Self-compassion has been defined as being kind to one’s self (Neff, 2003b) and being able to use self-reassurance and soothing in times of adversity (Gilbert, 2009; Neff, 2003b). It includes being nonjudgmental about one’s self (Gilbert, 2009; Neff, 2003b) and recognizing one’s experience as part of the human condition (Neff, 2003b). Self-criticism, on the other hand, is characterized by maladaptive emotion-regulation strategies such as being harsh and judgmental about one’s self (Gilbert, 2009; Neff, 2003b). It is associated with feeling isolated (Neff, 2003b) and being in flight or fight or social rank mode, therefore exacerbating a sense of threat in difficult times (Gilbert, 2009).

Whereas self-criticism has been associated with a number of mental health problems, such as depression and anxiety (Clark, Watson, & Mineka, 1994), there is growing evidence that self-compassion has beneficial effects on mental health and well-being (e.g., Galante, Galante, Bekkers, & Gallacher, 2014) from two lines of research. Cross-sectional, correlational studies investigating the associations between dispositional levels of self-compassion and psychological health using the Self-Compassion Scale (Neff, 2003a) revealed that higher levels of trait self-compassion are associated with higher levels of well-being (Zessin, Dickhauser, & Garbade, 2015) and quality of life (Wei, Liao, Ku, & Shaffer, 2011), health-related behaviors such as exercising (Magnus, Kowalski, & McHugh, 2010) or seeking medical treatment (Terry & Leary, 2011), and enhanced interpersonal functioning (Neff, 2003a; Neff & Beretvas, 2013). In contrast, lower levels of self-compassion were associated with mental health problems such as posttraumatic stress disorder (Thompson & Waltz, 2008) and depression (Kuyken et al., 2015). The correlational nature of these studies prevents causal conclusions of these associations.

A better understanding of the possible directionality comes from experimental and clinical studies assessing the effects of psychological interventions that directly or indirectly cultivate self-compassion and identify associated changes in psychological health. For example, kindness-based meditations drawing from Buddhists traditions, such as loving-kindness meditation (i.e., an exercise oriented toward enhancing unconditional kindness toward oneself and others), have been found to cultivate self-compassion (Galante et al., 2014; Neff & Germer, 2013) and self-acceptance (Fredrickson, Cohn, Coffey, Pek, & Finkel, 2008), increase positive (Fredrickson et al., 2008; Hofmann, Grossman, & Hinton, 2011; Klimecki, Leiberg, Lamm, & Singer, 2013; Kok et al., 2013) and decrease negative affect (Hofmann et al., 2011; Klimecki et al., 2013), increase empathy or warmth toward others (Ashar et al., 2016; Klimecki et al., 2013), and increase social connectedness (Hutcherson, Seppala, & Gross, 2008; Kok et al., 2013). In addition, mindfulness-based cognitive therapy (MBCT), an 8-week psychosocial program particularly designed for the treatment of depressive relapse (Segal, Teasdale, & Williams, 2002; Segal, Williams, & Teasdale, 2013), has been shown to increase self-compassion, which in turn predicted well-being 15 months later (Kuyken et al., 2010). MBCT uses mindfulness practices such as the body scan1 and breath awareness2 to teach mindfulness skills. Interestingly, even though it is not an explicit skill taught in MBCT, self-compassion is implicitly interwoven into the mindfulness instructions (e.g., “Whenever you notice that the mind has wandered off, bring it back with gentleness and kindness.”). This suggests that self-compassion can also be cultivated via more indirect interventions. Although it is unknown if the directness of the intervention is associated with differential processes, this may be important because direct cultivation of self-compassion could be more challenging when there is an underlying psychopathology such as depression (Gilbert, McEwan, Matos, & Rivis, 2010). Therefore, there is a need for research about the benefits of more indirect ways to cultivate self-compassion (e.g., via a compassionate body scan) in order to improve the acceptability of self-compassion interventions in these populations.

Critically, most of the above-mentioned experimental and clinical studies did not specifically assess self-compassion (Ashar et al., 2016; Hutcherson et al., 2008; Kok et al., 2013; Weng et al., 2013). The few studies that did (Kearney et al., 2013; Neff & Germer, 2013) relied on trait-level measures that may not be sensitive to transient state changes and, like all self-report measures, may be biased by demand characteristics (Orne, 1962). Finally, these studies do not allow conclusions about the underlying mechanisms of the beneficial effects of self-compassion.

A better understanding of underlying mechanisms may come from research suggesting that compassion exerts its protective effects by stimulating physiological systems associated with stress reduction and social affiliation (Engen & Singer, 2015; Gilbert, 2009) and by reducing threat and excessive motivational drive-related arousal (Gilbert, 2009). Compassion has been positioned within the soothing and contentment system of the tripartite affect-regulation system model (Gilbert, 2009). Activating this system is proposed to enhance feeling safe, securely attached, and affiliated with others, and to enable self-soothing when stressed (Porges, 2007). It is further proposed to enhance parasympathetic activity that gives rise to the beat-to-beat variability in heart rate known as heart rate variability (HRV), which has been linked to adaptive emotion regulation in threat contexts (Thayer & Lane, 2000). This system is also suggested to promote interpersonal approach and social affiliation (Depue & Morrone-Strupinsky, 2005) mediated by activations in the central oxytocin-opiate system (Carter, 1998; Depue & Morrone-Strupinsky, 2005; Insel, 2010; Porges, 2007).

The contentment system is distinguished from a negative threat-focused affect system and from another positive affect system that is associated with stimulation and excitement, the drive system; compassion is theorized to have a downregulating effect on both (Gilbert, 2014). To date, direct support for the complete tripartite model is scarce. Kelly, Zuroff, Leybman, and Gilbert (2012) found psychometric evidence for three distinct factors—negative affect, excited positive affect, and social safeness—and for an association between low daily levels of social safeness with low levels of self-esteem and high levels of self-criticism and anxious attachment.

More indirect evidence for the model’s two positive-affect systems comes from emerging neuroscience research. First, research studying the underpinnings of compassion identified higher HRV (Arch et al., 2014; Kok et al., 2013; Rockliff, Gilbert, McEwan, Lightman, & Glover, 2008; Tang et al., 2009), reduced sympathetic activity as indicated by reduced skin conductance (Ortner, Kilner, & Zelazo, 2007; Tang et al., 2009) and lower salivary α-amylase responses (Duarte, McEwan, Barnes, Gilbert, & Maratos, 2015), reduced cortisol stress response (e.g., Rockliff et al., 2008), improved immune functioning (e.g., Davidson et al., 2003), and activation of brain circuitries associated with positive affect, compassion, and social connectedness (Klimecki et al., 2013).

Second, within a biopsychological reward model, the soothing system has been related to a behaviorally deactivating, consummatory pleasure and social engagement system, the “liking” system, whereas Gilbert’s drive system has been linked to the reward model’s “wanting” system; for example, behavior activation, seeking of reward and success, energized positive affect (Berridge & Robinson, 2016; Gray, 1987; Panksepp, 1998), and social behaviors of comparison, competitiveness, or status seeking (Bushman, Moeller, Konrath, & Crocker, 2012; Gilbert, Allan, Brough, Melley, & Miles, 2002; Sloman, Gilbert, & Hasey, 2003). Increases in incentive salience and wanting are disconnected from increases in experienced pleasure (Liggins, Pihl, Benkelfat, & Leyton, 2012) and accompanied by physiological arousal (e.g., higher heart rate; Gruber, Harvey, & Johnson, 2009) and central dopaminergic system activation (Depue & Iacono, 1989; Depue & Morrone-Strupinsky, 2005). Whereas activation of the drive system has been associated with increased positive affect and increases in self-esteem (Wood, Heimpel, & Michela, 2003), its overactivation and dysregulation have been implicated in some mental health conditions such as bipolar disorder (e.g., Johnson, McKenzie, & McMurrich, 2008). On the other hand, parasympathetic activation has been associated with the controllability of positive emotions (Kang & Gruber, 2013) and the drive system.

Additional proof for the tripartite model comes from research suggesting a role of social evaluation (Dickerson & Kemeny, 2004) and isolation (e.g., Cacioppo et al., 2000) in activating the threat system. Psychosocial stress and self-focused rumination have been associated with increased heart rate (Christian & Stoney, 2006; Woody, Smolak, Rabideau, Figueroa, & Zoccola, 2015), enhanced release of the stress hormone cortisol (e.g., Young & Nolen-Hoeksema, 2001), and augmented amygdala activation (Mandell, Siegle, Shutt, Feldmiller, & Thase, 2014). The potential benefit of activating the soothing and contentment system and of a balanced control over the threat and drive system could therefore be understood within the tripartite affect system model, but to date the role of self-compassion within this context has not been studied.

Critically, none of the above-mentioned experimental inductions were specifically designed to cultivate self-compassion. They were either based on Buddhist meditative practices incorporating mindfulness and compassion toward various different other individuals (loved ones, neutral ones, and problematic ones) after briefly directing compassion to oneself (Arch et al., 2014; Ashar et al., 2016; Fredrickson et al., 2008; Hutcherson et al., 2008; Kok et al., 2013; Weng et al., 2013), or using compassion-focused imagery, whereby participants generate an imaginary, ideal compassionate figure sending oneself unconditional love and acceptance, similar to secure attachment priming (Mikulincer & Shaver, 2007b). Although these inductions are likely to translate into greater levels of self-compassion (e.g., Kuyken et al., 2010), to date this has not been adequately tested. Furthermore, the extent to which physiological effects are specific to inducing compassionate states rather than more general positive-mood states has not been explored. In addition, the majority of the above-mentioned studies have investigated psychophysiological effects of repeated or longer-term interventions (Arch et al., 2014; Kok et al., 2013). Hutcherson et al. (2008) showed that even a brief, one-off intervention can increase social connectedness, but have not studied changes in self-compassion or physiological responses. Short-term interventions may allow experimental study of temporal dynamics of self-compassion cultivation.

Summarized, we have identified three gaps in the current literature. First, to date, experimental research into the effects and underlying mechanisms of facilitating self-compassion is lacking. Second, there is also a lack of validated, experimental, short-term self-compassion interventions and well-matched control conditions (Galante et al., 2014), and there is a particular lack of indirect self-compassion inductions for experimental research, although they have been developed for clinical practice (e.g., compassionate body scan; Neff & Germer, 2013). Third, there is a need to triangulate measures of self-compassion mechanisms by complementing self-report with physiological measures (Holmes, Craske, & Graybiel, 2014). Therefore, the aim of this study was to investigate the mechanisms whereby self-compassion confers benefits, using a novel experimental paradigm employing carefully designed experimental and control manipulations and psychophysiological measures complementing self-report.

In addition to two short self-compassion inductions to stimulate a more positive self and the affiliative affect system, we developed three control conditions in line with Gilbert’s tripartite model. As a direct technique to cultivate state self-compassion, we developed Loving-Kindness Meditation for the Self (LKM-S) with a specific focus on directing kindness and soothing to oneself (adapted from Neff & Germer, 2013). As a more indirect approach, we used a compassionate body scan (CBS) to facilitate self-compassion (adapted from Neff & Germer, 2013). We consider this a more indirect condition because participants are guided through the body, invited to attend to bodily sensations with an attitude of interest and equanimity. The teacher’s tone embodies compassion as participants are invited to recognize and allow all experiences they encounter, whether they are pleasant or unpleasant. The LKM-S practice is more explicit in inviting participants to invoke compassionate attitudes toward themselves. To maximize the integrity of the exercises, they were developed and recorded together with mindfulness teachers and eminent researchers with extensive expertise in mindfulness training.

To stimulate the drive system (Gilbert, 2009), and thus test the specificity of any effect of the self-compassion inductions, a positive-excitement condition was designed. Moreover, we included a self-critical rumination condition designed to stimulate the threat system (adapted from Roberts, Watkins, & Wills, 2013) as well as a neutral control condition (adapted from Carnelley & Rowe, 2007).

We specifically chose to recruit a nondepressed sample for this study for two reasons. First, we wanted to avoid assigning a vulnerable group to a possible distressing situation such as self-critical rumination. Second, because of clinical observation that for some people (in particular depressed individuals and self-critics) focusing on compassion for the self at first might be unfamiliar and feel unsafe (Gilbert, Baldwin, Irons, Baccus, & Palmer, 2006), we wanted to investigate the effects of our self-compassion inductions in a healthy sample first to acquire reference data before using them in a clinical sample. On the basis of previous research on compassion, we hypothesized that techniques designed to cultivate self-compassion (as compared to the control conditions) increase a more positive self and state affiliative positive affect (i.e., feeling loved and safe, feeling securely attached) and reduce negative self. It was further expected that increased self-reported positive self and affiliative affect are associated with reduced skin conductance and heart rate (inferring physiological arousal suggestive of sympathetic activation) and increased heart-rate variability (inferring increased parasympathetic activation), a physiological response pattern associated with adaptive emotion regulation. In particular, it was hypothesized that changes in the psychophysiological responses mediate the effect of self-compassion exercises on positive affiliative affect.

Using Mplus, we then calculated a series of simple mediations with self-reported state change as outcome, experimental condition as predictor, and physiological response as mediator. To determine the size of direct and indirect effects, we followed principles suggested by Hayes (2012) , and to adjust for smaller samples we used bias corrected confidence intervals ( Efron & Tibshirani, 1993 ; Fritz & MacKinnon, 2007 ). To account for multiple testing, we adjusted the p value for number of tests.

In order to study the associations between experimental condition, psychophysiological responses, and state changes in self-report, we first calculated zero order correlations (Pearson) using SPSS. Residualized gain scores, as validated index of pre-post change that controls for variance in initial prescores, were calculated for each person by regression with postscore as outcome ( Mintz, Luborsky, & Christoph, 1979 ; Williams, Zimmerman, Rich, & Steed, 1984 ). Physiological change values as index of overall physiological change were calculated by averaging participants’ change values for each minute of the experimental condition together into a single variable. In order to study the associations between the experimental conditions, physiological responses, and state changes in self-report, we used dummy-coded variables for the experimental conditions CBS, LKM-S, Rumination, and Positive Excitement that were each contrasted against the neutral condition.

Model fit was determined using root-mean-square approximation (RMSEA), comparative fit index (CFI), the Tucker-Lewis index (TLI), and the standardized root-mean-square residual (SRMR; see Schermelleh-Engel, Moosbrugger, & Mueller, 2003 ). Comparisons between the different models within each outcome variable were made informal based on the sample size adjusted Bayesian Information Criterion (aBIC), the Akaike Information Criterion (AIC), whereby smaller values indicate a better model fit.

LGCM fits a basic growth model in which repeated measures of a variable represent indicators of continuous latent variables—growth factors—that represent different aspects of change and capture individual differences in a trajectory ( Meredith & Tisak, 1990 ). Typically, these are the intercept (i.e., mean starting value) and the linear (i.e., rate of growth) and quadratic (i.e., leveling off, or coming down) slopes. We initially centered the intercept at Minute 1 of the exercises. In order to understand the role of the different experimental conditions, we added dummy-coded variables CBS, LKM-S, Rumination, and Positive Excited conditions (thus running it against the neutral condition) as covariates to our growth curve model. The resulting coefficients therefore signify the contribution of each respective condition in the context of all other conditions; for example, whether each condition differed significantly from the neutral condition (which was expected to reveal no significant change). In addition to centering the intercept at Minute 1 of the exercises, we ran models with different center points from Minutes 2 to 11 to describe the influence of our conditions at different times during the exercises. We followed the suggested procedures of Muthén and Muthén (2000) , who stated that models with varying centering points are reparameterizations of each other. Analysis will therefore result in the same model fit and is superior to regular regressions, as it draws on information from all time points.

We screened participants for the exclusion criteria using an online survey. Eligible participants were invited to the laboratory session. Following informed consent, participants completed a self-referential task (repeated at the end but not reported here). Participants then completed an 8-min baseline (divided into eight, 1-min blocks—four with their eyes open and four with their eyes closed—for an electroencephalography study not reported here) in which subjects were invited to relax. Next, participants listened to one of the five induction tapes and finally were asked to complete a 1-min resting period with their eyes closed (the analyses of the postinduction findings are reported in the Supplemental Material ). Before and after the first baseline and following the induction, participants completed a manipulation check using visual analogue scales as described above. During the whole experimental procedure, ECG and SCL were continuously recorded.

To obtain measures of HR, HRV, and SCL change throughout the audio exercise and in order to control for individual differences, we calculated participants’ change values for each minute of the experimental condition by subtracting the participants’ averaged baseline value from the value for each subsequent 1-min section of the audio exercise. The last 30 s of each recording were excluded from the analyses because all experimental inductions lasted less than 11.5 min, and a recording of approximately 1 min is needed to reliably assess the physiological components analyzed in this study ( Berntson et al., 1997 ).

Skin conductance was applied as a measure of sympathetic activation and physiological defense response ( Sokolov, 1963 ). It was continuously recorded using a BIOPAC SCL100C amplifier and a skin-resistant transducer (TSD203) from the middle phalanx of the first and ring fingers of the participant’s nondominant hand at a sampling rate of 500 Hz with a low pass filter of 1.0 Hz. Mean skin conductance level (SCL), maximum SCL values, and minimum SCL values were extracted for the same time windows and a range correction ( Lykken, Rose, Luther, & Maley, 1966 ) was applied to each data section for each participant to give a mean SCL corrected for individual differences. The formula for this was Corrected SCL = (SCLmean – SCL min)/(SCL max – SCL min).

High-frequency heart-rate variability (HF-HRV) as an indicator of parasympathetic activation and adaptive physiological regulation capacity ( Thayer & Lane, 2000 ) was determined from the artefact-free ECG (see above) by submitting a time series of the R-peaks to a fast Fourier transformation that calculated the power spectrum of the R-R interval variation for the frequency range between 0.15 Hz and 0.4 Hz in a given time window ( Berntson et al., 1997 ). Mean HF HRV were then extracted for each data section similar to the heart rate. HRV values were log-transformed using the natural log to normalize data.

Heart rate (HR) was acquired as an indicator of physiological arousal. HR was determined from raw electrocardiography (ECG) in beats per min on the basis of a semiautomatic R-wave detection algorithm implemented in the software AcqKnowledge (version 4.2., BIOPAC Systems Inc., Goleta, CA). Raw ECG was acquired using a BIOPAC ECG100C amplifier at a sampling rate of 1 kHz and filtered using a band pass of 0.5–35 Hz. Artifact detection (i.e., noisy, missing, or ectopic beats) and removal was performed using a template correlation and interpolation from the adjacent R-peaks based on established procedures ( Berntson, Quigley, Jang, & Boysen, 1990 ). The interpolation procedure was used for less than 5% of the ECG data. Mean HR in beats per min was then extracted from the R-waves for each data section.

The autonomic nervous system measures described below were recorded using a BIOPAC MP150 system and the software AcqKnowledge 4.2 (BIOPAC Systems; Goleta, CA), with an acquisition sampling rate of 2000Hz. After recording, these data were processed using specialized analysis programs within the AcqKnowledge 4.2 software as described in the respective sections below.

The induction tapes for the five different conditions were developed and recorded together with an experienced MBCT therapist who had been trained in MBCT and taught > 10 courses. The tapes were matched in terms of length (11.5 min) and word density (610–630 words). Instructions were evenly distributed throughout the experimental inductions. In the CBS, participants were guided to direct kind and compassionate attention to their body sensations, starting from the top of the head and going down to the feet. In the LKM-S condition, participants were first guided to bring to mind a person they felt a natural sense of warmth toward and to direct friendly wishes toward this person. After this, participants were invited to offer the same friendly wishes toward themselves. In the self-critical rumination condition, participants were asked to dwell on something they felt they had not managed or achieved as they would have wanted to. In the control condition, participants were guided through a routine supermarket shopping scenario. In the positive excitement condition, participants were asked to think about certain aspects of a positive event or situation in which they were working through or achieving something great. Feedback on the final audio exercises was gathered from experienced mindfulness and meditation practitioners, as well as staff within our clinical department, to ensure ecological validity.

To assess the effectiveness of the experimental inductions on participants’ state self-compassion, positive affiliative affect, self-criticism, and feeling energized levels, a series of questions using visual analogue scales (VAS; ranging from 0 to 100) were used throughout the experiment (see Supplemental Material for full-prompt VAS). Three questions derived from the state adult attachment measure ( Gillath, Noftle, & Stockdale, 2009 ) asked participants about their state affiliative affect (i.e., feeling securely attached, safe, loved, and connected; Cronbach’s α = .66 in this sample). Two questions asked about participants’ state self-compassion (Cronbach’s r = .73 in this sample), adopted from the SCS ( Neff, 2003a ), one about their state self-criticism (based on the FSCRS; Gilbert et al., 2004 ), and one about how energized they felt.

To account for potential differences in trait levels of self-compassion and self-criticism, we assessed these variables across groups. We obtained a total score of the 26-item Self-Compassion Scale (SCS; Neff, 2003a ), on which each item is rated on a 5-point scale ranging from 1 ( almost never ) to 5 ( almost always ), with Cronbach’s α of .69 in this sample. We determined two forms of self-criticism (inadequate self and hated self) and one form of self-reassurance using the Forms of Self-Criticizing/Attacking & Self-Reassuring Scale (FSCRS; Gilbert, Clarke, Hempel, Miles, & Irons, 2004 ). The FSCRS is a 22-item measure identifying different ways people think and feel about themselves when things go wrong for them; it uses a 5-point Likert scale (ranging from 0 = not at all like me to 4 = extremely like me ), with Cronbach’s α in this sample of .73 for inadequate self, .76 for hated self, and .77 for reassured self.

We recruited a total of 135 university students in the United Kingdom (27 per experimental condition; see Fig. S1 in the Supplemental Material available online for the participant flow diagram). Participants were all students at the University of Exeter, native English speakers, right handed, with normal or corrected-to-normal vision and hearing. Exclusion criteria included current depression, currently taking psychopharmacological medication, epilepsy, cardiac problems, and a history of brain surgery. All participants provided written informed consent and received course credits or £10 for participation. The study protocol was approved by the University of Exeter School of Psychology Ethics Committee.

For the CBS condition, there were significant direct and indirect effects (see Figure 2 , Panel 2B), suggesting a partial mediation; for example, that the body scan exerts its effect on increasing positive affiliative affect directly and via a reduction in HR. Although the effects were in a similar direction for LKM-S, they failed to reach significance (see Figure 2 , Panel 1B).

For the CBS condition, there were again significant direct and indirect effects (see Figure 2 , panel 2A), suggesting a partial mediation; for example, that the body scan exerts its effect on decreasing self-criticism directly and via a reduction in HR. For the LKM-S, the direct path was not significant but a significant indirect effect was identified, suggesting a full mediation; for example, that LKM-S exerted its effect on reducing self-criticism only via a reduction in HR (see Figure 2 , Panel 1A).

Overall, the effects were small to medium, and HR change emerged as the only parameter that was significantly correlated with all predictor and outcome variables; therefore, mediation analyses were performed for this parameter to investigate if changes in HR precede changes in self-reported changes in both self-compassion conditions. In the Supplemental Material , we report findings for the other conditions.

Table 2 shows the zero order correlations. Both self-compassion conditions were significantly correlated with HR reduction and HRV increase, as well as with self-reported increase in self-compassion and positive affiliative affect. In contrast, the opposite pattern of significant correlations was found for the rumination condition. Being in the positive condition was significantly correlated with change in self-reported self-criticism and feeling energized but not change in self-compassion or positive affiliative affect. More interestingly, it was significantly associated with similar physiological response changes as in the rumination condition; for example, increased HR and reduced HRV. Feeling energized was significantly negatively correlated with the rumination condition and the CBS. The Supplemental Material includes a detailed consideration of effect sizes and differences between correlation coefficients.

The skin-conductance level results are depicted in Figure 1g . As the outcome variables were not multivariate normally distributed, we used the MLR. A piecewise model with continuous latent variables of intercept, one linear and quadratic slope of skin-conductance change from Minute 1 to Minute 7, and a second linear slope from 8 to 11 as outcomes, and the five experimental conditions as independent variables revealed a partially acceptable fit with χ 2 (80) = 266.81, p < .001, CFI = .915; TLI = .895; SRMR = .030; RMSEA = .132, 90% CI = [0.114, 0.149]; AIC = −3,082.63; aBIC = −3,093.21. Only the rumination condition had a significant effect on the intercept at Minute 1 ( b = 0.16, SE = 0.06, p = .005) but no other significant effects. This finding suggests that skin-conductance level was elevated at the start of the rumination condition as compared to the neutral condition. Moreover, during Minute 1 to Minute 7, the LKM-S had a significant effect on the linear ( b = −0.04, SE = 0.02, p = .038) and quadratic slope ( b = 0.01, SE = 0.003, p < .001), indicating that relative to the neutral condition, skin-conductance level decreased more steeply in this experimental condition but also moved up toward the end of the first 7 min. There were no significant slope effects for Minute 8 to Minute 11. Additional models recentering the intercept revealed that being in the rumination condition was significantly associated with SCL at all 11 min. Being in the LKM-S condition made a significant contribution to the intercept growth factor between Minutes 2 and 5.

Figure 1g depicts the pattern of change in heart-rate variability for the different experimental conditions. As the outcome variables were not multivariate normally distributed, we used the maximum likelihood estimation with robust standard errors (MLR). The model with continuous latent variables of intercept at Minute 1, slope, and quadratic growth of heart-rate variability as outcome and the five experimental conditions as independent variables revealed a good fit with χ 2 (89) = 176.83, p < .001, CFI = .943; TLI = .936; SRMR = .03; RMSEA = .08, 90% CI = [0.068, 0.105]; AIC = 2,145.70; aBIC = 2,136.50. The LKM-S ( b = 0.91, SE = 0.18, p < .001) and the rumination condition ( b = −0.39, SE = 0.10, p = .035) had a significant effect on the intercept at Minute 1 but there were no linear or quadratic effects, indicating that HRV was elevated at the start of the LKM-S and decreased at the start of the rumination condition relative to the neutral condition. The CBS not only had a significant effect on the intercept of HRV ( b = 0.40, SE = .17, p = .022), but also a significant linear ( b = 0.14, SE = .05, p = .013) and quadratic growth effect ( b = −0.01, SE < 0.01, p = .019). These results suggest that the HRV was elevated at the start of the CBS relative to the neutral condition and increased slowly over the first few minutes, plateaued at Minute 4, and then decreased again at the last minute of the intervention. Additional models recentering the intercept yielded that being in the two self-compassion conditions, CBS and LKM-S, was significantly associated with HRV intercept at all 11 min. Being in the rumination condition ceased to be significant in Minute 5, and being in the positive condition started to make a significant contribution in Minute 10.

Figure 1f shows the pattern of change in heart rate for the different experimental conditions. The outcome variables were multivariately normally distributed. The model with continuous latent variables of intercept of heart-rate change at Minute 1 and linear and quadratic slope as outcome and the five experimental conditions as independent variables revealed a good fit with χ 2 (89) = 164.66, p < .001; CFI = .968; TLI = .965; SRMR = .03; RMSEA = .08, 90% CI = [0.06, 0.09]; AIC = 6,648.53; aBIC = 6,639.80. It indicated that the CBS ( b = −3.66, SE = .99, p < .001), Rumination ( b = 2.32, SE = 1.00, p = .020), and LKM-S ( b = −4.54, SE = .99, p < .001) were significantly influencing the intercept, but there were no linear or quadratic slope effects for these conditions (all p > .05). This suggests that relative to the neutral condition, heart rate was decreased at the start of the CBS and the LKM-S, whereas it was elevated in the rumination condition. In contrast, the positive excitement condition had a significant effect on the linear slope ( b = 0.825, SE = .33, p = .012), suggesting that heart rate consistently increased over the course of this intervention. Additional models recentering the intercept revealed that whereas being in LKM-S and Rumination conditions was significantly associated with heart rate at all 11 min, being in the CBS condition ceased to be significant in Minute 8 and being in the positive condition made a significant contribution from Minute 4 through Minute 11.

The scores for the feeling energized ratings are depicted in Figure 1d . The Group × Time ANOVA revealed a significant main effect of group, F (4,130) = 3.63, p = .008, η p 2 = .01, that was qualified by the significant Time × Group interaction, F (4, 130) = 6.24, p < .001, η p 2 = .16. Post hoc analyses revealed that there was a significant pre-to-post increase in feeling energized in the positive excitement condition, F (1, 26) = 11.15, p = .003, η p 2 = .30, 95% CI = [4.95, 20.82]. In contrast, there was significant decrease in feeling energized for the CBS condition, F (1, 26) = 6.10, p = .021, η p 2 = .19, 95% CI = [–17.04, –1.55] and for the rumination condition, F (1, 26) = 4.68, p = .040, η p 2 = .15, 95% CI = [–16.03. 0.41]. No pre-to-post manipulation difference emerged for the control condition, F (1, 26) = 3.27, p = .082, η p 2 = .11, 95% CI = [–9.97, 0.64], or the LKM-S condition, F (1, 26) = .04, p = .847, η p 2 = .04, 95% CI = [–8.53, 7.05].

The scores for the positive affiliative affect ratings are depicted in Figure 1c . The Group × Time ANOVA revealed no significant main effect of group, F (4,130) = 1.03, p > .05, η p 2 = .03. However, the Time × Group interaction yielded significance, F (4, 130) = 24.46, p < .001, η p 2 = .43. Post hoc analyses revealed that there was a significant pre-to-post increase in positive affiliative affect in the CBS condition, F (1, 26) = 10.53, p = .003, η p 2 = .28, 95% CI = [2.00, 8.93], the LKM-S condition, F (1, 26) = 26.79, p < .001, η p 2 = .51, 95% CI = [5.43, 12.59] and, albeit smaller, for the positive condition, F (1, 26) = 6.12, p = .020, η p 2 = .19, 95% CI = [0.69, 7.46]. In the rumination condition there was a significant decrease in positive affiliative affect after the manipulation, F (1, 26) = 38.90, p < .001, η p 2 = .60, 95% CI = [–18.79, –9.48], whereas no pre-to-post manipulation difference emerged for the control condition, F (1, 26) = .49, p = 486, η p 2 = .01, 95% CI = [–4.77, 2.33]. Interestingly, an ANCOVA (see Supplemental Material ) revealed that after induction, only individuals in the LKM-S condition reported significantly higher positive affiliative affect than those in the neutral condition, and individuals in the rumination condition reported significantly lower positive affiliative affect.

Similarly, the Group × Time ANOVA yielded a main effect of Group, F (4,130) = 2.64, p = .037, η p 2 = .08, which again was qualified by a significant time by group interaction, F (4, 130) = 12.33, p < .001, η p 2 = .28. The scores for the state self-criticism ratings are depicted in Figure 1b . Post hoc analyses revealed that there was a significant pre-to-post decrease in self-critical ratings in the CBS group, F (1, 26) = 8.55, p = .006, η p 2 = .25, 95% CI = [–18.46, –3.66] and for the LKM-S condition, F (1, 26) = 7.00, p = .014, η p 2 = .21, 95% CI = [–7.69, 0.97]. A similar but smaller effect was found for the positive condition, F (1, 26) = 7.54, p = .044, η p 2 = .15, 95% CI = [–14.23, 0.22]. In contrast, there was a significant increase in self-critical ratings in the rumination condition, F (1, 26) = 21.11, p < .001, η p 2 = .45, 95% CI = [9.54, 24.98]. No pre-to-post manipulation difference emerged for the control condition, F (1, 26) = .03, p = .857, η p 2 < .00, 95% CI = [–5.93, 4.96]. Interestingly, an ANCOVA (see Supplemental Material ) revealed that after induction, only individuals in the rumination condition reported significantly higher state levels of self-criticism as compared to the neutral condition.

The scores for the state self-compassion ratings are depicted in Figure 1a . The Group × Time ANOVA revealed a main effect of Group, F (4, 130) = 2.86, p = .026, η p 2 = .08, which, in line with our hypothesis, was qualified by a significant Group × Time interaction, F (4,130) = 12.65, p < .001, η p 2 = .28. Post hoc analyses revealed that there was a significant increase in self-compassion in the CBS condition, F (1, 26) = 27.56, p < .001, η p 2 = .51, 95% confidence interval (CI) = [6.73, 15.41], and for the LKM-S condition, F (1, 26) = 23.30, p < .001, η p 2 = .47, 95% CI = [5.32, 13.20]. A similar but smaller effect could be found for the positive condition, F (1, 26) = 12.63, p = .001, η p 2 = .37, 95% CI = [2.96, 11.07]. In contrast, a significant decrease in self-compassion could be found in the rumination condition, F (1, 26) = 7.47, p = .011, η p 2 = .22, 95% CI = [–12.42, –1.76]. There was no pre-to-post difference in the control condition, F (1, 26) = .27, p = .607, η p 2 = .01, 95% CI = [–2.61, 4.96]. Interestingly, an ANCOVA on postinduction scores (see Supplemental Materials ) using pre-induction scores as the covariate revealed that after induction only individuals in the two self-compassion conditions (but not those in the positive excited condition) reported significantly higher self-compassion than the neutral condition, and individuals in the rumination condition reported significantly lower self-compassion.

Sample characteristics are shown in Table 1 . The average age of the sample was 19.34 years ( SD = 2.06). Trait levels of self-compassion in this sample were similar to published self-compassion scores for healthy young adults ( M = 19.51 out of 30, SD = 4.46, range = 8.60– 28.90 as compared to M = 18.25, SD = 3.75; Neff, 2003a ). Furthermore, participants in this study can be described as relative low in self-criticism as compared to previously published self-criticism scores for nonclinical populations (“inadequate self” subscale of the FSCRS: M = 12.97 out of 36, SD = 7.27, range = 0.00–33.00 vs. M = 17.72, SD = 8.29; Baiao, Gilbert, McEwan, & Carvalho, 2015 ). As shown in Table 1 , there were no significant differences between the groups in age, levels of self-compassion (SCS), and levels of self-criticism (FSCRS). Importantly, the different groups were comparable in terms of their self-reported state levels of self-compassion ( F (4,130) = 0.64, p = .637, η p 2 = .02), self-criticism ( F (4,130) = 0.35, p = .845, η p 2 = .01), positive affiliative affect ( F (4,130) < .40, p = .809, η p 2 = .01), and feeling energized ( F (4,130) = 1.06, p = .380, η p 2 = .03). In addition, no significant group differences emerged for the physiological parameters at baseline (see Table 1 ).

Discussion

In this study we used two short-term experimental inductions designed to temporarily increase self-compassion, as well as control conditions stimulating either the threat or the drive systems, to test the hypothesis that both a CBS and an LKM-S, as compared to the control conditions, reduce state self-criticism and physiological arousal on one hand and increase state positive affiliative affect, self-compassion, and parasympathetic activation on the other hand. Furthermore, we investigated whether changes in the psychophysiological responses mediate the effect of self-compassion exercises on state changes in self-compassion, self-criticism, and positive affiliative affect.

The results were largely in line with our expectations and lead us to suggest that self-compassion may exert its beneficial effects on mental and physical health in two possible ways, first, by temporarily activating a low-arousal parasympathetic positive affective system that has been associated with stress reduction, social affiliation, and effective emotion regulation, and second, by temporarily increasing positive self and reducing negative self, thus addressing cognitive vulnerabilities for mental health problems such as depression. Integrating our findings into the existing literature, we discuss this before providing a discussion of the relevance of our findings for the tripartite model of emotion regulation and the wider theoretical implication for the construct of self-compassion.