Subjects: Psychology >> Developmental Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： It is of great value to recognize concealed emotions for early warning of public security issues. Micro-expression is a vital channel to reveal concealed emotions. However, there are relatively few studies on concealed emotions, and micro-expressions are challenging to recognize because of their subtle magnitude and short duration. Existing Laboratory studies of micro-expression have few practical applications. Therefore, the perception and expression of concealed emotion should be systematically investigated by collecting micro-expression samples in an ecological situation, while synchronically collecting EEG signals for better labeling of micro-expressions. We spot and recognize concealed emotions mainly through micro-expressions, accompanied by face color analysis, gaze estimation, and contactless physiological signals measurement. Then, we verify and modify our system and method in two realistic public security related application scenarios: a Recognition Assistant System for the aggressive and suicidal behaviors of psychiatric patients and a Concealed Emotion Detection System for prisoners interview.
Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Micro-expressions are facial movements that are extremely short and not easily perceived, often generated under high pressure. Micro-expressions can reveal the individual's hidden real emotions and are important non-verbal communication clues, widely used in lies detection and other fields. Due to the difficulty of eliciting, collecting, and labeling micro-expression samples, micro-expression-related research becomes a typical small-sample-size (SSS) problem. In order to enlighten the application of micro-expression analysis technology in complex real-life scenarios such as national security and clinical consultation, this study focuses on the SSS problem and proposes a micro-expression spotting method based on human attention mechanism with multi-branching self-supervised learning through the intersection of computer and psychology. First, this study conducts an exploration related to attentional resources based on the cognitive mechanisms of psychological micro-expressions. A behavioral-experimental paradigm combining eye-movement techniques and a presentation-judgment paradigm with subthreshold emotion priming effects was used to examine the cognitive mechanisms of selective attention allocation in micro-expression recognition and to refine the distinct regions of interest in human recognition of micro-expressions. Thus, the model is effectively and directly enabled to acquire important micro-expression features from the input information. Then the relevant attention modules are further generated from multi-dimensions (time domain, spatial domain, and channel domain) by the deep learning network to improve the performance of the network in extracting micro-expression features with the limited sample size. Second, this study proposes a multi-branching self-supervised learning method based on the human attention mechanism for micro-expression spotting. Training in many unlabeled video samples for the pre-text tasks enables the model to extract features from regions of interest of micro-expressions, including structural and detail features and video dynamic change patterns. Thus, the limitation caused by the SSS problem could be avoided. Finally, the current data released for micro-expressions are video samples and do not include the corresponding depth information. This study will carry out a depth information-based micro-expression spotting method based on the first micro-expression database that includes image depth information being created by our research team. It enables self-supervised learning to learn the corresponding action patterns from the geometric information of the scene. This research will achieve theoretical and technological breakthroughs in the field of automatic micro-expression spotting, improve the accuracy and reliability, and lay the foundation for the application of micro-expression spotting in realistic and complex scenarios. Second, it can achieve the data augmentation of micro-expression samples by mining micro-expression clips in unlabeled videos. Thus, the micro-expression small sample problem could be solved, and the performance improvement of traditional supervised micro-expression spotting methods could be improved.
Subjects: Psychology >> Medical Psychology submitted time 2019-04-29
Abstract： " The Promoting the Emergence of Advanced Knowledge (PEAK) Relational Training System is the first verbal behavior assessment instrument and treatment protocol that integrates Skinner’s “Verbal Behavior” and Post-Skinnerism analysis of human language and cognition, “Relational Frame Theory”. It aims to address the language and cognitive deficits in children with autism. By the end of 2018, the PEAK system has published four modules: PEAK-Direct Training module (PEAK-DT), PEAK-Generalization module (PEAK-G), PEAK Equivalence module (PEAK-E) and PEAK-Transformation module (PEAK-T). Each of the modules contains a direct pre-assessment, a full 184-itemized skill assessment, and a 184 item curriculum. Based on the previous literature, PEAK-DT has broken the ceiling effect of the VB-MAPP milestone evaluation in patients with ASD, and the entirety PEAK system is prospected to provide a more advanced and comprehensive verbal behavior assessment and training system than VB-MAPP. Since the establishment of the PEAK system in 2014, many published empirical studies indicated that some properties of the PEAK system are: good reliability and validity as an assessment tool, effective treatment for the patients with ASD, and an easily-mastered operation in practice; which makes the PEAK system owning potential application value in the intervention delivered from behavioral analysists as well as autistic parents in the future.
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