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)

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)

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)

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)

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)

Facial information is a powerful channel for human-to-human communication. Characteristically, faces can be defined as biological objects that are four-dimensional (4D) patterns, whereby they have concurrently a spatial structure as well as temporal dynamics. The spatial characteristics of facial objects possess three dimensions (3D), namely breadth, height and importantly, depth. The temporal properties of facial objects are defined by how a 3D facial structure evolves dynamically over time; where time is referred to as the fourth dimension (4D). Our entire perspective (...) of another’s face, whether it be social, affective or cognitive perceptions, is therefore built on a combination of 3D and 4D visual cues. Counterintuitively, over the past few decades of experimental research in psychology, facial stimuli have largely been presented to participants as a spatial pattern that has been flattened into two dimensions (2D), while remaining largely static. The following review aims to advance and update facial researchers, on the recent revolution in computer-generated, realistic 4D facial models produced from real-life human subjects. We delve in-depth to summarize recent studies which have utilized facial stimuli that possess 3D structural cues (geometry and depth) and 4D temporal cues (3D structure + dynamic viewpoint and movement). In sum, we have found that higher-order perceptions such as identity, gender, ethnicity, emotion and personality, are critically influenced by the 4D characteristics embedded within natural human faces. In future, it is recommended that facial stimuli incorporate the 4D space-time perspective with the proposed time-resolved methods. (shrink)

People routinely use facial expressions to communicate successfully and to regulate other’s behaviour, yet modelling the form and meaning of these facial behaviours has proven surprisingly complex. One reason for this difficulty may lie in an over-reliance on the assumptions inherent in existing theories of facial expression – specifically that (1) there is a putative set of facial expressions that signal an internal state of emotion, (2) patterns of facial movement have been empirically linked to the prototypical emotions in this (...) set, and (3) static, non-social, posed images from convenience samples are adequate to validate the first two assumptions. These assumptions have guided the creation of datasets, which are then used to train unrepresentative computational models of facial expression. In this article, we discuss existing theories of facial expression and review how they have shaped current facial expression recognition tools. We then discuss the resources that are available to help researchers build a more ecologically valid model of facial expressions. (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)