Research on facial emotion expression has mostly focused on emotion recognition, assuming that a small number of discrete emotions is elicited and expressed via prototypical facial muscle configurations as captured in still photographs. These are expected to be recognized by observers, presumably via template matching. In contrast, appraisal theories of emotion propose a more dynamic approach, suggesting that specific elements of facial expressions are directly produced by the result of certain appraisals and predicting the facial patterns to be expected for (...) certain appraisal configurations. This approach has recently been extended to emotion perception, claiming that observers first infer individual appraisals and only then make categorical emotion judgments based on the estimated appraisal patterns, using semantic inference rules. Here we report two related studies to empirically investigate the facial action unit configurations that are used by actors to convey specific emotions in short affect bursts and to examine to what extent observers can infer a person’s emotions from the predicted facial expression configurations. The results show that 1) professional actors use many of the predicted facial action unit patterns to enact systematically specified appraisal outcomes in a realistic scenario setting, and 2) naïve observers infer the respective emotions based on highly similar facial movement configurations with a degree of accuracy comparable to earlier research findings. Based on estimates of underlying appraisal criteria for the different emotions we conclude that the patterns of facial action units identified in this research correspond largely to prior predictions and encourage further research on appraisal-driven expression and inference. (shrink)

Background: This study developed a photo and video database of 4-to-6-year-olds expressing the seven induced and posed universal emotions and a neutral expression. Children participated in photo and video sessions designed to elicit the emotions, and the resulting images were further assessed by independent judges in two rounds. Methods: In the first round, two independent judges, experts in the Facial Action Coding System, firstly analysed 3,668 emotions facial expressions stimuli from 132 children. Both judges reached 100% agreement regarding 1,985 stimuli, (...) which were then selected for a second round of analysis between judges 3 and 4. Results: The result was 1,985 stimuli were produced from 124 participants. A Kappa index of 0.70 and an accuracy of 73% between experts were observed. Lower accuracy was found for emotional expression by 4-year-olds than 6-year-olds. Happiness, disgust and contempt had the highest agreement. After a sub-analysis evaluation of all four judges, 100% agreement was reached for 1,381 stimuli which compound the ChildEFES database with 124 participants and 51% induced photographs. The number of stimuli of each emotion were: 87 for neutrality, 363 for happiness, 170 for disgust, 104 for surprise, 152 for fear, 144 for sadness, 157 for anger 157, and 183 for contempt. Conclusions: The findings show that this photo and video database can facilitate research on the mechanisms involved in early childhood recognition of facial emotions in children, contributing to the understanding of facial emotion recognition deficits which characterise several neurodevelopmental and psychiatric disorders. (shrink)

People dedicate significant attention to others’ facial expressions and to deciphering their meaning. Hence, knowing whether such expressions are genuine or deliberate is important. Early research proposed that authenticity could be discerned based on reliable facial muscle activations unique to genuine emotional experiences that are impossible to produce voluntarily. With an increasing body of research, such claims may no longer hold up to empirical scrutiny. In this article, expression authenticity is considered within the context of senders’ ability to produce convincing (...) facial displays that resemble genuine affect and human decoders’ judgments of expression authenticity. This includes a discussion of spontaneous vs. posed expressions, as well as appearance- vs. elicitation-based approaches for defining emotion recognition accuracy. We further expand on the functional role of facial displays as neurophysiological states and communicative signals, thereby drawing upon theencoding-decodingandaffect-inductionperspectives of emotion expressions. Theoretical and methodological issues are addressed with the aim to instigate greater conceptual and operational clarity in future investigations of expression authenticity. (shrink)

Emotion Review, Volume 14, Issue 1, Page 84-95, January 2022. Emotional stimuli such as images, words, or video clips are often used in studies researching emotion. New sets are continuously being published, creating an immense number of available sets and complicating the task for researchers who are looking for suitable stimuli. This paper presents the KAPODI-database of emotional stimuli sets that are freely available or available upon request. Over 45 aspects including over 25 key set characteristics have been extracted and (...) listed for each set. The database facilitates finding of and comparison between individual sets. It currently contains sets published between 1963 and 2020. A searchable online version allows users to select specific set characteristics and to find matching sets accordingly, as well as to add new published sets. (shrink)

ABSTRACTFacial stimuli are widely used in behavioural and brain science research to investigate emotional facial processing. However, some studies have demonstrated that dynamic expressions elicit stronger emotional responses compared to static images. To address the need for more ecologically valid and powerful facial emotional stimuli, we created Dynamic FACES, a database of morphed videos from younger, middle-aged, and older adults displaying naturalistic emotional facial expressions. To assess adult age differences in emotion identification of dynamic stimuli and to provide normative ratings (...) for this modified set of stimuli, healthy adults categorised for each video the emotional expression displayed, rated the expression distinctiveness, estimated the age of the face model, and rated the naturalness of the expression. We found few age differences in emotion identification when using dynamic stimuli. On... (shrink)

Android robots capable of emotional interactions with humans have considerable potential for application to research. While several studies developed androids that can exhibit human-like emotional facial expressions, few have empirically validated androids’ facial expressions. To investigate this issue, we developed an android head called Nikola based on human psychology and conducted three studies to test the validity of its facial expressions. In Study 1, Nikola produced single facial actions, which were evaluated in accordance with the Facial Action Coding System. The (...) results showed that 17 action units were appropriately produced. In Study 2, Nikola produced the prototypical facial expressions for six basic emotions, and naïve participants labeled photographs of the expressions. The recognition accuracy of all emotions was higher than chance level. In Study 3, Nikola produced dynamic facial expressions for six basic emotions at four different speeds, and naïve participants evaluated the naturalness of the speed of each expression. The effect of speed differed across emotions, as in previous studies of human expressions. These data validate the spatial and temporal patterns of Nikola’s emotional facial expressions, and suggest that it may be useful for future psychological studies and real-life applications. (shrink)

Most experimental studies of facial expression processing have used static stimuli (photographs), yet facial expressions in daily life are generally dynamic. In its original photographic format, the Karolinska Directed Emotional Faces (KDEF) has been frequently utilized. In the current study, we validate a dynamic version of this database, the KDEF-dyn. To this end, we applied animation between neutral and emotional expressions (happy, sad, angry, fearful, disgusted, and surprised; 1,033-ms unfolding) to 40 KDEF models, with morphing software. Ninety-six human observers categorized (...) the expressions of the resulting 240 video-clip stimuli, and automated face analysis assessed the evidence for 6 expressions and 20 facial action units (AUs) at 31 intensities. Low-level image properties (luminance, signal-to-noise ratio, etc.) and other purely perceptual factors (e.g., size, unfolding speed) were controlled. Human recognition performance (accuracy, efficiency, and confusions) patterns were consistent with prior research using static and other dynamic expressions. Automated assessment of expressions and AUs was sensitive to intensity manipulations. Significant correlations emerged between human observers’ categorization and automated classification. The KDEF-dyn database aims to provide a balance between experimental control and ecological validity for research on emotional facial expression processing. The stimuli and the validation data are available to the scientific community. (shrink)