Specificity in the Study of Mixed Emotions: A Theoretical Framework

A Multimodal Dataset for Mixed Emotion Recognition

Mixed emotions have attracted increasing interest recently, but existing datasets rarely focus on mixed emotion recognition from multimodal signals, hindering the affective computing of mixed emotions. On this basis, we present a multimodal dataset with four kinds of signals recorded while watching mixed and non-mixed emotion videos. To ensure effective emotion induction, we first implemented a rule-based video filtering step to select the videos that could elicit stronger positive, negative, and mixed emotions. Then, an experiment with 80 participants was conducted, in which the data of EEG, GSR, PPG, and frontal face videos were recorded while they watched the selected video clips. We also recorded the subjective emotional rating on PANAS, VAD, and amusement-disgust dimensions. In total, the dataset consists of multimodal signal data and self-assessment data from 73 participants. We also present technical validations for emotion induction and mixed emotion classification from physiological signals and face videos. The average accuracy of the 3-class classification (i.e., positive, negative, and mixed) can reach 80.96% when using SVM and features from all modalities, which indicates the possibility of identifying mixed emotional states.

Linking teacher support to achievement emotion profile: the mediating role of basic psychological need satisfaction

The current study used a person-centered approach to explore the co-occurrence of college students' achievement emotions. It also examined the impact of teacher support on achievement emotion profiles and the mediating effect of need satisfaction. A total of 866 college students participated in the survey. A robust three-step latent profile analysis was employed to analyze the data. Four profiles of achievement emotions were identified: moderate mixed emotions, the blends of high positive emotions, the blends of moderate positive emotions, and high mixed emotions. Higher perceived teacher support was associated with a greater likelihood of being classified into the blends of moderate positive emotion profile or the blends of high positive emotion profile. Moreover, basic psychological need satisfaction mediated the relationship between teacher support and the four emotion profiles. Our findings contribute to a more comprehensive understanding of the role of teacher support in shaping achievement emotion profiles, helping to broaden the application of self-determination theory to explain the mechanism by which external support influences emotion profiles.

The interplay of social rank perceptions of Trump and Biden and emotions following the U.S. presidential election 2020

The outcome of the 2020 U.S. election between Trump and Biden evoked strong emotions. In U.S. American (Study 1; N = 405) and German (Study 2; N = 123) samples, we investigated how observers' group membership (i.e. political orientation) and the social rank attainment of both candidates (i.e. dominance vs. prestige) predicted emotional reactions. Trump was generally perceived as more dominant, and Biden as more prestigious. However, perceptions of social rank attainment differed depending on the observers' political orientation, either matching or not matching with the leaders (i.e. Republicans and Democrats, respectively). The candidate who did not share the participants' political orientation was perceived as less prestigious and more dominant and elicited stronger contrastive emotions (i.e. schadenfreude, malicious envy) and weaker assimilative emotions (i.e. happy-for-ness, sympathy, anger), and vice versa. Crucially, dominance and prestige perceptions explained variance in the emotional reactions of more conservative and more liberal participants. Prestige positively predicted assimilative emotions and dominance contrastive emotions. Our work advances theorising by providing evidence that dominance and prestige perceptions contribute to the elicitation of various emotions. Furthermore, it suggests that prestige and dominance are not fixed characteristics of liberal and conservative leaders but depend on the observers' group membership.

Neural patterns associated with mixed valence feelings differ in consistency and predictability throughout the brain

Mixed feelings, the simultaneous presence of feelings with positive and negative valence, remain an understudied topic. They pose a specific set of challenges due to individual variation, and their investigation requires analtyic approaches focusing on individually self-reported states. We used functional magnetic resonance imaging (fMRI) to scan 27 subjects watching an animated short film chosen to induce bittersweet mixed feelings. The same subjects labeled when they had experienced positive, negative, and mixed feelings. Using hidden-Markov models, we found that various brain regions could predict the onsets of new feeling states as determined by self-report. The ability of the models to identify these transitions suggests that these states may exhibit unique and consistent neural signatures. We next used the subjects' self-reports to evaluate the spatiotemporal consistency of neural patterns for positive, negative, and mixed states. The insula had unique and consistent neural signatures for univalent states, but not for mixed valence states. The anterior cingulate and ventral medial prefrontal cortex had consistent neural signatures for both univalent and mixed states. This study is the first to demonstrate that subjectively reported changes in feelings induced by naturalistic stimuli can be predicted from fMRI and the first to show direct evidence for a neurally consistent representation of mixed feelings.

Affect dimensions and variability during major depressive episodes: Ecological momentary assessment of unipolar, bipolar, and borderline patients and healthy controls

Differentiating major depressive episodes (MDEs) of major depressive disorder (MDD), bipolar disorder (MDE/BD) and the MDEs comorbid with borderline personality disorder (MDE/BPD) is crucial for appropriate treatment, and knowledge of phenomenological differences may aid this. However, studies comparing affect experiences of these three patient groups and healthy subjects are scarce. In our study, participants (N = 114), including patients with MDD (n = 34), MDE/BD (n = 27), and MDE/BPD (n = 24), and healthy controls (HC, n = 29) responded to ecological momentary assessment (EMA) with ten circumplex model affect items ten times daily for seven days (7709 recordings). Explorative factor analysis resulted in two affect dimensions. The positive dimension included active, excited, cheerful (high arousal), and content (low arousal) affects, and the negative dimension irritated, angry, and nervous (high arousal) affects. Relative to HC, patients reported 3.5-fold negative affects (mean MDD 1.36 (SD 0.92), MDE/BD 1.43 (0.76), MDE/BPD 1.81 (0.95) vs. HC 0.44 (0.49) (p < 0.01)) but 0.5-fold positive affects (2.01 (0.90), 1.95 (0.89), 2.24 (1.03), vs. 3.2 (0.95), respectively (p < 0.01)). We used multilevel modelling. Negative-affect within-individual stability was lowest in MDE/BPD and highest in MDD. Negative affect predicted concurrent positive affect more in MDE/BPD than in MDD. Moderate size of subcohorts and no inpatients were limitations. Despite apparently similar MDEs, affective experiences may differ between BPD, BD, and MDD patients. Clinical subgroups of patients with depression may vary in affective instability and concurrent presence of negative and positive affects during depression.

Direct versus indirect measures of mixed emotions in predictive models: a comparison of predictive validity, multicollinearity, and the influence of confounding variables

Mixed emotions have been assessed using both direct measures that utilize self-report questionnaires as well as indirect measures that are computed from scores of positive and negative emotions. This study provides a pre-registered methodological examination on the use of direct and indirect measures of mixed emotions in predictive models. Two samples (N = 749) were collected, and path analyses were performed to compare direct measures and indirect measures in predicting psychological conflict, receptivity, and well-being, controlling for demographics, positive emotions, and negative emotions. We also tested whether trait dialecticism, need for cognition, social desirability, or acquiescence could account for these associations. In both samples, results suggest that indirect measures may be more susceptible to multicollinearity when controlling for positive and negative emotions. Specifically, variance inflation factors (VIF) were consistently higher for indirect measures calculated using the minimum index (MIN; VIFSample-1 = 3.53; VIFSample-2 = 9.46) than direct measures (VIFSample-1 = 2.52; VIFSample-2 = 1.68). Direct measures remained consistently associated with increased conflict and reduced coherence upon controlling for positive and negative emotions, while indirect measures remained consistently associated only with increased conflict. We found little evidence that response biases explained associations between direct measures or indirect measures with each of the outcomes. Specifically, associations between mixed emotions with psychological conflict, receptivity, and well-being largely remained unchanged in models that controlled for trait dialecticism, need for cognition, social desirability, or acquiescence. Implications and recommendations based on our findings are discussed.