Analysis of emotion regulation using posture, voice, and attention: A qualitative case study
Bibliographic record
Abstract
The ability to detect and regulate emotions is an important aspect of emotional intelligence that can benefit individuals in their personal well-being and social interactions (Mayer, Caruso, Salovey, 2016). This study examined emotion regulation (ER) in medical students as they practiced learning how best to communicate undesired news to patients in an international technology-rich learning environment (TRLE; Lajoie et al., 2012). Gross’ (2015) process model of ER served as the theoretical model that guided the analysis of regulatory strategies, in a case study (Yin, 2011) of four medical students. A qualitative approach was used to determine how multichannels of emotion representation (vocal characteristics, motor expressions, attention tendencies) indicate instances of conscious or unconscious ER. Analyses revealed four major findings as evidences of ER: (a) dissociation between emotion channels (e.g. a calm face accompanied by a nervous voice); (b) sudden changes in emotion expression without external triggers (e.g. from smile to a rapidly serious face); (c) unexpected emotions (e.g. smiling when expected to demonstrate empathy); and, (d) use of multiple emotion channels to demonstrate emotion regulatory responses. The findings demonstrate the power of multimodal emotion analysis towards accurate detection of ER, and how it may inform the relationship between experienced and expressed emotions. Multimodal ER detection approaches, as illustrated in this study, may have important implications for assisting learners in face to face and computer-supported collaborative settings in becoming more conscious of their ER, helping them regulate undesired self and peer emotions in challenging learning situations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".