The effects of emotionally congruent sad music listening in young adults high in rumination

Abstract

There is debate as to whether sad music is harmful or helpful when used to regulate emotions. Listeners’ trait level of rumination may influence their responses to sad music during sadness. This study used an online community sample of young adults (N = 386, 56% female, M_age = 21.89) in an induced sad state to understand the roles of listener rumination and the eight BRECVEMA musical emotion mechanisms (Brain Stem Reflex, Rhythmic Entrainment, Evaluative Conditioning, Contagion, Visual Imagery, Episodic Memory, Musical Expectancy, and Aesthetic Judgment) in determining changes in sadness during listening. Participants increased in sadness after listening to a self-nominated sad song. The increase in sadness observed was additionally moderated by rumination such that higher rumination predicted greater increases in sadness. People high in rumination were additionally more likely to experience musical entrainment, select a song with conditioned responses and associated memories, as well as experience emotional contagion while listening. Importantly, the effect of rumination was not significant when these BRECVEMA variables were added to the model. Results suggested that BRECVEMA mechanisms were more predictive of increases in sadness from pre- to post-listening than trait rumination levels. The findings suggest that attention should be given to individuals’ song choices and associated active BRECVEMA mechanisms in addition to their trait rumination.

Keywords

music emotion regulation rumination musical emotion mechanisms

Sadness is a negative emotional state frequently experienced in everyday life as a response to specific environmental factors and consists of a varied pattern of cognitions and bodily experiences (Barrett, 2006). However, sadness can also be a longer lasting, non-specific, mood state and this may be consequent to poor emotion regulation (Gross, 1998). Sadness is generally a transient emotion, with episodes typically remitting within 30 min of onset (Verduyn, Delvaux, Van Coillie, Tuerlinckx, & Van Mechelen, 2009). However, for episodes lasting longer than 30 min, only 10% remit within an hour of onset (Verduyn et al., 2009). Furthermore, difficulty regulating sadness has been linked to the development of mood disorders, such as depression (Aldao, Nolen-Hoeksema, & Schweizer, 2010). This points to the importance of effective and early regulation of specific instances of sadness.

Experience sampling and questionnaire studies both suggest that music is often used as a way to regulate negative emotions (Juslin & Laukka, 2004; Randall & Rickard, 2016; Thayer, Newman, & McClain, 1994; Vidas et al., 2020). During such episodes, a preference for music that expresses the experienced emotion may also be present (Chen, Zhou, & Bryant, 2007; Hunter, Schellenberg, & Griffith, 2011; Taylor & Friedman, 2015; cf. Knobloch & Zillmann, 2002). Young adults (17–25 years old) have reported a preference for using music to immerse in sadness compared to adults over the age of 25 (Dingle, Sharman, & Larwood, 2019). Dingle and colleagues (2019) argued that listening to emotionally congruent music is an adaptive way to regulate emotions. The Access–Awareness–Autonomy (AAA) model of musical social-emotional competence posits that the emotional properties of music allow for increased awareness of and access to emotional instances (Saarikallio, 2019). Indeed, interventions that employ the use of emotional exploration with emotionally congruent music listening have been related to improved emotion regulation and psychological health (Dingle & Fay, 2017; Dingle, Hodges, & Kunde, 2016). Such interventions that use individually selected emotionally congruent songs additionally respect the varied nature of songs that can be considered as either inducing or expressing a certain emotion (Cespedes-Guevara & Eerola, 2018).

Rumination is a personality trait that describes a style of emotion regulation where focus is given to the negative emotion itself, along with its negative consequences (Trapnell & Campbell, 1999). Trait rumination manifests itself early in the emotion regulation process and is related to both increased, non-clinical, negative mood states and to psychopathologies (Aldao et al., 2010; Kalokerinos et al., 2017). Music listening is an effective strategy used to regulate specific instances of sadness yet it is debated whether listening to sad music when sad is beneficial for people high in trait rumination (Garrido & Schubert, 2013). People high in rumination have shown increased preferences for sad music during experimentally induced sadness and in retrospective self-reports (Chen et al., 2007; Garrido & Schubert, 2013, 2015a). Without using a sadness induction, experimenter-selected and participant-selected sad music alike have been shown to increase sadness. When people are in an induced sad state, sadness levels have been found to decrease from pre– to post–sad music listening, although not to the same extent as when happy or self-selected music is listened to (Dingle et al., 2019; Zavoyskiy, Taylor, & Friedman, 2016).

Garrido and Schubert (2015a, 2015b) have considered trait rumination as a predictor of increases in sadness when listening to participant-selected sad music. In both studies, Garrido and Schubert (2015a, 2015b) found that when participants listened to self-nominated sadness-inducing music in a baseline state, sadness increased from pre- to post-listening. A larger increase in sadness for participants high compared to low in rumination was found in one study (Garrido & Schubert, 2015b) but not the other (Garrido & Schubert, 2015a). In a follow-up study, Garrido, Schubert, and Bangert (2016) created pre-determined sadness and happiness playlists based on songs selected by participants of previous studies. Results showed that rumination did not moderate the relationship between sad music listening and short-term changes in sadness from pre- to post-listening. Importantly, while participants with higher rumination had higher sadness scores at baseline, this measurement indicated a sad mood state rather than sad, contextual, emotional response. A previous study that investigated the effect of trait rumination when participants were in an explicitly sad emotional state found no evidence to support a moderating effect and found that sadness did not increase when listening to an experimenter-selected sad song (Dingle et al., 2019). The lack of increase observed in this study comparted to those of Garrido and colleagues may be attributed to participants not listening to a song they themselves had nominated as inducing sadness. Furthermore, power was insufficient in Dingle et al. (2019) to detect a meaningful moderating effect of rumination. It therefore remains unclear whether rumination moderates the influence of sad music and whether the relationship is present when music listening happens in the immediate time frame after the onset of sadness.

It is also unknown how rumination may relate to musical induction mechanisms proposed in the BRECVEMA model (Juslin, 2013). The BRECVEMA model provides mechanisms through which music may induce emotional states such as sadness. Of particular interest is the mechanism of emotional contagion, where the listener takes on the expressed emotion of the artist, their aesthetic judgment and song-specific associations or memories the listener may have (Juslin, Harmat, & Eerola, 2014). People recall feeling better after listening to sad music which suggests that a positive aesthetic judgment of sad songs may explain improvements in listeners’ mood (Eerola, Peltola, & Vuoskoski, 2015, Eerola, Vuoskoski, Peltola, Putkinen, & Schäfer, 2018; Sachs, Damasio, & Habibi, 2015; Vuoskoski & Eerola, 2017). Furthermore, people with clinical depression—who likely had increased trait rumination (although this was not directly measured)—showed the same response to sad music designed to trigger emotional contagion as participants without a diagnoses of depression (Sakka & Juslin, 2018). This again calls into question the moderating role of rumination.

It has recently been argued that the mechanisms triggered by a song arise from a combination of musical structure and the listener’s relationship with the song (Cespedes-Guevara & Eerola, 2018). The mechanism of cognitive appraisal has also been considered in conjunction with BRECVEMA mechanisms and may operate independently of musical features. However, appraisal has been implicated in sad music listening motivated by emotion regulation (Saarikallio, 2011; van den Tol, 2016). Subsequently, it is important to explore these mechanisms in relation to the effects of rumination on sad music outcomes. This is especially true as previous studies have used participant-nominated sad songs adding potential emotion induction confounds to the study design.

The current study aimed to test the hypothesis that people high in rumination would experience greater sadness after listening to sad music and extended this to examine participants in an induced sad emotional state, rather than a baseline sad mood state (cf. Garrido & Schubert, 2015a, 2015b). We additionally included exploratory measures of BRECVEMA mechanisms that occurred during listening to reflect music characteristics of the participants’ song choices. Furthermore, we focused on a young adult sample (18–25 years old) as this reflects the propensity of people under 25 to use music to regulate their emotions (Dingle et al., 2019; Vidas et al., 2020). The addition of these factors to the design of Garrido and Schubert (2015a, 2015b) makes this study well positioned to understand the influence of participant-level trait rumination and music-level emotional mechanisms when music is listened to in immediate response to sadness. Consistent with Garrido and Schubert (2015a, 2015b), participants nominated their own songs, allowing for variation in experienced BRECVEMA mechanisms, specifically associations and memories.

We predicted that sadness would decrease from pre– to post–music listening (H1) and—contrary to the rumination hypothesis—rumination would not moderate this effect (H2). This prediction was made in light of the aforementioned mixed findings in prior studies, where listening did not occur immediately subsequent to the occurrence of a sad emotional state and the evidence to suggest that sadness decreases when the listener is in an induced sad state. Exploratory analyses were conducted in relation to the BRECVEMA mechanisms and cognitive appraisal of the music to further understand the relationship between sad music listening and emotional responses. The musical characteristics of the nominated songs were also explored as potentially important variables when considering the relationship between rumination and sad music use.

Method

Participants

Participants were 386 residents of the United Kingdom aged 18 to 25 years (M_age = 21.89, SD_age = 2.30) who were recruited from prolific.co and paid £1.25 for their participation (£5 per hour). The sample was majority female (56%), with males making up 43% of the sample, and <1% not identifying as either. Participants reported that in daily life, they listened to music for an average of 3.76 hr per day.

Procedure

After consenting to participate in the study, participants were asked to nominate an artist and song “that makes you feel sad (or is likely to make you feel sad).” Participants then completed the baseline sadness measurement, before watching the sadness induction video, and then immediately completing the second measurement of sadness. Participants were then presented with the song title and artist they nominated and asked to listen to that song for 3 min. The page displaying the song and artist automatically advanced after 3 min and participants completed the final measure of sadness. Over all three sadness measurement points, participants were instructed to make the ratings specific to how they were feeling in that specific moment. Finally, participants then completed measures of BRECVEMA mechanisms, trait music use, and rumination before viewing a happiness induction video to counteract the sadness induction.

Measures

Sadness

Sadness was measured using the sadness items from the Discrete Emotion Questionnaire (Harmon-Jones, Bastian, & Harmon-Jones, 2016). The measure asked participants to rate the extent to which they were experiencing four different emotions that were part of the sadness family (lonely, grief, sad, and empty) in that specific moment on a scale from 1 = not at all to 7 = an extreme amount. The internal consistency at all times was good (Cronbach’s alpha’s = .84–.85).

Music emotion induction mechanisms

To measure the presence of a mechanism related to the induction of emotions through music, the MecScale was used (Juslin et al., 2014). The MecScale was responded to with “yes” and “no” responses for each of the BRECVEMA mechanisms of musical emotion induction (brainstem reflex, musical entrainment, conditioning, contagion, imagery, memory, expectancy, appraisal).

Song use for emotion regulation

A single item asked how likely participants were to listen to the song they had nominated when they were feeling sad. The artist and song title of the song nominated by the participant was presented in the item. Participants responded on a sliding scale from 0 = not at all likely to 100 = very likely.

Trait music use for emotion regulation

The cognitive and emotion regulation subscale of the Music Use and Background Questionnaire (MUSEBAQ; Chin, Coutinho, Scherer, & Rickard, 2018) was used to measure trait use of music to regulate emotions. Participants responded to items about their music use, such as “I use music to get through difficult times,” on a 1 (strongly disagree) to 5 (strongly agree) scale. Higher scores indicated greater trait use of music to regulate emotions, and internal consistency among the items was high, Cronbach’s alpha = .91.

Rumination

Rumination was measured using the rumination subscale of the Rumination Reflection Questionnaire (Trapnell & Campbell, 1999). Participants responded to self-referential statements, such as “Sometimes it is hard for me to shut off thoughts about myself,” on a 1 (strongly disagree) to 5 (strongly agree) scale. The final score was the mean of all items with a high score indicating higher trait rumination. The Rumination Reflection Questionnaire demonstrated high internal consistency in this sample, Cronbach’s alpha = .91.

Musical information

The songs that participants nominated as making them feel sad were matched with the spotify.com API “spotifyr” R package (Spotify, 2019; Thompson, Parry, Phipps, & Wolff, 2019). Possible valence and energy values ranged from 0 to 1 with 0 indicating lowest possible energy and valence and 1 indicating highest possible energy and valence. Lyrics were scraped from genius.com using the “genius” R package (Parry, 2019) and lyrics were analyzed for sentiment on the word level using the “NRC lexicon” and “tidytext” R package (Mohammad & Turney, 2013; Silge & Robinson, 2016). The ratio of negative words to total words in each song’s lyrics was used to quantify lyric negativity. Analysis of musical information can be found in Supplemental Online Material 1.

Stimuli

Sadness induction

To induce sadness, participants viewed a scene from The Lion King for 113 s, as described in Rottenberg, Ray, and Gross (2007). The manipulation was successful with sadness increasing from baseline (M = 9.51, SD = 5.06) to post-induction (M = 12.56, SD = 5.60), t(381) = 14.01, p < .001, d = 0.72.

Happiness induction

To counter sadness-inducing effects of the experiment, participants viewed positive Velten-type statements taken from Jennings, McGinnis, Lovejoy, and Stirling (2000).

Results

Sample size and data processing

Our sample size was based on the procedure and effect sizes in Garrido and Schubert (2015b). This study was larger than both Garrido and Schubert (2015a, 2015b) and Garrido et al. (2016). In addition, extra power was afforded to our analysis through the use of a general linear mixed model as opposed to dichotomizing rumination. Of the 437 participants recruited, 55 were deleted for the following reasons: (1) not finishing the survey (indicated by remaining on the survey until the final page and completing <95% of items; n = 30) and (2) completing the survey more than once (in these cases, the first survey was retained provided it was completed; n = 25).

Confirmatory analysis

Changes in sadness over time

A general linear mixed model was built to test the prediction that sadness would decrease from post-induction to post-listening (H1) and that rumination would not moderate this relationship (H2). Post-induction was set as the reference level and fixed effects were included for time and rumination as well as their interaction. Contrary to H1, a post hoc paired-samples t test suggested that sadness scores increased from post-induction to post-listening when the variance shared between rumination and the time by rumination interaction was not accounted for, t(381) = 6.36, p < .001, d = 0.33.

Interaction with rumination

An interaction was also present between time and rumination, such that there was a greater increase in sadness for participants with higher rumination scores. A Johnson–Neyman interval analysis suggested that the slope of change in sadness from post-induction to post-listening was non-significant at a rumination score of 2.87 (a score below the 10th percentile of the sample). The lower bound of the Johnson–Neyman interval was <1 indicating no possible rumination score predicted a decrease in sadness from pre- to post-listening. The hypothesis that rumination would not moderate the extent to which sadness scores changed from pre- to post-listening was therefore refuted.

Exploratory models

We investigated the effect of controlling for factors that may also contribute to changes in sadness.

Rumination and BRECVEMA mechanisms

To select BRECVEMA mechanisms to include in the exploratory model, we regressed rumination on to each measured mechanism and listener characteristic and retained variables where the p value was <.2. The BRECVEMA mechanisms of entrainment, conditioning, contagion, and memories were included in an exploratory model.

Rumination and listener characteristics

Participant gender, baseline sadness scores, the likelihood of listening to the nominated song during sadness, and the use of music for emotion regulation were also related to rumination at p < .05, with no other variables falling within p < .2. These variables were subsequently also included in the exploratory model. Supplemental Online Material 1 reports the results for all regression analyses carried out in selecting the variables predicted by rumination.

Exploratory timepoint model

The variables identified as relating to rumination at p < .2 were added to the confirmatory model. The fitted exploratory and fitted confirmatory model are shown in Table 1, and based on the Akaike information criterion (AIC), the exploratory model provided a better fit to the data.

Table 1.

Regression coefficients for the hypothesized and exploratory model.

Predictors	Sadness			Sadness
Predictors	Std. Beta	Standardized CI	p	Std. Beta	Standardized CI	p
(Intercept)	–0.11	[–0.20, –0.01]	.001	–0.06	[–0.41, 0.29]	.067
Timepoint	0.22	[0.15, 0.28]	.353	–0.38	[–0.70, –0.07]	.016
Rumination	0.23	[0.14, 0.33]	<.001	0.03	[–0.05, 0.11]	.45
Timepoint × Rumination	0.07	[0.00, 0.14]	.036	0.06	[–0.01, 0.13]	.088
Baseline				0.65	[0.57, 0.72]	<.001
Female				0.08	[–0.07, 0.23]	.306
Non-Binary				–0.19	[–1.03, 0.66]	.667
MUSEBAQ				0.03	[–0.06, 0.11]	.533
Entrainment				0.16	[–0.03, 0.34]	.098
Conditioning				–0.08	[–0.24, 0.09]	.35
Contagion				–0.09	[–0.27, 0.08]	.288
Memory				–0.11	[–0.39, 0.17]	.447
Likelihood of Listening				0.16	[0.07, 0.24]	<.001
Timepoint × Baseline				–0.07	[–0.14, –0.00]	.045
Timepoint × Female				0.05	[–0.08, 0.19]	.452
Timepoint × Non-Binary				0.25	[–0.50, 1.01]	.513
Timepoint × MUSEBAQ				0.02	[–0.06, 0.09]	.7
Timepoint × Entrainment				–0.11	[–0.28, 0.06]	.195
Timepoint × Conditioning				0.29	[0.15, 0.44]	<.001
Timepoint × Contagion				0.19	[0.04, 0.35]	.015
Timepoint × Memory				0.35	[0.10, 0.60]	.006
Timepoint × Likelihood of Listening				–0.05	[–0.13, 0.02]	.145
Random effects
σ²	7			6.63
T₀₀	22.21_Id			10.03_Id
ICC	.76			.6
n	382_Id			379_Id
Observations	764			758
Marginal R²/Conditional R²	.085/.781			.483/.794
AIC	4,427.978			4,169.811

CI: confidence interval; ICC: intraclass correlation; AIC: Akaike information criterion; Significant p-values are in bold.

The exploratory model indicated that when controlling for the effects of baseline sadness, gender, likelihood of listening to the nominated song, the use of music for emotion regulation, and selected BRECVEMA mechanisms, there was no longer a moderating effect of rumination. However, the model did indicate that when controlling for all other variables, including rumination, the BRECVEMA mechanisms of memory, conditioning, and contagion all predicted greater increases in sadness from pre- to post-listening.

Discussion

The current study sought to evaluate the effect of rumination in predicting changes in sadness when listening to sad music and an overtly sad emotional state. In this study, it was predicted that (1) when participants listened to their nominated sad music during a sad state, they would experience a decrease in sadness and (2) rumination would not influence this relationship. Contrary to predictions, sad music listening was related to increases in sadness when rumination was not controlled for in the model. Rumination was found to moderate the effect of music listening, such that people with higher rumination scores experienced greater levels of sadness after listening to sad music. However, a better fit was demonstrated when other variables (i.e., memories attached to the song) were entered into the model. Rumination did not moderate the relationship in this exploratory model.

Previous studies with participants in an induced sad state have indicated that listening to expressively sad music that is experimenter selected does not predict increases in sadness from pre- to post-listening (Dingle et al., 2019; Zavoyskiy et al., 2016). However, listening to self-selected negatively valanced music has been suggested to relate to increases in negative affect post-listening when the initial affective state is not sufficiently negative (Randall & Rickard, 2017). Furthermore, when listening occurred in a baseline state and self-identified sadness-inducing songs were listened to, sadness has been shown to increase from pre- to post-listening (Garrido & Schubert, 2015a, 2015b). As with Garrido and Schubert (2015a, 2015b), the current study asked participants to listen to a song they knew was likely to make them feel sad. While the current study differed from Garrido and Schubert (2015a, 2015b) through the addition of a sadness induction, an increase in sadness from pre- to post-listening was still observed. This use of participant-selected sad music may have enhanced the inductive effect of the music, potentially through BRECVEMA mechanisms. It is posited that when the song is known to induce sadness, the inductive effect may be larger than previously found in studies that did not consider the listener’s relationship with the music by using experimenter-selected musical clips (Dingle et al., 2019; Zavoyskiy et al., 2016). Extending on this, on average, participants did not rate it likely that the song nominated would be the song listened to for emotion regulation, perhaps suggesting that sadness-inducing songs are not equivalent to sad songs listened to during sadness. The nomination and subsequent listening to known sadness-inducing songs may have also introduced priming or demand effects, whereby participants expected or felt the need to report increased sadness after listening. The effect may also be an overestimation of the real-world use of sad music during sadness, given the range of variability of participant-rated likelihood of listening to the nominated song during sadness.

Consistent with Garrido and Schubert (2015b), a moderating effect of rumination was found such that participants high in the trait experienced greater increases in sadness from pre- to post-listening. Our result is contrary to previous studies on rumination and pre- to post-effects that utilized both participant- and experimenter-selected music (Dingle et al., 2019; Garrido & Schubert, 2015a; Garrido et al., 2016). In fact, results are consistent with Garrido and Schubert’s (2015b) rumination hypothesis.

While the planned analysis supported the rumination hypothesis, our exploratory analyses suggested that BRECVEMA mechanisms may be important in understanding the effect. In this model, we controlled for demographic and musical variables that were predicted by rumination. This model suggested that when controlling for all other variables, increases in sadness from pre- to post-music listening were predicted by the presence of emotional contagion, memories, and conditioning, but not rumination. Our findings concur with that of other researchers that BRECVEMA mechanisms occur at the interface of the song being listened to and the relationship the listener has with it (Cespedes-Guevara & Eerola, 2018).

Results from the exploratory analyses do not negate the finding that rumination predicted increased sadness from pre- to post-listening. However, it suggests that the results may not be purely attributable to rumination at the trait level. The analysis that included the BRECVEMA mechanisms that occurred in conjunction with rumination added nuance to how sad music listening may be maladaptive when regulating sadness (Garrido & Schubert, 2015b; McFerran & Saarikallio, 2014). Our results suggest a role for the emotion induction mechanisms in listening outcomes over and above rumination. However, while this nuance is added, more research is required. Our results are not able to establish the specific relationship between rumination and the experience of BRECVEMA mechanisms. It is possible that people high in rumination either select songs that are more likely to trigger certain BRECVEMA mechanisms or that people high in rumination are more susceptible to experiencing BRECVEMA mechanisms. Past studies have identified people high in rumination as experiencing more memories associated with a self-identified sad song along with a ruminative style of musical engagement which features the listener being reminded of bad memories (Garrido & Schubert, 2015a; Saarikallio, Gold, & McFerran, 2015). Given this, understanding whether differences observed in BRECVEMA mechanisms can be attributed to person-level variables or song-level variables is vital for effective recommendations around music listening and emotion regulation to be made.

People high in rumination would be well served by interventions that build awareness of their relationships with the songs they choose to listen to and the processes that occur during listening (see also McFerran & Saarikallio, 2014; Stewart et al., 2019). This listener awareness raising approach has been successful in improving psychological well-being through music-based emotion regulation programs that involve emotionally congruent music listening (Dingle & Fay, 2017; Dingle et al., 2016). In addition, the use of the questionnaire measures, such as the Healthy-Unhealthy Use of Music Scale has been suggested as a way to gain information surrounding ruminative patterns of music use that may be a suitable target for intervention (Hense et al., 2018; McFerran et al., 2018). Our findings and those of Garrido and Schubert (2013) suggest an increased preference for musically mediated emotion regulation as well as increased sad music consumption in people with higher trait rumination. Subsequently, and consistent with Saarikallio’s (2019) AAA model, we argue that promoting educated consumption of emotionally congruent sad music is a worthwhile avenue of practice.

The moderating relationship of rumination on changes in sadness over the duration of sad music listening is not wholly independent of other features of the musical emotion experience as captured by the BRECVEMA model. Future research is subsequently needed to establish whether trait rumination is influencing the selection of songs that induce sadness or whether trait rumination predicts a general sensitivity to BRECVEMA mechanisms. In addition, participants were instructed to listen to a song that they knew to make them sad. While this kept the methodology consistent with the work of Garrido and Schubert (2015a, 2015b), it makes the assumption that when listening to sad music to regulate sadness, participants are listening to songs that make them feel sad. This is an important consideration in light of the AAA model whereby participants in this study did not have autonomy over the song they chose to regulate sadness. Further it does not consider that participants may listen to songs that they consider to be expressively sad but associate with positive psychological consequences or strategies (Dingle et al., 2019). For instance, participants may not have explicitly liked or enjoyed the song, with greater liking of a song relating to increased positive affect consequent to listening (Randall & Rickard, 2017). Future work should therefore examine the rumination hypothesis where sad song selection happens at the beginning of the sad emotional state rather than prior to it. This may more accurately reflect real-world listening where listeners have vast control over song choices through streaming services, while maximizing the possibility of observing the positive effects of sad music conferred through aesthetic judgments and absorption (Eerola et al., 2018; van den Tol, 2016).

The current study investigated the moderating influence of rumination on changes in sadness when participants listened to self-identified sad music. An effect of rumination was found such that higher rumination scores predicted greater increases in sadness from pre- to post-listening. However, this effect did not appear to be independent of known musical emotion induction mechanisms. It is recommended that future basic and applied work consider both listener-level (i.e., rumination) and song-level (i.e., inductive mechanism) factors when evaluating the effects of sad music for emotion regulation. Finally, the results of the current study suggest that when considering music use as an emotion regulation strategy with young adults at increased risk of mood disorders, attention be given to their listening style, personality, and psychological processes associated with specific songs.

Supplemental Material

sj-docx-1-pom-10.1177_0305735620988793 – Supplemental material for The effects of emotionally congruent sad music listening in young adults high in rumination

Supplemental material, sj-docx-1-pom-10.1177_0305735620988793 for The effects of emotionally congruent sad music listening in young adults high in rumination by Joel L Larwood and Genevieve A Dingle in Psychology of Music

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Joel L Larwood is supported by a Commonwealth of Australia RTP scholarship.

ORCID iD

Joel L Larwood

Supplemental material

Supplemental material for this article is available online.

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