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In this paper, we use choice architecture techniques to activate both social and personal norms, seeking to increase pro-environmental choices and to better understand the effect of such norm types on post-choice emotional responses. In four experiments, we make different social or personal norms salient by aligning choice environments with psychosocial mechanisms that activate different types of norms. We use different choice architecture techniques to change information, alter product sets, and generate the social consequences of choices. The target behavior, purchasing a recycled paper notebook, is captured through direct purchase behaviors or willingness to pay commitments. We find that choice architecture activates personal but not social norms, and that associated positive and negative emotions (guilt, shame, regret and pride) are elicited by choices but not by willingness to pay. Moreover, manipulating choice environment moderates the relationship between choice and norm-related emotions, such that positive emotional responses seem to be stronger than negative ones. The results suggest that choice architecture interventions can activate individual level beliefs about sustainability and help reduce the attitude-behavior gap. [ABSTRACT FROM AUTHOR]

Aspect-level sentiment analysis (ABSA) is a pivotal task within the domain of neurorobotics, contributing to the comprehension of fine-grained textual emotions. Despite the extensive research undertaken on ABSA, the limited availability of training data remains a significant obstacle that hinders the performance of previous studies. Moreover, previous works have predominantly focused on concatenating semantic and syntactic features to predict sentiment polarity, which inadvertently severed the intrinsic connection. Several studies have attempted to utilize multi-layer graph convolution for the purpose of extracting syntactic characteristics. However, this approach has encountered the issue of gradient explosion. This paper investigates the possibilities of leveraging ChatGPT for aspect-level text augmentation. Furthermore, we introduce an improved gated attention mechanism specifically designed for graph convolutional networks to mitigates the problem of gradient explosion. By enriching the features of the dependency graph with a sentiment knowledge base, we strengthen the relationship between aspect words and the polarity of the contextual sentiment. It is worth mentioning that we employ cross-fusion to effectively integrate textual semantic and syntactic features. The experimental results substantiate the superiority of our model over the baseline models in terms of performance. [ABSTRACT FROM AUTHOR]

Background: Impairments in social cognition have been described in several psychiatric and neurological disorders. Given the importance of the relationship between social cognition and functioning and quality of life in these disorders, there is a growing interest in social cognition remediation interventions. The aim of this study was to carry out a systematic mapping review to describe the state of the art in social cognition training and remediation interventions. Methods: Publications from 2006 to 2016 on social cognition interventions were reviewed in four databases: Scopus, PsycINFO, PubMed and Embase. From the initial result set of 3229 publications, a final total of 241 publications were selected. Results: The study revealed an increasing interest in social cognition remediation interventions, especially in the fields of psychiatry and psychology, with a gradual growth in the number of publications. These were frequently published in high impact factor journals and underpinned by robust scientific evidence. Most studies were conducted on schizophrenia, followed by autism spectrum disorders. Theory of mind and emotional processing were the focus of most interventions, whilst a limited number of studies addressed attributional bias and social perception. Targeted interventions in social cognition were the most frequent practice in the selected papers, followed by non-specific treatment interventions and broad-based interventions. Conclusions: Research in social cognition remediation interventions is growing. Further studies are needed on attributional bias and social perception remediation programs, while the comparative efficacy of different interventions also remains unclear. [ABSTRACT FROM AUTHOR]

Emotional factors play a crucial role in streaming live marketing of agricultural products. Some literature explored several emotional factors' impact on consumers' purchase intention. Nonetheless, the interaction and integration effects of these factors have received less attention. Based on Consumer Engagement Theory, SOR model and TAM model, the paper constructs a moderated mediation model of the interactivity/presence, trust/resonance and purchase intention under rural sentiment. A quantitative study based on 365 valid samples is conducted to validate this model. The results indicate that interactivity and presence positively impact on consumers' purchase intention, trust and resonance play a mediating role between interactivity/presence and purchase intention separately. Contrary to our expectations, rural sentiment negatively moderates the relationship between interactivity and resonance. Differences of regression results between urban and rural group indicate that the cultural backgrounds of consumers have an impact on their emotional responses in live streaming of agricultural products. The results illustrate the mechanism of emotional factors in consumers' purchase decisions. Overall, this paper reveals the potential of emotional factors and the development of effective marketing strategies to improve agricultural products sales., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Han et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Feeling heard is considered a cornerstone of close relationships and crucial to healthy self-development, but psychologically, this sentiment of feeling heard remains understudied. The current paper therefore aims to define and measure the experience of feeling heard. Based on an integrative literature review, feeling heard is conceptualized as consisting of five components at two conceptual levels. At the interpersonal level people feel heard when they have 1) voice, and receive 2) attention, 3) empathy, 4) respect. At the collective level people should experience 5) common ground. In two population surveys (N = 194, N = 1000), we find that feeling heard is a unitary concept, and we develop and validate the feeling heard scale (FHS); a concise eight-item scale with good psychometric properties. Results show that the FHS is a distinct predictor of conversation intentions in many different contexts and relationships. In fact, the FHS is the strongest predictor of intentions for conflict behavior among a set of 15 related variables (e.g., acquaintance, intimacy). We conclude by reflecting on the potential applications of this scale: in interpersonal relations and professional contacts, the FHS enables the assessment of one crucial dimension of social interaction., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Roos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Altruistic communication by non-profit organizations plays a crucial role in shaping individuals' perceptions and beliefs about altruism. One of the indicators of effective communication is the anchoring of the messages. Therefore, understanding the underlying determinants of anchoring in altruistic communication is essential. Despite the importance of anchoring in the communication of altruism, extant research has not done much to examine the determinants of anchoring in altruistic communication. This paper investigates the determinants of anchoring in non-profit organizations' altruistic communication through the lens of the dual process theory. It applies the Fuzzy Decision Making Trial and Evaluation Laboratory (F-DEMATEL) method to analyze the causal and effect factors. Data were gathered from 12 social communication experts based in Taiwan. Out of the 12 proposed determinants, three factors, namely consistency, cultural consideration, and emotional anchoring, were established as significant causal factors. Consistency had causal effects on five other factors, namely, the use of metaphors, the use of antinomies, thematic anchoring, understanding the cognitive ability of the audience, and crafting engaging information. Cultural consideration had causal effects on feedback, naming, use of antinomies, thematic anchoring, emotional anchoring, and repetition. Emotional anchoring had causal effects on thematic anchoring, use of antinomies, use of metaphors, consistency, naming, feedback, understanding the cognitive ability of the audience, and repetition. On the other hand, feedback, naming, and use of antinomies were established as significant effect factors. The study's findings offer crucial contributions to the social communication literature and provide important insights for social communication practitioners., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Liao, Hsu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Negative emotion evoked in listeners of music can produce intense pleasure, but we do not fully understand why. The present study addressed the question by asking participants (n = 50) to self-select a piece of sadness-evoking music that was loved. The key part of the study asked participants to imagine that the felt sadness could be removed. Overall participants reported performing the task successfully. They also indicated that the removal of the sadness reduced their liking of the music, and 82% of participants reported that the evoked sadness also adds to the enjoyment of the music. The study provided evidence for a "Direct effect hypothesis", which draws on the multicomponent model of emotion, where a component of the negative emotion is experienced as positive during music (and other aesthetic) experiences. Earlier evidence of a mediator, such as 'being moved', as the source of enjoyment was reinterpreted in light of the new findings. Instead, the present study applied a semantic overlap explanation, arguing that sadness primes emotions that share meaning with sadness, such as being-moved. The priming occurs if the overlap in meaning is sufficient. The degree of semantic overlap was defined empirically. The present study therefore suggests that mediator-based explanations need to be treated with caution both as a finding of the study, and because of analytic limitations in earlier research that are discussed in the paper. [ABSTRACT FROM AUTHOR]

Low-resource languages are gaining much-needed attention with the advent of deep learning models and pre-trained word embedding. Though spoken by more than 230 million people worldwide, Urdu is one such low-resource language that has recently gained popularity online and is attracting a lot of attention and support from the research community. One challenge faced by such resource-constrained languages is the scarcity of publicly available large-scale datasets for conducting any meaningful study. In this paper, we address this challenge by collecting the first-ever large-scale Urdu Tweet Dataset for sentiment analysis and emotion recognition. The dataset consists of a staggering number of 1, 140, 821 tweets in the Urdu language. Obviously, manual labeling of such a large number of tweets would have been tedious, error-prone, and humanly impossible; therefore, the paper also proposes a weakly supervised approach to label tweets automatically. Emoticons used within the tweets, in addition to SentiWordNet, are utilized to propose a weakly supervised labeling approach to categorize extracted tweets into positive, negative, and neutral categories. Baseline deep learning models are implemented to compute the accuracy of three labeling approaches, i.e., VADER, TextBlob, and our proposed weakly supervised approach. Unlike the weakly supervised labeling approach, the VADER and TextBlob put most tweets as neutral and show a high correlation between the two. This is largely attributed to the fact that these models do not consider emoticons for assigning polarity., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ghafoor et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Recent deep-learning techniques have made it possible to manipulate facial expressions in digital photographs or videos, however, these techniques still lack fine and personalized ways to control their creation. Moreover, current technologies are highly dependent on large labeled databases, which limits the range and complexity of expressions that can be modeled. Thus, these technologies cannot deal with non-basic emotions. In this paper, we propose a novel interdisciplinary approach combining the Generative Adversarial Network (GAN) with a technique inspired by cognitive sciences, psychophysical reverse correlation. Reverse correlation is a data-driven method able to extract an observer's 'mental representation' of what a given facial expression should look like. Our approach can generate 1) personalized facial expression prototypes, 2) of basic emotions, and non-basic emotions that are not available in existing databases, and 3) without the need for expertise. Personalized prototypes obtained with reverse correlation can then be applied to manipulate facial expressions. In addition, our system challenges the universality of facial expression prototypes by proposing the concepts of dominant and complementary action units to describe facial expression prototypes. The evaluations we conducted on a limited number of emotions validate the effectiveness of our proposed method. The code is available at https://github.com/yansen0508/Mental-Deep-Reverse-Engineering., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Yan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The Dysarthric Expressed Emotional Database (DEED) is a novel, parallel multimodal (audio-visual) database of dysarthric and typical emotional speech in British English which is a first of its kind. It is an induced (elicited) emotional database that includes speech recorded in the six basic emotions: "happiness", "sadness", "anger", "surprise", "fear", and "disgust". A "neutral" state has also been recorded as a baseline condition. The dysarthric speech part includes recordings from 4 speakers: one female speaker with dysarthria due to cerebral palsy and 3 speakers with dysarthria due to Parkinson's disease (2 female and 1 male). The typical speech part includes recordings from 21 typical speakers (9 female and 12 male). This paper describes the collection of the database, covering its design, development, technical information related to the data capture, and description of the data files and presents the validation methodology. The database was validated subjectively (human performance) and objectively (automatic recognition). The achieved results demonstrated that this database will be a valuable resource for understanding emotion communication by people with dysarthria and useful in the research field of dysarthric emotion classification. The database is freely available for research purposes under a Creative Commons licence at: https://sites.google.com/sheffield.ac.uk/deed., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Alhinti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The study of the electroencephalogram signals recorded from subjects during an experience is a way to understand the brain processes that underlie their physical and emotional involvement. Such signals have the form of time series, and their analysis could benefit from applying techniques that are specific to this kind of data. Neuroaesthetics, as defined by Zeki in 1999, is the scientific approach to the study of aesthetic perceptions of art, music, or any other experience that can give rise to aesthetic judgments, such as liking or disliking a painting. Starting from a proprietary dataset of 248 trials from 16 subjects exposed to art paintings, using a real ecological context, this paper analyses the application of a novel symbolic machine learning technique, specifically designed to extract information from unstructured data and to express it in form of logical rules. Our purpose is to extract qualitative and quantitative logical rules, to relate the voltage at specific frequencies and in specific electrodes, and that, within the limits of the experiment, may help to understand the brain process that drives liking or disliking experiences in human subjects., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Mazzacane et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Despite growing interest in the value of human-animal interactions (HAI) to human mental and physical health the quality of the evidence on which postulated benefits from animals to human psychological health are based is often unclear. To date there exist no systematic reviews on the effects of HAI in educational settings specifically focussing on the perceived benefits to children of reading to dogs. With rising popularity and implementation of these programmes in schools, it is essential that the evidence base exploring the pedagogic value of these initiatives is well documented. Methods: Using PRISMA guidelines we systematically investigated the literature reporting the pedagogic effects of reading to dogs. Because research in this area is in the early stages of scientific enquiry we adopted broad inclusion criteria, accepting all reports which discussed measurable effects related to the topic that were written in English. Multiple online databases were searched during January-March 2015; grey literature searches were also conducted. The search results which met the inclusion criteria were evaluated, and discussed, in relation to the Oxford Centre for Evidence Based Medicine levels of evidence; 27 papers were classified as Level 5, 13 as Level 4, 7 as Level 2c and 1 as Level 2b. Conclusion: The evidence suggests that reading to a dog may have a beneficial effect on a number of behavioural processes which contribute to a positive effect on the environment in which reading is practiced, leading to improved reading performance. However, the evidence base on which these inferences are made is of low quality. There is a clear need for the use of higher quality research methodologies and the inclusion of appropriate controls in order to draw causal inferences on whether or how reading to dogs may benefit children’s reading practices. The mechanisms for any effect remain a matter of conjecture. [ABSTRACT FROM AUTHOR]

The development of believable, natural, and interactive digital artificial agents is a field of growing interest. Theoretical uncertainties and technical barriers present considerable challenges to the field, particularly with regards to developing agents that effectively simulate human emotions. Large language models (LLMs) might address these issues by tapping common patterns in situational appraisal. In three empirical experiments, this study tests the capabilities of LLMs to solve emotional intelligence tasks and to simulate emotions. It presents and evaluates a new Chain-of-Emotion architecture for emotion simulation within video games, based on psychological appraisal research. Results show that it outperforms control LLM architectures on a range of user experience and content analysis metrics. This study therefore provides early evidence of how to construct and test affective agents based on cognitive processes represented in language models. [ABSTRACT FROM AUTHOR]

Mental representations are of great importance for understanding human behaviour. The aim of this article is to present an innovative way to assess emotional scripts, which are a form of mental representations of emotional events, based on an analysis of narratives and their contents. Theoretical background on emotional schemas and scripts is provided along with information about types of related measures. Then, a rationale is presented for introducing an assessment of scripts related to specific emotions such as love, hate, and anxiety in a psychopathological context. This is followed by a perspective explaining the procedure of the relevant technique based on narrative data analysis. The technique has been successfully applied in two studies [I study (n- 200), II study (n- 280)]. A total of 1440 narratives about specific emotions have been analyzed to identify the indicators of scripts. The psychometric properties of the proposed technique have been established such as reliability, inter-rater agreement, and accuracy. The results show the value of the assessment of emotional script in individuals, particularly with high psychopathy and high trait anxiety. The contents of love and hate scripts are an illustration of cognitive distortions and deficits in the emotional information processing in individuals with psychopathy. The method enables the collection of informative data on romantic love, hate, and anxiety scripts which provides insight into how people may perceive and experience emotions and how they behave emotionally. Future research should focus on verification of the technique in other types of psychopathology and on the improvement of computer software dedicated to the narrative technique described in this paper., Competing Interests: The author has declared that no competing interests exist., (Copyright: © 2023 Barbara Gawda. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Users' emotions may influence the formation of presence in virtual reality (VR). Users' expectations, state of arousal and personality may also moderate the relationship between emotions and presence. An interoceptive predictive coding model of conscious presence (IPCM) considers presence as a product of the match between predictions of interoceptive emotional states and the actual states evoked by an experience (Seth et al. 2012). The present paper aims to test this model's applicability to VR for both high-arousal and low-arousal emotions. The moderating effect of personality traits on the creation of presence is also investigated. Results show that user expectations about emotional states in VR have an impact on presence, however, expression of this relationship is moderated by the intensity of an emotion, with only high-arousal emotions showing an effect. Additionally, users' personality traits moderated the relationship between emotions and presence. A refined model is proposed that predicts presence in VR by weighting emotions according to their level of arousal and by considering the impact of personality traits., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Jicol et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Past research has stressed the role of age and generation in climate change discourse, worries, and willingness to act. Therefore, the present paper aimed to examine the role of chronological age (as an arbitrary factor, which represents ageism) in lay people's climate change-related attitudes, feelings, and behavioral intentions. Two experiments in different countries, Australia and Israel, were conducted for this purpose. The first study examined the impact of the age of the speaker, who provides information about the climate crisis and the second examined the impact of the age of the group being blamed for the situation. Outcome variables included perceived responsibility and motivation for the current climate situation in study 1 and perceived climate change-related attitudes, feelings, and behavioral intentions in study 2. In study 1 (n = 250, Australia), the age of the speaker, a climate activist, varied randomly to test the hypothesis that a younger activist would be more influential and increase motivation and responsibility to act compared to an older activist. In study 2 (n = 179, Israel), the age (young vs. old) of the group identified as being responsible for the climate crisis varied randomly, to test the hypothesis that people would be more willing to identify older people as being responsible for the current climate situation, and this would impact climate change-related attitudes, feelings, and behavioral intentions. Both studies resulted in null effects. Additionally, there was no interaction between the age of the respondent and the age of the source of the message or the age group being blamed by the message. The present study has failed to show that strategies that emphasize intergenerational conflict and ageism impact people's attitudes, feelings, and behavioral intentions towards the current climate situation. This possibly can serve as an instigator for strategies that emphasize intergenerational solidarity, rather than conflict, as a guiding principle in future campaigns that advocate climate change adaptation and mitigation measures. [ABSTRACT FROM AUTHOR]

Given fault false alarm and fault control failure caused by the decrease of fault identification accuracy and fault delay of Switched Reluctance Motor (SRM) power converter in complex working conditions, a method based on the Interactive Multi-Model (IMM) algorithm was proposed in this paper. Besides, the corresponding equivalent circuit models were established according to the different working states of the SRM power converter. The Kalman filter was employed to estimate the state of the model, and the fault detection and location were realized depending on the residual signal. Additionally, a transition probability correction function of the IMM was constructed using the difference of the n-th order to suppress the influence of external disturbance on the fault diagnosis accuracy. Concurrently, a model jump threshold was introduced to reduce delay when the matched model was switched, so as to realize the rapid separation of faults and effective fault control. The simulation and experiment results demonstrate that the IMM algorithm based on low delay anti-interference can effectively reduce the influence of complex working conditions, improve the anti-interference ability of SRM power converter fault diagnosis, and identify fault information accurately and quickly., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Zhou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

It is unclear at present whether psychopathic tendencies are associated with lower or higher levels of emotional awareness (EA). Given that psychopathy includes a proficiency for manipulating others, one might expect an elevated ability to identify and use information about others' emotions. On the other hand, empathic deficits in psychopathy could arise from reduced emotional awareness. Further, heterogeneity in psychopathy may also play a role, wherein 'secondary' psychopathy is associated with early adversity and high negative affect, while 'primary' psychopathy is not. In this paper, we tested the relationship between EA and psychopathic tendencies in 177 undergraduate students (40 males) who completed the levels of emotional awareness scale (LEAS), the triarchic psychopathy measure (TPM), the affective (empathy-related) subscales of the interpersonal reactivity index (IRI), and two measures of early adversity: the childhood experiences of care and abuse questionnaire (CECA) and the childhood trauma questionnaire (CTQ). We found that lower LEAS scores were associated with higher TPM and lower IRI empathy scores, but these relationships were primarily present in those with early adversity and high negative affect. This suggests that lower EA may be selectively associated with higher levels of secondary psychopathy, while those with higher levels of primary psychopathy remain capable of higher EA., Competing Interests: Richard Lane has disclosed an outside interest in the Electronic Levels of Emotional Awareness Scale owned by Equanimity Health Technologies, LLC to the University of Arizona. Conflicts of interest resulting from this interest are being managed by The University of Arizona in accordance with its policies. All other authors have declared that no competing interests exist. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials., (Copyright: © 2022 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Recognizing the real emotion of humans is considered the most essential task for any customer feedback or medical applications. There are many methods available to recognize the type of emotion from speech signal by extracting frequency, pitch, and other dominant features. These features are used to train various models to auto-detect various human emotions. We cannot completely rely on the features of speech signals to detect the emotion, for instance, a customer is angry but still, he is speaking at a low voice (frequency components) which will eventually lead to wrong predictions. Even a video-based emotion detection system can be fooled by false facial expressions for various emotions. To rectify this issue, we need to make a parallel model that will train on textual data and make predictions based on the words present in the text. The model will then classify the type of emotions using more comprehensive information, thus making it a more robust model. To address this issue, we have tested four text-based classification models to classify the emotions of a customer. We examined the text-based models and compared their results which showed that the modified Encoder decoder model with attention mechanism trained on textual data achieved an accuracy of 93.5%. This research highlights the pressing need for more robust emotion recognition systems and underscores the potential of transfer models with attention mechanisms to significantly improve feedback management processes and the medical applications. [ABSTRACT FROM AUTHOR]

Research into clinical applications of speech-based emotion recognition (SER) technologies has been steadily increasing over the past few years. One such potential application is the automatic recognition of expressed emotion (EE) components within family environments. The identification of EE is highly important as they have been linked with a range of adverse life events. Manual coding of these events requires time-consuming specialist training, amplifying the need for automated approaches. Herein we describe an automated machine learning approach for determining the degree of warmth, a key component of EE, from acoustic and text natural language features. Our dataset of 52 recorded interviews is taken from recordings, collected over 20 years ago, from a nationally representative birth cohort of British twin children, and was manually coded for EE by two researchers (inter-rater reliability 0.84–0.90). We demonstrate that the degree of warmth can be predicted with an F1-score of 64.7% despite working with audio recordings of highly variable quality. Our highly promising results suggest that machine learning may be able to assist in the coding of EE in the near future. [ABSTRACT FROM AUTHOR]

Emotion lexicons became a popular method for quantifying affect in large amounts of textual data (e.g., social media posts). There are multiple independently developed emotion lexicons which tend to correlate positively with one another but not entirely. Such differences between lexicons may not matter if they are just unsystematic noise, but if there are systematic differences this could affect conclusions of a study. The goal of this paper is to examine whether two extensively used, apparently domain-independent lexicons for emotion analysis would give the same answer to a theory-driven research question. Specifically, we use the Linguistic Inquiry and Word Count (LIWC) and NRC Word-Emotion Association Lexicon (NRC). As an example, we investigate whether older people have more positive expression through their language use. We examined nearly 5 million tweets created by 3,573 people between 18 to 78 years old and found that both methods show an increase in positive affect until age 50. After that age, however, according to LIWC, positive affect drops sharply, whereas according to NRC, the growth of positive affect increases steadily until age 65 and then levels off. Thus, using one or the other method would lead researchers to drastically different theoretical conclusions regarding affect in older age. We unpack why the two methods give inconsistent conclusions and show this was mostly due to a particular class of words: those related to politics. We conclude that using a single lexicon might lead to unreliable conclusions, so we suggest that researchers should routinely use at least two lexicons. If both lexicons come to the same conclusion then the research evidence is reliable, but if not then researchers should further examine the lexicons to find out what difference might be causing inconclusive result., Competing Interests: The authors have declared that no competing interests exist.

In the field of Human-Computer Interaction (HCI), speech emotion recognition technology plays an important role. Facing a small number of speech emotion data, a novel speech emotion recognition method based on feature construction and ensemble learning is proposed in this paper. Firstly, the acoustic features are extracted from the speech signal and combined to form different original feature sets. Secondly, based on Light Gradient Boosting Machine (LightGBM) and Sequential Forward Selection (SFS) method, a novel feature selection method named L-SFS is proposed. And then, the softmax regression model is used to learn automatically the weights of the four single weak learners including Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost) and LightGBM. Lastly, based on the learned automatically weights and the weighted average probability voting strategy, an ensemble classification model named Sklex is constructed, which integrates the above four single weak learners. In conclusion, the method reflects the effectiveness of feature construction and the superiority and stability of ensemble learning, and gets good speech emotion recognition accuracy., Competing Interests: The authors have declared that no competing interests exist.

Neural open-domain dialogue systems often fail to engage humans in long-term interactions on popular topics such as sports, politics, fashion, and entertainment. However, to have more socially engaging conversations, we need to formulate strategies that consider emotion, relevant-facts, and user behaviour in multi-turn conversations. Establishing such engaging conversations using maximum likelihood estimation (MLE) based approaches often suffer from the problem of exposure bias. Since MLE loss evaluates the sentences at the word level, we focus on sentence-level judgment for our training purposes. In this paper, we present a method named EmoKbGAN for automatic response generation that makes use of the Generative Adversarial Network (GAN) in multiple-discriminator settings involving joint minimization of the losses provided by each attribute specific discriminator model (knowledge and emotion discriminator). Experimental results on two bechmark datasets i.e the Topical Chat and Document Grounded Conversation dataset yield that our proposed method significantly improves the overall performance over the baseline models in terms of both automated and human evaluation metrics, asserting that the model can generate fluent sentences with better control over emotion and content quality. [ABSTRACT FROM AUTHOR]

The proliferation of Social Media and Open Web data has provided researchers with a unique opportunity to better understand human behavior at different levels. In this paper, we show how data from Open Street Map and Twitter could be analyzed and used to portray detailed Human Emotions at a city wide level in two cities, San Francisco and London. Neural Network classifiers for fine-grained emotions were developed, tested and used to detect emotions from tweets in the two cites. The detected emotions were then matched to key locations extracted from Open Street Map. Through an analysis of the resulting data set, we highlight the effect different days, locations and POI neighborhoods have on the expression of human emotions in the cities. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new estimation method in estimating optimal dynamic treatment regimes. The quadratic inference functions in myopic regret-regression (QIF-MRr) can be used to estimate the parameters of the mean response at each visit, conditional on previous states and actions. Singularity issues may arise during computation when estimating the parameters in ODTR using QIF-MRr due to multicollinearity. Hence, the ridge penalty was introduced in rQIF-MRr to tackle the issues. A simulation study and an application to anticoagulation dataset were conducted to investigate the model's performance in parameter estimation. The results show that estimations using rQIF-MRr are more efficient than the QIF-MRr., Competing Interests: The authors have declared that no competing interests exist.

Background: 'Engager' is an innovative 'through-the-gate' complex care intervention for male prison-leavers with common mental health problems. In parallel to the randomised-controlled trial of Engager (Trial registration number: ISRCTN11707331), a set of process evaluation analyses were undertaken. This paper reports on the depth multiple case study analysis part of the process evaluation, exploring how a sub-sample of prison-leavers engaged and responded to the intervention offer of one-to-one support during their re-integration into the community., Methods: To understand intervention delivery and what response it elicited in individuals, we used a realist-informed qualitative multiple 'case' studies approach. We scrutinised how intervention component delivery lead to outcomes by examining underlying causal pathways or 'mechanisms' that promoted or hindered progress towards personal outcomes. 'Cases' (n = 24) were prison-leavers from the intervention arm of the trial. We collected practitioner activity logs and conducted semi-structured interviews with prison-leavers and Engager/other service practitioners. We mapped data for each case against the intervention logic model and then used Bhaskar's (2016) 'DREIC' analytic process to categorise cases according to extent of intervention delivery, outcomes evidenced, and contributing factors behind engagement or disengagement and progress achieved., Results: There were variations in the dose and session focus of the intervention delivery, and how different participants responded. Participants sustaining long-term engagement and sustained change reached a state of 'crises but coping'. We found evidence that several components of the intervention were key to achieving this: trusting relationships, therapeutic work delivered well and over time; and an in-depth shared understanding of needs, concerns, and goals between the practitioner and participants. Those who disengaged were in one of the following states: 'Crises and chaos', 'Resigned acceptance', 'Honeymoon' or 'Wilful withdrawal'., Conclusions: We demonstrate that the 'implementability' of an intervention can be explained by examining the delivery of core intervention components in relation to the responses elicited in the participants. Core delivery mechanisms often had to be 'triggered' numerous times to produce sustained change. The improvements achieved, sustained, and valued by participants were not always reflected in the quantitative measures recorded in the RCT. The compatibility between the practitioner, participant and setting were continually at risk of being undermined by implementation failure as well as changing external circumstances and participants' own weaknesses., Trial Registration Number: ISRCTN11707331, Wales Research Ethics Committee, Registered 02-04-2016-Retrospectively registered https://doi.org/10.1186/ISRCTN11707331., Competing Interests: The authors declare that they have no competing interests.

Recently, researchers have proposed four superordinate emotion beliefs that supposedly influence emotion regulation and emotion reactivity. Two of these proposed emotion beliefs are captured in the cognitive mediation beliefs questionnaire (CMBQa), namely stimulus-response (S-R) generation beliefs and cognitive mediation (C-M) change beliefs. The remaining two proposed emotion beliefs, C-M generation beliefs and S-R change beliefs, are yet to be operationalised in psychometric form. It is important to validate measurement for all four emotion beliefs in order for them to be used in research and practice. The current paper reports the development and initial validity testing of the CMBQb (studies 1-3), which concerns only C-M generation beliefs and S-R change beliefs, and then tests the four-factor structure of the combined CMBQa (S-R generation, C-M change) and CMBQb (C-M generation, S-R change): the CMBQc (study 4). Some support was found for the four-factor structure of the CMBQc, with factor analyses revealing good fit to the data with a four-factor solution. Also, scores indicating greater C-M generation and change beliefs, and lower S-R generation and change beliefs, were related to more adaptive, and less maladaptive, emotion regulation tendencies. In addition, there was some evidence that greater C-M change beliefs, and lower S-R generation and change belief, were related to better affective and emotion reactivity outcomes. Implications of the CMBQc for research and practice are discussed within the context and emotion regulation science, and cognitive behavioural psychotherapy., Competing Interests: The authors have declared that no competing interests exist.

The ongoing COVID-19 has led to an increase in negative emotions and health awareness among consumers. This paper discusses the emotion bias of Chinese consumers during the three periods: the pre-COVID-19 period, the COVID-19 lockdown period, and the COVID-19 normalization period. This study takes health products as the research object and crawls relevant reviews on the JD platform to classify products. The data were classified into emotion, the intensity of emotion was calculated, and the logistic regression model and variance analysis were used to analyze the difference in emotion expression. The study reveals that consumers are willing to express fear and sadness during the COVID-19 lockdown era and are willing to express like emotions before the pandemic compared to the three periods. There are also differences in the emotional intensity of different product reviews. The intensity of emotional expression is more vigorous for consumers who purchase nutritional products, while for those who purchase healthcare equipment, the intensity of emotional expression is lower. This study offers the emotion bias of consumers in response to COVID-19 to provide a theoretical basis and reference solution for implementing marketing strategies for health product companies. [ABSTRACT FROM AUTHOR]

In this paper, we propose an Emotional Trigger System to impart an automatic emotion expression ability within the humanoid robot REN-XIN, in which the Emotional Trigger is an emotion classification model trained from our proposed Word Mover’s Distance(WMD) based algorithm. Due to the long time delay of the WMD-based Emotional Trigger System, we propose an enhanced Emotional Trigger System to enable a smooth interaction with the robot in which the Emotional Trigger is replaced by a conventional convolution neural network and a long short term memory network (CNN_LSTM)-based deep neural network. In our experiments, the CNN_LSTM based model only need 10 milliseconds or less to finish the classification without a decrease in accuracy, while the WMD-based model needed approximately 6-8 seconds to give a result. In this paper, the experiments are conducted based on the same sub-data sets of the Chinese emotional corpus(Ren_CECps) used in former WMD experiments: one comprises 50% data for training and 50% for testing(1v1 experiment), and the other comprises 80% data for training and 20% for testing(4v1 experiment). The experiments are conducted using WMD, CNN_LSTM, CNN and LSTM. The results show that CNN_LSTM obtains the best F1 score (0.35) in the 1v1 experiment and almost the same accuracy of F1 scores (0.366 vs 0.367) achieved by WMD in the 4v1 experiment. Finally, we present demonstration videos with the same scenario to show the performance of robot control driven by CNN_LSTM-based Emotional Trigger System and WMD-based Emotional Trigger System. To improve the comparison, total manual-control performance is also recorded. [ABSTRACT FROM AUTHOR]

Understanding why parenting programmes work or do not work, and for whom, is crucial for development of more effective parenting interventions. In this paper we focus on a specific component of Mellow Bumps: reflection on one’s own childhood/past/life. We explore how this component was implemented, how participants engaged with it, the facilitating and constraining factors shaping this, whether and how it appeared to work, or not, and for whom. The paper analyses data from the Process Evaluation of the Trial of Healthy Relationships Initiatives for the Very Early years, which is evaluating two antenatal interventions delivered to vulnerable women, one of which is Mellow Bumps. Data were collected from January 2014 to June 2018 for 28 groups, 108 participants and 24 facilitators in a comprehensive and rigorous Process Evaluation designed to complement the Outcome Evaluation. Data were gathered at various time points using multiple methods, and were synthesised to triangulate findings. The reflective component was implemented with fidelity and participants engaged with it to varying degrees, dependent largely on the coherence of the group. Patchy attendance compromised the coherence of some groups, with the development of rapport, which is key to delivering reflective exercises, more difficult when group composition varied from week to week. Where there was a coherent group, powerful mechanisms of change, leading to stress reduction, included: relief through unburdening, empowerment through support given and received, reduced isolation through sharing anxieties, and control through self-care advice. A minority of highly vulnerable mothers seemed not to benefit from the reflective exercises and were marginalised within their groups. In order to minimise potential harmful effects of such exercises, allocation of participants to groups should strive to maximise group homogeneity. More research is needed to explore how very vulnerable parents can be supported in attending parenting interventions from start to finish. [ABSTRACT FROM AUTHOR]

Individuals tend to dehumanize the outgroup. In this paper, we explore whether the activation of attachment security can attenuate dehumanization. Two studies were performed. In Study 1, attachment security was primed by showing pictures that depicted relationships with attachment figures; the outgroup was the homeless and humanization was measured considering the attribution of uniquely human and non-uniquely human emotions to this group. In Study 2, the sense of interpersonal security was activated by inviting participants to relive a recent interaction that left them with a feeling of safety and warmth. Outgroup members were the Roma, and humanization was measured considering the attribution of uniquely human and human nature traits to them. In Study 2, the mediation effect of intergroup emotions was investigated. In both studies, outgroup humanization effects were highlighted. In Study 2, these effects were mediated by increased empathy toward the outgroup. Interestingly, the positive impact of security activation was not moderated by chronic attachment orientations. Findings suggest strategies that can be used to improve intergroup relations in specific contexts and in society at large., Competing Interests: The authors have declared that no competing interests exist.

Purpose: This paper evaluates a study which aimed to enhance clinical care of young people with Duchenne or Becker muscular dystrophy (MD) and their families in two Canadian neuromuscular clinics. We report on how/why the study changed clinical practices in relation to the 'human' (e.g., emotional, social, existential, cultural) dimensions of living with MD., Materials and Methods: The intervention involved regular dialogical exchanges with clinicians across the two sites, during which direct observations of the clinics' care practices were discussed and changes were planned. We drew from realist evaluation approaches to assess changes in clinical care associated with the intervention. Data sources included dialogical exchanges; clinic observations; interviews with clients, families and clinicians; and team analysis sessions., Results: Our evaluation suggests the clinical teams shifted their thinking and practices towards greater consideration of human aspects of living with MD including: more routinely attending to emotional, social and experiential dimensions of living with MD; reconceptualisation of risk; and considerations of affective aspects of clinical care. Not all clinicians changed their thinking and practices in the same ways, or to the same extent, and there were differences between the sites. These differences were likely due to numerous factors, including varying levels of clinician comfort with examining and shifting their own practices, and differing formal and informal clinic routines at each site., Conclusions: Overall, this intervention was able to shift clinic practices, and could feasibly be adapted across rehabilitation settings., Competing Interests: The authors have declared that no competing interests exist.

Emotions at work have long been identified as critical signals of work motivations, status, and attitudes, and as predictors of various work-related outcomes. When more and more employees work remotely, these emotional signals of workers become harder to observe through daily, face-to-face communications. The use of online platforms to communicate and collaborate at work provides an alternative channel to monitor the emotions of workers. This paper studies how emojis, as non-verbal cues in online communications, can be used for such purposes and how the emotional signals in emoji usage can be used to predict future behavior of workers. In particular, we present how the developers on GitHub use emojis in their work-related activities. We show that developers have diverse patterns of emoji usage, which can be related to their working status including activity levels, types of work, types of communications, time management, and other behavioral patterns. Developers who use emojis in their posts are significantly less likely to dropout from the online work platform. Surprisingly, solely using emoji usage as features, standard machine learning models can predict future dropouts of developers at a satisfactory accuracy. Features related to the general use and the emotions of emojis appear to be important factors, while they do not rule out paths through other purposes of emoji use., Competing Interests: The authors have declared that no competing interests exist.

Despite several attempts to provide a definite pattern regarding the effects of personality traits on performance in higher education, the debate over the nature of the relationship is far from being conclusive. The use of different subject pools and sample sizes, as well as the use of identification strategies that either do not adequately account for selection bias or are unable to establish causality between measures of academic performance and noncognitive skills, are possible sources of heterogeneity. This paper investigates the impact of the Big Five traits, as measured before the beginning of the academic year, on the grade point average achieved in the first year after the enrolment, taking advantage of a unique and large dataset from a cohort of Italian students in all undergraduate programs containing detailed information on student and parental characteristics. Relying on a robust strategy to credibly satisfy the conditional independence assumption, we find that higher levels of conscientiousness and openness to experience positively affect student score., Competing Interests: The authors have declared that no competing interests exist.

Background: Risk perception has been found to be a crucial factor explaining inconsistent or non-use of HIV prevention interventions. Considerations of risk need to expand beyond risk of infection to also include the personal, social, emotional, and economic risks associated with prevention intervention use. Objectives: This systematic review of qualitative peer-reviewed literature from sub-Saharan Africa examines perceptions of risk associated with HIV infection and HIV prevention intervention use. Data sources: We searched Medline, Embase, PsychInfo, Africa Wide Info, CINAHL, and Global Health for publications and screened them for relevance. Study eligibility criteria: Peer-reviewed qualitative studies published since 2003 were eligible for inclusion if they examined risk perception or uncertainty in the context of a medically regulated intervention. Only studies focusing on adults were included. Study appraisal and synthesis methods: Included publications were quality assessed using the Hawker method and coded thematically. Results: 10318 unique papers were identified, of which 29 are included. Among the themes identified, a particularly salient one was the potential of HIV prevention interventions to threaten the stability of a relationship and impact on how and when people may—or may not—choose to use prevention interventions. Limitations: This literature review excludes grey-literature, which may have distinct valuable insights. We also excluded quantitative studies that may have challenged or triangulated our findings. Conclusions and implications: When considering the risk of HIV acquisition, it is insufficient to examine biological risk in isolation from the personal, relational and economic costs associated with intervention use. This loss of emotional, physical, or material support may be perceived as more consequential than the prevention of a potential infection. [ABSTRACT FROM AUTHOR]

Communication policies employed by policymakers and non-governmental organizations (NGOs) often appeal to the emotions to persuade people to adopt virtuous behavior. The aim of this paper is to study the impact of induced emotions on pro-environmental behavior (PEB). We design a three-stage laboratory experiment. In the first stage, we determine the level of the subjects' environmental awareness. In the second stage, subjects read scripts that place them in realistic hypothetical scenarios designed to induce specific emotions. We implement a 2 x 2 in-between design by varying both the valence and social dimension of the four emotional states induced: happiness, sadness, pride and shame. In the third stage, subjects play a modified dictator game in which the recipient is an environmental non-governmental organization (ENGO). We show that the emotional states of subjects can influence PEB. In particular, negative emotions significantly reduce the average individual amount of donations made to ENGOs. We also find that the precise impact of the emotional states is more complex and appears to be dependent on individuals' characteristics and awareness for environmental issues. For instance, in positive emotional states, men donate significantly less than women. In addition, a high level of environmental awareness increases donations in subjects experiencing shame and decreases their likelihood to donate when feeling pride. Also, we observe behavioral consistency for negative emotions and rather compensatory behavior for positive emotions., Competing Interests: The authors have declared that no competing interests exist.

Emotion dynamics is a framework for measuring how an individual's emotions change over time. It is a powerful tool for understanding how we behave and interact with the world. In this paper, we introduce a framework to track emotion dynamics through one's utterances. Specifically we introduce a number of utterance emotion dynamics (UED) metrics inspired by work in Psychology. We use this approach to trace emotional arcs of movie characters. We analyze thousands of such character arcs to test hypotheses that inform our broader understanding of stories. Notably, we show that there is a tendency for characters to use increasingly more negative words and become increasingly emotionally discordant with each other until about 90% of the narrative length. UED also has applications in behavior studies, social sciences, and public health., Competing Interests: The authors have declared that no competing interests exist.

Previous studies have suggested that virtual reality (VR) can elicit emotions in different visual modes using 2D or 3D headsets. However, the effects on emotional arousal by using these two visual modes have not been comprehensively investigated, and the underlying neural mechanisms are not yet clear. This paper presents a cognitive psychological experiment that was conducted to analyze how these two visual modes impact emotional arousal. Forty volunteers were recruited and were randomly assigned to two groups. They were asked to watch a series of positive, neutral and negative short VR videos in 2D and 3D. Multichannel electroencephalograms (EEG) and skin conductance responses (SCR) were recorded simultaneously during their participation. The results indicated that emotional stimulation was more intense in the 3D environment due to the improved perception of the environment; greater emotional arousal was generated; and higher beta (21-30 Hz) EEG power was identified in 3D than in 2D. We also found that both hemispheres were involved in stereo vision processing and that brain lateralization existed in the processing., Competing Interests: The authors have declared that no competing interests exist.

Three studies provided initial laboratory tests of the effectiveness of a novel form of community-based environmental messaging intended to be deployed on public digital signs. In all studies, adult participants watched a slideshow of "Community Voices," a display that combines community images and quotes to celebrate and empower pro-environmental and pro-community thought and action. In addition to assessing the general efficacy of the approach, a central goal was to assess the impact of alternative messengers by comparing identical text associated with either adult or child messengers (Studies 1, 2, and 3). We also assessed the impact of alternative framing of the message itself by comparing: injunctive vs non-injunctive wording (Study 1), political vs non-political content (Study 1), and future vs. present-oriented framing (Study 2). Studies 1 and 2 were conducted on a national sample. In addition, to assess the impact of local vs. non-local messengers, Study 3 compared the response of a non-local sample to a local population in which subjects had personal connections with the people and places featured in the message content. Exposure to Community Voices messages resulted in significant increases in social norm perception, concern about environmental issues, commitment to action, and optimism, suggesting that this approach to messaging is potentially valuable for stimulating cultural change. However, messages attributed to child messengers were generally not more effective, and in some cases were less effective than the same message attributed to adults. We also found no significant difference in the impact of the alternative message frames studied., Competing Interests: All authors have read the journal’s policy on competing interests. CMF and KL declare that no competing interests exist for them with respect to this manuscript. JEP has the following competing interests: he is an owner of Community Hub Incorporated, an Ohio-based S-corporation that provides digital signage software as a service to organizations and communities. Installations of Community Hub’s digital signage in the City of Oberlin and the City of Cleveland currently display Community Voices content. The specific application that displays Community Voices content was developed as open-source computer code. None of the content discussed in this paper was developed with funds from Community Hub. None of the content discussed in this paper has been presented on Community Hub’s digital signage. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.

Learning to play a musical instrument is associated with different, partially conflicting emotions. This paper describes the development and psychometric properties of the Emotions while Learning an Instrument Scale (ELIS). In a longitudinal study with 545 German elementary school children factorial structure and psychometric properties were evaluated. Exploratory and confirmatory factor analyses confirmed a two-factor solution measuring Positive musical Emotions while Learning an Instrument (PELI) and Negative Emotions while Learning an Instrument (NELI). Both subscales yielded scores with adequate internal reliability (Cronbach's α = .74, .86) and relatively stable retest reliabilities over 18 months (r = .11 -.56). Preliminary evidence of congruent and divergent validity of the subscales is provided. Implications for future research of musical emotional experiences in children are discussed., Competing Interests: The authors have declared that no competing interests exist.

The dimensionality of the spatially distributed channels and the temporal resolution of electroencephalogram (EEG) based brain-computer interfaces (BCI) undermine emotion recognition models. Thus, prior to modeling such data, as the final stage of the learning pipeline, adequate preprocessing, transforming, and extracting temporal (i.e., time-series signals) and spatial (i.e., electrode channels) features are essential phases to recognize underlying human emotions. Conventionally, inter-subject variations are dealt with by avoiding the sources of variation (e.g., outliers) or turning the problem into a subject-deponent. We address this issue by preserving and learning from individual particularities in response to affective stimuli. This paper investigates and proposes a subject-independent emotion recognition framework that mitigates the subject-to-subject variability in such systems. Using an unsupervised feature selection algorithm, we reduce the feature space that is extracted from time-series signals. For the spatial features, we propose a subject-specific unsupervised learning algorithm that learns from inter-channel co-activation online. We tested this framework on real EEG benchmarks, namely DEAP, MAHNOB-HCI, and DREAMER. We train and test the selection outcomes using nested cross-validation and a support vector machine (SVM). We compared our results with the state-of-the-art subject-independent algorithms. Our results show an enhanced performance by accurately classifying human affection (i.e., based on valence and arousal) by 16%-27% compared to other studies. This work not only outperforms other subject-independent studies reported in the literature but also proposes an online analysis solution to affection recognition., Competing Interests: No, authors have no competing interests.

Expressing emotions through various modalities is a crucial function not only for humans but also for robots. The mapping method from facial expressions to the basic emotions is widely used in research on robot emotional expressions. This method claims that there are specific facial muscle activation patterns for each emotional expression and people can perceive these emotions by reading these patterns. However, recent research on human behavior reveals that some emotional expressions, such as the emotion "intense", are difficult to judge as positive or negative by just looking at the facial expression alone. Nevertheless, it has not been investigated whether robots can also express ambiguous facial expressions with no clear valence and whether the addition of body expressions can make the facial valence clearer to humans. This paper shows that an ambiguous facial expression of an android can be perceived more clearly by viewers when body postures and movements are added. We conducted three experiments and online surveys among North American residents with 94, 114 and 114 participants, respectively. In Experiment 1, by calculating the entropy, we found that the facial expression "intense" was difficult to judge as positive or negative when they were only shown the facial expression. In Experiments 2 and 3, by analyzing ANOVA, we confirmed that participants were better at judging the facial valence when they were shown the whole body of the android, even though the facial expression was the same as in Experiment 1. These results suggest that facial and body expressions by robots should be designed jointly to achieve better communication with humans. In order to achieve smoother cooperative human-robot interaction, such as education by robots, emotion expressions conveyed through a combination of both the face and the body of the robot is necessary to convey the robot's intentions or desires to humans., Competing Interests: The authors have declared that no competing interests exist.

Existing algorithms of speech-based deception detection are severely restricted by the lack of sufficient number of labelled data. However, a large amount of easily available unlabelled data has not been utilized in reality. To solve this problem, this paper proposes a semi-supervised additive noise autoencoder model for deception detection. This model updates and optimizes the semi-supervised autoencoder and it consists of two layers of encoder and decoder, and a classifier. Firstly, it changes the activation function of the hidden layer in network according to the characteristics of the deception speech. Secondly, in order to prevent over-fitting during training, the specific ratio dropout is added at each layer cautiously. Finally, we directly connected the supervised classification task in the output of encoder to make the network more concise and efficient. Using the feature set specified by the INTERSPEECH 2009 Emotion Challenge, the experimental results on Columbia-SRI-Colorado (CSC) corpus and our own deception corpus show that the proposed model can achieve more advanced performance than other alternative methods with only a small amount of labelled data. [ABSTRACT FROM AUTHOR]

Sentiment analysis is an evolving field of study that employs artificial intelligence techniques to identify the emotions and opinions expressed in a given text. Applying sentiment analysis to study the billions of messages that circulate in popular online social media platforms has raised numerous opportunities for exploring the emotional expressions of their users. In this paper we combine sentiment analysis with natural language processing and topic analysis techniques and conduct two different studies to examine whether engagement in entrepreneurship is associated with more positive emotions expressed on Twitter. In study 1, we investigate three samples with 6.717.308, 13.253.244, and 62.067.509 tweets respectively. We find that entrepreneurs express more positive emotions than non-entrepreneurs for most topics. We also find that social entrepreneurs express more positive emotions, and that serial entrepreneurs express less positive emotions than other entrepreneurs. In study 2, we use 21.491.962 tweets to explore 37.225 job-status changes by individuals who entered or quit entrepreneurship. We find that a job change to entrepreneurship is associated with a shift in the expression of emotions to more positive ones., Competing Interests: No competing interests exist.

An outcome of building sustainable urban forests is that people's well-being is improved when they are exposed to trees. Facial expressions directly represents one's inner emotions, and can be used to assess real-time perception. The emergence and change in the facial expressions of forest visitors are an implicit process. As such, the reserved character of Asians requires an instrument rating to accurately recognize expressions. In this study, a dataset was established with 2,886 randomly photographed faces from visitors at a constructed urban forest park and at a promenade during summertime in Shenyang City, Northeast China. Six experts were invited to choose 160 photos in total with 20 images representing one of eight typical expressions: angry, contempt, disgusted, happy, neutral, sad, scared, and surprised. The FireFACE ver. 3.0 software was used to test hit-ratio validation as an accuracy measurement (ac.) to match machine-recognized photos with those identified by experts. According to the Kruskal-Wallis test on the difference from averaged scores in 20 recently published papers, contempt (ac. = 0.40%, P = 0.0038) and scared (ac. = 25.23%, P = 0.0018) expressions do not pass the validation test. Both happy and sad expression scores were higher in forests than in promenades, but there were no difference in net positive response (happy minus sad) between locations. Men had a higher happy score but lower disgusted score in forests than in promenades. Men also had a higher angry score in forests. We conclude that FireFACE can be used for analyzing facial expressions in Asian people within urban forests. Women are encouraged to visit urban forests rather than promenades to elicit more positive emotions., Competing Interests: Authors do not have competing interests.

The main purpose of the current trial was to test if a brief trauma-focused cognitive-behaviour therapy protocol (prolonged exposure; PE) provided within 72 h after a traumatic event could be effective in decreasing the incidence of post-traumatic stress disorder (PTSD), thus replicating and extending the findings from an earlier trial. After a pilot study (N = 10), which indicated feasible and deliverable study procedures and interventions, we launched an RCT with a target sample size of 352 participants randomised to either three sessions of PE or non-directive support. Due to an unforeseen major reorganisation at the hospital, the RCT was discontinued after 32 included participants. In this paper, we highlight obstacles and lessons learned from our feasibility work that are relevant for preventive psychological interventions for PTSD in emergency settings. One important finding was the high degree of attrition, and only 75% and 34%, respectively, came back for the 2-month and 6-month assessments. There were also difficulties in reaching eligible patients immediately after the event. Based on our experiences, we envisage that alternative models of implementation might overcome these obstacles, for example, with remote delivery of both assessments and interventions via the internet or smartphones combined with multiple recruitment procedures. Lessons learned from this terminated RCT are discussed in depth., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: F.K.A receives royalties from Natur och Kultur for the Swedish translation of the prolonged exposure treatment manual. E.A and M.B report no competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Accurate inferences of the emotional state of conversation participants can be critical in shaping analysis and interpretation of conversational exchanges. In qualitative analyses of discourse, most labelling of the perceived emotional state of conversation participants is performed by hand, and is limited to selected moments where an analyst may believe that emotional information is valuable for interpretation. This reliance on manual labelling processes can have implications for repeatability and objectivity, both in terms of accuracy, but also in terms of changes in emotional state that might go unnoticed. In this paper we introduce a qualitative discourse analytic support method intended to support the labelling of emotional state of conversational participants over time. We demonstrate the utility of the technique using a suite of well-studied broadcast interviews, taking a particular focus on identifying instances of inter-speaker conflict. Our findings indicate that this two-step machine learning approach can help decode how moments of conflict arise, sustain, and are resolved through the mapping of emotion over time. We show how such a method can provide useful evidence of the change in emotional state by interlocutors which could be useful to prompt and support further in-depth study., Competing Interests: The authors have declared that no competing interests exist.

This paper examined, with a behavioral paradigm, to what extent people choose to view stimuli that portray death, violence or harm. Based on briefly presented visual cues, participants made choices between highly arousing, negative images and positive or negative alternatives. The negative images displayed social scenes that involved death, violence or harm (e.g., war scene), or decontextualized, close-ups of physical harm (e.g., mutilated face) or natural threat (e.g., attacking shark). The results demonstrated that social negative images were chosen significantly more often than other negative categories. Furthermore, participants preferred social negative images over neutral images. Physical harm images and natural threat images were not preferred over neutral images, but were chosen in about thirty-five percent of the trials. These results were replicated across three different studies, including a study that presented verbal descriptions of images as pre-choice cues. Together, these results show that people deliberately subject themselves to negative images. With this, the present paper demonstrates a dynamic relationship between negative information and behavior and advances new insights into the phenomenon of morbid curiosity. [ABSTRACT FROM AUTHOR]

Peer-reviewed publications focusing on climate change are growing exponentially with the consequence that the uptake and influence of individual papers varies greatly. Here, we derive metrics of narrativity from psychology and literary theory, and use these metrics to test the hypothesis that more narrative climate change writing is more likely to be influential, using citation frequency as a proxy for influence. From a sample of 732 scientific abstracts drawn from the climate change literature, we find that articles with more narrative abstracts are cited more often. This effect is closely associated with journal identity: higher-impact journals tend to feature more narrative articles, and these articles tend to be cited more often. These results suggest that writing in a more narrative style increases the uptake and influence of articles in climate literature, and perhaps in scientific literature more broadly. [ABSTRACT FROM AUTHOR]