The concept of emotion as defined by Rolls is based upon reinforcement mechanisms and their underlying neural networks. He shows how these networks process signals at many levels, through both separate and convergent pathways essential for adaptive action. While many behavioral issues related to emotion are omitted from his review, he succeeds admirably in summarizing both the “current state of the art” in single unit analyses and in pointing out how future research directions may be crafted.

Post-stroke depression is a serious complication of stroke that significantly restricts rehabilitation. The use of immersive virtual reality for stroke survivors is promising. Herein, we investigated the effects of a novel immersive virtual reality training system on PSD and explored induced effective connectivity alterations in emotional networks using multivariate Granger causality analysis. Forty-four patients with PSD were equally allocated into an immersive-virtual reality group and a control group. In addition to their usual rehabilitation treatments, the participants in the immersive-virtual (...) reality group participated in an immersive-virtual reality rehabilitation program, while the patients in the control group received 2D virtual reality rehabilitation training. The Hamilton Depression Rating Scale, modified Barthel Index, and resting-state functional magnetic resonance imaging data were collected before and after a 4-week intervention. rsfMRI data were analyzed using multivariate GCA. We found that the immersive virtual reality training was more effective in improving depression in patients with PSD but had no statistically significant improvement in MBI scores compared to the control group. The GCA showed that the following causal connectivities were strengthened after immersive virtual reality training: from the amygdala, insula, middle temporal gyrus, and caudate nucleus to the dorsolateral prefrontal cortex; from the insula to the medial prefrontal cortex; and from the thalamus to the posterior superior temporal sulcus. These causal connectivities were weakened after treatment in the control group. Our results indicated the neurotherapeutic use of immersive virtual reality rehabilitation as an effective non-pharmacological intervention for PSD; the alteration of causal connectivity in emotional networks might constitute the neural mechanisms underlying immersive-virtual reality rehabilitation in PSD. (shrink)

The traditional evaluation methods of foreign language communication skills cannot deal with emotional information in the process of communication. Psychologists believe that a real personalized evaluation system should be smart. Based on the emotion network technology of artificial emotion intelligence, aiming at the shortcomings of the traditional evaluation system, this paper puts forward a new language ability evaluation system with certain emotion judgment function. The system can easily obtain and identify emotions in foreign communication, and can also (...) carry out individual learning at the cognitive and emotional levels on the basis of comprehensive analysis of emotions and communication effects. (shrink)

A widespread perspective describes emotions as distinct categories bridged by fuzzy boundaries, indicating that emotions are distinct and dimensional at the same time. Theoretical and methodological approaches to this perspective still need further development. We conceptualize emotions as overlapping networks of causal relationships between emotion components—networks representing distinct emotions share components with and relate to each other. To investigate this conceptualization, we introduce network analysis to emotion research and apply it to the reanalysis of a data (...) set on multiple positive emotions. Specifically, we describe the estimation of networks from data, and the detection of overlapping communities of nodes in these networks. The network perspective has implications for the understanding of distinct emotions, their co-occurrence, and their measurement. (shrink)

Modeling emotion processes remains a conceptual and methodological challenge in affective sciences. In responding to the other target articles in this special section on “Emotion and the Brain” and the comments on our article, we address the issue of potentially separate brain networks subserving the functions of the different emotion components. In particular, we discuss the suggested role of component synchronization in producing information integration for the dynamic emergence of a coherent emotion process, as well (...) as the links between incentive salience and concern-relevance in the elicitation of emotion. (shrink)

Emotional openness is characterised by a capacity to tolerate threatening self-relevant material and an interest towards new emotional situations. We investigated how specific networks of memories could be an important contributing factor to emotional openness. At Phase 1, participants completed measures of personality traits and emotional intelligence, described a self-defining memory, provided other memories associated with it, and rated the valence of each of their memories. A score assessing the complexity of this memory network, comprising the number of memories (...) reported and their valence diversity, was created. Two weeks later, in laboratory, participants watched an anxiety-inducing film and took part in an interview assessing their emotional openness to the film. They completed a cognitive task before and after the film to measure ego depletion. Controlling for traits and emotional intelligence, memory network complexity was positively associated with emotional openness and negatively with ego depletion. The mental organisation of self-defining memories thus appears to be a critical factor contributing to emotional openness. (shrink)

The processing of emotional nonlinguistic information in speech is defined as emotional prosody. This auditory nonlinguistic information is essential in the decoding of social interactions and in our capacity to adapt and react adequately by taking into account contextual information. An integrated model is proposed at the functional and brain levels, encompassing 5 main systems that involve cortical and subcortical neural networks relevant for the processing of emotional prosody in its major dimensions, including perception and sound organization; related action (...) tendencies; and associated values that integrate complex social contexts and ambiguous situations. (shrink)

Theoretical research into the emotional attributes of ideological and political education can improve our ability to understand human emotion and solve socio-emotional problems. To that end, this study undertook an analysis of emotion in ideological and political education by integrating a gate recurrent unit with an attention mechanism. Based on the good results achieved by BERT in the downstream network, we use the long focusing attention mechanism assisted by two-way GRU to extract relevant information and global information of (...) ideological and political education and emotion analysis, respectively. The two kinds of information complement each other, and the accuracy of emotion information can be further improved by combining neural network model. Finally, the validity and domain adaptability of the model were verified using several publicly available, fine-grained emotion datasets. (shrink)

We investigated if emotion regulation can be improved through self-regulation training on non-emotional brain regions, as well as how to change the brain networks implicated in this process. During the training period, the participants were instructed to up-regulate their right dorsolateral prefrontal cortex activity according to real-time functional near-infrared spectroscopy neurofeedback signals, and there was no emotional element. The results showed that the training significantly increased emotion regulation, resting-state functional connectivity within the emotion regulation network and (...) frontoparietal network, and rsFC between the ERN and amygdala; however, training did not influence the rsFC between the FPN and the amygdala. However, self-regulation training on rDLPFC significantly improved emotion regulation and generally increased the rsFCs within the networks; the rsFC between the ERN and amygdala was also selectively increased. The present study also described a safe approach that may improve emotion regulation through self-regulation training on non-emotional brain regions. (shrink)

The target article is well in accordance with recent theoretical advances considering the complex large-scale brain network organization underlying emotions. Given current limitations of the methods in brain science, however, research is faced with the difficult question as to how it will be possible to elucidate the complex nonlinear interactions, the neurotransmitters involved, and the excitatory or inhibitory nature of neural processes underlying human emotion in such networks. Moreover, while investigating the network properties of neural processes underlying emotions, (...) it is also important to keep in mind that specific brain structures, or specific brain networks, generate specific emotions. Thus, while aiming at elucidating complex large-scale brain networks of emotion, it is important to identify emotional specificity of, or within, these networks. (shrink)

With the rapid development of Internet technology and the reform of the education model, online education has been widely recognized and applied. In the process of online learning, various types of browsing behavior characteristic data such as learning engagement and attitude will be generated. These learning behaviors are closely related to academic performance. In-depth exploration of the laws contained in the data can provide teaching assistance for education administrators. In this paper, the random forest algorithm is used to determine the (...) importance of factors for the relationship between 11 learning behavior data and students' psychological quality test data, a total of 12-dimensional feature data and grades, and extracts six factors that have a greater impact on grades. Through the research of this paper, the method of random forest is innovatively used, and it is found that the psychological factor is one of the six important factors. This paper innovatively uses BP neural network as the prediction model, takes six important factors as input, and establishes a complete method of online learning performance prediction. The research in this paper can help teachers monitor students' learning status, detect abnormal learning behaviors and problems in time, and make timely and effective teaching interventions and adjustments in advance according to the abnormal status of students found. (shrink)

ABSTRACTEmotion understanding, which can broadly be defined as expertise in the meaning of emotion, is a core component of emotional intelligence and facilitates better intra- and interpersonal outcomes. However, to date only very few standard tests to measure emotion understanding in healthy adults exist. Here, we present two new performance-based tests that were developed and are scored based on componential emotion theory and large-scale cross-cultural empirical findings. These instruments intend to measure facets of emotion understanding that (...) are not included in existing tests. The first test measures the ability to understand and label emotional experiences of a target person from a description of emotion features covering five emotion components embedded in a written vignette. The second test measures semantic knowledge about which features from each component a... (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)

The artificial neural network is employed to study children’s psychological emotion recognition to fully reflect the psychological status of preschool children and promote the healthy growth of preschool children. Specifically, the ANN model is used to construct the human physiological signal measurement platform and emotion recognition platform to measure the human physiological signals in different psychological and emotional states. Finally, the parameter values are analyzed on the emotion recognition platform to identify the children’s psychological and emotional states (...) accurately. The experimental results demonstrate that the recognition ability of children aged 4–6 to recognize the three basic emotions of happiness, calm, and fear increases with age. Besides, there are significant age differences in children’s recognition of happiness, calm, and fear. In addition, the effect of 4-year-old children on the theory of mind tasks is less than that of 5- to 6-year-old children, which may be related to more complex cognitive processes. Preschool children are experiencing a stage of rapid emotional development. If children cannot be guided to reasonably identify and deal with emotions at this stage, their education level and social ability development will be significantly affected. Therefore, this study has significant reference value for preschool children’s emotional recognition and guidance and can promote children’s emotional processing and mental health. (shrink)

Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use (...) a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient of the speech signal is used as the input of the improved long and short-term memory network. To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed. (shrink)

This study mainly focuses on the emotion analysis method in the application of psychoanalysis based on sentiment recognition. The method is applied to the sentiment recognition module in the server, and the sentiment recognition function is effectively realized through the improved convolutional neural network and bidirectional long short-term memory model. First, the implementation difficulties of the C-BiL model and specific sentiment classification design are described. Then, the specific design process of the C-BiL model is introduced, and the innovation of (...) the C-BiL model is indicated. Finally, the experimental results of the models are compared and analyzed. Among the deep learning models, the accuracy of the C-BiL model designed in this study is relatively high irrespective of the binary classification, the three classification, or the five classification, with an average improvement of 2.47% in Diary data set, 2.16% in Weibo data set, and 2.08% in Fudan data set. Therefore, the C-BiL model designed in this study can not only successfully classify texts but also effectively improve the accuracy of text sentiment recognition. (shrink)

We used social network analysis to examine how adaptive ER strategies and maladaptive ER strategies predict the creation and maintenance of friendship and conflict relationships within a mixed-gender social group. Participants reported on emotion regulation, friendship, and conflict nominations at two time points. Stochastic actor-oriented models revealed that similarity in endorsement of adaptive ER strategies predicted maintenance of friendship and conflict relationships over time. However, new conflict relationships were more likely to form between those who differed in use of (...) adaptive ER. Finally, more frequent use of maladaptive ER strategies was related to termination of existing conflict ties and the creation of new ones. Deploying social network analysis as a methodology for examining social relationships enables the unpacking the dynamics of multiple social relationships, identifying the role of ER for structuring of social relationships among group members. Although cognitive ER is an intra-individual process, it fundamentally occurs within a social environment and our results advance the knowledge of how ER contributes to how this social environment is created in a first place. (shrink)

In the human-computer interaction, electroencephalogram access for automatic emotion recognition is an effective way for robot brains to perceive human behavior. In order to improve the accuracy of the emotion recognition, a method of EEG access for emotion recognition based on a deep hybrid network was proposed in this paper. Firstly, the collected EEG was decomposed into four frequency band signals, and the multiscale sample entropy features of each frequency band were extracted. Secondly, the constructed 3D MSE (...) feature matrices were fed into a deep hybrid network for autonomous learning. The deep hybrid network was composed of a continuous convolutional neural network and hidden Markov models. Lastly, HMMs trained with multiple observation sequences were used to replace the artificial neural network classifier in the CNN, and the emotion recognition task was completed by HMM classifiers. The proposed method was applied to the DEAP dataset for emotion recognition experiments, and the average accuracy could achieve 79.77% on arousal, 83.09% on valence, and 81.83% on dominance. Compared with the latest related methods, the accuracy was improved by 0.99% on valence and 14.58% on dominance, which verified the effectiveness of the proposed method. (shrink)

Through the analysis of emotional tendency in online public opinion, governments and enterprises can stabilize people’s emotion more effectively and maintain social stability. The problem studied in this paper is how to analyze the emotional tendency of online public opinion efficiently, and finally, this paper chooses deep learning algorithm to perform fast analysis of emotional tendency of online public opinion. This paper briefly introduced the structure of the basic model used for emotional tendency analysis of online public opinion and (...) the convolutional neural network model used for text emotion classification. Then, the CNN model was improved by long short-term memory. A simulation experiment was carried out on MATLAB for the improved text emotion classification model to verify the influence of activation function type on the improved model and the performance difference between the improved model and support vector machine and traditional CNN models. The results showed that the improved classification model that adopted the sigmoid activation function had higher accuracy and was less affected by language than the relu and tanh activation functions; the improved classification model had the highest accuracy, recall rate, and F-value in classifying emotional tendency of web texts, followed by the traditional CNN model and the SVM model. (shrink)

This is an excerpt from a report that highlights and explores five questions which arose from the workshop on perceptual learning and perceptual recognition at the University of Toronto, Mississauga on May 10th and 11th, 2012. This excerpt explores the question: How do we recognize distinct types of emotion in music?

Building relationships is crucial for satisfaction and success, especially when entering new social contexts. In the present paper, we investigate whether attempting to improve others’ feelings helps people to make connections in new networks. In Study 1, a social network study following new networks of people for a twelve-week period indicated that use of interpersonal emotion regulation (IER) strategies predicted growth in popularity, as indicated by other network members’ reports of spending time with the person, in work (...) and non-work interactions. In Study 2, linguistic analysis of the tweets from over 8000 Twitter users from formation of their accounts revealed that use of IER predicted greater popularity in terms of the number of followers gained. However, not all types of IER had positive effects. Behavioral IER strategies (which use behavior to reassure or comfort in order to regulate affect) were associated with greater popularity, while cognitive strategies (which change a person’s thoughts about his or her situation or feelings in order to regulate affect) were negatively associated with popularity. Our findings have implications for our understanding of how new relationships are formed, highlighting the important the role played by intentional emotion regulatory processes. (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)

Emotion-based sentimental analysis has recently received a lot of interest, with an emphasis on automated identification of user behavior, such as emotional expressions, based on online social media texts. However, the majority of the prior attempts are based on traditional procedures that are insufficient to provide promising outcomes. In this study, we categorize emotional sentiments by recognizing them in the text. For that purpose, we present a deep learning model, bidirectional long-term short-term memory, for emotion recognition that takes (...) into account five main emotions. We use our experimental assessments on the emotion dataset to accomplish the emotion categorization job. The datasets were evaluated and the findings revealed that, when compared to state-of-the-art methodologies, the proposed model can successfully categorize user emotions into several classifications. Finally, we assess the efficacy of our strategy using statistical analysis. This research’s findings help firms to apply best practices in the selection, management, and optimization of policies, services, and product information. (shrink)

BackgroundThe neural activity and functional networks of emotion-based cognitive reappraisal have been widely investigated using electroencephalography and functional magnetic resonance imaging. However, single-mode neuroimaging techniques are limited in exploring the regulation process with high temporal and spatial resolution.ObjectivesWe proposed a source localization method with multimodal integration of EEG and fMRI and tested it in the source-level functional network analysis of emotion cognitive reappraisal.MethodsEEG and fMRI data were simultaneously recorded when 15 subjects were performing the emotional cognitive reappraisal (...) task. Fused priori weighted minimum norm estimation with sliding windows was proposed to trace the dynamics of EEG source activities, and the phase lag index was used to construct the functional brain network associated with the process of downregulating negative affect using the reappraisal strategy.ResultsThe functional networks were constructed with the measure of PLI, in which the important regions were indicated. In the gamma band source-level network analysis, the cuneus, the lateral orbitofrontal cortex, the superior parietal cortex, the postcentral gyrus, and the pars opercularis were identified as important regions in reappraisal with high betweenness centrality.ConclusionThe proposed multimodal integration method for source localization identified the key cortices involved in emotion regulation, and the network analysis demonstrated the important brain regions involved in the cognitive control of reappraisal. It shows promise in the utility in the clinical setting for affective disorders. (shrink)

In June 2014, a paper reporting the results of a study into ‘emotional contagion’ on Facebook was published. This research has already attracted a great deal of criticism for problems surrounding informed consent. While most of this criticism is justified, other relevant consent issues have gone unremarked, and the study has several other ethical flaws which collectively indicate the need for better regulation of health and mood research using social networks.

Emotion Review, Volume 14, Issue 2, Page 111-113, April 2022. Suri and Gross's 2022 connectionist emotion theory can be considered as one version of a family of theories known as network theories of emotion. It presents similarities and differences with older versions of network theories. Like previous network theories and several other traditional emotion theories, however, the connectionist theory remains a reactive theory. The class of reactive theories can be meaningfully contrasted with a class of instrumental (...) theories of which the goal-directed theory is a representative example. Although the latter theory does not deny the existence of emotion networks in memory, it does not grant them many causal powers, thereby seriously restricting their explanatory territory. Future research efforts may help disambiguate between both classes of theories. (shrink)

Dispositional mindfulness and emotion regulation are two psychological constructs closely interrelated, and both appear to improve with the long-term practice of mindfulness meditation. These constructs appear to be related to subcortical, prefrontal, and posterior brain areas involved in emotional processing, cognitive control, self-awareness, and mind wandering. However, no studies have yet discerned the neural basis of dispositional mindfulness that are minimally associated with emotion regulation. In the present study, we use a novel brain structural network analysis approach to (...) study the relationship between structural networks and dispositional mindfulness, measured with two different and widely used instruments [Mindfulness Attention Awareness Scale and Five Facet Mindfulness Questionnaire ], taking into account the effect of emotion regulation difficulties. We observed a number of different brain regions associated with the different scales and dimensions. The total score of FFMQ and MAAS overlap with the bilateral parahippocampal and fusiform gyri. Additionally, MAAS scores were related to the bilateral hippocampus and the FFMQ total score to the right insula and bilateral amygdala. These results indicate that, depending on the instrument used, the characteristics measured could differ and could also involve different brain systems. However, it seems that brain areas related to emotional reactivity and semantic processing are generally related to Dispositional or trait mindfulness, regardless of the instrument used. (shrink)

Although emotion is closely associated with motivation, and interacts with perception, cognition, and action, many conceptualizations still treat emotion as separate from these domains. Here, a comparative/evolutionary anatomy framework is presented to motivate the idea that long-range, distributed circuits involving the midbrain, thalamus, and forebrain are central to emotional processing. It is proposed that emotion can be understood in terms of large-scale network interactions spanning the neuroaxis that form “functionally integrated systems.” At the broadest level, the argument (...) is made that we need to move beyond a Newtonian view of causation to one involving complex systems where bidirectional influences and nonlinearities abound. Therefore, understanding interactions between subsystems and signal integration becomes central to unraveling the organization of the emotional brain. (shrink)

Lindquist et al. provide a convincing case against what they call the locationist account of emotion. Their quantitative approach elegantly illustrates the shortcomings of this still-entrenched viewpoint. Here, I discuss how a network perspective will advance our understanding of structure-function mappings in general, and the relationship between emotion and cognition in the brain.

Fieldwork experiences in the summer of 2003 resulted in confusion regarding the ethical positioning of myself (the interviewer) in relation to the multiple 'actants' that constituted the research subject(s). This paper explores some of these personal issues and conflicts in order to clarify, gain perspective on and critique the nature (and indeed the 'Nature') of my fieldwork. The multiple positioning of participants within networks of agricultural and social ethics is addressed. I borrow Lewis Holloway's idea of relational ethical identity, (...) in order to resituate and rethink the interviews in terms of actor-network theory. This paper argues that ethical identities and ethical 'natures' can be understood as relationally constructed and constituted within networks. The ways in which notions of (un)ethical agricultural relations shaped each interview experience are also explored. Specifically, how did my ideas of (un)ethical farming influence my 'ethical take' on how different farmers operated? I also argue that all encounters are ethically charged and, as such, encounters result in emotional tensions. (shrink)

Lonely students typically underperform academically. According to several studies, the COVID-19 pandemic is an important risk factor for increases in loneliness, as the contact restrictions and the switch to mainly online classes potentially burden the students. The previously familiar academic environment, as well as the exchange with peers and lecturers on site, were no longer made available. In our cross-sectional study, we examine factors that could potentially counteract the development of higher education student loneliness during the COVID-19 pandemic from a (...) social network perspective. During the semester, N = 283 students from across all institutional faculties of a German comprehensive university took part in an online survey. We surveyed their social and emotional experiences of loneliness, their self-reported digital information-sharing behavior, and their current egocentric networks. Here, we distinguished between close online contacts and close offline contacts. In addition, we derived the interconnectedness and heterogeneity of students’ contacts. To obtain the latter, we used a novel two-step method combining t-distributed stochastic neighbor embedding and cluster analysis. We explored the associations of the aforementioned predictors on social and emotional loneliness separately using two hierarchical multiple linear regression models. Our results suggest that social loneliness is strongly related to digital information-sharing behavior and the network structure of close contacts. In particular, high information-sharing behavior, high number of close contacts, a highly interconnected network, and a homogeneous structure of close contacts were associated with low social loneliness. Emotional loneliness, on the other hand, was mainly related to network homogeneity, in the sense that students with homogeneous close contacts networks experienced low emotional loneliness. Overall, our study highlights the central role of students’ close social network on feelings of loneliness in the context of COVID-19 restrictions. Limitations and implications are discussed. (shrink)

Suri and Gross's 2022 connectionist emotion theory can be considered as one version of a family of theories known as network theories of emotion. It presents similarities and differences with older...