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Contribution of Arousal and Mood States to Mozart Listening: Audiovisual Integration Study

2010· article· en· W1902672885 on OpenAlexvenueno aff
Phakkharawat Sittiprapaporn

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languageen
FieldPsychology
TopicMultisensory perception and integration
Canadian institutionsnot available
Fundersnot available
KeywordsMOZARTArousalPsychologyMoodActive listeningAffect (linguistics)Low arousal theoryPerceptionCognitive psychologyDevelopmental psychologyAudiologySocial psychologyCommunicationNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Several scientific evidences confirm that arousal and mood influence performance on a variety of cognitive tasks. We examined whether the Mozart effect is a consequence of between-condition differences in arousal and mood after simultaneously music and visual graphic presentation. Follow-up analyses were conducted separately for each musical excerpt, visual graphic piece, and simultaneously music and visual graphic presentation. The three posttest measures of arousal and mood were examined separately with mixed-design ANOVAs that had one within-subjects variable and one between-subjects variable. We found that effects of music attributed to differences in arousal and mood, as well as enjoyment. Participants who listened to Mozart scored significantly higher on positive mood and arousal and significantly lower on negative mood compared with their counterparts who listened to Albinoni. Enjoyable stimuli statistically induced positive affect and heightened levels of arousal, which lead to modest improvements in performance on visual graphic perception. Listeners’ use of music shows as an agent of emotional change. Our results support previous studies suggesting that the short-term effects of listening to Mozart on spatial ability are an artifact of arousal and mood. The present investigation is the first to examine directly the contribution of arousal and mood to the Mozart listening compared to seeing visual graphic condition. Changes in mood may be induced by giving participants a visual graphic, and changes in arousal occur in response to environmental events. Keywords: Music; Arousal and Mood; Audiovisual perception; Mozart; AlbioniResume: Plusieurs evidences scientifiques confirment que l'excitation et l'humeur influencent la performance dans une variete de tâches cognitives. Nous avons examine si l'effet Mozart est une consequence de differences entre les conditions de l'eveil et de l'humeur apres une presentation simultanee de la musique avec des visuels graphiques. Des analyses suivies ont ete realisees separement pour chaque extrait musical, chaque morceau visuel graphique, et chaque presentation simultanee de la musique avec des visuels graphiques. Les trois mesures post-test de l'exitation et de l'humeur ont ete examines separement avec le design mixte d'ANOVA qui avait une variable d'intra-sujets et une variable inter-sujets. Nous avons constate que les effets de la musique attribuaient a des differences dans l'excitation et l'humeur, ainsi que la jouissance. Les participants qui ecoutaient Mozart avaient eu des notes plus elevees dans l'humeur positive et l'excitation et des notes plus faibles sur l'humeur negative par rapport a leurs homologues qui ont ecoute Albinoni. Le stimulus agreable induit statistiquement un affect positif et augemente des niveaux d'excitation, qui conduisent a des ameliorations modestes dans la performance sur la perception visuelle graphique. L'utilisation de musique se montre en tant qu'un agent de changement affectif. Nos resultats confirment des etudes anterieures suggerant que les effets a court terme de l'ecoute de Mozart sur la capacite spatiale sont un artefact de l'excitation et de l'humeur. La presente enquete est la premiere a examiner directement la contribution de l'excitation et de l'humeur a l'ecoute de Mozart, comparee a la condition de visualisation graphique. Changements d'humeur peuvent etre induits en donnant aux participants une image visuelle, et des changements dans l'excitation se produit en reponse aux evenements environnementaux.Mots-cles: musique; exitation et humeur; perception audiovisuelle; Mozart; Albioni

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.733
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.025
GPT teacher head0.365
Teacher spread0.340 · 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 teacher head, 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

Citations3
Published2010
Admission routes1
Has abstractyes

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