Editorial: Dynamic Emotional Communication

Sato, Wataru; Krumhuber, Eva; Jellema, Tjeerd; Williams, Justin H. G.

doi:10.3389/fpsyg.2019.02836

Cited by 17 publications

(14 citation statements)

References 30 publications

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Section: Discussionmentioning

confidence: 99%

“…Based on a growing body of research arguing that facial displays of emotion are dynamic phenomena (Krumhuber et al, 2013 ; Sato et al, 2019 ), there has been a shift in interest towards dynamic expressions over the past two decades. This has led to a proliferation of stimuli available to the scientific community, amounting to a large number of datasets varying in size and properties (Krumhuber et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…Apart from their questionable ecological validity which renders them untypical of the displays encountered in everyday life (Russell, 1994 ), static portrayals may not convey the same affective information and communicative intent. There is now a growing body of evidence suggesting that the dynamics of facial expressions are crucial for the recognition (e.g., Wehrle et al, 2000 ; Kamachi et al, 2001 ) and interpretation of emotions (e.g., Ambadar et al, 2005 ; see Krumhuber et al, 2013 ; Sato et al, 2019 for reviews). Moreover, moving stimuli elicit different patterns of muscular/behavioral responses (Sato & Yoshikawa, 2007 ) and brain activation compared to static ones (Zinchenko et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Human and machine validation of 14 databases of dynamic facial expressions

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With a shift in interest toward dynamic expressions, numerous corpora of dynamic facial stimuli have been developed over the past two decades. The present research aimed to test existing sets of dynamic facial expressions (published between 2000 and 2015) in a cross-corpus validation effort. For this, 14 dynamic databases were selected that featured facial expressions of the basic six emotions (anger, disgust, fear, happiness, sadness, surprise) in posed or spontaneous form. In Study 1, a subset of stimuli from each database (N = 162) were presented to human observers and machine analysis, yielding considerable variance in emotion recognition performance across the databases. Classification accuracy further varied with perceived intensity and naturalness of the displays, with posed expressions being judged more accurately and as intense, but less natural compared to spontaneous ones. Study 2 aimed for a full validation of the 14 databases by subjecting the entire stimulus set (N = 3812) to machine analysis. A FACS-based Action Unit (AU) analysis revealed that facial AU configurations were more prototypical in posed than spontaneous expressions. The prototypicality of an expression in turn predicted emotion classification accuracy, with higher performance observed for more prototypical facial behavior. Furthermore, technical features of each database (i.e., duration, face box size, head rotation, and motion) had a significant impact on recognition accuracy. Together, the findings suggest that existing databases vary in their ability to signal specific emotions, thereby facing a trade-off between realism and ecological validity on the one end, and expression uniformity and comparability on the other.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Human and machine validation of 14 databases of dynamic facial expressions

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“…In the real world, faces are constantly in motion. As demonstrated by a growing body of research in cognitive science, the dynamics of facial movement convey communicative intent and emotion (for reviews, see Krumhuber et al, 2013;Krumhuber and Skora, 2016;Sato et al, 2019). While the role of fine-grained dynamics has been best explored in the context of smiling (e.g., Krumhuber et al, 2007Krumhuber et al, , 2009, they are believed to impact emotion judgments and behavioral responses more generally (Sato and Yoshikawa, 2007;Recio et al, 2013).…”

Section: Missing the Beat Of Fine-grained Expression Dynamicsmentioning

confidence: 99%

Opportunities and Challenges for Using Automatic Human Affect Analysis in Consumer Research

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The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions, and in naturalistic choice settings. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as a starting point for further analyses. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research.

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“…In the real world, faces are constantly in motion. As demonstrated by a growing body of research in cognitive science, the dynamics of facial movement convey communicative intent and emotion (for reviews (see Krumhuber et al, 2013;Krumhuber and Skora, 2016;Sato et al, 2019). While the role of fine-grained dynamics has been best explored in the context of smiling (e.g., Krumhuber et al, 2007Krumhuber et al, , 2009, they are believed to impact emotion judgements and behavioral responses more generally (Sato and Yoshikawa, 2007;Recio et al, 2013).…”

Section: Missing the Beat Of Fine-grained Expression Dynamicsmentioning

confidence: 99%

Opportunities and challenges for using automatic human affect analysis in consumer research

Küster¹,

Krumhuber²,

Steinert³

et al. 2020

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The ability to automatically assess emotional responses via contact-free video recording taps into a rapidly growing market aimed at predicting consumer choices. If consumer attention and engagement are measurable in a reliable and accessible manner, relevant marketing decisions could be informed by objective data. Although significant advances have been made in automatic affect recognition, several practical and theoretical issues remain largely unresolved. These concern the lack of cross-system validation, a historical emphasis of posed over spontaneous expressions, as well as more fundamental issues regarding the weak association between subjective experience and facial expressions. To address these limitations, the present paper argues that extant commercial and free facial expression classifiers should be rigorously validated in cross-system research. Furthermore, academics and practitioners must better leverage fine-grained emotional response dynamics, with stronger emphasis on understanding naturally occurring spontaneous expressions. We posit that applied consumer research might be better situated to examine facial behavior in socio-emotional contexts rather than decontextualized, laboratory studies, and highlight how AHAA can be successfully employed in this context. Also, facial activity should be considered less as a single outcome variable, and more as promising input for two-step machine learning in combination with other (multimodal) features. We illustrate this point in a case study using facial activity as input features to predict crying behavior in response to sad movies. Implications of this approach and potential obstacles that need to be overcome are discussed within the context of consumer research.

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Editorial: Dynamic Emotional Communication

Cited by 17 publications

References 30 publications

Human and machine validation of 14 databases of dynamic facial expressions

Human and machine validation of 14 databases of dynamic facial expressions

Opportunities and Challenges for Using Automatic Human Affect Analysis in Consumer Research

Opportunities and challenges for using automatic human affect analysis in consumer research

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