Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task

Goto, Nobuhiko; Mushtaq, Faisal; Shee, Dexter; Lim, Xue Li; Mortazavi, Matin; Watabe, Motoki; Schaefer, Alexandre

doi:10.1016/j.biopsycho.2017.06.004

Cited by 57 publications

(86 citation statements)

References 64 publications

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“…Telpaz, Webb, & Levy () found that the N200 component, a negative wave peaking between 200 and 350 ms after stimulus onset, can differentiate between high‐ and low‐preferred goods. This finding was later replicated by an independent research team (Goto et al, ). Moreover, Gajewski, Drizinsky, Zulch, & Falkenstein () reported an enhanced fronto‐central N200 during purchase decisions regarding overpriced and underpriced items.…”

Section: Prominent Candidate Eeg Featuresmentioning

confidence: 74%

A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography‐based prediction in neuromarketing

Hakim

Levy

2018

WIRES Cognitive Science

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In the last decade, the field of consumer neuroscience, or neuromarketing, has been flourishing, with numerous publications, academic programs, initiatives, and companies. The demand for objective neural measures to quantify consumers' preferences and predict responses to marketing campaigns is ever on the rise, particularly due to the limitations of traditional marketing techniques, such as questionnaires, focus groups, and interviews. However, research has yet to converge on a unified methodology or conclusive results that can be applied in the industry. In this review, we present the potential of electroencephalography (EEG)‐based preference prediction. We summarize previous EEG research and propose features which have shown promise in capturing the consumers' evaluation process, including components acquired from an event‐related potential design, inter‐subject correlations, hemispheric asymmetry, and various spectral band powers. Next, we review the latest findings on attempts to predict preferences based on various features of the EEG signal. Finally, we conclude with several recommended guidelines for prediction. Chiefly, we stress the need to demonstrate that neural measures contribute to preference prediction beyond what traditional measures already provide. Second, prediction studies in neuromarketing should adopt the standard practices and methodology used in data science and prediction modeling that is common in other fields such as computer science and engineering. This article is categorized under: Economics > Interactive Decision‐Making Economics > Individual Decision‐Making Psychology > Prediction Neuroscience > Cognition

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Section: Prominent Candidate Eeg Featuresmentioning

confidence: 74%

A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography‐based prediction in neuromarketing

Hakim

Levy

2018

WIRES Cognitive Science

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“…This relationship appeared only with negative emotions and moderate brand extensions. A second study [43] suggested that N200 could indicate the product preferences, as determined by spontaneous procedures, whereas the LPP and PSW could indicate the product preferences, as determined by the conscious cognitive procedures. In a third study, the Cerebro system [27] combined the N200 mean, N200 minima, and ERSP to rank products according to customer preferences.…”

Section: N200mentioning

confidence: 98%

“…We classified these predictive features based on the EEG signal types: (1) rhythms; and (2) transient activities. [17] Viewing brands and products ERP P300 BCI [11] Watching TV ads EEG Alpha (for emotional state) EMG, BCI and GSR [36] Viewing brands and images ERP N200 and P300 BCI [37] Watching TV ads EEG Alpha band frontal asymmetry BCI [35] Watching TV ads EEG Theta and gamma Heart rate, BCI and GSR [28] Watching TV ads EEG Asymmetrical increase in theta and alpha in PSD BCI [38] Viewing brand names ERP N400 BCI and EOG for eye movement [18] Viewing products and prices ERP FN400, LPC, and P200 BCI [39] Viewing products EEG Alpha, beta, theta, gamma, and delta BCI, Eye tracking [40] Viewing products ERP P300 BCI [41] Watching TV ads EEG Theta and alpha Heart rate, BCI and GSR [3] Viewing products ERSP and ERP Theta, N200, and FRN BCI [24] Watching ads (movie trailers) EEG (64 electrodes) Beta and gamma oscillations BCI and EOG for eye movement (2 electrode) [20] Watching TV ads dense-array EEG Three epochs: 200-350, 350-500, and 500-800 BCI [42] Viewing brand names ERP LPP BCI [43] Viewing product images ERP N200, LPP, and PSW BCI [30] Watching ad videos EEG Theta and alpha Heart rate, BCI and GSR [22] Viewing product images EEG Delta, theta, alpha, beta, and gamma BCI [29] Viewing product images EEG Alpha BCI [44] Viewing ads of food products EEG Delta, theta, alpha, beta, and gamma BCI [32] Viewing products and prices EEG Theta BCI [31] Tasting drinks EEG Alpha BCI [33] Viewing and touching products EEG Alpha and theta BCI [27] Viewing product images ERSP, ERP Theta, beta and N200 BCI [45] Viewing tourism images, videos and words EEG Delta, theta, alpha, beta and gamma BCI and GSR…”

Section: Predictive Features For the Preferencesmentioning

confidence: 99%

Deep Learning for EEG-Based Preference Classification in Neuromarketing

2020

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The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

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“…During the experiment, participants were asked to view each bundle and determine if they would buy it or not while their scalp electroencephalogram (EEG) were recorded. According to prior literature on purchase decision making (Zhao et al, 2015 ; Goto et al, 2017 ), the late positive potential (LPP) is of particular interest to the current study.…”

Section: Introductionmentioning

confidence: 99%

“…Pozharliev et al ( 2015 ), for instance, asked the participants to passively view pictures of luxury and basic branded products and noted increased LPP amplitude for luxury goods in the social context. Moreover, Goto et al ( 2017 ) designed a virtual shopping task which revealed a positive relationship between LPP amplitude and subjective preferences of products. Consequently, LPP could reflect preferences based on more elaborative and conscious cognitive processes (Goto et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

The Temptation of Zero Price: Event-Related Potentials Evidence of How Price Framing Influences the Purchase of Bundles

Zhang

et al. 2018

Front. Neurosci.

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Studies have revealed that consumers are susceptible to price framing effect, a common cognitive bias, due to their limited capacity in processing information. The effect of price framing in a bundling context and its neural correlates, however, remain not clearly characterized. The present study applied the event-related potentials (ERPs) approach to investigate the role of price framing in information processing and purchase decision making in a bundling context. Three price frames were created with practically identical total prices (with a maximum difference of ¥0.1, which was about equal to 0.016 US dollars) for a bundle with two components, a focal product and a tie-in product. In normal price condition (NP), both the focal and tie-in products were offered at a normal discounted price; in zero price condition (ZP), the tie-in product was offered free while the total price of the bundle remained the same as NP; whereas in low price condition (LP), the tie-in product was offered at a low token price (¥0.1), and the focal product shared the same price as the focal product of ZP. The behavioral results showed a higher purchase rate and a shorter reaction time for ZP in contrast to NP. Neurophysiologically, enlarged LPP amplitude was elicited by ZP relative to NP, suggesting that ZP triggered a stronger positive affect that could motivate decision to buy. Thus, this study provides both behavioral and neural evidence for how different price framing information is processed and ultimately gives rise to price framing effect in purchase decision making.

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Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task

Cited by 57 publications

References 64 publications

A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography‐based prediction in neuromarketing

A gateway to consumers' minds: Achievements, caveats, and prospects of electroencephalography‐based prediction in neuromarketing

Deep Learning for EEG-Based Preference Classification in Neuromarketing

The Temptation of Zero Price: Event-Related Potentials Evidence of How Price Framing Influences the Purchase of Bundles

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