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Record W4393031927 · doi:10.32920/25413148.v1

Music Hath Charms: The Effects of Valence and Arousal on Recovery Following an Acute Stressor

2024· preprint· en· W4393031927 on OpenAlexafffund
Gillian M. Sandstrom, Frank Russo

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsArousalStressorValence (chemistry)PsychologyEmotional valenceSocial psychologyClinical psychologyChemistryNeuroscienceCognition

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the effects of the valence and arousal dimensions of music over the time course of physiological (skin conductance level and heart rate) and subjective (Subjective Unit of Discomfort score) recovery from an acute stressor. Participants experienced stress after being told to prepare a speech, and were then exposed to happy, peaceful, sad, or agitated music. Music with a positive valence promoted both subjective and physiological recovery better than music with a negative valence, and low-arousal music was more effective than high-arousal music. Repeated measures analyses found that the emotion conveyed by the music affected skin conductance level recovery immediately following the stressor, whereas it affected heart rate recovery in a more sustained fashion. Follow-up tests found that positively valenced low-arousal (i.e., peaceful) music was more effective across the time course than an emotionally neutral control (white noise).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.033
GPT teacher head0.356
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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