is involved in several regulatory processes (Fowles et al., 1981), and numerous studies have shown that SC is not under conscious control and is associated with autonomous emotional regulation (Anders et al., 2004; Dawson et al., 2007; Fowles et al., 1981; Larsen et al., 2008). The autonomic nervous system includes a sympathetic division that prepares the body for fight-or-flight responses by increasing blood pressure, heart rate, and involuntary muscle tension (Andreassi, 2007) and a parasympathetic division that conserves energy by returning bodily functions to a state of rest and restoration (e.g., slowing heart rate, stimulating peristalsis, and secreting saliva; see LeDoux, 1986, 1994). The rationale for measuring SCR as an indicator of physiological response to a stimulus is that the stronger a person's reaction to a stimulus, the greater the sympathetic activation of the sweat glands, increasing secretion (Goldstein, 1980; Lang et al., 1998; North & Hargreaves, 1997). This increase is generally very small, but because sweat contains water and electrolytes, it enhances electrical conductivity and lowers the skin's electrical resistance. These changes, in turn, affect EDA.

SCR is proportional to the number of activated sweat glands, indicating a correlation between an individual's reactivity and SCR (Benedek & Kaernbach, 2010; Boucsein, 1992). An important aspect of SCR is that it reflects fluctuations in two underlying components of EDA: slowly varying tonic sympathetic activity and rapidly varying phasic sympathetic activity. The tonic component is expressed in units of electrodermal level, while the phasic component is measured in units of electrodermal responses. Changes in the faster, reactive phasic component (i.e., electrodermal responses) are short-lasting changes in EDA that occur in response to a specific stimulus (Boucsein, 2012; Critchley, 2002; see also Khalfa et al., 2002). Furthermore, sudden shifts in the faster, reactive phasic activity (i.e., electrodermal responses) above the slow and steady baseline tonic activity (i.e., electrodermal level) are called EDA peaks. When these peaks are triggered by a stimulus such as music, they are referred to as event-related SCR; when they do not appear to link to any observable external stimulus, they are termed nonspecific SCR (Boucsein, 2012; Dawson et al., 2007). Notably, counting EDA peaks over a given time frame provides quantified insight into an individual's level of autonomous arousal during exposure to musical stimuli (Khalfa et al., 2002). Therefore, we used the number of event-related SCR peaks during exposure to musical stimuli to assess individual predispositions for music-induced physiological response as the mental aspect of the perceptual domain within natural abilities (McPherson & Williamon, 2006, 2015).

Prior Evidence on SC in the Context of Music

Numerous studies have shown that the primary motivation for listening to music is the emotional responses it elicits (Juslin & Laukka, 2004; Juslin & Västfjäll, 2008; Panksepp, 1995; Scherer & Zentner, 2001, 2008; Sloboda, 1991; Zentner et al., 2008). Music can elicit genuine basic emotions (Egermann et al., 2015; Fritz et al., 2009; Västfjäll, 2001–2002) and is widely used for mood regulation (Carlson et al., 2015; Saarikallio, 2008, 2010; Skånland, 2013). Experimental studies have also used music to induce specific emotions in participants (see Västfjäll, 2001–2002 for a review) and have found that it can provoke intense peak experiences such as thrills or chills (Beier et al., 2022; Benedek & Kaernbach, 2011; Goldstein, 1980; Swaminathan & Schellenberg, 2015). Music triggers physiological responses across multiple systems, as studies across genres and paradigms reveal (e.g., Bartlett, 1996; Hodges, 2016; Panksepp & Bernatzky, 2002; Rickard, 2012). Research on the connections between music and emotion frequently measures autonomic nervous system responses such as SC (Bullack et al., 2018; Etzel et al., 2006; Gabrielsson, 2011; Khalfa et al., 2002; Krumhansl, 1997; Lundqvist et al., 2008; Merrill et al., 2020, 2023; Salimpoor et al., 2009; White & Rickard, 2016). For instance, Lundqvist et al. (2008) found that happy music increased zygomatic facial muscle activity, SC, and happiness while reducing sadness and finger temperature. White and Rickard (2016) demonstrated that happy and sad music influenced self-reported emotions, SC, and heart rate, with responses generally regulated except for heart rate. Salimpoor et al. (2009) identified a positive correlation between subjective pleasure and physiological arousal, noting a dissociation in individuals lacking pleasure responses. Arousal levels in response to musical stimuli, which reflect physiological changes in the autonomic nervous system, have been extensively studied (Eerola & Vuoskoski, 2011; Mauss & Robinson, 2009; Russell, 2003; van der Zwaag et al., 2011). Eerola and Vuoskoski (2011) created a movie music database to explore emotional processes. Meanwhile, van der Zwaag et al. (2011) investigated how musical features such as tempo and percussiveness affect arousal, tension, and physiological responses. Their findings confirm that music regulates emotional and physiological states and can elucidate emotional induction (Juslin et al., 2010; Scherer & Coutinho, 2013).

Overall, the evidence supporting the close connection between music and emotions, as well as the measurement of changes in autonomic nervous system responses, appears substantial. Moreover, subjective responses (e.g., perceptions of sadness, fear, happiness, and tension) and music-induced physiological responses (e.g., blood pressure, heart rate, respiration, SC, and skin temperature) can vary significantly among individuals. However, to our knowledge, no studies have examined links between individuals’ autonomic nervous system responses, such as event-related SCR (Khalfa et al., 2002), to musical stimuli, which reflect music-induced physiological response, active engagement with music, and musical competence.

Active Engagement with Music and Musical Competence

Conceptualizing Active Engagement with Music

According to Münte et al. (2002), music is a highly engaging and complex multisensory activity that involves interdependent processes of music perception and production (Elliott, 1995; see also Chin & Rickard, 2012). In this context, we define engagement consistent with earlier authors (Reeve et al., 2004; Russell et al., 2005), including Chin and Rickard (2012), who described it as “the connection between an individual and an activity of interest […] and reflects the individual's active involvement or participation in the activity […]” (p. 430). In line with Saks (2006), Maslach et al. (2001), Schaufeli and Bakker (2004), Schaufeli et al. (2002), and Chin and Rickard (2012), engagement is delineated as “an emotional or intellectual commitment to an activity or task – or a state of being – that occurs with the simultaneous presence of vigor, dedication, and absorption […]” (Chin & Rickard, 2012, p. 430). In other words, we define active engagement with music as the individual's active involvement in a wide range of musical activities (Chin & Rickard, 2012; Harter et al., 2002). In addition, we recognize that the construct of engagement is, on the one hand, related to intrinsic and extrinsic motivation (Chin & Rickard, 2012, p. 430; see also Sloboda, 2005); on the other hand, it can be measured partially in “the presence of certain behaviors and attitudinal terms” (Chin & Rickard, 2012, p. 430). Accordingly, in the present study, we chose to measure active engagement with music based on self-reported musical behaviors as suggested in the Music USE (MUSE) questionnaire (Chin & Rickard, 2012). These musical behaviors are measured and quantified using indices of music listening, music instrument playing, and music training. They “define ‘active engagement’ in behavioral terms as ‘high levels of activity, initiative, and responsibility’” (Chin & Rickard, 2012, p. 430). Therefore, active engagement with music is understood as “an individual's level of active participation in music activities, measured by the frequency and regularity of participation” (p. 430; see also Mankel & Bidelman, 2018; Mosing et al., 2014; Swaminathan & Schellenberg, 2018).

Conceptualizing Musical Competence

In line with Swaminathan and Schellenberg (2018), we defined musical competence as individuals’ “ability to perceive, remember, and discriminate musical melodies and rhythms” (p. 1). Accordingly, the term competence, unlike other terms such as aptitude, talent, or ability, is “meant to be neutral with respect to the relative roles of nature and nurture” (p. 1). In addition, following Swaminathan and Schellenberg (2018) and McPherson and Williamon (2006, 2015), we assumed that individual differences in musical competencies arise from individuals’ natural abilities and developmental process (2018; Slevc et al., 2016; Howe et al., 1998; Mosing et al., 2014, Swaminathan & Schellenberg, 2017). As a result, our measure of musical competence was defined as individuals’ performance on various computerized adaptive tests of musical listening, designed to identify individual differences in perceiving sequences of tones or beats (Swaminathan & Schellenberg, 2018; see also, e.g., Schellenberg & Weiss, 2013).

In the present study, we calculated a composite score of musical competence based on individuals’ performance on a set of computerized adaptive tests of musical listening. These tests included the mistuning perception test (Larrouy-Maestri et al., 2019), the beat alignment test (Harrison & Müllensiefen, 2018), and the melodic discrimination test (Harrison et al., 2017). Our composite score for musical competence aligned with Pausch et al. (2022; see also Liu et al., 2023), who demonstrated that a model similar to the general g-factor model of intelligence (Jensen, 1998, 2002; Mackintosh, 2011; Spearman, 1904a, 1904b) also applies to musical competence (see also, e.g., Boyle & Radocy, 1987; Wing, 1961).

Links Between Music-Induced Physiological Response, Active Engagement with Music, and Musical Competence

Building on McPherson and Williamon's differentiated model of musical giftedness and talent (2006, 2015) and the model-implied links and directional relationships from natural abilities through the developmental process to musical competencies, we hypothesized that music-induced physiological response is related to active engagement with music, and that active engagement with music, in turn, is associated with individuals’ musical competence (Howe et al., 1998; Mosing et al., 2014; Schellenberg, 2015; Swaminathan & Schellenberg, 2017, 2018; Slevc et al., 2016; Ullén et al., 2015). Specifically, we proposed that music-induced physiological response, measured as event-related skin SCR to musical stimuli (Khalfa et al., 2002), influences self-reported musical behaviors (i.e., music listening, music instrument playing, and music training), which reflect active engagement with music (Chin & Rickard, 2012). Active engagement with music, in turn, impacts musical competence as measured by a set of computerized adaptive tests of musical listening (Harrison et al., 2017; Harrison & Müllensiefen, 2018; Larrouy-Maestri et al., 2019).

Our reasoning for these propositions rests on the general assumption that emotions and motivation are interconnected (Lewis & Wolan Sullivan, 2005; Welch & Adams, 2003; for a review, see, e.g., McPherson & McCormick, 2006; Woody & McPherson, 2010; Woody, 2021). Thus, we proposed that music-induced physiological responses drive motivation to actively engage with music and, consequently, are linked to music listening, music instrument playing, and music training (see, e.g., Davidson, 2011; Kreutz et al., 2004; Lamont, 2012; Theorell et al., 2014; Valentine & Evans, 2001; Weinberg & Joseph, 2017). In particular, emotional responses are the primary reason people listen to music (Juslin & Laukka, 2004; Juslin & Västfjäll, 2008; Panksepp, 1995; Scherer & Zentner, 2001, 2008; Sloboda, 1991; Zentner et al., 2008). In addition, research by Persson (2001; see also Lamont, 2011) argued that music listening often precedes musical performance, and both activities share common underlying motives. One of these motives is that individuals pursue music listening and musical performance (i.e., playing a musical instrument and receiving music training) primarily for hedonic reasons (see, e.g., Fredrickson, 2001; Fredrickson & Levenson, 1998; Kahneman et al., 1999). Another common motive stems from strong emotional responses to music of both positive and negative valence (also known as peak experiences; for a review, see, e.g., Gabrielsson et al., 2016; Whaley et al., 2009). These responses can be impactful and influential in an individual's life (Gold et al., 2019; see also Brattico et al., 2016; Huron, 2006; Juslin & Västfjäll, 2008; Lamont, 2012; Mas-Herrero et al., 2013; Salimpoor et al., 2011, 2013, 2015).

Moreover, research has shown that active engagement with music (e.g., music production or music perception) is associated with widespread activation across distributed cortical and subcortical brain systems (see, e.g., Bhattacharya et al., 2001; Janata et al., 2002; Koelsch et al., 2004, 2006; Popescu et al., 2004; Sergent et al., 1992). Furthermore, active engagement with music positively correlates with a wide range of musical competencies (Swaminathan & Schellenberg, 2018; see also Law & Zentner, 2012; Slevc et al., 2016; Swaminathan et al., 2017, 2021; Swaminathan & Schellenberg, 2017, 2020; Ullén et al., 2014, 2015; Wallentin et al., 2010). However, changes in active engagement with music do not always correspond to changes in musical competencies; without active participation in music, it is difficult to envision how individuals’ musical competencies could develop (Schellenberg, 2015; Trainor, 2005; Ullén et al., 2015).

Aims and Hypotheses

The present study systematically examined the links among music-induced physiological response, active engagement with music, and musical competence. First, we considered music-induced physiological response as an indicator of emotional responsiveness and music preference (Brodsky, 2004; Csikszentmihalyi, 1998; Winner & Martino, 2000), measurable via event-related skin conductance responses to musical stimuli (Khalfa et al., 2002) and aligned with the perceptual domain of the mental aspect within the natural abilities outlined in McPherson and Williamon's (2006, 2015) model.

Next, we aimed to operationalize the model-implied developmental process using self-reported musical behaviors, quantified with indices of music listening, music instrument playing, and music training developed by Chin and Rickard (2012) to reflect individuals’ active engagement with music. In addition, we proposed evaluating individuals’ musical competence with a set of computerized adaptive tests of musical listening (Harrison et al., 2017; Harrison & Müllensiefen, 2018; Larrouy-Maestri et al., 2019). These tests consist of tasks that assess individuals’ musical listening abilities and detect individual differences in perceiving, remembering, and discriminating sequences of tones or beats (Liu et al., 2023; Pausch et al., 2022; Swaminathan & Schellenberg, 2018). Thus, our measure of individuals’ musical competence can be mapped to the analytical domain in McPherson and Williamon's (2006, 2015) model.

Finally, drawing on the previously mentioned theoretical and empirical assumptions, we hypothesized that music-induced physiological response influences individuals’ active engagement with music (i.e., music listening, music instrument playing, and music training). Active engagement with music, in turn, affects individuals’ musical competence (see Figure 1). However, several reasons (Brodsky, 2004; Csikszentmihalyi, 1998; Kirnarskaya, 2009; Kirnarskaya & Winner, 1997; Winner & Martino, 2000) suggest that music-induced physiological response is directly connected to individuals' musical competencies.

Figure 1.
The proposed path model shows the directional relationships among music-induced physiological response, active engagement with music, and musical competence, as suggested by the differentiated model of musical giftedness and talent (McPherson & Williamon, 2006, 2015).

Materials and Methods

Ethics Statement

All study procedures were approved by the Ethics Council of the Department of Psychology at the Ludwig-Maximilians-Universität München (LMU). In accordance with German and European data protection laws, participants were informed about the study before data collection (written consent form) and after data collection (debriefing letter). They provided their consent via the written consent form. Participants were also informed that their participation was voluntary and that their responses would be anonymous.

Participants

A total of N = 215 individuals participated in this study. Twelve participants were excluded based on visual inspection and quality measures of skin conductance data peaks to identify artifacts or signal quality issues. Two participants reported mild impairments related to tinnitus and autism but were still included in the sample. One participant had no data for the self-report questionnaire. The final sample comprised N = 203 participants: 48 identified as male, 152 as female, two as non-binary, and one with missing data. On average, they were 24.27 years old (SD = 5.86), with an age range of 18 to 44 years. In addition, participants had diverse musical backgrounds: 166 were musically active (i.e., playing a musical instrument, including singing), and 37 were not.

Materials

Musical Excerpts

The musical stimuli in this study were drawn from a database of 110 movie soundtracks created by Eerola and Vuoskoski (2011). Based on Eerola and Vuoskoski's ratings and categorizations of emotions into positive and negative valence, low and high tension, and low and high energy, Merrill et al. (2020) selected 32 musical excerpts, each 30 s long and maintaining a consistent musical expression (emotional quality), for their research on the impact of the locus of emotion on various physiological reactions to music, including SC. To optimize study time and measurement efficiency, we sought to reduce the number of musical excerpts. We conducted an exploratory factor analysis (EFA) using Merrill et al.'s (2020) SC data to select a subset of the initial 32 musical excerpts that showed a homogeneous SCR. Our final selection comprised 15 excerpts, each lasting 30 s, that were well balanced in terms of valence, tension, and energy (see Appendix A). We expected an overall, internally consistent music-induced physiological response, as assessed by event-related SCR, when listening to these excerpts (Merrill et al., 2020; see Appendix B).

Active Engagement with Music

The items used in this study were drawn from Chin and Rickard's (2012) MUSE questionnaire and translated into German (see Appendix C). From the MUSE questionnaire, we selected the items in Section A that assess self-reported musical behaviors and then quantified them into three indices: (1) music listening, measuring the amount of music listening by capturing the duration and frequency of intentional music listening; (2) music instrument playing, evaluating the intensity of practice based on the duration, frequency, and regularity of instrument playing; and (3) music training, reflecting an individual's musical background, assessed by the highest level of formal music training, other types of informal music training, and the completion of certified examinations. The index scores for music listening, music instrument playing, and music training were calculated using the MUSE questionnaire scoring sheet by Chin and Rickard (2012; see also Chin, 2025a, 2025b).

Set of Computerized Adaptive Tests of Musical Listening

Finally, to operationalize individuals’ musical competence as “ability to perceive, remember, and discriminate musical melodies and rhythms” (Swaminathan & Schellenberg, 2018, p. 1), we used a set of computerized adaptive musical listening tests, including the mistuning perception test (Larrouy-Maestri et al., 2019) with 18 items, the computerized adaptive beat alignment test (Harrison & Müllensiefen, 2018) with 18 items, and the melodic discrimination test (Harrison et al., 2017) with 15 items.

Each computerized adaptive test of musical listening began with two trials that provided feedback on whether each item was solved correctly. The mistuning perception test presented participants with two versions of the same musical excerpt, one in which the vocalist was “in tune” with the background music and the other in which the vocalist was “out of tune.” The task was to identify which version was “out of tune” (Larrouy-Maestri et al., 2019). The computerized adaptive beat alignment test presented participants with two 5-second musical tracks selected to represent a variety of musical genres. Each track was overlaid with a 20-ms beep tone consisting of a 1000-Hz sine tone with a 10-ms fade-out. In one version, the beep was “on” the beat; in the other, it was “off” the beat. The task was to determine whether the track with beeps “on” the beat came first or second (for details, see Harrison & Müllensiefen, 2018). Finally, the melodic discrimination test presented participants with three versions of the same melody. Two versions shared the same interval structure and were called lures, while one version had exactly one note altered. The task was to identify this latter tune as the “odd-one-out” (Harrison et al., 2017). Demos of the computerized adaptive tests of musical listening used in the present study are available on the LongGold project homepage (Müllensiefen et al., 2025).

Recruitment, Procedure, and Compensation

First, we attached two electrodes to the participants’ non-dominant palm to record SC. Afterward, participants completed self-report questionnaires and answered sociodemographic questions on the SoSci Survey online platform (Leiner, 2024). Second, before recording SC, we checked the devices’ functionality by instructing participants to bite their tongues, breathe rapidly, and keep their non-dominant hands still while the SC devices recorded (Pedersen, 2023). High-quality headphones (AKG K702) were used to present the musical excerpts, which were adjusted to a comfortable volume. All participants listened to the same set of musical excerpts (15 pieces, each 30 s long). After completing the second part, SC recording ended, and the SC devices were removed. The third part assessed musical competencies using the computerized adaptive test battery. Participants had compensation options, including 1.5 test-person hours, a chance to win restaurant vouchers (EUR 25), or payment for participation (EUR 15).

Signal Processing

All signal processing was performed using custom scripts (R Notebook) written in iMotions 8.2 (iMotions, 2021). To obtain a time series of the phasic component of the skin conductance (SC) data for each musical excerpt, the GSR (galvanic skin response) peak detection function in iMotions 8.2 was used to calculate the number of peaks and peaks per minute for each musical excerpt (event-related SCR; Benedek & Kaernbach, 2010). Note that no threshold was applied to estimate SC. However, as Benedek and Kaernbach (2010) recommended, time integration of the continuous measure of phasic activity (average value) was taken as a direct indicator of event-related sympathetic activity.

Statistical Analysis

All statistical analyses were conducted in R version 4.0.3 (R Core Team, 2020). We used several R packages, including dplyr (version 1.0.4; Wickham et al., 2023) and psych (version 2.0.12; Revelle, 2023), to prepare and organize the data. Initially, we used the psych package for reliability analysis (Cronbach, 1951; McDonald, 1999) and correlation analysis. For the correlation analysis, we used the mean score of music-induced physiological response, indicated by the number of peaks per minute (identified by the GSR peak detection algorithm included in iMotions 8.2), across the 15 musical excerpts; the mean scores of the quantified indices of music listening, music instrument playing, and music training for active engagement, as suggested by Chin and Rickard (2012); and composite scores (i.e., factor scores) for musical competence, computed using the lavPredict() function in the R package lavaan (Rosseel, 2012). For the latter, we computed a single factor for musical competence from the person estimators derived from each of the three distinct computerized adaptive tests of musical listening, following the recommendations of Pausch et al. (2022; see also Liu et al., 2023).

Second, using the R package lavaan, we conducted a path model analysis to specify directional relationships. Drawing on McPherson and Williamon's (2006, 2015) model, the path model (see Figure 1) specifies relationships from music-induced physiological response through active engagement with music to musical competence. We used the mean score for music-induced physiological response; standardized scores (z-scores) for the quantified indices of music listening, music instrument playing, and music training; and the composite factor score for musical competence. For the path model analysis, we selected the MLR estimator (maximum likelihood estimation with robust standard errors) with its default settings for standard errors and test statistics (Rosseel, 2012). To address missing data (n = 37 in the standardized index of music instrument playing), we applied full information maximum likelihood (FIML). To assess the overall goodness of fit, we reported several commonly accepted goodness-of-fit indices, including the comparative fit index (CFI), root-mean-square error of approximation (RMSEA), and standardized root-mean-square residual (SRMR) (Brown, 2006; Kline, 2016). In addition, we reported the MLR chi-square test statistic along with its degrees of freedom.

Finally, to assess whether the proposed path model strikes a good balance between data-model fit and model complexity, we created an alternative path model in which music-induced physiological response directly influences individuals’ musical competence. To compare the proposed path model with the alternative, we used the lavTestLRT() function in the R package lavaan. This function computed the MLR chi-square test statistic for the proposed model against the alternative, following the method described by Satorra and Bentler (2001). In addition, we used Akaike's information criterion (AIC; Akaike, 1974) and the sample-size-adjusted Bayesian information criterion (SABIC; Schwarz, 1978). To determine which model best fits the data, we considered the model with the (significantly) smallest MLR chi-square test statistic and/or the smallest AIC and SABIC values to be a better fit for the empirical data.

Results

Descriptive Statistics, Reliability Estimates, and Bivariate Correlations

Descriptive statistics, reliability estimates, and bivariate correlations of the measures are presented in Table 1. The reliability estimates indicate that our proposed indicator of music-induced physiological response, measured using event-related SCR to musical stimuli, demonstrates a highly satisfactory level of reliability. The alpha (α) and omega (ω) coefficients show good reliability, with α = .97 and ω = .97. In addition, the composite factor score of musical competence derived from our set of computerized adaptive tests of musical listening shows acceptable reliability, with α = .62 and ω = .68.

Table 1.
Descriptive statistics, reliability estimates, and correlations among music-induced physiological response, active engagement with music (i.e., music listening, music instrument playing, and music training), and musical competence.

Measures M SD α ω 1 2 3 4 5

Music-induced physiological response .97 .97

1. Mean score of skin conductance peaks per musical excerpt 3.72 2.95

Active engagement with music

2. Music listening 9.94 5.07 .19** [.05, .32]

3. Music instrument playing 12.54 26.32 .02 [−.13, .18] .23** [.08, .37]

4. Music training 4.15 1.81 −.03 [−.16, .11] .12 [−.02, .26] .26** [.11, .39]

Musical competence .62 .68

5. Factor score of person estimators derived from the computerized adaptive music tests 0.00 0.33 .06 [−.08, .20] .06 [−.08, .34] .19* [.04, .34] .27** [.14, .40]

Note. M and SD denote mean and standard deviation, respectively. Reliability estimates are α (alpha, Cronbach, 1951) and ω (omega, McDonald, 1999). Values in square brackets indicate the 95% confidence interval for each correlation. The confidence interval represents a plausible range of population correlations that could have produced the sample correlation (Cumming, 2014). * indicates p < .05. ** indicates p < .01. Non-significant correlation coefficients are italicized.

Table 1 also shows that music-induced physiological response is significantly and positively correlated with music listening, one facet of active engagement with music (r = .19, p < .01). However, it is not significantly correlated with the other two facets (i.e., music instrument playing and music training) or with musical competence. Nevertheless, music instrument playing is significantly and positively related to both music training (r = .23, p < .01) and music listening (r = .26, p < .01), whereas music listening and music training are not significantly correlated. Lastly, music instrument playing (r = .19, p < .05) and music training (r = .27, p < .01) are significantly and positively associated with musical competence, while music listening is not.

Path Model Analysis and Model Comparison

The proposed path model fits the data well (see Table 2), with χ2 = 0.99, df = 1, p = .32, CFI = 1.00, RMSEA = .00, and SRMR = .02. As shown in Figure 2, music-induced physiological response significantly and positively influences music listening (β = .19, p < .05); however, it does not affect music instrument playing or music training. Music instrument playing (β = .16, p < .01) and music training (β = .23, p < .01) significantly and positively influence individuals’ musical competence, whereas music listening does not. In the proposed path model, music listening is significantly and positively associated with music instrument playing (r = .22, p < .01) but not with music training (r = .13, p > .05). Lastly, music instrument playing is significantly and positively linked to music training (r = .28, p < .01).

Figure 2.
The proposed path model shows the path estimates among music-induced physiological response, active engagement with music (i.e., music listening, music instrument playing, and music training), and musical competence. Non-significant path estimates are shown in italics and with dashed lines. * p < .05, ** p < .01.

Table 2.
Information criteria for path model analysis and comparison.

Path models χ² d.f. p CFI RMSEA CI 90% SRMR AIC SABIC Δχ² Δd.f. p(Δχ²) ΔAIC ΔSABIC

Proposed path model 0.99 1 .32 1.00 .00 .00−.20 .02 2719.83 2722.58 0.99 1 .32 0.94 1.09

Alternative path model 0.00 0 NA 1.00 .00 .00−.00 .00 2720.77 2723.67

Note. χ² = MLR chi-square test, d.f. = degrees of freedom, CFI = comparative fit index, RMSEA = root mean square error of approximation, CI = confidence interval, SRMR = standard root mean residual, AIC = Akaike's information criterion, SABIC = sample-adjusted Bayesian information criterion, Δ = change.

As shown in Table 2, the proposed path model was compared with the alternative path model. The comparison indicates that both models fit the data similarly, as shown by the chi-square difference test. However, the proposed path model shows a slightly better balance between data–model fit and model complexity than the alternative path model, according to the AIC and SABIC criteria (ΔAIC = 0.94; ΔSABIC = 1.09). Consequently, we prefer the simpler model (i.e., the proposed path model).

Discussion

The present study systematically examined the links and directional relationships among music-induced physiological response, active engagement with music, and musical competence. Drawing on the differentiated model of musical giftedness and talent by McPherson and Williamon (2006, 2015), we hypothesized that natural abilities, as evidenced by music-induced physiological response measured with event-related SCR to musical stimuli (Khalfa et al., 2002), influence the developmental process operationalized through self-reported musical behaviors, quantified by indices of music listening, music instrument playing, and music training, which reflect individuals’ active engagement with music (Chin & Rickard, 2012). In line with the model's directional relationships, we further posited that active engagement with music shapes individuals’ musical competence (Howe et al., 1998; Mosing et al., 2014; Slevc et al., 2016; Swaminathan & Schellenberg, 2017, 2018).

First, our findings indicate that music-induced physiological response can be reliably measured. This supports the idea of consistent individual differences in overall physiological responses, specifically in music-induced physiological response measured with event-related SCR to musical stimuli (Khalfa et al., 2002). In addition, our findings demonstrate that a series of computerized adaptive tests of musical listening assessing mistuning perception, beat alignment, and melodic discrimination (Harrison et al., 2017; Harrison & Müllensiefen, 2018; Larrouy-Maestri et al., 2019) yields a reliable composite score of individuals’ musical competence, echoing prior findings by Pausch et al. (2022) and Liu et al. (2023) and defining individuals’ “ability to perceive, remember, and discriminate musical melodies and rhythms” (Swaminathan & Schellenberg, 2018, p. 1).

Second, the correlational findings show that music-induced physiological response is significantly, though weakly, correlated with music listening, one facet of individuals’ active engagement with music. However, our findings indicate that music-induced physiological response is not associated with music instrument playing and music training, two further facets. Furthermore, while the different facets of active engagement with music exhibit partial positive relationships, only music instrument playing and music training show significant small-to-moderate correlations with individuals’ musical competence, whereas music listening does not. These findings partially align with our hypotheses.

Third, inspired by McPherson and Williamon's (2006, 2015) model, which serves as the theoretical framework for the hypothesized links and directional relationships, the proposed path model shows a good fit. The model's path estimates fully align with the correlational findings that individuals’ music-induced physiological response significantly and positively impacts music listening. However, music-induced physiological response does not predict music instrument playing or music training. In the proposed path model, music instrument playing and music training positively and significantly predict individuals’ musical competence, whereas music listening does not. Finally, the path model comparison indicates that an alternative path model, which allows a directional relationship from music-induced physiological response to musical competence, does not achieve a better balance between data–model fit and model complexity, as measured by AIC and SABIC, than the proposed path model. Therefore, we prefer our proposed path model (see Figure 2).

Our findings indicate that music-induced physiological response is positively related to music listening, representing an important facet of individuals’ active engagement with music and operationalizing a specific aspect of individuals’ developmental process within McPherson and Williamon's (2006, 2015) model. Music listening positively correlates with music instrument playing, another important facet of active engagement with music. However, only music instrument playing and music training predict individuals’ musical competence. In other words, individuals with stronger music-induced physiological response are more likely to seek opportunities to listen to music, leading to more frequent and regular music listening. Typically, music listening accompanies playing a musical instrument (including singing) and receiving music training (Persson, 2001; see also Fredrickson, 2001; Fredrickson & Levenson, 1998; Kahneman et al., 1999; Lamont, 2011). These two facets of individuals’ active engagement with music – music instrument playing and music training – lead to greater musical competence. Interestingly, listening to music alone does not. Only musical activities, in which listening is part of perception and action cycles, foster individuals’ musical competence (Trainor, 2005; see also Law & Zentner, 2012; Slevc et al., 2016; Swaminathan et al., 2017, 2021; Swaminathan & Schellenberg, 2018, 2020; Ullén et al., 2014; Wallentin et al., 2010).

Demonstrated links and directional relationships between music-induced physiological response and music listening support theoretical and empirical assumptions that individuals’ emotional responsiveness and preference for music (Brodsky, 2004; Csikszentmihalyi, 1998; Winner & Martino, 2000) are primary reasons people listen to music (Carlson et al., 2015; Juslin & Laukka, 2004; Juslin & Västfjäll, 2008; Panksepp, 1995; Saarikallio, 2008, 2010; Scherer & Zentner, 2001, 2008; Skånland, 2013; Sloboda, 1991; Zentner et al., 2008). Therefore, these factors can sustain motivation, interest (Marin & Bhattacharya, 2013; Marion-St-Onge et al., 2020; Sinnamon et al., 2012), and flow (Csikszentmihalyi, 1990; Fritz & Avsec, 2007; Nakamura & Csikszentmihalyi, 2002). Moreover, links and directional relationships between music instrument playing, music training, and musical competence can be traced to individuals’ musical backgrounds, which are assessed by the intensity of practice (i.e., duration, frequency, and regularity of instrument playing; evaluated using self-reported musical behavior with the index of music instrument playing (Chin & Rickard, 2012) as well as the highest level of formal music training, other forms of informal music training, and completion of certified examinations (Chin & Rickard, 2012). This relates to individuals’ musical competencies, specifically the musical competence assessed with computerized adaptive tests of musical listening: mistuning perception, beat alignment, and melodic discrimination. This aligns, on the one hand, with the differentiated model of musical giftedness and talent (McPherson & Williamon, 2006, 2015); on the other hand, it is quite logical because changes in music instrument playing and music training often accompany changes in individuals’ musical competencies (Trainor, 2005; see also Law & Zentner, 2012; Slevc et al., 2016; Swaminathan et al., 2017, 2021; Swaminathan & Schellenberg, 2018, 2020; Ullén et al., 2014; Wallentin et al., 2010). However, it is also logical that changes in individuals’ musical competencies occur alongside changes in their musical backgrounds (i.e., playing a musical instrument and receiving music training).

Limitations and Directions for Future Research

The study has several limitations that must be considered when interpreting the findings and suggesting directions for future research. We measured event-related SCR to musical stimuli to assess individual differences in music-induced physiological response. Furthermore, we used self-reported musical behavior (i.e., music listening, music instrument playing, and music training) to operationalize different facets of individuals’ active engagement with music. Lastly, we evaluated individuals’ musical competence with computerized adaptive tests of musical listening (i.e., mistuning perception, beat alignment, and melodic discrimination). Future research should validate and extend these measures using other instruments related to the same constructs, notably the differentiated model of musical giftedness and talent (McPherson & Williamon, 2006, 2015). For example, future research should validate our measure of music-induced physiological response using additional psychophysiological measures, such as heart rate, respiratory rate, and electromyography (for a review, see e.g., Hodges, 2016), as well as in response to various stimuli, such as images or sounds. In this context, numerous studies have shown that music impacts autonomic nervous system responses, such as SC (Bullack et al., 2018; Merrill et al., 2020, 2023; Mori & Iwanaga, 2017; Rickard, 2004; White & Rickard, 2016), along with individuals’ natural predispositions (Zentner & Kagan, 1998). However, to our knowledge, no studies have demonstrated that physiological responses can be divided into distinct domains (e.g., music, arts, or movies). Therefore, future research should investigate whether participants primarily respond to musical stimuli compared to other stimulation modalities (e.g., visual images). This study indicates that individuals differ consistently in their physiological response to music, as measured by event-related SCR to musical stimuli. However, it does not establish that some individuals exhibit a greater physiological response to music than the general tendency in SCR. In addition, future research should extend our measures of active engagement with music using other reliable and valid instruments, such as the entire MUSE questionnaire (Chin & Rickard, 2012), self-report questionnaires on musical activities (Fernholz et al., 2019; Müllensiefen et al., 2015), the Goldsmith Musical Sophistication Inventory (Gold-MSI; Müllensiefen et al., 2014), or the Music Engagement Questionnaire (MusEQ) developed by Vanstone et al. (2015). Further measures of active engagement with music should aid in refining the analysis of links between music-induced physiological response, active engagement with music, and musical competence. In addition, future research should expand the set of computerized adaptive tests of musical listening by incorporating measures such as the music emotion discrimination test (MacGregor et al., 2023; MacGregor & Müllensiefen, 2019), the timbre perception test (Lee & Müllensiefen, 2020), the pitch imagery arrow task (Gelding et al., 2021), or the beat-drop test (Cinelyte et al., 2022).

Moreover, the associated data consist solely of adults aged 18 to 44. Future research should replicate our findings with children and adolescents from diverse educational tracks and music-related and sociodemographic backgrounds. In addition, future research should consider intentionally sampling different types of musical and non-musical activities and levels of musical training (e.g., novice vs. expert; see, e.g., Fiedler et al., 2024) or musical sophistication (Müllensiefen et al., 2014). Furthermore, the associated data are cross-sectional, which limits the ability to identify direct causal effects or to draw insights into developmental dynamics. Thus, longitudinal study designs (as implemented, e.g., by Müllensiefen et al., 2022) should be conducted to examine the consistency of directional relationships, allowing for the exploration of developmental trajectories in individuals’ musical competencies over time and addressing questions about individual differences throughout adolescence and adulthood. Finally, future research should explore the relationship between music-induced physiological response and other psychological constructs, such as individuals’ self-reported arousal and valence during exposure to musical excerpts (Bradley & Lang, 1994). Overall, we propose that future research on individuals’ motivation, interest, self-concept, active engagement with music, and musical competencies should consider music-induced physiological and psychological responses, using a range of physiological and self-reported measures.

Conclusions

Inspired by the differentiated model of musical giftedness and talent proposed by McPherson and Williamon (2006, 2015), along with further theoretical and empirical considerations, this study contributes to the ongoing debate about the interplay between nature and nurture in individuals’ musical development. It examines the links and directional relationships among music-induced physiological response as measured by event-related SCR to musical stimuli; self-reported musical behaviors, including listening to music, playing a musical instrument, and undergoing music training, to operationalize active engagement with music; and individuals’ musical competence, evaluated with a set of computerized adaptive tests of musical listening. The correlational and path analysis findings indicate that music-induced physiological response is positively linked to music listening but not to music instrument playing or music training. Music instrument playing and music training are positively associated with individuals’ musical competence. The results of our study suggest that individuals with stronger music-induced physiological response tend to seek out opportunities for music listening, which is also connected to playing a musical instrument and receiving music training. In addition, while playing a musical instrument and receiving music training enhance musical competence in individuals, listening to music does not.

Supplemental Material

sj-docx-1-mns-10.1177_20592043261420355 - Supplemental material for Examining Links Between Music-Induced Physiological Response, Active Engagement with Music, and Musical Competence

Supplemental material, sj-docx-1-mns-10.1177_20592043261420355 for Examining Links Between Music-Induced Physiological Response, Active Engagement with Music, and Musical Competence by Daniel Fiedler, A. C. Frenzel and Daniel Müllensiefen in Music & Science

Path models	χ²	d.f.	p	CFI	RMSEA	CI 90%	SRMR	AIC	SABIC	Δχ²	Δd.f.	p(Δχ²)	ΔAIC	ΔSABIC
Proposed path model	0.99	1	.32	1.00	.00	.00−.20	.02	2719.83	2722.58	0.99	1	.32	0.94	1.09
Alternative path model	0.00	0	NA	1.00	.00	.00−.00	.00	2720.77	2723.67

Footnotes

Acknowledgments

Our thanks to all study participants in the FEEL Laboratory, Department of Psychology, Ludwig-Maximilians-Universität München (LMU).

Action Editor

Youn Kim, The University of Hong Kong, Department of Music.

Peer Review

Julia Merrill, Max Planck Institute for Empirical Aesthetics Mark Reybrouck, University of Leuven, Musicology Research Group.

Authors’ Contributions

DF: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; software; supervision; validation; visualization; writing – original draft; writing – review & editing. ACF: Conceptualization; methodology; resources; writing – review & editing. DM: Conceptualization; writing - review & editing.

Data Availability Statement

The datasets generated for this study are available upon request from the corresponding author.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics Approval

The Ethics Council of the Ludwig-Maximilians-Universität München (LMU) approved all study procedures. In accordance with German and European data protection laws, participants were informed about the study both before (via a written consent form) and after (via a debriefing letter) the data assessment. They provided their consent via the written consent form. Participants were also informed that their participation was voluntary and that their responses would be kept entirely anonymous.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The Postdoc Support Fund 2021 and the Postdoc Support Fund 2022 from Ludwig-Maximilians-Universität München (LMU) supported the preparation of this article.

ORCID iDs

Daniel Fiedler

A. C. Frenzel

Daniel Müllensiefen

Supplemental Material

Supplemental material for this article is available online.

References

Akaike

(1974). A new look at statistical model identification. In Parzen

Tanabe

Kitagawa

(Eds.), Selected papers of Hirotugu Akaike (pp. 716–723). Springer. https://doi.org/10.1007/978-1-4612-1694-0_16

Anders

Lotze

Erb

Grodd

Birbaumer

(2004). Brain activity underlying emotional valence and arousal: A response-related fMRI study. Human Brain Mapping, 23(4), 200–209. https://doi.org/10.1002/hbm.20048

Andreassi

J. L.

(2007). Psychophysiology: Human emotions and physiological response (5th ed.). Lawrence Erlbaum Associates Publishers.

Bamberger

(2005). What develops in musical development? In McPherson

(Ed.), The child as musician (pp. 69–92). Oxford University Press.

Bartlett

D. L.

(1996). Physiological responses to music and sound stimuli. In Hodges

D. A.

(Ed.), Handbook of music psychology (pp. 343–385). IMR.

Beier

E. J.

Janata

Hulbert

J. C.

Ferreira

(2022). Do you chill when I chill? A cross-cultural study of strong emotional responses to music. Psychology of Aesthetics, Creativity, and the Arts, 16(1), 74–96. https://doi.org/10.1037/aca0000310

Benedek

Kaernbach

(2010). A continuous measure of phasic electrodermal activity. Journal of Neuroscience Methods, 190(1), 80–91. https://doi.org/10.1016/j.jneumeth.2010.04.028

Benedek

Kaernbach

(2011). Physiological correlates and emotional specificity of human piloerection. Biological Psychology, 86(3), 320–329. https://doi.org/10.1016/j.biopsycho.2010.12.012

Bhattacharya

Petsche

Pereda

(2001). Interdependencies in the spontaneous EEG while listening to music. International Journal of Psychophysiology, 42(3), 287–301. https://doi.org/10.1016/S0167-8760(01)00153-2

10.

Boucsein

(1992). Electrodermal activity. Plenum University Press.

11.

Boucsein

(2012). Electrodermal activity (2nd ed.). Springer. https://doi.org/10.1007/978-1-4614-1126-0

12.

Boyle

J. D.

Radocy

R. E.

(1987). Measurement and evaluation of musical experiences. Schirmer Books.

13.

Bradley

M. M.

Lang

P. J.

(1994). Measuring emotion: The Self-Assessment Manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry, 25(1), 49–59. https://doi.org/10.1016/0005-7916(94)90063-9

14.

Brattico

Bogert

Alluri

Tervaniemi

Eerola

Jacobsen

(2016). It’s sad but I like it: The neural dissociation between musical emotions and liking in experts and laypersons. Frontiers in Human Neuroscience, 9, 676. https://doi.org/10.3389/fnhum.2015.00676

15.

Brodsky

(2004). Developing the Keele Assessment of Auditory Style (KAAS): A factor-analytical study of cognitive trait predisposition for audition. Musicae Scientiae, 8(1), 83–108. https://doi.org/10.1177/102986490400800104

16.

Brown

T. A.

(2006). Confirmatory factor analysis for applied research. The Guilford Press.

17.

Buck

(1994). Social and emotional functions in facial expression and communication: The readout hypothesis. Biological Psychology, 38(2-3), 95–115. https://doi.org/10.1016/0301-0511(94)90032-9

18.

Bullack

Büdenbender

Roden

Kreutz

(2018). Psychophysiological responses to “happy” and “sad” music: A replication study. Music Perception, 35(4), 502–517. https://doi.org/10.1525/mp.2018.35.4.502

19.

Burgoyne

A. P.

Harris

L. J.

Hambrick

D. Z.

(2019). Predicting piano skill acquisition in beginners: The role of general intelligence, music aptitude, and mindset. Intelligence, 76, Article 101383. https://doi.org/10.1016/j.intell.2019.101383

20.

Carlson

Saarikallio

Toiviainen

Bogert

Kliuchko

Brattico

(2015). Maladaptive and adaptive emotion regulation through music: A behavioral and neuroimaging study of males and females. Frontiers in Human Neuroscience, 9, 466. https://doi.org/10.3389/fnhum.2015.00466

21.

Chin

(2025a, December 17). MUSE questionnaire. Retrieved from https://tcchin.com/wp-content/uploads/2021/06/Chin-Rickard_MUSE_Questionnaire.pdf.

22.

Chin

(2025b, December 17). MUSE questionnaire scoring sheet. Retrieved from https://tcchin.com/wp-content/uploads/2021/06/Chin-Rickard_MUSE_Scoring-Sheet.pdf.

23.

Chin

Rickard

N. S.

(2012). The Music Use (MUSE) questionnaire: An instrument to measure engagement in music. Music Perception, 29(4), 429–446. https://doi.org/10.1525/mp.2012.29.4.429

24.

Cinelyte

Cannon

Patel

A. D.

Müllensiefen

(2022). Testing beat perception without sensory cues to the beat: The Beat-Drop Alignment Test (BDAT). Attention, Perception, & Psychophysics, 84(8), 2702–2714. https://doi.org/10.3758/s13414-022-02592-2

25.

Cohrdes

Grolig

Schroeder

(2019). The development of music competencies in preschool children: Effects of a training program and the role of environmental factors. Psychology of Music, 47(3), 358–375. https://doi.org/10.1177/0305735618756764

26.

Correia

A. I.

Vincenzi

Vanzella

Pinheiro

A. P.

Schellenberg

E. G.

Lima

C. F.

(2023). Individual differences in musical ability among adults with no music training. Quarterly Journal of Experimental Psychology, 76(7), 1585–1598. https://doi.org/10.1177/17470218221128557

27.

Critchley

Nagai

(2013). Electrodermal Activity (EDA). In Gellman

M. D.

Turner

J. R.

(Eds.), Encyclopedia of behavioral medicine (pp. 666–669). Springer. https://doi.org/10.1007/978-1-4419-1005-9_13 .

28.

Critchley

H. D.

(2002). Electrodermal responses: What happens in the brain. The Neuroscientist, 8(2), 132–142. https://doi.org/10.1177/107385840200800209

29.

Cronbach

L. J.

(1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555

30.

Csikszentmihalyi

(1990). Flow: The psychology of optimal experience. Harper & Row.

31.

Csikszentmihalyi

(1998). Creativity and genius: A systems perspective. In Steptoe

(Ed.), Genius and mind: Studies of creativity and temperament (pp. 39–64). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198523734.003.0003

32.

Cumming

(2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29. https://doi.org/10.1177/0956797613504966

33.

Davidson

J. W.

(2011). Musical participation: Expectations, experiences, and outcomes. In Deliege

Davidson

J. W.

(Eds.), Music and the mind: Essays in honour of John Sloboda (pp. 65–87). Oxford University Press.

34.

Dawson

M. E.

Schell

A. M.

Filion

D. L.

(2007). The electrodermal system. In Cacioppo

J. T.

Tassinary

L. G.

Berntson

G. G.

(Eds.), Handbook of psychophysiology (pp. 159–181). Cambridge University Press. https://doi.org/10.1017/CBO9780511546396.007

35.

Eerola

Vuoskoski

J. K.

(2011). A comparison of the discrete and dimensional models of emotion in music. Psychology of Music, 39(1), 18–49. https://doi.org/10.1177/0305735610362821

36.

Egermann

Fernando

Chuen

McAdams

(2015). Music induces universal emotion-related psychophysiological responses: Comparing Canadian listeners to Congolese pygmies. Frontiers in Psychology, 5, 1341. https://doi.org/10.3389/fpsyg.2014.01341

37.

Ekman

(1993). Facial expression and emotion. American Psychologist, 48(4), 384–392. https://doi.org/10.1037/0003-066X.48.4.384

38.

Elliott

(1995). Music matters: A new philosophy of music education. Oxford University Press.

39.

Etzel

J. A.

Johnsen

E. L.

Dickerson

Tranel

Adolphs

(2006). Cardiovascular and respiratory responses during musical mood induction. International Journal of Psychophysiology, 61(1), 57–69. https://doi.org/10.1016/j.ijpsycho.2005.10.025

40.

Fernholz

Menzel

Jabusch

H. C.

Gembris

Fischer

Kendel

Kreutz

Schmidt

Willich

S. N.

Weikert

(2019). Musikalische Inaktivität – ein Risikofaktor? Vorstellung eines kurzen Fragebogens zur Erfassung der musikalischen Aktivität (MusA) [Musical inactivity – A risk factor? A short questionnaire to assess musical activity (MusA)]. Gesundheitswesen, 81(11), 907–910. https://doi.org/10.1055/s-0044-101143

41.

Fiedler

Hasselhorn

Arens

A. K.

Frenzel

A. C.

Vispoel

W. P.

(2024). Validating scores from the short form of the Music Self-Perception Inventory (MUSPI-S) with seventh- to ninth-grade school students in Germany. Psychology of Music, 53(5), 794–815. https://doi.org/10.1177/03057356241272999

42.

Fowles

D. C.

Christie

M. J.

Edelberg

Grings

W. W.

Lykken

D. T.

Venables

P. H.

(1981). Publication recommendations for electrodermal measurements. Psychophysiology, 18(3), 232–239. https://doi.org/10.1111/j.1469-8986.1981.tb03024.x

43.

Fredrickson

B. L.

(2001). The role of positive emotions in positive psychology: The broaden and build theory of positive emotions. American Psychologist, 56(3), 218–226. https://doi.org/10.1037/0003-066X.56.3.218

44.

Fredrickson

B. L.

Levenson

R. W.

(1998). Positive emotions speed recovery from the cardiovascular sequelae of negative emotions. Cognition and Emotion, 12(2), 191–220. https://doi.org/10.1080/026999398379718

45.

Fritz

B. S.

Avsec

(2007). The experience of flow and subjective well-being of music students. Psihološka Obzorja/Horizons of Psychology, 16(2), 5–17.

46.

Fritz

Jentschke

Gosselin

Sammler

Peretz

Turner

Friederici

A. D.

Koelsch

(2009). Universal recognition of three basic emotions in music. Current Biology, 19(7), 573–576. https://doi.org/10.1016/j.cub.2009.02.058

47.

Gabrielsson

(2011). Strong experiences with music: Music is much more than just music. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199695225.001.0001

48.

Gabrielsson

Whaley

Sloboda

(2016). Peak experiences in music. In Hallam

Cross

Thaut

(Eds.), The Oxford handbook of music psychology (pp. 745–758). Oxford University Press.

49.

Gagné

(2004). Transforming gifts into talents: The DMGT as a developmental theory. High Ability Studies, 15(2), 119–147. https://doi.org/10.1080/1359813042000314682

50.

Gagné

(2013). The DMGT: Changes within, beneath, and beyond. Talent Development & Excellence, 5(1), 5–19.

51.

Gardner

(1983). Frames of mind: The theory of multiple intelligences. Basic Books.

52.

Gelding

R. W.

Harrison

P. M. C.

Silas

Johnson

B. W.

Thompson

W. F.

Müllensiefen

(2021). An efficient and adaptive test of auditory mental imagery. Psychological Research, 85(3), 1201–1220. https://doi.org/10.1007/s00426-020-01322-3

53.

Gembris

(ed.). (2006). Musical development from a lifespan perspective. Peter Lang.

54.

Gold

B. P.

Mas-Herrero

Zeighami

Benovoy

Dagher

Zatorre

R. J.

(2019). Musical reward prediction errors engage the nucleus accumbens and motivate learning. Proceedings of the National Academy of Sciences of the United States of America, 116(8), 3310–3315. https://doi.org/10.1073/pnas.1809855116

55.

Goldstein

(1980). Thrills in response to music and other stimuli. Physiological Psychology, 8(1), 126–129. https://doi.org/10.3758/BF03326460

56.

Gordon

(1993). Learning sequences in music: Skill, content, and patterns. G.I.A. Publications.

57.

Gordon

E. E.

(2007). Learning sequences in music: A contemporary music learning theory. G.I.A. Publications.

58.

Habibi

Damasio

Ilari

Elliott Sachs

Damasio

(2018). Music training and child development: A review of recent findings from a longitudinal study. Annals of the New York Academy of Sciences, 1423(1), 73–81. https://doi.org/10.1111/nyas.13606

59.

Hallam

(2015). Musicality. In McPherson

G. E.

(Ed.), The child as musician: A handbook of musical development (2nd ed., pp. 67–80). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198744443.003.0004

60.

Hargreaves

Lamont

(2017). The psychology of musical development. Cambridge University Press.

61.

Harrison

P. M. C.

Collins

Müllensiefen

(2017). Applying modern psychometric techniques to melodic discrimination testing: Item response theory, computerised adaptive testing, and automatic item generation. Scientific Reports, 7(1), 3618. https://doi.org/10.1038/s41598-017-03586-z

62.

Harrison

P. M. C.

Müllensiefen

(2018). Development and validation of the computerised adaptive beat alignment test (CA-BAT). Scientific Reports, 8(1), 12395. https://doi.org/10.1038/s41598-018-30318-8

63.

Harter

J. K.

Schmidt

F. L.

Hayes

T. L.

(2002). Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis. Journal of Applied Psychology, 87(2), 268–279. https://doi.org/10.1037/0021-9010.87.2.268

64.

Harvey

A. R.

(2017). Music, evolution, and the harmony of souls. Oxford University Press.

65.

Hodges

D. A.

(2016). Bodily responses to music. In Hallam

Cross

Thaut

(Eds.), The Oxford handbook of music psychology (2nd ed., pp. 183–196). Oxford University Press.

66.

Honing

(2018). The origins of musicality. The MIT Press.

67.

Howe

M. J.

Davidson

J. W.

Sloboda

J. A.

(1998). Innate talents: Reality or myth? Behavioral and Brain Sciences, 21(3), 399–442. https://doi.org/10.1017/s0140525x9800123x

68.

Huron

(2006). Sweet anticipation: Music and the psychology of expectation. The MIT Press. https://doi.org/10.7551/mitpress/6575.001.0001

69.

iMotions. (2021). iMotions research platform (Version 8.2) [Computer software]. https://imotions.com/

70.

Izard

C. E.

(1977). Human emotions. Plenum Press.

71.

Izard

C. E.

(1989). The structure and functions of emotions: Implications for cognition, motivation, and personality. In Cohen

I. S.

(Ed.), The G. Stanley Hall lecture series (pp. 39–73). American Psychological Association. https://doi.org/10.1037/10090-002

72.

Janata

Birk

J. L.

Van Horn

J. D.

Leman

Tillmann

Bharucha

J. J.

(2002). The cortical topography of tonal structures underlying Western music. Science, 298(5601), 2167–2170. https://doi.org/10.1126/science.1076262

73.

Jensen

A. R.

(1998). The g factor: The science of mental ability. Praeger Publishers/Greenwood Publishing Group.

74.

Jensen

A. R.

(2002). Psychometric g: Definition and substantiation. In Sternberg

R. J.

Grigorenko

E. L.

(Eds.), The general factor of intelligence: How general is it? (pp. 39–53). Lawrence Erlbaum Associates Publishers.

75.

Juslin

P. N.

Laukka

(2004). Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening. Journal of New Music Research, 33(3), 217–238. https://doi.org/10.1080/0929821042000317813

76.

Juslin

P. N.

Liljeström

Västfjäll

Lundqvist

L.-O.

(2010). How does music evoke emotions? Exploring the underlying mechanisms. In Juslin

P. N.

Sloboda

(Eds.), Handbook of music and emotion: Theory, research, applications (pp. 605–643). Oxford University Press.

77.

Juslin

P. N.

Västfjäll

(2008). Emotional responses to music: The need to consider underlying mechanisms. Erratum. Behavioral and Brain Sciences, 31(6), 751. https://doi.org/10.1017/S0140525X08006079

78.

Kahneman

Diener

Schwarz

(1999). Well-being: The foundations of hedonic psychology. Russell Sage Foundation.

79.

Kaufman

S. B.

Duckworth

A. L.

(2017). World-class expertise: A developmental model. WIRES Cognitive Science, 8(1-2), 1–7. https://doi.org/10.1002/wcs.1365

80.

Khalfa

Peretz

Blondin

J.-P.

Manon

(2002). Event-related skin conductance responses to musical emotions in humans. Neuroscience Letters, 328(2), 145–149. https://doi.org/10.1016/s0304-3940(02)00462-7

81.

Kirnarskaya

(2009). The natural musician: On abilities, giftedness, and talent. Oxford University Press.

82.

Kirnarskaya

Winner

(1997). Musical ability in a new key: Exploring the expressive ear for music. Psychomusicology: A Journal of Research in Music Cognition, 16(1-2), 2–16. https://doi.org/10.1037/h0094071

83.

Kline

R. B.

(2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.

84.

Koelsch

Fritz

Cramon

Müller

D. Y.

& Friederici

(2006). Investigating emotion with music: An fMRI study. Human Brain Mapping, 27(3), 239–250. https://doi.org/10.1002/hbm.20180

85.

Koelsch

Jäncke

(2015). Music and the heart. European Heart Journal, 36(44), 3043–3049. https://doi.org/10.1093/eurheartj/ehv430

86.

Koelsch

Kasper

Sammler

Schulze

Gunter

Friederici

A. D.

(2004). Music, language and meaning: Brain signatures of semantic processing. Nature Neuroscience, 7(3), 302–307. https://doi.org/10.1038/nn1197

87.

Kreutz

Bongard

Rohrmann

Hodapp

Grebe

(2004). Effects of choir singing or listening on secretory immunoglobulin A, cortisol, and emotional state. Journal of Behavioral Medicine, 27(6), 623–635. https://doi.org/10.1007/s10865-004-0006-9

88.

Krumhansl

C. L.

(1997). An exploratory study of musical emotions and psychophysiology. Canadian Journal of Experimental Psychology, 51(4), 336–353. https://doi.org/10.1037/1196-1961.51.4.336

89.

Lamont

(2011). The beat goes on: Music education, identity and lifelong learning. Music Education Research, 13(4), 369–388. https://doi.org/10.1080/14613808.2011.638505

90.

Lamont

(2012). Emotion, engagement and meaning in strong experiences of music performance. Psychology of Music, 40(5), 574–594. https://doi.org/10.1177/0305735612448510

91.

Lang

P. J.

(1995). The emotion probe: Studies of motivation and attention. American Psychologist, 50(5), 372–385. https://doi.org/10.1037/0003-066X.50.5.372

92.

Lang

P. J.

Bradley

M. M.

Cuthbert

B. N.

(1998). Emotion, motivation, and anxiety: Brain mechanisms and psychophysiology. Biological Psychiatry, 44(12), 1248–1263. https://doi.org/10.1016/s0006-3223(98)00275-3

93.

Larrouy-Maestri

Harrison

P. M. C.

Müllensiefen

(2019). The mistuning perception test: A new measurement instrument. Behavior Research Methods, 51(2), 663–675. https://doi.org/10.3758/s13428-019-01225-1

94.

Larsen

J. T.

Berntson

G. G.

Poehlmann

K. M.

Ito

T. A.

Cacioppo

J. T.

(2008). The psychophysiology of emotion. In Lewis

Haviland-Jones

J. M.

Barrett

L. F.

(Eds.), Handbook of emotions (pp. 180–195). The Guilford Press.

95.

Law

L. N. C.

Zentner

(2012). Assessing musical abilities objectively: Construction and validation of the profile of music perception skills. PLoS ONE, 7(12), e52508. https://doi.org/10.1371/journal.pone.0052508

96.

LeDoux

J. E.

(1986). Sensory systems and emotion: A model of affective processing. Integrative Psychiatry, 4(4), 237–243.

97.

LeDoux

J. E.

(1994). Emotion, memory and the brain. Scientific American, 270(6), 50–57. https://doi.org/10.1038/scientificamerican0694-50

98.

Lee

Müllensiefen

(2020). The timbre perception test (TPT): A new interactive musical assessment tool to measure timbre perception ability. Attention, Perception, & Psychophysics, 82(7), 3658–3675. https://doi.org/10.3758/s13414-020-02058-3

99.

Leiner

D. J.

(2024). SoSci survey (Version 3.5.02) [Computer software]. https://www.soscisurvey.de

100.

Levenson

R. W.

(1994). Human emotions: A functional view. In Ekman

Davidson

R. J.

(Eds.), The nature of emotion: Fundamental questions (pp. 123–126). Oxford University Press.

101.

Leventhal

E. A.

(1984). Aging and the perception of illness. Research on Aging, 6(1), 119–135. https://doi.org/10.1177/0164027584006001007

102.

Levitin

D. J.

(2006). This is your brain on music: The science of a human obsession. Dutton/Penguin Books.

103.

Lewis

Wolan Sullivan

(2005). The development of self-conscious emotions. In Elliot

A. J.

Dweck

C. S.

(Eds.), Handbook of competence and motivation (pp. 185–201). Guilford Publications.

104.

Liu

Hilton

C. B.

Bergelson

Mehr

S. A.

(2023). Language experience predicts music processing in a half-million speakers of fifty-four languages. Current Biology, 33(10), 1916–1925. https://doi.org/10.1016/j.cub.2023.03.067

105.

Lundqvist

L.-O.

Carlsson

Hilmersson

Juslin

P. N.

(2008). Emotional responses to music: Experience, expression, and physiology. Psychology of Music, 37(1), 61–90. https://doi.org/10.1177/0305735607086048

106.

MacGregor

Müllensiefen

(2019). The musical emotion discrimination task: A new measure for assessing the ability to discriminate emotions in music. Frontiers in Psychology, 10, 1955. https://doi.org/10.3389/fpsyg.2019.01955

107.

MacGregor

Ruth

Müllensiefen

(2023). Development and validation of the first adaptive test of emotion perception in music. Cognition and Emotion, 37(2), 284–302. https://doi.org/10.1080/02699931.2022.2162003

108.

Mackintosh

(2011). IQ and human intelligence (2nd ed.). Oxford University Press.

109.

Mainwaring

(1941). The meaning of musicianship: A problem in the teaching of music. British Journal of Educational Psychology, 11(3), 205–214.

110.

Mainwaring

(1947). The assessment of musical ability. British Journal of Educational Psychology, 17(1), 83–96. https://doi.org/10.1111/j.2044-8279.1947.tb02214.x

111.

Mankel

Bidelman

G. M.

(2018). Inherent auditory skills rather than formal music training shape the neural encoding of speech. Proceedings of the National Academy of Sciences of the United States of America, 115(51), 13129–13134. https://doi.org/10.1073/pnas.1811793115

112.

Marin

M. M.

Bhattacharya

(2013). Getting into the musical zone: Trait emotional intelligence and amount of practice predict flow in pianists. Frontiers in Psychology, 4, 853. https://doi.org/10.3389/fpsyg.2013.00853

113.

Marion-St-Onge

Weiss

M. W.

Sharda

Peretz

(2020). What makes musical prodigies? Frontiers in Psychology, 11, 566373. https://doi.org/10.3389/fpsyg.2020.566373

114.

Mas-Herrero

Marco-Pallares

Lorenzo-Seva

Zatorre

R. J.

Rodriguez-Fornells

(2013). Individual differences in music reward experiences. Music Perception, 31(2), 118–138. https://doi.org/10.1525/mp.2013.31.2.118

115.

Maslach

Schaufeli

W. B.

Leiter

M. P.

(2001). Job burnout. Annual Review of Psychology, 52, 397–422. https://doi.org/10.1146/annurev.psych.52.1.397

116.

Mauss

I. B.

Robinson

M. D.

(2009). Measures of emotion: A review. Cognition and Emotion, 23(2), 209–237. https://doi.org/10.1080/02699930802204677

117.

McDonald

R. P.

(1999). Test theory: A unified treatment. Lawrence Erlbaum.

118.

McPherson

G. E.

Hallam

(2016). Musical potential. In Hallam

Cross

Thaut

(Eds.), The Oxford handbook of music psychology (pp. 433–448). Oxford University Press.

119.

McPherson

G. E.

McCormick

(2006). Self-efficacy and music performance. Psychology of Music, 34(3), 322–336. https://doi.org/10.1177/0305735606064841

120.

McPherson

G. E.

Williamon

(2006). Giftedness and talent. In McPherson

G. E.

(Ed.), The child as musician: A handbook of musical development (pp. 239–256). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198530329.003.0012

121.

Mcpherson

G. E.

Williamon

(2015). Building gifts into musical talents. In McPherson

G. E.

(Ed.), The child as musician: A handbook of musical development (2nd ed., pp. 340–360). Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198744443.003.0018

122.

Merrill

Ackermann

T. I.

Czepiel

(2023). Effects of disliked music on psychophysiology. Scientific Reports, 13(1), 20641. https://doi.org/10.1038/s41598-023-46963-7

123.

Merrill

Omigie

Wald-Fuhrmann

(2020). Locus of emotion influences psychophysiological reactions to music. PLoS ONE, 15(8), e0237641. https://doi.org/10.1371/journal.pone.0237641

124.

Mori

Iwanaga

(2017). Two types of peak emotional responses to music: The psychophysiology of chills and tears. Scientific Reports, 7, 46063. https://doi.org/10.1038/srep46063

125.

Morley

(2013). The prehistory of music: Evolutionary origins and archaeology of human musicality. Oxford University Press.

126.

Mosing

M. A.

Pedersen

N. L.

Madison

Ullén

(2014). Genetic pleiotropy explains associations between musical auditory discrimination and intelligence. PLoS ONE, 9(11), e113874. https://doi.org/10.1371/journal.pone.0113874

127.

Müllensiefen , et al. (2025, December 17). LongGold Battery Demo. Retrieved from https://shiny.gold-msi.org/longgold_demo/?language=en&p_id=954516c7b69bdbc827afdb 110c823e8c1473396528954b6535a75d568f82521e.

128.

Müllensiefen

Elvers

Frieler

(2022). Musical development during adolescence: Perceptual skills, cognitive resources, and musical training. Annals of the New York Academy of Science, 1518(1), 264–281. https://doi.org/10.1111/nyas.14911

129.

Müllensiefen

Gingras

Musil

Stewart

(2014). The musicality of non-musicians: An index for assessing musical sophistication in the general population. PLoS ONE, 9(2), e89642. https://doi.org/10.1371/journal.pone.0089642

130.

Müllensiefen

Harrison

P. M. C.

Caprini

Fancourt

(2015). Investigating the importance of self-theories of intelligence and musicality for students’ academic and musical achievement. Frontier in Psychology, 6, 1702. https://doi.org/10.3389/fpsyg.2015.01702

131.

Münte

T. F.

Altenmüller

Jäncke

(2002). The musician's brain as a model of neuroplasticity. Nature Reviews. Neuroscience, 3(6), 473–478. https://doi.org/10.1038/nrn843

132.

Nakamura

Csikszentmihalyi

(2002). The concept of flow. In Snyder

C. R.

Lopez

S. J.

(Eds.), Handbook of positive psychology (pp. 89–105). Oxford University Press.

133.

North

A. C.

Hargreaves

D. J.

(1997). Liking, arousal potential, and the emotions expressed by music. Scandinavian Journal of Psychology, 38(1), 45–53. https://doi.org/10.1111/1467-9450.00008

134.

Pace-Schott

E. F.

Amole

M. C.

Aue

Balconi

Bylsma

L. M.

Critchley

Demaree

H. A.

Friedman

B. H.

Gooding

A. E. K.

Gosseries

Jovanovic

(2019). Physiological feelings. Neuroscience and Biobehavioral Reviews, 103, 267–304. https://doi.org/10.1016/j.neubiorev.2019.05.002

135.

Panksepp

(1995). The emotional sources of “chills” induced by music. Music Perception, 13(2), 171–207. https://doi.org/10.2307/40285693

136.

Panksepp

Bernatzky

(2002). Emotional sounds and the brain: The neuro-affective foundations of musical appreciation. Behavioural Processes, 60(2), 133–155. https://doi.org/10.1016/s0376-6357(02)00080-3

137.

Papoušek

(1996). Musicality in infancy research: Biological and cultural origins of early musicality. In Deliège

Sloboda

J. A.

(Eds.), Musical beginnings: Origins and development of musical competence (pp. 37–55). Oxford University Press.

138.

Patel

A. D.

(2008). Music, language, and the brain. Oxford University Press.

139.

Pausch

Müllensiefen

Kopiez

(2022). Musikalischer g-Faktor oder multiple Faktoren? Struktur und Leistungskennwerte der musikalischen Hörfähigkeit von Jugendlichen [Musical g factor or multiple factors? Structure and normas of musical ability of adolescents]. In Fischinger

Louven

(Eds.), Musikpsychologie – Empirische Forschungen – Ästhetische Experimente (Vol. 30, pp. 1–25). Waxmann. https://doi.org/10.5964/jbdgm.89

140.

Pedersen

(2023). Moving towards a clear and effective validation protocol for galvanic skin response research. Research project. Ludwig-Maximilians-Universität München (LMU).

141.

Persson

R. S.

(2001). The subjective world of the performer. In Juslin

P. N.

Sloboda

J. A.

(Eds.), Music and emotion: Theory and research (pp. 275–289). Oxford University Press.

142.

Plutchik

(1994). The psychology and biology of emotion. Harper Collins College Publishers.

143.

Popescu

Otsuka

Ioannides

A. A.

(2004). Dynamics of brain activity in motor and frontal cortical areas during music listening: A magnetoencephalographic study. NeuroImage, 21(4), 1622–1638. https://doi.org/10.1016/j.neuroimage.2003.11.002

144.

Putkinen

Tervaniemi

Saarikivi

De Vent

Huotilainen

(2014). Investigating the effects of musical training on functional brain development with a novel melodic MMN paradigm. Neurobiology of Learning and Memory, 110, 8–15. https://doi.org/10.1016/j.nlm.2014.01.007

145.

R Core Team. (2020). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/ .

146.

Reeve

Jang

Carrell

Jeon

Barch

(2004). Enhancing students’ engagement by increasing teachers’ autonomy support. Motivation and Emotion, 28(2), 147–169. https://doi.org/10.1023/B:MOEM.0000032312.95499.6f

147.

Revelle

(2023). psych: Procedures for psychological, psychometric, and personality research. Northwestern University, Evanston, Illinois. R package version 2.3.6, https://CRAN.R-project.org/package=psych.

148.

Reybrouck

Eerola

(2017). Music and its inductive power: A psychobiological and evolutionary approach to musical emotions. Frontiers in Psychology, 8, Article 494. https://doi.org/10.3389/fpsyg.2017.00494

149.

Rickard

N. S.

(2004). Intense emotional responses to music: A test of the physiological arousal hypothesis. Psychology of Music, 32(4), 371–388. https://doi.org/10.1177/0305735604046096

150.

Rickard

N. S.

(2012). Music listening and emotional well-being. In Rickard

N. S.

McFerran

(Eds.), Lifelong engagement with music: Benefits for mental health and well-being (pp. 209–240). Nova Science Publishers.

151.

Rosseel

(2012). Lavaan: An R package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36. https://doi.org/10.18637/jss.v048.i02

152.

Russell

J. A.

(2003). Core affect and the psychological construction of emotion. Psychological Review, 110(1), 145–172. https://doi.org/10.1037/0033-295X.110.1.145

153.

Russell

V. J.

Ainley

Frydenberg

(2005). Schooling issues digest: Student motivation and engagement.

154.

Saarikallio

S. H.

(2008). Music in mood regulation: Initial scale development. Musicae Scientiae, 12(2), 291–309. https://doi.org/10.1177/102986490801200206

155.

Saarikallio

S. H.

(2010). Music as emotional self-regulation throughout adulthood. Psychology of Music, 39(3), 307–327. https://doi.org/10.1177/0305735610374894

156.

Saks

A. M.

(2006). Antecedents and consequences of employee engagement. Journal of Managerial Psychology, 21(7), 600–619. https://doi.org/10.1108/02683940610690169

157.

Salimpoor

Benovoy

Larcher

Dagher

Zatorre

R. J.

(2011). Anatomically distinct dopamine release during anticipation and experience of peak emotion to music. Nature Neuroscience, 14, 257–262. https://doi.org/10.1038/nn.2726

158.

Salimpoor

V. N.

Benovoy

Longo

Cooperstock

J. R.

Zatorre

R. J.

(2009). The rewarding aspects of music listening are related to degree of emotional arousal. PLoS ONE, 4(10), e7487. https://doi.org/10.1371/journal.pone.0007487

159.

Salimpoor

V. N.

van den Bosch

Kovacevic

Mcintosh

A. R.

Dagher

Zatorre

R. J.

(2013). Interactions between the nucleus accumbens and auditory cortices predict music reward value. Science, 340(6129), 216–219. https://doi.org/10.1126/science.1231059

160.

Salimpoor

V. N.

Zald

D. H.

Zatorre

R. J.

Dagher

Mcintosh

A. R.

(2015). Predictions and the brain: How musical sounds become rewarding. Trends in Cognitive Sciences, 19(2), 86–91. https://doi.org/10.1016/j.tics.2014.12.001

161.

Satorra

Bentler

P. M.

(2001). A scaled difference chi-square test statistic for moment structure analysis. Psychometrika, 66(4), 507–514. https://doi.org/10.1007/BF02296192

162.

Schäfer

Sedlmeier

Städtler

Huron

(2013). The psychological functions of music listening. Frontiers in Psychology, 4, 511. https://doi.org/10.3389/fpsyg.2013.00511

163.

Schaufeli

W. B.

Bakker

A. B.

(2004). Job demands, job resources and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior, 25(3), 293–315. https://doi.org/10.1002/job.248

164.

Schaufeli

W. B.

Salanova

González-Romá

Bakker

A. B.

(2002). The measurement of engagement and burnout: A two sample confirmatory factor analytic approach. Journal of Happiness Studies: An Interdisciplinary Forum on Subjective Well-Being, 3(1), 71–92. https://doi.org/10.1023/A:1015630930326

165.

Schellenberg

E. G.

(2015). Music training and speech perception: A gene-environment interaction. Annals of the New York Academy of Sciences, 1337, 170–177. https://doi.org/10.1111/nyas.12627

166.

Schellenberg

E. G.

Weiss

M. W.

(2013). Music and cognitive abilities. In Deutsch

(Ed.), The psychology of music (3rd ed., pp. 499–550). Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-381460-9.00012-2

167.

Scherer

Zentner

(2008). Music evoked emotions are different—More often aesthetic than utilitarian. Behavioral and Brain Sciences, 31(5), 595–596. https://doi.org/10.1017/S0140525X08005505

168.

Scherer

K. R.

Coutinho

(2013). How music creates emotion: A multifactorial process approach. In Cochrane

Fantini

Scherer

K. R.

(Eds.), The emotional power of music: Multidisciplinary perspectives on musical arousal, expression, and social control (pp. 121–145). Oxford University Press.

169.

Scherer

K. R.

Zentner

M. R.

(2001). Emotional effects of music: Production rules. In Juslin

P. N.

Sloboda

J. A.

(Eds.), Music and emotion: Theory and research (pp. 361–392). Oxford University Press.

170.

Schwarz

(1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136

171.

Sergent

Zuck

Terriah

MacDonald

(1992). Distributed neural network underlying musical sight-Reading and keyboard performance. Science, 257(5066), 106–109. https://doi.org/10.1126/science.1621084

172.

Shifriss

Bodner

Palgi

(2015). When you’re down and troubled: Views on the regulatory power of music. Psychology of Music, 43(6), 793–807. https://doi.org/10.1177/0305735614540360

173.

Sinnamon

Moran

O'Connell

(2012). Flow among musicians: Measuring peak experiences of student performers. Journal of Research in Music Education, 60(1), 6–25. https://doi.org/10.1177/0022429411434931

174.

Skånland

M. S.

(2013). Everyday music listening and affect regulation: The role of MP3 players. International Journal of Qualitative Studies on Health and Well-Being, 8, 20595. https://doi.org/10.3402/qhw.v8i0.20595

175.

Slevc

L. R.

Davey

N. S.

Buschkuehl

Jaeggi

S. M.

(2016). Tuning the mind: Exploring the connections between musical ability and executive functions. Cognition, 152, 199–211. https://doi.org/10.1016/j.cognition.2016.03.017

176.

Sloboda

J. A.

(1991). Music structure and emotional response: Some empirical findings. Psychology of Music, 19(2), 110–120. https://doi.org/10.1177/0305735691192002

177.

Sloboda

J. A.

(2005). Exploring the musical mind: Cognition, emotion, ability, function. Oxford University Press.

178.

Spearman

(1904a). The proof and measurement of association between two things. The American Journal of Psychology, 15(1), 72–101. https://doi.org/ 10.2307/1412159

179.

Spearman

(1904b). “General intelligence”: Objectively determined and measured. The American Journal of Psychology, 15(2), 201–292. https://doi.org/ 10.2307/1412107

180.

Swaminathan

Kragness

H. E.

Schellenberg

E. G.

(2021). The Musical Ear Test: Norms and correlates from a large sample of Canadian undergraduates. Behavior Research Methods, 53(5), 2007–2024. https://doi.org/10.3758/s13428-020-01528-8

181.

Swaminathan

Schellenberg

E. G.

(2015). Current emotion research in music psychology. Emotion Review, 7(2), 189–197. https://doi.org/10.1177/1754073914558282

182.

Swaminathan

Schellenberg

E. G.

(2017). Musical competence and phoneme perception in a foreign language. Psychonomic Bulletin & Review, 24(6), 1929–1934. https://doi.org/10.3758/s13423-017-1244-5

183.

Swaminathan

Schellenberg

E. G.

(2018). Musical competence is predicted by music training, cognitive abilities, and personality. Scientific Reports, 8, 9223. https://doi.org/10.1038/s41598-018-27571-2

184.

Swaminathan

Schellenberg

E. G.

(2020). Musical ability, music training, and language ability in childhood. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(12), 2340–2348. https://doi.org/10.1037/xlm0000798

185.

Swaminathan

Schellenberg

E. G.

Khalil

(2017). Revisiting the association between music lessons and intelligence: Training effects or music aptitude? Intelligence, 62, 119–124. https://doi.org/10.1016/j.intell.2017.03.005

186.

Tan

Y. T.

McPherson

G. E.

Peretz

Berkovic

S. F.

Wilson

S. J.

(2014). The genetic basis of musical ability. Frontiers in Psychology, 5, Article 658. https://doi.org/10.3389/fpsyg.2014.00658

187.

Theorell

T. P.

Lennartsson

A.-K.

Mosing

M. A.

Ullén

(2014). Musical activity and emotional competence – a twin study. Frontiers in Psychology, 5, 774. https://doi.org/10.3389/fpsyg.2014.00774

188.

Trainor

L. J.

(2005). Are there critical periods for musical development? Developmental Psychobiology, 46(3), 262–278. https://doi.org/10.1002/dev.20059

189.

Ullén

Mosing

M. A.

Holm

Eriksson

Madison

(2014). Psychometric properties and heritability of a new online test for musicality, the Swedish Musical Discrimination Test. Personality and Individual Differences, 63, 87–93. https://doi.org/10.1016/j.paid.2014.01.057

190.

Ullén

Mosing

M. A.

Madison

(2015). Associations between motor timing, music practice, and intelligence studied in a large sample of twins. Annals of the New York Academy of Sciences, 1337(1), 125–129. https://doi.org/10.1111/nyas.12630

191.

Valentine

Evans

(2001). The effects of solo singing, choral singing, and swimming on mood and physiological indices. British Journal of Medical Psychology, 74(1), 115–120. https://doi.org/10.1348/000711201160849

192.

van der Zwaag

Westerink

J. H. D. M.

van den Broek

(2011). Emotional and psychophysiological responses to tempo, mode, and percussiveness. Musicae Scientiae, 15(2), 250–269. https://doi.org/10.1177/1029864911403364

193.

Vanstone

A. D.

Wolf

Poon

Cuddy

L. L.

(2015). Measuring engagement with music: Development of an informant-report questionnaire. Aging & Mental Health, 20(5), 474–484. https://doi.org/10.1080/13607863.2015.1021750

194.

Västfjäll

(2001-2002). Emotion induction through music: A review of the musical mood induction procedure. Musicae Scientiae, Spec Issue, 2001-2002, 173–211. https://doi.org/10.1177/10298649020050S107

195.

Wallentin

Nielsen

A. H.

Friis-Olivarius

Vuust

(2010). The Musical Ear Test, a new reliable test for measuring musical competence": Corrigendum. Learning and Individual Differences, 20(6), 705. https://doi.org/10.1016/j.lindif.2010.10.001

196.

Weinberg

M. K.

Joseph

(2017). If you’re happy and you know it: Music engagement and subjective wellbeing. Psychology of Music, 45(2), 257–267. https://doi.org/10.1177/0305735616659552

197.

Welch

Adams

(2003). How is music learning celebrated and developed? A Professional User Review of UK and related international research undertaken for the British Educational Research Association. British Educational Research Association.

198.

Whaley

Sloboda

J. A.

Gabrielsson

(2009). Peak experiences in music. In Hallam

Cross

Thaut

(Eds.), The Oxford handbook of music psychology (pp. 452–461). Oxford University Press.

199.

White

E. L.

Rickard

N. S.

(2016). Emotion response and regulation to “happy” and “sad” music stimuli: Partial synchronization of subjective and physiological responses. Musicae Scientiae, 20(1), 11–25. https://doi.org/10.1177/1029864915608911

200.

Wickham

François

Henry

Müller

Vaughan

(2023). dplyr: A Grammar of Data Manipulation. https://dplyr.tidyverse.org, https://github.com/tidyverse/dplyr.

201.

Wing

H. D.

(1961). Standardized tests of musical intelligence. NFER Pub.

202.

Winner

Martino

(2000). Giftedness in non-academic domains: The case of the visual arts and music. In Heller

K. A.

Mönks

F. J.

Sternberg

R. J.

Subotnik

R. F.

(Eds.), International handbook of giftedness and talent (2nd ed., pp. 95–110). Elsevier.

203.

Woody

R. H.

(2021). Music education students’ intrinsic and extrinsic motivation: A quantitative analysis of personal narratives. Psychology of Music, 49(5), 1321–1343. https://doi.org/10.1177/0305735620944224

204.

Woody

R. H.

McPherson

G. E.

(2010). Emotion and motivation in the lives of performers. In Juslin

P. N.

Sloboda

J. A.

(Eds.), Handbook of music and emotion: Theory, research, applications (pp. 401–424). Oxford University Press.

205.

Zentner

Grandjean

Scherer

K. R.

(2008). Emotions evoked by the sound of music: Characterization, classification, and measurement. Emotion, 8(4), 494–521. https://doi.org/10.1037/1528-3542.8.4.494

206.

Zentner

M. R.

Kagan

(1998). Infants’ perception of consonance and dissonance in music. Infant Behavior & Development, 21(3), 483–492. https://doi.org/10.1016/S0163-6383(98)90021-2

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Measures	M	SD	α	ω	1	2	3	4
Music-induced physiological response			.97	.97
1. Mean score of skin conductance peaks per musical excerpt	3.72	2.95
Active engagement with music
2. Music listening	9.94	5.07			.19** [.05, .32]
3. Music instrument playing	12.54	26.32			.02 [−.13, .18]	.23** [.08, .37]
4. Music training	4.15	1.81			−.03 [−.16, .11]	.12 [−.02, .26]	.26** [.11, .39]
Musical competence			.62	.68
5. Factor score of person estimators derived from the computerized adaptive music tests	0.00	0.33			.06 [−.08, .20]	.06 [−.08, .34]	.19* [.04, .34]	.27** [.14, .40]