Emotional Context Research Articles

Effects of congruent emotional contexts during encoding on recognition: An ERPs study.

Past research showed that emotional contexts can impair recognition memory for the target item. Given that item-context congruity may enhance recognition memory, the present study aims to examine the effect of the congruent emotional encoding contexts on recognition memory. Participants studied congruent word-picture pairs (e.g., the word "cow" - a picture describing a cow) and incongruent word-picture pairs (e.g., the word "cow" - a picture describing a goat) and, subsequently, were asked to report the nature of the picture (emotional or neutral). Behavioral results revealed that emotional contexts impaired source but not item recognition, with congruent word-context mitigating this impairment and enhancing item recognition. Neural results from ERPs and theta oscillations found the recollection process, as shown by the LPC old/new effect and theta oscillations, for both item and source recognition across emotional contexts, irrespective of congruity. Meanwhile, the familiarity process as indexed by the FN400 old/new effect was found only for item recognition in congruent emotional contexts. These findings suggest that the congruent relationship of item-context could mitigate the emotion-induced source memory impairment and enhance item memory, with neural results elucidating the memory processes involved in retrieval of emotional information. Specifically, while emotion-related information generally elicits the recollection-based memory process, only congruent emotional information elicits the familiarity-based process.

The semantics of gaze in person perception: a novel qualitative-quantitative approach

Interpreting gaze behavior is essential in evaluating interaction partners, yet the ‘semantics of gaze’ in dynamic interactions are still poorly understood. We aimed to comprehensively investigate effects of gaze behavior patterns in different conversation contexts, using a two-step, qualitative-quantitative procedure. Participants watched video clips of single persons listening to autobiographic narrations by another (invisible) person. The listener’s gaze behavior was manipulated in terms of gaze direction, frequency and direction of gaze shifts, and blink frequency; emotional context was manipulated through the valence of the narration (neutral/negative). In Experiment 1 (qualitative-exploratory), participants freely described which states and traits they attributed to the listener in each condition, allowing us to identify relevant aspects of person perception and to construct distinct rating scales that were implemented in Experiment 2 (quantitative-confirmatory). Results revealed systematic and differential meanings ascribed to the listener’s gaze behavior. For example, rapid blinking and fast gaze shifts were rated more negatively (e.g., restless and unnatural) than slower gaze behavior; downward gaze was evaluated more favorably (e.g., empathetic) than other gaze aversion types, especially in the emotionally negative context. Overall, our study contributes to a more systematic understanding of flexible gaze semantics in social interaction.

An Emotional Analysis of Korean Topics based on Social Media Big Data Clustering

An innovative approach is introduced in this paper to address the challenges in emotional topic interpretation and accuracy in emotional situation assessment. Utilizing large data from social media to improve the accuracy of emotional analysis in online debates, with a specific emphasis on Korean themes. The proposed solution, the Online Topic Emotion Recognition Model (OTSRM), builds upon the foundational Online Latent Dirichlet Allocation (OLDA) model. The OTSRM integrates the concept of emotion intensity and introduces an inventive emotion iteration framework to tackle these issues. Key innovations of the OTSRM include establishing an affective evolution channel by augmenting affective heritability using a β priori. Additionally, the model generates two critical distribution matrices: one for characteristic words and another for affective words, facilitating a deeper understanding of emotional context within topics. The relative entropy method is employed to discern emotional tones in textual content, calculating maximum emotion values for topic focus within adjacent time segments. Validation experiments using five diverse network event datasets and comparisons to mainstream models demonstrate the OTSRM's effectiveness with emotion recognition accuracy rates of 85.56% and 81.03%. The OTSRM represents significant progress in addressing challenges associated with emotional topic analysis and precise emotional dynamics assessment in Korean social media data.

Emotional Context Effects on the Rating of Ambiguous Facial Expressions in Depression and Schizophrenia Spectrum Disorders

Objective To investigate the influence of visual contextual information on emotion recognition of ambiguous facial expressions in depression and schizophrenia spectrum disorders. Method: Ambiguous facial expressions and emotional contexts representing anger, disgust, fear, joy, sadness and surprise were validated in a pre-test with healthy independent raters. Afterwards, 20 healthy participants (8 women, 12 men; mean age 24.35 ± 2.85 years), 20 participants with schizophrenia spectrum disorders (9 women, 11 men; mean age 40.25 ± 11.68 years) and 19 participants with depression (11 women, 8 men; mean age 43.74 ± 12.65 years) rated the emotional content of nine different faces in seven different emotion-suggesting contexts. The proportions of context-congruent answers and differences between emotion ratings in each context were analysed using non-parametric Kruskal-Wallis and explorative, paired Wilcoxon tests. Correlational analyses explored the influence of clinical symptoms assessed by clinician-administered scales. Results: The overall proportion of context-congruent answers did not differ between participants with depression and schizophrenia spectrum disorders compared to healthy participants. Participants with schizophrenia spectrum disorders were more susceptible to anger-suggesting contexts and participants with depression were more susceptible to fear-suggesting contexts. Differences in emotion recognition were associated with the severity of depressive, but not psychotic, symptoms. Conclusion: Despite increased susceptibility to anger-suggesting cues in schizophrenia and to fear-suggesting cues in depression, visual contextual influence remains largely consistent with healthy participants. Preserved emotional responsiveness suggests an efficacy of emotion training but emphasizes the need for additional research focusing on other factors contributing to social interaction deficits.

Three-Dimensional View Relationship-Based Context-Aware Emotion Recognition.

Context-aware emotion recognition (CAER) leverages comprehensive scene information, including facial expressions, body postures, and contextual background. However, current studies predominantly rely on facial expressions, body postures, and global contextual features; the interaction between the agents (target individuals) and other objects in the scene is usually absent or incomplete. In this article, a three-dimensional view relationship-based CAER (TDRCer) method is proposed, which comprises two branches: the personal emotional branch (PEB) and the contextual emotional branch (CEB). First, PEB is designed for the extraction of facial expression features and body posture features from the agent. A vision transformer (ViT), pretrained by contrastive learning with a novel loss function combining Euclidean distance and cosine similarity, is applied to enhance the robustness of facial expression features. Meanwhile, the human body contour images extracted by semantic segmentation are fed into another ViT to extract body posture features. Second, CEB is constructed for the extraction of global contextual features and interactive relationships among objects in the scene. The images masked by the agents' bodies are fed into a ViT to extract global contextual features. By leveraging both the gaze angle and depth map, a three-dimensional view graph (3DVG) is constructed to represent the interactive relationships between agents and objects in the scene. Then, a graph convolutional network is employed to extract interactive relationship features from the 3DVG. Finally, the multiplicative fusion strategy is applied to fuse the features of two branches, and the fused features are utilized to classify the emotions. TDRCer achieves an accuracy of 89.90% on the CAER-S dataset and a mean average precision (mAP) of 36.02% on the EMOTIons in context (EMOTIC) dataset. The code can be accessed at https://github.com/mengTender/TDRCer.

ПРОЯВЛЕНИЕ НАРРАТИВНОЙ ЭКОНОМИКИ В США: ВЛИЯНИЕ НА РЫНОЧНУЮ АКТИВНОСТЬ И ВОЛАТИЛЬНОСТЬ

Against the background of growing interest in the role and influence of information noise in economic activity, this study aims to analyze the manifestation of the narrative economy using the example of the United States. The aim of the work is to understand the components of narrative economics and their impact on the market and its participants. This analysis helps to explain which narratives and in which cases incline investors to change their strategies in the market. The object of the study is the growth rate of GDP (economic activity), the profitability of the market and its volatility in the United States. The subject of the study is the influence of narratives on the projected GDP growth rate, profitability and volatility of the US market. The research methodology includes the identification of narratives with a narrative structure using thematic modeling and the use of machine learning methods to identify causeandeffect relationships. Linear regression and machine learning methods, in particular, random forest and XGBoost, were chosen as tools for identifying causeandeffect relationships. The study revealed that narratives have a significant impact on projected GDP growth and economic activity, and there is also a link between market volatility and narratives with an emotional context. However, there is no direct relationship between narratives and market returns. The results obtained may be important for understanding the dynamics of market processes and decisionmaking in the investment sector.

White Tears

ABSTRACT In this article, I explore the rhetorical deployment of White tears, tears that are circulated within narratives of interracial conflict as evidence for the rightness of White supremacist norms. More specifically, White tears are those that are framed as deictic indicators of a White victim versus a Black, Indigenous, people of color (BIPOC) threat. These presumed relations of cause and effect are made possible within an emotional context that assumes a baseline White rationality as the norm; distress signals the threat of non-White aggression. Analysis of several prominent cases of crying demonstrates how tears can be marshaled as evidence for the legitimacy of White bodies at the expense of those of color. Although considering the rhetorical force of affect and emotion is important for critical rhetorical analyses, such work needs to contend with how scripts for emotional engagement are already inclined toward or against certain bodies. It then becomes possible to develop alternative, subversive framings.

Active consideration in an emotional context: implications for information processing.

Active consideration in an emotional context: implications for information processing.

Traversing Emotional Spaces: Social Media Affordances and Emotion Regulation in Times of Physical Isolation

People’s emotional life is increasingly interwoven with social media. Against the background of the COVID-19 pandemic and lockdown in China, this study investigated social media affordances enacted during people’s emotion regulation processes. By analyzing in-depth interviews with 27 Shanghai residents going through a 2-month lockdown, we identified intersections between social media affordances and different emotion regulation processes: selectivity and intrapersonal emotion regulation; visibility and emotional self-presentation; bandwidth and interpersonal emotion regulation; connectivity and collective emotion regulation. This study extended our knowledge of mediated emotion regulation processes and enriched our understanding of the dynamic relations between users, media technologies, and emotional and social contexts.

Disease Discourse through Sentiment and Network Analysis

In the realm of public health, the discourse surrounding diseases significantly impacts awareness, funding, and stigma. Traditional methods of analyzing disease mentions often overlook the intricate relationships and sentiments expressed in text, which can provide deeper insights into public perception and information dissemination. Existing literature on disease discourse primarily focuses on quantitative metrics such as disease frequency counts or associations based on semantic similarity. However, such analyses rarely account for the sentiment of the discourse, which can play a critical role in shaping public perception and response to health information. Moreover, the complexity of textual data and the subtleties of language usage present significant challenges for extracting meaningful patterns, especially when considering the emotional context of disease-related discussions. This study introduces a novel approach that integrates sentiment analysis and network visualization to examine the discourse on diseases within a comprehensive textual dataset. By applying the TextBlob library, we analyze sentence-level sentiment and categorize it as positive or negative. Utilizing these sentiment scores, we construct two distinct network graphs to depict the relationships between diseases based on their co-occurrence within sentiment-laden sentences. The analysis reveals contrasting landscapes of disease discourse: the positive sentiment network highlights diseases frequently mentioned in the context of successful treatment or optimistic outcomes, forming distinct clusters around well-managed conditions. In contrast, the negative sentiment network elucidates diseases that coalesce around shared public concerns, fears, or complications, marking out critical areas for public health intervention. The findings underscore the importance of sentiment in understanding disease discourse, offering a novel perspective that can assist public health officials in tailoring communication strategies. By recognizing patterns in disease associations within negative contexts, interventions can be more effectively targeted to address misconceptions and alleviate public health anxieties. Conversely, identifying diseases with strong positive connections can guide campaigns to bolster preventive measures and highlight medical advancements. This methodology serves as a blueprint for leveraging sentiment analysis in the exploration of health-related textual data, paving the way for more nuanced and actionable insights into public health narratives.

Lexical and contextual emotional valence in foreign language vocabulary retention

Abstract Studies suggest that not only words per se but also surrounding contexts play significant roles in foreign language vocabulary retention. This study investigated how different conditions of lexical and contextual emotional valence differently affect foreign language vocabulary retention. The target words were either positive (LexVal+), neutral (LexVal=), or negative (LexVal−) in meaning. Each visually enhanced target word was embedded in a sentence either positive (CtxVal+), neutral (CtxVal=), or negative (CtxVal−) in meaning. Sentences with different combinations of lexical valence and contextual valence were presented in the Study Session, which were later incidentally recalled in the Test Session. It was revealed that positive and negative words were remembered more often than neutral words and that negative contexts resulted in better retention of the embedded words than neutral contexts. These findings are in accordance with the predictions of the emotionally enhanced memory, the Emotion-Involved Processing Hypothesis, and the role of affect in the Modular Cognition Framework. Further, even if the target item is not emotional, embedding it in emotional context may result in better retention, a finding with potential pedagogical implications. Interestingly, words embedded in emotionally congruent contexts were not learned better than those in incongruent contexts, a finding contrary to expectation. The result may be explainable via the Deep Epistemic Emotion Hypothesis, calling for more empirical study.

An emotion-driven and topic-aware dialogue framework for human–robot interaction

ABSTRACT Developing dialogue services for robots has been promoted nowadays for providing natural human–robot interactions to enhance user experiences as conversation is a key instrument for creating and maintaining their mutual relationships. In this work, we present a trainable framework for modeling context-aware human–robot dialogues, and apply it to a live streaming application to demonstrate the need for social robots. Our social chatting robot framework takes into account both emotional context and topic-focused content information to generate appropriate responses. This framework has some unique features. It adopts a multimodal deep learning model to recognize the participants’ emotions. Moreover, our work includes a topic-aware neural model that enables the social robot to follow certain conversation topics to fulfill the specific dialoguing goals and to enhance the chatting coherence. Most importantly, we design a strategy able to take emotions as the drive to control over the neural method of generating utterances. To evaluate the proposed approach, we have conducted several sets of quantitative and quantitative experiments. The results highlight the importance of multimodal emotion recognition and topic-awareness in dialoguing and confirm the feasibility and effectiveness of our framework for human–robot interaction.

Patterns of disruptions: Complexities of discursive-embodied triggers and resilience responses of individuals with autoimmune diseases

ABSTRACT Individuals with autoimmune diseases face a multiplicity of adverse disruptions throughout their lives that can span physical, emotional, social, and financial contexts. We employed the communication theory of resilience as a theoretical framework to explore how individuals with an autoimmune disease construct the connections between triggers and communicative resilience responses within their narratives. Utilizing abductive analysis, we identified four overarching trigger patterns: (1) linear, (2) cyclical, (3) compounding, and (4) branching. In examining the triggers and health journeys of individuals with autoimmune diseases, we can practically aid physician communication approaches for patients with complex symptomologies, diagnostic journeys, and trigger patterns.

Emotion and prospective memory: effects of emotional targets and contexts.

Event-based prospective memory (PM) refers to the ability to remember to perform a delayed and intended action when an event is encountered in the future. Whether emotional targets promote PM performance is still controversial. The reason for these inconsistent findings may be related to the degree of target arousal and context valence (the valence of ongoing task trials) in the previous studies. This study aimed to investigate the separate and combined effects of target valence, arousal, and context valence on event-based PM through two experiments. The results showed that the participants were faster and more accurate in responding to positive, negative, and high-arousal PM targets. Interestingly, an interaction effect of target valence, arousal, and context valence was observed, implying that their individual effects on PM performance cannot be understood in isolation. These findings demonstrate that positive, negative, and high-arousal PM targets can enhance PM performance. In addition, the results provided support for both the emotion enhancement account and the emotion-saliency account, depending upon whether the valence of the PM target matched or did not match the valence of the context. Moreover, context valence can modulate the effect of arousal on PM across different target valences.

The influence of emotional context on emotional word processing in discourse comprehension: evidence from event-related potential.

This research utilized event-related potential (ERP) recording technology to examine the effect of emotional context on the processing of emotional information in sentences. Three types of emotion-consistent discourse materials (neutral-neutral, positive-positive and negative-negative) were constructed to specifically express neutral, positive and negative emotions, respectively. Each discourse comprised two sentences, with the emotionally significant words embedded at the penultimate position of the second sentence. Participants were asked to read these texts, respond to reading comprehension questions and the ERP amplitude induced by the emotional words was recorded. The results indicated a tripartite interaction in the N400 and Late positive component amplitudes involving emotional context, emotional words and brain hemispheres, observed in both frontal and central brain regions. Notably, there was a significant difference in response to positive words between positive and negative contexts. The findings suggest that emotional context has a substantial effect on the processing of emotional words. Positive words, in comparison to negative ones, are more influenced by emotional context, particularly in the frontal and central regions of the brain.

AGE DIFFERENCES IN EMOTION REGULATION AND EXECUTIVE FUNCTION

Abstract Age-related improvements in emotion regulation (ER) are thought to account for the relatively healthy levels of well-being observed in late adulthood. However, extant laboratory-based evidence on age differences in the effective implementation of ER strategies―such as reappraisal (i.e., reframing the meaning of an emotion-eliciting event) and distraction (i.e., redirecting attention away from emotional to non-emotional information)―remains equivocal (Isaacowitz, 2022). Moreover, developmental differences in ER monitoring (i.e., adjusting regulatory approach as needed, such as maintaining or switching strategies), and factors that underpin such age-related differences, are poorly understood. Therefore, we investigated how younger and older adults (N = 335) differ in the implementation and monitoring aspects of ER, and how executive function (EF)―a collection of higher-order control operations that underlie ER processes (Pruessner et al., 2020; Schmeichel & Tang, 2015)―could explain such age-related differences in ER. Using a comprehensive battery of nine EF and two ER tasks, results indicated age-related decline for the implementation of reappraisal but not distraction. For monitoring, advancing age was associated with a less flexible pattern of strategy maintenance and switching (of reappraisal and distraction) across high- and low-intensity emotional contexts. Importantly, these age-related differences in ER implementation and monitoring were significantly mediated by declines in EF (βs = .25‒.29, ps < .007), thereby alluding to the role of EF as a potential mechanism. Our findings suggest that certain ER processes are more cognitively demanding than others, and that EF could be a viable target of intervention to improve such ER processes in late adulthood.

Music emotion classification using a hybrid CNN-LSTM model

The emotional content of music, interwoven with the intricacies of human affect, poses a unique challenge for computational recognition and classification. With the digitalization of music libraries expanding exponentially, there is a pressing need for precise, automated tools capable of navigating and categorizing vast musical repositories based on emotional contexts. This study advances music emotion classification in the field of music information retrieval by developing a deep learning model that accurately predicts emotional categories in music. The goal of this research is to advance the field of music emotion classification by leveraging the capabilities of convolutional neural networks combined with long short-term memory within deep learning frameworks. The contribution of this study is to provide a refined approach to music emotion classification, combining the power of convolutional neural networks and long short-term memory architectures with sophisticated preprocessing of the Emotify dataset for a deeper and more accurate analysis of musical emotions. The research introduces a novel architecture combining Convolutional Neural Networks and Long Short-Term Memory networks designed to capture the intricate emotional nuances in music. The model leverages convolutional neural networks for robust feature detection and Long Short-Term Memory networks for effective sequence learning, addressing the temporal dynamics of musical features. Utilizing the Emotify dataset, comprising tracks annotated with nine emotional features, the study expands the dataset by segmenting each track into 20 parts, thereby enriching the variety of emotional expressions. Techniques like the synthetic minority oversampling technique were implemented to counter dataset imbalance, ensuring equitable representation of various emotions. The spectral characteristics of the samples were analyzed using the Fast Fourier Transform, contributing to a more comprehensive understanding of the data. Through meticulous fine-tuning, including dropout implementation to prevent overfitting and learning rate adjustments, the developed model achieved a notable accuracy of 94.7 %. This high level of precision underscores the model's potential for application in digital music services, recommendation systems, and music therapy. Future enhancements to this music emotion classification system include expanding the dataset and refining the model architecture for even more nuanced emotional analysis

Contextual emotion detection using ensemble deep learning

Emotion detection from online textual information is gaining more attention due to its usefulness in understanding users’ behaviors and their desires. This is driven by the large amounts of texts from different sources such as social media and shopping websites. Recent studies investigated the benefits of deep learning in the detection of emotions from textual conversations. In this paper, we study the performance of several deep learning and transformer-based models in the classification of emotions in English conversations. Further, we apply ensemble learning using a majority voting technique to improve the overall classification performance. We evaluated our proposed models on the SemEval 2019 Task 3 public dataset that categorizes emotions as Happy, Angry, Sad, and Others. The results show that our models can successfully distinguish the three main classes of emotions and separate them from Others in a highly imbalanced dataset. The transformer-based models achieved a micro-averaged F1-score of up to 75.55%, whereas the RNN-based models only reached 67.03%. Further, we show that the ensemble model significantly improves the overall performance and achieves a micro-averaged F1-score of 77.07%.

If it Ain't Broke, Don't Fix it: Positive Versus Negative Emotion Regulation in Daily Life and Depressive Symptoms

BackgroundDespite the importance of positive emotions for affective psychopathology, prior research primarily focused on negative emotion regulation. To address this gap, this ecological momentary assessment study compared a broad set of emotion regulation strategies in the context of positive versus negative emotions regarding their effectiveness and associations with depressive symptoms. MethodsWe analyzed data from 1066 participants who were notified five times daily for seven consecutive days to complete a smartphone survey assessing their predominant emotions, strategies to regulate them, and subsequent emotional outcomes. ResultsFindings show that the effectiveness of most regulation strategies depended on whether the emotional context was positive or negative. While acceptance and savoring predicted improved emotional outcomes across emotional contexts, reappraisal and problem-solving were associated with deteriorated emotional outcomes and increased depressive symptoms when regulating positive but not negative emotions. LimitationsFuture studies should replicate our findings in demographically and culturally diverse clinical samples to improve generalizability. ConclusionThese results emphasize that strategies effective for regulating negative emotions may be less helpful in the context of positive emotions. Thus, context-specific interventions may be a promising approach to improve the treatment of affective disorders.

OLD-NEW COLONIAL TENDENCIES IN SOCIAL ANTHROPOLOGY:

Taking autoethnographic and reflexive approaches as a background, this article reflects on the tendency of a number of Western Anglophone academic writings to impose a patronising perspective on, and indeed try to silence, commentary on Ukraine concerning the ongoing Russian invasion. This line of argumentation has become known as “westplaining”, and it seems to have taken the place of the old “orientalism”. Such interventions neglect or elide the variety of regional perspectives and their entangled histories, embodied experiences and emotional contexts that are all too germane to those of us who have been doing fieldwork in Ukraine for years now. Such a regrettable imposition of ill-equipped “westplaining” thinking results in a presentation of a distanced, patronising, sometimes partisan and too-commonly facile view of the complexity of current events. Through ostensibly disinterested and compassionate appeals to listen to the “western” perspective first, the local insiders’ voices are effectively silenced. In contrast, I discuss the importance of emotional testimonies and active empathy in social anthropology as responses to collective evil and violence, and as one possible way to overcome the borders that intellectual colonialism creates within the academic community.