Estimating affective taste experience using combined implicit behavioral and neurophysiological measures

A.-M. Brouwer; T.J. van den Broek; M.A. Hogervorst; D. Kaneko; A. Toet; V. Kallen; J.B.F. van Erp

doi:10.1109/TAFFC.2020.3032236

Estimating affective taste experience using combined implicit behavioral and neurophysiological measures

A.-M. Brouwer, T.J. van den Broek, M.A. Hogervorst, D. Kaneko, A. Toet, V. Kallen, J.B.F. van Erp

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Abstract

We trained a model to distinguish an extreme high arousal, unpleasant drink from regular drinks based on a range of implicit behavioral and physiological responses to naturalistic tasting. The trained model predicted arousal ratings of regular drinks, highlighting the possibility to estimate affective experience without having to rely on subjective ratings.

Original language	English
Number of pages	8
Journal	IEEE transactions on affective computing
Early online date	19 Oct 2020
DOIs	https://doi.org/10.1109/TAFFC.2020.3032236
Publication status	Published - 2020

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title = "Estimating affective taste experience using combined implicit behavioral and neurophysiological measures",

abstract = "We trained a model to distinguish an extreme high arousal, unpleasant drink from regular drinks based on a range of implicit behavioral and physiological responses to naturalistic tasting. The trained model predicted arousal ratings of regular drinks, highlighting the possibility to estimate affective experience without having to rely on subjective ratings.",

author = "A.-M. Brouwer and {van den Broek}, T.J. and M.A. Hogervorst and D. Kaneko and A. Toet and V. Kallen and {van Erp}, J.B.F.",

year = "2020",

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language = "English",

journal = "IEEE transactions on affective computing",

issn = "1949-3045",

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AU - van den Broek, T.J.

AU - Hogervorst, M.A.

AU - Kaneko, D.

AU - Toet, A.

AU - Kallen, V.

AU - van Erp, J.B.F.

PY - 2020

Y1 - 2020

N2 - We trained a model to distinguish an extreme high arousal, unpleasant drink from regular drinks based on a range of implicit behavioral and physiological responses to naturalistic tasting. The trained model predicted arousal ratings of regular drinks, highlighting the possibility to estimate affective experience without having to rely on subjective ratings.

AB - We trained a model to distinguish an extreme high arousal, unpleasant drink from regular drinks based on a range of implicit behavioral and physiological responses to naturalistic tasting. The trained model predicted arousal ratings of regular drinks, highlighting the possibility to estimate affective experience without having to rely on subjective ratings.

U2 - 10.1109/TAFFC.2020.3032236

DO - 10.1109/TAFFC.2020.3032236

M3 - Article

JO - IEEE transactions on affective computing

JF - IEEE transactions on affective computing

SN - 1949-3045

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