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Enregistrement W3016722988

The effect of musical mode on emotional state.

2006· article· en· W3016722988 sur OpenAlexaff
Anthony DiGiacomo, Brenda J. Kirby

Notice bibliographique

RevueCanadian journal of music therapy · 2006
Typearticle
Langueen
DomainePsychology
ThématiqueHealth and Well-being Studies
Établissements canadiensCentre for Addiction and Mental Health
Organismes subventionnairesnon disponible
Mots-clésMusicalMode (computer interface)PsychologyState (computer science)Cognitive psychologyCommunicationComputer scienceArtVisual artsHuman–computer interaction
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The authors would like to acknowledge Stacy Bird, Joshua Nelson, and the SORG research team at LeMoyne College for research assistance on this project.The idea of music as an emotionally stimulating medium is not new. Historically, music has been utilized for a variety of purposes worldwide and throughout cultures. In the recent past, music has been investigated and incorporated into various medical and psychological theories and interventions. Loewy (1997) looked at the role of music in assessing and attending to pain management. Courtnage (2000) developed a rationale for the use of live music in the Neonatal Intensive Care Unit to regulate body functions and as a non-invasive method of treatment. Shiraishi (1997) showed that a structured music listening protocol decreased depression-anxiety levels and increased self-esteem levels in a sample of multi-risk mothers. Intensive investigations into the root causes of this phenomenon have led to research looking into different types of responses to music. Chief among these is the association between music and emotional response.Emotional Response to MusicVarious studies have looked at emotional response to musical stimuli in general. In a study of the effects of music on mood and memory, Sousou (1997) concluded that music had a significant effect on mood. Krumhansl ( 1997) studied whether music elicits an emotional response in listeners (the emotivist position) or simply expresses emotions that listeners recognize in the music (the cognitivist position). Krumhansl's research concluded that the effects of music observed support the emotivist view of musical emotions. North and Hargreaves (1997) found that emotions expressed through music are closely associated with the pleasing aspects of that stimulus. They concluded that response to music and other stimuli interact because they are based on like dimensions. Miller and Strongman (2002) looked at the emotional effects of music on religious experience. They found that the positive mood of the participants increased significantly from directly before the service until directly after the music and worship portions of the service. Vaestfjaell (2002) reviewed a body of research that showed music could alter moods and emotions. Vaestfjaell looked at various Mood Induction Procedures (MIPs) that were specifically developed to aid in determining how mood influences the cognitive process. Many of these operate by musical mood induction. Vaestfjaell concluded that Musical Mood Induction Procedures (MMIPs) could induce positive, negative and neutral moods.Research has also looked at musical influence on emotion from a biological and developmental standpoint. Bartlett ( 1996) compiled an overview of physiological responses to music. Bartlett reported a collection of studies describing music's effect on heart/pulse rate, skin conductivity, respiration, blood pressure, muscular tension/motor activities, motor/postural response, skin temperature, blood volume, stomach contractions and biochemical response studies. Nyklicek, Thayer and Van Doornen (1997) looked at the cardiorespiratory differentiation of emotions induced musically. They were able to conclude that emotions could be categorized indicating a clear physiological differentiation. Blood, Zatorre, Bermudez and Evans (1999) used positron emission tomography (PET) to examine cerebral blood flow (CBF) changes related to affective responses to music. They found that CBF covariations existed in distinct paralimbic and neocortical regions as a function of the perceived pleasantness or unpleasantness of the music. Blood, Zatorre, Bermudez and Evans conclude it is possible that music uses neural mechanisms similar to those associated with pleasant and unpleasant emotional states. Panksepp and Bernatzky (2002) described the potential role of the brain's emotional systems in music appreciation citing examples of how music may promote behavioral changes including effects on memory, mood, brain activity and autonomie responses. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,007
Score d'incertitude au seuil0,022

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,300
Écart entre enseignants0,284 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2006
Routes d'admission1
Résumé présentnon

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