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
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.
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.
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.
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).
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.
Information criteria for path model analysis and comparison.
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
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.
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References
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