Autonomic Nervous System Activity During Positive Emotions: A Meta-Analytic Review. Maciej Behnke et al. Emotion Review, February 4, 2022. https://doi.org/10.1177/17540739211073084
Abstract: Autonomic nervous system (ANS) activity is a fundamental component of emotional responding. It is not clear, however, whether positive emotional states are associated with differential ANS reactivity. To address this issue, we conducted a meta-analytic review of 120 articles (686 effect sizes, total N = 6,546), measuring ANS activity during 11 elicited positive emotions, namely amusement, attachment love, awe, contentment, craving, excitement, gratitude, joy, nurturant love, pride, and sexual desire. We identified a widely dispersed collection of studies. Univariate results indicated that positive emotions produce no or weak and highly variable increases in ANS reactivity. However, the limitations of work to date – which we discuss – mean that our conclusions should be treated as empirically grounded hypotheses that future research should validate.
Keywords: positive emotions, autonomic nervous system, cardiovascular activity, electrodermal activity
Discussion
In this quantitative review, we aimed to inspect, evaluate, and synthesize (to the extent possible) findings from past research that measured a physiological component of positive emotions. We found the available data to be quantitatively imbalanced, with many studies focused on some positive emotions and physiological signals and few studies focused on other positive emotions and physiological signals. Furthermore, we found high variability in methods used for emotion elicitation and data collection. Recognizing that the empirical evidence might be insufficient to test some effects, we aimed to use all available empirical data and stringent criteria for multiple hypothesis testing to examine whether the currently available empirical findings allow us to conclude that positive emotions elicit ANS reactivity. We also explored whether the ANS reactivity is specific to discrete emotions (in terms of patterns and magnitudes) or—alternatively—whether similar ANS reactivity accompanies all positive emotions. Finally, we tested participant characteristics and methodological factors as moderators of the ANS reactivity to discrete positive emotions. One main and three secondary findings emerged.
Based on univariate analyses, most discrete positive emotions elicited no or weak ANS reactivity. Moreover, half of the effect sizes in ANS responses were highly inconsistent, suggesting that other significant physiological variability sources exist. We also found that similarities outweighed differences in ANS responses during positive emotions. This contrasts the literature suggesting a stronger physiological differentiation among discrete positive emotions (Kreibig, 2010; Shiota et al., 2017). Finally, we found few moderating effects of study or participant characteristics. Thus, the current empirical material supports the view that positive emotions produce no or only a weak and nonspecific ANS response relative to baseline and neutral conditions (Cacioppo et al., 2000; Lench et al., 2011; Siegel et al., 2018).
However, we emphasize that these conclusions must be considered tentative because they are based upon imbalanced and incomplete data and one type of analysis (univariate, not multivariate). This suggests the need for more systematic research on the physiology of positive emotions that will fill existing gaps and provide material for future robust evaluation of positive emotions and ANS activity. For instance, impedance cardiography that is often applied to the study of stress and negative emotions is relatively underrepresented in the study of positive emotions. Moreover, the psychophysiological study of amusement is greatly overrepresented relative to gratitude, pride, or love. Finally, we advocate more multivariate sampling and analysis of emotional responses in positive emotions.
Positive Emotions and ANS Reactivity: The State-Of-The-Art
We based this review on the most extensive collection of available studies, which produced over 686 effect sizes derived from 6,546 participants. However, in the coding process, we observed substantial variability across this large number of studies, which resulted in the collection of a widely dispersed dataset. The studies were conducted in different settings. For instance, the laboratories used various equipment, procedures, and data cleaning and analysis techniques. Most studies examined only reactivity related to two emotions using 2-3 ANS measures with a single elicitation method. Furthermore, when coding the possible moderators, in many cases, we were unable to determine whether participants were alone during the experimental task or the experimenter stayed in the room after placing the physiological sensors. We found that only 44% of studies explicitly stated that they video recorded participants during the experiments. In this way, our meta-analysis supports the message from the recent comment on the current state of the science of positive emotion (Shiota, 2017) - although the field made incredible progress in the last decades, affective scientists are still far away from the promise of this field being fully realized. This also includes the call for more detailed reporting of procedures (e.g., were participants explicitly observed) and data use (e.g., duplicate datasets).
The data collected so far on positive emotion is insufficient to strongly support the ANS specificity versus similarity for a wide range of positive emotions presented in our investigation. Our analyses were challenging due to a widely dispersed dataset with small numbers of studies per emotion and per ANS measure. Moreover, some comparisons and analyses were performed on a relatively small number of effect sizes. Although the field of affective science struggles to just-decide-already whether specificity or similarity of ANS emotion-related reactivity is the ground truth, with this review, we observed that the current state-of-the-art is not sufficient to address this expectation for definite conclusions. While reviewing hundreds of studies, we also observed that researchers moved quickly to asking complex questions related to functions of targeted emotion without addressing more basic questions, e.g., which ANS parameters are adequate for studying a specific positive emotion. We suggest that a return to more basic questions might advance the field of psychophysiology of emotions. It would be beneficial for the field of positive emotions to further examine ANS reactivity, in particular, to positive emotions that have not been explored yet, such as hope or schadenfreude, using multiple ANS measures. We advocate that the field of positive emotions would benefit from greater integration and uniform standards/rigor for emotion elicitation and data curation, analysis, and reports.
Do Positive Emotions Produce Robust Changes in ANS Reactivity?
We found that the set of inspected positive emotions produce no or weak increases in ANS reactivity in both SNS and PNS. Our findings are consistent with models of positive emotions that assumed that positive emotions do not generate independent sympathetic responses (Fredrickson & Levenson, 1998; Fredrickson et al., 2000; Fredrickson, 2013; Folkman, 2008; Levenson, 1988, 1999). Moreover, we did not observe increased PNS reactivity in positive emotions, as suggested by the polyvagal theory (Porges, 2011). One explanation for these null results is that large differences across experiments might be responsible for the responding range. Figure 2 presents that even the mean effects interpreted as medium sizes had wide confidence intervals that prevented them from being significant. These findings are consistent regardless of focusing only on separate ANS measures, measures that had more than ten studies, or measures aggregated into broader categories.
Our findings match previous meta-analyses focused on happiness that concluded that happiness produces weak ANS reactivity and that this reactivity is not different from neutral conditions (Lench et al., 2011; Siegel et al., 2018). Our findings do not support conclusions from qualitative reviews in which some positive emotions such as contentment and love decreased cardiovascular or electrodermal activity (Kreibig, 2010). However, more than half of the physiological responses’ directions in the qualitative review were based on fewer than three studies, suggesting that these findings were preliminary (Kreibig, 2010). With additional studies that were published over the last decade, we found support for the previously found directions of the electrodermal reactivity (increases) to positive emotions. We also found support for increased ANS activity to joy and amusement (Kreibig, 2010).
Are ANS Reactivity Patterns Specific to Particular Positive Emotions?
The main goal was to provide a quantitative review of the body of research related to ANS reactivity and positive emotions. However, we also evaluated the specificity or generality of ANS reactivity to discrete emotions. The basic expectation in this meta-analysis was that discrete positive emotions produce specific adaptive changes in physiology (Ekman & Cordaro, 2011; Levenson, 2011; Panksepp & Watt, 2011; Kreibig, 2010).
We found that similarities outweighed differences in ANS responses during positive emotions. This finding is consistent with the models that view ANS reactivity to emotion as context-sensitive and not discrete-emotion-sensitive (Barrett, 2013, 2017; Quigley & Barrett, 2014). Thus, the ANS reactivity is not random but is specific and supports actions in the specific context, which could vary for the same discrete emotion (Barrett, 2006; Barrett & Russell, 2015; Quigley, & Barrett, 2014). Theorists suggest that multiple distinct, context-sensitive physiological responses to discrete emotion are possible, as long as both serve the same adaptive function, e.g., freezing versus fleeing from a threat in fear (Ekman, 1992).
However, the ANS reactivity is only one component of emotional responding. Thus, major judgments about the structure of emotions should be interpreted along with affective and behavioral responses and should not be based solely on any one component.
Shared ANS reactivity to positive emotions might be related to common neural origin from a highly conserved circuit of neural structures, namely the mesolimbic pathway, often called the “reward system” (see Shiota et al., 2017 for discussion). The activation along the mesolimbic pathway has been linked to a wide range of stimuli associated with the family of positive emotions, including delicious foods (Berridge, 1996), monetary incentives (Knutson et al., 2001), babies (Glocker et al., 2009), loved ones (Bartels & Zeki, 2004), humor (Mobbs et al., 2003), and favorite music (Blood & Zatorre, 2001). It may explain the mechanism by which the discrete positive emotions share some overlapping properties that might be further differentiated depending on the conditions in which positive emotions are activated. Overlapping properties of positive emotions and continuous gradients between discrete emotion categories have been found in recent large-scale investigations (Cowen & Keltner, 2017). That study has shown that emotions were more precisely conceptualized in terms of continuous categories, rather than discrete emotions, showing smooth gradients between emotions, such as from calmness to aesthetic appreciation to awe (Cowen & Keltner, 2017).
Supporting the dimensionality of emotions, we found differences along the dimension of approach motivation (Gable & Harmon-Jones, 2010; Harmon-Jones et al., 2013). Positive emotions characterized by strong approach tendencies, such as joy and excitement, were accompanied by a higher sympathetic reactivity (e.g., DBP, MAP) than low-approach positive emotions like amusement. Our investigation may serve future studies to conceptualize positive emotions in terms of physiological arousal starting from the least arousing and ending with the most arousing positive emotions, namely awe, attachment love, gratitude, nurturant love, contentment, excitement, amusement, pride, craving, sexual desire, and joy.
Are There Moderators of ANS Reactivity to Positive Emotions?
We investigated several moderators that we thought might influence physiological responsiveness to emotions, but most did not moderate the observed effects. Only in craving did we observe a significant moderating effect of the elicitation method on the physiological response. We observed that behavioral methods, namely, exposure to food, produced stronger ANS reactivity than pictures, films, and imagery. This observation indicates the advantage of using active rather than passive emotion elicitation methods. Furthermore, in line with Lench and colleagues (2011), we found that reactions to craving were stronger when the proportion of women in samples increased.
Contrary to our expectations, we found no influence of several continuous variables on physiological reactivity to positive emotions, including age, sex proportion, participant number, and study quality (Kret & De Gelder, 2012; Stevens and Hamann, 2012; Mill et al., 2009; Sullivan et al., 2007). Like the previous meta-analysis of Lench and colleagues (2011), we found no evidence that the participants’ age influenced the degree of emotional reactivity. However, this may be due to imbalanced age distribution (skewed young), meaning we were underpowered to detect age differences. A thorough examination of how age influences the emotional experience's intensity requires additional research with older participants. In line with previous meta-analyses, we found no sex differences in response to positive emotions (Joseph et al., 2020; Siegel et al., 2018). Finally, although we found indications of publication bias for some pairs of ANS reactivity and positive emotion, we conclude that most mean effect sizes seem to be robust and unlikely to be an artifact of systematic error.
Limitations and Future Directions
First, as we emphasize throughout the paper, the conclusions we present are provisional and contingent upon current data availability. More definitive conclusions will await additional research, particularly on under-researched positive emotions and measures.
Second, in this project, we used a univariate approach to analyze the mean ANS reactivity to discrete positive emotions in a series of meta-analyses. Although available multivariate meta-analytic approaches (Riley et al., 2017) would provide a better fit to the characteristic of emotions (Kragel & LaBar, 2013; Stephens et al., 2010), several factors militated against using a multivariate approach. For instance, a multivariate meta-analysis requires a correlation matrix between the ANS measures. This was not possible to obtain because only 7 out of 128 articles included in our investigation reported correlations between some ANS measures. Along similar lines, for many analyses (e.g., amusement), two or more ANS measures were never observed jointly in the same study. Moreover, a previous meta-analysis found that multivariate pattern classifiers did not provide strong evidence of a consistent multivariate pattern for any emotion category (Siegel et al., 2018). Of note, the multivariate and univariate models produce similar point estimates, but the multivariate approach usually provides more precise estimates. Thus, the benefits of a multivariate meta-analysis are small (Riley et al., 2017). The advantages of using multivariate meta-analysis of multiple outcomes are greatest when the magnitude of correlation among outcomes is large, which was not the case for most of our analyses. In conclusion, our approach can produce statistically valid results for each pair of positive emotion and ANS reactivity measures (Pustejovsky & Tipton, 2021). Future studies might collect multiple physiological measures when studying ANS reactivity to emotions (Cacioppo et al., 2000) to provide data that allows for robust multivariate analyses.
Third, we used univariate statistics, which disrupt the physiological response's continuity and treat the entire emotion manipulation as a separate piece. Although univariate methods have historically dominated the literature (see Cacioppo et al., 2000; Kreibig, 2010, for the reviews), future reviews may use multivariate approach data to replicate our findings.
Fourth, during the coding, we relabeled examined emotions in many studies. However, we found no differences in effect sizes extracted from studies with the original emotion label and effect sizes extracted from studies for which we renamed the emotion label. Moreover, the conclusions that come from our literature research stress the importance of using precise terminology in emotion-related literature. The overview of existing empirical and theoretical models indicates not only a variety of discrete positive emotions but also a variety of terms used to describe them. For instance, researchers used different labels for emotions elicited by funny situations, such as amusement (Kreibig et al., 2013), happiness (Kring & Gordon, 1998), or mirth (Foster et al., 2003). The heterogeneity of labels suggests problems with discrete emotions’ construct validity and measurement invariance. Future research would benefit from a more uniform nomenclature and definitions accepted by researchers within affective science.
Fifth, most of the theoretical and empirical models conceptualize individual positive emotions without clearly addressing how different positive emotions might be interrelated (e.g., Cowen and Keltner, 2017; Ekman and Cordaro, 2011; Tong, 2015; Weidman & Tracy, 2020, with the exception of Kreibig, 2014; and Shiota et al., 2017). It might be useful to group discrete emotions into families or clusters based on their similarities. For instance, joy and excitement are similar emotions associated with progress in achieving one's goals, but excitement has an anticipatory response compared to joy that brings well-being and good fortune after an event (Lazarus, 1991; Shiota et al., 2017; Smith & Kirby, 2010). Future studies may focus on examining similarities rather than differences between positive emotions. Researchers should also balance between generalization and differentiation in studying emotions.
A sixth limitation resulted from including studies that examined physiological reactivity from baseline or neutral conditions. The results produced by these studies differ due to differences in the design of these studies. Some research used neutral movies as a baseline (e.g., De Wied et al., 2009), whereas other studies used neutral videos in the experiment (e.g., Codispoti et al., 2008). We followed the theoretical premise that both baselines and neutral conditions should be emotionally impartial. A moderation analysis showed that the type of comparison used in primary studies had no effect on the size of the physiological reactivity. These results allowed us to examine further hypotheses, but both decisions of including different types of comparison and relabeling the emotion categories may have produced bias in the results of our meta-analysis. Given the considerable increase in psychophysiological research on emotions in recent years, future meta-analytic work would provide empirical support for the emotional impartiality of baseline and neutral conditions.
Seventh, we found substantial research methodology variability in primary studies. We tested whether the study quality moderated the effect sizes by assessing the presence of exclusion criteria, manipulation check procedures, and protocols for reporting or handling artifacts and missing data. We found those three measures to be objective indicators of study quality. However, the study quality had no effect on the size of physiological reactivity. More studies that include multiple ANS measures and multiple discrete positive emotions with diverse samples are required to strengthen broad inferences about ANS responses to positive emotions. Future studies might also examine how emotions differ in ANS reactivity rather than asking whether emotions generally differ physiologically (e.g., Berntson et al., 1991; Stemmler, 1992). ANS reactivity produces the optimal bodily milieu to provide physiological support for behaviors associated with discrete emotion (Levenson, 2014). This requires unique configurations of multiple physiological responses rather than a single unique physiological change (Levenson, 2014). Single ANS measures might not be sufficient, given that most physiological measures used in the emotion-related literature constitute the physiological outcome of emotion-related states, showing a one-to-many relation between the physiological measure and emotions (see Cacioppo et al., 2000; Richter & Slade, 2017 for the discussion). Groups of emotions may lead to similar general activation that occurs in response to an upcoming action (Brehm, 1999; Fredrickson & Levenson, 1998; Frijda, 1987). For instance, excitement, craving, or sexual desire prepare the organism to “be ready for action,” and they produce similar sympathetic activation. However, more specific activity might be observed in targeted organs. For instance, craving might be observed in the gastrointestinal tract, sexual arousal in the genital system, and excitement in the locomotor system (Levenson, 2011, 2014).
Eighth, most of the ANS variables included in our meta-analysis are blends of SNS and PNS activation (e.g., HR). The measures more specifically related to PNS or SNS measures (e.g., RSA or PEP) were not broadly assessed across the positive emotions. Thus, we could not fully address whether positive emotions produce pure SNS or PNS activity but rather the co-product of one of the two systems.
Nineth, we believe that our meta-analysis opened the discussion for methodological issues in the psychophysiology of emotions that would be worth testing empirically. For instance, for moderators, we focused on the length of the time interval used to calculate the physiological levels for baselines and emotion manipulations. Although we did not find effects of the time interval on mean ANS reactivity to positive emotions, we believe that comparing different time intervals of the same physiological measure is problematic. Similarly, researchers usually used the same time intervals to present the reactivity of all ANS measures despite differences across the family of ANS variables. Thus, scientists tended to sacrifice the specificity of particular ANS measures for the sake of a uniform data analysis strategy.
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