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Facial expression perception is influenced by body posture, with perception biased toward the body emotion. Previous research has suggested that the magnitude of this biasing influence of body posture is driven by individual differences in the precision of facial expression representations underlying discrimination abilities, where lower precision leads to a greater influence of body posture. It is unclear however whether similar mechanisms might drive the influence of contextual cues in Autism, which is often characterised by reduced facial expression discrimination abilities. Here, we addressed this question by using online psychophysical methods to determine the precision of isolated face and body expression representations of anger and disgust, and the influence of body on facial expression perception, in autistic and non-autistic adults. Both groups showed a strong influence of body context on facial expression perception, but this influence was larger in the autistic group, mirroring their lower overall precision of facial expression representations relative to non-autistic individuals. Crucially, the magnitude of the biasing influence of body posture in both groups was related to the precision of individuals' facial expression representations. The results suggest that similar principles govern the integration of facial expression and body posture information in both autistic and non-autistic individuals. [ABSTRACT FROM AUTHOR]

One of the main challenges in the process of recognizing emotion through the voice are related to the specific characteristics of an individual's sound spectrum, such as accent and speech rhythm, as well as regionalism and wide variability of spoken phrases. Despite efforts to propose emotion recognition models, providing an increase in accuracy in classifying emotion in a specialized way is an open research question. Faced with these challenges, this work proposes DEEP (DEtection of voice Emotion in Portuguese language), an architecture for detecting voice emotion based on patterns present in the sound spectrum generated by the voice of Brazilian Portuguese speakers. DEEP recognizes each emotion by using a set of specialist Convolutional Neural Networks that receive as input the features extracted from the sound spectrum. With this, DEEP aims to specialize each emotion to increase the rate of correct answers and adapt to different tones and voice conditions that may occur in everyday life. Our results show that DEEP outperforms the emotion recognition measures of other state of art techniques for all evaluated scenarios. [ABSTRACT FROM AUTHOR]

Behavioral contagion is thought to play a significant role in social synchronization and coordination across animal taxa. While there is extensive evidence of behavioral contagion in Haplorrhines (i.e. monkeys and apes), limited research exists in Strepsirrhines (i.e. lemurs). Here, we aimed to investigate the presence of contagious yawning and scratching in two captive groups of black-and-white ruffed lemurs (Varecia variegata) (N = 4) and red ruffed lemurs (Varecia rubra) (N = 4), and further test whether behavioral contagion is modulated by the model's social integration in the group. We conducted all occurrence sampling to examine whether individuals observing a yawning or scratching event (i.e. trigger event) were more likely to yawn or scratch in the following 2 min, as compared to individuals who did not observe it. We ran generalized linear mixed models and found that the likelihood of yawning and scratching was higher for subjects observing the trigger event than for subjects who did not observe the event, although the model's social integration had no modulating effect on the probability of showing behavioral contagion. Our findings represent the first evidence of behavioral contagion in this genus and contribute to shed light on the distribution and the possible adaptive function of this phenomenon in primates. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between emotional trends derived from X platform data and the market dynamics of prominent cryptocurrencies—Cardano, Binance, Fantom, Matic, and Ripple—during the period from October 2022 to March 2023. Utilizing SenticNet, key emotions such as fear and anxiety, rage and anger, grief and sadness, delight and pleasantness, enthusiasm and eagerness, and delight and joy were identified. The emotional data and cryptocurrency price data, sourced bi-weekly, were analyzed to uncover significant correlations. The findings reveal that emotions such as delight and pleasantness and delight and joy have the strongest positive correlations with Fantom's price, while delight and pleasantness exhibit the strongest negative correlations with Cardano and Binance. The study highlights the nuanced impact of specific emotional states on cryptocurrency prices, offering valuable insights for market participants. [ABSTRACT FROM AUTHOR]

In communication, emotional information is crucial, yet its preservation in tweet embeddings remains a challenge. This study aims to address this gap by exploring three distinct methods for generating embedding vectors of tweets: word2vec models, pre-trained BERT models, and fine-tuned BERT models. We conducted an analysis to assess the degree to which emotional information is conserved in the resulting embedding vectors. Our findings indicate that the fine-tuned BERT model exhibits a higher level of preservation of emotional information compared to other methods. These results underscore the importance of utilizing advanced natural language processing techniques for preserving emotional context in text data, with potential implications for enhancing sentiment analysis and understanding human communication in social media contexts. [ABSTRACT FROM AUTHOR]

Emotion enhances the subjective experience of recollection. Previous research examined associations between memory accuracy and metamemory confidence judgments, but these studies have not quantified the relationship between accuracy and metacognitive confidence judgments. In this study we utilize signal detection theory frameworks to investigate how memory accuracy (measured by discrimination sensitivity) and the alignment between metamemory confidence judgments and memory accuracy (ie. metacognitive efficiency) varies for neutral and negative valence, as well as item and associative detail memory types. Our results indicate that valence and memory type have different effects on accuracy, confidence, and metacognitive efficiency. Negative valence was associated with enhanced accuracy for both items and associated details, but its relationship with response bias varied across memory types, with conservative recognition responses observed for items and liberal responses for associative details. We also observed a double dissociation between metamemory confidence judgements across valence and memory type with negative valence associated with increased confidence for item memory, but decreased confidence for associated details. Examining the association between memory accuracy and metamemory confidence revealed that metacognitive efficiency was greater for negatively valenced items compared to neutral, but this effect did not generalize to details associated with negatively valenced items. These findings advance our understanding of how arousing, negatively valenced information modulates memory and metacognition. [ABSTRACT FROM AUTHOR]

Episodic autobiographical memory (EAM) is a long-term memory system of personally experienced events with their context – what, where, when – and subjective elements, e.g., emotions, thoughts, or self-reference. EAM formation has rarely been studied in a controlled, real-life-like paradigm, and there is no predictive model of long-term retrieval from self-rated subjective experience at encoding. The present longitudinal study, with three surprise free recall memory tests immediately, one-week and one-month after encoding, investigated incidental encoding of EAM in an immersive virtual environment where 30 participants either interacted with or observed specific events of varying emotional valences with simultaneous physiological recordings. The predictive analyses highlight the temporal dynamics of the predictors of EAM from subjective ratings at encoding: common characteristics related to sense of remembering and infrequency of real-life encounter of the event were identified over time, but different variables become relevant at different time points, such as the emotion and mental imagery or prospective aspects. This dynamic and time-dependent role of memory predictors challenges traditional views of a uniform influence of encoding factors over time. Current evidence for the multiphasic nature of memory formation points to the role of different mechanisms at play during encoding but also consolidation and subsequent retrieval. [ABSTRACT FROM AUTHOR]

Multisensory integration of emotion is crucial for social interactions. However, existing research on emotional multisensory integration has primarily focused on adults and infants, with less attention paid to children. To explore the cognitive and neural mechanisms underlying emotional multisensory integration in children, we recorded event-related potentials from 42 children aged 5 to 6 years when they identified emotional stimuli. The emotional stimuli, including pictures, sounds, or both presented simultaneously, were randomly presented. Children were required to recognize the emotions of the stimuli. Results showed that (1) children identified audiovisual emotional stimuli faster and more accurately than visual stimuli; (2) multimodal stimuli elicited a larger N1 component than visual stimuli did, which may indicate the early development of emotional multisensory integration in children; (3) the audiovisual P2 component did not exhibit an inhibitory effect compared with visual stimuli, indicating information redundancy resulting from incomplete cognitive development in children. In conclusion, these results suggest that emotional multisensory integration in 5- to 6-year-old children occurs early rather than late. [ABSTRACT FROM AUTHOR]

Behavioral and physiological synchrony facilitate emotional closeness in attachment relationships. The aim of this pseudorandomized cross-over study was to investigate the emotional and physiological link, designated as co-modulation, between dogs and their owners. We measured the heart rate variability (HRV) and physical activity of dogs belonging to co-operative breeds (n = 29) and their owners during resting baselines and positive interaction tasks (Stroking, Training, Sniffing, Playing) and collected survey data on owner temperament and dog–owner relationship. Although overall HRV and activity correlated between dogs and their owners across tasks, task-specific analyses showed that HRV of dogs and owners correlated during free behaving (Pre- and Post-Baseline), whereas the activity of dogs and owners correlated during predefined interaction tasks (Stroking and Playing). Dog overall HRV was the only predictive factor for owner overall HRV, while dog height, ownership duration, owner negative affectivity, and dog–owner interaction scale predicted dog overall HRV. Thus, the characteristics of dog, owner, and the relationship modified the HRV responses in dog–owner dyads. The physiology and behavior of dogs belonging to co-operative breeds and their owners were therefore co-modulated, demonstrating physiological and emotional connection comparable to those found in attachment relationships between humans. [ABSTRACT FROM AUTHOR]

The emotional valence of animals is challenging to assess, despite being a key component of welfare. In this study, we attempted to assess emotional valence through memory in 1- and 3-week-old piglets. It was hypothesized that piglets would spend less time in a pen where they experienced a negative event (castration) and more time in a pen where they experienced a positive event (enrichment). A testing apparatus was designed with three equally sized pens: two outer sections serving as treatment pens containing unique visual and tactile cues and a center section remaining neutral. Piglets received either negative or positive condition in one outer treatment pen and a sham treatment in the opposite. Various methods were tested (age of piglets, number and length of conditioning sessions, passive vs. active conditioning). Contrary to expectations, piglets did not decrease their time in the pen associated with the negative condition or increase their time in the pen associated with the positive condition. However, when exposed to the positive condition, results indicate older piglets developed an aversion towards the sham treatment. This study provides methodological groundwork for the application of place conditioning in piglets and highlights the nuances important for the use of cognitive tests to assess animal welfare. [ABSTRACT FROM AUTHOR]

Recognizing the pivotal role of tourists as ambassadors for their hometown is imperative, as it relates to the dynamic host-guest relationship, thereby significantly influencing the sustainable tourism progress in both their originating locations and the visited destinations. To delve deeper into this ambassadorial role, this study employed the tripartite model of resident attitude, which bridges the intricate interplay between residents' cognitive, affective, and conative attitudes towards tourists. 204 U.S. people have been enrolled through Amazon's Mechanical Turk to finish semi-structured questionnaire about their attitudes towards Chinese outbound tourists. Leximancer, a computer-assisted qualitative data analysis approach was applied. Results demonstrated that neutral or positive cognitive-attitudes U.S. residents held towards Chinese tourists exceeded negative cognitive attitudes. Further analysis inferred that it was the interpretation or attribution of cognitive attitudes that would induce residents' different emotional feelings and behavioral intention. Positive interpretation would lead to positive feeling (i.e., admiration) and positive intention to visit China. Negative interpretation would lead to negative feeling (i.e., contempt) and negative intention to visit China. This research provided implications for destination management organization of tourists' origin place to incorporate outbound tourists as their place ambassadors. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG)-based Brain-Computer Interface (BCI) systems for emotion recognition have the potential to assist the enrichment of human–computer interaction with implicit information since they can enable understanding of the cognitive and emotional activities of humans. Therefore, these systems have become an important research topic today. This study aims to present trends and gaps on this topic by performing a systematic literature review based on the 216 published scientific literature gathered from various databases including ACM, IEEE Xplore, PubMed, Science Direct, and Web of Science from 2016 to 2020. This review gives an overview of all the components of EEG based BCI system from the signal stimulus module which includes the employed device, signal stimuli, and data processing modality, to the signal processing module which includes signal acquisition, pre-processing, feature extraction, feature selection, classification algorithms, and performance evaluation. Thus, this study provides an overview of all components of an EEG-based BCI system for emotion recognition and examines the available evidence in a clear, concise, and systematic way. In addition, the findings are aimed to inform researchers about the issues on what are research trends and the gaps in this field and guide them in their research directions. [ABSTRACT FROM AUTHOR]

Emotion is an episode involving changes in multiple components, specifically subjective feelings, physiological arousal, expressivity, and action tendencies, all these driven by appraisal processes. However, very few attempts have been made to comprehensively model emotion episodes from this full componential perspective, given the statistical and methodological complexity involved. Recently, network analyses have been proposed in the field of emotion and cognition as an innovative theoretical and statistical framework able to integrate several properties of emotions. We therefore addressed the call for more multi-componential evidence by modeling the network of a comprehensive list of emotion components drawn from the Component Process Model of Emotion. Five-hundred students were confronted with mildly ambiguous scenarios from everyday life, and reported on their situational appraisals and emotion responses. Network analyses were applied to the emotion components related to a positive and a negative scenario to explore 1) how the components organize themselves into networks and dimensions; 2) which components are the most central within networks and dimensions; and 3) the patterns of components relation between and within dimensions. A three-dimensional solution emerged in both scenarios. Additionally, some appraisals and responses appeared to be differentially relevant and related to each other in both scenarios, highlighting the importance of context in shaping the strength of emotion component relations. Overall, we enriched the field of affective science by exploring the connections between emotion components in three novel ways: by using network analyses, by integrating them into a multi-componential framework, and by providing context to our emotion components. Our results can also potentially inform applied research, where understanding the interconnections and the centrality of components could aid the personalization of interventions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic presented a series of challenges to organizations. Among those that successfully continued operating, the subsequent recovery period catalyzed pressure to redefine work structure after social distancing restrictions lifted. Here, we observed the benefits of transformational leadership in this historically unique context of organizational distress by applying an adjusted version of Ashkanasy and Dorris's (Ashkanasy and Dorris in Annu Rev Organ Psych Organ Behav 4:67–90, 2017) framework for workplace emotions to a large, multi-campus university. We quantitatively content-analyzed semi-structured interviews of more than 300 divisional leaders and their staff from across the organization. Interviews occurred in the months following the first semester of continuous in-person service delivery, when most employees returned to working in employer-operated space. Despite disproportionate emphasis on negative and self-focused emotions, negative emotions clustered in individuals' empathic recognition of others' emotions; though efforts to regulate those emotions proved scant. Positive emotions primarily emerged in response to local leadership efforts to mitigate the negative emotions of students, staff, and faculty. This data pattern suggests that individuals experienced negative emotions, recognized others' negative emotions, and appreciated leaders' interventions to ameliorate those negative emotions. Strategies reminiscent of transformational leadership therefore productively addressed the negative impact of workplace stress imposed by the pandemic, helping to facilitate compliance and enthusiasm with return-to-work efforts. The findings thus illustrate how a transformational style of leadership can address individuals' negative experiences during a period of pronounced existential stress. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to investigate the relationship between childhood polycyclic aromatic hydrocarbon (PAH) exposure and emotional and behavioral problems in adolescence. Methods: Participants included 998 school-age children aged 7–12 years (514 girls and 484 boys). Metabolite concentrations of four PAHs (1-hydroxypyrene [1-OHPyr], 2-hydroxynaphthalene [2-OHNap], 2-hydroxyfluorine [2-OHFlu], and 9-hydroxyphenanthrene [9-OHPhe]) were measured in urine samples at baseline (Dec 2014–Dec 2015). During adolescence, we measured emotional and behavioral problems in study participants. We used logistic regression models to assess the effects of different levels of PAH metabolite concentrations on emotional and behavioral problems for boys and girls, separately. Results: Boys exposed to 1-OHPyr and 2-OHFlu had a significantly higher risk of externalizing problems (OR: 2.62, 95% CI: 1.09 ~ 6.29; OR: 2.92, 95% CI: 1.15 ~ 7.42). 2-OHNap exposure faced a higher risk of internalizing problems (OR: 3.85, 95% CI: 1.28 ~ 11.58; OR: 3.63, 95% CI: 1.13 ~ 11.63) and externalizing problems (OR: 4.27, 95% CI: 1.44 ~ 12.70; OR: 4.68, 95% CI: 1.49 ~ 14.73). Moreover, boys exposed to 9-OHPhe exhibited a significant risk of anxiety (OR: 2.84, 95% CI: 1.01 ~ 7.97; OR: 3.00, 95% CI: 1.04 ~ 8.68). Similarly, girls exposed to 9-OHPhe had a significant risk of anxiety (OR: 2.41, 95% CI: 1.25 ~ 4.64). Conclusion: Childhood PAH exposures are associated with emotional and behavioral problems in adolescence, and boys seem more susceptible than girls. [ABSTRACT FROM AUTHOR]

This study uses autoethnography to examine the development of and changes in my professional identity through my study and work experiences since I came to Australia from Japan. I am currently an academic teaching Japanese language and research in the field of applied linguistics in an Australian university. I wrote a self-narrative about my experiences and the related emotions covering the time of my arrival in Australia from Japan in 1997. Because teaching and research are the major roles in my current position, I coded the narrative for statements concerning my teacher identity and my researcher identity. After reading the narratives and repeatedly reviewing the coding, I produced stories about my teacher and researcher identities. The development and changes of my professional identity were closely linked to sociopolitical aspects of my work in my immediate teaching and research contexts and to my linguistic and cultural backgrounds. My relationships with students and research colleagues tended to trigger positive emotions and contribute to the development of a positive professional identity, whereas relationships with higher management at the university were likely to cause negative emotions and contribute to the development of a negative professional identity. [ABSTRACT FROM AUTHOR]

Music is a powerful medium that influences our emotions and memories. Neuroscience research has demonstrated music's ability to engage brain regions associated with emotion, reward, motivation, and autobiographical memory. While music's role in modulating emotions has been explored extensively, our study investigates whether music can alter the emotional content of memories. Building on the theory that memories can be updated upon retrieval, we tested whether introducing emotional music during memory recollection might introduce false emotional elements into the original memory trace. We developed a 3-day episodic memory task with separate encoding, recollection, and retrieval phases. Our primary hypothesis was that emotional music played during memory recollection would increase the likelihood of introducing novel emotional components into the original memory. Behavioral findings revealed two key outcomes: 1) participants exposed to music during memory recollection were more likely to incorporate novel emotional components congruent with the paired music valence, and 2) memories retrieved 1 day later exhibited a stronger emotional tone than the original memory, congruent with the valence of the music paired during the previous day's recollection. Furthermore, fMRI results revealed altered neural engagement during story recollection with music, including the amygdala, anterior hippocampus, and inferior parietal lobule. Enhanced connectivity between the amygdala and other brain regions, including the frontal and visual cortex, was observed during recollection with music, potentially contributing to more emotionally charged story reconstructions. These findings illuminate the interplay between music, emotion, and memory, offering insights into the consequences of infusing emotional music into memory recollection processes. [ABSTRACT FROM AUTHOR]

Video recordings accurately capture facial expression movements; however, they are difficult for face perception researchers to standardise and manipulate. For this reason, dynamic morphs of photographs are often used, despite their lack of naturalistic facial motion. This study aimed to investigate how humans perceive emotions from faces using real videos and two different approaches to artificially generating dynamic expressions – dynamic morphs, and AI-synthesised deepfakes. Our participants perceived dynamic morphed expressions as less intense when compared with videos (all emotions) and deepfakes (fearful, happy, sad). Videos and deepfakes were perceived similarly. Additionally, they perceived morphed happiness and sadness, but not morphed anger or fear, as less genuine than other formats. Our findings support previous research indicating that social responses to morphed emotions are not representative of those to video recordings. The findings also suggest that deepfakes may offer a more suitable standardized stimulus type compared to morphs. Additionally, qualitative data were collected from participants and analysed using ChatGPT, a large language model. ChatGPT successfully identified themes in the data consistent with those identified by an independent human researcher. According to this analysis, our participants perceived dynamic morphs as less natural compared with videos and deepfakes. That participants perceived deepfakes and videos similarly suggests that deepfakes effectively replicate natural facial movements, making them a promising alternative for face perception research. The study contributes to the growing body of research exploring the usefulness of generative artificial intelligence for advancing the study of human perception. [ABSTRACT FROM AUTHOR]

EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-'action'-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses. [ABSTRACT FROM AUTHOR]

Surface facial electromyography (EMG) is commonly used to detect emotions from subtle facial expressions. Although there are established procedures for collecting EMG data and some aspects of their processing, there is little agreement among researchers about the optimal way to process the EMG signal, so that the study-unrelated variability (noise) is removed, and the emotion-related variability is best detected. The aim of the current paper was to establish an optimal processing pipeline for EMG data for identifying emotional expressions in facial muscles. We identified the most common processing steps from existing literature and created 72 processing pipelines that represented all the different processing choices. We applied these pipelines to a previously published dataset from a facial mimicry experiment, where 100 adult participants observed happy and sad facial expressions, whilst the activity of their facial muscles, zygomaticus major and corrugator supercilii, was recorded with EMG. We used a resampling approach and subsets of the original data to investigate the effect and robustness of different processing choices on the performance of a logistic regression model that predicted the mimicked emotion (happy/sad) from the EMG signal. In addition, we used a random forest model to identify the most important processing steps for the sensitivity of the logistic regression model. Three processing steps were found to be most impactful: baseline correction, standardisation within muscles, and standardisation within subjects. The chosen feature of interest and the signal averaging had little influence on the sensitivity to the effect. We recommend an optimal processing pipeline, share our code and data, and provide a step-by-step walkthrough for researchers. [ABSTRACT FROM AUTHOR]

The current study used event-related potentials (ERPs) to examine the ability of task demand in modulating the effect of reward association on the processing of emotional faces. In the learning phase, a high or low reward probability was paired with male or female facial photos of angry, happy, or neutral expressions. Then, in the test phase, task demand was manipulated by asking participants to discriminate the emotionality or the gender of the pre-learned face with no reward at stake. The ERP results in the test phase revealed that the fronto-central N1 (60–100 ms) and the VPP (160–210 ms) components were sensitive to the interaction between reward and emotion, in that the differences between the mean amplitudes for high- and low-reward conditions were significantly larger in the neutral face and angry face conditions than in the happy face condition. Moreover, reward association and task demand showed a significant interaction over the right hemisphere for the N170 component (140–180 ms), with amplitude difference between high- and low-reward conditions being larger in the emotion task than that in the gender task. The later N2pc component exhibited an interaction between task demand and emotionality, in that happy faces elicited larger N2pc difference waves than angry and neutral faces did in the emotion task, but neutral faces elicited larger N2pc difference waves than angry faces did in the gender task. The N2pc effect aligned with behavioral performance. These results suggest that reward association acts as an 'emotional tagging' to imbue neutral or angry faces with motivational significance at early time windows. Task demand functions in a top-down way to modulate the deployment of attentional resources at the later attentional selection stage, but does not affect the early automatic processing of either emotion or reward association. [ABSTRACT FROM AUTHOR]

Producing a high-quality review translation is a multifaceted process. It goes beyond successful semantic transfer and requires conveying the original message's tone and style in a way that resonates with the target audience, whether they are human readers or Natural Language Processing (NLP) applications. Capturing these subtle nuances of the review text demands a deeper understanding and better encoding of the source message. In order to achieve this goal, we explore the use of self-supervised masked language modeling (MLM) and a variant called polarity masked language modeling (p-MLM) as auxiliary tasks in a multi-learning setup. MLM is widely recognized for its ability to capture rich linguistic representations of the input and has been shown to achieve state-of-the-art accuracy in various language understanding tasks. Motivated by its effectiveness, in this paper we adopt joint learning, combining the neural machine translation (NMT) task with source polarity-masked language modeling within a shared embedding space to induce a deeper understanding of the emotional nuances of the text. We analyze the results and observe that our multi-task model indeed exhibits a better understanding of linguistic concepts like sentiment and emotion. Intriguingly, this is achieved even without explicit training on sentiment-annotated or domain-specific sentiment corpora. Our multi-task NMT model consistently improves the translation quality of affect sentences from diverse domains in three language pairs. [ABSTRACT FROM AUTHOR]

Perfectionism cognitions are associated with cognitive biases (including memory), depression, and anxiety. Memory biases related to perfectionism may contribute to perfectionism's role as a transdiagnostic risk factor and high comorbidity rates. However, very limited research exists on the relationship between perfectionism and memory. The current study examined (1) if perfectionism cognitions are related to memory performance, and (2) if perfectionism cognitions are related to sensitivity or decision bias. A convenience sample of 191 university students completed a recognition task with perfectionism-relevant, negative, positive, and neutral stimuli, and the Perfectionism Cognitions Inventory. The results showed perfectionism cognitions were correlated with and predicted memory accuracy for perfectionism-relevant and negative words only, suggesting individuals higher in perfectionism cognitions may exhibit an enhanced memory for perfectionism-relevant and negative stimuli. Signal detection analysis showed higher perfectionism cognitions were also related to (1) a higher memory discriminative ability, and (2) a more liberal response bias, for perfectionism words only. Furthering our understanding of how perfectionism cognitions impact cognitive processes is an important step to identify potential mechanisms in the relation between perfectionism cognitions and distress, and identify targets for intervention. [ABSTRACT FROM AUTHOR]

This research investigates the nuanced influence of internally experienced moods, specifically focusing on positive and negative moods, on individuals' propensity for risk-taking behavior. The objective is to discern the intricate impact of mood on risk-taking, a subject that has elicited varying results in diverse studies. A systematic search for experimental-research studies, spanning from the present to 1983, was conducted using PubMed and Semantic Scholar databases. After a meticulous examination of the complete texts, a final set of 13 studies was chosen for analysis. The results revealed divergent findings: some studies indicated that positive mood increased risk-taking behavior, while others suggested a similar effect for negative mood. These opposing outcomes are explained by two hypotheses: the Mood-Maintenance Hypothesis and the Affect Infusion Model. This study aims to comprehensively explore and compare the substantiation of these two theories across a range of experimental studies, contributing to a deeper understanding of the complex interplay between mood and risk-taking behavior. [ABSTRACT FROM AUTHOR]

Research suggests that making Judgments of Learning (JOLs) can significantly influence memory performance, commonly known as JOL reactivity. While recent investigations have highlighted the role of learning material attributes in JOL reactivity, the impact of emotions on this process has received less attention. Additionally, the cognitive mechanisms underlying JOL reactivity remain debated. This study aimed to investigate the influence of emotion on JOL reactivity and its associated cognitive mechanisms. In Experiment 1, participants were assigned to make JOLs or refrain from such judgments while learning adjectives with positive, negative, and neutral valences. Subsequently, they underwent a recognition test. The results revealed that participants displayed JOL reactivity when exposed to positive and neutral words, but not when encountering negative words. Experiment 2, utilizing nouns as learning material and manipulating the emotion of new words in the recognition test, replicated the findings of Experiment 1. Collectively, both experiments demonstrated distinct effects of emotions on JOL reactivity. These findings provide support for the enhanced learning engagement theory of JOL reactivity. [ABSTRACT FROM AUTHOR]

I offer a philosophical account of shared grief, on which it is a process, undergone by a group, of recognising and accommodating significant possibilities that are lost to that group. In setting out from an understanding of grief's distinctive characteristics, a philosophically interesting, metaphysically undemanding, and practically useful account of shared grief comes into view, that has broader consequences for understanding shared emotion. [ABSTRACT FROM AUTHOR]

Drawing inspiration from the sociocultural turn in language teacher cognition research, this conceptual article argues for the utilisation of cultural-historical activity theory (CHAT) as a theoretical framework for researching and understanding teacher cognition as a social phenomenon. In this article, three CHAT-related concepts, namely mediation, including contradictions; emotion; and agency are expounded, drawn together, and proven as pertinent and useful analytical tools for the study of the complexity of teacher cognition. Its central thesis posits that CHAT accommodates teachers' sociocultural activity systems whose mechanism is characterised by mediation that stimulates their emotional responses and agency for taking actions—a process through which teacher cognition unfolds in dynamic, developmental manners. By mapping these concepts, the article contributes to the extant scholarship by highlighting language teacher cognition as a mediated, emotional, and agentive process of transformation and providing critical epistemological implications for prospective empirical attempts in this line of research. [ABSTRACT FROM AUTHOR]

The way in which climate change-related information is framed can influence not only cognitive outcomes but also emotional responses. This web-based experiment (n = 331) investigated the effects of strategy framing and issue framing on emotions and online climate action intentions in the context of climate relationship between China and the United States. Results indicated that strategy framing increased negative emotions (i.e., anxiety and anger), whereas issue framing triggered more positive emotions (i.e., pride and excitement). No significant difference was observed regarding climate action intentions between two groups. Additionally, anxiety and anger mediated the effects of message framing on online climate action intentions. Habitual worry about climate change and attitude toward China-U.S. climate collaboration moderated the effects of strategy framing and issue framing on discrete emotions. The findings offer theoretical insights regarding emotional responses to strategy framing and practical implications on climate communication. [ABSTRACT FROM AUTHOR]

The present study was conducted to understand the essence of the lived experience of the stateless erstwhile enclave dwellers in India and Bangladesh who later chose Indian citizenship during land and population swap between the two countries as per Land Boundary Agreement (2015). The study is phenomenological in nature and studies the phenomenon of living in statelessness in the Enclaves for generations, their emotions during transition and their pollyannaism after getting citizenship. The researchers conducted unstructured open ended interviews among the erstwhile enclave dwellers who were bestowed Indian citizenship and are living in India. Samples were taken both from the enclave dwellers who have been living in their own place and not displaced during land and population swap as per Land and Boundary Agreement (LBA) 2015, between India and Bangladesh and also from the enclave dwellers who had to move to the Indian mainland and are living in Permanent Rehabilitation Clusters (PRCs) provided by the Indian Authorities. All the participants of the study were male and their real names have been replaced by the pseudo names. The enclave dwellers reflected that the decades and generations of suffering from statelessness, lawlessness, lack of schools, hospitals and other civic amenities has cost their education, mental and physical health, dignity and identity. Though all the erstwhile enclave dwellers reflect on the statelessness and express their emotion on getting citizenship in the same way, but the pollyannaism of enclave dwellers who were not displaced by LBA 2015 and living in the ancestral place and those who are living in the PRCs are quite different. [ABSTRACT FROM AUTHOR]

Humans express emotions through various modalities such as facial expressions and natural language. However, the relationships between emotions expressed through different modalities and their correlations with neural activities remain uncertain. Here, we aimed to unveil some of these uncertainties by investigating the similarity of emotion representations across modalities and brain regions. First, we represented various emotion categories as multi-dimensional vectors derived from visual (face), linguistic, and visio-linguistic data, and used representational similarity analysis to compare these modalities. Second, we examined the linear transferability of emotion representation from other modalities to the visual modality. Third, we compared the representational structure derived in the first step with those from brain activities across 360 regions. Our findings revealed that emotion representations share commonalities across modalities with modality-type dependent variations, and they can be linearly mapped from other modalities to the visual modality. Additionally, emotion representations in uni-modalities showed relatively higher similarity with specific brain regions, while multi-modal emotion representation was most similar to representations across the entire brain region. These findings suggest that emotional experiences are represented differently across various brain regions with varying degrees of similarity to different modality types, and that they may be multi-modally conveyable in visual and linguistic domains. [ABSTRACT FROM AUTHOR]

Childhood trauma is associated with poor health outcomes in adulthood, largely due to the impact of chronic stress on the body. Fortunately, there are certain protective characteristics, such as constraint (i.e., impulse control, inhibition, and avoidance of unconventional behavior and risk) and cognitive reappraisal (i.e., reframing circumstances in a more positive light). In the present study, we investigated the interaction between childhood trauma, resilience, and neural correlates of emotion processing. Participants responded to survey questions regarding childhood trauma and resilient characteristics. They were later invited to passively view neutral, unpleasant, and pleasant images while their brain activity was recorded via electroencephalography (EEG). We analyzed two event-related potential (ERP) components of interest: the Early Posterior Negativity (EPN) and Late Positive Potential (LPP). We found that childhood trauma was associated with decreased constraint and reduced sensitivity to unpleasant images (i.e., decreased LPP amplitude differences between neutral and unpleasant images as compared to controls). Further, constraint predicted increased sensitivity to pleasant images. In a hierarchical linear regression analysis, we found that constraint moderated the relation between childhood trauma and emotion processing, such that it predicted increased sensitivity to unpleasant images for adults with childhood trauma in particular. Childhood trauma and cognitive reappraisal independently predicted decreased sensitivity to unpleasant images, (i.e., decreased LPP amplitude differences between neutral and unpleasant images). Our findings suggest that childhood trauma and resilient characteristics independently and interactively influence emotion processing. [ABSTRACT FROM AUTHOR]

This study aims to develop a predictive model for predicting gifted students' engagement levels and to investigate the features that are important in such predictions. Features reflecting students' emotions, social-emotional learning skills, learning approaches and video-watching behaviours were used in the prediction models. The study group consisted of 36 gifted students between the ages of 12 and 14 who attended an information technologies course, where students engaged with Arduino-based learning tasks. Data related to only one task were analysed. Prediction models were developed using different machine learning algorithms, and information gain scores were used to investigate important features in the prediction models. The results show that all prediction models achieved a higher classification accuracy than the base model. The highest classification accuracy (83%) was achieved with the Support Vector Machine (SVM) algorithm. Students' self-reported emotions while performing the task were found to be the most important feature in predicting their level of engagement. Other important features in predicting engagement level were the students' social-emotional learning skills, deep and surface learning approaches and their video-watching behaviours. As a result, it was found that the engagement level of gifted students could be predicted with high accuracy. These results can be used to predict students' engagement levels and to develop interventions for low-engaged students. The obtained results are discussed within the scope of emotion-aware learning design and social-emotional learning. [ABSTRACT FROM AUTHOR]

The literature has demonstrated the potential for detecting accurate electrical signals that correspond to the will or intention to move, as well as decoding the thoughts of individuals who imagine houses, faces or objects. This investigation examines the presence of precise neural markers of imagined motivational states through the combining of electrophysiological and neuroimaging methods. 20 participants were instructed to vividly imagine the desire to move, listen to music or engage in social activities. Their EEG was recorded from 128 scalp sites and analysed using individual standardized Low-Resolution Brain Electromagnetic Tomographies (LORETAs) in the N400 time window (400–600 ms). The activation of 1056 voxels was examined in relation to the 3 motivational states. The most active dipoles were grouped in eight regions of interest (ROI), including Occipital, Temporal, Fusiform, Premotor, Frontal, OBF/IF, Parietal, and Limbic areas. The statistical analysis revealed that all motivational imaginary states engaged the right hemisphere more than the left hemisphere. Distinct markers were identified for the three motivational states. Specifically, the right temporal area was more relevant for "Social Play", the orbitofrontal/inferior frontal cortex for listening to music, and the left premotor cortex for the "Movement" desire. This outcome is encouraging in terms of the potential use of neural indicators in the realm of brain-computer interface, for interpreting the thoughts and desires of individuals with locked-in syndrome. [ABSTRACT FROM AUTHOR]

Sentiment analysis in natural language processing (NLP) is used to understand the polarity of human emotions (e.g., positive and negative) and preferences (e.g., price and quality). Reinforcement learning (RL) enables a decision maker (or agent) to observe the operating environment (or the current state) and select the optimal action to receive feedback signals (or reward) from the operating environment. Deep reinforcement learning (DRL) extends RL with deep neural networks (i.e., main and target networks) to capture the state information of inputs and address the curse of dimensionality issue of RL. In sentiment analysis, RL and DRL reduce the need for a large labeled dataset and linguistic resources, increasing scalability and preserving the context and order of logical partitions. Through enhancement, the RL and DRL algorithms identify negations, enhance the quality of the generated responses, predict the logical partitions, remove the irrelevant aspects, and ultimately capture the correct sentiment polarity. This paper presents a review of RL and DRL models and algorithms with their objectives, applications, datasets, performance, and open issues in sentiment analysis. [ABSTRACT FROM AUTHOR]

Rhombencephalosynapsis (RES) is a hindbrain malformation characterized by a missing cerebellar vermis with apposition or fusion of the cerebellar hemispheres. The present clinical case report provides a comprehensive, longitudinal overview of cognitive and affective manifestations in a 22-year-old patient with RES. The patient shows clinical signs of emotional reactivity and dysregulation, impulsivity, and impairments in executive functioning since early childhood. These features fit the constellation of neuropsychiatric symptoms observed in patients with congenital and acquired abnormalities of the posterior vermis. It is proposed that patients with RES may show affective and cognitive difficulties which increase their vulnerability to psychological stress and risk of developing mental health issues. [ABSTRACT FROM AUTHOR]

Affective neuroscience has traditionally relied on cross-sectional studies to uncover the brain correlates of affects, emotions, and moods. Such findings obfuscate intraindividual variability that may reveal meaningful changing affect states. The few functional magnetic resonance imaging longitudinal studies that have linked changes in brain function to the ebbs and flows of affective states over time have mostly investigated a single individual. In this study, we explored how the functional connectivity of brain areas associated with affective processes can explain within-person fluctuations in self-reported positive and negative affects across several subjects. To do so, we leveraged the Day2day dataset that includes 40 to 50 resting-state functional magnetic resonance imaging scans along self-reported positive and negative affectivity from a sample of six healthy participants. Sparse multivariate mixed-effect linear models could explain 15% and 11% of the within-person variation in positive and negative affective states, respectively. Evaluation of these models' generalizability to new data demonstrated the ability to predict approximately 5% and 2% of positive and negative affect variation. The functional connectivity of limbic areas, such as the amygdala, hippocampus, and insula, appeared most important to explain the temporal dynamics of affects over days, weeks, and months. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic in early 2020, face masks have become a common experience for many people to reduce the spread of the disease. Although recent research has shown that face masks impair emotion recognition, it is unclear how this impairment differs from other familiar types of face covering, such as sunglasses. In the present study, participants identified expressions and rated their confidence in this assessment of six affective expressions (anger, disgust, fear, surprise, sadness, and happiness) on faces wearing masks or sunglasses at four different time points during the pandemic (June 2020, March 2021, September 2021, June 2022). They also provided judgements of emotion intensity and genuineness. Overall, emotion identification of faces with masks was less accurate and had lower ratings of confidence and emotion intensity than faces with sunglasses. Faces with sunglasses, alternatively, were rated as less genuine than faces with masks. Furthermore, this pattern for both masks and sunglasses remained stable across two years of the pandemic. This study provides new insights on the differential effects of face masks and sunglasses on emotion perception and highlights the importance of face coverings for emotion communication and social interactions. [ABSTRACT FROM AUTHOR]

The Xing Zi Ming Chu 性自命出 (Nature Derives from Decree) is one of the eighteen pieces that were recorded in Guodian 郭店 bamboo slips, which were excavated in 1993 and thought to be buried around 300 BCE. We can observe from this text detailed discussions surrounding terms such as xing 性 (nature), qing 情 (emotion), xin 心 (heart-mind), and yue 樂 (music), which played crucial roles in producing early Chinese philosophical discourses, particularly in the area of moral psychology. Since its publication in 1998, this text has gained huge interest from Chinese scholars. This article illustrates how Chinese scholars, through their concrete studies, reveal the complicated textual nature of the Xing Zi Ming Chu and its intricate relations to different philosophical streams which followed. This article also points out a problematic assumption that lies in the majority of Chinese scholarship and proposes a more sensible approach to examine this text. [ABSTRACT FROM AUTHOR]

In ths paper I outline my development over fifty years as a therapist, researcher and theorist. I started with empathy as a core helping process seeing it as a moment-bymoment information processing aid as well as a provision of the curative effects of human understanding. Over the years I added a focus on emotion as central to change, and proposed a number of necessary distinctions that needed to be made when working with emotion and concluded that one of the best ways to change emotion was with another emotion. In addition to my focus on moment by moment process I saw that identification of in-session experiential states and case formulation went beyond just being in the moment and helped provide more focus to an essentially phenomenologically based experiential approach. [ABSTRACT FROM AUTHOR]

Aging is a multifactorial biological process that may be associated with cognitive decline. Photobiomodulation (PBM) is a non-pharmacological therapy that shows promising results in the treatment or prevention of age-related cognitive impairments. The aim of this review is to compile the preclinical and clinical evidence of the effect of PBM during aging in healthy and pathological conditions, including behavioral analysis and neuropsychological assessment, as well as brain-related modifications. 37 studies were identified by searching in PubMed, Scopus, and PsycInfo databases. Most studies use wavelengths of 800, 810, or 1064 nm but intensity and days of application were highly variable. In animal studies, it has been shown improvements in spatial memory, episodic-like memory, social memory, while different results have been found in recognition memory. Locomotor activity improved in Parkinson disease models. In healthy aged humans, it has been outlined improvements in working memory, cognitive inhibition, and lexical/semantic access, while general cognition was mainly enhanced on Alzheimer disease or mild cognitive impairment. Anxiety assessment is scarce and shows mixed results. As for brain activity, results outline promising effects of PBM in reversing metabolic alterations and enhancing mitochondrial function, as evidenced by restored CCO activity and ATP levels. Additionally, PBM demonstrated neuroprotective, anti-inflammatory, immunomodulatory and hemodynamic effects. The findings suggest that PBM holds promise as a non-invasive intervention for enhancing cognitive function, and in the modulation of brain functional reorganization. It is necessary to develop standardized protocols for the correct, beneficial, and homogeneous use of PBM. [ABSTRACT FROM AUTHOR]

In e-learning environments, most adaptive systems do not consider the learner's emotional state when recommending activities for learning difficulties, blockages, or demotivation. In this paper, we propose a new approach of emotion-based adaptation in e-learning environments. The system will allow recommendation resources/activities to motivate and support the learner in learning. Our first contribution is modeling the learner's emotion by exploiting the facial expressions generated during the pedagogical activities. For this purpose, a probability-based emotion quantification algorithm has been proposed. To recommend support resources, we presented our adaptation approach that leverages a set of adopted adaptation criteria, where the weighting of these criteria differs from one support resource to another. Five experiments aimed at validating the approach were conducted on two groups of students (test and control groups). The results show our approach's impact on improving the learner's cognitive level, engagement time, and motivation. [ABSTRACT FROM AUTHOR]

In today's digital world, understanding how YouTube's recommendation systems guide what we watch is crucial. This study dives into these systems, revealing how they influence the content we see over time. We found that YouTube's algorithms tend to push content in certain directions, affecting the variety and type of videos recommended to viewers. To uncover these patterns, we used a mixed methods approach to analyze videos recommended by YouTube. We looked at the emotions conveyed in videos, the moral messages they might carry, and whether they contained harmful content. Our research also involved statistical analysis to detect biases in how these videos are recommended and network analysis to see how certain videos become more influential than others. Our findings show that YouTube's algorithms can lead to a narrowing of the content landscape, limiting the diversity of what gets recommended. This has important implications for how information is spread and consumed online, suggesting a need for more transparency and fairness in how these algorithms work. In summary, this paper highlights the need for a more inclusive approach to how digital platforms recommend content. By better understanding the impact of YouTube's algorithms, we can work towards creating a digital space that offers a wider range of perspectives and voices, affording fairness, and enriching everyone's online experience. [ABSTRACT FROM AUTHOR]

Sleep and mood/emotion influence learning and academic achievement. However, inconclusive/contradictory findings have been reported. Unlike previous studies that examined either sleep or mood and grade-point-average (combined result of several assessments over long period), this study examined UK university students for the relationship of test scores with both, previous night's sleep-duration, and mood/emotion approximately an hour before the test. Mood/feelings data were collected by presenting a selection of emojis and text. Here, test scores did not associate with night sleep-duration. There were no differences in distribution of scores across sleepers of 5, 6, 7 and 8 h and sleeping for less than these hours did not increase the odds of scoring low (scoring ≤ 25%). There was no significant difference in distribution of night-sleep-hours between high scorers (score ≥ 75%) and low scorers (score ≤ 25%). Furthermore, test scores did not associate with feelings/emotions. There were no significant differences in the distribution of scores across different feelings. Unhappy/negative feelings did not increase the odds of scoring low. However, feelings (specifically expressed through selection of words/phrases) predicted scores (p = 0.01) and both negative and positive feelings associated with increased odds of high score (p ≤ 0.04). Night-sleep-duration positively associated with feelings (p < 0.001). Distribution of night-sleep-duration differed between feelings (p ≤ 0.003). Night sleep of less than 5, 6, 7 or 8 h increased the odds of feeling unhappy/negative (p ≤ 0.01). This data (i) defies the generic notion of a night of sub-optimal sleep-duration negatively affecting scores, (ii) provides empirical evidence of association between night-sleep-duration and emotions/mood/feelings, and (iii) proposes that sleep may affect learning via mood. [ABSTRACT FROM AUTHOR]

While the importance of fostering individual taxpayers' (hereafter taxpayers) trust in government to encourage tax compliance is widely acknowledged, how policy actions can increase trust in government remains unclear. Thus, the main purpose of our research is to see whether policy actions that signal government benevolence during a crisis can quickly increase trust in government, and its positive implications for tax compliance intentions. Another goal of our research is to see whether such a quick change of trust is driven by emotions. To test this, we use the British COVID-19 Self-Employment Income Support Scheme (hereafter SEISS) as a natural experiment. Right after the SEISS was launched to the British public, we recruited a sample of 500 British self-employed taxpayers via Prolific. Our results suggest that receiving (vs. not receiving) government support increases trust in government and enhances tax compliance intentions. In addition, trust in government mediates the positive impact of receiving (vs. not receiving) government support on tax compliance intentions. More important, our results suggest that policy actions that signal government benevolence during a crisis can increase trust in government quickly because it is driven by taxpayers' emotions such as gratitude. These results have important implications for governments to efficiently deal with the sharp rise in public debts caused by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Background: Dominant psychotherapies target how individuals experience and understand their daily emotion. Therefore, research examining how daily emotions influence long-term mental health outcomes may help inform treatment development. Methods: This investigation applied a multi-cohort (n = 378; n = 460), longitudinal design to test how reports of daily emotion predict psychological symptoms, loneliness, and wellbeing one-year later. Dynamic indices (polarity, inertia) reflecting "how" emotional experiences are conceptualized moment-to-moment and static indices (person-mean, standard deviation) of emotion were extracted from 10 daily reports. Each index was modelled individually, in concert with others, and in relation to a key dispositional factor in symptom development: trait anxiety. Results: Dynamic indices predicted outcomes one-year later, but only the effect of positive emotional inertia remained significant after accounting for mean intensity of affect. Daily reports of emotion also predicted small but significant variance in outcomes beyond trait anxiety. Conclusions: Results highlight the role of daily subjective experiences of emotion in long-term mental health outcomes and reinforce their importance as targets for treatment. [ABSTRACT FROM AUTHOR]

Autobiographical memory specificity is known to contribute to better mental health, social problem-solving, and episodic future thinking. While numerous studies have addressed variables that affect autobiographical memory specificity, little is known regarding the meta-memory processes that underpin memory retrieval. In this study, we introduced two meta-memory constructs, ease of retrieval judgments and anticipation of negative emotion evoked, which potentially affect autobiographical memory specificity. Participants (N = 109) first rated the ease of retrieval and anticipated emotions for positive and negative words used in a subsequent autobiographical memory test. We used the Optional Instructions of the Autobiographical Memory Test, in which participants were instructed that "specific memories are better, but other memories are permissible," allowing them to adjust how much cognitive effort they spent on generative retrieval after a failure of direct retrieval. They also self-judged whether each retrieval was generative (using additional cues with cognitive effort) or direct (immediate recall without much cognitive effort). Results showed that for generative retrieval, ease of retrieval was associated with greater specific and general memories and fewer omissions. A more negative anticipated emotion was associated with fewer specific memories and greater omissions, but was not with general memories. These results suggest that low retrievability and anticipated negative emotion prevent individuals from devoting efforts to generative retrieval. The lack of association between anticipated negative emotion and general memory calls into question the functional avoidance hypothesis regarding autobiographical memory specificity. We discussed how participants judged these meta-memories and directions for future research. [ABSTRACT FROM AUTHOR]

Beyond motor deficits, spinocerebellar ataxia (SCA) patients also suffer cognitive decline and show socio-affective difficulties, negatively impacting on their social functioning. The possibility to modulate cerebello-cerebral networks involved in social cognition through cerebellar neurostimulation has opened up potential therapeutic applications for ameliorating social and affective difficulties. The present review offers an overview of the research on cerebellar neurostimulation for the modulation of socio-affective functions in both healthy individuals and different clinical populations, published in the time period 2000–2022. A total of 25 records reporting either transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS) studies were found. The investigated clinical populations comprised different pathological conditions, including but not limited to SCA syndromes. The reviewed evidence supports that cerebellar neurostimulation is effective in improving social abilities in healthy individuals and reducing social and affective symptoms in different neurological and psychiatric populations associated with cerebellar damage or with impairments in functions that involve the cerebellum. These findings encourage to further explore the rehabilitative effects of cerebellar neurostimulation on socio-affective deficits experienced by patients with cerebellar abnormalities, as SCA patients. Nevertheless, conclusions remain tentative at this stage due to the heterogeneity characterizing stimulation protocols, study methodologies and patients' samples. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs) have become a popular platform owing to their open nature. However, the shortage of emotional interaction in MOOCs can cause a high attrition rate among learners. Appropriate emotional expression can enhance knowledge delivery and students' learning performance. To tackle this issue, exploring the correlation between teachers' emotions and students' learning behaviors is of great value for instructors to understand how their emotions can impact students' learning performance and improve their teaching skills. However, manually watching and studying MOOCs videos are often tedious and time-consuming. There is a lack of efficient methods to help users conduct correlation exploration, which is challenging due to the large-scale, multi-dimensional, and time-series nature of students' learning behaviors and their complex correlation to teachers' multimodal emotions. In this paper, we propose an interactive visual system called TSSeer to facilitate correlation analysis in MOOCs videos. Specifically, the level view and correlation view allow users to obtain a quick overview of the correlation at different video levels. The detail view shows detailed temporal changes and enables a deeper understanding of the correlation with time-series visualization. Through comprehensive evaluations, including two usage scenarios and five expert interviews, the effectiveness and usefulness of TSSeer are demonstrated. [ABSTRACT FROM AUTHOR]

Generalization enables individuals to respond to novel stimuli based on previous experiences. The degree to which organisms respond is determined by their physical resemblance to the original conditioned stimulus (CS+), with a stronger response elicited by more similar stimuli, resulting in similarity-based generalization gradients. Recent research showed that cognitive or conceptual dimensions also result in gradients similar to those observed with manipulations of physical dimensions. Such findings suggest that attributes beyond physical similarity play a role in shaping generalization gradients. However, despite its adaptive relevance for survival, there is no study exploring the effectiveness of affective dimensions in shaping generalization gradients. In two experiments (135 Spanish and 150 English participants, respectively), we used an online predictive learning task, in which different stimuli (words and Gabor patches) were paired with the presence – or absence – of a fictitious shock. After training, we assessed whether valence (i.e., hedonic experience) conveyed by words shape generalization gradients. In Experiment 1, the outcome expectancy decreased monotonically with variations in valence of Spanish words, mirroring the gradient obtained with the physical dimension (line orientation). In Experiment 2, conducted with English words, a similar gradient was observed when non-trained (i.e., generalization) words varied along the valence dimension, but not when words were of neutral valence. The consistency of these findings across two different languages strengthens the reliability and validity of the affective dimension as a determinant of generalization gradients. Furthermore, our data highlight the importance of considering the role of affective features in generalization responses, advancing the interplay between emotion, language, and learning. [ABSTRACT FROM AUTHOR]

The perception of threatening facial expressions is a critical skill necessary for detecting the emotional states of others and responding appropriately. The anger superiority effect hypothesis suggests that individuals are better at processing and identifying angry faces compared with other nonthreatening facial expressions. In adults, the anger superiority effect is present even after controlling for the bottom-up visual saliency, and when ecologically valid stimuli are used. However, it is as yet unclear whether this effect is present in children. To fill this gap, we tested the anger superiority effect in children ages 6–14 years in a visual search task by using emotional dynamic stimuli and equating the visual salience of target and distractors. The results suggest that in childhood, the angry superiority effect consists of improved accuracy in detecting angry faces, while in adolescence, the ability to discriminate angry faces undergoes further development, enabling faster and more accurate threat detection. [ABSTRACT FROM AUTHOR]