Judgements of Positive Emotions Support a Unidimensional Structure of Positive Valence

doi:10.21203/rs.3.rs-7864910/v1

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Judgements of Positive Emotions Support a Unidimensional Structure of Positive Valence

https://doi.org/10.21203/rs.3.rs-7864910/v1

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Positive valence is the intrinsic positivity or pleasantness of an emotion, feeling, or mood. Affective scientists disagree on whether positive valence is unidimensional (i.e., a single spectrum ranging from minimally positive to maximally positive) or multidimensional (i.e., a complex construct that accommodates multiple distinct ways an emotion can be experienced as positive). In this study, we examined the structure of positive valence through exploratory factor analyses of participants’ (N = 378) evaluations of positive emotions along candidate valence dimensions, as assessed through questionnaires. Across multiple factor analyses—both across all emotions, and within each emotion analysed separately—our results supported a single-factor model, suggesting a unidimensional structure of positive valence. Further, predictive regression analyses showed that this single positive valence factor best accounted for variations in participants’ preference-based rankings of positive emotions, over and above all individual aspects and additional factors, establishing predictive validity and providing further support for the unidimensionality of positive valence. Together, these findings support a unidimensional conceptualisation of positive valence, and help to clarify the underlying structure of people’s judgements about their positive emotional experiences.

Positive valence

pleasant

emotion structure

factor analysis

dimensionality

affective science

Emotion is a fundamental component of human life; it shapes decisions, drives behaviours, guides social interactions, influences our understanding of ourselves and the world, and colours our daily experience with complexity, passion, and meaning (Dukes et al., 2021; Ekman, 1994; Lerner et al., 2015). At the heart of emotion lies valence, a contemporary term for the positive or negative character of emotional experiences (Colombetti, 2005), which is often regarded as the defining quality of emotion (Cabanac, 2002; Fernandez Velasco & Loev, 2021; Rolls, 1995). Valence is therefore fundamental to the nature of subjective experience. This is further evidenced by the fact that the positive and negative qualities of experience have been debated for millennia (Aristotle, 1991; Bentham, 1789; Briesemeister et al., 2012; Epicurus, 300BC; Plato, 375BC), and still are being debated across numerous disciplines (Adolphs & Anderson, 2018; Bellocchi & Turner, 2019; Bliss-Moreau et al., 2020; Ekman, 1994; Lutz & White, 1986; Solomon, 1976; Walle & Dukes, 2023). Despite its central role, there is still no consensus on the best characterisation of valence, the best operationalisation of valence, or – most relevant to the present paper – the structural dimensionality of valence (Walle & Dukes, 2023). A better understanding of valence is therefore an important step towards achieving a complete picture of the nature of subjective experience.

Valence has been conceptualised in many different ways, including as (i) a single bipolar continuum (Barrett & Bliss-Moreau, 2009; Bradley & Lang, 2000; Feldman Barrett & Russell, 1998; Lang et al., 1993; Russell, 2003; Wundt, 1912); (ii) two independent unipolar positive and negative dimensions (Cacioppo & Berntson, 1994; Diener & Emmons, 1984; Green et al., 1999; Lewis et al., 2007; Norman et al., 2011; Thayer et al., 1994; Viinikainen et al., 2010; Warr et al., 1983; Watson et al., 1999); (iii) a binary qualitative characteristic or classification (Colombetti, 2005); (iv) a multidimensional construct (Labukt, 2012; Scherer et al., 2001; Solomon, 2003; Varela, 2005); (v) a multilevel construct (Shuman et al., 2013); and (vi) a heterogeneous phenomenon—i.e., always idiosyncratic to the individual, context, and emotional episode (Lambie & Marcel, 2002). These varying perspectives highlight the depth of ongoing debates about the structural dimensionality of valence. Despite this, there remains a lack of research focusing on the nature and structure of positive valence itself. This gap in the literature is likely also exacerbated by the fact that early and influential taxonomies of emotion focused disproportionately on negative emotions, often including only one or very few positive emotions (Ekman, 1992; Keltner & Cowen, 2021). Because of this, the positive side of emotion has frequently been treated as largely homogeneous, with emotion researchers often boiling down the whole range of positive emotions to a single happiness construct (Keltner, 2019). Consequently, positive valence has often been oversimplified or considered solely in opposition to negative valence, rather than as an independent construct. For these reasons, the current paper investigates whether positive valence, in itself, consists of a single dimension or multiple dimensions. Phrased in less technical terms: do all pleasant emotions share a common kind of positivity, such that there is only one way an emotion can be experienced as positive, or are there multiple kinds of positivity, such that there are multiple distinct ways an emotional experience can be experienced as positive? Given this goal, we focus solely on positive emotions, as the inclusion of negative emotions can make the positive–negative contrast the dominant dimension in factor analyses, potentially obscuring finer-grained distinctions among various kinds of positivity, if they indeed exist.

Given ongoing disagreements about the structure of valence, factor analytic approaches have been proposed as particularly well-suited to investigating the dimensionality of the valence construct (Walle & Dukes, 2023). By applying factor analysis to participants’ responses about various candidate dimensions of positive valence, it is possible to determine which positive aspects of emotional experience are independent versus overlapping (Walle & Dukes, 2023; Weidman & Tracy, 2017). As such, in the current study we used factor analysis to investigate the structure of participants’ decontextualised judgements about positive emotions. By decontextualised we mean abstracted context-free judgements about emotions as categories (e.g., how meaningful is awe “in general”?), rather than judgements of a particular emotional episode, stimulus or memory. Unlike more commonly used autobiographical recall (Holland & Kensinger, 2010) and experience sampling (Koval et al., 2020; Kuppens et al., 2022; Van Berkel et al., 2017) methods, measuring participants’ decontextualised judgments allowed us to explore how people mentally structure positive emotions and their features, while avoiding the confounds of personal memories, contextual variability of emotions, or emotion-regulation strategies that might influence the reporting of actual or recalled emotional experiences. If positive valence is unidimensional, we would expect people’s judgements of various positive emotions along a range of valenced aspects to load onto a single latent factor, which should account for the majority of variance between emotions. In contrast, if people’s judgements of positive emotions along a range of criteria cannot be well accounted for by a single dimension, but rather load onto multiple distinct factors, this would support a multidimensional view of positive valence. Additionally, we investigated the predictive validity of identified factor(s) by examining how well they can account for participants’ preference-based rankings of positive emotions.

Participants

A total of 405 psychology students at the University of Melbourne were recruited for participation in exchange for course credit. The only inclusion criterion was that they were aged 17 years or older. Participants were provided with an online consent form, outlining that participation was voluntary and that they could withdraw at any time. The sample comprised predominantly female (76.2%) students, aged 18–60 years (Mage = 21.1, SD = 4.89). Most participants identified as Asian (52.1%) or White/Caucasian (35.98%), with smaller percentages identifying as Other or Multiple Ethnicities (4.23%), Black or African American (0.56%), American Indian or Alaskan Native (0.26%), or Hispanic or Latino (0.26%). The majority were undertaking an undergraduate degree (94.5%), with smaller percentages having completed postgraduate degrees (4.20%), professional degrees (0.80%), or technical and further education (0.50%). Ethical approval for this project (ID 23720) was provided by the Human Research Ethics Committee of the University of Melbourne.

Emotion Taxonomy and Elicitation

To ensure emotional distinctiveness, breadth, and non-redundancy, this study drew positive emotions from Weidman and Tracy (2020a) work which provides a thorough analysis of distinctive subjective emotional content and a provisional taxonomy of positive emotions. Initially, all 15 positive emotions from Weidman and Tracy’s (2020a) taxonomy, and the two facets of pride (Tracy & Robins, 2007) were included. However, Tenderness was removed as many individuals found the emotion too difficult to conceptualise during piloting, with several respondents claiming tenderness was better characterised as a type of behaviour, rather than an emotion (e.g., one acts tenderly, rather than experiences tenderness). Nurturant love was removed, as participants in the sample were young (aged 17–22 years) and therefore unlikely to have children, thus rendering the emotion less relevant. Finally, emotions that may have a negative or mixed-valence (e.g., hubristic pride, schadenfreude, sympathy, empathy) were excluded, as their inclusion may have artificially consolidated positive and negative valence into a single dominant factor, biasing the results in favour of unidimensionality.

Weidman and Tracy’s (2020a) State-Trait Scales for Distinct Positive Emotions were adapted verbatim into prompts to help participants imagine each emotion. Since these scale items have been empirically validated to measure the presence of specific emotions, this study aimed to use these scale items to evoke those same specific emotions (see Table 1 for the final list of included emotions and their respective prompts). The included emotions were also found to appear frequently in our own brief literature review (see supplementary materials), as well as within several meta-analyses on the number of positive emotions (Cowen et al., 2019; Keltner, 2019; Keltner & Cowen, 2021; Weidman & Tracy, 2020b). Happiness (or Joy, which are often considered synonymous) was not included because it typically corresponds more closely to the general or basic category of positive emotions, rather than a specific instantiation or kind of positive emotion.

Table 1
*The Included Emotions and Their Four Respective Prompts*
Emotion	Prompts To help you imagine the emotion, you can think about moments of your life in which you…
Admiration	Felt a desire to become more like a specific person; felt as if you could learn a lot from a specific person; had a great deal of respect toward a specific person; strongly valued a specific person’s opinion.
Amusement	Giggled; laughed; were entertained; thought that what you saw was funny.
Attachment Love	Felt accepted by someone; felt like you could rely on someone; felt secure; trusted someone else.
Awe	Continued to think about what you just saw; could not believe what you had just seen; felt you were in the presence of something quite out of the ordinary; were rendered speechless.
Contentment	Enjoyed the situation; felt that all was right in the world; wanted to stay in the moment.
Enthusiasm	Felt adventuresome; wanted to get others excited; were eager; were on top of the world.
Gratitude	Felt appreciative toward a specific person; felt lucky to know a specific person; wanted to express thanks; thought that a specific person who helped you should be acknowledged.
Hope	Drew on your inner strength; engaged in some wishful thinking; had a great desire for a certain outcome; tried to believe in yourself.
Interest	Felt engaged with what you were doing; paid close attention to what you saw and heard; wanted to seek out more information; were curious about what you were seeing.
Pride (Authentic)	Felt accomplished; felt successful; felt fulfilled; achieved something.
Romantic Love	Could not stop thinking about someone; felt butterflies in your stomach; had a craving for someone; felt giddy.
Note. Each individual prompt is separated by a semicolon.

Positive Aspects

Participants were asked to rate each included positive emotion on 15 different phenomenal aspects of positive emotion. These positive aspects were determined by surveying the literature for various definitions of valence, as well as for any element of affective experience argued to be imbued with positivity in some way. A variety of positive aspects of emotion were included to ensure the positive space of emotion was sufficiently captured. Specifically, participants rated each positive emotion on (1) pleasantness, one of the most widely used definitions of positive valence, appearing in several prominent models of emotion (Barrett & Bliss-Moreau, 2009; Russell, 1980); (ii) meaning, which contrasts with pleasure in the classical distinction between hedonic (i.e., pleasure) and eudaimonic (i.e., meaning) components of affective experience (Deci & Ryan, 2008; Huta & Waterman, 2014; Kringelbach & Berridge, 2017); (iii) aesthetic pleasure, to capture the experiences of beauty or aesthetic appreciation, which can evoke positivity (Cupchik, 2020; Menninghaus et al., 2019); (iv) reward, which captures the positive reinforcement function of positive emotions (Berridge & Kringelbach, 2015; Sander & Nummenmaa, 2021); (v) flow, as the experience of being deeply engaged has been argued to be fundamentally positive (Csikszentmihalyi et al., 2005); (vi) novelty, as the surprising quality of experience can enhance enjoyment (Scherer et al., 2010; Skavronskaya et al., 2020); (vii) ethicality, as the positive-negative status of emotions was historically linked to moral evaluations (Solomon & Stone, 2002); (viii) power, since being in control is often experienced as positive (Fontaine et al., 2007; Scherer, 2013); (ix) self-congruence, as the degree to which emotional experiences align with one’s identity or values can be a source of positivity (Solomon, 1993); (x) approach tendency, which represents a behaviour-oriented definition of valence—as an approach motivation—found in several emotion theories (Cacioppo et al., 1999; Lang et al., 1998; Watson, 2000); (xi) object and (xii) subject evaluation represent seemingly different positive appraisals about emotional experiences (Carruthers, 2018; Cochrane, 2019; Solomon, 2003); (xiii) inner marker, which aligns with imperativism, the view that positive valence functions as a signal that impels individuals to reinforce the associated emotion (Barlassina & Hayward, 2019; Carruthers, 2023; Martínez & Klein, 2016); (xiv) goal conduciveness, which represents the positivity of emotions in terms of their alignment with personal goals (Kreibig et al., 2012; Lazarus, 1991; Shuman et al., 2013); and finally, (xv) positivity, which represents the broadest or most general conceptualisation of an emotion’s positivity, also aligning somewhat with Shuman and colleagues’ (2013) concept of macro-valence.

Table 2
*Positive Aspects of Emotion and Corresponding Questions*
Dimension (Items)	Question When experiencing this emotion…
Pleasant	… how pleasant or pleasurable is it?
Meaning	… how meaningful or profound is it?
Aesthetic	… how beautiful or aesthetically pleasing is it?
Reward	… how strongly does this emotion serve as a reward or reinforcement?
Flow	… how strongly does this emotion engage you or captivate your attention?
Novelty	… how novel or unfamiliar is it?
Ethical	… how much do you feel like this emotion aligns with your morals or ethics?
Power	… how much power do you feel you have to influence or control the situation?
Self-Congruence	… how much do you feel like this emotion aligns with your identity or your true self?
Approach Tendency	… how much do you want to approach or move towards the source of the emotion?
Subject Evaluation	… how favourable is your view or evaluation of yourself?
Object Evaluation	… how positive is your evaluation of the thing (or person) that the emotion is about?
Inner Marker	… how much do you want the emotion to continue or persist?
Goal Conduciveness	… how much does this emotion support you in reaching or realising your goals?
Positivity	… how positive is the emotional experience overall or on the whole?

Procedure

Participants were first provided with four prompts to help them imagine each specific positive emotion (e.g., awe). Next, participants rated 15 different positive aspects of each emotion. The emotion prompts and items measuring the positive aspects of each emotion were presented on a dedicated page (i.e., the survey comprised 11 pages, with each page corresponding to a different positive emotion). The order in which the emotions were presented was randomised for each participant, but the aspects were always presented in the same fixed order shown in Table 2. Participants rated the phenomenal aspects of each positive emotion on a 10-point scale ranging from 1 (“not at all”) to 10 (“most in my life”) adapted from the Affect Rating Dial (Ruef & Levenson, 2007). Finally, participants rated all the included emotions in terms of their overall preference using a drag and drop rank ordering task.

Data Analytic Strategy

Exploratory Factor Analysis

Regarding the data-analytic strategy used, all participants rated all 11 positive emotions on all 15 candidate valence dimensions, resulting in a total of 62,370 observations. To investigate the underlying structure of positive valence across emotions, participants’ ratings were first averaged across all 11 positive emotions for each candidate valence dimension, reducing the data to a total of 378 (participants) x 15 (phenomenal aspects) = 5,670 observations for analysis. This produced a dataset where each row represented a participant, and each column represented their average rating for one specific emotional aspect across all 11 emotions. Additionally, 11 separate exploratory factor analyses were conducted for each of the 11 positive emotions using participants’ unaggregated ratings of the candidate valence dimensions. These emotion-specific analyses explored whether the dimensionality of positive valence was consistent across different emotions.

Additionally, to check the robustness of the main findings, additional analyses were performed on a subset of data (one random emotion per participant) to rule out the influence of repeated measures, and at the individual participant level to assess within-participant variance.

Predictive Regression Analysis

As an additional test of the positive valence structure, a series of predictive regression models were conducted to examine the functional validity of the extracted factors. Specifically, this analysis investigated whether the primary positive valence factor could meaningfully predict how participants ranked different emotions, over and above the original individual aspects and any other additional latent factors. To this end, all possible combinations of 17 predictors—15 positive emotional aspects and the first two extracted factor scores (calculated for each participant–emotion combination)— were used to predict emotion rankings. For each unique combination of the predictors, an Ordinary Least Squares (OLS) regression model was fitted. This meant that 131,071 models were run 11 times, with each run including data from only one specific emotion, resulting in 1,441,781 total models. Average coefficients for each predictor were then aggregated across the subset of models containing that predictor to determine which variables were most predictive of emotion rankings for each specific emotion (see Fig. 6). This approach allowed for different predictors to dominate for different emotions. The same analysis was also conducted whilst including only factors as predictors. This was repeated four times, each time including a different number of factors: two factors, three factors, four factors and five factors.

Software

The study was administered online via the Qualtrics platform. Data were prepared and analyses were conducted using Python (3.11). The pandas (2.2.0) library was used for data manipulation, numpy (1.26.3) for numerical computations, matplotlib (3.8.2) and seaborn (0.13.1) for data visualisation, the factor_analyzer (0.5.0) library for factor analyses, including calculating the Kaiser-Meyer-Olkin measure and Bartlett’s test of sphericity, and pingouin (0.5.4) was used to compute Cronbach’s alpha.

Of the 405 participants that took part in the study, 27 were excluded due to failure to complete the survey. Responses from all remaining 378 participants were included. The median time to complete the study was 21 minutes. Included in this section are analyses ensuring the suitability of factor analysis, analyses identifying the optimal number of factors to retain, exploratory factor analyses to explore the underlying factor structure, and regression models to test the predictive validity of the identified factors.

Factor Analysis Suitability

To ensure the appropriateness of the factor analysis, several key assumptions were tested and satisfied. A very high Kaiser-Meyer-Olkin (KMO) measure (0.955) confirmed sufficient common variance among variables. Bartlett’s Test (χ² = 6271.52, p < .001) validated that the variables were interrelated. The total variance explained by a single factor (66.65%) indicated the strength of a general factor and a high Cronbach’s alpha (0.968) indicated high internal consistency among the 15 aspects. Therefore, factor analysis was deemed appropriate. Regarding the extraction method, a Henze-Zirkler (1990) test revealed that the assumption of multivariate normality was violated (HZ = 1.46, p < 0.001). Consistent with Zygmont and Smith’s (2014) discussion, the MINRES method was selected because it is robust under non-normality. Regarding the factor rotation method, a correlation heatmap (see Figure S3 in supplementary materials) showed that most variables demonstrated moderate (> 0.3) to strong (> 0.7) correlations. Therefore, consistent with Howard (2016) and Fabrigar and Wegener’s (2012) advice to use an oblique rotation method, Promax rotation was selected because it allows the resultant factors to be correlated. For comprehensiveness, 20 separate analyses were run, combining four extraction methods—maximum likelihood (ML), unweighted least squares (ULS), principal axis factoring (PAF), and minimum residual (MINRES)—with five rotations—varimax, quartimax, equamax (orthogonal), and direct oblimin and promax (oblique). Results demonstrated consistent factor structure across approaches (see Figure S4 in supplementary materials), thereby alleviating concerns about sensitivity to the specific extraction and rotation method selected.

Factor Retention

To determine the number of factors to retain for factor analysis we applied multiple retention criteria to the initial eigenvalues to ensure a reliable and accurate decision. The subjective scree test (i.e., identify a clear inflection point or ‘elbow’ on the scree plot) suggested the retention of one factor (Fig. 1), and this was also true when observing scree plots for each individual emotion (Fig. 2). Parallel analysis (i.e., compare actual eigenvalues with those from randomly generated data; Horn, 1965) suggested retaining a single factor by showing that only the first factor’s eigenvalue (10) exceeded the 95th percentile of eigenvalues derived from random data, compared with the significantly lower eigenvalues of the second (1.27) and third (0.89) factors. The Kaiser criterion (i.e., retain eigenvalues greater than 1) suggested the retention of two factors (Fig. 1), however the Kaiser criterion has been considered less accurate than other criteria (Costello & Osborne, 2005; Preacher & MacCallum, 2003; Velicer et al., 2000). Minimum Average Partial analysis (i.e., analyse partial correlations; Velicer, 1976) suggested retaining two components. Therefore, we ran both one-factor and two-factor solutions.

Exploratory Factor Analyses

Two main EFAs were run on the data. The first EFA retained one factor and the second EFA retained two factors (see Table S2 in supplementary materials). The factor loadings in the one-factor solution are unrotated, as rotation is only meaningful when two or more factors are retained. Most variables demonstrated high communalities, suggesting a shared underlying factor, with ‘Novelty’ as an exception. However, excluding ‘Novelty’ from the analysis did not significantly alter the results. The first factor was strongly loaded by ‘Positivity’, ‘Pleasantness’, ‘Flow’, ‘Reward’, ‘Inner Marker’, ‘Approach Tendency’, and ‘Meaning’ (see Table 3), suggesting that the first factor is best characterised by positive valence.

Table 3
*Factor Loadings and Communalities of Positive Aspects in the One-Factor Solution*
Aspect	F1 Loading	Communality
Positivity	0.92	0.85
Inner Marker	0.89	0.80
Reward	0.89	0.79
Flow	0.89	0.79
Approach Tendency	0.88	0.78
Goal Conduciveness	0.88	0.78
Pleasant	0.88	0.77
Meaning	0.88	0.77
Self-Congruence	0.86	0.74
Object Evaluation	0.81	0.66
Ethical	0.81	0.66
Aesthetic	0.77	0.60
Subject Evaluation	0.67	0.44
Power	0.55	0.30
Novelty	0.02	0.00
Note. F1 (Factor 1) = Positive Valence.

In the two-factor EFA (see supplementary materials), Factor 2 had a much smaller eigenvalue and accounted for a far smaller share of the variance relative to Factor 1. Factor 2 was primarily loaded by ‘Power’ (.75), with ‘Subject Evaluation’, ‘Self-Congruence’, and ‘Ethical’ showing only moderate loadings that also loaded strongly on Factor 1. The small eigenvalue, the concentration of unique variance on ‘Power’, and the fact that the other aspects were also captured by the first valence factor, suggests that if ‘Power’ is present in the dataset, it likely reflects a distinct component of emotion rather than an additional dimension of positive valence. This factor likely aligns with the emotional component of ‘Dominance’ (Mehrabian, 1996), ‘Potency-Control’ (Fontaine et al., 2007; Gehm & Scherer, 2013; Herrmann & Raybeck, 1981), or ‘Power’ appraisal (Scherer, 2013). Interestingly, across both factor solutions, Novelty had negligible loadings on both factors, suggesting that if novelty is indeed a component of emotion it is markedly distinct from positive valence.

Therefore, considering the retention tests and EFAs collectively, although a secondary power component may be present, in light of the parallel analysis, subjective scree test, factor loading patterns, and the substantially greater variance explained by Factor 1, these data support a one-factor solution as the most accurate representation of positive valence in emotional experiences.

Analysing the Influence of Repeated Measures on Factor Structure

To address the issue of non-independence of observations caused by repeated measures, the same analyses were run on a subset of the data, in which only one randomised emotion per participant was included. The analyses on this subset of data were compared to the analyses on the entire dataset. Results showed stable loadings on the two first factors across datasets, consistent results from factor retention criteria, and high factor congruence, with Tucker’s Phi values exceeding 0.85 (see supplementary materials; Lorenzo-Seva & Ten Berge, 2006). These findings suggest that the factor structure observed in the entire dataset is not significantly influenced by the problem of repeated measures.

Within-Participant Variance

Additionally, factor analyses, as well as parallel analyses, were performed on each individual participant to ensure that within-participant variance was not confounding the factor structure. Consistent with previous analyses, the first factor remained dominant, followed by a steep drop-off in eigenvalues, and the aggregated observed eigenvalues exceeded the aggregated random eigenvalues from the parallel analyses only for the first factor (see Fig. 3).

Predictive Regression Analyses

Next, OLS regression analyses were conducted to assess the predictive validity of the extracted factors in accounting for participants’ preference-based emotion rankings. Below are the average coefficients for each factor and aspect, aggregated across all OLS models in which they were present, in predicting the ranking of each specific emotion (see Fig. 4).

The same analysis was then conducted whilst including only factors as predictors.

This was repeated four times, each time including a different number of factors: two factors, three factors, four factors and five factors. Across all configurations Factor 1 remained the most predictive of emotion rankings (see supplementary materials for each of these runs).

This study provides evidence for the unidimensionality of positive valence as a fundamental dimension of emotional experiences. By factor-analysing participants’ judgments of a broad set of positive emotions against along a comprehensive set of candidate positive (valence) dimensions, this study showed that a single factor subsumes a large variety of ways an emotion can be construed as positive. This was evident both at the level of each individual emotion, and for all included emotions considered collectively. Additionally, regression analyses showed that the first (positive valence) factor consistently outperformed all individual aspects and additional factors in accounting for participants’ preference-based emotion rankings, demonstrating the predictive validity of a single valence dimension. These findings support the existence of a common core of positivity shared by a diverse range of positive emotional experiences and, in turn, constrain models of overall valence by reducing the plausibility of accounts that posit multiple orthogonal positive dimensions.

Previous Research on Valence

Many previous studies have shown that valence emerges as the factor with the most explanatory power in analyses of a variety of affective phenomena (e.g., Bottenberg, 1975; Cochrane, 2009; Feldman, 1995a; Fillenbaum, 1971; Fontaine et al., 2007; Galati et al., 2008; Gehm & Scherer, 2013; Kring et al., 2003; Lutz, 1982; Reisenzein, 1994; Russell, 1978, 1983; Russell & Mehrabian, 1977; Russell & Ridgeway, 1983; Shuman et al., 2013; Smith & Ellsworth, 1985; Storm & Storm, 1987). The present findings align with previous research, demonstrating that valence has substantial explanatory power. However, the present study extended upon previous work by including a comprehensive set of candidate dimensions that have been previously proposed as possible (independent) positive valence dimensions. By doing so, we sought to comprehensively map the positive section of the affective landscape, as opposed to the affective landscape in its entirety, thereby maximising the chances of recovering additional positive valence dimensions, if such dimensions indeed exist.

Positive Valence Dimensionality for Each Individual Emotion

The 11 positive emotions included represent a broad range of positive experiences, from low-activation (e.g., contentment) to high-activation (e.g., interest, enthusiasm) pleasant feelings, as well as self-focused (e.g., pride) and other-focused (e.g., gratitude, admiration) emotions. Nevertheless, the variance of all included emotions was best captured by a single factor, both when analysing average ratings of the positive aspects of all positive emotions and when analysing each positive emotion separately. This uniformity across emotions casts doubt on the idea that laypeople’s judgements of emotions are characterised by multiple distinct positive valence dimensions. Therefore, positive valence appears to be unidimensional across most, if not all, of the positive emotion landscape.

Characterising and Operationalising Positive Valence

The positive aspects with the strongest loadings on the first factor were ‘Positivity’, ‘Inner Marker’, ‘Pleasantness’, and ‘Flow’. Of these positive aspects, the first three seem like more accurate approximations of the positive valence concept than the last (‘Flow’). One interpretation for this, however, is that engagement is not necessarily synonymous with positive valence but rather highlights the positive valence that is already there, present in the experience, waiting to be attended to. In any case, there are several implications to be drawn from these positive aspects loading strongly on the first factor. First, they suggest that, of the included positive aspects, these specific descriptors are the most accurate characterisations of positive valence itself. Second, these questions can be operationalised in a scale for measurement of positive emotion valence in future research. Third, given the variety of terms that are attributable to this underlying construct of positive valence, it is clear that most common definitions of valence are insufficient. This may highlight a version of the jangle fallacy (Weidman et al., 2017), where different terms are intended to pick out distinct phenomena, but instead mistakenly refer to the same phenomenon. It is possible that positive valence is not merely the ‘pleasantness’ (Cabanac, 1992), the ‘approach tendency’ (Chen & Bargh, 1999; Harmon-Jones & Allen, 1998), or the ‘inner marker’ (Prinz, 2010) of a positive emotion. Rather, positive valence may be better characterised as the broadest construal of an emotion’s positivity, superordinate to, and underlying, many of these specific positive aspects. Indeed, latent dimensions often combine multiple observable dimensions, resulting in constructs that are not always easy to define (Roads & Love, 2024).

Dimensionality Analysis Considerations

Following Roads and Love’s (2024) recommendations, it is important to clarify that we prioritised the latent space property of global interpretability in our factor analyses. Additionally, we did not explicitly prioritise intrinsic dimensionality—the smallest number of dimensions required to capture a dataset’s structure—but nevertheless, our findings converged on a single dimension, providing further support for the unidimensionality of positive valence.

Conceptualisations of Positive Valence, and Positive Valence Itself

While the current findings suggest that positive valence is unidimensional, this finding is derived from self-report measures and the abstract reflective process employed by participants when responding. For this reason, our findings may merely reflect that people’s conceptualisation (or folk-psychological understanding), rather than phenomenological experience, of positive valence is unidimensional. However, researchers who hold a constructionist view of emotion might push back against the starkness of this distinction, arguing that our conceptualisations invariably shape our emotional experiences (Barrett, 2006). Nonetheless, perhaps a cohort of participants who hold a belief in a multidimensional conceptualisation of positive valence would yield a different result. As might a cohort of participants who conceptualise emotions more discretely, with lower levels of valence-focus (Feldman, 1995b). This issue is characteristic of challenges in emotion research, which, due to its fundamentally subjective nature, is often reliant on self-report measures (Barrett, 2004).

At the same time, the present findings are made even more interesting by the fact that folk-psychological judgements about decontextualised emotional experiences were assessed, rather than emotional episodes directly. One might expect that folk-psychological beliefs about emotion—shaped by personal experience, literature, media and culture—would be more differentiated than emotional experiences themselves. This expectation follows from the implications of research demonstrating that conceptual categorisation sharpens perceptual distinctions (Barrett, 2006; Goldstone, 1994; Kay & Kempton, 1984), which suggest folk-psychological conceptualisations are inherently more differentiated than perception or experience. Yet despite this, a unidimensional structure underlying these folk-psychological judgements still emerged. Having said that, it is possible that participants imagined specific emotional experiences while making their evaluations, so these judgements may partly reflect episodic recall rather than purely decontextualised beliefs about emotion.

This study shows that a single positive valence dimension can meaningfully represent how people subjectively evaluate positive emotions along a variety of positive aspects, as well as predict participants’ preference-based rankings of those emotions. While the landscape of emotional phenomena is vast and far from being fully understood, the present findings support a unidimensional conceptualisation of positive valence. Affective scientists should continue to refine our characterisation and the structural dimensionality of valence, given the significance of valence as both a cornerstone of affective science and also of people’s daily emotional lives.

The authors declare no competing interests.

This research was supported by an Australian Government Research Training Program Scholarship

Author Contribution

G.B designed and ran the study, conducted the data analyses and drafted the manuscript. P.K and O.C contributed to the analysis plan, data interpretation and revision of the manuscript text.

Data Availability

All data available here: https://doi.org/10.17605/OSF.IO/VQDG9

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Judgements of Positive Emotions Support a Unidimensional Structure of Positive Valence

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Abstract

Figures

Introduction

Method

Participants

Emotion Taxonomy and Elicitation

Positive Aspects

Procedure

Data Analytic Strategy

Exploratory Factor Analysis

Predictive Regression Analysis

Software

Results

Factor Analysis Suitability

Factor Retention

Exploratory Factor Analyses

Analysing the Influence of Repeated Measures on Factor Structure

Within-Participant Variance

Predictive Regression Analyses

Discussion

Previous Research on Valence

Positive Valence Dimensionality for Each Individual Emotion

Characterising and Operationalising Positive Valence

Dimensionality Analysis Considerations

Conceptualisations of Positive Valence, and Positive Valence Itself

Conclusion

Declarations

Author Contribution

Data Availability

References

Additional Declarations

Supplementary Files

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