Artificial Intelligence (AI) Applied to Computational Psychology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 3204

Special Issue Editors

School of Computer Science and Technology, East China Normal University, Shanghai 200241, China
Interests: computational affection; AI; blockchain
Special Issues, Collections and Topics in MDPI journals
School of Computer Science and Technology, East China Normal University, Shanghai 200241, China
Interests: evolutionary optimization and learning; computational affection; AI for education
Counseling and Psychological Services Center, East China Normal University, Shanghai 200241, China
Interests: mindfulness interventions; sleep; emotion and stress
School of Computer Science and Technology, East China Normal University, Shanghai 200241, China
Interests: microexpression and gesture recognition; landmark detection

Special Issue Information

Dear Colleagues,

With the development of psychology, artificial intelligence (AI) techniques and quantitative analysis techniques in social sciences, emotion analysis has received a great deal of attention across multiple domains. The field of Computational Affection (CA), particularly human-centered CA, has become popular for the quantitative analysis of emotional intelligence at the intersection of AI techniques and psychological theory. AI-based emotional recognition has been sufficiently studied considering various types of the biological information of human beings, such as facial expressions, speech semantics, gestures and physiological electrical signals. To reveal emotion more precisely, researchers are focusing on how to further integrate emotional psychology and cognitive psychology, and apply computational emotions to daily life successfully.

The topics for this Special Issue include, but are not limited to:

  • CA-based emotional study integrating psychology and AI.
  • CA-based theory, methods and applications of facial expression/micro-expression recognition.
  • CA-based theory, methods and applications of audio emotional recognition.
  • CA-based theory, methods and applications of semantic emotional recognition for natural language processing.
  • CA-based theory, methods and applications of gestural emotional recognition.
  • CA-based theory, methods and applications of EEG signal emotion recognition incorporating psychology.
  • CA-based theory, methods and applications of emotion recognition for heartbeat signals.
  • CA-based theory, methods and applications of virtual reality, augmented reality, and mixed reality.
  • CA-based theory, methods and applications of human-computer interaction.

References:

  1. Zhou, A., Yang, X., Wu, W., Zhou, N., Liu, F., & He, L. . (2021). Computational affection: a catalyst for human-centered education. Science(374-Oct.1 App. TN.6563).
  2. F. Liu et al., "OPO-FCM: A Computational Affection Based OCC-PAD-OCEAN Federation Cognitive Modeling Approach," in IEEE Transactions on Computational Social Systems, 2022. https://doi.org/10.1109/TCSS.2022.3199119.
  3. Liu, H. Wang, J. Zhang, et al. EvoGAN: An evolutionary computation assisted GAN, Neurocomputing, Vol. 469, 2022, pp.81-90. https://doi.org/10.1016/j.neucom.2021.10.060.
  4. Liu F, Shen S-Y, Fu Z-W, Wang H-Y, Zhou A-M, Qi J-Y. LGCCT: A Light Gated and Crossed Complementation Transformer for Multimodal Speech Emotion Recognition. Entropy. 2022; 24(7):1010. https://doi.org/10.3390/e24071010.
  5. Ziwang Fu, Feng Liu, Qing Xu, iayin Qi, Xiangling Fu, Aimin Zhou, Zhibin Li. "NHFNET: A Non-Homogeneous Fusion Network for Multimodal Sentiment Analysis," 2022 IEEE International Conference on Multimedia and Expo (ICME), 2022, pp. 1-6. https://doi.org/10.1109/ICME52920.2022.9859836.

Dr. Feng Liu
Prof. Dr. Aimin Zhou
Dr. Ran Wu
Dr. Fei Jiang
Guest Editors

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Keywords

  • computational affection
  • facial expression
  • voice emotion
  • semantic emotion
  • gestural emotion
  • EEG emotion
  • heartbeat emotion
  • psychology of emotion
  • emotion computing

Published Papers (2 papers)

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22 pages, 6337 KiB  
Article
Cascaded Convolutional Recurrent Neural Networks for EEG Emotion Recognition Based on Temporal–Frequency–Spatial Features
by Yuan Luo, Changbo Wu and Caiyun Lv
Appl. Sci. 2023, 13(11), 6761; https://doi.org/10.3390/app13116761 - 02 Jun 2023
Cited by 1 | Viewed by 1121
Abstract
Emotion recognition is a research area that spans multiple disciplines, including computational science, neuroscience, and cognitive psychology. The use of electroencephalogram (EEG) signals in emotion recognition is particularly promising due to their objective and nonartefactual nature. To effectively leverage the spatial information between [...] Read more.
Emotion recognition is a research area that spans multiple disciplines, including computational science, neuroscience, and cognitive psychology. The use of electroencephalogram (EEG) signals in emotion recognition is particularly promising due to their objective and nonartefactual nature. To effectively leverage the spatial information between electrodes, the temporal correlation of EEG sequences, and the various sub-bands of information corresponding to different emotions, we construct a 4D matrix comprising temporal–frequency–spatial features as the input to our proposed hybrid model. This model incorporates a residual network based on depthwise convolution (DC) and pointwise convolution (PC), which not only extracts the spatial–frequency information in the input signal, but also reduces the training parameters. To further improve performance, we apply frequency channel attention networks (FcaNet) to distribute weights to different channel features. Finally, we use a bidirectional long short-term memory network (Bi-LSTM) to learn the temporal information in the sequence in both directions. To highlight the temporal importance of the frame window in the sample, we choose the weighted sum of the hidden layer states at all frame moments as the input to softmax. Our experimental results demonstrate that the proposed method achieves excellent recognition performance. We experimentally validated all proposed methods on the DEAP dataset, which has authoritative status in the EEG emotion recognition domain. The average accuracy achieved was 97.84% for the four binary classifications of valence, arousal, dominance, and liking and 88.46% for the four classifications of high and low valence–arousal recognition. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Applied to Computational Psychology)
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17 pages, 7480 KiB  
Article
Towards Automatic Detection of Social Anxiety Disorder via Gaze Interaction
by Sara Shafique, Iftikhar Ahmed Khan, Sajid Shah, Waqas Jadoon, Rab Nawaz Jadoon and Mohammed ElAffendi
Appl. Sci. 2022, 12(23), 12298; https://doi.org/10.3390/app122312298 - 01 Dec 2022
Cited by 2 | Viewed by 1185
Abstract
Social anxiety disorder (SAD) is an extreme fear of underperformance in various social situations. It is necessary to detect people with or without SAD for counseling and treatment. A few manual techniques in the existing literature show the possibility of SAD detection from [...] Read more.
Social anxiety disorder (SAD) is an extreme fear of underperformance in various social situations. It is necessary to detect people with or without SAD for counseling and treatment. A few manual techniques in the existing literature show the possibility of SAD detection from gaze interaction. However, an automated prediction of SAD is scarce. In this research, an automatic technique to predict SAD using gaze interaction/avoidance is proposed, where a custom application was developed that used the Haar Cascade classifier to predict gaze interaction/avoidance. The experiments were conducted on 50 participants in a live environment using the developed application. SAD classes were predicted by using decision tree classifiers from the created gaze dataset. The results proved that SAD could be predicated with an overall accuracy of 80%. Furthermore, four classes of SAD (Mark, Moderate, Severe, Very Severe along with ‘No SAD’) could be predicted with an accuracy of 80%, 70%, 90%, 80%, and 80%, respectively. The research proved the possibility to predict SAD using computer-based methods without human intervention. Furthermore, it created the possibility of aiding a subjective Liebowitz Social Anxiety Scale (LSAS) with an objective technique described in this research. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) Applied to Computational Psychology)
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