Emotion Generation Research Articles

Exploring the direction of the English translation of environmental protection articles based on the robot cognitive- emotional interaction model

To broaden the application area of the cognitive-emotional interaction model for robots. In this paper, an algorithmic model for the English translation of environmental articles based on a cognitive-emotional interaction model for robots is used to model the process of emotion generation using reinforcement learning. Similarly, positivity and empathy are used to quantify the reward function for emotional state assessment, and the optimal emotional strategy selection is derived based on the utility function. In the process of article translation by the robot, Lagrangian factors are introduced to make the translation probability maximum process transformed into the process of obtaining the highest value of the auxiliary function at a random state. Finally, the effectiveness of the robot's cognitive-emotional interaction model in the English translation of environmental protection articles is verified by the Chinese-English parallel question-and-answer dataset. The experimental results demonstrate that this model can not only be used for the English translation of environmental protection articles but also can give the corresponding English translation work similar to human emotions, which can better help people understand the meaning of English. It also provides a basis and direction for the subsequent in-depth application of the robot cognitive-emotional interaction model in various fields.

On the latency of object recognition and affect: Evidence from speeded reaction time tasks.

According to the semantic primacy hypothesis of emotion generation, stimuli must be semantically categorized to evoke emotions. This hypothesis was tested in two speeded reaction time experiments that also explored the processes underlying valence judgments. Participants viewed pleasant and unpleasant pictures. In different blocks of trials, they pressed a key as soon as they experienced the feeling evoked by a picture, recognized the depicted object, or detected the valence (pleasant/unpleasant) of the picture. Object recognition was significantly earlier than affect onset, and the two latencies were positively correlated. The latency of valence detection was in between the latencies of object recognition and affect and correlated with both. Experiment 2 additionally found that blurring the pictures delayed the onset of affect and that this effect was partially mediated by delayed object recognition. In contrast, false-coloring the pictures was found to delay affect mainly by reducing its intensity. Coloring also delayed valence judgments of pleasant pictures. The findings provide further chronometric support for the semantic primacy hypothesis of emotion generation and shed light on the processes underlying valence judgments. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Functional connectivity profiles of the default mode and visual networks reflect temporal accumulative effects of sustained naturalistic emotional experience

Determining and decoding emotional brain processes under ecologically valid conditions remains a key challenge in affective neuroscience. The current functional Magnetic Resonance Imaging (fMRI) based emotion decoding studies are mainly based on brief and isolated episodes of emotion induction, while sustained emotional experience in naturalistic environments that mirror daily life experiences are scarce. Here we used 12 different 10-minute movie clips as ecologically valid emotion-evoking procedures in n = 52 individuals to explore emotion-specific fMRI functional connectivity (FC) profiles on the whole-brain level at high spatial resolution (432 parcellations including cortical and subcortical structures). Employing machine-learning based decoding and cross validation procedures allowed to investigate FC profiles contributing to classification that can accurately distinguish sustained happiness and sadness and that generalize across subjects, movie clips, and parcellations. Both functional brain network-based and subnetwork-based emotion classification results suggested that emotion manifests as distributed representation of multiple networks, rather than a single functional network or subnetwork. Further, the results showed that the Visual Network (VN) and Default Mode Network (DMN) associated functional networks, especially VN-DMN, exhibited a strong contribution to emotion classification. To further estimate the temporal accumulative effect of naturalistic long-term movie-based video-evoking emotions, we divided the 10-min episode into three stages: early stimulation (1∼200 s), middle stimulation (201∼400 s), and late stimulation (401∼600 s) and examined the emotion classification performance at different stimulation stages. We found that the late stimulation contributes most to the classification (accuracy=85.32%, F1-score=85.62%) compared to early and middle stimulation stages, implying that continuous exposure to emotional stimulation can lead to more intense emotions and further enhance emotion-specific distinguishable representations. The present work demonstrated that sustained happiness and sadness under naturalistic conditions are presented in emotion-specific network profiles and these expressions may play different roles in the generation and modulation of emotions. These findings elucidated the importance of network level adaptations for sustained emotional experiences during naturalistic contexts and open new venues for imaging network level contributions under naturalistic conditions.

Emotional change: Neural mechanisms based on semantic pointers.

Emotional change includes generation of new emotions, switching from one emotion to another, and alteration of the frequency and intensity of emotions. Psychotherapists help clients to reduce negative emotions such as sadness and anxiety and increase positive emotions such as happiness and hope. We explain such emotional shifts by the semantic pointer theory of emotions, which views them as brain processes that integrate neural representations of situations, appraisals of the goal-relevance of those situations, and physiological reactions to the situations. This theory can explain many kinds of emotional change, including the generation and shifting of mixed, nested, and dispositional emotions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Executive functioning and nontarget emotions in late life.

When confronted with an emotion prototype (e.g., loss), individuals may experience not only target emotions (e.g., sadness), but also nontarget emotions (emotions that are atypical or incongruent with an emotion prototype; e.g., gratitude in response to loss). What are the cognitive correlates of nontarget emotions? Drawing from models of emotion generation, the present laboratory-based study examined associations between aspects of executive functioning (i.e., working memory, inhibition, verbal fluency) and the subjective experience of positive and negative nontarget emotions in response to sad and awe film clips in 129 healthy older adults. Findings showed that (a) lower working memory was associated with higher levels of positive and negative nontarget (but not target) emotions in response to sad and awe film clips. Moreover, (b) associations were specific to working memory and not found for other aspects of executive functioning. Associations were (c) robust when accounting for age, gender, education, target emotion and physiological arousal (except for negative nontarget emotions in response to the sad film clips). Finally, (d) findings were driven by awe, happiness, calm, and gratitude for the sad film clips and disgust, fear, sadness, compassion, happiness, love, and excitement for the awe film clips. Overall, these findings show a link between lower working memory function and elevated nontarget emotional experiences in late life. Directions for future research are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Individual differences in harm-related moral values are associated with functional integration of large-scale brain networks of emotional regulation.

Emotions affects moral judgements, and controlled cognitive processes regulate those emotional responses during moral decision making. However, the neurobiological basis of this interaction is unclear. We used a graph theory measurement called participation coefficient ('PC') to quantify the resting-state functional connectivity within and between four meta-analytic groupings (MAGs) associated with emotion generation and regulation, to test whether that measurement predicts individual differences in moral foundations-based values. We found that the PC of one of the MAGs (MAG2) was positively correlated with one of the five recognized moral foundations-the one based on harm avoidance. We also found that increased inter-module connectivity between the ventromedial prefrontal cortex, dorsolateral prefrontal cortex and middle temporal gyrus with other nodes in the four MAGs was likewise associated with higher endorsement of the Harm foundation. These results suggest that individuals' sensitivity to harm is associated with functional integration of large-scale brain networks of emotional regulation. These findings add to our knowledge of how individual variations in our moral values could be reflected by intrinsic brain network organization and deepen our understanding of the relationship between emotion and cognition during evaluations of moral values.

Memristive Circuit Design of Brain-Like Emotional Learning and Generation.

In this work, a bionic memristive circuit with the functions of emotional learning and generation is proposed, which can perform brain-like emotional learning and generation based on various types of input information. The proposed circuit is designed based on the brain emotional learning theory in the limbic system, which mainly includes three layers of design: 1) the bottom layer is the design of the basic unit modules, such as neuron and synapse; 2) the middle layer is the design of the functional modules related to emotional learning in the limbic system, such as the amygdala, thalamus, and so on; and 3) the top layer is the design of the overall circuit, which is used to realize the function of the emotional generation. A 2-D emotional space composed of valence and arousal signals is adopted. According to the above bottom-up circuit design method, the valence and arousal signals can be generated, respectively, by designing corresponding emotional learning circuits, so as to form continuous emotions. The volatile and nonvolatile memristors are mainly used to mimic the functions of the neuron and synapse at the bottom layer of the circuit to achieve the core emotional learning function of the middle layer, thereby constructing a brain-like information processing architecture to realize the function of the emotional generation in the top layer. The simulation results in PSPICE show that the proposed circuit can learn and generate emotions like humans. If the proposed circuit is applied to a humanoid robot platform through further research, the robot may have the ability of personalized emotional interaction with humans, so that it can be effectively used in emotional companionship and other aspects.

Emotionally Controllable Talking Face Generation from an Arbitrary Emotional Portrait

With the continuous development of cross-modality generation, audio-driven talking face generation has made substantial advances in terms of speech content and mouth shape, but existing research on talking face emotion generation is still relatively unsophisticated. In this work, we present Emotionally Controllable Talking Face Generation from an Arbitrary Emotional Portrait to synthesize lip-sync and an emotionally controllable high-quality talking face. Specifically, we take a facial reenactment perspective, using facial landmarks as an intermediate representation driving the expression generation of talking faces through the landmark features of an arbitrary emotional portrait. Meanwhile, decoupled design ideas are used to divide the model into three sub-networks to improve emotion control. They are the lip-sync landmark animation generation network, the emotional landmark animation generation network, and the landmark-to-animation translation network. The two landmark animation generation networks are responsible for generating content-related lip area landmarks and facial expression landmarks to correct the landmark sequences of the target portrait. Following this, the corrected landmark sequences and the target portrait are fed into the translation network to generate an emotionally controllable talking face. Our method controls the expressions of talking faces by driving the emotional portrait images while ensuring the generation of animated lip-sync, and can handle new audio and portraits not seen during training. A multi-perspective user study and extensive quantitative and qualitative evaluations demonstrate the superiority of the system in terms of visual emotion representation and video authenticity.

The Process Model of Emotion Regulation Questionnaire: Assessing Individual Differences in Strategy Stage and Orientation.

The study of individual differences in emotion regulation has typically focused exclusively either on the stage of the emotion generation process at which regulation occurs or on the engagement versus disengagement orientation of the regulation efforts. We introduce a new measure that samples equally across each stage of the process model of emotion regulation and from both engagement and disengagement orientations. Through five studies (ntotal = 2,543), we present the development and convergent, discriminant, and predictive validity evidence for the Process Model of Emotion Regulation Questionnaire (PMERQ). We show the final 10-scale 45-item questionnaire has acceptable internal consistency, is invariant between genders and across the age range, and has sufficient convergent and discriminant validity. The PMERQ also predicts affective and relationship functioning with strategies occurring earlier in the process model generally showing the strongest effects. We propose the PMERQ is a valuable measure to assess individual differences in the use of emotion regulation strategies.

Increasing empathic concern relates to salience network hyperconnectivity in cognitively healthy older adults with elevated amyloid-β burden

Enhanced emotional empathy, the ability to share others’ affective experiences, can be a feature of Alzheimer’s disease (AD), but whether emotional empathy increases in the preclinical phase of the disease is unknown. We measured emotional empathy over time (range = 0 – 7.3 years, mean = 2.4 years) in 86 older adults during a period in which they were cognitively healthy, functionally normal, and free of dementia symptoms. For each participant, we computed longitudinal trajectories for empathic concern (i.e., an other-oriented form of emotional empathy that promotes prosocial actions) and emotional contagion (i.e., a self-focused form of emotional empathy often accompanied by feelings of distress) from informant ratings of participants’ empathy on the Interpersonal Reactivity Index. Amyloid-β (Aβ) positron emission tomography (PET) scans were used to classify participants as either Aβ positive (Aβ+, n = 23) or negative (Aβ-, n = 63) based on Aβ-PET cortical binding. Participants also underwent structural and task-free functional magnetic resonance imaging approximately two years on average after their last empathy assessment, at which time most participants remained cognitively healthy. Results indicated that empathic concern, but not emotional contagion, increased more over time in Aβ+ participants than in Aβ- participants despite no initial group difference at the first measurement. Higher connectivity between certain salience network node-pairs (i.e., pregenual anterior cingulate cortex and periaqueductal gray) predicted longitudinal increases in empathic concern in the Aβ+ group but not in the Aβ- group. The Aβ+ participants also had higher overall salience network connectivity than Aβ- participants despite no differences in gray matter volume. These results suggest gains in empathic concern may be a very early feature of AD pathophysiology that relates to hyperconnectivity in the salience network, a system that supports emotion generation and interoception. A better understanding of emotional empathy trajectories in the early stages of AD pathophysiology will broaden the lens on preclinical AD changes and help clinicians to identify older adults who should be screened for AD biomarkers.

Emotional state analysis based on speech recognition signals

At present, as intelligent speech recognition is widely used, the importance of speech emotion recognition technology is also gradually gaining recognition. Considering the current social pressure, the generation of negative emotions has become particularly common. Therefore, it is necessary to increase the recognition of different emotions by using the speech recognition system, which has been perfected, so as to play a role in anticipation and prevention. In this paper, we focus on the negative effects of negative emotions on people's lives, and use K-nearest classifier to identify different emotions, and accurately identify them, and prevent and monitor them. Finally, the feasibility of this method is confirmed by simulating the process.

Corrigendum to “Emotion Generation and Emotion Regulation: The Role of Emotion Beliefs”. [Journal of Affective Disorders Reports 9C (2022) 100351

Corrigendum to “Emotion Generation and Emotion Regulation: The Role of Emotion Beliefs”. [Journal of Affective Disorders Reports 9C (2022) 100351

Associations of sleep and emotion regulation processes in childhood and adolescence - a systematic review, report of methodological challenges and future directions.

Sleep and emotions are closely associated; however, the methodological challenges in the examination of sleep and the processes of emotion regulation in children and adolescents have not been investigated so far. Additionally, there is the demand to identify the levels of emotion regulating processes in which problematic or restricted sleep causes effect. Experimental sleep deprivation as well as prevalent sleep problems have been found to have negative influence on mental health and regulating functions. This review focuses first on the methodological protocols of the included studies. Subsequently, the results are summarized in the context of a multilevel model of emotion regulation. Thereafter, suggestions for future directions are given. Sleep problems and sleep deprivation are associated with a decrease of functional emotion regulating behavior and impaired emotion generation, and prolonged sleep enhances better mood and affect states, positive emotion expression, and faster sensory processing in response to emotional stimuli. This literature review highlights the limitations in current research, focusing on types of measurements, task characteristics, and data analysis. At the conclusion, suggestions are given for the future research direction in the field of sleep and emotion regulation in children and adolescents.

On the latency of object recognition and affect: Evidence from temporal order and simultaneity judgments.

According to the semantic primacy hypothesis of emotion generation, stimuli must be semantically categorized to evoke emotions. This hypothesis was tested for the subjective component of emotions in four chronometric experiments in which the conscious recognition of emotion-eliciting objects and the onset of affect was timed using temporal order judgments (TOJs, Exp. 1a, 1b, and 3) and simultaneity judgments (SJs, Exp. 2). Participants viewed pictures that elicited pleasant or unpleasant feelings. At varying intervals before and after picture onset, a visual probe stimulus was presented. In separate blocks of trials, the participants judged when they recognized the object shown in the picture and noticed the feeling evoked by the picture: Before or after the probe (TOJs), or simultaneously/not simultaneously with the probe (SJs). Psychometric functions were fitted to the data of the individual participants to determine the point of subjective simultaneity (PSS) of the target events and the probe. In both tasks, the mean PSS of affect occurred significantly later than the PSS of object recognition, with an average delay of about 120 ms. A positive lag between object recognition and affect was found for nearly all participants and pictures. In addition, the latencies of object recognition and affect were positively correlated. The found temporal order of object recognition and affect is consistent with the findings of previous studies using speeded reaction time tasks. Implications of the findings for the cognition-emotion debate and for bodily feedback theories of emotional experience are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Study on Two Conditions for the Realization of Artificial Empathy and Its Cognitive Foundation

The realization of artificial empathy is conditional on the following: on the one hand, human emotions can be recognized by AI and, on the other hand, the emotions presented by artificial intelligence are consistent with human emotions. Faced with these two conditions, what we explored is how to identify emotions, and how to prove that AI has the ability to reflect on emotional consciousness in the process of cognitive processing, In order to explain the first question, this paper argues that emotion identification mainly includes the following three processes: emotional perception, emotional cognition and emotional reflection. It proposes that emotional display mainly includes the following three dimensions: basic emotions, secondary emotions and abstract emotions. On this basis, the paper proposes that the realization of artificial empathy needs to meet the following three cognitive processing capabilities: the integral processing ability of external emotions, the integral processing ability of proprioceptive emotions and the processing ability of integrating internal and external emotions. We are open to whether the second difficulty can be addressed. In order to gain the reflective ability of emotional consciousness for AI, the paper proposes that artificial intelligence should include consistency on identification of external emotions and emotional expression, processing of ontological emotions and external emotions, integration of internal and external emotions and generation of proprioceptive emotions.

Evaluation of film stimuli for the assessment of social-emotional processing: a pilot study.

Difficulties in top-down and bottom-up emotion generation have been proposed to play a key role in the progression of psychiatric disorders. The aim of the current study was to develop more ecologically valid measures of top-down interpretation biases and bottom-up evoked emotional responses. A total of 124 healthy female participants aged 18-25 took part in the study. We evaluated two sets of 18 brief film clips. The first set of film clips presented ambiguous social situations designed to examine interpretation biases. Participants provided written interpretations of each ambiguous film clip which were subjected to sentiment analysis. We compared the films in terms of the valence of participants interpretations. The second set of film clips presented neutral and emotionally provoking social scenarios designed to elicit subjective and facial emotional responses. While viewing these film clips participants mood ratings and facial affect were recorded and analysed using exploratory factor analyses. Most of the 18 ambiguous film clips were interpreted in the expected manner while still retaining some ambiguity. However, participants were more attuned to the negative cues in the ambiguous film clips and three film clips were identified as unambiguous. These films clips were deemed unsuitable for assessing interpretation bias. The exploratory factor analyses of participants' mood ratings and evoked facial affect showed that the positive and negative emotionally provoking film clips formed their own factors as expected. However, there was substantial cross-loading of the neutral film clips when participants' facial expression data was analysed. A subset of the film clips from the two tasks could be used to assess top-down interpretation biases and bottom-up evoked emotional responses. Ambiguous negatively valenced film clips should have more subtle negative cues to avoid ceiling effects and to ensure there is enough room for interpretation.

Recovering Individual Emotional States from Sparse Ratings Using Collaborative Filtering

A fundamental challenge in emotion research is measuring feeling states with high granularity and temporal precision without disrupting the emotion generation process. Here we introduce and validate a new approach in which responses are sparsely sampled and the missing data are recovered using a computational technique known as collaborative filtering (CF). This approach leverages structured covariation across individual experiences and is available in Neighbors, an open-source Python toolbox. We validate our approach across three different experimental contexts by recovering dense individual ratings using only a small subset of the original data. In dataset 1, participants (n=316) separately rated 112 emotional images on 6 different discrete emotions. In dataset 2, participants (n=203) watched 8 short emotionally engaging autobiographical stories while simultaneously providing moment-by-moment ratings of the intensity of their affective experience. In dataset 3, participants (n=60) with distinct social preferences made 76 decisions about how much money to return in a hidden multiplier trust game. Across all experimental contexts, CF was able to accurately recover missing data and importantly outperformed mean and multivariate imputation, particularly in contexts with greater individual variability. This approach will enable new avenues for affective science research by allowing researchers to acquire high dimensional ratings from emotional experiences with minimal disruption to the emotion-generation process.

Mood Variability, Craving, and Substance Use Disorders: From Intrinsic Brain Network Connectivity to Daily Life Experience

BackgroundSubstance use disorders (SUDs) are major contributors to morbidity and mortality rates worldwide, and this global burden is attributable in large part to the chronic nature of these conditions. Increased mood variability might represent a form of emotional dysregulation that may have particular significance for the risk of relapse in SUD, independent of mood severity or diagnostic status. However, the neural biomarkers that underlie mood variability remain poorly understood. MethodsEcological momentary assessment was used to assess mood variability, craving, and substance use in real time in 54 patients treated for addiction to alcohol, cannabis, or nicotine and 30 healthy control subjects. Such data were jointly examined relative to spectral dynamic causal modeling of effective brain connectivity within 4 networks involved in emotion generation and regulation. ResultsDifferences in effective connectivity were related to daily life variability of emotional states experienced by persons with SUD, and mood variability was associated with craving intensity. Relative to the control participants, effective connectivity was decreased for patients in the prefrontal control networks and increased in the emotion generation networks. Findings revealed that effective connectivity within the patient group was modulated by mood variability. ConclusionsThe intrinsic causal dynamics in large-scale neural networks underlying emotion regulation play a predictive role in a patient’s susceptibility to experiencing mood variability (and, subsequently, craving) in daily life. The findings represent an important step toward informing interventional research through biomarkers of factors that increase the risk of relapse in persons with SUD.

Research on the influence of college students' participation in sports activities on their sense of inferiority based on self-esteem and general self-efficacy.

College students need to face various problems and conflicts, and are prone to many negative emotions, such as depression, low self-esteem, social anxiety, low sense of belonging, lack of self-identity, and so on. The generation of these bad emotions will bring unexpected negative effects to college students. Taking Chinese college students as the research object, this study analyzes the influence of sports participation on inferiority. Furthermore, we explored whether self-esteem and general self-efficacy mediated the relationship between physical activity and inferiority. In this study, 115 students were selected to conduct the experiment for 12 weeks. After reliability testing, the collected data were analyzed by multivariate analysis of variance to verify the research model. The results show that sports has a significant positive correlation with the reduction of college students' inferiority complex. What is important in this model is that self-esteem and general self-efficacy are enhanced during physical activity and decreased during inferiority complex. In addition, this study created three sports situations: competition group, entertainment group and control group. The comparison results show that competitive sports situation is better than leisure sports situation in terms of the influence on college students' inferiority complex. Through the theoretical and empirical research on college students' inferiority complex, it is concluded that sports is an effective means to reduce college students' inferiority complex.

DEGARI 2.0: A diversity-seeking, explainable, and affective art recommender for social inclusion

We present DEGARI 2.0 (Dynamic Emotion Generator And ReclassIfier): an explainable, affective-based, art recommender relying on the commonsense reasoning framework TCL and exploiting an ontological model formalizing the Plutchik’s theory of emotions. The main novelty of this system relies on the development of diversity-seeking affective recommendations obtained by exploiting the spatial structure of the Plutchik’s ‘wheel of emotion’. In particular, such development allows to classify and to suggest, to museum users, cultural items able to evoke not only the very same emotions of already experienced or preferred objects (e.g. within a museum exhibition), but also novel items sharing different emotional stances. The system’s goal, therefore, is to break the filter bubble effect and open the users’ view towards more inclusive and empathy-based interpretations of cultural content. The system has been tested, in the context of the EU H2020 SPICE project, on the community of deaf people and on the collection of the GAM Museum of Turin. We report the results and the lessons learnt concerning both the acceptability and the perceived explainability of the received diversity-seeking recommendations.