Emotions in Everyday Life

Trampe, Debra; Quoidbach, Jordi; Taquet, Maxime

doi:10.1371/journal.pone.0145450

Cited by 178 publications

(196 citation statements)

References 42 publications

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“…According to Oatley and Johnson-Laird (1996), people react to events by making multiple cognitive evaluations, which may, in turn, elicit different emotional reactions simultaneously or in rapid alternation. This would be in agreement with the literature that has shown the existence of mixed emotions, even when these are opposite in valence (Berrios, Totterdell, & Kellett, 2015;Larsen & McGraw, 2014;Trampe, Quoidbach, & Taquet, 2015). Of note, it has been demonstrated recently that these emotionally ambivalent states can be captured by people's ratings of emotionally charged words (Briesemeister, Kuchinke, & Jacobs, 2012).…”

Section: Distribution Of Words Among the Five Discrete Emotionssupporting

confidence: 92%

Moved by words: Affective ratings for a set of 2,266 Spanish words in five discrete emotion categories

et al. 2016

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The two main theoretical accounts of the human affective space are the dimensional perspective and the discrete-emotion approach. In recent years, several affective norms have been developed from a dimensional perspective, including ratings for valence and arousal. In contrast, the number of published datasets relying on the discrete-emotion approach is much lower. There is a need to fill this gap, considering that discrete emotions have an effect on word processing above and beyond those of valence and arousal. In the present study, we present ratings from 1,380 participants for a set of 2,266 Spanish words in five discrete emotion categories: happiness, anger, fear, disgust, and sadness. This will be the largest dataset published to date containing ratings for discrete emotions. We also present, for the first time, a fine-grained analysis of the distribution of words into the five emotion categories. This analysis reveals that happiness words are the most consistently related to a single, discrete emotion category. In contrast, there is a tendency for many negative words to belong to more than one discrete emotion. The only exception is disgust words, which overlap least with the other negative emotions. Normative valence and arousal data already exist for all of the words included in this corpus. Thus, the present database will allow researchers to design studies to contrast the predictions of the two most influential theoretical perspectives in this field. These studies will undoubtedly contribute to a deeper understanding of the effects of emotion on word processing.

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Section: Distribution Of Words Among the Five Discrete Emotionssupporting

confidence: 92%

Moved by words: Affective ratings for a set of 2,266 Spanish words in five discrete emotion categories

et al. 2016

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“…The fact that we only studied high school studnets may limit the generalizability of our findings. However, that Trampe et al (2015) found similarly frequent co-occurrences between mixed-valence emotions in their more diverse sample suggests that our findings might be relevant beyond the school context.…”

Section: Discussionsupporting

confidence: 49%

“…Based on results of Study 1 and previous findings by Trampe et al (2015), we expected that positive and negative emotions would occur frequently together, within individuals and situations.…”

Section: Research Questionsmentioning

confidence: 92%

Mixed emotions: Network analyses of intra-individual co-occurrences within and across situations.

Moeller¹,

Ivčević

Brackett

et al. 2018

Emotion

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This study revisits the structure of emotions by employing a co-occurrence network analysis. While previous studies have examined the structure of emotions primarily through inter-individual correlations, we investigated how often and which specific positive and negative emotions occur together within individuals. Two studies were conducted with high school students, one (N=21,678) using retrospective emotion measures (open-ended questions and 28 rated items) and the other (N = 471) using in-the-moment emotion measures (experience sampling).As in previous studies, positive and negative emotion ratings were negatively correlated across individuals, and this negative correlation became stronger when

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“…19, study 3 from ref. 18-were obtained via correspondence with the authors. The first two datasets were each comprised of 40 participants, all recruited in the United States and performing the study in English.…”

Section: Methodsmentioning

confidence: 99%

“…Studies 1-3 measured the actual rates of transitions between emotions using existing experience-sampling datasets (18,19). These data served as the "ground truth" against which we could test the accuracy of people's mental models.…”

mentioning

confidence: 99%

Mental models accurately predict emotion transitions

Thornton

Tamir

2017

Proc. Natl. Acad. Sci. U.S.A.

110

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Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.emotion | experience-sampling | social cognition | theory of mind H umans must navigate a wide variety of stimuli in everyday life, ranging from apples and oranges to automobiles and computer operating systems. However, other humans are perhaps the most consequential stimuli of all, potentially driving the very evolution of the human brain (1). Despite the dazzling array of actions and internal states of which humans are capable, people are remarkably good at understanding each other (2-4). Indeed, the social mind appears particularly attuned to the problem of predicting other people (5). Perceivers make use of a wide variety of perceptible cues-including social context, facial expression, and tone of voiceto infer what emotions others' are feeling (6-8), likely because emotions predict behavior (9, 10). However, these perceptual mechanisms only get us so far: we cannot see what expression our friend will wear next week, nor hear tomorrow's tone of voice. How might we make social predictions beyond the immediate future? Such foresight could convey significant strategic advantages: in the social domain, as in the game of chess (11), success may depend on the depth and breadth of a player's search through others' possible future moves. Here we propose that people use a powerful mechanism for gain...

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Emotions in Everyday Life

Cited by 178 publications

References 42 publications

Moved by words: Affective ratings for a set of 2,266 Spanish words in five discrete emotion categories

Moved by words: Affective ratings for a set of 2,266 Spanish words in five discrete emotion categories

Mixed emotions: Network analyses of intra-individual co-occurrences within and across situations.

Mental models accurately predict emotion transitions

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