Components of cultural complexity relating to emotions: A conceptual framework

Many cultural variations in emotions have been documented in previous research, but a general theoretical framework involving cultural sources of these variations is still missing. The main goal of the present study was to determine what components of cultural complexity interact with the emotional experience and behavior of individuals. The proposed framework conceptually distinguishes five main components of cultural complexity relating to emotions: 1) emotion language, 2) conceptual knowledge about emotions, 3) emotion-related values, 4) feelings rules, i.e. norms for subjective experience, and 5) display rules, i.e. norms for emotional expression

EEG-Based Emotion Recognition Using Regularized Graph Neural Networks

Electroencephalography (EEG) measures the neuronal activities in different brain regions via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit the topology of EEG channels. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. RGNN considers the biological topology among different brain regions to capture both local and global relations among different EEG channels. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in a graph neural network where the connection and sparseness of the adjacency matrix are inspired by neuroscience theories of human brain organization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL), to better handle cross-subject EEG variations and noisy labels, respectively. Extensive experiments on two public datasets, SEED and SEED-IV, demonstrate the superior performance of our model than state-of-the-art models in most experimental settings. Moreover, ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our RGNN model. Finally, investigations on the neuronal activities reveal important brain regions and inter-channel relations for EEG-based emotion recognition

Emotion and Sentiment Guided Paraphrasing

Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, including moderating online dialogues and preventing cyberbullying. We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine-grained settings following smooth variations in affective dimensions while preserving the meaning of the original text. We reconstruct several widely used paraphrasing datasets by augmenting the input and target texts with their fine-grained emotion labels. Then, we propose a framework for emotion and sentiment guided paraphrasing by leveraging pre-trained language models for conditioned text generation. Extensive evaluation of the fine-tuned models suggests that including fine-grained emotion labels in the paraphrase task significantly improves the likelihood of obtaining high-quality paraphrases that reflect the desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROUGE, and METEOR.Comment: 13th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2023 at The 61st Annual Meeting of the Association for Computational Linguistics (ACL) 2023. arXiv admin note: substantial text overlap with arXiv:2212.0329

Interactions Among Amygdala Volume, Cortical Thickness, and Structural Connectivity in Youth: Relationship to Emotion Regulation

Emotion regulation includes adaptive (e.g., reappraisal) and non-adaptive behaviors (e.g., avoidance) designed to alter ones’ affective responses. The central hypothesis is that emotional consciousness – being self-aware that you are currently in a particular emotional state – and emotion regulation share the same underlying brain mechanisms/networks. In addition, it is argued that the more appropriate dichotomy, in regard to non-adaptive and adaptive emotion regulation strategies, is dependent on whether they are unconscious or conscious (respectively), positing a two-system framework of emotion regulation. Evidence for the proposed framework draws and builds off of recent theories of higher-order emotional consciousness (LeDoux & Brown, 2017) and supported frameworks of fear/anxiety (LeDoux & Pine, 2016). The literature reviewed suggests that the difference between emotional consciousness and emotion regulation lies in the variations in recruitment of lower-order, subcortical networks and the higher-order interpretation by the same overarching general network of cognition. In the second section, an empirical examination of this theory was conducted using neuroimaging and self-reported anxiety in a sample of youth. I provide evidence for my first hypothesis by identifying significant clusters of grey-matter thickness in the general linear analyses that qualitatively overlap with the general network of cognition proposed to underlie emotional consciousness. Our second hypothesis was partially supported as grey-matter thickness of these regions of the PFC, but not amygdala volume, significantly related to self-reported anxiety. Next, it is demonstrated that this relationship was significantly moderated by youths’ structural connectivity. Post-hoc analyses indicated that prefrontal grey-matter cortical thickness had a significant indirect effect on the relationship between amygdala volume and youth’s self-reported anxiety. The current results provide support for the central hypothesis that emotional consciousness and emotion regulation share many of the same underlying brain networks and mechanisms

The Many Moods of Emotion

This paper presents a novel approach to the facial expression generation problem. Building upon the assumption of the psychological community that emotion is intrinsically continuous, we first design our own continuous emotion representation with a 3-dimensional latent space issued from a neural network trained on discrete emotion classification. The so-obtained representation can be used to annotate large in the wild datasets and later used to trained a Generative Adversarial Network. We first show that our model is able to map back to discrete emotion classes with a objectively and subjectively better quality of the images than usual discrete approaches. But also that we are able to pave the larger space of possible facial expressions, generating the many moods of emotion. Moreover, two axis in this space may be found to generate similar expression changes as in traditional continuous representations such as arousal-valence. Finally we show from visual interpretation, that the third remaining dimension is highly related to the well-known dominance dimension from psychology