Consumer nueroscience: a new area of study for biomedical engineers

Sonic Influence on Initially Neutral Brands: Using EEG to Unveil the Secrets of Audio Evaluative Conditioning

The present study addresses the question of whether explicit, survey-type measures of attitude differ in sensitivity when compared to implicit, non-conscious measures of attitude in the context of attitude changes in response to evaluative conditioning (EC). In the frame of a pre-test, participants rated 300 brand names on a Likert-type scale, the results of which were then used to create personalised lists of neutral brands. After this initial online component, the participants were exposed to one, five, and ten rounds of EC (during three separate sessions), during which half of the brands were paired with pleasant audio excerpts (positive EC) and the remainder were paired with unpleasant audio excerpts (negative EC). Following each conditioning round, the participants rated the brand names again, whilst changes in the brain's electrical activity in response to the brands were recorded via electroencephalography (EEG). After having rated the brand names, the participants also completed two implicit association tests (IAT; one for each of the neutral conditions). The results revealed that self-reported, explicit responses of brand names remained unchanged despite having been conditioned. Similarly, the IAT did not reveal any declines in reaction time. In contrast, the EEG data appeared to not only be sensitive to initial brand ratings, but also the conditioning effects of initially neutral brands. Respective neurophysiological effects were found at frontal electrode locations AF3 and AF4 for a 1 s-long time window starting at 400 ms after stimulus onset. Furthermore, the EEG revealed that changes in brand attitude are more susceptible to the effects of negative conditioning than positive conditioning. Given the rather small sample size, any generalizability seems vague, but the present results provide scientific evidence that EEG could indeed be a valuable additional method to investigate EC effects. The results of this study support the notion of utilising a multidimensional approach, inclusive of neuroscience, to understanding consumer attitudes instead of solely relying on self-report measures. In the end, the brain knows more than it admits to consciousness and language, which is why objective methods should always be included in any study.

Can Evaluative Conditioning Change Well-Established Attitudes Towards Popular Brands? Your Brain Says Yes Even Though Your Mouth Says No

In the present study, using both implicit and explicit measures, we addressed the issue of whether strongly developed relationships towards brands could be modified through the use of evaluative conditioning. Using an online survey, individual participant brand lists were created, and formed the basis of this experiment. Participants were then exposed to conditioning during a longitudinal study. Throughout the experiment, a combination of explicit and implicit measures was used to assess changes in attitude. Specifically, participants were asked to rate the brand names on a Likert-type scale. Simultaneously, changes in the brains electrical activity in response to the brands were recorded via electroencephalography (EEG). Upon completion of this task, participants underwent two Implicit Association Tests (IAT; one for liked brands and one for disliked brands). There were two main findings of this study. Firstly, no significant changes in attitude were observed via the use of explicit measures, and those that were found relating to the IAT were regarded as questionable. Secondly, EEG presented consistent results which showed that conditioning elicited changes in cortical activity towards both liked and disliked brands, which suggest it may be a useful tool in measuring the impact of evaluative conditioning that is not reflected in verbal responses.

Exploration of Web-Sites Affects Autonomic Responses Related to Unconscious Emotions

In this work we are interested in analyzing any correlations between physiological parameters, extracted from signals such as Electrocardiogram, respiratory signal and Skin Conductance, and self-reported indices related to emotional or cognitive stimulations. For this purpose, an experiment involving twenty participants with a mean age of 25±5 years of both sexes (13 males and 7 females) was carried out. The protocol included the navigation in simulated web-sites and the vision of two different commercial products (utilitarian and hedonistic). At the end of the navigation, a questionnaire was submitted to the subject in order to measure his/her feelings and emotions in a qualitative and subjective way. Quantitative features were extracted from the physiological signals recorded during the execution of the protocol. We performed a correlation analysis between self-reported and physiological responses related to Arousal, Pleasure, Expectancy and Situational Involvement. Findings showed that when a consumer is exposed to a utilitarian product, the physiological emotional responses are disassociated from the self-reported ones. For the hedonistic product, instead, self-reported measures significantly correlate with physiological arousal features like the combined effect of cardiac and respiratory activity and the Heart Rate.

Consumer Neuroscience-Based Metrics Predict Recall, Liking and Viewing Rates in Online Advertising

The purpose of the present study is to investigate whether the effectiveness of a new ad on digital channels (YouTube) can be predicted by using neural networks and neuroscience-based metrics (brain response, heart rate variability and eye tracking). Neurophysiological records from 35 participants were exposed to 8 relevant TV Super Bowl commercials. Correlations between neurophysiological-based metrics, ad recall, ad liking, the ACE metrix score and the number of views on YouTube during a year were investigated. Our findings suggest a significant correlation between neuroscience metrics and self-reported of ad effectiveness and the direct number of views on the YouTube channel. In addition, and using an artificial neural network based on neuroscience metrics, the model classifies (82.9% of average accuracy) and estimate the number of online views (mean error of 0.199). The results highlight the validity of neuromarketing-based techniques for predicting the success of advertising responses. Practitioners can consider the proposed methodology at the design stages of advertising content, thus enhancing advertising effectiveness. The study pioneers the use of neurophysiological methods in predicting advertising success in a digital context. This is the first article that has examined whether these measures could actually be used for predicting views for advertising on YouTube.