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Abstract The integrated model of rumination argues that two trait factors—negative thinking habits and processing modes—get people stuck in maladaptive rumination. There is little evidence showing whether these factors influence the daily dynamic associations between rumination and negative moods. To address this, in this study, we conducted an experience-sampling method on Japanese university students. We recruited 92 Japanese university students and assessed their daily rumination and negative affect (NA) eight times a day for seven days. We examined the effects of habits and processing modes on the dynamic associations between rumination and negative moods using dynamic structural equation modeling. We found that individuals were more likely to ruminate when they experienced NA. However, contrary to previous findings, this study’s participants did not experience NA after engaging in rumination. Moreover, we did not detect any significant trait factor effect on these dynamic associations. Our findings imply that individuals are more likely to engage in rumination after experiencing NA, but the reverse association, particularly the autoregression of rumination, may not be maintained in natural daily life. Furthermore, negative thinking habits and processing modes may not influence the daily dynamic associations between rumination and NA among Japanese university students.

The rapid detection of neutral faces with emotional value plays an important role in social relationships for both young and older adults. Recent psychological studies have indicated that young adults show efficient value learning for neutral faces and the detection of “value-associated faces,” while older adults show slightly different patterns of value learning and value-based detection of neutral faces. However, the mechanisms underlying these processes remain unknown. To investigate this, we applied hierarchical reinforcement learning and diffusion models to a value learning task and value-driven detection task that involved neutral faces; the tasks were completed by young and older adults. The results for the learning task suggested that the sensitivity of learning feedback might decrease with age. In the detection task, the younger adults accumulated information more efficiently than the older adults, and the perceptual time leading to motion onset was shorter in the younger adults. In younger adults only, the reward sensitivity during associative learning might enhance the accumulation of information during a visual search for neutral faces in a rewarded task. These results provide insight into the processing linked to efficient detection of faces associated with emotional values, and the age-related changes therein.

Abstract The development of facial expressions with sensing information is progressing in multidisciplinary fields, such as psychology, affective computing, and cognitive science. Previous facial datasets have not simultaneously dealt with multiple theoretical views of emotion, individualized context, or multi-angle/depth information. We developed a new facial database (RIKEN facial expression database) that includes multiple theoretical views of emotions and expressers’ individualized events with multi-angle and depth information. The RIKEN facial expression database contains recordings of 48 Japanese participants captured using ten Kinect cameras at 25 events. This study identified several valence-related facial patterns and found them consistent with previous research investigating the coherence between facial movements and internal states. This database represents an advancement in developing a new sensing system, conducting psychological experiments, and understanding the complexity of emotional events.

Abstract The physical properties of genuine and deliberate facial expressions remain elusive. This study focuses on observable dynamic differences between genuine and deliberate expressions of surprise based on the temporal structure of facial parts during emotional expression. Facial expressions of surprise were elicited using multiple methods and video recorded: senders were filmed as they experienced genuine surprise in response to a jack-in-the-box (Genuine), other senders were asked to produce deliberate surprise with no preparation (Improvised), by mimicking the expression of another (External), or by reproducing the surprised face after having first experienced genuine surprise (Rehearsed). A total of 127 videos were analyzed, and moment-to-moment movements of eyelids and eyebrows were annotated with deep learning-based tracking software. Results showed that all surprise displays were mainly composed of raising eyebrows and eyelids movements. Genuine displays included horizontal movement in the left part of the face, but also showed the weakest movement coupling of all conditions. External displays had faster eyebrow and eyelid movement, while Improvised displays showed the strongest coupling of movements. The findings demonstrate the importance of dynamic information in the encoding of genuine and deliberate expressions of surprise and the importance of the production method employed in research.

Although results of many psychology studies have shown that sharing emotion achieves dyadic interaction, no report has explained a study of the transmission of authentic information from emotional expressions that can strengthen perceivers. For this study, we used computational modeling, which is a multinomial processing tree, for formal quantification of the process of sharing emotion that emphasizes the perception of authentic information for expressers’ feeling states from facial expressions. Results indicated that the ability to perceive authentic information of feeling states from a happy expression has a higher probability than the probability of judging authentic information from anger expressions. Next, happy facial expressions can activate both emotional elicitation and sharing emotion in perceivers, where emotional elicitation alone is working rather than sharing emotion for angry facial expressions. Third, parameters to detect anger experiences were found to be correlated positively with those of happiness. No robust correlation was found between the parameters extracted from this experiment task and questionnaire-measured emotional contagion, empathy, and social anxiety. Results of this study revealed the possibility that a new computational approach contributes to description of emotion sharing processes.

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 (anger, disgust, fear, happiness, sadness, and surprise), 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.

Reading the genuineness of facial expressions is important for increasing the credibility of information conveyed by faces. However, it remains unclear which spatio-temporal characteristics of facial movements serve as critical cues to the perceived genuineness of facial expressions. This study focused on observable spatio-temporal differences between perceived-as-genuine and deliberate expressions of happiness and anger expressions. In this experiment, 89 Japanese participants were asked to judge the perceived genuineness of faces in videos showing happiness or anger expressions. To identify diagnostic facial cues to the perceived genuineness of the facial expressions, we analyzed a total of 128 face videos using an automated facial action detection system; thereby, moment-to-moment activations in facial action units were annotated, and nonnegative matrix factorization extracted sparse and meaningful components from all action units data. The results showed that genuineness judgments reduced when more spatial patterns were observed in facial expressions. As for the temporal features, the perceived-as-deliberate expressions of happiness generally had faster onsets to the peak than the perceived-as-genuine expressions of happiness. Moreover, opening the mouth negatively contributed to the perceived-as-genuine expressions, irrespective of the type of facial expressions. These findings provide the first evidence for dynamic facial cues to the perceived genuineness of happiness and anger expressions.

Facial expressions of emotion can convey information about the world and disambiguate elements of the environment, thus providing direction to other people’s behavior. However, the functions of facial expressions from the perspective of learning patterns over time remain elusive. This study investigated how the feedback of facial expressions influences learning tasks in a context of ambiguity using the Iowa Gambling Task. The results revealed that the learning rate for facial expression feedback was slower in the middle of the learning period than it was for symbolic feedback. No difference was observed in deck selection or computational model parameters between the conditions, and no correlation was observed between task indicators and the results of depressive questionnaires.

This study reveals associative processes for novel words in a second language (L2) and their referents. Thirty Japanese participants learned associative pairs for novel words in Chinese and pictorial referents (CP), as well as novel words in Chinese and words in Japanese (CJ), against a condition in which they learned only novel words in Chinese (C). After the learning phase, participants conducted two learning condition retrieval tasks for word recognition and three recognition tasks for the source-monitoring of the referents. The correct answers for each recognition task were provided to participants after each trial. Although the correct answers in all conditions increased in both the recognition and learning condition retrieval tasks, there was no significant difference among conditions. In contrast, the response times of the correct trials in all recognition tasks and the first learning condition retrieval tasks were faster for the CP condition than the CJ condition. Additionally, in the second learning condition retrieval task, missed items in associative conditions (CP and CJ conditions) were judged to be learned items more often than unlearned items, whereas missed items in the non-associative condition (C condition) were judged to be unlearned items more than learned items. These findings suggest that pictures contribute to the recognition and retrieval speeds of associations between novel words in L2 and referents, and that associative learning of L2 words and referents could enhance more familiarity effects than the learning of L2 words only.

Verbal sounds can be associated with specific meanings, a phenomenon called sound symbolism. Previous findings of sound symbolism have shown that words including specific consonants or vowels or mouth shapes to pronounce specific vowels associate with specific and subjective physical and emotional evaluations. The purpose of this study was to examine whether each written vowel in a given language was individually associated with specific subjective evaluations. Six hundred and thirteen participants used five-point semantic differential scales for 10 features (size, closeness, thickness, width, weight, height, depth, affection, excitement and familiarity) to rate written Japanese vowels (a, i, u, e, and o). The results showed that the size, closeness, thickness and width of a, u and o were significantly higher than those of i and e, whereas the affection and familiarity of a were higher than the others. These results were consistent with previous findings in which vowels in sound-symbolic words have been associated with physical (i.e., size, closeness, thickness and width) and emotional (i.e., affection) evaluations. Our findings suggest that each written Japanese vowel itself, with its individual characteristics, could individually contribute to specific and subjective physical and emotional evaluations. These findings provide insights on how we could better use letters for communicative relationships among writers and readers.

Automatic facial action detection is important, but no previous studies have evaluated pre-trained models on the accuracy of facial action detection as the angle of the face changes from frontal to profile. Using static facial images obtained at various angles (0°, 15°, 30°, and 45°), we investigated the performance of three automated facial action detection systems (FaceReader, OpenFace, and Py-feat). The overall performance was best for OpenFace, followed by FaceReader and Py-Feat. The performance of FaceReader significantly decreased at 45° compared to that at other angles, while the performance of Py-Feat did not differ among the four angles. The performance of OpenFace decreased as the target face turned sideways. Prediction accuracy and robustness to angle changes varied with the target facial components and action detection system.

In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.

Previous studies have reported that verbal sounds are associated—non-arbitrarily—with specific meanings (e.g., sound symbolism and onomatopoeia), including visual forms of information such as facial expressions; however, it remains unclear how mouth shapes used to utter each vowel create our semantic impressions. We asked 81 Japanese participants to evaluate mouth shapes associated with five Japanese vowels by using 10 five-item semantic differential scales. The results reveal that the physical characteristics of the facial expressions (mouth shapes) induced specific evaluations. For example, the mouth shape made to voice the vowel “a” was the one with the biggest, widest, and highest facial components compared to other mouth shapes, and people perceived words containing that vowel sound as bigger. The mouth shapes used to pronounce the vowel “i” were perceived as more likable than the other four vowels. These findings indicate that the mouth shapes producing vowels imply specific meanings. Our study provides clues about the meaning of verbal sounds and what the facial expressions in communication represent to the perceiver.

Accurately gauging the emotional experience of another person is important for navigating interpersonal interactions. This study investigated whether perceivers are capable of distinguishing between unintentionally expressed (genuine) and intentionally manipulated (posed) facial expressions attributed to four major emotions: amusement, disgust, sadness, and surprise. Sensitivity to this discrimination was explored by comparing unstaged dynamic and static facial stimuli and analyzing the results with signal detection theory. Participants indicated whether facial stimuli presented on a screen depicted a person showing a given emotion and whether that person was feeling a given emotion. The results showed that genuine displays were evaluated more as felt expressions than posed displays for all target emotions presented. In addition, sensitivity to the perception of emotional experience, or discriminability, was enhanced in dynamic facial displays, but was less pronounced in the case of static displays. This finding indicates that dynamic information in facial displays contributes to the ability to accurately infer the emotional experiences of another person.

While numerous studies have examined the relationships between facial actions and emotions, they have yet to account for the ways that specific spontaneous facial expressions map onto emotional experiences induced without expressive intent. Moreover, previous studies emphasized that a fine-grained investigation of facial components could establish the coherence of facial actions with actual internal states. Therefore, this study aimed to accumulate evidence for the correspondence between spontaneous facial components and emotional experiences. We reinvestigated data from previous research which secretly recorded spontaneous facial expressions of Japanese participants as they watched film clips designed to evoke four different target emotions: surprise, amusement, disgust, and sadness. The participants rated their emotional experiences via a self-reported questionnaire of 16 emotions. These spontaneous facial expressions were coded using the Facial Action Coding System, the gold standard for classifying visible facial movements. We corroborated each facial action that was present in the emotional experiences by applying stepwise regression models. The results found that spontaneous facial components occurred in ways that cohere to their evolutionary functions based on the rating values of emotional experiences (e.g., the inner brow raiser might be involved in the evaluation of novelty). This study provided new empirical evidence for the correspondence between each spontaneous facial component and first-person internal states of emotion as reported by the expresser.