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The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applications (BCI). However, there are scarce algorithms and procedures for real-time classification of emotions. The present study aims to investigate the feasibility of real-time emotion recognition implementation by the selection of parameters such as the appropriate time window segmentation and target bandwidths and cortical regions. We recorded the EEG-neural activity of 24 participants while they were looking and listening to an audiovisual database composed of positive and negative emotional video clips. We tested 12 different temporal window sizes, 6 ranges of frequency bands and 60 electrodes located along the entire scalp. Our results showed a correct classification of 86.96% for positive stimuli. The correct classification for negative stimuli was a little bit less (80.88%). The best time window size, from the tested 1s to 12s segments, was 12s. Although more studies are still needed, these preliminary results provide a reliable way to develop accurate EEG-based emotion classification.

Understanding the neurophysiology of emotions, the neuronal structures involved in processing emotional information and the circuits by which they act, is key to designing applications in the field of affective neuroscience, to advance both new treatments and applications of brain–computer interactions. However, efforts have focused on developing computational models capable of emotion classification instead of on studying the neural substrates involved in the emotional process. In this context, we have carried out a study of cortical asymmetries and functional cortical connectivity based on the electroencephalographic signal of 24 subjects stimulated with videos of positive and negative emotional content to bring some light to the neurobiology behind emotional processes. Our results show opposite interhemispheric asymmetry patterns throughout the cortex for both emotional categories and specific connectivity patterns regarding each of the studied emotional categories. However, in general, the same key areas, such as the right hemisphere and more anterior cortical regions, presented higher levels of activity during the processing of both valence emotional categories. These results suggest a common neural pathway for processing positive and negative emotions, but with different activation patterns. These preliminary results are encouraging for elucidating the neuronal circuits of the emotional valence dimension.

Models of message flows in an artificial group of users communicating via the Internet are introduced and investigated using numerical simulations. We assumed that messages possess an emotional character with a positive valence and that the willingness to send the next affective message to a given person increases with the number of messages received from this person. As a result, the weights of links between group members evolve over time. Memory effects are introduced, taking into account that the preferential selection of message receivers depends on the communication intensity during the recent period only. We also model the phenomenon of secondary social sharing when the reception of an emotional e-mail triggers the distribution of several emotional e-mails to other people.

Long-range interactions are introduced to a two-dimensional model of agents with time-dependent internal variables e_i = 0, ±1 corresponding to valencies of agent emotions. Effects of spontaneous emotion emergence and emotional relaxation processes are taken into account. The valence of agent i depends on valencies of its four nearest neighbors but it is also influenced by long-range interactions corresponding to social relations developed for example by Internet contacts to a randomly chosen community. Two types of such interactions are considered. In the first model the community emotional influence depends only on the sign of its temporary emotion. When the coupling parameter approaches a critical value a phase transition takes place and as result for larger coupling constants the mean group emotion of all agents is nonzero over long time periods. In the second model the community influence is proportional to magnitude of community average emotion. The ordered emotional phase was here observed for a narrow set of system parameters.

The implications and contagion effect of emotion cannot be ignored in rumor spreading. This paper sheds light on how decision makers’ (DMs) emotion type and intensity affect rumor spreading. Based on the rank-dependent expected utility (RDEU) and evolutionary game theory (EGT), we construct an evolutionary game model between rumormongers (RMs) and managers (Ms) by considering emotions. We use MATLAB to simulate and reveal the influencing mechanism of DMs’ emotion type and intensity on rumor spreading. The results indicate that the DMs’ strategy choice is not only affected by their own emotion preference and intensity, but also by the other players in rumor spreading. Moreover, pessimism has a more significant influence than optimism on the stability of the evolutionary game, Ms’ emotion is more sensitive to the game results than RMs’ emotion and the emotion intensity is proportional to the evolution speed. More significantly, some earthshaking emotional thresholds are found, which can be used to predict RMs’ behavior, help Ms gain critical time to deal with rumors, and avoid the Tacitus Trap crisis. Furthermore, the evolution results fall into five categories: risk, opportunity, ideal, security and hostility. The results of this work can benefit Ms’ public governance.

According to the World Health Organization (WHO), depression is one of the largest contributors to the burden of mental and psychological diseases with more than 300 million people being affected; however a huge portion of this does not receive effective diagnosis. Traditional techniques to diagnose depression were based on clinical interviews. These techniques had several limitations based on duration and variety of symptoms, due to which these methods lacked subjectivity and accuracy. Speech is tested to be an important tool in diagnosis as they carry the impression of one’s thoughts and emotions. Speech signals not only carry the linguistic feature but they also contain several other features (paralinguistic features) which can reflect the emotional state of the speaker. The analysis of these features can be used for the diagnosis of depression. With the advancement of artificial techniques and algorithms, they have become popular and are widely used in tasks of pattern recognition and signal processing. These algorithms can easily extract the features from the data and learn to recognize patterns from them. Although these algorithms can successfully recognize emotions, their efficiency is often argued. The main objective of this paper is to propose a strategy to efficiently diagnose depression from the analysis of speech signals. The analysis is performed in the following two ways: First, by considering the male and female emotions combined (gender-neutral) where they are classified into two classes, and second, separately for the male and female emotions (gender-based) for a total of four classes. Experiments conducted show the advantages and shortcomings of paralinguistic features for diagnosis of depression. During experimentation we tested several architectures by efficiently tuning the hyperparameters. For K-nearest neighbors (KNN), best attained accuracy was 86%, whereas for Multi-Layer Perceptron (MLP) architecture the accuracy attained was 87.8%. Best results were obtained from hybrid 1D-Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) architecture with the accuracy of 88.33% and 90.07% for gender-neutral and gender-based respectively.

The purpose of this study was to investigate the relationship of certain characteristics of family business decision-making processes (customer orientation and open and negotiating family decision-making styles) to business and family goal achievement as mediated by emotions (family supportiveness). We undertook this study to better understand why certain family businesses make consistently better decisions than others, leading them to earn more money and have family members who are happier in their home lives. Decision theory undergirded the study development. The sample consisted of 277 family business owners, and the data are from the National Family Business Panel data set. Our results showed that if the business owners focused on customer satisfaction and product quality when making decisions, they tended to make more money and tended to be happier at home. If families made business decisions in open and negotiating ways, their members were happier about their decisions because they felt supported by the other family members. Furthermore, family members who felt good about the support they got from their family members in their business decision-making were also happier in their home lives in general.

This article introduces a model of rationality that combines procedural utility over actions with consequential utility over payoffs. It applies the model to the Prisoners' Dilemma and shows that empirically observed cooperative behaviors can be rationally explained by a procedural utility for cooperation. The model characterizes the situations in which cooperation emerges as a Nash equilibrium. When rational individuals are not solely concerned by the consequences of their behavior but also care for the process by which these consequences are obtained, there is no one single rational solution to a Prisoners' Dilemma. Rational behavior depends on the payoffs at stake and on the procedural utility of individuals. In this manner, this model of procedural utility reflects how ethical considerations, social norms or emotions can transform a game of consequences.

本研究旨在了解内地乳腺癌患者的情感体验特徵，进而探索乳腺癌患者在自助组织获得的情感支持。研究发现，乳腺癌患者的情感体验特徵表现为：对死亡的种种不确定所产生的恐惧和沮丧；对婚姻解体的焦虑和无助；女性角色的负重和内疚等等。患者在自助组织中感受到的情感支持，在分享、情感讨论和互助方面均有较好的情感支持功效。

This study aims to understand Chinese breast cancer patients' emotions and the emotional support they receive from self-help groups. The findings showed various responses, such as fear and depression of the uncertainty of death; helplessness towards the eventual parting with spouse; feeling of guilt resulting from inability to meet traditional female role expectations and others. The emotional support that the patients received in the self-help groups through sharing and discussion showed a positive turn.

Decision-makers often struggle to terminate unsuccessful new product development (NPD) projects, so that escalating commitment occurs. Although research shows that rational and intuitive decision-making styles (DMS) as well as a decision-maker’s affective state determines the performance of NPD decisions, little is known about their influences on escalating commitment. By applying the affect infusion model in an experimental study, we investigate how a decision-maker’s affective state influence their escalating commitment by focusing on their use of a rational and an intuitive DMS. Our findings, based on 366 respondents, show that a rational DMS is unable to reduce commitment escalation. Surprisingly, an intuitive DMS is able to reduce a decision-maker’s commitment in the case of a positive affect, whereas a rational DMS increases their commitment in the case of a negative affect. Thus, our interdisciplinary research on affect and decision-making extends and contributes to research into decision-making during the NPD process as well as into escalating commitment.

This paper investigates the impact of aesthetics in early game development based on a quantitative analysis of 367 early access games. We identified the relationship between aesthetic perception in early video games reflected in the user reviews, comments, and subsequent positive and negative video game recommendations over time. We find that customer co-creation in product innovation is increasingly negative feedback over time when the game’s aesthetic early impression is perceived as negative. The implications for innovation management are that aesthetics design impacts the response to customer-ready prototypes. Managers should take the aesthetic design and user perception in early development into account and not delay the attention to aesthetics to a later product release stage.

Computing with words, CWW, is considered in the context of natural language functioning, unifying language with thinking. Previous attempts at modeling natural languages as well as thinking processes in artificial intelligence have met with computational complexity. To overcome computational complexity we use dynamic logic (DL), an extension of fuzzy logic describing fuzzy to crisp transitions. We suggest a possible architecture motivated by mathematical and neural considerations. We discuss the reasons why CWW has to be modeled jointly with thinking and propose an architecture consistent with brain neural structure and with a wealth of psychological knowledge. The proposed architecture implies the existence of relationships between languages and cultures. We discuss these implications for further evolution of English and Chinese cultures, and for cultural effects of interactions between natural languages and CWW.

Successes of information and cognitive science brought a growing understanding that mind is based on intelligent cognitive processes, which are not limited by language and logic only. A nice overview can be found in the excellent work of Jeff Hawkins "On Intelligence." This view is that thought is a set of informational processes in the brain, and such processes have the same rationale as any other systematic informational processes. Their specifics are determined by the ways of how brain stores, structures and process this information. Systematic approach allows representing them in a diagrammatic form that can be formalized and programmed. Semiotic approach allows for the universal representation of such diagrams. In our approach, logic is just a way of synthesis of such structures, which is a small but clearly visible top of the iceberg. However, most of the efforts were traditionally put into logics without paying much attention to the rest of the mechanisms that make the entire thought system working autonomously. Dynamic fuzzy logic is reviewed and its connections with semiotics are established. Dynamic fuzzy logic extends fuzzy logic in the direction of logic-processes, which include processes of fuzzification and defuzzification as parts of logic. This extension of fuzzy logic is inspired by processes in the brain-mind. The paper reviews basic cognitive mechanisms, including instinctual drives, emotional and conceptual mechanisms, perception, cognition, language, a model of interaction between language and cognition upon the new semiotic models. The model of interacting cognition and language is organized in an approximate hierarchy of mental representations from sensory percepts at the "bottom" to objects, contexts, situations, abstract concepts-representations, and to the most general representations at the "top" of mental hierarchy. Knowledge instinct and emotions are driving feedbacks for these representations. Interactions of bottom-up and top-down processes in such hierarchical semiotic representation are essential for modeling cognition. Dynamic fuzzy logic is analyzed as a fundamental mechanism of these processes. In this paper we are trying to formalize cognitive processes of the human mind using approaches above, and provide interfaces that could allow for their practical realization in software and hardware. Future research directions are discussed.

There is a long-standing debate regarding the nature of the relationship between emotions and consciousness. Majority of existing computational models of emotions largely avoid the issue, and generally do not explicitly address distinctions between the conscious and the unconscious components of emotions. This paper highlights the importance of developing an adequately differentiated vocabulary describing the mental states of interest, and their features and components, for the development of computational models of the relationships between emotions and consciousness. We discuss current psychological theories of emotion, highlighting specific points in the affective processes where links exist with consciousness, and possible roles played by each. We discuss examples of models that are beginning to address components of the interface between emotions and consciousness: models of social emotions, requiring explicit representations of the self; models of affective biases on attention; models of emotion and metacognition; and models of emotion regulation. We conclude with a discussion of some of the challenges associated with modeling mental states whose core distinguishing characteristic is an awareness of affective feelings, and highlight the importance of integrating the diverse approaches to emotion research and modeling currently existing within psychology and neuroscience.

For over a decade neuroscience has uncovered that appropriate decision-making in daily life decisions results from a strong interplay between cognition and covert biases produced by emotional processes. This interplay is particularly important in social contexts: lesions in the pathways supporting these processes provoke serious impairments on social behavior. One important mechanism in social contexts is empathy, fundamental for appropriate social behavior. This paper presents arguments supporting this connection between cognition and emotion, in individual as well as in social contexts. The central claim of this paper is that biologically inspired cognitive architectures ought to include these mechanisms. A taxonomy of computational models addressing emotions is presented, together with a brief survey of the research published in this area. The Prisoner Dilemma game is used as a case study exposing the trade-off between individual rationality and cooperative behavior. Experiments using a simple implementation of empathy and emotion expression, employing an Iterated Prisoner Dilemma setup, illustrate the emergence of a cooperative behavior mutually beneficial for both players.

The current study evaluates the dynamics of the electroencephalogram signals during specific emotional states in order to obtain a detailed understanding of the affective EEG patterns. Employing recurrence analysis, the dynamical states of the emotional brain during visual stimuli is evaluated. Three channels of electroencephalogram time series (Fz, Cz, and Pz) available in eNTERFACE06_EMOBRAIN database are used in this study. Electroencephalogram signals are recorded from 5 subjects in three emotional categories: exciting negative (disgust), neutral and exciting positive (happy). Recurrence quantification analysis (RQA) was applied to study electroencephalogram morphological changes in different emotional states (happy, disgust, neutral). The ANOVA and $t$-test are done to detect significant differences in RQA measures of the EEGs. It has shown that the phase space trajectory becomes more periodic during exciting negative. In addition, the results reveal that in comparison with negative emotion and neutral, the behavior of EEGs in positive emotion is highly chaotic. Performing statistical analysis, significant differences were observed in recurrence rate, determinism, average diagonal, line length, Shannon entropy, laminarity, trapping time among three emotional evoked groups. Although these changes occurred in all EEG channels, a better distinction between each emotional state can be observed in Pz location. It seems that recurrence analysis is a promising non-linear approach for detecting instantaneous changes in the emotional EEG induced by visual stimuli. RQA has the potential to discover differences of signal features in response to an emotional stimulus.

Cognitive load and emotional states may impact the cognitive learning process. Detection of reliable emotions and cognitive load would benefit the design of the emotional intelligent mobility system for visually impaired peoples (VIPs). Application of learning process using electroencephalography (EEG) offers novel and promising approaches to measure cognitive load and emotional states. EEG is used to identify the physiological index that can lead to detecting cognitive load and emotions which can help to explore the knowledge of learning processes. Basically, EEG is a record of ongoing electrical signals to represent the human brain activity due to external and internal stimuli. Therefore, in this study EEG signals are captured from participants with nine different degrees of sight loss people. EEG signals are then used to measure various cognitive load and emotional states to evaluate cognitive learning process for the VIPs. To support the argument of cognitive learning process, the complexity of the tasks in terms of cognitive load and emotional states are quantified considering diverse factors by extracting features from various well-established metrics such as permutation entropy, event related synchronization/desynchronization, arousal, and valence when VIPs are navigating unfamiliar indoor environments. A classification accuracy of door is 86.67% which is achieved by the proposed model. It has almost 10% of improvement compared to another state-of the-art method who have used same dataset. Moreover, we have achieved 10% and 1% more accuracy in the corridor and open space conditions compared to the existing method. Experimental results also demonstrated that learning process is significantly improved considering wide range of obstacles when they are navigating indoor environments.

Investment beliefs, serving as a bridge between high-level objectives and practical decision-making, are increasingly implemented in the investment industry. The present web-based study compares the beliefs of Swedish professional (N = 64) and non-professional (N = 278) investors, testing the links between investment beliefs and portfolio risk-taking in both samples. The results expose significant differences between the beliefs of professionals and others, also showing that the portfolio risk-taking of non-professionals is susceptible to self-confidence and emotional effects while the professionals respond to investment beliefs and risk attitude. The results confirm that disclosure of investment beliefs may reduce tensions between stakeholders and investment managers for the industry’s benefit.

Previous research examined the causes for suboptimal financial decisions. However, scant research has been devoted to examine the psychological effects of suboptimal financial decisions. This chapter empirically examines the emotional outcomes of pension and provident suboptimal financial decisions. Study 1 reveals that the word pension elicits many negative associations. Study 2 reveals that suboptimal pension and provident decisions lead to greater accessibility of negative thoughts. Moreover, the greater accessibility of negative thoughts following suboptimal financial decisions was found to be more salient among male then female participants. In this chapter, we provide theoretical as well as practical applications of the current findings. We also suggest future research directions that could be conducted to better enable capture this important topic.

The past decades have witnessed the rise of normative theorising in International Relations (IR) scholarship with cosmopolitanism emerging as one hegemonic response. However, the IR literature has been so far dominated by Euro-centric philosophies, especially Kantian, and highly grounded in reason and rationality. More recent drifts in IR have foregrounded emotions as a crucial IR variable. In this backdrop, this chapter explores the Indian variant of a rooted sentimental cosmopolitan world order inherent in the Gandhian philosophy. While there is adequate literature on the Gandhian world order, there has been scant focus on the sentimental or emotional core of his vision which is forged on ahimsa and translates to unconditional love which involves a complex range of affective processes, resulting in various cosmopolitan principles like an ethic of duty and equality to fellow citizens as well as a sense of trusteeship between rich and poor nations among others. Besides highlighting how this qualifies as a sentimental cosmopolitan vision from the Global South, the later section of the chapter draws upon contemporary IR research on the circulation of affect and individual transformation to highlight how this world order can be realised. Besides, serving as a possible panacea to the current global landscape characterised by increasing inwardness and coercive manifestations of practical cosmopolitanism, this work seeks to expand a research agenda to uncover and foreground other emotional cosmopolitan visions from the Global South and India, in particular.