The Measurement of Aesthetic Emotion in Music

Crickmore, Leon

doi:10.3389/fpsyg.2017.00651

Cited by 6 publications

(5 citation statements)

References 17 publications

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“…Different measurement instruments have been implemented to assess aesthetic emotions (for an overview see Schindler et al, 2017). These include emotions elicited by music (e.g., Zentner et al, 2008;Peltola and Eerola, 2016;Schubert et al, 2016;Coutinho and Scherer, 2017;Crickmore, 2017), visual art (e.g., Silvia and Nusbaum, 2011;Augustin et al, 2012) and films (e.g., Renaud and Unz, 2006;Bartsch, 2012). However, emotion terms such as being moved, fascination, awe, and wonder-considered as typical aesthetic emotions (Keltner and Haidt, 2003;Scherer, 2004;Frijda, 2007;Scherer and Coutinho, 2013;Fingerhut and Prinz, 2020)-are not only reserved for questionnaires assessing aesthetic emotions, but are likewise used to characterize emotional experiences that do not involve aesthetic evaluations.…”

Section: Aesthetic Emotionsmentioning

confidence: 99%

Dimensions and Clusters of Aesthetic Emotions: A Semantic Profile Analysis

et al. 2021

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Aesthetic emotions are elicited by different sensory impressions generated by music, visual arts, literature, theater, film, or nature scenes. Recently, the AESTHEMOS scale has been developed to facilitate the empirical assessment of such emotions. In this article we report a semantic profile analysis of aesthetic emotion terms that had been used for the development of this scale, using the GRID approach. This method consists of obtaining ratings of emotion terms on a set of meaning facets (features) which represent five components of the emotion process (appraisal, bodily reactions, action tendencies, expression, and feelings). The aims here were (a) to determine the dimensionality of the GRID features when applied to aesthetic emotions and compare it to published results for emotion terms in general, and (b) to examine the internal organization of the domain of aesthetic emotion terms in order to identify salient clusters of these items based on the similarity of their feature profiles on the GRID. Exploratory Principal Component Analyses suggest a four-dimensional structure of the semantic space consisting of valence, power, arousal, and novelty, converging with earlier GRID studies on large sets of standard emotion terms. Using cluster analyses, 15 clusters of aesthetic emotion terms with similar GRID feature profiles were identified, revealing the internal organization of the aesthetic emotion terms domain and meaningful subgroups of aesthetic emotions. While replication for further languages is required, these findings provide a solid basis for further research and methodological development in the realm of aesthetic emotions.

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Section: Aesthetic Emotionsmentioning

confidence: 99%

Dimensions and Clusters of Aesthetic Emotions: A Semantic Profile Analysis

et al. 2021

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“…rough the experimental study of the volume of music and people's aesthetic point of view, the acceptance limit of music art is measured (Nilesh [23]). e students are put in music environment and the music content and memory they can bear in a day is measured through the method of experimental verification (García González et al [24]), that is, through experiments and comparative studies, to determine the effects of prenatal music stimulation on fetal heart condition, neonatal physical measurement, and vital signs of pregnant women and to scientifically analyze the effects of music on human beings (Crickmore [25]). It is an effective measurement and research on the cultivation effect of music and chorus art on people's aesthetic taste and draws corresponding research conclusions (Tai et al [26]).…”

Section: Research Status Of Chorus Education and College Culture At Home And Abroadmentioning

confidence: 99%

Research on the Evaluation and Optimization Method of the Impact of Chorus Education on University Culture Based on Coevolution Model in the Background of Artificial Intelligence

Lin

Zhuo

2021

Scientific Programming

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The development of artificial intelligence technology is a field where all walks of life need to carry out in-depth research in the future, and the introduction of artificial intelligence technology in the field of university evaluation has become an inevitable trend. Through the collection and collation of the literature at home and abroad, the influence of chorus education on college culture in China has long remained in qualitative and experiential judgment and the significance and value of chorus education to colleges and universities are relatively single. Therefore, it is of great innovative value and practical significance to establish a scientific, systematic, and comprehensive evaluation mechanism for the impact of chorus education on university culture and to scientifically analyze key issues, establish evaluation criteria, and inject new research perspectives into the promotion of chorus education in colleges and universities in China, combining with the mature coevolution theoretical model of management science. It is of great innovative value and significance to combine the DEMATEL research method with the current practice of promoting chorus education in China’s colleges and universities and to systematically and comprehensively construct the evaluation system and research paradigm in line with chorus education by using the qualitative and quantitative methods.

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“…In the process of music appreciation, some people may not understand, but everyone is very fond of music, so the audience can calm their emotions and inspire their thoughts during the process of listening [3][4][5]. From the current research analysis, the education of music related to music can help schools and students improve the standard of aesthetic education [6][7][8]. But in the current education situation and the environment, the rigid and traditional marks are serious [9][10].…”

Section: Introductionmentioning

confidence: 99%

Diversified curriculum innovation of college music education under deep learning model

Xin

2023

Applied Mathematics and Nonlinear Sciences

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In order to innovate the college music education model, this paper first analyzes the design ideas of music-diversified courses based on deep learning and carries out the feature extraction of music course content. Secondly, combined with the diversified music wisdom environment and curriculum evaluation, a diversified music curriculum system teaching model based on deep learning is designed. Finally, the teaching practice is conducted, and a control experiment is designed to analyze the effectiveness of diversified curriculum innovations. The results showed that after the teaching practice, the basic level of the experimental class increased by 0.26, the cognitive ability increased by 0.23, the learning attitude increased by 0.23, the learning style increased by 0.23, and the interests and hobbies increased by 0.25. The diversified curriculum based on this paper can effectively carry out music teaching innovation.

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The Measurement of Aesthetic Emotion in Music

Cited by 6 publications

References 17 publications

Dimensions and Clusters of Aesthetic Emotions: A Semantic Profile Analysis

Dimensions and Clusters of Aesthetic Emotions: A Semantic Profile Analysis

Research on the Evaluation and Optimization Method of the Impact of Chorus Education on University Culture Based on Coevolution Model in the Background of Artificial Intelligence

Diversified curriculum innovation of college music education under deep learning model

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