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1,836 results for “emotion”
Dataset: Age effects in emotional memory and associated eye movements
<p><span>Mnemonic-enhanced memory has been observed for negative events. Here, we investigate its association with spatiotemporal attention, consolidation, and age. An ingenious method to study visual attention for emotional stimuli is eye tracking. Twenty young adults and twenty-one older adults encoded stimuli depicting neutral faces, angry faces, and houses while eye movements were recorded. The encoding phase was followed by an immediate and delayed (48 h) recognition assessment. Linear mixed model analyses of recognition performance with group, emotion, and their interaction as fixed effects revealed increased performance for angry compared to neutral faces in the young adults group only. Furthermore, young adults showed enhanced memory for angry faces compared to older adults. This effect was associated with a shorter fixation duration for angry faces compared to neutral faces in the older adults group. Furthermore, the results revealed that total fixation duration was a strong predictor for face memory performance.</span></p>
Using social media and personality traits to assess software developers' emotional polarity
<p>Companion DATA</p> <p>Title:<br> Using social media and personality traits to assess software developers' emotional polarity</p> <p>Authors: <br> Leo Moreira Silva<br> Marília Gurgel Castro<br> Miriam Bernardino Silva<br> Milena Santos<br> Uirá Kulesza<br> Margarida Lima<br> Henrique Madeira</p> <p>Journal:<br> PeerJ Computer Science</p> <p>Github:<br> <a href="https://github.com/leosilva/peerj_computer_science_2022">https://github.com/leosilva/peerj_computer_science_2022</a></p> <p>------------------------------------------------------------<br> The folders contain:</p> <p>Experiment_Protocol.pdf: document that present the protocol regarding recruitment protocol, data collection of public posts from Twitter, criteria for manual analysis, and the assessment of Big Five factors from participants and psychologists. English version.</p> <p><br> /analysis<br> analyzed_tweets_by_psychologists.csv: file containing the manual analysis done by psychologists<br> analyzed_tweets_by_participants.csv: file containing the manual analysis done by participants<br> analyzed_tweets_by_psychologists_solved_divergencies.csv: file containing the manual analysis done by psychologists over 51 divergent tweets' classifications</p> <p><br> /dataset<br> alldata.json: contains the dataset used in the paper</p> <p>/ethics_committee<br> committee_response_english_version.pdf: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. English version.<br> committee_response_original_portuguese_version: contains the acceptance response of Research Ethics and Deontology Committee of the Faculty of Psychology and Educational Sciences of the University of Coimbra. Portuguese version.<br> committee_submission_form_english_version.pdf: the project submitted to the committee. English version.<br> committee_submission_form_original_portuguese_version.pdf: the project submitted to the committee. Portuguese version.<br> consent_form_english_version.pdf: declaration of free and informed consent fulfilled by participants. English version.<br> consent_form_original_portuguese_version.pdf: declaration of free and informed consent fulfilled by participants. Portuguese version.<br> data_protection_declaration_english_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. English version.<br> data_protection_declaration_original_portuguese_version.pdf: personal data and privacy declaration, according to European Union General Data Protection Regulation. Portuguese version.</p> <p>/notebooks<br> General - Charts.ipynb: notebook file containing all charts produced in the study, including those in the paper<br> Statistics - Lexicons and Ensembles.ipynb: notebook file with the statistics for the five lexicons and ensembles used in the study<br> Statistics - Linear Regression.ipynb: notebook file with the multiple linear regression results<br> Statistics - Polynomial Regression.ipynb: notebook file with the polynomial regression results<br> Statistics - Psychologists versus Participants.ipynb: notebook file with the statistics between the psychologists and participants manual analysis<br> Statistics - Working x Non-working.ipynb: notebook file containing the statistical analysis for the tweets posted during work period and those posted outside of working period</p> <p><br> /surveys<br> Demographic_Survey_english_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. English version.<br> Demographic_Survey_portuguese_version.pdf: survey inviting participants to enroll in the study. We collect demographic data and participants' authorization to access their public Tweet posts. Portuguese version.<br> Demographic_Survey_answers.xlsx: participants' demographic survey answers<br> ibf_pt_br.doc: the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_en.doc: translation in English of the Portuguese version of the Big Five Inventory (BFI) instrument to infer participants' Big Five polarity traits.<br> ibf_answers.xlsx: participantes' and psychologists' answers for BFI</p> <p><br> ------------------------------------------------------------</p> <p>We have removed from dataset any sensible data to protect participants' privacy and anonymity.<br> We have removed from demographic survey answers any sensible data to protect participants' privacy and anonymity.</p>
Workplace bullying, emotional regulation self-efficacy and well-being.
<p>Workplace bullying is one of the most relevant social stressors at work. Although previous research has shown its negative consequences on health and well-being, scarce evidence is available about the emotional mechanisms of this relationship. Thus, we conducted a longitudinal study. The final sample comprised 139 participants over 4 waves</p>
Emotions Iliad Dataset
<p> a CSV file with four columns: "chap", "text", "label", "annotator_code", and "label_code". </p> <p>The first row of the file contains the headers for each column. </p> <p>Each subsequent row contains data for a particular text. </p> <p>The "chap" column represents the chapter number of the text. </p> <p>The "text" column contains a quote from the text. </p> <p>The "label" column is a classification label assigned to the quote by an annotator. </p> <p>The "annotator_code" column is an identifier for the annotator who assigned the label. </p> <p>The "label_code" column is a numeric code assigned to the label. </p> <p> it is related to the labeling or classification of quotes from a text based on the emotional tone conveyed by the text.</p>
The EmoHI Test stimuli: Measuring vocal emotion recognition in hearing-impaired populations
<p>Before reading this file, make sure you have read the <strong>README.1.pdf</strong> file. That file also contains information about the <strong>license</strong> these materials are distributed under.</p> <p><em><strong>Versions</strong></em></p> <ul> <li><strong>Version 2</strong>: This is the current version. To cite this version specifically, use DOI 10.5281/zenodo.7997063. In this version we fixed some naming mistakes in the files (in 8 of the files the sentence was identified as <code>t2</code> instead of <code>s2</code>), and added two missing stimuli (<code>t5_neutral_t2_u04.wav</code> and <code>t5_sad_t1_u05.wav</code>).</li> <li><strong>Version 1</strong>: This was the initial version. To cite that version specifically, use DOI 10.5281/zenodo.3689710.</li> </ul> <p>The latest version of the EmoHI material can be downloaded from <a href="https://doi.org/10.5281/zenodo.3689709">https://doi.org/10.5281/zenodo.3689709</a>. Please always check that you have the latest version, and that you comply with the current license requirements.</p> <p><em><strong>The EmoHI Test</strong></em></p> <p>The EmoHI Test was developed to measure the accuracy at which participants can recognize vocal emotions based on pseudospeech sentences that were produced in a happy, angry sad, or neutral manner. The EmoHI Test recordings are particularly suitable for testing hearing-impaired populations due to their high sound quality. All recordings, including the ones that were used in Nagels <em>et al.</em> (2020, <em>PeerJ</em>, <a href="https://doi.org/10.7717/peerj.8773">doi: 10.7717/peerj.8773</a>), are made available here.</p> <p>The stimuli were recorded in an anechoic room at a sampling rate of 44.1 kHz. The microphone was placed at a distance of approximately 30 cm (12 in) from the speaker. The recordings were made by connecting a standing Røde NT1 microphone to a Presonus TubePre V2 preamplifier and a TASCAM DR-100 portable digital recorder. The gain of the recordings was adjusted for each emotion production using the preamplifier to record the stimuli at an intensity level that was approximately the same across emotions to reduce large intensity differences between the recordings of different emotions. The files are not RMS equalized.</p> <p><em><strong>Citation</strong></em></p> <p>When using this repository in your research, please cite the repository itself. For this version:</p> <blockquote> <p>Nagels L., Gaudrain E., Hendriks P., & Başkent D. (2023, June 2). The EmoHI Test stimuli: Measuring vocal emotion recognition in hearing-impaired populations. Version 2. <em>Zenodo</em>. <a href="https://doi.org/10.5281/zenodo.7997063">https://doi.org/10.5281/zenodo.7997063</a></p> </blockquote> <p>Also cite the PeerJ article that describes the material:</p> <blockquote> <p>Nagels L., Gaudrain E., Vickers D., Matos Lopes M., Hendriks P., Başkent D. (2020). Development of vocal emotion recognition in school-age children: The EmoHI test for hearing-impaired populations. <em>PeerJ</em> 8:e8773 <a href="https://doi.org/10.7717/peerj.8773">https://doi.org/10.7717/peerj.8773</a></p> </blockquote> <p><em><strong>Sound file name structure</strong></em></p> <p>The sound files are named using the following convention:</p> <p><code>t[1-6]_{emotion}_s{1,2}_u[01-18].wav</code></p> <ul> <li><code>t[1-6]</code> represents the <strong>talker</strong> who produced the stimulus: <code>t1</code>, <code>t2</code>, <code>t3</code>, <code>t4</code>, <code>t5</code>, or <code>t6</code></li> <li><code>{emotion}</code> is the label of the <strong>emotion</strong> that was produced: <code>neutral</code>, <code>happy</code>, <code>angry</code>, or <code>sad</code></li> <li><code>s{1,2}</code> is the <strong>pseudospeech sentence</strong> that was used: <code>s1</code> for "Koun se mina lod belam." <code>s2</code> for "Nekal ibam soud molen."</li> <li><code>u[01-18]</code> is the <strong>utterance</strong> number: Number ranging from <code>u01</code> to <code>u18</code></li> </ul> <p>For instance, <code>t1_happy_s2_u01.wav</code> is utterance 1 of talker <code>t1</code> producing emotion "happy" using sentence 2.</p> <p><em><strong>Talker demographic information</strong></em></p> <p>The table below gives an overview of the voice characteristics from the talkers who produced the EmoHI test stimuli.</p> <table> <thead> <tr> <th scope="col">Talker</th> <th scope="col">Age (years)</th> <th scope="col">Gender</th> <th scope="col">Height (m)</th> <th scope="col">Mean F0 (Hz)</th> <th scope="col">F0 range (Hz)</th> </tr> </thead> <tbody> <tr> <td>t1</td> <td>48</td> <td>f</td> <td>1.72</td> <td>253.14</td> <td>179.97 – 421.81</td> </tr> <tr> <td>t2</td> <td>36</td> <td>f</td> <td>1.68</td> <td>302.23</td> <td>200.71 – 437.38</td> </tr> <tr> <td>t3</td> <td>27</td> <td>m</td> <td>1.85</td> <td>166.92</td> <td>100.99 – 296.47</td> </tr> <tr> <td>t4</td> <td>45</td> <td>m</td> <td>1.90</td> <td>149.41</td> <td>96.97 – 274.72</td> </tr> <tr> <td>t5</td> <td>25</td> <td>f</td> <td>1.63</td> <td>282.89</td> <td>199.49 – 429.38</td> </tr> <tr> <td>t6</td> <td>24</td> <td>m</td> <td>1.75</td> <td>167.76</td> <td>87.46 – 285.79</td> </tr> </tbody> </table> <p><em><strong>Supporting data</strong></em></p> <p>The behavioural data from the PeerJ article is accessible at <a href="https://doi.org/10.34894/BDMX6D">https://doi.org/10.34894/BDMX6D</a>.</p>
Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking
<p>It has been indicated that extreme sport activities, despite providing fear, stress and anxiety, also result in a highly rewarding experience. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate – HR, and breathing rate – BR) and neural (electroencephalographic – EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m – low-walk – and 45 m – high-walk – from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network – VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network – DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e., configured as a ‘task-constantly-opened-to-subtle-information experience’, rather than a ‘task-focused experience’.</p>
Psycho-physio-neurological correlates of qualitative attention, emotion and flow experiences in a close-to-real-life extreme sports situation: low- and high-altitude slackline walking
<p>It has been indicated that extreme sport activities, despite providing fear, stress and anxiety, also result in a highly rewarding experience. Studies have related this experience to the concept of flow, a positive feeling that individuals undergo when they are completely immersed in an activity. However, little is known about the exact nature of these experiences, and, there are still no empirical results to characterize the brain dynamics during extreme sport practice. This work aimed at investigating changes in psychological responses while recording physiological (heart rate – HR, and breathing rate – BR) and neural (electroencephalographic – EEG) data of eight volunteers, during outdoors slackline walking in a mountainous environment at two different altitude conditions (1 m – low-walk – and 45 m – high-walk – from the ground). Low-walk showed a higher score on flow scale, while high-walk displayed a higher score in the negative affect aspects, which together point to some level of flow restriction during high-walk. The order of task performance was shown to be relevant for the physiological and neural variables. The brain behavior during flow, mainly considering attention networks, displayed the stimulus-driven ventral attention network – VAN, regionally prevailing (mainly at the frontal lobe), over the goal-directed dorsal attention network – DAN. Therefore, we suggest an interpretation of flow experiences as an opened attention to more changing details in the surroundings, i.e., configured as a ‘task-constantly-opened-to-subtle-information experience’, rather than a ‘task-focused experience’.</p>
Appendix - Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition
<p>Appendix tables for the paper "Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition".</p> <p>Feature informativeness information was gathered using SHAP values.</p> <p>TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE X: Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values</p> <p>TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values</p>
Data on the Model of Loneliness and Smartphone Use Intensity as a Mediator of Self-Control, Emotion Regulation, and Spiritual Meaningfulness in Nomophobia
<p><em>The data collection was conducted in June-July 2022, with 355 participants completing the scales provided through offline paper-pencil surveys in the classroom. The research data was collected from three cities, namely Palembang, Jambi, and Yogyakarta, and consisted of junior high school and high school students, as well as university students. The university student participants were recruited from Ahmad Dahlan University, Jambi University, and Charitas Musi Catholic University. The participants completed the Nomophobia NMP-Q scale (Yildirim & Correia, 2015), the R-UCLA Loneliness Scale (Russell et al., 1980), self-control (Tangney, Baumeister & Boone, 2004), The Difficulties in Emotion Regulation Scale (DERS; Gratz & Roemer, 2004), and Spiritual Meaningfulness which was developed based on the theory of Pargament (2007). All the questionnaires for data collection were in the Indonesian version.</em></p>
Emotional intelligence and school climate data from Spain, Norway, and Poland in 2019
<p>Dataset of the published open access article: Luque González, R., Romera, E., Gómez-Ortiz, O., Wiza, A., Laudańska-Krzemińska, I., Antypas, K., & Muller, S. (2022). Emotional intelligence and school climate in primary school children in Spain, Norway, and Poland. <em>Psychology, Society & Education</em>, <em>14</em>(3), 29–37. https://doi.org/10.21071/psye.v14i3.15122</p>
Implementing and Evaluating a Social-Emotional Learning Program for Refugee Children During the COVID-19 Pandemic
ClinicalTrials.gov study NCT04931888. IPD Sharing: NO. Countries: 1. Publications: 1.
Multicomponent FIBROWALK Therapy Plus Unified Protocol for Emotional Regulation in Individuals With Fibromyalgia
ClinicalTrials.gov study NCT06906991. IPD Sharing: NO. Countries: 1. Publications: 6.
Effects of Transdermal Nicotine on Response Inhibition to Emotional Cues in Schizophrenia
ClinicalTrials.gov study NCT03838484. IPD Sharing: NO. Countries: 1. Publications: 3.
Smartphone-based Cognitive Emotion Regulation Training for Unpaid Primary Caregivers of Persons With Alzheimer's Disease
ClinicalTrials.gov study NCT05949047. IPD Sharing: YES. Countries: 1. Publications: 8.
A One-Day ACT Workshop for Emotional Eating
ClinicalTrials.gov study NCT03744780. IPD Sharing: YES. Countries: 1. Publications: 10.
Pharmacogenetics, Emotional Reactivity and Smoking
ClinicalTrials.gov study NCT00507728. IPD Sharing: Not stated. Countries: 1. Publications: 16.
Cognitive vs. Emotional Psychopharmacological Manipulations of Fear vs. Anxiety
ClinicalTrials.gov study NCT02153944. IPD Sharing: Not stated. Countries: 1. Publications: 1.
MOST EMPOWER: Optimizing An Emotion Regulation Intervention
ClinicalTrials.gov study NCT04317417. IPD Sharing: NO. Countries: 1. Publications: 0.
A Study in People With Depression to Test the Effects of BI 1358894 on Parts of the Brain That Are Involved in Emotions
ClinicalTrials.gov study NCT03854578. IPD Sharing: NO. Countries: 1. Publications: 1.
Virtual Reality Rehabilitation for Cognitive, Emotional, and Motor Recovery in Neurological Disorders
ClinicalTrials.gov study NCT06838975. IPD Sharing: YES. Countries: 1. Publications: 3.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.