Approaches to studying emotion using physiological responses to spoken narratives: A scoping review
Bibliographic record
Abstract
Narratives are effective tools for evoking emotions, and physiological measurements provide a means of objectively assessing emotional reactions - making them a potentially powerful pair of tools for studying emotional processes. However, extent research combining emotional narratives and physiological measurement varies widely in design and application, making it challenging to identify previous work, consolidate findings, and design effective experiments. Our scoping review explores the use of auditory emotional narratives and physiological measures in research, examining paradigms, study populations, and represented emotions. Following the PRISMA-ScR Checklist, we searched five databases for peer-reviewed experimental studies that used spoken narratives to induce emotion and reported autonomic physiological measures. Among 3466 titles screened and 653 articles reviewed, 110 studies were included. Our exploration revealed a variety of applications and experimental paradigms; emotional narratives paired with physiological measures have been used to study diverse topics and populations, including neurotypical and clinical groups. Although incomparable designs and sometimes contradictory results precluded general recommendations as regards which physiological measures to use when designing new studies, as a whole, the body of work suggests that these tools can be valuable to study emotions. Our review offers an overview of research employing narratives and physiological measures for emotion study, and highlights weaknesses in reporting practices and gaps in our knowledge concerning the robustness and specificity of physiological measures as indices of emotion. We discuss study design considerations and transparent reporting, to facilitate future using emotional narratives and physiological measures in studying emotions.
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".