情動体験を伴う表情刺激の作成と情動伝染モデルの妥当性の検討

内容の要約広島大学(Hiroshima University)博士(心理学)Doctor of Philosophy in Psychologydoctora

Distinct temporal features of genuine and deliberate facial expressions of surprise

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

Bayesian statistical analysis for psychologists using JASP

In this paper, we introduce the free statistical software, JASP (JASP Team, 2016). For psychologists who want to use statistical analysis, JASP has several advantages, one of which is that it has a graphical user interface that is easy to operate. Moreover, JASP makes Bayesian statistical analysis accessible. In this paper, we provide examples of how to use JASP using actual Big 5 data

Improving method of listener monitoring by elucidator in the face-to-face explanation

In this study, we have developed teaching methods to improve transmission efficiency of messages in face-to-face explanation. In the current school education system, to improve communication skills of students, the opportunities for students to carry out group work and class presentations are increasing. In general, imparting information is easier with face-to-face communication than in the situations where one cannot see the face of the listener. However, in face-to-face communication, it has been reported that it is possible that the degree of understanding has been overestimated, based on the face of listener. Therefore, it is considered that the efficiency of communication improves by directly asking the degree of the listener's understanding, regardless of the listener's face. In this paper, we used a task which listener draws a geometric figure by listening elucidator's verbal explanation and studied teaching methods improving the metacognitive monitoring of elucidator, especially confirmation to the listener. In the experiment, participants were divided into two groups. One group performed reflection of their explanations by using the checklist for metacognitive monitoring after explaining, and repeating the explanation. Another group repeated explanations without reflection of their explanations. We compared the two groups about their frequency of confirmation to the listener in the second explanation. As a result, the frequency of confirmation increased with reflection with the checklist. This result suggested that using the checklist as a teaching method is effective for improving the efficiency of communication

Human and machine validation of 14 databases of dynamic facial expressions

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