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8 results for “Valence; Arousal”
Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses
<p>This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).</p>
Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses
<p>This repository contains extracted data features and all questionnaires from our study "Estimating the valence, arousal and balance of dyadic conversations using regression algorithms with autonomic nervous system responses". </p><p> </p><p>Data_FinalFeatureSet.xlsx contains data for the 42 dyads who completed the study protocol. Rows represent individual participants, with the two participants in the same dyad always on consecutive rows. Columns consist of:</p><ul><li>Participant gender and age.</li><li>Group that dyads were assigned to. PosInit/NeutInit/NegInit represent positive, neutral or negative initial prompts. Devil1st/NoEmot1st represent which of the two secret prompts was presented first ("devil's advocate" or "no emotion").</li><li>A column stating which of the two participants was given the secret prompts (participant on left or right).</li><li>A column stating whether the participants had already known each other before the session (Y/N).</li><li>Extracted physiological features for 12 intervals: the first baseline (interval 1), 10 conversation intervals (intervals 2-11), and the second baseline (interval 12). Individual features are present for all individual participants while synchrony features exist for dyads (not individuals) and are thus present for only one row of a dyad.</li><li>Raw data from three personality questionnaires: the Brief Fear of Negative Evaluation Scale (BFNES), the Questionnaire of Cognitive and Affective Empathy (QCAE) and the Center for Epidemiologic Studies Depression Scale (CESD).</li><li>Self-reported results of the Self-Assessment Manikin (SAM) for the 10 conversation intervals, with the three columns in each interval corresponding to valence, arousal and balance.</li></ul><p>Note that one dyad's physiological data were corrupted and that dyad was not used for further analysis. Their demographics and questionnaire data are included, but no physiological features were calculated.</p><p> </p><p>Questionnaire files include the BFNES, QCAE and CESD as well as three versions of our modified SAM: one with no secret prompts, one with secret prompts for participants who saw the "devil's advocate" prompt first, and one with secret prompts for participants who saw the "no emotion" prompt first.</p>
US and Japanese adolescents’ emotions across time: variation by valence and arousal
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Perceiving arousal and valence in facial expressions: Differences between children and adults
<p>Arousal and valence have long been studied as the two primary dimensions for<br> the perception of emotional stimuli such as facial expressions. Prior correlational<br> studies that tested emotion perception along these dimensions found broad<br> similarities between adults and children. However, few studies looked for<br> direct differences between children and adults in these dimensions beyond<br> correlation. We tested 9-year-old children and adults on rating positive and<br> negative facial stimuli based on emotional arousal and valence. Despite high<br> significant correlations between children’s and adults’ ratings, our findings also<br> showed significant differences between children and adults in terms of rating<br> values: Children rated all expressions as significantly more positive than adults<br> in valence. Children also rated positive emotions as more arousing than adults.<br> Our results show that although perception of facial emotions along arousal and<br> valence follows similar patterns in children and adults, some differences in ratings<br> persist, and vary by emotion type.</p>
Perception of valence and arousal in German emotion terms – A comparison between 9-year-old children and adults
<p>Two major semantic features of emotion concepts have been shown to impact performance in<br> emotion perception tasks: valence and arousal. To design psycholinguistic experiments with<br> emotion terms as stimuli, norms are required which indicate valence and arousal values for<br> individual words. Although such norms are usually obtained from ratings of adults, they are<br> often also used in developmental studies. This procedure raises the question of whether<br> children and adults perceive emotional valence and arousal of words in the same way, and<br> consequently, whether adults’ ratings are adequate when constructing stimulus sets for<br> children. The present study obtained valence and arousal ratings for 48 German emotion<br> terms from three different groups: 9-year-old children and adults tested in a controlled<br> laboratory setting, and adults tested via online survey. Results demonstrate high correlations<br> for valence and arousal across settings. The comparison between children and adults also<br> revealed high correlations, suggesting that children at the age of 9 already display adult-like<br> behavior in their evaluation of emotion terms. A small difference was found for absolute<br> rating values of arousal, with children rating words less arousing than adults. Overall, using<br> adults’ norms in developmental studies on word processing seems to be a valid procedure.</p>
Crowding modulates time perception while controlling for valence and arousal
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Crowding modulates time perception while controlling for valence and arousal
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OASIS EEG Dataset: Decoding the Neural Signatures of Valence and Arousal From Portable EEG Headset
<p>Emotion classification using electroencephalography (EEG) data and machine learning techniques have been on the rise in the recent past. However, past studies use data from medical-grade EEG setups with long set-up times and environment constraints. The images from the OASIS image dataset were used to elicit valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. We propose a novel feature ranking technique and incremental learning approach to analyze performance dependence on the number of participants. The analysis is carried out on publicly available datasets: DEAP and DREAMER for benchmarking. Leave-one-subject-out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The collected dataset and pipeline are made open source. </p> <p>Code: <a href="https://github.com/rohitgarg025/Decoding_EEG">https://github.com/rohitgarg025/Decoding_EEG</a></p>
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