Phenomenal Properties of Positive Emotion


This research is about whether positive emotion valence is unidimensional or multidimensional. Some emotional theories, such as the component process model (Shuman et al., 2013), propose that there are multiple kinds of valence (i.e., multidimensional), and as possibilities the authors include what they term microvalences of pleasantness/beauty, goal conduciveness, power, self-congruence, moral goodness, novelty, and certainty. However, most theories, such as the circumplex model, conceptualise valence as being one single kind of phenomenon (i.e., unidimensional; Shuman et al., 2013).

Research Questions / Hypotheses

This research is aiming to learn two things. Firstly, we want to better understand which of the positive emotions, if any, should be treated as distinct emotions, as opposed to lumping all positive emotions into the one umbrella term: Happiness. Secondly, we want to understand whether the positive quality (referred to in the literature as positive valence) of different positive emotions is the same. For example, is the positive quality that makes the emotion of Love a positive thing to experience, the same positive quality that makes the emotion of Awe a positive thing to experience?


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Principal Component Analysis (PCA), a type of exploratory factor analysis, will be applied to reduce dimensionality, group question types that are most interrelated, and identify underlying unobserved factors that explain the variance and covariance between the reported phenomenal properties. The number of factors used will be determined by calculating eigenvalues. Regarding data visualisation, each emotion’s distribution along the different factors will be mapped using t-distributed stochastic neighbour embedding (t-SNE), which represents high-dimensional information in two-dimensional space while preserving data structure (Balamurali, 2020). Additionally, Hierarchical Clustering, a type of exploratory data analysis, will be used to group sets of similar emotions and reported phenomenal properties into clusters, and to identify intra-class and inter-class similarities.


It is predicted that different subcategories of positive emotion will cluster in different ways. It is further predicted that the degree of correlation between variables of pleasantness, meaningfulness, preference, and ranking will illuminate if positive valence is unidimensional or multidimensional as well as what the most likely description of positive valence in. This survey will form the basis of a future journal article.