Analysing the Impact of Music on the Perception of Red Wine via Temporal Dominance of Sensations.


Journal

Multisensory research
ISSN: 2213-4808
Titre abrégé: Multisens Res
Pays: Netherlands
ID NLM: 101604290

Informations de publication

Date de publication:
01 01 2019
Historique:
received: 07 01 2019
accepted: 29 03 2019
pubmed: 23 5 2019
medline: 3 6 2020
entrez: 23 5 2019
Statut: epublish

Résumé

Several studies have examined how music may affect the evaluation of food and drink, but the vast majority have not observed how this interaction unfolds in time. This seems to be quite relevant, since both music and the consumer experience of food/drink are time-varying in nature. In the present study we sought to fix this gap, using Temporal Dominance of Sensations (TDS), a method developed to record the dominant sensory attribute at any given moment in time, to examine the impact of music on the wine taster's perception. More specifically, we assessed how the same red wine might be experienced differently when tasters were exposed to various sonic environments (two pieces of music plus a silent control condition). The results revealed diverse patterns of dominant flavours for each sound condition, with significant differences in flavour dominance in each music condition as compared to the silent control condition. Moreover, musical correspondence analysis revealed that differences in perceived dominance of acidity and bitterness in the wine were correlated in the temporality of the experience, with changes in basic auditory attributes. Potential implications for the role of attention in auditory flavour modification and opportunities for future studies are discussed.

Identifiants

pubmed: 31117045
doi: 10.1163/22134808-20191401
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

455-472

Auteurs

Qian Janice Wang (QJ)

1Crossmodal Research Laboratory, Department of Experimental Psychology, Oxford University, Oxford, UK.
2Department of Food Science, Aarhus University, Aarslev, Denmark.

Bruno Mesz (B)

3MUNTREF Tecnópolis, Universidad Nacional de Tres de Febrero, Buenos Aires, Argentina.

Pablo Riera (P)

4Laboratorio de Inteligencia Artificial Aplicada, Instituto de Ciencias de la Computación, Universidad de Buenos Aires, CONICET, Argentina.

Marcos Trevisan (M)

5Department of Physics, University of Buenos Aires and Institute of Physics Buenos Aires (IFIBA), CONICET, Argentina.

Mariano Sigman (M)

6Laboratorio de Neurociencia, CONICET, Universidad Torcuato Di Tella, C1428BIJ Buenos Aires, Argentina.
7Facultad de Lenguas y Educación, Universidad Nebrija, Madrid, Spain.

Apratim Guha (A)

8Production, Operations and Decision Sciences Area, XLRI, Xavier School of Management, Jamshedpur, India.

Charles Spence (C)

1Crossmodal Research Laboratory, Department of Experimental Psychology, Oxford University, Oxford, UK.

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