The capacity to appreciate music is a universal human phenomenon that helps inspire individual and social life. Neuroscientific studies have shown that music is processed in a cascade of steps that begins with segregation within the auditory stream, followed by the extraction and integration of a variety of acoustic features, and leading to cognitive memory-related processes that induce personal, often emotional, experiences. In the past 25 years, a wealth of brain-imaging studies has explored critical components of music and how they are processed in the underlying neural networks [e.g., chords (Tervaniemi et al., 2011), musical syntax (Koelsch, 2009), major/minor keys (Virtala and Tervaniemi, 2017), consonance/dissonance (Virtala and Tervaniemi, 2017), timing (Istók et al., 2013), absolute pitch (Hirata et al., 1999; Schaal et al., 2015), harmony (Passynkova et al., 2005), rhythm (Grahn and Rowe, 2009), and timbre (Meyer et al., 2006; Alluri et al., 2012) (for a summary see Jäncke, 2008; Koelsch, 2012)].

A major question in neuroscientific music research is whether musical pieces evoke specific subjective emotional and arousal reactions, which are related to neurophysiological activation patterns. A simple and often-used strategy in this context is to ask subjects to rate the musical pieces. Rating can be done either using questionnaires, which are applied after the presentation of the musical pieces, or by having subjects continuously rate the musical piece as it is played. These ratings can be made either verbally or along several dimensions (e.g., relaxation vs. arousal and/or sadness vs. happiness). The continuous rating of musical pieces is a relatively new strategy that has aided the identification of the time course of subjective experiences during music listening (Hutcherson et al., 2005; Mikutta et al., 2012, 2014; Jancke et al., 2015; Trost et al., 2015). For example, Mikutta et al. (2014) instructed subjects to move a computer mouse forward when they experienced increased arousal caused by the music, independently of their affective valence. The subjects were instructed to move the mouse backward when they experienced decreased arousal. The ratings were recorded and stored for offline analysis with a 100-Hz sampling rate. A slightly different strategy was used by Jancke et al. (2015). In their experiment, subjects rated the musical piece according to valence and arousal using analog scales (a vertical line for valence and a horizontal line for arousal). The subjects were instructed to click on the positions of the valence and arousal lines that best represented their actual valence and arousal ratings, respectively. As with Mikutta et al. (2014), these ratings were recorded for offline analysis with a sampling rate of 100 Hz. These continuous ratings have been applied in three different ways so far: (1) continuous rating of the musical piece simultaneously during neurophysiological recording [Listening and Rating (LR)] (Hutcherson et al., 2005); (2) continuous rating during a second presentation of the musical piece, without neurophysiological recording (LR without neurophysiological recording in a second separate session) (Mikutta et al., 2012, 2013, 2014; Jancke et al., 2015; Trost et al., 2015); (3) continuous rating during the first presentation of the musical piece, without neurophysiological recording (LR without neurophysiological recording in an initial separate session).

Although these approaches have yielded some insight into subjective emotional reactions and the related neurophysiological underpinnings, they are nevertheless associated with methodological problems. When continuously rating a musical piece while listening to it, the cognitive and motor processes are active, and are inactive during passive music listening (Hutcherson et al., 2005). Subjects must continuously self-monitor their feelings and thus must direct their attention not only to the musical piece but also to their own feelings. Basically, this is a multitasking situation during which the subject must: (1) listen attentively to the musical piece, (2) monitor their feelings, (3) transform subjective feelings into psychological categories, and (4) indicate these categories with motor responses. Thus, several psychological functions must be supervised and orchestrated. During passive and attentive listening, the subject has the opportunity to listen to a musical piece unconstrained by any other task. This provides the opportunity to be “drawn” into the music without external distraction. Thus, incoming stimuli other than the musical piece are inhibited and the music has the power to elicit a particular brain activation pattern that most likely can induce the extraordinary feelings and subjective experiences encountered when listening to music. This music experience is definitively different from one in which the listener is required to continuously rate the musical piece to which they are listening.

In addition, directing attention to one’s own emotions might also influence the subjective emotions and the associated neurophysiological and vegetative reactions. Some researchers suggest that focus on one’s own emotions may activate processes that might alter or modulate the emotional response (on both the neural and subjective level). As outlined by Hutcherson et al. (2005), three competing hypotheses can be derived regarding the effect of focusing on emotion on subjective, neural, and vegetative responses, which could be: (1) amplified, (2) weakened, or (3) uninfluenced. The findings based on these hypotheses have largely been inconsistent, supporting any one of the hypotheses (for a summary, see Hutcherson et al., 2005). However, the last fMRI paper on this topic revealed that subjective rating of ongoing emotional responses during music listening did not “decrease either self-reported experience of emotion or neural activations relative to passive viewing in any brain regions” (Hutcherson et al., 2005). However, relative to passive viewing, the act of rating increased activity in brain areas involved in the control of executive functions and emotions (e.g., anterior cingulate, insula).

Neural responses to musical pieces have been studied in the last 50 years using methods ranging from EEG/MEG to fMRI and PET. Although these methods have supported our understanding of the neural underpinnings of music listening, they are associated with their respective advantages and disadvantages. fMRI measurements during music listening are particularly problematic for several methodological and psychological reasons: (1) the obtrusive and unavoidable scanner noise induces undesirable activations in the auditory system (Herrmann et al., 2000; Novitski et al., 2006); (2) subjects have to suppress their processing of the background scanner noise in order to focus on the music stimuli, thus additional executive functions that are inactive during undisturbed music listening have to be activated; (3) the scanner environment is uncomfortable and frequently associated with negative emotions such as discomfort, claustrophobia, pain, low-level anxiety, and other variants of negative emotion (Heinrich et al., 2014; Keulers et al., 2014; Mutschler et al., 2014); (4) the fixation of the subject’s head in the coil is often associated with feelings of malaise and pain; (5) the background scanner noise is often so obtrusive that it greatly disturbs the aesthetic enjoyment of music. The latter especially has largely been neglected in fMRI-based music research, given that we do much to exploit modern methods (e.g., sophisticated HiFi presentation methods) and/or specific presentation environments (opera hall with world-class orchestra) to derive maximum enjoyment from music listening. Music presentations in fMRI environments are definitively of much lower quality than the music presentations to which we normally listen. Other studies have used PET to study blood flow responses to musical stimuli (Satoh et al., 2006). Although PET measurements are silent, they are associated with tracer injections into the subject’s blood, which is a stressful intervention for many subjects, thus most likely mitigating the enjoyment of music listening. Thus, fMRI and PET environments are definitively not the perfect experimental environments in which to study neural and emotional responses to aesthetically appealing music stimuli.

EEG and MEG, on the other hand, measure neurophysiological responses in an ecologically valid setting (e.g., sitting on a chair while listening to HiFi music without interfering noise). The relative disadvantage of EEG recordings is the lower spatial resolution (if the aim is to estimate the underlying cortical sources of EEG activity), although the correspondence between EEG-based estimations of intracortical sources and fMRI measurements is astonishingly high (Britz et al., 2010; Van de Ville et al., 2010). Several published studies have used EEG to measure neural responses during music listening (Petsche et al., 1993; Iwaki et al., 1997; Sarnthein et al., 1997; Bhattacharya and Petsche, 2001; Bhattacharya et al., 2001a,b; Altenmuller et al., 2002; Jaušovec and Habe, 2005; Baumgartner et al., 2006; Jausovec et al., 2006; Peterson and Thaut, 2007; Sammler et al., 2007; Schaefer et al., 2009, 2011a,b, 2013; Mikutta et al., 2012, 2014; Wu et al., 2012; Jancke et al., 2015; Jancke and Alahmadi, 2016; Rogenmoser et al., 2016). Some of these studies focused on the functional network characteristics during music listening and identified specific network features in various frequency bands. A further set of studies focused on the frontal activation asymmetry patterns during the listening of differently valenced and arousing music (Tsang et al., 2001; Altenmuller et al., 2002; Mikutta et al., 2012, 2014).

In the context of this paper, the study by Schaefer et al. (2011b) is of particular interest. The authors reported increased alpha band power during imagery of listening to a musical piece as compared to during the perception of the musical piece. The authors explained this increase during imagery as an indicator of a modulation of the attentional network. They related their finding to Klimesch’s “inhibition concept,” where alpha band oscillations are a neurophysiological indicator of an active inhibition of non-task-relevant cortical areas. Thus, when directing attention internally (as when imagining listening to the musical piece), numerous networks, especially those processing incoming information, must be inhibited. However, several studies have shown that alpha band power is increased also while listening to rhythms, tone sequences, and even natural musical pieces. In a previous study, we identified increased power during music listening not only in the alpha band, but also in the theta and beta bands (Jancke et al., 2015). We interpreted this general synchronizing in different frequency bands as a neural indicator of a psychological process during which the subject is torn into the music.

However, the question arises as to whether this general synchronization only occurs during unconstrained music listening or whether it is disrupted or diminished when music listening is accompanied by a task that must be performed simultaneously. In this context, we reconsidered the question of whether simultaneous music listening and rating (LR condition) will induce different neural and vegetative activations compared to passive listening without rating (listening: L condition). We were also interested in examining whether the sequence in which these experimental conditions are followed is important. Particularly, we were interested in whether the LR condition influences the subsequent L condition in terms of the neural, vegetative, and subjective responses. Thus, it is possible that when the subjects have rated the musical pieces first (LR condition), they might implicitly do so even when not required (L condition) subsequently. To measure the neural responses, we focused (similarly as in our previous paper) on the power in the theta, alpha, and beta bands. In addition, we used subjective ratings of the musical pieces and heart rate (HR) and electrodermal (EDA) responses. The HR and EDA were used because these measures are good indicators of vegetative arousal responses during music listening (Jancke et al., 2015; Koelsch and Jancke, 2015). Using these measures, we addressed the following research questions:

In this study, we show that passive unconstrained music listening (L condition) is associated with increased power values in all examined frequency bands (theta, lower alpha, upper alpha, lower beta, upper beta). However, this power increase was only apparent when the subjects followed the L condition first. Thus, after following the LR condition first and the L condition second, the subjects obviously did not listen to the music during the L condition in an unconstrained manner. They most likely applied the same (or similar) mental state as used during the LR condition when listening to the music in the L condition. Possibly, they implicitly rated the musical pieces although they are not required to do so.

We also observed HR increases during the L condition but HR decreases during the LR condition. HR decreases are often reported during the performance of long-lasting and partly boring tasks, while HR increases are generally observed during emotionally arousing situations (i.e., during passive listening of emotionally arousing music) (Koelsch and Jancke, 2015). We interpreted the HR increase during the L condition as emotional arousal, which is evoked by listening to positively valenced music. The HR decrease during the LR condition might indicate less emotional arousal. For the EDA measures, the findings are less clear, as we obtained only a marginally significant effect for the Group × Condition interaction. However, this marginally significant interaction fits the general findings of our study. The subjects who began with the L condition demonstrated decreased EDA responses (indicating less vegetative arousal) during the LR condition. Thus, simultaneous music listening and rating appears less emotionally arousing after unconstrained music listening. In summary, the findings for both vegetative measures may indicate that simultaneous listening and rating is associated (at least slightly) with less emotional/vegetative arousal.

Nevertheless, what is the functional meaning of our findings? An initial approach to interpreting our findings is interpreting the power in the EEG frequency bands as indicators of the activity of different cortical and subcortical networks. The best-elaborated frequency band in this context is the alpha band. Meanwhile, several studies support the notion that alpha band oscillations are strongly related to cortical inhibition, especially of task-irrelevant brain areas (Klimesch et al., 2007). It has also been argued that alpha band oscillations indicate a neurophysiological process during which the engaged neural networks need to maintain optimal neural activation. This is most likely realized by maintaining an optimal level of excitation–inhibition through the suppression of neural networks, which might “disturb” or “interfere” with the on-going processing of the relevant task. This is consistent with findings demonstrating strong alpha power increases during such processes, which are characterized by redirecting attention from external events to internal thoughts. Typical examples of such psychological processes are meditation (Aftanas and Golocheikine, 2001; Faber et al., 2012); effortful cognition, such as perception of degraded speech (Weisz et al., 2011); imagination (Cooper et al., 2003; Schaefer et al., 2011b); creative thinking (Benedek et al., 2011); working memory tasks (Klimesch, 1999; Palva and Palva, 2007); or during unconstrained music listening (Iwaki et al., 1997; Schaefer et al., 2011b; Jancke et al., 2015). Previously, we speculated that this could indicate a “dragging into the music” while simultaneously neglecting other stimuli or even thoughts (Jancke et al., 2015). Thus, subjects let their minds wander per the musical rhythm, harmonies, and/or melody. In a way, this is similar to what occurs during meditation or imagination, psychological states that are also accompanied by increased alpha band power.

Explaining the functional meaning of the other frequency bands during music listening is less easy and straightforward. In our study, we also identified an increase in theta band power. Increased theta band power has been associated with three psychological functions: (a) the so-called frontal midline theta (Fm theta), which is generally related to cognitive effort, working memory, and emotion processing (Gevins et al., 1997; Sammler et al., 2007; Maurer et al., 2015; Wisniewski et al., 2015); (b) the widespread theta most prominent at the frontal and parietal scalp locations, which is associated with low-level alertness, drowsiness, and “mind-wandering” (Braboszcz and Delorme, 2011; Platt and Riedel, 2011; Baumeister et al., 2012; Park et al., 2014; Poudel et al., 2014); and (c) the widespread theta with parietal dominance, which has been related to the effective encoding of new memories (Klimesch, 1999). Theta increases have also been reported during meditation (Lagopoulos et al., 2009; Baijal and Srinivasan, 2010; Faber et al., 2012). The theta band power increase in our study was more widespread but also with a strong power increase over frontal midline EOIs. The musical pieces used in our study were all positively evaluated, thus it is possible that the frontal midline theta power increases observed were due to positively valenced arousal, as in the study of Sammler et al. (2007). It is also possible that the subjects exerted more mental effort to perceive and process the musical pieces. However, we find this explanation implausible, as the LR condition is definitively more demanding than the L condition. Thus, we would anticipate a greater workload and therefore more frontal midline theta during the LR condition than during the L condition. As we have argued previously, it is more plausible that our subjects were “drawn into” the music, resulting in a state that shares some similarities with the “mind-wandering” state, a state during which theta increases have frequently been reported (Braboszcz and Delorme, 2011). Mind-wandering is characterized by the experience of one’s attention drifting from a task or from external matters toward internal, mostly personal, issues. Braboszcz and Delorme (2011) argue that mind-wandering is a form of low-alertness and low-concentration state of rest.

Increased beta power during unconstrained music listening is, on one hand, difficult to explain, as tonic beta band power increases are generally reported during conditions of increased tonic alertness. During the resting state, upper alpha and beta band power both positively correlate with tonic alertness network activity (Sadaghiani et al., 2010). Thus, it is possible that the subjects were tonically alert (but with a more internal direction of attention) during the L condition. Increased beta band power has also been reported during emotional processing (Sebastiani et al., 2003; Aftanas et al., 2006), which is consistent with the fact that the musical pieces we used are also emotionally arousing. Increases in beta oscillations have also been observed during the “minimally conscious state” (MCS; Schiff et al., 2014). This state is characterized by intermittent or inconsistent evidence of consciousness. Typical behavioral signs are intermittent or inconsistent responses to verbal commands, reduced verbal output or object use, as well as intermittent or inconsistent purposeful eye movements. In terms of the EEG pattern, MCS is characterized by a coupled increase in theta and beta oscillations, which is thought to indicate functional or structural deafferentation of the thalamus from its cortical inputs.

One striking finding is that the power values in all examined frequency bands in our study were highly intercorrelated, suggesting strong functional interplay between the frequency bands (see, for example, Steriade, 2006). One reason could be that the different cognitive processes and associated networks, and their specific frequency bands, are simultaneously or sequentially active. Thus, when calculating power values for the frequency bands across longer periods (as we have done), it is most likely that they correlated substantially during the experiment. A further possibility is that all other frequencies are “grouped” around the alpha band oscillation according to harmonic rules (Klimesch, 2012). It has been suggested that this so-called “harmonic coupling” provides an optimal basis for a functional interchange between two or more oscillatory systems (Klimesch, 2012). In addition, we also should take into account the fact that the definition of the frequency bands used in the present study is somewhat arbitrary (although we used a traditional frequency band definition that has been used in most EEG studies so far), especially considering the borders between adjacent frequency bands. Thus, it is most likely that some overlap and leakage between the adjacently located band borders caused spurious correlations between the frequency bands.

However, we propose a further possibility for explaining the general power increase in all examined frequency bands during the L condition. It is possible that the active networks are highly synchronized and are not disrupted by interfering psychological processes. Thus, they can synchronize without any disturbance because they do the same during the entire music-listening process. Consequently, all networks oscillate synchronically, resulting in generally increased power values in several frequency bands. In future studies and analyses, it would thus be interesting to study the functional network architecture and the associated coherences during music listening. Some first studies have shown changed (mostly increased) coherences during unconstrained music listening (e.g., Bhattacharya and Petsche, 2001).

Our most important finding, however, is that the pattern of brain activation during unconstrained and undisturbed music listening depends on whether the subjects had followed the L condition before or after the LR condition. The LR condition is a multitasking condition wherein subjects were required to listen to the music, evaluate their emotional feelings, transfer these feelings to reportable (verbal) categories, and to indicate these feelings with the appropriate motor output. These psychological functions obviously disrupt unconstrained music listening and the associated brain activations. As mentioned above, we speculate that during unconstrained music listening, the subjects employed increased internal attention, accompanied by reduced external attention, increased inhibition of brain networks uninvolved in generating this internal state, and a mind-wandering state. Thus, subjects engaged in the LR condition could not let their mind wander; rather, they had to focus their attention on the manipulandum with which they indicated their internal state. They also focused their attention on their feelings and on the processes necessary for transferring the feelings to reportable categories. These different processes would prevent the development of mind-wandering as in unconstrained music listening. Interestingly, when the subjects were confronted with this particular state, it was obviously not that easy to switch to the unconstrained listening style.

A possible limitation of our study is that we did not explicitly examine whether repeated exposure to the L and LR conditions (i.e., L → L and LR → LR) might have revealed similar or different results to the L → LR and/or LR → L conditions. However, we refrained from using these conditions in our experiment because: (1) Previously (Jancke et al., 2015), we showed that during repeated presentation of a musical piece, with the same listening context, the EEG and vegetative responses are strikingly similar across the repeated presentations. Thus, we are certain that the neurophysiological and vegetative responses would have been the same across the repeated presentation of the L and LR conditions. (2) Including the abovementioned conditions would have increased the duration of the entire experiment for each subject. As our plan was to study the neurophysiological and vegetative responses to musical pieces in an ecologically valid setting, we wanted to keep the subjects’ experimental load as low as possible. Nevertheless, we are confident that we have shown that the neurophysiological and vegetative reactions to musical pieces depends, at least partly, on the listening context.

Taken together, we show that the power in all examined frequency bands increases during the L condition, but only when subjects have not had previous experience with the LR condition. During the LR condition, the power values are all substantially lower. We interpret the association between unconstrained music listening and increased power in all examined frequency bands as a neural indicator of a mental state, which can best be described as a mind-wandering state, during which subjects are “drawn into the music.” Further studies are needed to delineate possible characteristic differences (if they exist) between what has typically been described as mind-wandering or meditation.

LJ designed the experimental paradigm, performed the statistical analysis and drafted the manuscript. AM conducted the experiments, did the analysis of the EEG, vegetative, and behavioral data, and drafted the manuscript. JK designed the experimental paradigm, performed the statistical analysis and drafted the manuscript. All authors read and approved the final manuscript.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.