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Dataset . 2017
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2017
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2017
License: CC BY
Data sources: ZENODO
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Predicting Subjective Liking Of Bitter And Sweet Liquid Solutions Using Facial Electromyography: Mixed Model Dataset

Authors: Cannon, Peter Robert; Li, Bei; Grigor, John M.;

Predicting Subjective Liking Of Bitter And Sweet Liquid Solutions Using Facial Electromyography: Mixed Model Dataset

Abstract

Hedonic responses to foods are often measured using subjective liking ratings scales. This project investigated the potential use of electromyography as a means to predict subjective liking ratings using affective facial muscle activity recorded at different phases of oral processing while tasting liquids. Using linear mixed models, muscle activity recorded while emptying into the mouth, swirling, and thinking about the taste of bitter and sweet liquid solutions was used to predict subjective liking ratings. During different phases of the tasting, these mixed models demonstrate that zygomaticus major activity predicted increased liking and that corrugator supercilii and levator labii superioris predicted decreased liking. The change in liking ratings predicted by each muscle varied depending on whether participants were emptying, swirling, or thinking about the taste.

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Keywords

Liking; Food; Liquid; Electromyography; Affect

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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