Arousal Dimensions Research Articles

5956 German affective norms for atmospheres in organizations (GANAiO)

This article develops a comprehensive database comprising 5956 German affective norms specifically tailored for the study of organizational atmospheres through computational verbal language analysis. This dictionary adopts both dimensional and categorical approaches. The theoretical foundation of this study is the circumplex model of affective atmospheres. Similar to established methodologies, each word is rated based on the dimensions of valence and arousal. Going beyond the dimensional approach, this article introduces a classification system with 11 distinct atmospheric categories, assigning the words to their corresponding categories. This dictionary represents the first attempt to apply computer-aided text analysis (CATA) to the study of organizational atmospheres, providing a practical tool to support research in this developing area.

EEG Emotion Recognition Based on GADF and AMB‐CNN Model

ABSTRACTDeep learning has achieved better results in natural language processing, computer vision, and other fields. Nowadays, more deep learning algorithms have also been applied in brain‐based emotion recognition. In the studies on brain‐based emotion recognition, deep learning models typically use one‐dimensional time series as the input and cannot fully leverage the advantages of the models in image classification or recognition. To address this issue, based on the publicly available SEED and DEAP datasets, the Gramian angular difference field (GADF) method was proposed to construct two‐dimensional image representation datasets: SEED‐GADF and DEAP‐GADF datasets, in the paper. Additionally, a convolutional attention mechanism model (AMB‐CNN) was introduced and its classification performance was validated on SEED‐GADF and DEAP‐GADF datasets. AMB‐CNN achieved an average accuracy of 90.8%, a recall rate of 90%, and AUC of 96.86% on SEED‐GADF. On DEAP‐GADF, the average accuracy, recall rate, and AUC respectively reached 96.06%, 96.06%, and 98.58% in the valence dimension and 96.11%, 96.11%, and 98.73% in the arousal dimension. Finally, the comparison results with various algorithms and ablation experiments proved the superiority of the proposed model.

The construction of a Chinese fine-grained sentiment dictionary for Chinese domestic investors (CN-FSD) and its application

Stock bar comment texts are used to forecast stock market trends because they contain sentiment data provided by investors. However, previous research has relied on generic or polar sentiment dictionaries to construct sentiment indicators, which has limited progress in this field. This paper crawls nearly 1.8 million stock bar comment texts from the Eastmoney (www.eastmoney.com) Stock Bar Forum to perform sentiment analysis in this specialized area of the financial market. It reclassifies existing Chinese sentiment dictionaries, and utilizes Word2Vec and CNN models and word frequency statistics with a penalty mechanism to extract polar sentiment words. Additionally, the improved PMI (Pointwise Mutual Information) algorithm was utilized for fine-grained segmentation of negative emotion words. Finally, a fine-grained sentiment dictionary (four categories: happy, anger, sad and fear; with a total of 6155 words) was established for Chinese domestic investors (denoted by CN-FSD). We found that the improved PMI algorithm increased accuracy by 14%. In addition, we introduce the investor sentiment quantification indicators from two aspects, i.e., fine-grained sentiments and on the emotion theory dimension of psychology from the valence and arousal dimensions. This is done to verify the validity and robustness of the constructed dictionary. The case study from 2019 to 2022 discovered that under the influence of extreme occurrences like COVID-19, the fine-grained sentiment indicators constructed in this study are found to be significantly more strongly correlated with market trend indicators like index return and trading volume than the existing polar or fine-grained sentiment dictionaries. ‘fear’ emotions in fine-grained emotions dominate the stock market. Just as this case study emphasize the importance of ‘fear’ sentiment, studying the indicators of high arousal sentiment is also important. This case study further validates the efficacy of CN-FSD.

Jazz hands and jitters: Exploring valence and arousal dimensions with multidimensional scaling techniques

Utilizing the two-dimensional plane of valence and arousal with multi-dimensional scaling (MDS), the current study sought to better understand the structure of affect and the relationship between emotion items. The positive and negative affect schedule (PANAS) is the most frequently applied self-report measure of affect but has been criticized for only including high arousal emotions. A between-subjects, two-part experimental study was conducted. Participants (N = 548) were recruited online and asked to rate the similarity between one of two target words (anger: n = 271; calm: n = 277) and 143 emotion words. Our results provided evidence for the two-dimensional plane of valence and arousal, indicating that all four quadrants are important when studying affect. Overall, the current study found support for the activation-arousal theory of affectivity with MDS analyses. Emotion words clustered in all four quadrants of the two-dimensional plane, demonstrating the necessity of an affect measure that encompasses all four quadrants. Furthermore, our results demonstrated that the PANAS does not include items in the positive valence/low activation quadrant, however, a newly published measure was found to have increased item dispersion, with items included in all four quadrants of affect. Limitations and proposed future directions are discussed.

Visual experience shapes bodily representation of emotion.

Philosophers and experimentalists have long debated whether bodily representation of emotion is grounded in our sensory experience. Indeed, we are used to observe emotional reactions expressed through the bodies of others, yet it is still unknown whether this observation influences how we experience affective states in our own bodies. To delve into this question, we developed a naturalistic haptic task and asked a group of early (n = 20) and late (n = 20) blind, as well as sighted individuals (n = 20) to indicate where in the body they perceive changes associated with affective states. Our results show that visual experience shapes bodily representation of emotion. Blind and sighted individuals attribute different importance to body regions in relation to specific emotional states, as sighted people focus more on visceral sensations, while blind report as more relevant the mouth and the hand areas. We also observe differences in the coherence of bodily maps of specific emotions, such as aggressiveness, for which early and late blind are homogenous in reporting the mouth, while sighted subjects demonstrate a scattered pattern of activation across the body. Finally, our findings show that blind people rely on a different organization of affect, as only sighted categorize bodily maps of emotion through the valence and arousal dimensions. In summary, we demonstrate that sensory experience impacts the bodily representation of affect by modulating the relevance that different body parts have in emotional reactions, modifying the weights attributed to interoceptive and exteroceptive signals, and changing how emotions are conceptualized in the body. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Differentiating anticipated and anticipatory emotions and their sensitivity to depressive symptoms.

Anticipated emotions are the feelings one expects if a hypothetical future event were to occur, whereas anticipatory emotions are those one experiences right now while imagining the event. There has been little direct comparison of these two forms of future-oriented emotion, and authors have typically focused on positive emotions (e.g., pleasure). Besides, their sensitivity to depressive symptoms-which may help to explain motivational problems in depression-has only recently been investigated (e.g., Anderson et al., 2023; Gamble et al., 2021). The present study (conducted September-November 2022) used innovative picture-and-text vignettes depicting everyday positive and negative future events, to which participants rated their anticipated and anticipatory responses on separate dimensions of valence (i.e., how positive or negative) and arousal (i.e., emotional intensity). Based on prior literature, anticipatory emotions were expected to be correlated with, yet weaker than, anticipated emotions, reflecting a conceptualization of anticipatory emotions as a "foretaste" of the affective response one expects in the future. We also predicted that high depressive symptoms would coincide with diminished emotion ratings overall and specifically for anticipatory emotions (tightly coupled with event expectations; Carrera et al., 2012). Results largely supported these preregistered predictions, yet anticipatory emotions (positive and negative) were only weaker in more highly depressed participants. Depressive symptoms may therefore affect how one currently feels about future possibilities without altering one's expectations of how such events would actually feel. Implications and future research objectives arising from this are discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Exploring Emotion Control and Alexithymia in Autistic Adults: An Ecological Momentary Assessment Study.

Difficulties in controlling emotions - a proxy for emotion dysregulation (ED)-and difficulties in expressing feelings in words-'absence of emotion labelling' or alexithymia-co-exist in autism and contribute to elevated levels of impulsive and suicidal behaviour. To date, studies linking the two phenomena have relied on retrospective self-reported measures, lacking support for generalizability to real-life situations. The present study investigated in vivo emotion labelling and its impact on emotion control in 29 autistic adults without intellectual disability (ASC) and 28 neurotypical (NT) individuals of similar age, sex, and educational level. Participants were trained in an Ecological Momentary Assessment (EMA) to label their emotions, the arousal dimension, and their emotion control via smartphone over a one-week period. Findings showed that the ASC group experienced more instances of 'having an emotion that I cannot name' and, when they were able to label their emotions, they reported higher rates of negative and conflicting (simultaneously positive and negative) emotions. In both groups, the absence of emotion labelling, and intense negative emotions were associated with impaired emotion control. However, the association between lack of emotional awareness-'I have no emotion'-and impaired emotion control was only evident in ASC individuals. Our study highlights a nuanced facet of emotional processing in the ASC population. Further research is needed to gain a deeper understanding of the complex relationship between ED and alexithymia in autism.

Adaptation of the normative rating procedure for the International Affective Picture System to a remote format

BackgroundThe Self-Assessment Manikin (SAM), a pictorial scale for the measurement of pleasure and arousal dimensions of emotions, is one of the most applied tools in the emotion research field.ObjectiveWe present a detailed description of a remote method to collect affective ratings in response to pictures by using the SAM scale.MethodsTo empirically validate our remote method, we conducted a study using a digitized version of the SAM scale and delivered online didactic instructions that followed the normative rating procedure for the International Affective Picture System (IAPS) to the participants. We presented 70 pictures from the IAPS and an additional set of 22 food pictures to the participants.ResultsWe found strong correlations between the ratings of IAPS pictures obtained in our sample and those reported by North American and Brazilian participants in previous in-person studies that applied the same pictures and methodology. We were also able to obtain an additional standardized set of food pictures.ConclusionThe protocols described here may be useful for researchers interested in collecting remotely valid and reliable affecting ratings.

Electroencephalography Emotion Recognition Based on Rhythm Information Entropy Extraction

Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person’s emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%.

Brain Emotion Perception Inspired EEG Emotion Recognition With Deep Reinforcement Learning.

Inspired by the well-known Papez circuit theory and neuroscience knowledge of reinforcement learning, a double dueling deep Q network (DQN) is built incorporating the electroencephalogram (EEG) signals of the frontal lobe as prior information, which is named frontal lobe double dueling DQN (FLD3QN). The framework of FLD3QN is constructed in accord with the brain emotion mechanism which takes the frontal lobe and the thalamus as the core, in which the part of the Papez circuit is simulated by the bifrontal lobe residual convolution neural network (BiFRCNN). Moreover, a step penalty factor is designed to constrain the number of mistakes of the agent. The ablation studies results on the public EEG emotion dataset DEAP verified the important roles of the frontal lobe and the Papez circuit in modeling the procedure of learning rewards during the perception of emotions, with a great increase in the average accuracies by 25.24% and 23.31% in valence and arousal dimensions.

Ecological momentary assessment of the unique effects of trait worry on daily negative emotionality: does arousal matter?

ABSTRACT Worry proneness is a transdiagnostic trait that predicts increased negative affect (NA), potentially in the service of preventing negative emotional contrasts. Although discrete types of NA vary along the dimension of arousal, the extent to which trait worry predicts high vs. low arousal forms of NA in daily life is unclear. This distinction has important implications for conceptualising how worry may perturb adaptive emotionality in various disorders. The present study (not pre-registered) aimed to isolate the effects of trait worry on high (N = 88) and low (N = 122) arousal NA in daily life using ecological momentary assessment while controlling for potential physical and psychological confounds. Participants were assessed for trait worry and depressive symptoms at baseline then reported their affect, heart rate, and exercise three times per day for one week. Multilevel models revealed that trait worry predicted both increased high and low arousal NA after controlling for momentary heart rate, daily exercise, and depression. In contrast, baseline depressive symptoms only predicted low arousal NA in daily life. Findings support the contrast avoidance model of worry and suggest that worry is linked to increased state NA in daily life, independent of arousal.

Dissonant music engages early visual processing

The neuroscientific examination of music processing in audio-visual contexts offers a valuable framework to assess how auditory information influences the emotional encoding of visual information. Using fMRI during naturalistic film viewing, we investigated the neural mechanisms underlying the effect of music on valence inferences during mental state attribution. Thirty-eight participants watched the same short-film accompanied by systematically controlled consonant or dissonant music. Subjects were instructed to think about the main character's intentions. The results revealed that increasing levels of dissonance led to more negatively valenced inferences, displaying the profound emotional impact of musical dissonance. Crucially, at the neuroscientific level and despite music being the sole manipulation, dissonance evoked the response of the primary visual cortex (V1). Functional/effective connectivity analysis showed a stronger coupling between the auditory ventral stream (AVS) and V1 in response to tonal dissonance and demonstrated the modulation of early visual processing via top-down feedback inputs from the AVS to V1. These V1 signal changes indicate the influence of high-level contextual representations associated with tonal dissonance on early visual cortices, serving to facilitate the emotional interpretation of visual information. Our results highlight the significance of employing systematically controlled music, which can isolate emotional valence from the arousal dimension, to elucidate the brain's sound-to-meaning interface and its distributive crossmodal effects on early visual encoding during naturalistic film viewing.

Transfer of affective connotations in the basic lexicon of Spanish learners of L2 English

This study addresses emotion in second languages with the aim of understanding how late bilinguals incorporate the affective connotations of core vocabulary into their mental lexicon. Specifically, it examines whether there are emotionality differences between the L2 and the native languages of reference, and whether linguistic competence, contact with the L2 and its culture, and attitudes explain the different tendencies. For this purpose, a group of Spanish learners of L2 English assessed a corpus of 300 words on the affective dimensions of valence and arousal. Different statistical analyses showed that English learners value the lexical repertoire in a very similar way to Spanish speakers, differing markedly from English speakers. It is mainly concluded that the mother tongue exerts a significant influence on this perceptual process and that the emotional connotations of the L2 are incorporated into the mental lexicon through translations from the L1 as a result of an emotional transfer.

Deep learning-based dimensional emotion recognition for conversational agent-based cognitive behavioral therapy.

Internet-based cognitive behavioral therapy (iCBT) offers a scalable, cost-effective, accessible, and low-threshold form of psychotherapy. Recent advancements explored the use of conversational agents such as chatbots and voice assistants to enhance the delivery of iCBT. These agents can deliver iCBT-based exercises, recognize and track emotional states, assess therapy progress, convey empathy, and potentially predict long-term therapy outcome. However, existing systems predominantly utilize categorical approaches for emotional modeling, which can oversimplify the complexity of human emotional states. To address this, we developed a transformer-based model for dimensional text-based emotion recognition, fine-tuned with a novel, comprehensive dimensional emotion dataset comprising 75,503 samples. This model significantly outperforms existing state-of-the-art models in detecting the dimensions of valence, arousal, and dominance, achieving a Pearson correlation coefficient of r=0.90, r=0.77, and r=0.64, respectively. Furthermore, a feasibility study involving 20 participants confirmed the model's technical effectiveness and its usability, acceptance, and empathic understanding in a conversational agent-based iCBT setting, marking a substantial improvement in personalized and effective therapy experiences.

Assessing physiological arousal and emotional valence during behavioral intervention for pediatric feeding difficulties: A pilot study

AbstractThis study's purpose is to introduce a new measurement system that objectively assesses the social validity of behavioral feeding intervention from the child's perspective via the concurrent measurement of two dimensions of emotion. To date, the primary measures of social validity for behavioral feeding intervention are caregiver treatment acceptability and satisfaction surveys. This is the first study to objectively measure two dimensions of emotion, physiological arousal and emotional valence, while children received behavioral intervention for feeding difficulties. Data collectors used a new open‐source data collection software, cometrics, developed to synchronize and record physiological and observational data. Physiological data was collected using a wearable biosensor and observers recorded an index of child happiness, unhappiness, or neither using definitions by Phipps et al. (2022) for six children with a pediatric feeding disorder. Two out of six children needed programmed habituation to the device before data collection. All children assented to wearing the device during data collection. Recorded indices mapped to separable physiological states using electrodermal activity and its constituent components, skin conductance responses and skin conductance level, in addition to skin temperature. Findings demonstrated the feasibility of measuring two dimensions of child emotions during behavioral feeding intervention and revealed that children's physiological responses were distinctly different during intervals scored as happy, unhappy, or neither. This new data collection system has implications for future research on the child's emotional experience of behavioral feeding treatment and increases the possibilities for improving clinical practice.

Contradictory findings in the study of emotional false memory: a review on the inadvisability of controlling valence and arousal.

Emotional false memories are the erroneous recollection of events accompanied by an emotional experience. In high-risk domains like psychotherapy and the legal system, emotional false memories are of particular importance. Despite the systematic research conducted on emotional false memories in recent years, findings remain contradictory. Some studies have suggested that negative emotion reduces false memories, while others have suggested that negative emotion increases false memories. Research has mainly employed words and pictures as experimental stimuli, and studies using both types of memory stimuli are reviewed here. From this examination, it emerged that the main reasons for contradictory findings are as follows: (1) different materials have varying effects on inducing false memories, with pictures demonstrating a memory advantage compared to words; (2) recall and recognition tests have been used interchangeably, leading to different false-memory effects depending on the memory test employed; and (3) different studies have adopted different levels of control over valence and arousal when manipulating emotional variables. Future studies should distinguish between the use of different memory materials, examine specific differences in recall and recognition tests, and measure the impact of specific emotions on false memory beyond the dimensions of valence and arousal.

PSPN: Pseudo-Siamese Pyramid Network for multimodal emotion analysis.

Emotion recognition plays an important role in human life and healthcare. The EEG has been extensively researched as an objective indicator of intense emotions. However, current existing methods lack sufficient analysis of shallow and deep EEG features. In addition, human emotions are complex and variable, making it difficult to comprehensively represent emotions using a single-modal signal. As a signal associated with gaze tracking and eye movement detection, Eye-related signals provide various forms of supplementary information for multimodal emotion analysis. Therefore, we propose a Pseudo-Siamese Pyramid Network (PSPN) for multimodal emotion analysis. The PSPN model employs a Depthwise Separable Convolutional Pyramid (DSCP) to extract and integrate intrinsic emotional features at various levels and scales from EEG signals. Simultaneously, we utilize a fully connected subnetwork to extract the external emotional features from eye-related signals. Finally, we introduce a Pseudo-Siamese network that integrates a flexible cross-modal dual-branch subnetwork to collaboratively utilize EEG emotional features and eye-related behavioral features, achieving consistency and complementarity in multimodal emotion recognition. For evaluation, we conducted experiments on the DEAP and SEED-IV public datasets. The experimental results demonstrate that multimodal fusion significantly improves the accuracy of emotion recognition compared to single-modal approaches. Our PSPN model achieved the best accuracy of 96.02% and 96.45% on the valence and arousal dimensions of the DEAP dataset, and 77.81% on the SEED-IV dataset, respectively. Our code link is: https://github.com/Yinyanyan003/PSPN.git.

Prosocial emotions predict individual differences in economic decision-making during ultimatum game with dynamic reciprocal contexts.

Social decision-making is known to be influenced by predictive emotions or the perceived reciprocity of partners. However, the connection between emotion, decision-making, and contextual reciprocity remains less understood. Moreover, arguments suggest that emotional experiences within a social context can be better conceptualised as prosocial rather than basic emotions, necessitating the inclusion of two social dimensions: focus, the degree of an emotion's relevance to oneself or others, and dominance, the degree to which one feels in control of an emotion. For better representation, these dimensions should be considered alongside the interoceptive dimensions of valence and arousal. In an ultimatum game involving fair, moderate, and unfair offers, this online study measured the emotions of 476 participants using a multidimensional affective rating scale. Using unsupervised classification algorithms, we identified individual differences in decisions and emotional experiences. Certain individuals exhibited consistent levels of acceptance behaviours and emotions, while reciprocal individuals' acceptance behaviours and emotions followed external reward value structures. Furthermore, individuals with distinct emotional responses to partners exhibited unique economic responses to their emotions, with only the reciprocal group exhibiting sensitivity to dominance prediction errors. The study illustrates a context-specific model capable of subtyping populations engaged in social interaction and exhibiting heterogeneous mental states.

DLEmotion: Deep learning-based emotion classification using visual attention location information

The correct recognition of each person's emotion can be an important step in knowing mental states and reactions. Because of the complexities of human personality, this is a difficult task and providing a method that can classify emotions with high accuracy can be useful. The purpose of doing this work is to provide a suitable method for recognizing emotions in human subjects. In other words, in this research the aim is to analyze eye tracking data using deep learning approaches and for this purpose, deep neural networks are used to classify fixations in different emotional states. The eye data of 10 women and 7 men who watched 79 emotional images are first collected. In this way, for each fixation point obtained from the subjects, a patch of 80 x 80 pixels was selected from the images and we used these patches of the obtained image as the input to the deep neural networks. The images shown to the subjects have three basic dimensions of emotion, i.e. Valence, Arousal and Dominance. Data classification was done by transfer learning and using Xception, InceptionV3 and DenseNet201 deep neural networks whose weights were trained on ImageNet datasets. We used these three neural networks in our work because they have relatively equal number of parameters and will help us in better analysis of the obtained results. In general, the results show that the points obtained from subjects' fixations location on images with three basic dimensions of emotion can be used to classify emotions. We will also see that changing the size of the patches obtained from the subject's fixation will not have much effect on the test results, which shows that deep neural networks are a powerful and accurate tool for processing people's eye fixation points.

Emotion recognition with reduced channels using CWT based EEG feature representation and a CNN classifier

Objective. Although emotion recognition has been studied for decades, a more accurate classification method that requires less computing is still needed. At present, in many studies, EEG features are extracted from all channels to recognize emotional states, however, there is a lack of an efficient feature domain that improves classification performance and reduces the number of EEG channels. Approach. In this study, a continuous wavelet transform (CWT)-based feature representation of multi-channel EEG data is proposed for automatic emotion recognition. In the proposed feature, the time-frequency domain information is preserved by using CWT coefficients. For a particular EEG channel, each CWT coefficient is mapped into a strength-to-entropy component ratio to obtain a 2D representation. Finally, a 2D feature matrix, namely CEF2D, is created by concatenating these representations from different channels and fed into a deep convolutional neural network architecture. Based on the CWT domain energy-to-entropy ratio, effective channel and CWT scale selection schemes are also proposed to reduce computational complexity. Main results. Compared with previous studies, the results of this study show that valence and arousal classification accuracy has improved in both 3-class and 2-class cases. For the 2-class problem, the average accuracies obtained for valence and arousal dimensions are 98.83% and 98.95%, respectively, and for the 3-class, the accuracies are 98.25% and 98.68%, respectively. Significance. Our findings show that the entropy-based feature of EEG data in the CWT domain is effective for emotion recognition. Utilizing the proposed feature domain, an effective channel selection method can reduce computational complexity.