2021
DOI: 10.3389/fpsyg.2021.622433
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Understanding the Multidimensional and Dynamic Nature of Facial Expressions Based on Indicators for Appraisal Components as Basis for Measuring Drivers' Fear

Abstract: Facial expressions are one of the commonly used implicit measurements for the in-vehicle affective computing. However, the time courses and the underlying mechanism of facial expressions so far have been barely focused on. According to the Component Process Model of emotions, facial expressions are the result of an individual's appraisals, which are supposed to happen in sequence. Therefore, a multidimensional and dynamic analysis of drivers' fear by using facial expression data could profit from a considerati… Show more

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Cited by 2 publications
(2 citation statements)
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“…More recently, in addition to valence and arousal, more dimensions (e.g., novelty) were suggested to describe the full space of emotional experience [ 30 , 31 ]. In this vein, a recent study suggested understanding facial expressions with regard to the time courses of and the mechanisms underlying the generation of emotional appraisals [ 32 ].…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…More recently, in addition to valence and arousal, more dimensions (e.g., novelty) were suggested to describe the full space of emotional experience [ 30 , 31 ]. In this vein, a recent study suggested understanding facial expressions with regard to the time courses of and the mechanisms underlying the generation of emotional appraisals [ 32 ].…”
Section: Introductionmentioning
confidence: 99%
“…This result is consistent with the result from a questionnaire study [ 41 ]. In a driving simulator study [ 32 ], facial expressions based on the FACS were used to successfully indicate the appraisal of high novelty and low power in fearful traffic situations. However, reliable multimodal measurements of the feeling of risk in vehicles with consideration of the individual appraisal of the situation still need more empirical validation.…”
Section: Introductionmentioning
confidence: 99%