Positive Emotion Differentiation and the relationship with Well-Being
Joyce Helsloot 5994225
University of Amsterdam
Master’s Thesis Social Psychology July 2016
Supervised by: Dr. Disa Sauter
Second assessor: Prof. dr. Agneta Fischer
Table of Contents
Abstract ………. 6
Introduction ………... 7
Comparing Emotions by Appraisals ………. 9
Positive Emotion Granularity relates with Well-Being ……… 11
Current Research ………...12 Methods ……… 13 Participants ………13 Procedure ……….. 13 Design ………... 14 Materials ………... 14
Emotional Experience Questionnaire ………... 14
Appraisal Questionnaire ………15
Positive Emotion Granularity ………... 15
Ryff Scales of Psychological Well-Being ……….15
Results ………... 16
Manipulation Check ……….. 16
NA Responses ………... 17
Comparing Differentiation Strength of Two Sets of Appraisals ………….. 17
Results for Both General and Specific Appraisals ………17
Results for General Appraisals ………. 18
Results for Specific Appraisals ………. 19
Comparison of General with Specific Appraisals ……….19
Correlation between Positive Emotion Differentiation and Well-Being ….. 20
Discussion ………. 23
Current study ……….23
Emotion Intensity ……….. 23
Classifying Emotions by General and Specific Appraisals ……….. 24
Comparing General With Specific Appraisals ……….24
Are Both Sets Complementary? ………25
Previous Research ………. 25
Limitations ……… 26
Positive Emotion Granularity and Well-Being ………. 27
Emotion Profiles ………... 28 Amusement ………... 28 Awe ………... 28 Compassion ………... 28 Gratitude ………... 29 Interest ………...29 Relief ………. 29 Love ……….. 29 Pride ……….. 30 General Limitations ……….. 30 General Contributions ………... 31 References ………. 33
Appendix A. Attention Checks ………. 38
Appendix B. Recall Experience ……… 40
Appendix C. Emotional Experience Questionnaire ……….. 41
Appendix E. Ryff’s Psychological Well-Being Scales (RPWB) ……….. 48 Appendix F. Confusion Matrixes resulting from Discriminant Analysis …………. 52
Abstract
Despite the importance of positive emotions for human health and well-being, past research has primarily focused on negative emotions. This might be due to the suggestion that positive emotions are less differentiated than negative emotions. Studies about differentiating positive emotions have been done with appraisals that were mostly applied on negative emotions before. Current research’s aim was to compare the differentiation strength from 2 sorts of appraisals: general and emotion-specific appraisals. Another aim of this research was to see if positive emotion granularity (the extent to which people distinguish between their positive emotions) was related to well-being. 37 participants recalled experiences of eight emotions (amusement, awe, compassion, gratitude, interest, love, pride, and relief) and filled in appraisal questionnaires and well-being questionnaires. Results indicated that specific appraisals differentiated better between positive emotions than general appraisals, but both sets of appraisals were apt in classifying positive emotions. No relationship between positive emotional granularity and well-being has been found. Additionally, the appraisal patterns of each emotion were described.
Positive Emotion Differentiation and the relationship with Well-Being
Positive emotions are beneficial for human health and well-being, and they aid our coping ability during times of stress (Tugade, Fredrickson, & Feldman Barrett, 2004; Pressman & Cohen, 2005; Folkman & Moskowitz, 2000). According to
functional perspectives1 emotions are indicators of one’s state of well-being and they assist us in survival and adaptation (Lazarus, 1991). For example negative emotions activate an impulse that causes us to avoid or deal with situations that are a threat to our individual well-being (Lazarus, 1991). In contrast, results suggest that positive emotions are correlated with approach motivation, and regulate interdependent relationships on which we depend for survival and reproductive success (Gonzaga, Keltner, Londahl, & Smith, 2001; Shiota, Campos, Keltner, & Hertenstein, 2004).
Even though positive emotions are of great importance, the structure of positive emotions has been studied far less than the structure of negative emotions (Lazarus, 1991; Ekman, 1992). One way to generate a better understanding of the structure of emotions is investigating the differences and similarities among them (Ekman, 1992; Lazarus, 1991). Differentiating positive emotions could help us understand specific emotional states, so they can function as an important source of information about how to cope with our environment adaptively. But although there has been growing attention focused on positive emotions in recent years, knowledge about the differentiation of positive emotions remains limited (Campos, Shiota, Keltner, Gonzaga, & Goetz, 2013; Tong, 2014; Cong, 2015). This might be due to the suggestion that positive emotions are less categorically differentiated than negative emotions (Ekman, 1992).
1
Functional perspectives argue psychological phenomena are functions that help us cope with the environment in an adaptive manner.
This is illustrated by the fact that only one of the six basic emotions2 is a positive emotion, namely happiness, which is assumed to reflect all positive emotions (Sauter, 2010). Also, previous research found that reported positive emotions
correlate more highly than reported negative emotions (Ellsworth & Smith, 1988). And Ekman (1992) describes the ‘problem of positive emotions’ by arguing that their expressions do not have distinctive features, whereas each of the basic negative emotions has such distinctive features.
But there is also reason to believe that positive emotions, like negative emotions, are differentiated. Not only do we experience these emotions as distinct from one another, empirical studies have shown that subjective experiences (measured by ‘core relational themes’) and facial expressions of eight positive emotional states - amusement, awe, contentment, gratitude, interest, joy, love, and pride - are distinct (Campos et al., 2013). Also, some positive emotions have been found to differ in persuasion strength, which suggests they differ in how they are cognitively being processed (Griskevicius, Shiota, & Neufeld, 2010). Enthusiasm, love, amusement and awe show distinct profiles of activation in the autonomic
nervous system (Shiota, Neufeld, Yeung, Moser, & Perea, 2011), which suggests the existence of multiple physiologically distinct positive emotions. These findings suggest there are significant differences between positive emotions, but not every aspect of positive emotional responding have been shown to be distinct.
Comparing Emotions by Appraisals
Current research aimed to examine the overlap and differences between
2
Basic emotions are a set of six emotions (sadness, anger, disgust, fear, happiness, and surprise), which are considered to be (1) universal, and (2) fundamentally distinct from one another (Ekman, 1992; Ekman & Cordaro, 2011).
multiple positive emotions, using appraisals. Appraisals are evaluations about the anticipated consequences of emotion-eliciting situations (Smith & Ellsworth, 1985), and are made on dimensions such as: pleasantness, control and certainty in relation to one’s needs, goals and beliefs (Smith & Lazarus, 1993). For example, when a man meets a bear, he could appraise this event as dangerous. This appraisal causes a specific emotion – in this case probably fear – that motivates him to deal with the appraised advantage or disadvantage in an adaptive way. Different patterns of appraisal would inescapably result in different emotional states. This leads to the suggestion that emotions can be differentiated by appraisals, which has already been found for negative emotions (Ellsworth & Smith, 1985; Smith & Ellsworth, 1988).
One of few studies on positive emotion differentiation3, carried out by Tong (2014), looked at how strongly emotional experiences were differentiated by appraisals. They found a differentiation strength4 of 42.4% across 13 positive emotions (awe, pride, amusement, gratitude, love, interest, compassion, challenge, contentment, hope, relief, romantic love, serenity). This shows above chance differentiation and provides a piece of evidence for the differentiation between positive emotions on appraisals. Tong used a set of 13 appraisals that cover most of the appraisals often used for differentiating emotions (Smith, Tong, & Ellsworth, 2014): Pleasantness, Relevance, Problem, Goal attainment, self, Agency-other, Agency-circumstances, Control-self, Control-Agency-other, Control-circumstances, Certainty, Predictability, and Effort (hereinafter referred to as general appraisals).
3
Positive emotion differentiation is the extent in which people distinguish between their positive emotions.
4
Differentiation strength is the accuracy rate in which the appraisals differentiated between the emotions (Tong, 2014).
However, the appraisals were not effective at classifying all positive emotions, because interest, relief and joy5 could not be differentiated from other positive
emotions (Tong, 2014). Also, appraisal patterns found in this study may not entirely cover the main essence of some of the emotions. Namely, there are facets in the conceptualizations of some positive emotions, which are not reflected in these appraisals. For instance, Tong (2014) describes the appraisal profile of each emotion, with awe rated as very pleasant; and awe was seen as evoked and controlled by something other than the self. This profile does not cover the conceptualization of awe, whereby awe involves the sense that the object of attention is difficult to grasp, and a sense of smallness of oneself in the presence of something greater than oneself (Tong, 2014; Keltner & Haidt, 2003; Shiota, Keltner & Mossman, 2007).
These limitations of appraisals might be due to the fact that the appraisals have been designed for negative emotions, and likewise have mostly been used for
differentiating negative emotions (Smith, Tong, & Ellsworth, 2014). Therefore these general appraisals might be more apt in classifying negative emotions than positive emotions. This may be a reason for the relatively low differentiation rates found among positive emotions so far, and consequently it is interesting to explore if perhaps other appraisals are more successful for differentiating between positive emotions. Cong (2015) designed a set of new appraisals specifically for
differentiating positive emotional states, which showed a lot cross-cultural variation, unlike the general appraisals. If the specific appraisals were better in detecting
cultural differences, they might also be better in differentiating between emotions. No study to date has compared the differentiation strength of these specific appraisals
5
Interest, relief and joy could not be differentiated above chance, for example there were no differences found between contentment and joy (Tong et al., 2014).
with the general ones. One of the primary goals of current research is to compare the differentiation strength of the two sets of appraisals. It is expected that the specific set of appraisals distinguish better between positive emotions than the general set of appraisals (Hypothesis 1).
Eight positive emotions are tested in current study: amusement, awe, compassion, gratitude, interest, love, pride and relief6. As a result of testing the hypothesis, we will find appraisal patterns of these eight emotions. They are described afterwards to extend the knowledge of similarities and differences among positive emotions.
Positive Emotion Granularity relates with Well-Being
The second aim of this research is to look at the individual ability to distinguish between positive emotions and see if it correlates with other individual differences. Research has shown that people differ in the extent to which they distinguish between their emotions, called emotional granularity7 (Feldman Barrett, Gross, Christensen, & Benvenuto, 2001). High granular individuals experience emotions in a differentiated way, whereas low granular individuals would experience emotions in a relatively undifferentiated way. Negative emotional granularity has also been shown to correlate with individual differences. For example, Erbas, Ceulemans,
6
These emotions are adapted from Cong (2015), except for embarrassment and desire. Embarrassment was not used because this is considered to be a negative emotion rather than a positive emotion (Keltner, 1996). Desire was not used because the study was limited to one hour and including all nine emotions it would simply take too long; there is no available data on the appraisals of desire (Tong, 2014).
7
Emotional granularity is individual difference in how precise and specific one characterises an emotional experience (Tugade et al., 2004).
Lee Pe, Koval, and Kuppens (2014) found a positive relationship between negative emotion granularity and indicators of personal well-being. Well-being is a construct that includes subjective, social, physical and psychological comfort (Seifert, 2005). There is reason to believe that positive-, as well as negative emotional granularity, correlates with well-being. Having a more differentiated knowledge of one’s emotions is more likely to result in understanding the cause of it, its relational meaning, and the action required to deal with the emotion (Feldman Barrett et al., 2001). This would make a person more capable to adaptively react to events and emotions (Erbas et al., 2014). In accordance with this, highly granular persons have been found to use more effective coping styles (Tugade et al., 2004). It is likely that therefore positive emotional granularity improves well-being. No study examined the direct link
between positive emotional granularity and well-being yet. The current study aims to see if positive emotional granularity is related with well-being. It is expected that positive emotional granularity correlates positively with well-being (Hypothesis 2). Current Study
The current study aims to contribute to the knowledge about positive emotion differentiation. Appraisals of eight positive emotions are tested in current study: amusement, awe, compassion, gratitude, interest, love, pride and relief. One of the primary goals of current study is to compare the differentiation strength of the two sets of appraisals. It is expected that the specific set of appraisals distinguishes better between positive emotions than the general set of appraisals (Hypothesis 1). The second goal is to see if positive emotion granularity is related with individual
differences. It is expected that positive emotion granularity correlates positively with well-being (Hypothesis 2).
Participants
Forty-two American8 participants (17 females and 20 males) did take part in this study. Seven participants were excluded9, so the remaining 37 were taken into analysis. Participant mean age was 34.9 years (SDage = 8.6). The ethnic composition of the participants was European American (73.0%), Asian American (13.5%), African American (8.1%), and Latino American (5.4%). The participants were recruited online via the website Amazon Mechanical Turk (MTurk)10. They received a monetary reward of $3.00 for participation.
Procedure
Participants could sign up for the study at MTurk, where they filled in the questionnaires online. The questionnaires were designed in in Qualtrics. To check if participants were really paying attention some attention checks were included (see Appendix A). Before participating in the study, participants were asked to read the information brochure and sign the informed consent. Participants filled in the questionnaires about every one of the eight emotions in a randomized order. At first, participants were instructed to recall a personal experience in which they had felt the
8
American participants will be used, so this research can afterwards (in another study) be compared well with other research with similar samples (Cong, 2015; Tong et al., 2014; Erbas et al., 2014).
9
The aimed sample size of 50 was based on a power calculation done of expected effect sizes in personality research (r = .20 to .30), but due to recruitment problems only 42 participated. Seven participants were excluded from analysis, because the manipulation of emotion did not work on them or they did not describe a genuine situation during the recall procedure (see results – manipulation check).
10
Amazon Mechanical Turk (MTurk) is an American website on which people can make tests for money.
emotion, for example amusement. The participants were then asked to describe the feeling of amusement vividly and detailed, as if they were describing it to someone that could not experience emotions. Second, the participants asked to rate the given appraisals about the emotion; the general and specific appraisals were randomly intermixed. During the rating, participants were asked to keep the target emotion in mind. Then, they were asked to what extent they felt the specific emotion (i) during the situation, and (ii) while recalling the situation, in a manner of manipulation check. This procedure was undergone for every one of the eight emotions. Then they filled in the self-reporting questionnaire Ryff’s Scales of Psychological Well-Being (RPWB) as a measure for well-being. Finally, they were asked for demographic information (gender, age, profession, ethnicity, nationality, number of years lived in US). Completing the whole task took about one hour.
Design
The study had a within-subject design, whereas each participant was asked to recall all eight positive emotions (amusement, awe, compassion, gratitude, interest, love, pride and relief), so emotional granularity could be measured. The recall procedure was adapted from Smith and Ellsworth (1985). The participants were told to recall and describe the emotion and experience as vividly as possible (see
Appendix B). Materials
Emotional Experience Questionnaire (Cong, 2015). This 7-item questionnaire measured emotional intensity. We adapted it as a manner of
manipulation check. It included questions like “How strongly did you experience the emotion during the situation?” and “How strongly did you experience the emotion while recalling the situation?”. For the full version of these questions see Appendix
C. The emotion intensity was rated on a Likert-scale ranging from not at all (0) to extremely (10).
Appraisal Questionnaire (Cong, 2015). This 29-item questionnaire consisted of 13 general appraisals (one item per appraisal) and 16 emotion-specific items (two appraisals per emotion). The questionnaire included for example an item for
pleasantness (general appraisal): “How pleasant was the situation?” and an item for awe (emotion-specific): “To what extent did you feel small relative to the
environment when you were recalling this event just now?” For the full version of these questions see Appendix D. The items were rated on a Likert-scale ranging from not at all (0) to extremely (10). There was given a not applicable (NA) option to indicate whether participants thought a certain appraisal was not applicable to their personal experience.
Positive Emotion Granularity. We measured positive emotion granularity in terms of an average intra-class correlation coefficient (ICC) between emotions for each participant. A high ICC reflected high correlation between emotions, and indicated that a participant did not strongly distinguished between his/her positive emotions, and was therefore low in positive emotion differentiation. To make the interpretation of ICCs easier, we reversed the ICCs, so a high ICC reflects that a person is high in positive emotion differentiation (hereinafter called positive emotion differentiation index).
Ryff Scales of Psychological Well-Being, the mid-length version (RPWB; Ryff, 1989). This 21-item questionnaire measured psychological well-being. The original version focuses on six dimensions of well-being (autonomy, environmental mastery, personal growth, positive relations, purpose in life, and self-acceptance). We have only used three due to limited time (personal growth, purpose in life, and
self-acceptance), with each of them containing 7 items. A separate score was given for each of the dimensions. Statements were rated on a scale of one to six, with one indicating strong disagreement and six indicating strong agreement. Examples of statements are: “I think it is important to have new experiences that challenge how you think about yourself and the world.” (Dimension: Personal growth), or “Some people wander aimlessly through life, but I am not one of them.” (Dimension:
Purpose in life), see Appendix E for full version. The personal growth subscale has a good internal consistency (Cronbach’s α = .85), as does the purpose in life subscale (Cronbach’s α = .88), and the self-acceptance subscale (Cronbach’s α = .91) (Seifert, 2005).
Results Manipulation Check
As part of the manipulation check, participants were removed from the study if they answered with a zero or one to the manipulation check question (“How strongly did you experience the emotion while recalling the situation?”), because the manipulation of emotion did not work in these cases. Participants were removed that did not describe a serious situation11 during the recall procedure. Seven participants were excluded from the study because the manipulation check did not work with all their emotions.
The second part of the manipulation check was testing whether the emotion intensity differed between the actual situation and the recalled situation. A One Sample T-test was conducted to compare the emotion intensity during the situation and emotion intensity during the recall. There was a significant difference in the
11
We checked if the stories were written seriously. A story was considered serious when (1) it had a minimum of two sentences, and (2) participants had used existing words (in English).
scores for emotion intensity during the situation and the recall, t(37) = 63.2, p < 0.01. These results suggest that there is a significant difference in emotion intensity
between the actual situation and the recalled situation. Even though the emotion intensity reported was high in both situations, emotions were experienced more intense during the actual situation (M = 9.2, SD = 0.9) than during the recall of the situation (M = 8.0, SD = 1.4).
NA Responses
A total of 8584 appraisal evaluations (37 participants x 29 appraisals x 8 emotions) were collected. Of these 670 (7.8%) were not applicable responses (NA responses). This is lower than the overall average of 16.3% found by Scherer (1997) and 10% found by Tong (2015), but was higher than the overall average of 3.6% found by Cong (2015). The amount of NA responses in the specific appraisals was slightly bigger (8.7%) than the amount of NA responses in the general appraisals (6.9%). Cong (2015) found a response rate of 0.5% for the specific appraisals, and 6.7% for the general appraisals. Even though response rates found were higher than in previous studies, both NA response rates were quite low, which indicates that the appraisals were suitable for classifying the emotions.
Comparing Differentiation Strength of Two Sets of Appraisals
Results for Both General and Specific Appraisals. To test how strongly the two kinds of appraisals differentiate between positive emotions, a Discriminant Analysis was used to measure differentiation strength of the general and specific appraisals of the positive emotions. The appraisals were taken into account as predictors of the emotions, so it was tested how accurate appraisals could classify
emotions. Table 1 shows the classification rates, leave-one-out classifications rates12 and chance levels13 per emotion as predicted by the general appraisals, the specific appraisals and both. All appraisals together classified 67.3% of the 296 emotion cases (37 participants x 8 emotions) accurately. The leave-one-out classification rate was 37.7% with both sets of appraisals.
Results for General Appraisals. The general appraisals classified 46.5% accurately. The leave-one-out classification rate was 34.1%. Both rates were higher than the overall chance level of 12.5%. Tested with a One Sample T-test, the general appraisals classified the emotions above chance, with a mean difference of M = 34.1, t(7) = 6.4, p < 0.01. A Proportion Test in R was used to test the classification rates against chance level. A Proportion Test can be used for testing the null hypothesis that the proportions (probabilities of success) in several groups equal a given value (the chance levels in this case). This test is calculated on the basis of chance levels and
12
Classification rates are based on all emotion cases, whereas leave-one-out classification rates are cross-validated, which means each emotion case is classified by the functions derived from all other cases than that case. Leave-one-out classification rates are less optimistic, and used to be seen as more accurate (Betz, 1987). Nowadays the original
classification rates are mostly used. In order to compare the rates with other recent studies, we focus mainly on the original classification rates. Both are shown to provide a completer picture of the differentiation strength.
13
A chance level (chance classification rate) reflects the probability that an appraisal will classify an emotion right, before data is collected. The calculation is based on assumptions of normality and group equality. With eight emotions, the overall chance an appraisal will classify an emotion right is 12.5%. Chance levels could differ slightly for each specific emotion, because some emotions had more data points than others (the more data points, the bigger the chance the appraisal will classify the emotion right).
original classification rates per emotion. A p-value smaller than 0.05 means the classification rate does not equals the chance level, which could be interpreted as the emotion being classified above chance. All emotions in this study were classified above chance. When the cross-validated classification rate was tested against chance, all emotions were classified above chance, except for compassion and relief.
Results for Specific Appraisals. The specific appraisals classified 58.9% correctly. The leave-one-out classification rate was 38.9%. These rates were higher than the chance level of 12.5%. Tested with a One Sampled T-test, the specific appraisals classified the emotions above chance, with a mean difference of M = 46.0, t(7) = 12.2, p < 0.01. Tested with a Proportion Test in R, all emotions were classified above chance, whether the original or the cross-validated classification rate was used.
Comparison of General with Specific Appraisals. The differentiation strength of the specific appraisals (58.9%, 38.9%) was higher than the differentiation strength of the general appraisals (46.5%, 34.1%). All emotions were classified above chance level by both of the appraisals. Most emotions had higher classification rates when predicted by the specific appraisals than when predicted by the general
appraisals, except for awe. When predicted by the specific appraisals, no emotion had a lower classification rate than 50.0%. When predicted by the general appraisals relief, love and gratitude were least accurately classified with lower classification rates than 35.0%. These results altogether provide support for the hypothesis that the specific appraisals are more apt at differentiating positive emotions than the general appraisals.
Classification Rates (in Percentages) of Individual Emotions
Note. General = General appraisals, Specific = Specific appraisals, Both = Both general and specific appraisals, Chance = Chance levels, Origin = Original classification rates, LEO = Leave-one-out classification rates
Correlation between Positive Emotion Differentiation and Well-Being
To test hypothesis two, first the mean ICC per participant is calculated for 33 participants14. Then the ICC was reverse-coded and transformed it into the positive emotion differentiation index. The average positive emotion differentiation index (reversed ICC) was 0.37 (SD = 0.17). The range of positive emotion differentiation was 0.09 to 0.74. Compared to other studies on negative emotion differentiation the range and mean are quite low (Erbas, 2014; Kashdan, Ferssizidis, Collins & Muraven, 2010), which might indicate people experience their negative emotions as more differentiated from each other. Pearson Bivariate Correlation was used to test correlation between the RPWB-score and the positive emotion differentiation index. There was no significant correlation between well-being and the positive emotion differentiation index, r(33) = -.26, p = 0.15.
14
Four participants were removed from this analysis, because they had a negative ICC or zero variance.
General Specific Both
Emotion Chance Origin LEO Chance Origin LEO Chance Origin LEO Amusement Awe Compassion Gratitude 13.8 11.5 14.3 12.0 50.0 52.0 32.3 65.4 40.0 40.0 19.4 53.8 13.7 62.5 37.5 14.2 60.9 43.5 11.4 50.0 35.0 11.1 72.2 33.3 13.7 50.0 41.7 14.2 60.9 17.4 12.6 68.2 54.5 13.0 71.4 52.4 Interest 12.9 67.9 50.0 13.1 78.3 65.2 12.3 95.0 75.0 Love 12.0 34.6 19.2 12.6 59.1 31.8 12.3 60.0 15.0 Pride 11.5 44.0 36.0 11.4 50.0 20.0 11.1 66.7 38.9 Relief 12.0 26.9 15.4 11.4 50.0 20.0 11.7 52.6 26.3
Appraisal Patterns
As a result of measuring eight positive emotions with 29 appraisals, appraisal patterns15 of these emotions were established (see Table 2). An emotion was
considered high on an appraisal when it either (1) was rated as higher than 7.0; or (2) was rated highest of all emotions with a score above 6.5. An emotion was considered low on an appraisal if it was rated as lower than 2.0.
15
Note these appraisal pattern findings are post hoc, which means they are not based on a hypothesis or theoretical frame. The results should be treated carefully.
Table 2
Mean (SD) Score on Appraisals per Emotion
Note. The orange numbers are considered to be high. The blue numbers are considered to be low.
Emotion Amusement Awe Gratitude Compassion Interest Relief Love Pride
Pleasantness Relevance Problem Goal-attainment 8.2 (2.6) 4.6 (3.6) 1.7 (3.0) 3.7 (3.7) 8.3 (2.9) 5.2 (3.4) 1.6 (2.9) 5.5 (3.9) 6.3 (3.7) 7.2 (3.3) 5.6 (3.9) 6.3 (3.7) 2.5 (3.2) 8.0 (2.0) 4.1 (5.1) 8.9 (2.3) 8.8 (1.9) 2.9 (3.7) 7 (3.4) 6.4 (3.5) 7.1 (3.5) 6.8 (3.4) 7.3 (2.6) 4.4 (3.8) 5.8 (3.8) 3.1 (3.6) 2.3 (3.6) 3.6 (3.6) 6.8 (3.2) 5.2 (4.0) 7.2 (3.4) 7.3 (3.2) Agency-self 3.3 (3.9) 2.9 (3.6) 3.0 (3.0) 1.6 (3.0) 3.2 (3.5) 3.2 (3.3) 5.5 (2.9) 5.1 (4.1) Agency-other 6.4 (3.8) 5.5 (3.6) 7.7 (3.3) 5.4 (4.0) 5.1 (3.6) 5.3 (4.1) 7.1 (3.2) 6.1 (3.9) Agency-circumstances 3.4 (3.6) 6.3 (3.7) 6.1 (4.1) 7.4 (3.2) 3.2 (3.3) 6.6 (3.4) 4.4 (3.5) 4.7 (3.7) Control-self 3.6 (3.5) 3.2 (3.5) 2.5 (2.4) 2.1 (3.1) 4.7 (3.3) 2.5 (3.4) 6.1 (3.1) 4.9 (4.0) Control-other 4.9 (3.8) 4.8 (4.1) 7.0 (3.2) 4.6 (4.0) 3.4 (3.5) 5.4 (3.6) 4.9 (3.5) 5.3 (3.9) Control-circumstances 3.4 (3.5) 7.0 (3.4) 5.9 (3.5) 6.5 (3.6) 2.7 (3.1) 7.0 (4.1) 4.4 (3.4) 4.5 (3.5) Certainty 7.5 (2.9) 7.5 (3.2) 6.7 (3.2) 6.0 (3.4) 6.3 (3.4) 5.7 (3.6) 7.8 (2.5) 7.7 (2.3) Predictability 3.8 (3.0) 3.4 (3.5) 3.6 (3.4) 3.2 (3.1) 3.2 (3.1) 2.9 (2.9) 5.8 (3.2) 4.0 (3.0) Effort 1.7 (2.7) 2.8 (3.5) 5.3 (3.8) 6.6 (3.2) 5.0 (3.9) 6.8 (3.1) 3.4 (3.9) 2.5 (3.4) Playfulness (amusement) 8.1 (2.9) 5.3 (3.4) 1.9 (2.9) 1.6 (2.8) 4.4 (3.8) 1.5 (2.8) 6.4 (3.0) 4.7 (4.0) Incongruity (amusement) 2.2 (3.2) 1.8 (2.2) 1.7 (2.5) 2.0 (2.5) 3.1 (2.8) 2.3 (2.7) 2.0 (2.8) 1.7 (2.7) Smallness (awe) 5.2 (2.6) 7.3 (2.8) 6.7 (2.5) 7.0 (2.7) 5.1 (3.1) 7.1 (2.9) 4.1 (3.1) 3.6 (2.9) Understanding (awe) 1.0 (2.2) 1.4 (2.6) 2.5 (3.4) 2.9 (2.7) 1.0 (1.8) 3.3 (3.4) 2.0 (3.1) 1.1 (2.2) Helplessness (compassion) 0.7 (1.8) 2.4 (3.0) 4.9 (3.7) 6.9 (3.2) 1.6 (2.7) 6.2 (3.8) 2.1 (3.4) 1.0 (2.0) Change (compassion) 1.5 (2.8) 1.8 (3.2) 4.7 (3.9) 7.6 (3.4) 2.7 (3.4) 6.6 (3.8) 2.1 (3.7) 2.1 (3.4) Benefit (gratitude) 5.0 (4.0) 6.1 (3.8) 8.1 (3.2) 4.0 (3.6) 6.0 (3.8) 3.6 (4.0) 7.4 (2.8) 5.7 (4.0) Return (gratitude) 3.9 (3.7) 5.2 (3.8) 5.4 (3.9) 7.4 (2.6) 4.9 (3.8) 4.0 (3.8) 7.2 (3.1) 4.9 (3.4) Learning (interest) 4.1 (3.8) 6.2 (3.4) 3.2 (3.3) 6.9 (3.2) 8.6 (2.4) 3.1 (3.3) 5.5 (3.8) 5.0 (3.9) Curiosity (interest) 5.8 (3.4) 6.9 (2.6) 3.1 (3.5) 3.6 (2.9) 8.8 (2.2) 3.8 (4.0) 5.1 (3.6) 3.4 (3.5) Vulnerability (love) 1.7 (3.1) 2.2 (3.2) 6.0 (3.3) 2.9 (3.5) 2.4 (3.4) 4.5 (3.8) 5.1 (4.1) 2.5 (3.1) Connectedness (love) 6.9 (3.4) 7.8 (2.6) 8.1 (2.7) 3.6 (2.9) 5.6 (3.7) 5.4 (4.0) 9.3 (1.4) 7.8 (3.0) Strong (pride) 5.8 (3.3) 4.7 (3.6) 3.2 (2.8) 3.8 (3.7) 6.2 (2.9) 4.1 (3.7) 6.5 (3.1) 7.2 (3.3) Social value (pride) 5.3 (3.3) 4.7 (3.2) 6.0 (3.7) 5.1 (3.5) 4.9 (3.2) 3.8 (3.7) 6.8 (3.0) 6.3 (3.5) Satisfaction (relief) 8.2 (2.4) 8.3 (2.7) 7.6 (3.0) 3.3 (3.7) 7.9 (2.5) 6.0 (3.8) 9.2 (1.4) 9.1 (1.7) Relaxation (relief) 7.3 (2.8) 6.3 (3.6) 4.1 (3.4) 2.7 (3.2) 6.6 (2.7) 4.3 (4.1) 7.1 (3.0) 6.2 (3.4)
Discussion Current Study
In current study appraisals of eight positive emotions were tested: amusement, awe, compassion, gratitude, interest, love, pride and relief. One of the primary goals of current study was to compare the differentiation strength of the two sets of appraisals. It was expected that the specific set of appraisals distinguished better between positive emotions than the general set of appraisals (Hypothesis 1). We found support for this hypothesis, because (1) the differentiation strength of the specific appraisals was higher than the differentiation strength of the general appraisals, and (2) in general, most emotions had higher classification rates when predicted by the specific appraisals than when predicted by the general appraisals. Both sets did well in classifying the emotions, because (1) both differentiation
strengths were high, and (2) all emotions were classified above chance by both sets of appraisals.
The second goal was to see if positive emotion granularity was related with individual differences. It was expected that positive emotion granularity correlated positively with well-being (Hypothesis 2), but no significant relationship between well-being and the positive emotion differentiation has been found.
Emotion Intensity
The results suggest that emotions experienced were more intense during the actual situation (M = 9.2) than during the recalled situation (M = 8.0). Even so, the emotion intensity reported in the recalled situation was high compared to previous research. Cong (2015), for example, found a lower emotional intensity (M = 6.7) with a story telling method; the participants were being video-recorded while they were sharing memories of personal emotional events. This indicates that the method used in
this study was effective in provoking emotions. Another advantage about the method is that it was efficient in effort, costs and time, because participants could recall and describe personal emotional events behind their own computers. This suggests the recall method used in this study is a preferable way to collect data in the future. Classifying Emotions By General and Specific Appraisals
To see whether there was a difference in the general appraisals and the
appraisals used to determine positive emotions, the differentiation strength of the two sets of appraisals was compared over eight positive emotions. The general appraisals have been derived from research on negative emotions; the specific appraisals have been designed to evaluate positive emotions. It was therefore expected that the
specific set of appraisals would distinguish better between positive emotions than the general set of appraisals.
Comparing General with Specific Appraisals. The results indicate that both sets of appraisals were apt at predicting positive emotions, because both sets of appraisals had high differentiation strengths. Like expected, the overall differentiation strength of the specific appraisals (58.9%, 38.9%, with 16 appraisals) was higher than the differentiation strength of the general appraisals (46.5%, 34.1%, with 13
appraisals). When interpreting these numbers, note that the number of appraisals influenced the classification rates16. In general, studies with more appraisals gain higher classification rates, because a wider range of appraisals is able to classify an emotion. Thus caution is needed in comparing classification rates with each other.
16
The higher the amount of appraisals is, the higher the classification rate will be. This is because a wider range of appraisals is able to classify an emotion. The more emotions analyzed the stricter the test of differentiation strength is, because there is a higher chance to classify an emotion wrong.
When looking into the rest of the results, both sets of appraisals could classify the emotions above chance. The cross-validated data showed the general appraisals were less effective at predicting relief and compassion, where the specific appraisals were effective at classifying all emotions. Also, most individual emotions had higher classification rates when predicted by the specific appraisals than when predicted by the general appraisals, except for awe. The findings altogether do support the idea the specific appraisals are more apt at differentiating positive emotions, though both sets do a good job. This is in line with previous research, in which specific appraisals were better in detecting cultural differences than general appraisals, which made the
suggestion they might be better in differentiating between positive emotions as well (Cong, 2015).
Are Both Sets Complementary? As we compare the separate sets of appraisals with the appraisals altogether, we see that both sets of appraisals together had the highest differentiation strength (67.3%, 37.7%). By comparing the confusion matrixes of the general, specific and both appraisals in Appendix F, it stands out that both general and specific added new information. Moreover, most emotions were classified best by both sets of appraisals, except for amusement (amusement was best classified by the specific appraisals). These results indicate that both sets
complemented each other. It is therefore assumed that the specific appraisals contribute to a more complete picture of the emotions than solely the general appraisals.
Previous Research. When the results are compared to previous research, the classification rates were comparable to or higher than the rates found by others. In Scherer (1997) seven emotions were classified by seven appraisals (one positive, six negative), with an overall classification rate of 39.2%. In Tong et al. (2014) 13
emotions were classified by 13 appraisals with an overall accuracy rate of 42.4%. Most comparable to our study with both sets of appraisals is a study by Reisenzein & Spielhofer (1994, study 3), in which 22 appraisals classified eight emotions, with an overall classification rate of 57.0%, which is 10.3% lower than our differentiation strength.
Limitations. This study had some limitations to be mindful of. Firstly, the specific appraisals were designed specifically for the used positive emotions. Two appraisals per emotion were adapted from the scientific definition of this emotion. Therefore it is logical that these appraisals would differentiate better between the positive emotions. We would expect participants to rate the two appraisals higher when recalling the emotion they were specifically designed for, than recalling other positive emotions. Notable is though, that a lot of the specific appraisals were well rated by other positive emotions than the one they were specifically designed for. For example, the appraisal “To what extent did you feel small relative to the
environment?” was designed specifically for awe, whereas a specific feature known from awe is a sense of smallness of oneself relative to the environment (Shiota et al., 2007). This appraisal was also relevant for the experience of compassion and relief, but not for the other tested positive emotions. This indicates the specific appraisals are apt to discriminate between more positive emotions than just the ones they were specifically designed for. Moreover, the general appraisals were originally designed specifically for negative emotions too.
Secondly, with this study we cannot say anything about a difference between positive emotion differentiation and negative emotion differentiation. Despite this limitation, the results indicate that measured with standards designed for positive emotions, positive emotion differentiation is also high. This could lead to the idea that
the former suggestion that ‘positive emotions are less differentiated than negative emotions’ is due to the fact that in former research the same measures (based on negative emotions) were used to measure positive emotions as well. Future research could add to this knowledge to compare the differentiation strength of both positive and negative emotions, and measure them both with general and specific appraisals.
Despite the limitations, this study showed that both the general and the specific appraisals are important to describe the emotions. For future research we recommend to use both sets of appraisals rather than replace the general by the specific appraisals, because they complement each other.
Positive Emotion Granularity and Well-Being
The second aim of this study was to look at the individual ability to
distinguish between positive emotions and see if it related positively with well-being. To do so the positive emotional differentiation index per person was calculated as an indication of positive emotion granularity. The positive granularity measured in this study was lower than the negative granularity found in previous studies (Erbas, 2014; Kashdan, Ferssizidis, Collins & Muraven, 2010). This might indicate that people may differentiate more between their negative emotions than their positive emotions. Further research is necessary to clarify this issue. Another reason for the low granularity found in this study could be the specific sample, which might not be representative for the population. For example, because all participants join Internet studies for money they could have lower socioeconomic status, which might influence emotional experience as argued by Gallo & Matthews (2003).
No significant relationship has been found between positive emotion granularity and well-being. A possible explanation for the failure to establish a
relationship is that this study was underpowered17, due to the small sample. We estimate a minimum of 62 participants is needed to establish a relationship with a high power (0.8). Future studies could replicate this study with a minimum of 62 participants to see if they are able to establish a relationship between positive emotion granularity and well-being. The findings altogether did not support the suggestion that positive emotional granularity is related to indicators of well-being. More research with larger and more representative samples is required to clarify the relation between positive emotion granularity and well-being.
Emotion Profiles
Down below a brief summary of the definitions and highlights in appraisal patterns for each emotion is given. No emotion had an appraisal pattern similar to another emotion. The only similarity was that most positive emotions were associated with satisfaction, except for relief and compassion. Because all appraisal profiles were unique, only the main differences are described.
Amusement. Amusement was rated as highly pleasant and there was an absence of problems to be solved in this situation, which was also found by Tong (2014). The appraisals indicated that amusement feels effortless and relaxed. When participants experienced amusement they felt playful, which is in line with a
previously found association with social or cognitive play (Griskevicius et al., 2010). Awe. Awe has been conceptualised as the feeling of wonder and astonishment experienced in the presence of something novel and difficult to grasp (Keltner & Haidt, 2003). Unsurprisingly, events were viewed as pleasant and controlled by something other than the self, which was also found by Tong (2014). Furthermore,
17
This study was underpowered because a total of 11 participants were excluded from this study, which left 33 participants for the second study.
awe was associated with a sense of smallness of oneself relative to the environment, which is in accordance with earlier findings on awe (Shiota et al., 2007).
Compassion. Compassion is the feeling of concern for another’s well-being (Griskevicius et al., 2010). A situation in which compassion occurs is experienced as being controlled and elicited by circumstances beyond own control, which was also found by Tong (2014). The appraisals indicated that participants felt they wanted to change the situation and do something that would benefit others. This is in line with the wish that the victim does not have to suffer any longer (Tong, 2014), and the motivation to offer assistance (Goetz, Keltner, & Simon-Thomas, 2010).
Gratitude. Gratitude is conceptualised as a response to being benefited by another’s intentional action (Algoe, Haidt & Gable, 2008). Unsurprisingly, a situation in which gratitude was felt was rated as being elicited and controlled by someone else. Accordingly, participants felt they benefited from someone else’s action, and therefor feeling vulnerable to and connected with the other. The situation was very important for ones needs or desires, which was also found by Tong (2014).
Interest. Interest was rated as highly pleasant, which was also found by Tong (2014). It was also rated as relevant to ones goals and wishes, which indicates that we are interested in things that are relevant to us (Connelly, 2011). Interest was rated as absorbed by learning something new, which reflects it functions as a motivator for exploration and learning (Silvia, 2005). Accordingly, interest felt like curiosity, which is in line with earlier research as well (Sylvia, 2005).
Relief. Relief is the response to an averted undesired outcome (Roseman et al., 1996). This might explain situations were experienced as controlled by the
circumstances, which was also found by Tong (2014). On the other hand participants reported they had put a lot of effort in dealing with the situation themselves, which
implies relieving situations also ask for self-agency (Frijda, Kuipers & ter Schure, 1989).
Love. Love is an emotion that is hard to define, but the Oxford American dictionary (2010) defines it as a variety of different feelings that ranges from interpersonal affection to interpersonal pleasure. Love was rated as highly pleasant, contributed to ones goals and desires, and was experienced as caused by another, which was also showed by Tong (2014). Participants felt socially valued and relaxed while recalling their love experience. They had the feeling they benefitted from someone else’s actions, and wanted to do something that would benefit the other. Furthermore they felt connected with someone in the situation, which is in line with the finding that love promotes social bonding (Shiota et al., 2004).
Pride. People feel pride when they achieve personal accomplishment (Campos, 2013). Expectedly, situations in which pride was felt contributed to ones goals and wishes. Moreover, pride felt pleasant according to the participants, which was also shown by Tong (2014). Other appraisals found on pride include feeling strong, certain and connected with others in the situation.
General Limitations
This study had some limitations to be aware of. Firstly, the measurement of appraisals brings a few issues to mind, as it was a self-report and people are not always aware of the appraisals they have about emotions. This is why they may have not answered accordingly. The self-report method is also sensitive for forms of response bias, which is a cognitive bias that influences the response of participants away from an accurate response (Furnham, 1986). For example, participants could have answered everything alike. Or participants could have overestimated the differences between emotions, because the emotions have been questioned in
succession. Future research could measure different emotions over multiple days, so participants answer every emotion as new. Even so, the appraisals gave a lot of information about how people experience the emotions and how they think about it.
Secondly, this study did not show how emotions differed from one another, because they were not directly compared to each other. This might be a problem if one of the emotions, for example, would not be experienced intensely and therefor would not rate high in any appraisal. In this study, all emotions rated high or low on some of the appraisals, but future research could focus on comparing emotions from the appraisal profiles.
Lastly, these appraisal patterns have been described ad hoc, and so findings should be interpreted carefully. The reason there were no expectations about the appraisal patterns, is that no study before has described the specific appraisal patterns of these 8 emotions. However, the specific appraisal patterns reflect more facets in conceptualizations of positive emotions than general appraisal patterns do. For example, our study added the feeling of smallness of oneself relative to the
environment, whereas Tong (2014) just found awe rated as pleasant and evoked and controlled by something other than the self. Another example is interest, which felt as being absorbed by learning something new, whereas Tong (2014) just found interest was pleasant, relevant and associated with high personal control.
General Contributions
This study contributes to an analytical view on how positive emotions have been studied and how they should be studied. In previous research on the appraisals of positive emotions, emotions have mostly been evaluated by the same standards
(appraisals) as negative emotions. This study looks at the difference between the old appraisals and the appraisals used to determine positive emotions. The findings
altogether do support the idea that the specific appraisals are more apt at
differentiating positive emotions. Moreover, the specific appraisals provided a more complete picture of the positive emotions, and were able to reflect more facets in conceptualizations of positive emotions than general appraisal patterns do. Both sets of appraisals complemented each other, because most emotions were classified best by a combination of both sets of appraisals. It is therefore supported that the specific appraisals contribute to providing a more complete picture of positive emotions, and it is not proposed that they replace the general appraisals. We delivered support for a whole new set of appraisals that did really well in classifying positive emotions, and these could be used for future research as well.
We also delivered evidence for an effective and efficient method to provoke emotions, namely recalling and describing emotions behind the computer. Lastly, this study extends knowledge of similarities and differences among positive emotions by describing the appraisal patterns found. In all, this study contributes to knowledge about positive emotions.
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Appendix A Attention Checks
Added to Informed consent:
“Participating in this study means accepting the terms of the Amazon Mechanical Turk policy, which states ‘a Requester may reject your work if the HIT was not completed correctly or the instructions were not followed’. We added some questions that checks to see if you read and understood the instructions. If you do not answer this question right, which is only possible if you didn't read it, you didn't met the requirements in order to participate and receive compensation and the study will end.”
Attention check questions in the questionnaire:
1. I'd like to ask you a random question to check if you're not a computer. Please answer "No" on this question. Are you a computer?
A. Yes, I will be a computer B. Yes, I am a computer C. Yes, I have been a computer
D. No, I am a human and I pay attention to this task
2. Please select 3. 1 – 2 – 3
If the attention checks were failed, the test ended, and participants did see this following text:
“Thank you for taking our survey. As stated in the Informed Consent, there are certain requirements that must be met in order to participate and receive compensation.
You are seeing this message because you are not eligibly to complete the study and receive compensation. This may be due to any of the following reasons:
• You do not agree to participate. • You are under 18 years old.
• You failed to answer a question that checked to see if you read and understood the instructions.
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Appendix B Recall Experience Smith and Ellsworth, 1985
“Now, can you to think of a past situation or event where you felt amusement? Picture this situation in your mind. Try and remember as vividly as you can what this
past amusing situation was like. Think of what happened to make you
feel amusement, and what it felt like to be amused in this particular situation. Go on to the next page if you are ready and have this amusing situation in your mind, and some questions about this emotional experience will arise.”
* next page *
“Please describe this past amusing situation. Try to describe the emotion as if you are explaining it to someone who cannot experience emotions. What was is like to feel amused in this situation? What did it feel like?”
Appendix C
Emotional Experience Questionnaire Cong, 2015
1. How strongly did you experience the emotion during the situation? 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
2. How strongly did you experience the emotion while recalling the situation? 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
3. How long did your emotion last during your experience? A. Less than a minute
B. A couple of minutes C. A couple of hours D. A day E. A couple of days F. A couple of weeks G. A couple of months H. One year or longer
4. When did the event occur?
6. Did you experience any other emotions during or immediately after the situation? What are they?
7. Did you experience any other emotions while recalling the event just now? What are they?
Appendix D Appraisal Questionnaire
Cong, 2015
“We will now ask you some questions about the emotion you just described. Please keep in mind how you felt during this experience of amusement.
Please rate the following items on a scale from 0 – 10. 0 means not at all, 10 means extremely. Please select NA if you think the question is not applicable to your experience. Please note that all questions refer to the situation when you experienced the emotion you just described.”
General appraisals:
1. How pleasant was this situation? (Pleasantness) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
2. How important in this situation was your needs or desires? (Relevance) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
3. Did you feel that there were problems that had to be solved in the situation? (Problem)
NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
4. Did you achieve important goals or wishes in the situation? (Goal-attainment) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
5. Did you cause what happened? (Agency-self) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
6. Did someone else cause what happened? (Agency-other) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
7. Did circumstances or forces beyond your control cause what happened? (Agency-circumstances)
NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
8. Did you feel that you were in control of what was happening? (Control-self) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
9. Did you feel that someone else was controlling what was happening? (Control-other)
NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
10. Did you feel that circumstances or forces beyond your control were controlling what was happening? (Control-circumstances)
NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
11. How certain were you about what was happening? (Certainty) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
12. How well could you predict what was going to happen next? (Predictability) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
13. Did you feel that you needed to make an effort to deal with this situation? (Effort)
NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
Emotion-specific appraisals18:
14. a. To what extent did you feel small relative to the environment? (Smallness)19 NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
b. To what extent did you feel large relative to the environment? NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
18
Some of the emotion-specific items were adapted slightly relative to Cong’s items. Sometimes Cong’s items contained “…when you were recalling this event just now?” at the end of an item. In this study these parts were erased, because this could possibly be confusing since the explanation is saying that all questions refer to the situation when experienced the emotion.
19
This item measures the degree of feeling small relative to the environment. That is why the score of 14.b (feeling large relative to the environment) is reverse-coded. After, the mean score of 14.a and 14.b was calculated.
15. Did you feel that you had difficulty understanding what was happening in the situation? (Understanding)
NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
16. Did you feel strong when you were recalling this event? (Strong) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
17. Did you feel socially valued? (Social value)
NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
18. Did you feel playful? (Playfulness)
NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
19. Did you recognize any incongruity in the situation? (Incongruity) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
20. Did you feel you had benefitted from someone else’s action(s)? (Benefit) NA 0 – 1 – 2 – 3 – 4 – 5 – 6 – 7 – 8 – 9 – 10
21. Did you feel that you wanted to do something that would benefit others? (Return)
