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@ARTICLE{Goto:863832,
author = {Goto, Nobuhiko and Lim, Xue Li and Shee, Dexter and Hatano,
Aya and Khong, Kok Wei and Buratto, Luciano Grüdtner and
Watabe, Motoki and Schaefer, Alexandre},
title = {{C}an {B}rain {W}aves {R}eally {T}ell {I}f a {P}roduct
{W}ill {B}e {P}urchased? {I}nferring {C}onsumer
{P}references {F}rom {S}ingle-{I}tem {B}rain {P}otentials},
journal = {Frontiers in integrative neuroscience},
volume = {13},
issn = {1662-5145},
address = {Lausanne},
publisher = {Frontiers Research Foundation},
reportid = {FZJ-2019-03812},
pages = {19},
year = {2019},
abstract = {Recent research has shown that event-related brain
potentials (ERPs) recorded while participants view lists of
different consumer goods can be modulated by their
preferences toward these products. However, it remains
largely unknown whether ERP activity specific to a single
consumer item can be informative about whether or not this
item will be preferred in a shopping context. In this study,
we examined whether single-item ERPs could reliably predict
consumer preferences toward specific consumer goods. We
recorded scalp EEG from 40 participants while they were
viewing pictures of consumer goods and we subsequently asked
them to indicate their preferences for each of these items.
Replicating previous results, we found that ERP activity
averaged over the six most preferred products was
significantly differentiated from ERP activity averaged
across the six least preferred products for three ERP
components: The N200, the late positive potential (LPP) and
positive slow waves (PSW). We also found that using
single-item ERPs to infer behavioral preferences about
specific consumer goods led to an overall predictive
accuracy of $71\%,$ although this figure varied according to
which ERPs were targeted. Later positivities such as the LPP
and PSW yielded relatively higher predictive accuracy rates
than the frontal N200. Our results suggest that ERPs related
to single consumer items can be relatively accurate
predictors of behavioral preferences depending on which type
of ERP effects are chosen by the researcher, and ultimately
on the level of prediction errors that users choose to
tolerate.},
cin = {INM-3},
ddc = {610},
cid = {I:(DE-Juel1)INM-3-20090406},
pnm = {572 - (Dys-)function and Plasticity (POF3-572)},
pid = {G:(DE-HGF)POF3-572},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:31316357},
UT = {WOS:000474661700001},
doi = {10.3389/fnint.2019.00019},
url = {https://juser.fz-juelich.de/record/863832},
}