Considering cross-cultural context in the automatic recognition of emotions

Quirós-Ramírez, María Alejandra; Onisawa, Takehisa

doi:10.1007/s13042-013-0192-2

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…The current study predicts that social interactions should be most successful between interaction partners who move similarly, as greater understanding of others' internal states is likely to result in more successful social interactions. For example, differences in the production of actions may impact cross-cultural affect perception (Hareli, Kafetsios, & Hess, 2015;Matsumoto, Seung Hee Yoo, & Fontaine, 2008;Quiros-Ramirez & Onisawa, 2015). Consistent with this speculation, individuals with Autism Spectrum Disorder (ASD), who have a range of social and communicative impairments, have been shown to move with atypical action kinematics compared to 14 typical individuals, and individuals with the most atypical kinematics are also those with the most severe social difficulties (Cook, Blakemore, & Press, 2013;Cook, 2016; see also Edey et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Our own action kinematics predict the perceived affective states of others.

Edey¹,

Yon²,

Cook³

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

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Words in body of text: 2434 (<2500). Acknowledgements:We are grateful to Kaat Alaerts for providing the original stimuli and much useful advice concerning their generation, as well as Nicholas Holmes for advice concerning the bootstrapping analysis. RE was funded by a graduate teaching assistantship from Birkbeck, University of London and DY was funded by an ESRC Studentship. JC is supported by the Birmingham Fellows programme. 2 AbstractOur movement kinematics provide useful cues about our affective states. Given that our experiences furnish models that help us to interpret our environment, and that a rich source of action experience comes from our own movements, the present study examined whether we use models of our own action kinematics to make judgments about the affective states of others. For example, relative to one's typical kinematics, anger is associated with fast movements. Therefore, the extent to which we perceive anger in others may be determined by the degree to which their movements are faster than our own typical movements. We related participants' walking kinematics in a neutral context to their judgments of the affective states conveyed by observed point-light walkers (PLWs). As predicted, we found a linear relationship between one's own walking kinematics and affective state judgments, such that faster participants rated slower emotions more intensely relative to their ratings for faster emotions. This relationship was absent when observing PLWs where differences in velocity between affective states were removed. These findings suggest that perception of affective states in others is predicted by one's own movement kinematics, with important implications for perception of, and interaction with, those who move differently.Keywords: Action perception; emotion; affective states; point-light walkers; expertise Public significance statement: The way that we move provides useful cues about our emotions. For example, we move more quickly than our average speed when we are feeling angry and more slowly when we are feeling sad. The present study shows that we make judgments about others' emotional expressions relative to how we move ourselves. To give an example, rather than everyone interpreting movements of a certain speed as angry, individuals may only think that others feel angry if these movements are faster than their own typical movement speed. Therefore, we are better placed to understand the emotions of others who tend to move more similarly to us. These findings have 3 important implications for our understanding of and interactions with clinical and cultural groups whose movements are dramatically different from our own. 4

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Section: Discussionmentioning

confidence: 99%

Our own action kinematics predict the perceived affective states of others.

Edey¹,

Yon²,

Cook³

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

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“…It consists of at least two categories that presuppose a particular level of measurement coders, for example via ProLific (Suviranta & Hiippala, 2022). Another noteworthy omission is the possibility of biases in cross-cultural analyses (Peña, 2007;Quiros-Ramirez & Onisawa, 2015).…”

Section: Unitmentioning

confidence: 99%

Taming our Wild Data

Van Enschot,

Spooren,

Van den Bosch

et al. 2024

DuJAL

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Many research questions in the field of applied linguistics are answered by manually analyzing data collections or corpora: collections of spoken, written and/or visual communicative messages. In this kind of quantitative content analysis, the coding of subjective language data often leads to disagreement among raters. In this paper, we discuss causes of and solutions to disagreement problems in the analysis of discourse. We discuss crucial factors determining the quality and outcome of corpus analyses, and focus on the sometimes tense relation between reliability and validity. We evaluate formal assessments of intercoder reliability. We suggest a number of ways to improve the intercoder reliability, such as the precise specification of the variables and their coding categories and carving up the coding process into smaller substeps. The paper ends with a reflection on challenges for future work in discourse analysis, with special attention to big data and multimodal discourse.

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“…Generally, the term emotion describes the subjective feelings in short periods of time which are related to events, persons, or objects [10,11]. Since the emotional state of human is a highly subjective experience, it is hard to find objective and universal definitions.…”

Section: Emotions In Violence Eventsmentioning

confidence: 99%

Emotion recognition and school violence detection from children speech

Han

Zhang

Zhu

et al. 2018

J Wireless Com Network

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School violence is a serious problem all over the world, and violence detection is significant to protect juveniles. School violence can be detected from the biological signals of victims, and emotion recognition is an important way to detect violence events. In this research, a violence simulation experiment was designed and performed for school violence detection system. Emotional voice from the experiment was extracted and analyzed. Consecutive elimination process (CEP) algorithm was proposed for emotion recognition in this paper. After parameters optimization, SVM was chosen as the classifier and the algorithm was validated by Berlin database which is an emotional speech database of adults, and the mean accuracy for seven emotions was 79.05%. The emotional speech database of children extracted in violence simulation was also classified by SVM classifier with proposed CEP algorithm, and the mean accuracy was 66.13%. The results showed that high classification performance could be achieved with the CEP algorithm. The classification result was also compared with database of adults, and the results indicated that children and adults' voice should be treated differently in speech emotion recognition researches. The accuracy of children database is lower than adult database; the accuracy of violence detection will be improved by other signals in the system.

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Considering cross-cultural context in the automatic recognition of emotions

Cited by 9 publications

References 18 publications

Our own action kinematics predict the perceived affective states of others.

Our own action kinematics predict the perceived affective states of others.

Taming our Wild Data

Emotion recognition and school violence detection from children speech

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