Human Emotions Research Articles

From Psychostasis to the Discovery of Cardiac Nerves: The Origins of the Modern Cardiac Neuromodulation Concept.

This review explores the historical development of cardiology knowledge, from ancient Egyptian psychostasis to the modern comprehension of cardiac neuromodulation. In ancient Egyptian religion, psychostasis was the ceremony in which the deceased was judged before gaining access to the afterlife. This ritual was also known as the "weighing of the heart" or "weighing of the soul". The Egyptians believed that the heart, not the brain, was the seat of human wisdom, emotions, and memory. They were the first to recognize the cardiocentric nature of the body, identifying the heart as the center of the circulatory system. Aristotle (fourth century BC) considered the importance of the heart in human physiology in his philosophical analyses. For Galen (third century AD), the heart muscle was the site of the vital spirit, which regulated body temperature. Cardiology knowledge advanced significantly in the 15th century, coinciding with Leonardo da Vinci and Vesalius's pioneering anatomical and physiological studies. It was William Harvey, in the 17th century, who introduced the concept of cardiac circulation. Servet's research and Marcello Malpighi's discovery of arterioles and capillaries provided a more detailed understanding of circulation. Richard Lower emerged as the foremost pioneer of experimental cardiology in the late 17th century. He demonstrated the heart's neural control by tying off the vagus nerve. In 1753, Albrecht von Haller, a professor at Göttingen, was the first to discover the heart's automaticity and the excitation of muscle fibers. Towards the end of the 18th century, Antonio Scarpa challenged the theories of Albrecht von Haller and Johann Bernhard Jacob Behrends, who maintained that the myocardium possessed its own "irritability", on which the heartbeat depended, and was independent of neuronal sensitivity. Instead, Scarpa argued that the heart required innervation to maintain life, refuting Galenic notions. In contemporary times, the study of cardiac innervation has regained prominence, particularly in understanding the post-acute sequelae of SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection (PASC), which frequently involves cardiorespiratory symptoms and dysregulation of the intrinsic cardiac innervation. Recently, it has been recognized that post-acute sequelae of acute respiratory infections (ARIs) due to other pathogens can also be a cause of long-term vegetative and somatic symptoms. Understanding cardiac innervation and modulation can help to recognize and treat long COVID and long non-COVID-19 (coronavirus disease 2019) ARIs. This analysis explores the historical foundations of cardiac neuromodulation and its contemporary relevance. By focusing on this concept, we aim to bridge the gap between historical understanding and modern applications. This will illuminate the complex interplay between cardiac function, neural modulation, cardiovascular health, and disease management in the context of long-term cardiorespiratory symptoms and dysregulation of intrinsic cardiac innervations.

Yes, I am afraid of the sharks and also wild lions!: A multitask framework for enhancing dialogue generation via knowledge and emotion grounding

Current end-to-end neural conversation models inherently lack the capability to generate coherently engaging responses. Efforts to boost informativeness have an adversarial effect on emotional and factual accuracy, as validated by several sequence-based models. While these issues can be alleviated by access to emotion labels and background knowledge, there is no guarantee of relevance and informativeness in the generated responses. In real dialogue corpus, informative words like named entities, and words that carry specific emotions can often be infrequent and hard to model, and one primary challenge of the dialogue system is how to promote the model’s capability of generating high-quality responses with those informative words. Furthermore, earlier approaches depended on straightforward concatenation techniques that lacked robust representation capabilities in order to account for human emotions. To address this problem, we propose a novel multitask hierarchical encoder–decoder model, which can enhance the multi-turn dialogue response generation by incorporating external textual knowledge and relevant emotions. Experimental results on a benchmark dataset indicate that our model is superior over competitive baselines concerning both automatic and human evaluation.

Letter to the editor: Gut feelings: associations of emotions and emotion regulation with the gut microbiome in women.

Letter to the editor: Gut feelings: associations of emotions and emotion regulation with the gut microbiome in women.

Evaluation of Eye-Blinking Dynamics in Human Emotion Recognition Using Weighted Visibility Graph

Purpose: Designing an automated emotion recognition system using biosignals has become a hot and challenging issue in many fields, including human-computer interferences, robotics, and affective computing. Several algorithms have been proposed to characterize the internal and external behaviors of the subjects in confronting emotional events/stimuli. Eye movements, as an external behavior, are habitually analyzed in a multi-modality system using classic statistical measures, and the evaluation of its dynamics has been neglected so far.
 Materials and Methods: This experiment intended to provide an innovative single-modality scheme for emotion classification using eye-blinking data. The dynamics of eye-blinking data have been characterized by weighted visibility graph-based indices. The extracted measures were then fed to the different classifiers, including support vector machine, decision tree, k-Nearest neighbor, Adaptive Boosting, and random subset to complete the process of classifying sad, happy, neutral, and fearful affective states. The scheme has been evaluated utilizing the available signals in the SEED-IV database.
 Results: The proposed framework provided significant performance in terms of recognition rates. The highest average recognition rates of > 90% were achieved using the decision tree.
 Conclusion: In brief, our results showed that eye-blinking data has the potential for emotion recognition. The present system can be extended for designing future affect recognition systems.

Experimental Evaluation of Noise Exposure Effects on Subjective Perceptions and Cognitive Performance

Individuals exposed to elevated noise levels experience heightened emotional intensity, leading to increased cognitive disruption and a higher likelihood of accidents. This study seeks to investigate the impact of noise exposure on human cognitive performance, and the moderating role of emotion. Twelve healthy male college-age students underwent exposure to three noise conditions, each characterized by different sound pressure levels and sharpness. Each condition included an initial acoustic/thermal adaption period lasting approximately 40 min, followed by intermittent questionnaire tests and a battery of computerized cognitive tests. Statistical analysis revealed that reducing noise levels proved advantageous, enhancing perceived sound quality, positive emotions, and auditory perception abilities, while concurrently reducing false alerts and accelerating execution speed. Many of these effects were found to be counteracted by elevated sharpness. Correlation analyses and partial least squares structural equation modeling (PLS-SEM) results suggested that human emotions mediate the relationship between noise exposure and cognitive performance. The potential underlying mechanism suggests that negative feelings towards noise contribute to poor emotional states, subsequently influencing cognitive processes and impairing executive function. The outcomes of this study provide valuable insights into the mechanism of noise exposure and its effects on human cognition and subjective perceptions.

Deep operational audio-visual emotion recognition

Emotions play a large role in interpersonal communication, marketing, healthcare and the service industry. For this reason, much research has been carried out on emotion classification until today. Audio-visual emotion recognition is a field within artificial intelligence and machine learning that focuses on recognizing and understanding human emotions from both visual and audio cues. It combines computer vision and audio processing techniques to analyze and interpret emotional states expressed by individuals. This paper presents a deep learning model developed over an operational neural network using multiple inputs and aimed at audio-visual emotion recognition. The proposed network utilizes both visual and audio information in an end to end approach. The primary objective of this work is to demonstrate that multi-input models can produce more efficient outcomes compared to single-input models in emotion classification. Another objective is to demonstrate the superior performance of weight calculation methods employed in operational neural networks compared to the conventional weight calculation methods used in convolutional neural networks. Therefore, we want to demonstrate that substituting convolutional neural network approaches with operational neural network methods can yield superior outcomes in emotion categorization models. In the proposed architecture, regular convolutional layers are replaced with operational layers. The experimental results demonstrate that the operational convolutional architecture performs better compared to the classical convolutional neural network architecture.

A multimodal shared network with a cross-modal distribution constraint for continuous emotion recognition

Continuous emotion recognition has been a compelling topic in affective computing because it can interpret human emotions subtly and continuously. Existing studies have achieved advanced emotion recognition performance using multimodal knowledge. However, these studies generally ignore the circumstances where some particular modalities are missing in the inference phase and thus become sensitive to the absence of modalities. To resolve this issue, we propose a novel multimodal shared network with a cross-modal distribution constraint, i.e. the DS-Net, which aims to improve the robustness of the model to missing modalities. The training process of the proposed network generally includes two components: multimodal shared space modeling and a cross-modal distribution matching constraint. The former utilizes the local and temporal information of multimodal signals for multimodal shared space modeling, while the latter further enhances the multimodal shared space via a loose constraint method. Coupled with the latter, the former can effectively exploit the complementarity between videos and peripheral physiological signals (PPSs), thus enhancing the discriminative capability of the shared space. Based on the shared space, the DS-Net works during the inference phase with only one modality input and can leverage multimodal knowledge to improve emotion recognition accuracy. Comprehensive experiments were conducted on two public datasets. Results demonstrate that the proposed method is competitive or superior to the current state-of-the-art methods. Further experiments indicate that the proposed method can be extended to handle other modalities and to deal with partially missing modalities, demonstrating its potential in real-world applications.

Application of Stereo Digital Image Correlation on Facial Expressions Sensing.

Facial expression is an important way to reflect human emotions and it represents a dynamic deformation process. Analyzing facial movements is an effective means of understanding expressions. However, there is currently a lack of methods capable of analyzing the dynamic details of full-field deformation in expressions. In this paper, in order to enable effective dynamic analysis of expressions, a classic optical measuring method called stereo digital image correlation (stereo-DIC or 3D-DIC) is employed to analyze the deformation fields of facial expressions. The forming processes of six basic facial expressions of certain experimental subjects are analyzed through the displacement and strain fields calculated by 3D-DIC. The displacement fields of each expression exhibit strong consistency with the action units (AUs) defined by the classical Facial Action Coding System (FACS). Moreover, it is shown that the gradient of the displacement, i.e., the strain fields, offers special advantages in characterizing facial expressions due to their localized nature, effectively sensing the nuanced dynamics of facial movements. By processing extensive data, this study demonstrates two featured regions in six basic expressions, one where deformation begins and the other where deformation is most severe. Based on these two regions, the temporal evolutions of the six basic expressions are discussed. The presented investigations demonstrate the superior performance of 3D-DIC in the quantitative analysis of facial expressions. The proposed analytical strategy might have potential value in objectively characterizing human expressions based on quantitative measurement.

Medicine, emotience, and reason

Medicine is faced with a number of intractable modern challenges that can be understood in terms of hyper-intellectualization; a compassion crisis, burnout, dehumanization, and lost meaning. These challenges have roots in medical philosophy and indeed general Western philosophy by way of the historic exclusion of human emotion from human reason. The resolution of these medical challenges first requires a novel philosophic schema of human knowledge and reason that incorporates the balanced interaction of human intellect and human emotion. This schema of necessity requires a novel extension of dual-process theory into epistemology in terms of both intellect and emotion each generating a distinct natural kind of knowledge independent of the other as well as how these two forms of mental process together construct human reason. Such a novel philosophic schema is here proposed. This scheme is then applied to the practice of medicine with examples of practical applications with the goal of reformulating medical practice in a more knowledgable, balanced, and healthy way. This schema’s expanded epistemology becomes the philosophic foundation for more fully incorporating the humanities in medicine.

EmoStyle: Emotion-Aware Semantic Image Manipulation with Audio Guidance

With the flourishing development of generative models, image manipulation is receiving increasing attention. Rather than text modality, several elegant designs have delved into leveraging audio to manipulate images. However, existing methodologies mainly focus on image generation conditional on semantic alignment, ignoring the vivid affective information depicted in the audio. We propose an Emotion-aware StyleGAN Manipulator (EmoStyle), a framework where affective information from audio can be explicitly extracted and further utilized during image manipulation. Specifically, we first leverage the multi-modality model ImageBind for initial cross-modal retrieval between images and music, and select the music-related image for further manipulation. Simultaneously, by extracting sentiment polarity from the lyrics of the audio, we generate an emotionally rich auxiliary music branch to accentuate the affective information. We then leverage pre-trained encoders to encode audio and the audio-related image into the same embedding space. With the aligned embeddings, we manipulate the image via a direct latent optimization method. We conduct objective and subjective evaluations on the generated images, and our results show that our framework is capable of generating images with specified human emotions conveyed in the audio.

EmoAsst: emotion recognition assistant via text-guided transfer learning on pre-trained visual and acoustic models

Children diagnosed with Autism Spectrum Disorder (ASD) often struggle to grasp social conventions and promptly recognize others' emotions. Recent advancements in the application of deep learning (DL) to emotion recognition are solidifying the role of AI-powered assistive technology in supporting autistic children. However, the cost of collecting and annotating large-scale high-quality human emotion data and the phenomenon of unbalanced performance on different modalities of data challenge DL-based emotion recognition. In response to these challenges, this paper explores transfer learning, wherein large pre-trained models like Contrastive Language-Image Pre-training (CLIP) and wav2vec 2.0 are fine-tuned to improve audio- and video-based emotion recognition with text- based guidance. In this work, we propose the EmoAsst framework, which includes a visual fusion module and emotion prompt fine-tuning for CLIP, in addition to leveraging CLIP's text encoder and supervised contrastive learning for audio-based emotion recognition on the wav2vec 2.0 model. In addition, a joint few-shot emotion classifier enhances the accuracy and offers great adaptability for real-world applications. The evaluation results on the MELD dataset highlight the outstanding performance of our methods, surpassing the majority of existing video and audio-based approaches. Notably, our research demonstrates the promising potential of the proposed text-based guidance techniques for improving video and audio-based Emotion Recognition and Classification (ERC).

DEVELOPING STRATEGIES FOR JUNIOR DESIGNERS TO MANAGE THEIR DESIGN AND EMOTIONS

Junior designers may encounter difficulties when considering customers from a distinct generation who possess dissimilar physical and physiological requirements compared to their own. The understanding of the sensory and physical sensations of design is crucial as younger designers cater to the needs of future generations of users and consumers. Understanding includes both the factual and emotional components of the users' and the audience's genuine experiences. Together, these tools help junior designers consider user safety. Techniques for simulation, have been effectively used by the design industries, that combine cognitive thinking abilities with knowledge outputs to better understand users and their needs. It is essential to explore possible approaches to help junior designers improve cognitive and affective skills (including emotional changes). The effectiveness of simulation guidelines on teaching junior designers about human emotion management abilities and limitations was examined, as well as how affective skill development was influenced.

Token-disentangling Mutual Transformer for multimodal emotion recognition

Multimodal emotion recognition presents a complex challenge, as it involves the identification of human emotions using various modalities such as video, text, and audio. Existing methods focus mainly on the fusion information from multimodal data, but ignore the interaction of the modality-specific heterogeneity features that contribute differently to emotions, leading to sub-optimal results. To tackle this challenge, we propose a novel Token-disentangling Mutual Transformer (TMT) for robust multimodal emotion recognition, by effectively disentangling and interacting inter-modality emotion consistency features and intra-modality emotion heterogeneity features. Specifically, the TMT consists of two main modules: multimodal emotion Token disentanglement and Token mutual Transformer. In the multimodal emotion Token disentanglement, we introduce a Token separation encoder with an elaborated Token disentanglement regularization, which effectively disentangle the inter-modality emotion consistency feature Token from each intra-modality emotion heterogeneity feature Token; consequently, the emotion-related consistency and heterogeneity information can be performed independently and comprehensively. Furthermore, we devise the Token mutual Transformer with two cross-modal encoders to interact and fuse the disentangled feature Tokens by using bi-directional query learning, which delivers more comprehensive and complementary multimodal emotion representations for multimodal emotion recognition. We evaluate our model on three popular three-modality emotion datasets, namely CMU-MOSI, CMU-MOSEI, and CH-SIMS, and the experimental results affirm the superior performance of our model compared to state-of-the-art methods, achieving state-of-the-art recognition performance. Evaluation Codes and models are released at https://github.com/cug-ygh/TMT.

Synthesis of memoirs and autobiography in Rifat Sverigin’s book “Every Bird Has His Own Perch”

The article analyzes Rifat Sverigin’s work “Every Bird Has Its Own Perch” (“Һәр кошның үз куначасы”), which, according to the author’s definition, is an ironic autobiographical elegy, but in form, it is closer to a documentary cycle of memoirs. The book has attracted the readers’ attention due to the true-to-life description of the events and its humor with a dose of irony. The main focus is on the unique emotional atmosphere of the text, which combines light sadness and a deep sense of satisfaction, gratitude to fate, thus allowing a deeper understanding of the main character’s inner world and the complexity of human emotions. The purpose of the study is to reveal the multilayered emotional and ideological content of the work, demonstrating its significance in the context of contemporary literature. It is from different perspectives that the book reveals the Soviet era, the era of perestroika, glasnost and our modern times through the prism of the protagonist’s personal fate.

The Governance of Algorithms: Profiling and Personalisation of Online Content in the Context of European Consumer Law

This contribution aims at analysing the current European regulatory framework aimed at protecting online users against the risks of digital content profiling and personalisation practices by online platforms.
 Indeed, algorithms exploit human weaknesses and emotions to influence users' purchasing behaviour in a context where the line between mere persuasion (accepted within a certain limit by law) becomes manipulation. After analysing the current regulation on the subject and giving an account of its major criticalities, some final considerations will be provided in order to identify possible future developments in the EU consumer discipline to ensure greater protection against algorithm abuse.

A software package for the investigation of spatio-temporal feature descriptors

At present time researchers investigate approaches to the automatic facial micro-expression analysis to implement the possibility of the recognition of true human emotions from the image sequence of his face. The modern technologies are based on the use of complex mathematical algorithms with many adjusted parameters; in particular, such algorithms are the algorithms of spatio-temporal feature descriptors. The scientists who are not technical specialists, e.g., psychologists or psychotherapists, when investigating these algorithms may encounter difficulties in writing the program code. Therefore, it became necessary to develop a special software package for the investigation of spatio-temporal feature descriptors. Aim of the work is development of a special software package for studying various spatial and temporal descriptors of facial micromovement features in video images A software package for the investigation of spatio-temporal feature descriptors with a graphical interface is developed. The software package makes it possible to investigate the influence of the selection of the descriptor (LBP-TOP, FHOOF or FHOFO) and its parameters on the micro-facial movement detection accuracy for different input datasets of human face image sequences. The developed software package for the investigation of spatio-temporal feature descriptors can be applied as a software tool for carrying out investigations in the facial micro-expression analysis field.

ERASE: a feasible early warning tool for elder abuse, developed for use in the Dutch emergency department

BackgroundElder abuse is a worldwide problem with serious consequences for individuals and society. To effectively deal with elder abuse, a timely identification of signals as well as a systematic approach towards (suspected) elder abuse is necessary. This study aimed to develop and test the acceptability and appropriateness of ERASE (EldeR AbuSE) in the emergency department (ED) setting. ERASE is an early warning tool for elder abuse self-administered by the healthcare professional in patients ≥ 70 years.MethodsA systematic literature review was previously conducted to identify potential available instruments on elder abuse for use in the ED. Furthermore, a field consultation in Dutch hospitals was performed to identify practice tools and potential questions on the recognition of elder abuse that were available in clinical practice. Based on this input, in three subsequent rounds the ERASE tool was developed. The ERASE tool was tested in a pilot feasibility study in healthcare professionals (n = 28) working in the ED in three Dutch hospitals. A semi-structured online questionnaire was used to determine acceptability and appropriateness of the ERASE tool.ResultsThe systematic literature review revealed seven screening instruments developed for use in the hospital and/or ED setting. In total n = 32 (44%) hospitals responded to the field search. No suitable and validated instruments for the detection of elder abuse in the ED were identified. The ERASE tool was developed, with a gut feeling awareness question, that encompassed all forms of elder abuse as starting question. Subsequently six signalling questions were developed to collect information on observed signs and symptoms of elder abuse and neglect. The pilot study showed that the ERASE tool raised the recognition of healthcare professionals for elder abuse. The tool was evaluated acceptable and appropriate for use in the ED setting.ConclusionsERASE as early warning tool is guided by an initial gut feeling awareness question and six signalling questions. The ERASE tool raised the recognition of healthcare professionals for elder abuse, and was feasible to use in the ED setting. The next step will be to investigate the reliability and validity of the ERASE early warning tool.

On the Information Data Length Theory of Human Emotions

The current paper provides a cutting-edge information data length-theoretic approach to human emotions. This is a revolutionary breakthrough that unifies information data length and the dynamics of emotions. The significant impact of both time and its mean value associated on both the upper and lower bounds of the obtained information data length is observed and noted. The paper concludes with some challenging open problems, combined with concluding remarks and future research pathways.

AI ChatGPT and student engagement: Unraveling dimensions through PRISMA analysis for enhanced learning experiences

This PRISMA-based literature review aims to assess and analyze the measurement of student engagement dimensions within AI ChatGPT interactions. The central question is how to effectively evaluate these dimensions using established methods and leverage insights to enhance AI ChatGPT’s capacity to foster student engagement. The systematic review of PRISMA methodology identifies 16 relevant peer-reviewed research. All relevant and eligible research according to PRISMA methodology are analyzed to comprehend the intricacies of student engagement in AI ChatGPT interactions. The synthesis of these findings unveils the current state of knowledge on AI ChatGPT’s influence on student engagement and uncovers opportunities for future research. This review underscores AI ChatGPT’s potential as an educational tool, offering personalized experiences that bolster student engagement and learning outcomes. The systematic review established that, even though using ChatGPT has many advantages such as enhancing student engagement and academic involvement and supporting inquiry-based learning. However, there are some negative aspects such as lacking empathy and human emotions, limited contextual understanding, increased technology dependence and possibility of Inaccurate or Biased Information. In summary, this PRISMA-based review contributes to understanding the measurement of student engagement within AI ChatGPT. It identifies best practices, laying the foundation for further research and development. By optimizing AI ChatGPT’s effectiveness, educators and developers can craft more engaging and tailored learning experiences, ultimately enhancing educational outcomes.

IJCM_418A: Perceived Social Isolation amongst Medical Undergraduates of Mangalore

Background: Human emotions often tend to be fragile and gullible, changing with time and understanding but is almost always engaged in a competitive manner wherever there is intra-specific human competition leading to social isolation and loneliness. In a professional course like Medicine, there is a lot of competition and academic pressure which to a certain extent impacts the mental health of the individual and this is a crucial time as medical undergraduates generally in age group- 18-24 are in a phase of transition to adulthood. Perceived Social Isolation is generally seen associated with decline in cognitive and conative activities, not only the mental health is affected but chronic feelings of loneliness are a causal factor for CVDs and neuropathies. Our study will help us to identify the proportion of the medical undergraduates with perceived social isolation and its possible association with depression. This becomes greatly significant because of the strong competition and hectic schedule, characteristic of medical curriculum. Objectives: The objective of the study was to assess the proportion of Perceived Social Isolation in Medical Undergraduates and to assess a possible association between Perceived Social Isolation and Depression in Medical Undergraduates. Methodology: A Google Form was circulated amongst the students of KMC, Mangalore to collect the data from the participants. Subjects response to the designated questionnaire (circulated through google form) served as the sources of information, based on the responses in the Public Health Questionnaire -2 and University of California Los Angeles Loneliness Scale- the scores were evaluated and the data was compiled to comment on the study cohort. Results: Out of 219 participants, 48.9% of study subjects had moderately high degree of loneliness and 11.9% had relatively high degree of loneliness. Only 9.6% were reported to have low degree of loneliness followed by 29.7% having moderate degree of loneliness. Correlation between Loneliness Scale and PHQ-2 for study subjects with perceived social isolation and individuals who are likely to be screened for depression showed r-value of 0.400 and a significant p-value of <0.001. Individuals who had high degree of loneliness also scored significantly high in PHQ-2, 84.6% of all subjects who had high degree of loneliness also reported as potential candidates for screening of depression. Conclusion: The study concludes that a high proportion of medical undergraduates were reported to have either a high or moderately high degree of loneliness. It was also found that people who had high degree of loneliness significantly correlated with the likelihood of being screened for depression as based on their PHQ-2 scores.