Article
Psychology, Multidisciplinary
Hua Zhao, Danli Li, Yong Zhong
Summary: This study explored teacher emotion labor and its impact on teachers' pedagogical decision-making, finding that teachers face major emotion labor related to student disengagement in the classroom. Teacher emotion labor serves as a key factor in their pedagogical adjustments, with the belief in attending to students' needs supporting teachers to subvert institutional feeling rules and critically reflect on curriculum dysfunctions.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Psychology, Multidisciplinary
Melissa T. Buelow, Sammy Moore, Jennifer M. Kowalsky, Bradley M. Okdie
Summary: Decision-making is a complex executive function that involves cognitive, emotional, and personality-based components. The lack of consistency in performance across different decision-making tasks makes it difficult to compare and track changes over time. Differing theories about the factors that influence decision-making contribute to the inconsistency in measurement. The low criterion-related validity and lack of consistent measurement pose challenges for scholars studying emotion and decision-making.
FRONTIERS IN PSYCHOLOGY
(2023)
Article
Computer Science, Information Systems
Rendhir R. Prasad, Ciza Thomas
Summary: This paper introduces an artificial emotion-based decision-making mechanism, drawing inspiration from emotional decision-making in the natural world and considering the structural similarities between the biological and cyber worlds. Emotions are modeled as manifestations of agents' goals, and stimuli and responses are linked through emotions using an ontological structure.
Article
Behavioral Sciences
Francisco Molins, Carla Ayuso, Miguel Angel Serrano
Summary: Studies indicate that emotional stress can decrease loss aversion, despite increasing stress response, thus the exact impact of stress on loss aversion remains unclear.
STRESS-THE INTERNATIONAL JOURNAL ON THE BIOLOGY OF STRESS
(2021)
Review
Psychology, Applied
Godelieve Hofstee, Paul G. W. Jansen, Annet H. De Lange, Brian R. Spisak, Maaike Swinkels
Summary: In an increasingly service-oriented world, employees are facing emotional demands in client interactions. Studies have shown that emotional regulation can interfere with cognitive performance, but research in this area is still limited. The interaction between emotion and cognition may be more complex than currently understood.
Article
Psychology, Multidisciplinary
Samantha E. Clark, Robin L. Locke, Sophia L. Baxendale, Ronald Seifer
Summary: This study investigated the roles of withdrawal, language, and context-inappropriate anger in the development of emotion knowledge among preschoolers. The findings showed that receptive language mediated the relationship between withdrawal behavior and situational emotion knowledge. However, context-inappropriate anger significantly interacted with receptive language, and moderate levels of context-inappropriate anger rendered the indirect effect of withdrawal behavior on situational emotion knowledge via receptive language insignificant.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Dillam Jossue Diaz-Romero, Adriana Maria Rios Rincon, Antonio Miguel-Cruz, Nicholas Yee, Eleni Stroulia
Summary: This study utilized EEG, EOG, and kinematic motion data to classify emotional states of players in the Whack-a-Mole game. Results showed that EC and RF classifiers performed the best, achieving accuracies of 73% for Arousal and 80% for Valence. This suggests that these biosignals and machine learning techniques can effectively classify emotional states during gameplay.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2021)
Article
Business
Antoine Gilbert-Saad, Frank Siedlok, Rod B. McNaughton
Summary: Entrepreneurs, especially those in uncertain environments, rely heavily on heuristics when making strategic decisions. In our study, we conducted 27 semi-structured interviews with founders of newly established ventures to identify the heuristics they used and analyzed their roles and functions. Our findings indicate that heuristics are the primary decision-making strategy for inexperienced entrepreneurs, which help with organizing the venture, projecting the founder's identity onto their ventures, and exchanging information between the venture and the market. We also introduce a new type of heuristics called metacognitive heuristics.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Business
Joseph McManus
Summary: Emotions play a crucial role in promoting ethical decision making within organizations. Norms that discourage the expression of strong emotions at work inhibit individuals' ability to generate moral intuitions and reason about ethical issues they encounter. This lack of emotional awareness in organizational decision processes increases the prevalence of amoral decision making.
JOURNAL OF BUSINESS ETHICS
(2021)
Article
Computer Science, Software Engineering
Hansen Yang, Yangyu Fan, Guoyun Lv, Shiya Liu, Zhe Guo
Summary: The research proposes a novel method for image emotion recognition, leveraging emotional concepts as intermediaries to connect images and emotions by organizing the relationship between concepts and emotions in the form of a knowledge graph. By exploring the relation between images and emotions in the semantic embedding space and using a multi-task learning deep model, the method successfully recognizes image emotions from a visual perspective, with the fusion strategy showing promising experimental results.
Review
Psychology, Multidisciplinary
Tse Yen Tan, Louise Wachsmuth, Michele M. Tugade
Summary: This review focuses on positive emotional granularity, specifically emphasizing the lack of research in this area compared to negative emotional granularity. It highlights how positive emotions can motivate individuals to broaden their cognition, attention, and behavior, and suggests that distinct positive emotion concepts provide more informational value. Individuals with higher positive emotional granularity report better coping with stress.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Psychology, Multidisciplinary
Foteini Aikaterini Pikouli, Despina Moraitou, Georgia Papantoniou, Maria Sofologi, Vasileios Papaliagkas, Georgios Kougioumtzis, Eleni Poptsi, Magdalini Tsolaki
Summary: This study aimed to investigate whether metacognitive strategy training could improve decision-making abilities in patients with amnestic mild cognitive impairment (MCI). The results showed that the experimental group, receiving the metacognitive strategy training, improved their ability to make decisions based on analytical thinking about economic and healthcare-related everyday decision-making situations. However, the ability to apply decision rules, which requires high cognitive effort, did not improve.
JOURNAL OF INTELLIGENCE
(2023)
Article
Mathematics
Fernando Chacon-Gomez, M. Eugenia Cornejo, Jesus Medina
Summary: This paper investigates different methods for classifying new objects using decision rules in decision-making processes. These methods determine the best possible decision based on various indicators associated with the decision rules.
Article
Anatomy & Morphology
Yuying He, Francesco Margoni, Yanjing Wu, Huanhuan Liu
Summary: Research indicates that foreign language can influence decision making by regulating emotional response to negative stimuli and enhancing emotional response to positive stimuli. This study explores the neural mechanisms of Chinese-English bilinguals during decision making, revealing that the second language can mediate loss aversion through the dorsolateral prefrontal cortex while enhancing the response to positive feedbacks via the hippocampus.
BRAIN STRUCTURE & FUNCTION
(2021)
Article
Green & Sustainable Science & Technology
Vaclav Beran, Marek Teichmann, Frantisek Kuda
Summary: This paper discusses how problems faced by decision-makers can escalate under various influences, emphasizing the complexity and chaos of processes in technical and economic structures. It explores the possibility of choosing the right time and place for process activities and illustrates the positive impact of such decision-making.
Article
Computer Science, Artificial Intelligence
Bahare Kiumarsi, Frank L. Lewis, Daniel S. Levine
Article
Computer Science, Artificial Intelligence
Ruizhuo Song, Frank Lewis, Qinglai Wei, Hua-Guang Zhang, Zhong-Ping Jiang, Dan Levine
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2015)
Article
Computer Science, Artificial Intelligence
Daniel S. Levine
COGNITIVE SYSTEMS RESEARCH
(2012)
Editorial Material
Biology
Daniel S. Levine
PHYSICS OF LIFE REVIEWS
(2010)
Article
Computer Science, Artificial Intelligence
Leonid I. Perlovsky, Daniel S. Levine
COGNITIVE COMPUTATION
(2012)
Article
Psychology, Multidisciplinary
Stephen M. Doerfler, Maryam Tajmirriyahi, Amandeep Dhaliwal, Aaron J. Bradetich, William Ickes, Daniel S. Levine
Summary: This study investigated the impact of message framing on risky decision-making during the COVID-19 crisis, finding that both gain- and loss-framing influenced risk choice. Among the Dark Triad traits, psychopathy emerged as a significant predictor of risk taking behavior. The study suggests that decision-makers with psychopathic tendencies may take greater risks with other people's lives during a pandemic.
INTERNATIONAL JOURNAL OF PSYCHOLOGY
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel S. Levine, Kay-Yut Chen, Bakur AlQaudi
2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2017)
Article
Computer Science, Artificial Intelligence
Daniel S. Levine
COGNITIVE SYSTEMS RESEARCH
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel S. Levine
2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2016)
Article
Social Sciences, Mathematical Methods
Tyler Hamby, Daniel S. Levine
APPLIED PSYCHOLOGICAL MEASUREMENT
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Bakur AlQaudi, Daniel S. Levine, Frank L. Lewis
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniel S. Levine
2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2012)
Proceedings Paper
Computer Science, Artificial Intelligence
Leon C. Hardy, Daniel S. Levine, Dahai Liu
2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2012)
Article
Psychology, Applied
Paul B. Paulus, Daniel S. Levine, Vincent Brown, Ali A. Minai, Simona Doboli
SMALL GROUP RESEARCH
(2010)
Article
Computer Science, Artificial Intelligence
Hamdan Abdellatef, Lina J. Karam
Summary: This paper proposes performing the learning and inference processes in the compressed domain to reduce computational complexity and improve speed of neural networks. Experimental results show that modified ResNet-50 in the compressed domain is 70% faster than traditional spatial-based ResNet-50 while maintaining similar accuracy. Additionally, a preprocessing step with partial encoding is suggested to improve resilience to distortions caused by low-quality encoded images. Training a network with highly compressed data can achieve good classification accuracy with significantly reduced storage requirements.
Article
Computer Science, Artificial Intelligence
Victor R. Barradas, Yasuharu Koike, Nicolas Schweighofer
Summary: Inverse models are essential for human motor learning as they map desired actions to motor commands. The shape of the error surface and the distribution of targets in a task play a crucial role in determining the speed of learning.
Article
Computer Science, Artificial Intelligence
Ting Zhou, Hanshu Yan, Jingfeng Zhang, Lei Liu, Bo Han
Summary: We propose a defense strategy that reduces the success rate of data poisoning attacks in downstream tasks by pre-training a robust foundation model.
Article
Computer Science, Artificial Intelligence
Hao Sun, Li Shen, Qihuang Zhong, Liang Ding, Shixiang Chen, Jingwei Sun, Jing Li, Guangzhong Sun, Dacheng Tao
Summary: In this paper, the convergence rate of AdaSAM in the stochastic non-convex setting is analyzed. Theoretical proof shows that AdaSAM has a linear speedup property and decouples the stochastic gradient steps with the adaptive learning rate and perturbed gradient. Experimental results demonstrate that AdaSAM outperforms other optimizers in terms of performance.
Article
Computer Science, Artificial Intelligence
Juntong Yun, Du Jiang, Li Huang, Bo Tao, Shangchun Liao, Ying Liu, Xin Liu, Gongfa Li, Disi Chen, Baojia Chen
Summary: In this study, a dual manipulator grasping detection model based on the Markov decision process is proposed. By parameterizing the grasping detection model of dual manipulators using a cross entropy convolutional neural network and a full convolutional neural network, stable grasping of complex multiple objects is achieved. Robot grasping experiments were conducted to verify the feasibility and superiority of this method.
Article
Computer Science, Artificial Intelligence
Miaohui Zhang, Kaifang Li, Jianxin Ma, Xile Wang
Summary: This paper proposes an unsupervised person re-identification (Re-ID) method that uses two asymmetric networks to generate pseudo-labels for each other by clustering and updates and optimizes the pseudo-labels through alternate training. It also designs similarity compensation and similarity suppression based on the camera ID of pedestrian images to optimize the similarity measure. Extensive experiments show that the proposed method achieves superior performance compared to state-of-the-art unsupervised person re-identification methods.
Article
Computer Science, Artificial Intelligence
Florian Bacho, Dominique Chu
Summary: This paper proposes a new approach called the Forward Direct Feedback Alignment algorithm for supervised learning in deep neural networks. By combining activity-perturbed forward gradients, direct feedback alignment, and momentum, this method achieves better performance and convergence speed compared to other local alternatives to backpropagation.
Article
Computer Science, Artificial Intelligence
Xiaojian Ding, Yi Li, Shilin Chen
Summary: This research paper addresses the limitations of recursive feature elimination (RFE) and its variants in high-dimensional feature selection tasks. The proposed algorithms, which introduce a novel feature ranking criterion and an optimal feature subset evaluation algorithm, outperform current state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Naoko Koide-Majima, Shinji Nishimoto, Kei Majima
Summary: Visual images observed by humans can be reconstructed from brain activity, and the visualization of arbitrary natural images from mental imagery has been achieved through an improved method. This study provides a unique tool for directly investigating the subjective contents of the brain.
Article
Computer Science, Artificial Intelligence
Huanjie Tao, Qianyue Duan
Summary: In this paper, a hierarchical attention network with progressive feature fusion is proposed for facial expression recognition (FER), addressing the challenges posed by pose variation, occlusions, and illumination variation. The model achieves enhanced performance by aggregating diverse features and progressively enhancing discriminative features.
Article
Computer Science, Artificial Intelligence
Zhenyi Wang, Pengfei Yang, Linwei Hu, Bowen Zhang, Chengmin Lin, Wenkai Lv, Quan Wang
Summary: In the face of the complex landscape of deep learning, we propose a novel subgraph-level performance prediction method called SLAPP, which combines graph and operator features through an innovative graph neural network called EAGAT, providing accurate performance predictions. In addition, we introduce a mixed loss design with dynamic weight adjustment to improve predictive accuracy.
Article
Computer Science, Artificial Intelligence
Yiyang Yin, Shuangling Luo, Jun Zhou, Liang Kang, Calvin Yu-Chian Chen
Summary: Medical image segmentation is crucial for modern healthcare systems, especially in reducing surgical risks and planning treatments. Transanal total mesorectal excision (TaTME) has become an important method for treating colon and rectum cancers. Real-time instance segmentation during TaTME surgeries can assist surgeons in minimizing risks. However, the dynamic variations in TaTME images pose challenges for accurate instance segmentation.
Article
Computer Science, Artificial Intelligence
Teng Cheng, Lei Sun, Junning Zhang, Jinling Wang, Zhanyang Wei
Summary: This study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, called Fast Spectrum-Size Self-Training network (FSSNet). By utilizing start-stop points to build the signal model, this method successfully solves the difficulty of existing deep learning methods in detecting discontinuous signals and achieves satisfactory detection speed.
Article
Computer Science, Artificial Intelligence
Wenming Wu, Xiaoke Ma, Quan Wang, Maoguo Gong, Quanxue Gao
Summary: The layer-specific modules in multi-layer networks are critical for understanding the structure and function of the system. However, existing methods fail to accurately characterize and balance the connectivity and specificity of these modules. To address this issue, a joint learning graph clustering algorithm (DRDF) is proposed, which learns the deep representation and discriminative features of the multi-layer network, and balances the connectivity and specificity of the layer-specific modules through joint learning.
Article
Computer Science, Artificial Intelligence
Guanghui Yue, Guibin Zhuo, Weiqing Yan, Tianwei Zhou, Chang Tang, Peng Yang, Tianfu Wang
Summary: This paper proposes a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. BUNet utilizes a pyramid vision transformer encoder to learn multi-scale features and incorporates a boundary exploration module (BEM) and a boundary uncertainty aware module (BUM) to handle boundary areas. Experimental results demonstrate that BUNet outperforms other methods in terms of performance and generalization ability.
