Author: Donovan, Ryan; Johnson, Aoife; deRoiste, Aine; O’Reilly, Ruairi
Title: Quantifying the Links Between Personality Sub-traits and the Basic Emotions Cord-id: hat7exyq Document date: 2020_8_19
ID: hat7exyq
Snippet: This article describes an exploratory study that aimed to analyse the relationship between personality traits and emotions. In particular, it investigates to what extent the sub-traits of the Five Factor Model has an empirically quantifiable correlation with the Basic Emotions (Anger, Anxiety, Disgust, Fear, Joy, Sadness, Surprise). If links between these personality traits and the basic emotions can be found, then this would enable an emotional-state-to-personality-trait mapping. In this study,
Document: This article describes an exploratory study that aimed to analyse the relationship between personality traits and emotions. In particular, it investigates to what extent the sub-traits of the Five Factor Model has an empirically quantifiable correlation with the Basic Emotions (Anger, Anxiety, Disgust, Fear, Joy, Sadness, Surprise). If links between these personality traits and the basic emotions can be found, then this would enable an emotional-state-to-personality-trait mapping. In this study, 38 participants answered a Big Five Aspects Scale (BFAS) questionnaire and then watched 12 emotionally provocative film clips along with answering 12 short emotional Likert-scales on their emotional experiences during each film clip. The results showed that (i) four of the seven Basic Emotions outright significantly correlated, while two emotions (Fear and Disgust) approached statistical significance, with at least one of the personality traits and (ii) significant correlations between personality traits and basic emotions could only be identified at the sub-trait level, demonstrating the value in adopting a higher-resolution personality model. The results supports the long-term goal of this research, which is the enabling of state-to-trait inferences. A method for analysing and visualising such a mapping, that differentiates mappings based on the direction and magnitude of the effect size was also developed. The study contributes a blueprint towards utilising Affective Computing methodology to automatically map these phenomena.
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