When Cues Collide: Facial versus Nonlinguistic Vocal Dominance in Emotion Decoding

2014 ◽  
Author(s):  
Emily Zetzer ◽  
Sandra K. Webster
Keyword(s):  
Emotion Decoding

scholarly journals Exploring Emotion Recognition and the Understanding of Others’ Unspoken Thoughts and Feelings when Narrating Self-Experienced Emotional Events

2021 ◽  
Vol 45 (1) ◽  
pp. 67-81
Author(s):  
Anders Flykt ◽  
Tina Hörlin ◽  
Frida Linder ◽  
Anna-Karin Wennstig ◽  
Gabriella Sayeler ◽  
...  
Keyword(s):  
Social Work ◽  
Emotion Recognition ◽  
Clinical Psychology ◽  
Recognition Accuracy ◽  
Social Work Students ◽  
Empathic Accuracy ◽  
Emotional Event ◽  
Emotion Decoding ◽  
Emotional Events

AbstractEmotion decoding competence can be addressed in different ways. In this study, clinical psychology, nursing, or social work students narrated a 2.5–3 min story about a self-experienced emotional event and also listened to another student’s story. Participants were video recorded during the session. Participants then annotated their own recordings regarding their own thoughts and feelings, and they rated recordings by other participants regarding their thoughts and feelings [empathic accuracy, EA, task]. Participants further completed two emotion recognition accuracy (ERA) tests that differed in complexity. The results showed that even though significant correlations were found between the emotion recognition tests, the tests did not positively predict empathic accuracy scores. These results raise questions regarding the extent to which ERA tests tap the competencies that underlie EA. Different possibilities to investigate the consequences of method choices are discussed.

scholarly journals Tackling heterogeneity: Individual variability of emotion decoding deficits in severe alcohol use disorder

2021 ◽  
Vol 279 ◽  
pp. 299-307
Author(s):  
Pierre Maurage ◽  
Arthur Pabst ◽  
Séverine Lannoy ◽  
Fabien D'Hondt ◽  
Philippe de Timary ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Alcohol Use Disorder ◽  
Individual Variability ◽  
Emotion Decoding

scholarly journals Vocal emotion decoding in the subthalamic nucleus: An intracranial ERP study in Parkinson’s disease

2017 ◽  
Vol 168 ◽  
pp. 1-11 ◽  
Author(s):  
Julie Péron ◽  
Olivier Renaud ◽  
Claire Haegelen ◽  
Lucas Tamarit ◽  
Valérie Milesi ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Subthalamic Nucleus ◽  
Emotion Decoding ◽  
Vocal Emotion

scholarly journals Hemispheric specialization of the basal ganglia during vocal emotion decoding: Evidence from asymmetric Parkinson's disease and 18FDG PET

2018 ◽  
Vol 119 ◽  
pp. 1-11 ◽  
Author(s):  
Nancy Stirnimann ◽  
Karim N'Diaye ◽  
Florence Le Jeune ◽  
Jean-François Houvenaghel ◽  
Gabriel Robert ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Basal Ganglia ◽  
Hemispheric Specialization ◽  
Emotion Decoding ◽  
18Fdg Pet ◽  
Vocal Emotion

scholarly journals Unsupervised learning of facial emotion decoding skills

2014 ◽  
Vol 8 ◽  
Author(s):  
Jan O. Huelle ◽  
Benjamin Sack ◽  
Katja Broer ◽  
Irina Komlewa ◽  
Silke Anders
Keyword(s):  
Unsupervised Learning ◽  
Facial Emotion ◽  
Decoding Skills ◽  
Emotion Decoding

scholarly journals Unsupervised Learning of Brain State Dynamics during Emotion Imagery using High-Density EEG

2020 ◽  
Author(s):  
Sheng-Hsiou Hsu ◽  
Yayu Lin ◽  
Julie Onton ◽  
Tzyy-Ping Jung ◽  
Scott Makeig
Keyword(s):  
Temporal Dynamics ◽  
Emotional Experience ◽  
High Density ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Brain State ◽  
Emotion Decoding ◽  
Emotional Imagery ◽  
Time Courses ◽  
Dynamic States

AbstractHere we assume that emotional states correspond to functional dynamic states of brain and body, and attempt to characterize the appearance of these states in high-density scalp electroencephalographic (EEG) recordings acquired from 31 participants during 1-2 hour sessions, each including fifteen 3-5 min periods of self-induced emotion imagination using the method of guided imagery. EEG offers an objective and high-resolution measurement of whatever portion of cortical electrical dynamics is resolvable from scalp recordings. Despite preliminary progress in EEG-based emotion decoding using supervised machine learning methods, few studies have applied data-driven, unsupervised decomposition approaches to investigate the underlying EEG dynamics by characterizing brain temporal dynamics during emotional experience. This study applies an unsupervised approach – adaptive mixture independent component analysis (adaptive mixture ICA, AMICA) that learns a set of ICA models each accounted for portions of a given multi-channel EEG recording. We demonstrate that 20-model AMICA decomposition can identify distinct EEG patterns or dynamic states active during each of the fifteen emotion-imagery periods. The transition in EEG patterns revealed the time-courses of brain-state dynamics during emotional imagery. These time-courses varied across emotions: “grief” and “happiness” showed more abrupt transitions while “contentment” was nearly indistinguishable from the preceding rest period. The spatial distributions of independent components (ICs) of the AMICA models showed higher similarity within-subject across emotions than within-emotion across subjects. No significant differences in IC distributions were found between positive and negative emotions. However, significant changes in IC distributions during emotional imagery compared to rest were identified in brain areas such as the left prefrontal cortex, the posterior cingulate cortex, the motor cortex, and the visual cortex. The study demonstrates the feasibility of AMICA in modeling high-density and nonstationary EEG and its utility in providing data-driven insights into brain state dynamics during self-paced emotional experiences, which have been difficult to measure. This approach can advance our understanding of highly dynamical emotional processes and improve the performance of EEG-based emotion decoding for affective computing and human-computer interaction.

scholarly journals Προσανατολισμός δεσμού και κοινωνική αντίληψη του συναισθήματος: Ο ρόλος των κοινωνικών κινήτρων

2020 ◽  
Vol 21 (3) ◽  
pp. 319
Author(s):  
Πανωραία Ανδριοπούλου ◽  
Κωνσταντίνος Καφέτσιος
Keyword(s):  
Negative Emotion ◽  
Relevant Literature ◽  
Social Goals ◽  
Insecure Attachment ◽  
Facial Emotion ◽  
Relational Context ◽  
Emotion Decoding ◽  
The Social ◽  
Decoding Accuracy

Despite the seminal role of emotion perception in social and personal relationships, there is limited understanding of how adult attachment organization affects the decoding of facial emotion expressions. Previousresearch has focused on how insecure attachment-related strategies for emotion regulation influence early stages of emotion information processing. However, recent studies highlight the importance of socialprocesses and motivational factors in the perception of positive and negative emotion (see e.g., Vrtička, Sander, & Vuilleumier, 2012). Based on a critical review of the relevant literature, the present articlepresents findings from a recent series of studies that reveal the effects senders' social moti ves (relational context, social goals) have on emotion decoding accuracy in adults with insecure attachment. The findings from these studies are discussed in the context of theories of motivated social cognition and the social perception of emotion.

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