Emotion recognition technology uses artificial intelligence to make inferences about a person’s emotions, on the basis of their facial expressions, body language, tone of voice, or other types of input. Underlying such technology are a variety of assumptions about the manifestation, nature, and value of emotions. To assure the quality and desirability of emotion recognition technology, it is important to critically assess the assumptions embedded in the technology. Within philosophy, there is a (...) long tradition of epistemological, ontological, phenomenological, and ethical reflection on the manifestation, nature, and value of emotions. This article draws from this tradition of philosophy of emotions, in order to challenge the assumptions underlying current emotion recognition technology and to promote a more critical engagement with the concept of emotions in the tech-industry. (shrink)

With the development of modern information technology, the flipped classroom teaching mode came into being. It has gradually become one of the hotspots of contemporary educational circles and has been applied to various disciplines at the same time. The domestic research on the flipped classroom teaching mode is still in the exploratory stage. The application of flipped classroom teaching mode is still in the exploratory stage. It also has many problems, such as low class efficiency, poor teacher-student interaction, outdated (...) teaching modes, not student-centered, etc., which lead to poor students’ enthusiasm for learning. Therefore, the current English flipped classroom teaching mode still needs to be tested and revised in practice. Combined with emotion recognition technology, this paper analyzes speech emotion recognition, image emotion recognition, and audition emotion recognition technology and conducts a revision test for the current English flipped classroom teaching mode. It uses the SVM algorithm for one-to-one method and dimension discretization for emotion recognition, and finds that the recognition results after different dimension classification recognition are improved for each emotion. Among them, the recognition rate of different dimension classification recognition methods is 2.6% higher than that of one-to-one method. This shows that under the same conditions, the emotion recognition technology of different dimension classification recognition methods is higher. (shrink)

Ambient Assisted Living (AAL) refers to technologies that track daily activities of persons in need of care to enhance their autonomy and minimise their need for assistance. New technological developments show an increasing effort to integrate automated emotion recognition and regulation (ERR) into AAL systems. These technologies aim to recognise emotions via different sensors and, eventually, to regulate emotions defined as “negative” via different forms of intervention. Although these technologies are already implemented in other areas, AAL stands out (...) by its tendency to enable an inconspicuous 24-hour surveillance in the private living space of users who rely on the technology to maintain a certain degree of independence in their daily activities. The combination of both technologies represents a new dimension of emotion recognition in a potentially vulnerable group of users. Our paper aims to provide an ethical contextualisation of the novel combination of both technologies. We discuss different concepts of emotions, namely Basic Emotion Theory (BET) and the Circumplex Model of Affect (CMA), that form the basis of ERR and provide an overview over the current technological developments in AAL. We highlight four ethical issues that specifically arise in the context of ERR in AAL systems, namely concerns regarding (1) the reductionist view of emotions, (2) solutionism as an underlying assumption of these technologies, (3) the privacy and autonomy of users and their emotions, (4) the tendency of machine learning techniques to normalise and generalise human behaviour and emotional reactions. (shrink)

Financial market and economic growth and development trends can be regarded as an extremely complex system, and the in-depth study and prediction of this complex system has always been the focus of attention of economists and other scholars. Emotion recognition algorithm is a pattern recognition technology that integrates a number of emerging science and technology, and has good non-linear system fitting capabilities. However, using emotion recognition algorithm models to analyze and predict financial market (...) and economic growth and development trends can yield more accurate prediction results. This article first gives a detailed introduction to the existing financial development and economic growth status and development trend forecasting problems, and then gives a brief overview of the concept of emotion recognition algorithms. Then, it describes the emotion recognition methods, including statistical emotion recognition methods, mixed emotion recognition methods, and emotion recognition methods based on knowledge technology, and conducts in-depth research on the three algorithm models of statistical emotion recognition methods, they are the support vector machine algorithm model, the artificial neural network algorithm model, and the long and short-term memory network algorithm model. Finally, these three algorithm models are applied to the financial market and economic growth and development trend prediction experiments. Experimental results show that the average absolute error of the three algorithms is below 25, which verifies that the emotion recognition algorithm has good operability and feasibility for the prediction of financial market and economic growth and development trends. (shrink)

Purpose This study responds to Agnieszka Landowska’s paper about the lack of accuracy in emotion recognition. Design/methodology/approach The approach is purely theoretical. The paper also refers to empirical studies. Findings The author first elaborates on Landowska’s “postulates” and then shortly expands on how virtual chatbots such as “AI therapists” pose considerable challenges to emotion recognition algorithms as well. Originality/value This viewpoint’s value is to elaborate and expand on an ongoing discussion on emotion recognition technologies.

Purpose The purpose of this paper is to explore uncertainty inherent in emotion recognition technologies and the consequences resulting from that phenomenon. Design/methodology/approach The paper is a general overview of the concept; however, it is based on a meta-analysis of multiple experimental and observational studies performed over the past couple of years. Findings The main finding of the paper might be summarized as follows: there is uncertainty inherent in emotion recognition technologies, and the phenomenon is not (...) expressed enough, not addressed enough and unknown by the users of the technology. Practical implications Practical implications of the study are formulated as postulates for the developers, users and researchers dealing with the technologies of automatic emotion recognition. Social implications As technologies that recognize emotions are becoming more and more common, and perhaps more decisions influencing people lives are to come in the next decades, the trustworthiness of the technology is important from a scientific, practical and ethical point of view. Originality/value Studying uncertainty of emotion recognition technologies is a novel approach and is not explored from such a broad perspective before. (shrink)

Tactile emotion recognition provides a lot of valuable information in human-computer interaction, and it has strong application prospects in many aspects such as smart home and medical treatment. So this situation raises a question: How to quickly and efficiently let the robot perform the correct emotion recognition? In this work, we develop a lifelong learning algorithm which is based on the efficient dictionary learning technology, to tackle the tactile emotion recognition across different tasks. (...) To verify the efficiency of the proposed method, we applied it to two data sets for experimentation: Corpus of Social Touch (CoST) and our dataset(We built it with a 12X12 array sensor). The results show that the proposed lifelong learning algorithm achieves satisfactory results. (shrink)

Purpose Uncertainty is an under-respected issue when it comes to automatic assessment of human emotion by machines. The purpose of this paper is to highlight the existent approaches towards such measurement of uncertainty, and identify further research need. Design/methodology/approach The discussion is based on a literature review. Findings Technical solutions towards measurement of uncertainty in automatic emotion recognition exist but need to be extended to respect a range of so far underrepresented sources of uncertainty. These then need (...) to be integrated into systems available to general users. Research limitations/implications Not all sources of uncertainty in automatic emotion recognition including emotion representation and annotation can be touched upon in this communication. Practical implications AER systems shall be enhanced by more meaningful and complete information provision on the uncertainty underlying their estimates. Limitations of their applicability should be communicated to users. Social implications Users of automatic emotion recognition technology will become aware of their limitations, potentially leading to a fairer usage in crucial application context. Originality/value There is no previous discussion including the technical view point on extended uncertainty measurement in automatic emotion recognition. (shrink)

Pulmonary ventilation and respiration are considered to be primarily involved in oxygenation of blood for oxygen delivery to cells throughout the body for metabolic purposes. Other pulmonary physiological observations, such as respiratory sinus arrhythmia, Hering Brewer reflex, cardiorespiratory synchronization, and the heart rate variability (HRV) relationship with breathing rhythm, lack complete explanations of physiological/functional significance. The spectrum of waveforms of breathing activity correlate to anxiety, depression, anger, stress, and other positive and negative emotions. Respiratory pattern has been thought not only (...) to be influenced by emotion but to itself influence emotion in a bi-directional relationship between the body and the mind. In order to show how filling in gaps in understanding could lead to certain future developments in mind–body medicine, biofeedback, and personal health monitoring, we review and discuss empirical work and tracings to express the vital role of bodily rhythms in influencing emotion, autonomic nervous system activity, and even general neural activity. Future developments in measurement and psychophysiological understanding of the pattern of breathing in combination with other parameters such as HRV, cardiorespiratory synchronization, and skin conductivity may allow for biometric monitoring systems to one day accurately predict affective state and even affective disorders such as anxiety. Better affective prediction based on recent research when incorporated into personal health monitoring devices could greatly improve public mental health by providing at-home biofeedback for greater understanding of one’s mental state and for mind–body affective treatments such as breathing exercises. (shrink)

To solve a series of pension problems caused by aging, based on the emotional recognition of the Internet of Things, the control method and system research of smart homes are proposed. This article makes a detailed analysis and research on the necessity, feasibility, and how to realize speech emotion recognition technology in smart families, introduces the definition and classification of emotion, and puts forward five main emotions to be recognized in speech emotion recognition (...) based on smart family environment. Then, based on this, it analyses the acquisition methods of emotional speech data. On this premise, this article discusses and analyses the related problems of voice data acquisition in smart homes, such as the voice characteristics and acquisition methods, puts forward three rules for voice text design, and determines the relatively suitable hybrid recording acquisition method applied in a smart home environment. At the same time, the design and establishment process of intelligent family emotional speech database is described in detail. The related problems of feature extraction in speech emotion recognition are studied. Starting from the definition of feature extraction, this article expounds on the necessity of feature extraction in the process of recognition and analyses the characteristics of the speech signals. For the specific environment of the smart family, the speech signal required to be processed needs to be close to the auditory characteristics of the human ears, and the speech signal contains enough information. Finally, the Mel frequency cepstrum coefficient is selected as the feature parameter applied in this article, and the extraction process of MFCC is introduced in detail. (shrink)

Facial recognition is a promising emerging technology, but it sometimes fails to recognize people adequately. Facial recognition applications have been found to regularly misidentify certain demographics, misinterpret traits like gender, age, beliefs, or emotions, and categorize individuals in ways that do not resonate with their own sense of identity. In this paper, I argue that in each of these cases, the person who has their face analyzed is not merely misidentified or misunderstood, but misrecognized in an ethically (...) relevant sense. Following Charles Taylor’s The Politics of Recognition (1992) and Axel Honneth’s The Struggle for Recognition (1996), I describe how those subjected to facial recognition systems on the one hand struggle to obtain adequate recognition on a universal level, as being equally important to others, and lack recognition for their individual uniqueness, on the other hand. These forms of misrecognition are of ethical concern, because they can harm a person’s identity formation. So, ironically, facial recognition technology can give rise to a struggle for recognition. (shrink)

The research on technologies and methodologies for (accurate, real-time, spontaneous, three-dimensional…) facial expression recognition is ongoing and has been fostered in the past decades by advances in classification algorithms like deep learning, which makes them part of the Artificial Intelligence literature. Still, despite its upcoming application to contexts such as human–computer interaction, product and service design, and marketing, only a few literature studies have investigated the willingness of end users to share their facial data with the purpose of detecting (...) emotions. This study investigates the level of awareness and interest of 373 potential consumers towards this technology in the car insurance sector, particularly in the contract drafting phase, with a focus on differentiating the respondents between generation Y and Z. Results show that younger people, individuals with higher levels of education, and social network users feel more confident about this innovative technology and are more likely to share their expressive facial data. (shrink)

Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, (...) Microsoft; Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions. (shrink)

This article focuses on emotions, conceptualised as emotional labour, evoked during data practices used to repurpose and enable healthcare data journeys for Finnish public healthcare. Combined approaches from critical data studies and the sociology of emotions were used to contribute to a better understanding of the mundane but often invisible work of the emotions of experts involved in data practices, such as facilitating data journeys and building data technologies. The article is based on a two-and-a-half-year ethnographic study conducted in a (...) Finnish regional public healthcare and social service organisation. The study results were derived from the analysis of 39 interviews and fieldnotes produced by observing 170 h of various meetings, events and work activities performed by experts. The results were organised into three forms of observed experts’ emotional labour related to three phases of healthcare data journeys: caring for data production and preparing data for travel, managing excitement and frustration in data processing for continually building the data management system, and reassuring users in making sense of obtained data analytics. The results contribute to a greater understanding of the emotions and emotional labour generated by healthcare data journeys and in relation to the volatile nature of healthcare data and the collaborative character of data practices. This work advocates for a better recognition of the emotional aspects of data practices and their implications on data-based knowledge and datafication processes in healthcare. (shrink)

This paper interrogates the growing pervasiveness of affect recognition tools as an emerging layer human-centric automated management in the global workplace. While vendors tout the neoliberal incentives of emotion-recognition technology as a pre-eminent tool of workplace wellness, we argue that emotional AI recalibrates the horizons of capital not by expanding outward into the consumer realm (like surveillance capitalism). Rather, as a new genus of digital Taylorism, it turns inward, passing through the corporeal exterior to extract greater (...) surplus value and managerial control from the affective states of workers. Thus, empathic surveillance signals a profound shift in the ontology of human labor relations. In the emotionally quantified workplace, employees are no longer simply seen as physical capital, but conduits of actuarial and statistical intelligence gleaned from their most intimate subjective states. As a result, affect-driven automated management means that priority is often given to actuarial rather than human-centered managerial decisions. (shrink)

Resumen El reconocimiento facial es una tecnología que se ha utilizado para el control de la población a fin de combatir los efectos de la pandemia por COVID-19. Sin embargo, esta tecnología se utiliza hace bastante tiempo en muchos ámbitos del quehacer económico, social y político. Este trabajo analiza el reconocimiento fa cial entendida como la captura de micro expresiones faciales mediante la cual se puede medir y evaluar emociones con el fin de vigilar movimiento, predecir com portamientos y anticipar (...) intenciones de compra y de voto. Nuestro objetivo es revisar los enfoques teóricos que sustentan esta tecnología que ha devenido en una nueva oportunidad de negocio, para proponer después algunas reflexiones éticas derivadas.Facial recognition is a technology that is used to control people in order to fight the impacts of COVID-19. However, this technology has been used formany years in many economical, social and political areas. This work provides an analysis of the new business based on technologies of facial micro-expressions recognition. By means of this procedure, the involved companies measure and evaluate emotions with the aim of monitoring the movement, predicting behaviors, intentions of purchase and vote, between other applications. Our purpose is to check the theoretical suppositions that sustain this business, to propose later some ethical derivative reflections. (shrink)

This chapter identifies and analyzes ethical issues in video surveillance, with a focus on applications that involve video analytics and facial recognition technology. The first part of the analysis focuses on ethical issues in video analytics, which are techniques that automatically analyze preexisting or gathered video data. Ethical issues are discussed in relation to processes of identification, classification, detection, and tracking of persons. Next, facial recognition technology is discussed, particularly its use in surveillance, and its ethical (...) challenges are investigated. Finally, we focus on three advanced video analytics techniques: facial analysis, behavior analysis, and emotional analysis, which raise additional serious ethical issues. (shrink)

Augmentative and Alternative Communication Technology [AAC Tech] is a relatively young, multidisciplinary field aimed at developing technologies for people who are unable to use their natural speaking voice due to congenital or acquired disability. In this paper, we take a look at the role of AAC Tech in promoting an ‘empathic turn’ in the perception of non-speaking autistic persons. By the empathic turn we mean the turn towards a recognition of non-speaking autistic people as persons whose ways of (...) engaging the world and expressing themselves are indicative of psychologically rich and intrinsically meaningful experiential lives. We first identify two ways in which AAC Tech contributes positively to this development. We then discuss how AAC Tech can simultaneously undermine genuine empathic communication between autistic persons and typically developed communicators (or neurotypicals). To mitigate this concern, we suggest the AAC field should incorporate philosophical insights from Design for Emotions and enactive embodied cognitive science into its R&D practices. To make our proposal concrete, we home in on stimming as an autistic form of bodily expressivity that can play an important role in empathic communicative exchanges between autistic persons and neurotypicals and that could be facilitated in AAC Tech designed for autistic people. (shrink)

Biometric technologies are becoming more pervasive in the workplace, augmenting managerial processes such as hiring, monitoring and terminating employees. Until recently, these devices consisted mainly of GPS tools that track location, software that scrutinizes browser activity and keyboard strokes, and heat/motion sensors that monitor workstation presence. Today, however, a new generation of biometric devices has emerged that can sense, read, monitor and evaluate the affective state of a worker. More popularly known by its commercial moniker, Emotional AI, the technology (...) stems from advancements in affective computing. But whereas previous generations of biometric monitoring targeted the exterior physical body of the worker, concurrent with the writings of Foucault and Hardt, we argue that emotion-recognition tools signal a far more invasive disciplinary gaze that exposes and makes vulnerable the inner regions of the worker-self. Our paper explores attitudes towards empathic surveillance by analyzing a survey of 1015 responses of future job-seekers from 48 countries with Bayesian statistics. Our findings reveal affect tools, left unregulated in the workplace, may lead to heightened stress and anxiety among disadvantaged ethnicities, gender and income class. We also discuss a stark cross-cultural discrepancy whereby East Asians, compared to Western subjects, are more likely to profess a trusting attitude toward EAI-enabled automated management. While this emerging technology is driven by neoliberal incentives to optimize the worksite and increase productivity, ultimately, empathic surveillance may create more problems in terms of algorithmic bias, opaque decisionism, and the erosion of employment relations. Thus, this paper nuances and extends emerging literature on emotion-sensing technologies in the workplace, particularly through its highly original cross-cultural study. (shrink)

Emotional AI is an emerging technology used to make probabilistic predictions about the emotional states of people using data sources, such as facial (micro)-movements, body language, vocal tone or the choice of words. The performance of such systems is heavily debated and so are the underlying scientific methods that serve as the basis for many such technologies. In this article I will engage with this new technology, and with the debates and literature that surround it. Working at the (...) intersection of criminology, policing, surveillance and the study of emotional AI this paper explores and offers a framework of understanding the various issues that these technologies present particularly to liberal democracies. I argue that these technologies should not be deployed within public spaces because there is only a very weak evidence-base as to their effectiveness in a policing and security context, and even more importantly represent a major intrusion to people’s private lives and also represent a worrying extension of policing power because of the possibility that intentions and attitudes may be inferred. Further to this, the danger in the use of such invasive surveillance for the purpose of policing and crime prevention in urban spaces is that it potentially leads to a highly regulated and control-oriented society. I argue that emotion recognition has severe impacts on the right to the city by not only undertaking surveillance of existing situations but also making inferences and probabilistic predictions about future events as well as emotions and intentions. (shrink)

Electroencephalography is the measurement of neuronal activity in different areas of the brain through the use of electrodes. As EEG signal technology has matured over the years, it has been applied in various methods to EEG emotion recognition, most significantly including the use of convolutional neural network. However, these methods are still not ideal, and shortcomings have been found in the results of some models of EEG feature extraction and classification. In this study, two CNN models were (...) selected for the extraction and classification of preprocessed data, namely, common spatial patterns- CNN and wavelet transform- CNN. Using the CSP-CNN, we first used the common space model to reduce dimensionality and then applied the CNN directly to extract and classify the features of the EEG; while, with the WT-CNN model, we used the wavelet transform to extract EEG features, thereafter applying the CNN for classification. The EEG classification results of these two classification models were subsequently analyzed and compared, with the average classification accuracy of the CSP-CNN model found to be 80.56%, and the average classification accuracy of the WT-CNN model measured to 86.90%. Thus, the findings of this study show that the average classification accuracy of the WT-CNN model was 6.34% higher than that of the CSP-CNN. (shrink)

Metaverse is to build a virtual world that is both mapped and independent of the real world in cyberspace by using the improvement in the maturity of various digital technologies, such as virtual reality, augmented reality, big data, and 5G, which is important for the future development of a wide variety of professions, including education. The metaverse represents the latest stage of the development of visual immersion technology. Its essence is an online digital space parallel to the real world, (...) which is becoming a practical field for the innovation and development of human society. The most prominent advantage of the English-teaching metaverse is that it can provide an immersive and interactive teaching field for teachers and students, simultaneously meeting the teaching and learning needs of teachers and students in both the physical world and virtual world. This study constructs experiential situational English-teaching scenario and convolutional neural networks –recurrent neural networks fusion models are proposed to recognize students’ emotion electroencephalogram in experiential English teaching during the feature space of time domain, frequency domain, and spatial domain. Analyzing EEG data collected by OpenBCI EEG Electrode Cap Kit from students, experiential English-teaching scenario is designed into three types: sequential guidance, comprehensive exploration, and crowd-creation construction. Experimental data analysis of the three kinds of learning activities shows that metaverse-powered experiential situational English teaching can promote the improvement of students’ sense of interactivity, immersion, and cognition, and the accuracy and analysis time of CNN–RNN fusion model is much higher than that of baselines. This study can provide a nice reference for the emotion recognition of students under COVID-19. (shrink)

The student management work model in colleges and universities is an effective plan for college student management, but the traditional college student management work is not very good in terms of student psychology, resulting in negative attitudes such as low learning desire, low learning efficiency, and inactive learning. In recent years, with the development of artificial intelligence technologies such as sentiment analysis and incentive theory, emotional management and incentive theory have been applied to the management of college students. The emotional (...) management and incentive model is a way to help college students get rid of psychological obstacles and guide students to establish positive and correct values by predict and analyze the psychological state of college students through language emotion recognition and BP neural network. This paper compares the college student management work model based on emotional management and incentives with the traditional college management work mode through experiments. The results show that the students’ learning enthusiasm is better than the traditional college student management work mode based on emotional management and incentives. The student management work model in colleges and universities is 15.8% better, and the students’ grades have improved by 12.5%; the college student management work model based on emotional management and incentives also has a positive role in helping students’ mental health. The way of emotional management and motivation can make better use of college students’ psychology to effectively manage students and guide students to develop in a good direction. (shrink)

With the development of artificial intelligence, it is imperative to combine design methods with new technologies. From the perspective of the personalized design of derived images of art paintings, this study analyzes the new user demand generated by the current situation and background of personalized design, puts forward a new method of derivative design based on AI emotion analysis, verifies the feasibility of the new method by constructing a personalized design system of derived images of art paintings driven by (...) facial emotion features, and explores the method of combining AI emotion recognition, emotion analysis, and personalized design. This study provides new ideas for the design of art derivatives for the future with massive personalized demand. Thinking and practicing from the perspective of the development of new technology will promote the change of design paradigms in the digital age. (shrink)

In response to the prospects and challenges of emerging digital communication technologies, higher education level courses are increasingly being developed to address the ethical implications of these technologies. To this end, an undergraduate course has been developed that emphasizes the importance of process-oriented and case-based approaches to the ethical implications of data-driven communication technologies such as X (Twitter), digital crowdsourcing, smart cities, and face/emotion recognition. The course is structured around a model of case-based learning; starting with basic concepts (...) and theories given by the instructor. This is followed by collaborative problem-solving sessions where students and the instructor work together to consider responses to problems that are relevant to particular digital communication technologies and of interest to course participants. This chapter attempts to motivate the adoption of this approach to teach the ethics of digital communication technologies. The first section offers a conceptual take on digital communication technologies and highlights the ethical implications of the process of datafication that underlies many of these data-driven platforms. In addition, the rationale for doing applied ethics together with media and communication students is explained. The second section briefly describes the design of the course. (shrink)

Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to the (...) music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion. (shrink)

Algorithms increasingly make managerial decisions that people used to make. Perceptions of algorithms, regardless of the algorithms' actual performance, can significantly influence their adoption, yet we do not fully understand how people perceive decisions made by algorithms as compared with decisions made by humans. To explore perceptions of algorithmic management, we conducted an online experiment using four managerial decisions that required either mechanical or human skills. We manipulated the decision-maker, and measured perceived fairness, trust, and emotional response. With the mechanical (...) tasks, algorithmic and human-made decisions were perceived as equally fair and trustworthy and evoked similar emotions; however, human managers' fairness and trustworthiness were attributed to the manager's authority, whereas algorithms' fairness and trustworthiness were attributed to their perceived efficiency and objectivity. Human decisions evoked some positive emotion due to the possibility of social recognition, whereas algorithmic decisions generated a more mixed response – algorithms were seen as helpful tools but also possible tracking mechanisms. With the human tasks, algorithmic decisions were perceived as less fair and trustworthy and evoked more negative emotion than human decisions. Algorithms' perceived lack of intuition and subjective judgment capabilities contributed to the lower fairness and trustworthiness judgments. Positive emotion from human decisions was attributed to social recognition, while negative emotion from algorithmic decisions was attributed to the dehumanizing experience of being evaluated by machines. This work reveals people's lay concepts of algorithmic versus human decisions in a management context and suggests that task characteristics matter in understanding people's experiences with algorithmic technologies. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Reviewed by:The Secret History of Emotion: From Aristotle's ‘Rhetoric’ to Modern Brain ScienceMichael J. HydeThe Secret History of Emotion: From Aristotle's ‘Rhetoric’ to Modern Brain Science. Daniel M. Gross. Chicago: University of Chicago Press, 2006. Pp. x + 194. $35.00, Hardcover.The twofold goal of this book is clearly stated by its author: "to reconstitute by way of critical intellectual history a deeply nuanced, rhetorical understanding of (...) class='Hi'>emotion that prevailed until the triumph of psychophysiology; second, to show by way of literary and philosophical example how this rhetorical perspective helps us read anew the emotional complex of modernity, whether early or late" (8). Gross' appreciation of the function of emotion emphasizes a phenomenological orientation that is inspired by Martin Heidegger's unpublished 1924 lecture course on Aristotle's Rhetoric. Gross recently co-edited with Ansgar Kemmann a book of original essays dedicated to a critical assessment of this lecture course (Gross and Kemmann 2006). In concluding her contribution to the book, the well-known Renaissance scholar Nancy S. Struever maintains that the course "remains, arguably, the best twentieth-century reading of Aristotle's Rhetoric" (Struever 2006: 127). Gross advances this reading with his creative and insightful assessment of his topic.This advance unfolds as Gross, abiding by phenomenology's well-known critique of the matter, takes issue with the reductive psychophysiology of emotion that Descartes first proposed and that informs latter-day sciences of the mind and the brain. Gross turns to the research program of the distinguished cognitive scientist Antonio Damasio as a way of arguing "how brain science of emotion goes awry when it blunders into social fact" (29). The reductionism of science, in other words, misses the forest for the trees. Admitting that Damasio is open to inquiries outside the boundaries of neurobiology, Gross nevertheless takes particular issue with Damasio's model for such a project: Edward O. Wilson's, Consilience: The Unity of Knowledge. Indeed, Wilson never gives rhetoric a nod when he acknowledges the worth of the humanities in his book.This slight, coming from Wilson, Damasio, and other scientists, fuels Gross's enthusiasm for turning to Aristotle's analysis of the pathe in book two of the Rhetoric. With Aristotle's discussion of "anger" in mind, for example, Gross emphasizes how we live in "a contoured world of emotional investments, where some people have significantly more liabilities than others" (3). [End Page 326]The point warrants elaboration: "The contours of our emotional world have been shaped by institutions such as slavery and poverty that simply afford some people greater emotional range than others, as they are shaped by publicity that has nothing to do with the inherent values of each human life and everything to do with technologies of social recognition and blindness" (4). Gross links this Aristotelian based social-psychological insight with such early modern philosophers, psychologists, and cultural critics as Thomas Hobbes, David Hume, William Perfect, and then with such contemporary intellectuals as Judith Butler. Gross' discussion of this linkage enables him to argue a key point in his overall project: the constitutive power of emotions depends not on how the social passions originate in a moral sense equally shared by all, but rather on the "uneven distribution" of these passions in our social and political lives.Gross makes much of how the rhetoric of this uneven distribution is at work in such particular cases as Aristotle's apathetic slave, Seneca's angry tyrant, Hobbes's resentful preacher, Sarah Fielding's humble hero, and Adam Smith's compassionate spectator. Gross's intellectual acumen shines as he weaves together these cases and expands on their implications for developing a rhetorical understanding of emotion. For example, in chapter three ("Virtues of Passivity in the English Civil War"), Gross offers a critical assessment of Michael Walzer's classic The Revolution of the Saints: A Study in the Origins of Radical Politics in order to show how the emotional makeup of passivity, too often "relegated to a diminished femininity" (87), not only informs the virtue of humility, but also helps it serve rhetorically "the most dramatic revolutionary ends" (110). Additional extended case studies emphasize the specific emotions of apathy, pride, pity... (shrink)

Contextual affective information influences the processing of facial expressions at the relatively early stages of face processing, but the effect of the context on the processing of facial expressions with varying intensities remains unclear. In this study, we investigated the influence of emotional scenes on the processing of fear expressions at different levels of intensity during the early stages of facial recognition using event-related potential technology. EEG data were collected while participants performed a fearful facial expression recognition (...) task. The results showed that the recognition of high-intensity fear expression was higher than that of medium- and low-intensity fear expressions. Facial expression recognition was the highest when faces appeared in fearful scenes. Emotional scenes modulated the amplitudes of N170 for fear expressions with different intensities. Specifically, the N170 amplitude, induced by high-intensity fear expressions, was significantly higher than that induced by low-intensity fear expressions when faces appeared in both neutral and fearful scenes. No significant differences were found between the N170 amplitudes induced by high-, medium-, and low-intensity fear expressions when faces appeared in happy scenes. These results suggest that individuals may tend to allocate their attention resources to the processing of face information when the valence between emotional context and expression conflicts i.e., when the conflict is absent or is low. (shrink)

The purpose is to explore new cultivation modes of college students’ national cognition and cultural acceptance. Deep learning technology and Educational Psychology theory are introduced, and the influence of art journal reading on college students’ national cognition and cultural acceptance is analyzed under Educational Psychology. Firstly, the background of Educational Psychology, national cognition and cultural acceptance, and learning system are discussed following a literature review. The DL technology is introduced to construct the journal reading guidance system. The system (...) can provide users with art journals and record the user habits like reading duration and preferences. Secondly, hypotheses are proposed, and a questionnaire survey is designed, with 12 specific indicators to investigate and collect research data. Finally, the collected data are analyzed. The results show that women’s cognition of Chinese traditional culture, Chinese excellent revolutionary culture, and Chinese national identity is higher than that of men. By comparison, men’s cognition of Chinese advanced socialist culture is higher than women’s. After using the journal reading guidance system, the cognition of female college students on traditional Chinese culture is improved by 16.3%. Before and after reading art journals, the overall national cognition and cultural acceptance of Minority students are higher than that of Han students. The overall cognition of Literature and History students is higher than that of Science and Engineering students in traditional Chinese culture and China’s excellent revolutionary culture and lower in advanced Chinese socialist culture and Chinese national identity. The overall cognition of college students’ party members to the advanced socialist culture is higher than league members. As students read more art journals through the guidance system, their overall national cognition and cultural acceptance have increased. Therefore, reading art journals can promote college students’ national cognition and cultural acceptance. A national cognition and cultural acceptance promotion system that conforms to the current situation of college students is constructed. The finding provides a reference for developing complex emotion recognition technology in human-computer interaction. (shrink)

With the deepening of globalization, brand plays an important role in determining the competitiveness of enterprises. It is worth thinking about how to quantify the brand value reasonably to achieve the purpose of improving the competitiveness of enterprises. The research of commercial brands based on emotion analysis extracts the views of consumers on the evaluation data of brand attributes, analyzes the emotional tendency of consumers' views, and then helps enterprises adjust their production strategies. The purpose of emotion analysis (...) is to judge users' views and attitudes toward goods or services by analyzing texts. In this paper, the linear support vector machine model is used to extract and study the features in sentiment analysis. Then, the brand based on the fusion model is evaluated, and the experimental conclusions are drawn: the method of fusing the depth features extracted by word vector and the named entity extracted by CRF makes the brand effect of fusing emotional factors better than the feature extraction method only using CRF named entity recognition. It is proved that the model method proposed in this paper has a certain role in actual business. Feature fusion often combines shallow model methods and fuses various shallow feature screening technologies based on word segmentation results. This paper introduces fusion features and supplements feature vectors based on the results of the deep learning word vector model. Support Vector Machine maps feature vectors to some sample points in the space, finds a plane that can realize sample classification, and maximizes the distance between the two sample points closest to the plane in two types of samples, to maximize the classification performance and improve the generalization of the model. (shrink)

This chapter argues that emotion recognition is a skill. A skill perspective on emotion recognition draws attention to underappreciated features of this cornerstone of social cognition. Skills have a number of characteristic features. For example, they are improvable, practical, and flexible. Emotion recognition has these features as well. Leading theories of emotion recognition often draw inadequate attention to these features. The chapter advances a theory of emotion recognition that is better (...) suited to this purpose. It proposes that emotion recognition involves scripts. Emotion scripts describe how people are likely to behave in different emotional contexts. Scripts can be improved with the addition of richer social knowledge, they are practical in that they describe and guide social interaction, and they are flexible because they must accommodate the fact that emotions have different behavioral impact depending on the agents involved and the circumstances at play. Learning to recognize emotions through scripts qualifies as a skill. (shrink)

There is debate within the literature as to whether emotion dysregulation (ED) in Attention-Deficit Hyperactivity Disorder (ADHD) reflects deviant attentional mechanisms or atypical perceptual emotion processing. Previous reviews have reliably examined the nature of facial, but not vocal, emotion recognition accuracy in ADHD. The present meta-analysis quantified vocal emotion recognition (VER) accuracy scores in ADHD and controls using robust variance estimation, gathered from 21 published and unpublished papers. Additional moderator analyses were carried out to (...) determine whether the nature of VER accuracy in ADHD varied depending on emotion type. Findings revealed a medium effect size for the presence of VER deficits in ADHD, and moderator analyses showed VER accuracy in ADHD did not differ due to emotion type. These results support the theories which implicate the role of attentional mechanisms in driving VER deficits in ADHD. However, there is insufficient data within the behavioural VER literature to support the presence of emotion processing atypicalities in ADHD. Future neuro-imaging research could explore the interaction between attention and emotion processing in ADHD, taking into consideration ADHD subtypes and comorbidities. (shrink)

According to a dominant interpretation of the simulation hypothesis, in recognizing an emotion we use the same neural processes used in experiencing that emotion. This paper argues that the view is fundamentally misguided. I will examine the simulational arguments for the three basic emotions of fear, disgust, and anger and argue that the simulational account relies strongly on a narrow sense of emotion processing which hardly squares with evidence on how, in fact, emotion recognition is (...) processed. I contend that the current body of empirical evidence suggests that emotion recognition is processed in an integrative system involving multiple cross-regional interactions in the brain, a view which squares with understanding emotion recognition as an information-rich, rather than simulational, process. In the final section, I discuss possible objections. (shrink)

We develop a version of a direct perception account of emotion recognition on the basis of a metaphysical claim that emotions are individuated as patterns of characteristic features. On our account, emotion recognition relies on the same type of pattern recognition as is described for object recognition. The analogy allows us to distinguish two forms of directly perceiving emotions, namely perceiving an emotion in the absence of any top-down processes, and perceiving an (...) class='Hi'>emotion in a way that significantly involves some top-down processes ; and, in addition, an inference-based evaluation of an emotion. Our model clarifies the epistemology of emotion recognition. (shrink)

Background: Clinical and experimental findings have suggested that insomnia is associated with altered emotion processing, such as facial emotion recognition and impairments in executive functions. However, the results still appear non-consensual and have recently been presented by a few number of studies. Accordingly, the aim of the present study was to investigate whether patients with Insomnia disorder will present alterations in recognition of facial emotions and that such alterations will be related to Executive Functions and that (...) Insomnia Disorder patients will exhibit a lower Executive Functions performance when compared to normal controls individuals. Method: A total of 11 individuals diagnosed with insomnia disorder and 15 control participants took part in the present cross-sectional study. They completed questionnaires covering socio-demographic data, a sleep and mood evaluation, and a cognitive assessment. Moreover, they underwent a computerized Facial Emotional Recognition test, consisting of facial emotion labeling and facial emotion matching. Results: Individuals with insomnia presented a lower recognition of facial expressions of fear and sadness in comparison to control volunteers. Perceptual Organization Index was found to be statistically significant lower in the insomnia group compared to the control group. There was a statistically significant association between facial recognition emotional and cognitive processes of in executive functions performance, especially in processes related to the inhibitory control, planning capacity, problem solving and cognitive flexibility in insomnia group. Conclusions: Insomnia disorder impairs the facial emotion recognition of the fear and sadness. Insomniacs with impairment in the facial emotion recognition had impaired performance of executive functions. There is a negative impact on processes related to cognitive-emotional capacity than on processes related to social-emotional recognition. Keywords: insomnia, facial emotional recognition, sleep deprivation, cognition, executive functions. (shrink)

BackgroundParanoia is associated with a multitude of social cognitive deficits, observed in both clinical and subclinical populations. Empathy is significantly and broadly impaired in schizophrenia, yet its relationship with subclinical paranoia is poorly understood. Furthermore, deficits in emotion recognition – a very early component of empathic processing – are present in both clinical and subclinical paranoia. Deficits in emotion recognition may therefore underlie relationships between paranoia and empathic processing. The current investigation aims to add to the (...) literature on social cognition and paranoia by: (1) characterizing the relationship between paranoia and empathy, and (2) testing whether there is an indirect effect of emotion recognition on the relationship between empathy and paranoia.MethodsParanoia, empathy, and emotion recognition were assessed in a non-clinical sample of adults (n= 226) from the Nathan Kline Institute-Rockland (NKI-Rockland) dataset. Paranoia was measured using the Peters Delusions Inventory-21 (PDI-21). Empathy was measured using the Interpersonal Reactivity Index (IRI), a self-report instrument designed to assess empathy using four subscales: Personal Distress, Empathic Concern, Perspective Taking, and Fantasy. Emotion recognition was assessed using the Penn Emotion Recognition Test (ER-40). Structural equation modeling (SEM) was used to estimate relationships between paranoia, the four measures of empathy and emotion recognition.ResultsParanoia was associated with the Fantasy subscale of the IRI, such that higher Fantasy was associated with more severe paranoia (p< 0.001). No other empathy subscales were associated with paranoia. Fantasy was also associated with the emotion recognition of fear, such that higher Fantasy was correlated with better recognition of fear (p= 0.008). Paranoia and emotion recognition were not significantly associated. The Empathic Concern subscale was negatively associated with emotion recognition, with higher empathic concern related to worse overall emotion recognition (p= 0.002). All indirect paths through emotion recognition were non-significant.DiscussionThese results suggest that imaginative perspective-taking contributes to paranoia in the general population. These data do not, however, point to robust global relationships between empathy and paranoia or to emotion recognition as an underlying mechanism. Deficits in empathy and emotion recognition observed in schizophrenia may be associated with the broader pathology of schizophrenia, and therefore not detectable with subclinical populations. (shrink)

BackgroundWe review original papers on ovarian-hormone status in two areas of emotional processing: facial emotion recognition and emotional memory. Ovarian-hormone status is operationalized by the levels of the steroid sex hormones 17β-estradiol and progesterone, fluctuating over the natural menstrual cycle and suppressed under oral contraceptive use. We extend previous reviews addressing single areas of emotional processing. Moreover, we systematically examine the role of stimulus features such as emotion type or stimulus valence and aim at elucidating factors that (...) reconcile the inconsistent results.MethodsWe followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and included papers published until September 2020 indexed in PubMed and Web of Science databases. Search terms were MeSH terms AND AND with representing our separately searched areas, resulting in, and. To be included, articles had to be written and published in English, examine healthy, non-pregnant adult women in their reproductive age, and measure or at least estimate levels of E2 and P4. In PubMed, the search was limited to humans and to the search term present in the title or abstract.ResultsFeatures of the provided stimulus material constitute a relevant influence that interacts with E2- and P4-related ovarian-hormone status. For instance, recognition of basic emotions appears to be more related to P4- than E2-levels. Quite consistent, OC intake was accompanied by impaired recognition accuracy of basic and also complex emotions, although not in a recent large-sample study assessing complex emotions. Memory recall of negative content was mainly enhanced by P4, especially after having been stressed.Discussion and ConclusionWe document the methodological diversity in the field, presumably contributing to the heterogeneity of results. More studies explicitly contrasting the early follicular phase, mid-cycle phase, mid-luteal, and OC intake while standardizing tasks are needed. Research would take advantage of using within-subject designs and accounting for the recognition of complex emotions. (shrink)

Emotion recognition skills and the ability to understand the mental states of others are crucial for normal social functioning. Conversely, delays and impairments in these processes can have a profound impact on capability to engage in, maintain, and effectively regulate social interactions. Therefore, this study aimed to compare the performance of 42 autistic children, 45 unaffected siblings, and 41 typically developing controls on the Affect Recognition and Theory of Mind subtests of the Developmental Neuropsychological Assessment Battery. There (...) were no significant differences between siblings and TD controls. Autistic children showed significantly poorer performance on AR when compared to TD controls and on TOM when compared to both TD controls and unaffected siblings. An additional comparison of ASD, unaffected sibling and TD control subsamples, matched on full-scale IQ, revealed no group differences for either AR or TOM. AR and TOM processes have received less research attention in siblings of autistic children and remain less well characterized. Therefore, despite limitations, findings reported here contribute to our growing understanding of AR and TOM abilities in siblings of autistic children and highlight important future research directions. (shrink)

There exists substantial cultural variation in how emotions are expressed, but there is also considerable evidence for universal properties in facial and vocal affective expressions. This is the first empirical effort examining the perception of vocal emotional expressions across cultures with little common exposure to sources of emotion stimuli, such as mass media. Shuar hunter-horticulturalists from Amazonian Ecuador were able to reliably identify happy, angry, fearful and sad vocalizations produced by American native English speakers by matching emotional spoken utterances (...) to emotional expressions portrayed in pictured faces. The Shuar performed similarly to English speakers who heard the same utterances in a content-filtered condition. These data support the hypothesis that vocal emotional expressions of basic affective categories manifest themselves in similar ways across quite disparate cultures. (shrink)

Despite the lower levels of psychopathy in women than in men, the scientific interest in studying psychopathy in female participants is increasing. Nevertheless, the number of studies investigating psychopathy in women and associated phenomena remains low. The influence of psychopathy in women inmates on experimental tasks of emotional recognition and moral judgment was evaluated, aiming to contribute to this field of research. Utilitarian moral judgment was predicted by psychopathy, specifically by primary and secondary psychopathy, while primary psychopathy predicted a (...) worse performance on the emotional recognition task. There was no significant influence of general intellectual abilities on either task. Contrarily to what was expected, emotional recognition did not prove to be a significant mediator of the relationship between psychopathy and utilitarian moral judgment. These results emphasize that the tendency to utilitarian moral judgment and worse recognition of facial expressions of emotion are associated to higher psychopathy scores in female inmates (especially primary psychopathy), but more studies are necessary to address the role of the emotional component in the process of moral judgment. (shrink)

Results from studies on gender differences in emotion recognition vary, depending on the types of emotion and the sensory modalities used for stimulus presentation. This makes comparability between different studies problematic. This study investigated emotion recognition of healthy participants (N = 84; 40 males; ages 20 to 70 years), using dynamic stimuli, displayed by two genders in three different sensory modalities (auditory, visual, audio-visual) and five emotional categories. The participants were asked to categorise the stimuli (...) on the basis of their nonverbal emotional content (happy, alluring, neutral, angry, and disgusted). Hit rates and category selection biases were analysed. Women were found to be more accurate in recognition of emotional prosody. This effect was partially mediated by hearing loss for the frequency of 8,000 Hz. Moreover, there was a gender-specific selection bias for alluring stimuli: Men, as compared to women, chose “alluring” more often when a stimulus was presented by a woman as compared to a man. (shrink)

At present, there are so many learners in online classroom that teachers cannot master the learning situation of each student comprehensively and in real time. Therefore, this paper first constructs a multimodal emotion recognition model based on CNN-BiGRU. Through the feature extraction of video and voice information, combined with temporal attention mechanism, the attention distribution of each modal information at different times is calculated in real time. In addition, based on the recognition of learners’ emotions, a prediction (...) model of learners’ achievement based on emotional state assessment is proposed. C4.5 algorithm is used to predict students’ academic achievement in the multi-polarized emotional state, and the relationship between confusion and academic achievement is further explored. The experimental results show that the proposed multi-scale self-attention layer and multi-modal fusion layer can improve the achievement of ER task; moreover, there is a strong correlation between students’ confusion and foreign language achievement. Finally, the model can accurately and continuously observe students’ learning emotion and state, which provides a new idea for the reform of education modernization. (shrink)

Linguistic communication is an important part of the cross-cultural perspective, and linguistic textual emotion recognition is a key massage in interpersonal communication. Spanish is the second largest language system in the world. The purpose of this paper is to identify the emotional features in Spanish texts. The improved BiLSTM framework is proposed. We select three widely used Spanish dictionaries as the datasets for our experiments, and then we finally obtain text sentiment classification results through text preprocessing, text (...) class='Hi'>emotion feature extraction, text topic detection, and emotion classification. We inserted the attention mechanism in the improved BiLSTM framework. It enables the shared feature encoder to obtain weighted representation results in the extraction of emotion features, which enhances the generalization ability of the model for text emotion feature recognition. Experimental results demonstrate that our approach performs better for specialized Spanish dictionary datasets. In terms of emotion recognition accuracy, the average value is as high as 76.21%. The overall performance outperforms current comparable machine learning methods and convolutional neural network methods. (shrink)

The purpose of this study was to investigate emotion recognition and processing in patients with mild cognitive impairment in order to update the state of current literature on this important but undervalued topic. We identified 15 papers published between 2012 and 2022 that meet the inclusion criteria. Paper search, selection, and extraction followed the PRISMA guidelines. We used a narrative synthesis approach in order to report a summary of the main findings taken from all papers. The results collected (...) are still ambiguous: some studies did not find any differences between MCI and healthy controls groups in emotion recognition and processing, and other results reported emotion-specific deficits in emotion recognition regarding MCI patients. It is essential to underline that these findings could not be generalized to the whole MCI population due to the heterogeneous use of measures and composition of the sample. This does not allow us to make a comprehensive comparison between the results. Our suggestion for future research is to align the results using the same type of tests and emotion recognition assessment. (shrink)

Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side-scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, (...) diversity, multisources, and small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K-means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images. (shrink)

Most research on cross-cultural emotion recognition has focused on facial expressions. To integrate the body of evidence on vocal expression, we present a meta-analysis of 37 cross-cultural studies...