Human Emotions Research Articles

As virtual work becomes more prevalent, an important question arises: how do surface colors and light color temperature in a virtual environment impact creativity?

ABSTRACT Colors and light affect human emotions, and positive emotions encourage human creativity. To enhance student creativity, the appropriate surface colors and light color temperatures in a virtual environment must be determined. This study explores how color and light color temperature in a virtual space impact student creativity. In Experiment 1, color and light color temperature were chosen by surveying 189 architect students who answered questionnaires after watching 10 pictures of a workspace with five different surface colors and two light color temperatures. The workspace with white walls and daylight had the highest mode scores for positive emotion perception. Experiment 2 assessed the creativity of 46 participants using virtual reality (VR) and the alternative uses test (AUT). Participants were divided into two groups of 23. The first group was exposed to white surfaces under daylight. The second group interacted with red surfaces under warm light. The results indicate that a space with white surfaces and daylight significantly enhances creativity, specifically in originality component, compared to a space with red surfaces and warm light. Using light colors and daylight in VR workspace design may promote positive emotions and enhance creativity.

Polyfluorene Bearing Quinoline and Ferrocene as the Pendant Groups for Bioelectronics

Abstract Analog-type polymer memristors are preeminent candidates for neuromorphic computation and synaptic biomimicking. A novel polyfluorene bearing quinoline and ferrocene as the pendant groups (PQF) is synthesized. The PQF film sandwiched between the Al and ITO electrodes exhibits outstanding analog-type memristive performance at a small sweep voltage range of ±1V, with 42 distinguishable conductance states. As compared, poly{[9,9-di(2,4-diphenylquinoline)-fluorene]-alt-[9,9-bis(6-bromohexyl)-9H-fluorene]}, and poly {[9H-fluorene]-alt-[fluorene with triazole and ferrocene moieties in the sidechains] only show 12 and 15 distinguishable conductance states, respectively. The former only involves intramolecular charge transfer from the polymer backbone to the quinolone moieties, while the latter mainly concern redox effect from the ferrocene moieties. These results demonstrate that the combined action of intramolecular electron transfer and redox effect could dramatically increase the number of distinguishable conductive states of the resultant materials. Associated with the exceptional memristive performance, the PQF-based memristor can accurately recognize human emotions. After 150 training sessions, the accuracy of emotion recognition reaches up to 98.74%.

Reinforcement learning in sentiment analysis: a review and future directions

Sentiment analysis in natural language processing (NLP) is used to understand the polarity of human emotions (e.g., positive and negative) and preferences (e.g., price and quality). Reinforcement learning (RL) enables a decision maker (or agent) to observe the operating environment (or the current state) and select the optimal action to receive feedback signals (or reward) from the operating environment. Deep reinforcement learning (DRL) extends RL with deep neural networks (i.e., main and target networks) to capture the state information of inputs and address the curse of dimensionality issue of RL. In sentiment analysis, RL and DRL reduce the need for a large labeled dataset and linguistic resources, increasing scalability and preserving the context and order of logical partitions. Through enhancement, the RL and DRL algorithms identify negations, enhance the quality of the generated responses, predict the logical partitions, remove the irrelevant aspects, and ultimately capture the correct sentiment polarity. This paper presents a review of RL and DRL models and algorithms with their objectives, applications, datasets, performance, and open issues in sentiment analysis.

Affective Computing in Mental Health: The Role of Facial Expression Recognition

Affective computing has emerged as one of the leading interdisciplinary disciplines that utilize technology to detect and interpret human emotions, and so it provides critical information for the mental health monitoring process. The present study addresses the facial expression recognition as an important tool in the diagnosis and treatment of mental health disorders. By discussing the physiological and psychological implications that facial expressions may have, we analyze how FER systems can supplement the traditional approach to mental health evaluation by providing real-time feedback and timely intervention. Improved algorithms for machine learning and computer vision techniques in FER systems enhance the accuracy and efficiency of FER systems, thus increasing their applicability in teletherapy, clinical assessment, and personal well-being applications. This discussion paper shall also touch on the issues and ethical concerns of FER technology, such as privacy and the potential for misinterpretation. Our results indicate that FER can be a meaningful contributor to care for personal mental health by continuous monitoring and understanding of emotional well-being.

A Study on “Chou” Metaphors in 300 Song Lyrics

Advancements in modern cognitive linguistics have rendered metaphor a conceptual tool for understanding the world. The interplay between emotion and cognition highlights the importance of studying human emotions, which are often metaphorized by people. The expression “Chou” is one of the most prevalent emotions in Chinese Song Lyrics. This paper adopts a cognitive linguistics perspective, utilizing 300 Song Lyrics as the corpus, to conduct a detailed exploration and analysis of the types and meanings of “Chou” metaphors by applying Conceptual Metaphor Theory. The study identifies six main types of “Chou” metaphors in 300 Song Lyrics, namely ontological metaphors, image metaphors, body metaphors, personification, synaesthetic metaphors, and reverse metaphors. Among them, the most common are ontological metaphors and image metaphors. This paper aids in a better understanding of the emotion of “Chou” in Song Lyrics. It is also significant for enhancing appreciation of Chinese poetry and literature, while facilitating a deeper understanding of the characteristics of national metaphorical thinking and promoting cultural exchange.

IDEOGRAPHIC MODELLING OF THE EMOTIONAL PICTURE OF THE WORLD IN ENGLISH PHRASEOLOGY

Emotion is a mental reflection in the form of a direct biased experience of the content of life phenomena and situations, caused by the relation of their objective properties to the needs of the subject. Emotions are essential components of life activity, a powerful means of activating the sensory and perceptual activity of the individual. In the theory of activity, they are defined as a reflection of the relationship between the result of an activity and its motive. If the activity is successful in terms of the motive, positive emotions (interest, satisfaction) arise, and if it is unsuccessful, negative emotions arise. Emotions arise only in connection with events or results of actions that are related to motives. The highest product of the development of human emotions is strong feelings for objects that meet one’s highest needs. A strong, absolutely dominant feeling is called passion. Events signalling possible changes in a person’s life, along with specific emotions, can cause changes in the general emotional background – mood. The attitude to the reflected phenomena as the main property of emotions is presented: 1) in their qualitative characteristics, which include: a) sign – positive, negative; b) modality – anger, contempt, embarrassment, guilt, interest, sadness, surprise, disgust, pleasure, fear, shame; 2) in the dynamics of the course of emotions themselves – duration, intensity, etc.; 3) in the dynamics of external expression of emotions – emotional expression – in facial expressions, speech, pantomime.

Survey on Multimodal Emotion Detection

As we know, many people suffer from unhappiness in today’s world, so there is a requirement for a system that recommends music based on human emotions such as anger, sadness, happiness, etc. In this paper, we propose a system named Multimodal Emotion Detection which helps people by suggesting music and movies based on their emotions. We implemented this system using the FER-2013 dataset, which contains 35,887 images of different emotions such as sadness, happiness, and anger. We used Python’s TensorFlow framework and Haar Cascade algorithms to recommend movies and music based on human facial emotions. The main concern in existing recommendation systems is manual sorting. To avoid this, we propose this model, which automatically plays music and movies without requiring much browsing time

An interview with Professor Benjamin Becker: understanding our brain and mental disorders requires collaboration across all disciplines

Abstract From July 20th to 22nd, 2024, the ISMRM Endorsed Workshop on MR for Psychiatry was held in Chengdu City, China. This prestigious event attracted numerous academic elites worldwide, and Professor Benjamin Becker from the University of Hong Kong was invited. On the morning of July 20, during the “Advances in MR Technology” session, Professor Becker delivered an engaging lecture entitled “Novel approaches to precision MRI-imaging of human emotion”. His presentation was met with great enthusiasm and sparked lively discussions among the participants. Following the conference, the Psychoradiology journal interviewed Professor Benjamin Becker. In the interview, Benjamin emphasized the significant role of interdisciplinary collaboration, spanning various fields including psychology, neuroscience, clinical medicine, biomedical engineering, and computer science. Professor Becker firmly believed that such collaboration was crucial for a deeper understanding of the brain and psychiatric disorders. Additionally, he highly valued the importance of international cooperation, especially in addressing global mental health issues and challenges related to psychiatric disorders.

The Integration of Arts-Based Research Methods in Scientific Studies

Arts-based research (ABR) integrates creative and artistic practices into academic research, offering novel methodologies that enrich scientific inquiry. This interdisciplinary approach enables the exploration of complex human experiences, emotions, and phenomena through mediums like visual art, drama, poetry, and music. This paper examines the benefits and challenges of integrating arts-based research into scientific studies, highlighting its capacity to elicit deeper, embodied insights that complement traditional methods. While ABR fosters innovative data collection and engagement, it faces skepticism regarding rigor and validity in the scientific community. This study examines how the collaboration between scientists and artists can bridge these challenges, emphasizing best practices for effective integration. The paper concludes by offering examples of successful arts-based research in scientific contexts and suggests strategies for future implementation. Keywords: Arts-based research, Interdisciplinary research, Artistic methodologies, Scientific inquiry, Creative expression, Data collection.

Xanthan gum biopolymer gated low voltage-operating photo synaptic organic field-effect transistor for neuromorphic electronics

Low voltage-operating light-stimulated organic field effect transistors (OFETs) are attracting huge attention due to their potential for developing photonic neuromorphic synapse-based neural networks resembling the human brain. The OFETs based on environment friendly biocompatible materials and processability are highly desired for realizing photo synaptic elements. In this report, we demonstrate a low voltage operating xanthan gum-gated poly(2,5-bis(3-alkylthiophen-2-yl) thieno[3,2-b] thiophene) [PBTTT-C14] based OFETs, exhibiting photo synaptic behavior at −0.3V. The basic photo response parameters such as photoresponsivity, detectivity, and photocurrent to dark current ratio of the OFETs are estimated to quantify the photo synaptic behavior of the OFETs. We report the fundamental neurobiological characteristics such as excitatory post-synaptic current (EPSC), pair pulse facilitation (PPF), short-term plasticity (STP), long-term plasticity (LTP), learning-forgetting-memorizing (LFM), and conversion of STP to LTP for the xanthan gum-gated PBTTT-C14-based OFETs. Furthermore, we showcase the electronic "OR" logic operations by employing the OFETs and replicate the Pavlovian conditioning experiment to mimic the classical learning phenomenon. Using these photo synaptic OFETs, we replicate human emotion and mood-swing-dependence learning and memory behavior which is an important aspect of cognitive learning. The OFETs exhibit fairly low energy consumption ~100 pJ per optical operation to perform synaptic responses. We interpret and attribute the observed neuromorphic behavior to the charge-trapping occurring at the semiconductor-dielectric interface and bulk traps in OFETs. The presented phenomena broaden opportunities to the device communities for building an artificial complex neural network using energy-efficient bio-polymer gated photo synaptic OFETs.

혐오의 정치를 넘어선 연대를 위한 포스트휴머니즘

This study examines the politics of disgust facing contemporary democracy and the possibility of solidarity as its alternative from a posthumanist perspective. It demonstrates that the issues of disgust and solidarity extend beyond mere human emotions or political cooperation, encompassing complex sociopolitical implications, while critically examining the fundamental limitations of the autonomous subject concept presupposed by humanism. Recent neuroscientific findings reveal that brain plasticity operates in close conjunction with sociocultural practices, and the neurobiological mechanisms of affect are continuously reconstructed within sociopolitical contexts. This suggests that disgust is not an inevitable universal phenomenon but rather a component of brain plasticity, and the biological possibility of solidarity should also be understood in terms of the complexity of brain plasticity. The study concludes that within the posthumanist paradigm, genuine solidarity must be reconstructed at the point of actively affirming difference, discord, and the (im)possibility of disgust, and that this transition is an imperative task directly linked to the survival of contemporary democracy.

Application and renovation evaluation of Dalian's industrial architectural heritage based on AHP and AIGC.

This paper takes the example of industrial architectural heritage in Dalian to explore design scheme generation methods based on generative artificial intelligence (AIGC). The study compares the design effects of three different tools using the Analytic Hierarchy Process (AHP). It first establishes the key indicator weights for the renovation of industrial architectural heritage, with the criterion layer weights as follows: building renovation 0.230, environmental landscape 0.223, economic benefits 0.190, and socio-cultural value 0.356. Among the goal layer weights, the highest weight is for the improvement of living quality at 0.129, followed by resident satisfaction at 0.096, and educational and display functions at 0.088, while the lowest is for renovation costs at only 0.035. The design schemes are generated using Stable Diffusion, Mid Journey, and Adobe Firefly tools, and evaluated using a weighted scoring method. The results show that Stable Diffusion excels in overall image control, Mid Journey demonstrates strong artistic effects, while Adobe Firefly stands out in generation efficiency and ease of use. In the overall score, Stable Diffusion leads the other two tools with scores of 6.1 and 6.3, respectively. Compared to traditional design processes, these tools significantly shorten the design workflow and cycle, improving design quality and efficiency while also providing rich creative inspiration. Overall, although current generative artificial intelligence tools still have limitations in understanding human emotions and cultural differences, with continuous technological iteration, this method is expected to play a larger role in the design field, offering more innovative solutions for the renovation of industrial architectural heritage.

Generative technology for human emotion recognition: A scoping review

Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify and interpret human emotional states from different modalities, such as speech, facial images, text, and physiological signals. In recent years, important progress has been made in generative models, including Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model. These models, with their powerful data generation capabilities, emerge as pivotal tools in advancing emotion recognition. However, up to now, there remains a paucity of systematic efforts that review generative technology for emotion recognition. This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 330 research papers until June 2024. Specifically, this survey will firstly introduce the mathematical principles of different generative models and the commonly used datasets. Subsequently, through a taxonomy, it will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities in several aspects, including data augmentation, feature extraction, semi-supervised learning, cross-domain, etc. Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems.

Implementation of the YOLO Method for Detection of Human Emotions Based on Facial Mimics

Emotion detection through facial expression recognition plays an important role in everyday life, such as how to respond correctly to emotional expressions in social interactions, so that you can establish and build verbal or nonverbal communication with other people and so on. Facial expressions are facial changes in response to a person's emotional state, intentions, or social communication. Face detection is the first step that must be taken in facial analysis, including facial expression recognition. There are many methods that can be used to carry out the face detection process, such as the YOLO method. This YOLO method reframes object detection as a single regression problem, directly from image pixels to bounding box coordinates and class probabilities. By using the YOLO method, the process only needs to look once at the input image, to predict what objects are in the image and where those objects are. Based on the results of the tests carried out, the YOLO method can be used to detect human facial expressions with a success rate of 80%, with neutral, surprise and disgust facial expressions having a good level of accuracy and fear facial expressions having a poor accuracy level. The YOLO method is able to detect facial expressions of humans who wear accessories such as glasses.

Lou Reed’s Emotional Geography

Starting with his band the Velvet Underground, Lou Reed wrote songs imbued with nonmoral depictions of people’s emotional experiences with drug use, sex work, marriage, poverty, friendships, and other aspects of life and survival in New York City. This paper presents an interpretation of Reed’s words and music from the twin perspective of geography and psychology. Drawing on materials in the Lou Reed Archive at the New York Public Library for the Performing Arts, the author conducted a comprehensive analysis of song recordings released over three distinct periods of Reed’s music career. Each song was considered within the framework of a psychoevolutionary theory of human emotions and situated in New York’s diverse spaces and places. Reed’s grounded lyrics convey a deep empathy for people’s triumphs, setbacks, and conflicts as they search for love and act on a desire for transcendence and freedom. The paper recommends further research into songwriters who use their biography to tell geographic stories.

A comprehensive design for biomimetic behavior of robotic head

In robotics and human-robot interaction, the ability to express emotions and respond accurately to human emotions is essential. An important aspect of this ability involves controlling the robot’s facial actuators in a way that resonates with human emotions. This study systematically presented the construction of a robot head based on the theoretical foundation of human sustainability. Facial landmarks are extracted for various purposes, such as to determine anthropometric metrics for robot head design, a comprehensive design for the robotic head is proposed in this study including anthropometric measurements used as a theoretical basis for the design, the calculations, and design, control system, electricity, and robot skin are presented. The robotic head with 24 degrees of freedom (DoF) aims to express all human emotions, signals are fed back from strain gauge sensors and cameras to calibrate the response of the robot’s prosthetic muscles. Strain gauge sensors are set up at locations with large deformations to double-check the functioning of prosthetic muscles. The two experiments are set up to evaluate the completeness of emotions, the results showed that both experiments achieved 93.88% and 91.85% respectively.

The Importance of Data Analytics in Modern Fashion Industry

The growth of the internet and the abundance of data it currently offers has caused one of the most significant developments in recent years. Fashion data scientists can now examine and benefit from a multitude of information because of the introduction of social media. These tips are invaluable for fashion campaign managers and designers. This paper examined the importance of data analytics in modern fashion industry. The paper collected secondary data from existing journals, articles, and companies’ blogs and websites. The overall result from this study indicates that data analytics have significant roles in modern fashion industry. For one thing, decision making in relation to customers’ needs and expectation, trend, quality, etc are based on data collected through different means. Thus, the fashion retail market is a quickly evolving field, with trends shifting within a few years. Intuition and gut feelings alone are not enough. In order to adapt to shifting consumer tastes and preferences, the fashion industry needs a data-driven, analytical approach.

Machine Learning Techniques Towards Accurate Emotion Classification from EEG Signals

This article delves into using machine learning algorithms for emotion classification via EEG brain signals. The goal is to discover an accurate model beyond traditional methods, necessitating AI for classifying emotional EEG signals. This study, motivated by the complex link between emotions and neural activity, employs Random Forest, Support Vector Machines, and K-Nearest Neighbors. Notably, Random Forest achieves 99% accuracy, SVM 98%, and KNN 94%. These impressive results, backed by performance metrics like confusion matrices, reveal each model’s effectiveness in emotion classification. The dataset, rich in varied emotional stimuli and EEG placements, provides a robust foundation for detailed analysis. This research underscores significant applications in affective computing and mental health, offering a promising path to understanding the intricate relationship between EEG signals and human emotions.

Music Generation Using Dual Interactive Wasserstein Fourier Acquisitive Generative Adversarial Network

Music composition, an intricate blend of human creativity and emotion, presents substantial challenges when generating melodies from lyrics which hinders effective learning in neural networks and the inadequate depiction of harmonic structure that fails to encapsulate the complex relationships between lyrics and melodies. The existing methods often struggle to balance emotional depth and structural coherence, leading to compositions that lack both the intended emotional resonance and musical consistency. To overcome these issues, this research introduces a novel approach named Dual Interactive Wasserstein Fourier Acquisitive Generative Adversarial Network (DIWFA-GAN), which integrates innovative techniques like swish activation functions and the Giant Trevally Optimizer (GTO) for parameter optimization. Meanwhile, the GTO, inspired by the movement patterns of the Giant Trevally fish, provides efficient and effective parameter optimization, improving the model’s convergence speed and accuracy. Comparative analysis against recent existing models reveals superior performance for both the LMD-full MIDI and Reddit MIDI datasets, with impressive metrics including inception scores of 9.36 and 2.98, Fréchet inception distances of 35.29 and 135.54 and accuracies of 99.98% and 99.95%, respectively. The DIWFA-GAN significantly outperforms existing models in generating high-fidelity melodies, as evidenced by superior inception scores, Fréchet inception distances, and accuracies on both datasets.

Who Would Taste It? Exploring Decision-Making Styles and Intention to Eat Insect-Based Food among Italian University Students

Background: Although insect-based foods (IBFs) have been recently proposed as a way to face climate crisis and starvation, they encounter aversion from Western countries, which express fear, disgust, and high risk. The contribution of psychology research to food choices highlights how decisions are made, not only through reasoned attitudes and goal-directed behavior, but also through more automatic associations (dual-system models). Methods: In this paper, we investigated people’s dispositions towards IBFs by combining (a) explicit attitudes (as assessed via self-report scales), (b) automatic associations (as measured via indirect measures), and (c) intention to taste, and comparing different profiles based on (d) psychological factors, including decision-making style, food neophobia, and trust in science and scientist. A pilot sample of 175 Italian university students participated in the study. Results: The analyses of the general sample highlighted rather negative attitudes. The cluster analysis identified 4 decision-making profiles: ‘the gut feeling’, ‘the suspicious’, ‘the vicarious’, and ‘the mind’. It revealed more favorable opinions in ‘the mind’ profile, characterized by a rational decision-making style and high trust in science, and very aversive reactions from ‘the suspicious’ profile, characterized by high food neophobia and low trust in science. Conclusions: The results underline the importance of psychological factors in interpreting people’s reactions to IBF and changes in dietary habits based on the decision-making process. They suggest possible strategies to promote eco-friendly diets.