Examining the Intensity and Frequency of Experience of Discrete Positive Emotions

Linley, P. Alex; Dovey, Helen; Beaumont, Sarah; Wilkinson, Joann; Hurling, Robert

doi:10.1007/s10902-015-9619-7

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…Romance is experienced in a romantic relationship and motivates approach towards a romantic target [45]. Friendship refers to the friendly feeling and is frequently experienced and reported in daily life [46]. Tender-ness is characterized as a warm-and-fuzzy feeling corresponding to family love and attachment in present study.…”

Section: Positive Emotion Categoriesmentioning

confidence: 77%

CPED: A Chinese Positive Emotion Database for Emotion Elicitation and Analysis

Zhang

Zhao

Shu

et al. 2023

IEEE Trans. Affective Comput.

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Positive emotions are of great significance to people's daily life, such as human-computer/robot interaction. However, the structure of extensive positive emotions is not clear yet and effective standardized inducing materials containing as many positive emotional categories as possible are lacking. Thus, this paper aims to establish a Chinese positive emotion database (CPED) to (1) effectively elicit positive emotion categories as many as possible, (2) provide both the subjective feelings of different positive emotions and a corresponding peripheral physiological database, and (3) explore the structure and framework of positive emotion categories. 42 video clips of 16 positive emotion categories were screened from more than 1000 online clips. Then a total of 312 participants watched and rated these video clips during which PPG and GSR signals were recorded. 34 video clips that met hit rate and intensity standards were systemically clustered into four emotion categories (empathy, fun, creativity and esteem). Eventually, 22 film clips of these four major categories formed the CPED database. 113 features from PPG and GSR signals were extracted and entered into a SVM classifier that serves as a baseline classification method. A classification accuracy of 45.84% for four major categories of positive emotions was achieved.

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Section: Positive Emotion Categoriesmentioning

confidence: 77%

CPED: A Chinese Positive Emotion Database for Emotion Elicitation and Analysis

Zhang

Zhao

Shu

et al. 2023

IEEE Trans. Affective Comput.

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“…However, it can be assumed that some students may not admit their anxiety, especially boys. This may also be the reason for the significantly higher levels of anxiety among girls, as girls generally show more emotions than boys (Linley, Dovey, Beaumont, Wilkinson, & Hurling, 2016). Behavioral observations of students' learning by teachers during exams or by parents at home would, thus, be a good extension to the AFS.…”

Section: Limitations and Conclusionmentioning

confidence: 99%

Factorial Validity of the Anxiety Questionnaire for Students (AFS): Bifactor Modeling and Measurement Invariance

Lohbeck

Petermann

2018

Journal of Psychoeducational Assessment

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The present study with 2,273 students aimed to examine the factorial validity of the Anxiety Questionnaire for Students (AFS) by using the bifactor modeling framework, that is, contrasting a confirmatory factor analysis (CFA) model to an exploratory structural equation model (ESEM) and two bifactor models (B-CFA and B-ESEM). In addition, measurement invariance and latent mean differences in the three facets of the AFS (test anxiety, manifest anxiety, dislike of school) across gender, age groups, and school types were explored. Results provided strong support for the multidimensionality of the AFS. The B-ESEM showed the best fit to the data as opposed to the other models. Also, measurement invariance across gender, age groups, and school types was fully supported. Girls and younger students generally reported higher levels of anxiety than boys and older students, while the latter stated more dislike of school than girls and younger students. Furthermore, elementary school students showed generally higher levels of anxiety than students of the other school types.

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“…Evidence for the differentiated subjective experiences of positive emotions has been found both from people’s reports of the induced emotion states and daily emotion experiences. Recently, Linley et al (2016) collected people’s retrospective recall of the frequency and intensity of positive emotion experience in their daily life, and provided support for a set of 50 discrete positive emotions. Another study by Cowen and Keltner (2017) used 2,185 short video clips to elicit emotion responses from 853 participants and found 27 distinct varieties of the reported emotion experiences.…”

Section: Introductionmentioning

confidence: 99%

fNIRS Evidence for Recognizably Different Positive Emotions

Zhuang

Wang

et al. 2019

Front. Hum. Neurosci.

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The behavioral differentiation of positive emotions has recently been studied in terms of their discrete adaptive functions or appraising profiles. Some preliminary neurophysiological evidences have been found with electroencephalography or autonomic nervous system measurements such as heart rate, skin conductance, etc. However, the brain’s hemodynamic responses to different positive emotions remain largely unknown. In the present study, the functional near-infrared spectroscopy (fNIRS) technique was employed. With this tool, we for the first time reported recognizable discrete positive emotions using fNIRS signals. Thirteen participants watched 30 emotional video clips to elicit 10 typical kinds of positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love), and their frontal neural activities were simultaneously recorded with a 24-channel fNIRS system. The multidimensional scaling analysis of participants’ subjective ratings on these 10 positive emotions revealed three distinct clusters, which could be interpreted as “playfulness” for amusement, joy, interest, “encouragement” for awe, gratitude, hope, inspiration, pride, and “harmony” for love, serenity. Hemodynamic responses to these three positive emotion clusters showed distinct patterns, and HbO-based individual-level binary classifications between them achieved an averaged accuracy of 73.79 ± 11.49% (77.56 ± 7.39% for encouragement vs. harmony, 73.29 ± 11.87% for playfulness vs. harmony, 70.51 ± 13.96% for encouragement vs. harmony). Benefited from fNIRS’s high portability, low running cost and the relative robustness against motion and electrical artifacts, our findings provided support for implementing a more fine-grained emotion recognition system with subdivided positive emotion categories.

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Examining the Intensity and Frequency of Experience of Discrete Positive Emotions

Cited by 9 publications

References 29 publications

CPED: A Chinese Positive Emotion Database for Emotion Elicitation and Analysis

CPED: A Chinese Positive Emotion Database for Emotion Elicitation and Analysis

Factorial Validity of the Anxiety Questionnaire for Students (AFS): Bifactor Modeling and Measurement Invariance

fNIRS Evidence for Recognizably Different Positive Emotions

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