An assessment of mood or emotion is important in developing mental health measures, and facial expressions are strongly related to mood or emotion. This study thus aimed to examine the relationship between levels of negative mood and characteristics of mouth parts when moods are drawn as facial expressions on a common platform. A cross-sectional study of Japanese college freshmen was conducted, and 1,068 valid responses were analyzed. The questionnaire survey consisted of participants’ characteristics, the Profile of Mood States, and a (...) sheet of facial expression drawing, and the sheet was digitized and analyzed using an image-analysis software. Based on the total POMS score as an index of negative mood, the participants were divided into four groups: low, normal, high, and very high. Lengths of drawn lines and between both mouth corners were significantly longer, and circularity and roundness were significantly higher in the L group. With increasing levels of negative mood, significant decreasing trends were observed in these lengths. Convex downward and enclosed figures were significantly predominant in the L group, while convex upward figures were significantly predominant and a tendency toward predominance of no drawn mouths or line figures was found in the H and VH groups. Our results suggest that mood states can be significantly related to the size and figure characteristics of drawn mouths of FACED on a non-verbal common platform. That is, these findings mean that subjects with low negative mood may draw a greater and rounder mouth and figures that may be enclosed and downward convex, while subjects with a high negative mood may not draw the line, or if any, may draw the line shorter and upward convex. (shrink)

The study of emotional concepts stands at a very interesting intersection between the theoretical debate about the nature of emotions and the debate about the nature of processing concrete concepts and abstract concepts. On the one hand, it is debated whether it is possible to differentiate basic emotions from secondary emotions and, on the other hand, whether emotional concepts differ from abstract concepts. In this regard, the prototypical perceptual aspects are considered an important factor both for the differentiation between concrete (...) and abstract concepts and for the differentiation between basic and secondary emotions (facial expressions). Thus, the objective has been to determine if (a) the presence or absence of a prototypical perceptual referent, and (b) the type of concept (referring to emotion and not referring to emotion), produce differences between concepts of basic emotions, secondary emotions and concepts not related to emotions, concrete and abstract, in the tasks of qualification of concreteness, imageability and availability of context and the task of the list of properties, that have been used in previous studies. A total of 86 university students from the suburbs of La Serena - Coquimbo (Chile), all native Spanish speakers, participated in the study. The results show that in the perception of concreteness and in the total of enumerated properties, emotional concepts presented similar results to abstract concepts not related to emotion and there was no difference between basic and secondary emotion concepts. In imageability and context availability, emotional concepts were perceived as different from and more concrete than abstract concepts. In addition, the cause-effect type attributes allowed to clearly differentiate emotional concepts from those not related to emotion and to differentiate between basic and secondary emotion concepts. These types of attributes appear almost exclusively in emotional concepts and are more frequent in basic emotions. These results are partially consistent with the predictions of Neurocultural and Conceptual Act theories about emotions. (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)

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; Face++, (...) Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions. (shrink)