|其他题名||Self-monitoring of Facial Expression and Its Influencing Factors|
|关键词||面部表情 自我监控 实时觉察 工作记忆 自我监控倾向 数据 库|
研究二通过两个表情编码实验（实验2 和实验3）考察个体面部表情的自我监控在实时和事后范式下的关系，以及监控对情绪表达和觉察的影响。实验2要求个体在表情压抑条件下对泄露表情进行实时和事后自我觉察。结果发现，实时觉察条件下的觉察率为57.79%，事后觉察条件下的觉察率为75.92%。面部表情的自我觉察与表情的表达特征（如强度和时长）之间的相关关系分析发现，实时觉察条件下，面部表情的自我觉察只受表情强度的影响，而事后觉察条件下，自我觉察既受表情强度又受表情时长的影响。同时，个体对面部表情的事后觉察能够显著地预测其实时觉察情况，表明实时觉察与事后觉察能力之间具有一定的关系，通过测量个体在事后表情自我觉察的成绩可以在一定程度上预测其在实时人际互动中的自我觉察绩效。在实验2 的基础上，实验3 设置四组监控条件（有无控制×有无觉察），考察实时表情自我监控对表情表达的影响，同时试图分离表情自我控制和自我觉察之间的相互影响。结果发现，表情控制显著影响表情的表达特点，与无控制条件相比，控制条件下面部表情的频次更少、时长更短、表情强度和体验强度更低；而表情自我觉察对表情表达特点的影响并不显著；并且对于高兴表情的控制要由于对愤怒、厌恶表情的控制。控制与觉察之间的关系分析发现，对面部表情的控制会显著影响个体对面部表情的觉察。
研究四基于实验2 采集的面部表情数据，根据面部表情编码系统（Facial Action Coding System），对表情数据进行面部动作单元（Action Unit）的编码，建立压抑条件下面部表情的数据库CAS(ME)2。相对以往数据库，该数据库既包含宏表情又包含微表情，同时，在情绪类型标定上结合FACS、表情诱发视频材料的情绪类型以及被试对每一个面部动作发生时情绪体验的主观报告，获取更高的情绪标定准确性和生态效度。同时，在新建数据库的基础上，使用LBP 和LBP-TOP 算法进行表情自动检测和识别算法测试。
|其他摘要||As an important component of the self, self-monitoring implies one's self-awareness and self-control over thoughts, emotions, and behaviors. As an important nonverbal visual cue, self-monitoring of facial expression can help individuals better adjust their expressed emotion and adapt to social interaction.|
However, previous researches on facial expression self-monitoring and its influencing factors was not sufficient. By employing the real-time self-monitoring paradigm and micro-analysis of facial expression, the present research aimed to investigate the performance of individuals’ self-awareness and self-control on their facial expression under both real-time and post awareness condition, as well as several possible influencing factors that may influence self-awareness and control.
In study 1, we used the general rating on facial expression expressivity as the index of facial expression self-control ability. In addition, we also investigated individuals’ performance on the facial expression self-control task and its relationship with Working Memory Capacity (WMC) and emotion expression tendency. Results suggested that participants' facial expression self-control performance was positively related with one’s WMC; no relationship was found with emotion expression tendency.
These results suggested that the self-control of facial expression was related with the WMC, which was responsible for the target goal updating, however, self-control of facial expression was not influenced one’s emotional expressive tendency.
In study 2, two experiments were conducted to investigate participants' ability to self-monitor facial expression. Experiment 2 explored individuals' self-awareness of leaked facial expression in both real-time and post awareness condition. Results suggested that the awareness rates were 57.79% and 75.92% for real-time and post awareness condition respectively. Furthermore, results indicate that awareness rate was influenced by expression intensity in real-time condition; in contrast both intensity and duration of facial expression influenced awareness rate in the post-awareness condition. Finally, individuals' awareness rate of changes in facial expression under real-time condition can predict one's performance in post-awareness condition. In experiment 3, we set four different conditions to explore the influence of self-monitoring on facial expression in order to disassociate self-awareness and self-control and investigate them separately. Results suggested that the inhibition of facial expression greatly influenced the characteristic of facial expression by reducing the frequency, duration, and intensity. While the self-focused attention didn’t influence the characteristic of facial expression. Analysis on the relationship between self-awareness and self-control showed that the inhibition of facial expression may exert a significant influence on self-awareness of facial expression.
In study 3, we investigated the influencing factors of facial expression self-monitor through micro-analysis of facial expression. Results revealed that the self-control of facial expression was significantly related with ones’ WMC and self-monitoring scores, no relationship was found between self-control ability and duration/intensity of facial expression. Self-awareness of facial expression was not related with both the WMC and the self-monitoring scores.
Finally, in study 4, we constructed a facial expression database, the Chinese Academy of Sciences CAS(ME)2, which contained both macro-expressions and micro-expressions. Extending on our previous works, the labeling of emotion type in this database uses a combination of Facial Action Coding System (FACS), the emotion type of elicitation stimuli, and the self-report of subjects on each of their facial movement, to greatly improve the accuracy and ecological validity of the emotion labeling. LBP and LBP-TOP algorithms were then employed to achieve automatic facial expression detection and recognition on this database.
Overall, the present study investigated one’s performance of facial expression self-monitoring under both real-time and post monitoring conditions. Besides, the self-monitoring of facial expression was found to be influenced by one’s WMC and self-monitoring tendency. This study further facilitated our understanding on the self-consciousness characteristics and self-control mechanisms of facial expression and can benefit studies in the field of automatic facial expression detection and recognition.
|曲方炳. 面部表情的自我监控及影响因素[D]. 北京. 中国科学院研究生院,2016.|