Established liked versus disliked brands: Brain activity, implicit associations and explicit responses

Bosshard, Shannon; Bourke, Jesse D.; Kunaharan, Sajeev; Koller, Monika; Walla, Peter

doi:10.1080/23311908.2016.1176691

Cited by 52 publications

(57 citation statements)

References 65 publications

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“…The procedures used in the two studies are too different to draw definitive conclusions about this potential contradiction, but it could be speculated that the possibility of buying products in the current study may have enhanced the motivational engagement towards preferred products, which could potentially explain this difference. This result is also consistent with a study that found a greater LPP for written words referring to brand names that were liked (Bosshard et al, 2016) and a study that found a greater positivity between the 400-1000 latencies while participants were shown cheap vs. expensive prices during a virtual shopping task (Schaefer et al, 2016).…”

Section: Discussionsupporting

confidence: 91%

“…Although these studies provide evidence in favour of the notion of the involvement of early attentional responses in consumer preferences, evidence regarding later effects is scant and contradictory. Pozharliev et alÕs (2015) study reported a relationship between the LPP and preferences for luxury goods only when participants were in a social context, and Bosshard et al (2016), although not using pictures of consumer goods, found a relationship between the LPP and the extent to which brand names were liked. In contrast to these studies, the work by Telpaz et al (2015) described above did not find effects in the LPP or in any later time window.…”

Section: Introductionmentioning

confidence: 99%

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

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Mushtaq

Shee

et al. 2017

Biological Psychology

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We investigated whether well-known neural markers of selective attention to motivationally-relevant stimuli were modulated by variations in subjective preference towards consumer goods in a virtual shopping task. Specifically, participants viewed and rated pictures of various goods on the extent to which they wanted each item, which they could potentially purchase afterwards. Using the event-related potentials (ERP) method, we found that variations in subjective preferences for consumer goods strongly modulated positive slow waves (PSW) from 800 to 3000 milliseconds after stimulus onset. We also found that subjective preferences modulated the N200 and the late positive potential (LPP). In addition, we found that both PSW and LPP were modulated by subsequent buying decisions. Overall, these findings show that well-known brain event-related potentials reflecting selective attention processes can reliably index preferences to consumer goods in a shopping environment. Based on a large body of previous research, we suggest that early ERPs (e.g. the N200) to consumer goods could be indicative of preferences driven by unconditional and automatic processes, whereas later ERPs such as the LPP and the PSW could reflect preferences built upon more elaborative and conscious cognitive processes.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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

Goto

Mushtaq

Shee

et al. 2017

Biological Psychology

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“…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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“…Preferences related to urban neighbourhoods were reported to be subject to discrepancies between conscious and non-conscious information processing [4]. Brand attitude can change non-consciously without affecting conscious processing [5][6][7] and emotion pictures are processed differently depending on conscious or non-conscious measures being taken [8,9]. SRM became a highly appreciated tool to quantify raw affective responses in the absence of any awareness and it got introduced to various different disciplines including clinical and industrial areas, even marketing [10][11][12][13][14][15].…”

mentioning

confidence: 99%

Editorial: Sub- and Unconscious Information Processing in the Human Brain

Walla

2018

Applied Sciences

Self Cite

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This Special Issue about sub-and unconscious information processing in the human brain finally became a collection of 6 accepted contributions, 2 articles, 2 reviews, 1 opinion and 1 concept paper. Even though more papers were submitted this rather small number of accepted contributions mirrors the still existing lack of focus on non-conscious human brain processes that surely influence human behavior to a much larger extent than one would ever imagine. Our brains contain evolutionary old neural structures that much more primitive organisms have in their brains too and crucially those old structures have more or the less the same functional properties regardless of what brain they belong to. Consciousness as an individual experience is a rather young evolutionary product, which means that those older structures that are strongly involved in the generation of human behavior work largely without being associated with conscious experience. This explains why people not always do what they say. In other words, the brain knows more than it admits to consciousness and since we have more and more access to that knowledge we should be interested in gaining it. The evidence grows that demonstrates how non-conscious processing occurs and influences our decision making.

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Established liked versus disliked brands: Brain activity, implicit associations and explicit responses

Cited by 52 publications

References 65 publications

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

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

Deep Learning for EEG-Based Preference Classification in Neuromarketing

Editorial: Sub- and Unconscious Information Processing in the Human Brain

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