Multiple “buy buttons” in the brain: Forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI

Neural correlates of chocolate brand preference: A functional MRI study

BACKGROUND AND PURPOSE

Preferences can be developed for, or against, specific brands and services. Using two functional magnetic resonance imaging (fMRI) experiments, this study investigated two dissociable aspects of reward processing, craving and liking, in chocolate lovers. The goal was to further delineate the neural basis supporting branding effects using familiar chocolate (FC) and unfamiliar chocolate (UC) brand images.

METHODS

In the first experiment, subjects rated their subjective craving and liking on a scale of 1-5 (weak-strong) for each FC and UC image. In the second experiment, they performed a choice task between FC and UC images.

RESULTS

Both the craving and liking ratings were significantly greater for FC and were differentially correlated with choice behavior. Craving ratings predicted greater preference for UC, and liking ratings predicted greater preference for FC. A contrast of neural activity for UC versus FC choice trials revealed significantly greater activation for UC choices in the bilateral inferior frontal gyrus and right caudate head. Response times for the FC images were faster than UC images; fMRI activity in the ventromedial prefrontal cortex was significantly correlated with response times during FC trials, but not UC trials. These correlations were significantly different from each other at the group level.

CONCLUSIONS

The choices for branded chocolate products are driven by higher subjective reward ratings and lower neural processing demands.

Consistent movement of viewers' facial keypoints while watching emotionally evocative videos

Neuropsychological research aims to unravel how diverse individuals' brains exhibit similar functionality when exposed to the same stimuli. The evocation of consistent responses when different subjects watch the same emotionally evocative stimulus has been observed through modalities like fMRI, EEG, physiological signals and facial expressions. We refer to the quantification of these shared consistent signals across subjects at each time instant across the temporal dimension as Consistent Response Measurement (CRM). CRM is widely explored through fMRI, occasionally with EEG, physiological signals and facial expressions using metrics like Inter-Subject Correlation (ISC). However, fMRI tools are expensive and constrained, while EEG and physiological signals are prone to facial artifacts and environmental conditions (such as temperature, humidity, and health condition of subjects). In this research, facial expression videos are used as a cost-effective and flexible alternative for CRM, minimally affected by external conditions. By employing computer vision-based automated facial keypoint tracking, a new metric similar to ISC, called the Average t-statistic, is introduced. Unlike existing facial expression-based methodologies that measure CRM of secondary indicators like inferred emotions, keypoint, and ICA-based features, the Average t-statistic is closely associated with the direct measurement of consistent facial muscle movement using the Facial Action Coding System (FACS). This is evidenced in DISFA dataset where the time-series of Average t-statistic has a high correlation (R2 = 0.78) with a metric called AU consistency, which directly measures facial muscle movement through FACS coding of video frames. The simplicity of recording facial expressions with the automated Average t-statistic expands the applications of CRM such as measuring engagement in online learning, customer interactions, etc., and diagnosing outliers in healthcare conditions like stroke, autism, depression, etc. To promote further research, we have made the code repository publicly available.

Food and non-alcoholic beverage marketing in children and adults: A systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies

Food marketing impacts the food behaviors of children and adults, but the underpinning neural mechanisms are poorly understood. This systematic review and meta-analysis pooled evidence from neuroimaging studies of exposure to food marketing stimuli (vs. control) on brain activations in children and adults to clarify regions associated with responding. Databases were searched for articles published to March 2022. Inclusion criteria included human functional magnetic resonance imaging (fMRI) studies employing a contrast between a food marketing stimulus and a non-food/non-exposure control, published in English in a peer-reviewed journal, reporting whole brain (not Region of Interest [ROI] only) co-ordinates. Eleven studies met inclusion criteria, of which eight were included in the quantitative synthesis (Activation Likelihood Estimation [ALE] meta-analysis). Food marketing exposures (vs. controls) produced greater activation in two clusters lying across the middle occipital gyrus, lingual gyrus, and cuneus (cluster 1), and the postcentral gyrus, precentral gyrus, and the inferior parietal lobule/supramarginal gyrus (cluster 2). Brain responses to food marketing are most consistently observed in areas relating to visual processing, attention, sensorimotor activity, and emotional processing. Subgroup analyses (e.g., adults vs. children) were not possible because of the paucity of data, and sensitivity analyses highlighted some instability in the clusters; therefore, conclusions remain tentative pending further research.

Implicit IAT Measures and Neurophysiological fNIRS Markers in Response to High-Engagement Advertising

Self-report measures partially explain consumers' purchasing choices, which are inextricably linked to cognitive, affective processes and implicit drives. These aspects, which occur outside of awareness and tacitly affect the way consumers make decisions, could be explored by exploiting neuroscientific technology. The study investigates implicit behavioural and neurovascular responses to emotionally arousing and high-engagement advertisements (COVID-19 content). High-engagement advertisements and control stimuli were shown in two experimental sessions that were counterbalanced across participants. During each session, hemodynamic variations were recorded with functional Near-Infrared Spectroscopy (fNIRS) of the prefrontal cortex (PFC), a neurophysiological marker for emotional processing. The implicit association task (IAT) was administered to investigate the implicit attitude. An increase in the concentration of oxygenated haemoglobin (O2Hb) was found for the high-engagement advertising when this category of stimuli was seen first. Specular results were found for deoxygenated haemoglobin (HHb) data. The IAT reported higher values for highly engaging stimuli. Increased activity within the PFC suggests that highly engaging content may be effective in generating emotional arousal and increasing attention when presented before other stimuli, which is consistent with the higher IAT scores, indicating more favourable implicit attitudes. This evidence suggests that the effectiveness of highly engaging advertising-related messages may be constrained by the order of advertisement administration.

Central EEG Beta/Alpha Ratio Predicts the Population-Wide Efficiency of Advertisements

Recent studies have demonstrated that the brain activity of a group of people can be used to forecast choices at the population level. In this study, we attempted to neuroforecast aggregate consumer behavior of Internet users. During our electroencephalography (EEG) and eye-tracking study, participants were exposed to 10 banners that were also used in the real digital marketing campaign. In the separate online study, we additionally collected self-reported preferences for the same banners. We explored the relationship between the EEG, eye-tracking, and behavioral indexes obtained in our studies and the banners' aggregate efficiency provided by the large food retailer based on the decisions of 291,301 Internet users. An EEG-based engagement index (central beta/alpha ratio) significantly correlated with the aggregate efficiency of banners. Furthermore, our multiple linear regression models showed that a combination of eye-tracking, EEG and behavioral measurements better explained the market-level efficiency of banner advertisements than each measurement alone. Overall, our results confirm that neural signals of a relatively small number of individuals can forecast aggregate behavior at the population level.

Neural correlates of food labels on brand, nature, and nutrition: An fMRI meta-analysis

Eating is an essential act of our everyday life, and it involves complicated cognitive appraisal and gustatory evaluation. This study meta-analyzed the functional magnetic resonance imaging (fMRI) studies about food labels on brand, nature and nutrition. Web of Science Core Collection (WoS), Scopus, and PubMed were queried to identify human fMRI studies written in English and published in peer-reviewed journals and used taste or food related labels. Studies were excluded if they reported no results from taste/food related stimuli versus control, no task-based fMRI results, or no results from whole-brain analysis. Nineteen studies entered the analysis. Results for the meta-analysis on food nutrition revealed that the precuneus on the right hemisphere was significantly activated, a brain region related to internal mentation of self-consciousness and nutritional evaluation. Results for the overall analysis on all 19 studies, the analysis on food brand, and the analysis on food nature revealed no significant brain regions. Food nutrition labels were generally processed by brain regions related to internal mentation of self-consciousness and nutritional evaluation. However, the neural correlates of labels of food brand and food nature were inconsistent across studies. More future studies are needed to better understand the cognitive processing of different kinds of food labels in our brain.

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

Introduction

The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking.

Methods

Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review.

Results

Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour.

Conclusions and implications

FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

Human’s Intuitive Mental Models as a Source of Realistic Artificial Intelligence and Engineering

Despite the success of artificial intelligence (AI), we are still far away from AI that model the world as humans do. This study focuses for explaining human behavior from intuitive mental models’ perspectives. We describe how behavior arises in biological systems and how the better understanding of this biological system can lead to advances in the development of human-like AI. Human can build intuitive models from physical, social, and cultural situations. In addition, we follow Bayesian inference to combine intuitive models and new information to make decisions. We should build similar intuitive models and Bayesian algorithms for the new AI. We suggest that the probability calculation in Bayesian sense is sensitive to semantic properties of the objects’ combination formed by observation and prior experience. We call this brain process as computational meaningfulness and it is closer to the Bayesian ideal, when the occurrence of probabilities of these objects are believable. How does the human brain form models of the world and apply these models in its behavior? We outline the answers from three perspectives. First, intuitive models support an individual to use information meaningful ways in a current context. Second, neuroeconomics proposes that the valuation network in the brain has essential role in human decision making. It combines psychological, economical, and neuroscientific approaches to reveal the biological mechanisms by which decisions are made. Then, the brain is an over-parameterized modeling organ and produces optimal behavior in a complex word. Finally, a progress in data analysis techniques in AI has allowed us to decipher how the human brain valuates different options in complex situations. By combining big datasets with machine learning models, it is possible to gain insight from complex neural data beyond what was possible before. We describe these solutions by reviewing the current research from this perspective. In this study, we outline the basic aspects for human-like AI and we discuss on how science can benefit from AI. The better we understand human’s brain mechanisms, the better we can apply this understanding for building new AI. Both development of AI and understanding of human behavior go hand in hand.

Visual communication via the design of food and beverage packaging

A rapidly growing body of empirical research has recently started to emerge highlighting the connotative and/or semiotic meanings that consumers typically associate with specific abstract visual design features, such as colours (either when presented individually or in combination), simple shapes/curvilinearity, and the orientation and relative position of those design elements on product packaging. While certain of our affective responses to such basic visual design features appear almost innate, the majority are likely established via the internalization of the statistical regularities of the food and beverage marketplace (i.e. as a result of associative learning), as in the case of round typeface and sweet-tasting products. Researchers continue to document the wide range of crossmodal correspondences that underpin the links between individual visual packaging design features and specific properties of food and drink products (such as their taste, flavour, or healthfulness), and the ways in which marketers are now capitalizing on such understanding to increase sales. This narrative review highlights the further research that is still needed to establish the connotative or symbolic/semiotic meaning(s) of particular combinations of design features (such as coloured stripes in a specific orientation), as opposed to individual cues in national food markets and also, increasingly, cross-culturally in the case of international brands.

Decision neuroscience and neuroeconomics: Recent progress and ongoing challenges

In the past decade, decision neuroscience and neuroeconomics have developed many new insights in the study of decision making. This review provides an overarching update on how the field has advanced in this time period. Although our initial review a decade ago outlined several theoretical, conceptual, methodological, empirical, and practical challenges, there has only been limited progress in resolving these challenges. We summarize significant trends in decision neuroscience through the lens of the challenges outlined for the field and review examples where the field has had significant, direct, and applicable impacts across economics and psychology. First, we review progress on topics including reward learning, explore-exploit decisions, risk and ambiguity, intertemporal choice, and valuation. Next, we assess the impacts of emotion, social rewards, and social context on decision making. Then, we follow up with how individual differences impact choices and new exciting developments in the prediction and neuroforecasting of future decisions. Finally, we consider how trends in decision-neuroscience research reflect progress toward resolving past challenges, discuss new and exciting applications of recent research, and identify new challenges for the field. This article is categorized under: Psychology > Reasoning and Decision Making Psychology > Emotion and Motivation.

How Does the Implicit Awareness of Consumers Influence the Effectiveness of Public Service Announcements? A Functional Near-Infrared Spectroscopy Study

A large number of scholars have conducted detailed studies on the effectiveness of commercial advertising by using neuroimaging methods, but only a few scholars have used this method to study the effectiveness of public service announcements (PSAs). To research the relationship between the effectiveness of PSAs and the audience’s implicit awareness, functional near-infrared spectroscopy (fNIRS) was employed to record the neural activity data of participants in this study. The results showed that there was a correlation between activation of dorsolateral prefrontal cortex (dlPFC) and the effectiveness of PSAs; The activation of the dlPFC could also be used as an indicator to represent the appeal of advertising content. The results means that neuroimaging tool can also be used to investigate the effectiveness of PSAs, not just commercial advertisements and a few PSAs study, and that neural activity can predict and improve the effectiveness of PSAs before they are released.

Recent updates of eye movement abnormalities in patients with schizophrenia: A scoping review

AIM

Although eye-tracking technology expands beyond capturing eye data just for the sole purpose of ensuring participants maintain their gaze at the presented fixation cross, gaze technology remains of less importance in clinical research. Recently, impairments in visual information encoding processes indexed by novel gaze metrics have been frequently reported in patients with schizophrenia. This work undertakes a scoping review of research on saccadic dysfunctions and exploratory eye movement deficits among patients with schizophrenia. It gathers promising pieces of evidence of eye movement abnormalities in attention-demanding tasks on the schizophrenia spectrum that have mounted in recent years and their outcomes as potential biological markers.

METHODS

The protocol was drafted based on PRISMA for scoping review guidelines. Electronic databases were systematically searched to identify articles published between 2010 and 2020 that examined visual processing in patients with schizophrenia and reported eye movement characteristics as potential biomarkers for this mental illness.

RESULTS

The use of modern eye-tracking instrumentation has been reported by numerous neuroscientific studies to successfully and non-invasively improve the detection of visual information processing impairments among the screened population at risk of and identified with schizophrenia.

CONCLUSIONS

Eye-tracking technology has the potential to contribute to the process of early intervention and more apparent separation of the diagnostic entities, being put together by the syndrome-based approach to the diagnosis of schizophrenia. However, context-processing paradigms should be conducted and reported in equally accessible publications to build comprehensive models.

Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.

Measuring dlPFC Signals to Predict the Success of Merchandising Elements at the Point-of-Sale - A fNIRS Approach

The (re-)launch of products is frequently accompanied by point-of-sale (PoS) marketing campaigns in order to foster sales. Predicting the success of these merchandising elements at the PoS on sales is of interest to research and practice, as the misinvestments that are based on the fragmented PoS literature are tremendous. Likewise, the predictive power of neuropsychological methods has been demonstrated in various research work. Nevertheless, the practical application of these neuropsychological methods is still limited. In order to foster the application of neuropsychological methods in research and practice, the current research work aims to explore, whether mobile functional near-infrared spectroscopy (fNIRS) - as a portable neuroimaging method - has the potential to predict the success of PoS merchandising elements by rendering significant neural signatures of brain regions of the dorsolateral prefrontal cortex (dlPFC), highlighting its potential to forecast shoppers' behaviour aka sales at the PoS. Building on previous research findings, the results of the given research work indicate that the neural signal of brain regions of the dlPFC, measured with mobile fNIRS, is able to predict actual sales associated with PoS merchandising elements, relying on the cortical relief effect. More precisely, the research findings support the hypothesis that the reduced neural activity of brain regions associated with the dlPFC can predict sales at the PoS, emphasising another crucial neural signature to predict shoppers' purchase behaviour, next to the frequently cited reward association system. The research findings offer an innovative perspective on how to design and evaluate PoS merchandising elements, indicating fruitful theoretical and practical implications.

EEG distinguishes heroic narratives in ISIS online video propaganda

The Islamic State (ISIS) was uniquely effective among extremist groups in the Middle East at recruiting Westerners. A major way ISIS accomplished this was by adopting Hollywood-style narrative structures for their propaganda videos. In particular, ISIS utilized a heroic martyr narrative, which focuses on an individual’s personal glory and empowerment, in addition to traditional social martyr narratives, which emphasize duty to kindred and religion. The current work presented adult participants (n = 238) video clips from ISIS propaganda which utilized either heroic or social martyr narratives and collected behavioral measures of appeal, narrative transportation, and psychological dispositions (egoism and empathy) associated with attraction to terrorism. Narrative transportation and the interaction between egoism and empathy predicted video recruitment appeal. A subset of adults (n = 80) underwent electroencephalographic (EEG) measurements while watching a subset of the video-clips. Complementary univariate and multivariate techniques characterized spectral power density differences when perceiving the different types of narratives. Heroic videos show increased beta power over frontal sites, and globally increased alpha. In contrast, social narratives showed greater frontal theta, an index of negative feedback and emotion regulation. The results provide strong evidence that ISIS heroic narratives are specifically processed, and appeal to psychological predispositions distinctly from other recruitment narratives.

Data Triangulation in Consumer Neuroscience: Integrating Functional Neuroimaging With Meta-Analyses, Psychometrics, and Behavioral Data

This article reviews a wide range of functional magnetic resonance imaging (fMRI) studies conducted in the field of consumer neuroscience to (1) highlight common interpretative approaches of neuroimaging data (i.e., forward inference and reverse inference), (2) discuss potential interpretative issues associated with these approaches, and (3) provide a framework that employs a multi-method approach aimed to possibly raise the explanatory power and, thus, the validity of functional neuroimaging research in consumer neuroscience. Based on this framework, we argue that the validity of fMRI studies can be improved by the triangulation of (1) careful design of neuroimaging studies and analyses of data, (2) meta-analyses, and (3) the integration of psychometric and behavioral data with neuroimaging data. Guidelines on when and how to employ triangulation methods on neuroimaging data are included. Moreover, we also included discussions on practices and research directions that validate fMRI studies in consumer neuroscience beyond data triangulation.

A Multisensor Data Fusion Approach for Predicting Consumer Acceptance of Food Products

Sensory experiences play an important role in consumer response, purchase decision, and fidelity towards food products. Consumer studies when launching new food products must incorporate physiological response assessment to be more precise and, thus, increase their chances of success in the market. This paper introduces a novel sensory analysis system that incorporates facial emotion recognition (FER), galvanic skin response (GSR), and cardiac pulse to determine consumer acceptance of food samples. Taste and smell experiments were conducted with 120 participants recording facial images, biometric signals, and reported liking when trying a set of pleasant and unpleasant flavors and odors. Data fusion and analysis by machine learning models allow predicting the acceptance elicited by the samples. Results confirm that FER alone is not sufficient to determine consumers' acceptance. However, when combined with GSR and, to a lesser extent, with pulse signals, acceptance prediction can be improved. This research targets predicting consumer's acceptance without the continuous use of liking scores. In addition, the findings of this work may be used to explore the relationships between facial expressions and physiological reactions for non-rational decision-making when interacting with new food products.

Brain activity forecasts video engagement in an internet attention market

People currently spend over a billion of hours a day watching internet video content. To understand why, we combined neuroimaging with a behavioral video viewing task that simulated an internet attention market (i.e., youtube.com). While brain activity at video onset (increased nucleus accumbens [NAcc] and medial prefrontal cortex but decreased anterior insula [AIns]) predicted individuals’ choices to start and stop viewing, only activity in a subset of these regions implicated in anticipatory affect (increased NAcc and decreased AIns) at video onset forecasts aggregate video view frequency and duration on the internet. These findings suggest that brain activity can reveal “hidden” information capable of forecasting video engagement in attention markets.

The growth of the internet has spawned new “attention markets,” in which people devote increasing amounts of time to consuming online content, but the neurobehavioral mechanisms that drive engagement in these markets have yet to be elucidated. We used functional MRI (FMRI) to examine whether individuals’ neural responses to videos could predict their choices to start and stop watching videos as well as whether group brain activity could forecast aggregate video view frequency and duration out of sample on the internet (i.e., on youtube.com). Brain activity during video onset predicted individual choice in several regions (i.e., increased activity in the nucleus accumbens [NAcc] and medial prefrontal cortex [MPFC] as well as decreased activity in the anterior insula [AIns]). Group activity during video onset in only a subset of these regions, however, forecasted both aggregate view frequency and duration (i.e., increased NAcc and decreased AIns)—and did so above and beyond conventional measures. These findings extend neuroforecasting theory and tools by revealing that activity in brain regions implicated in anticipatory affect at the onset of video viewing (but not initial choice) can forecast time allocation out of sample in an internet attention market.

Journey of neuroscience: marketing management to organizational behavior

Purpose

The purpose of this paper is to provide an overview of the advancement of neuroscience research works in the domains of marketing management and organizational behavior and its future scope for expansion in the area of organizational behavior.

Design/methodology/approach

A total of 77 neuroscience research articles in the area of marketing management and organizational behavior published between 2004 and 2017 were reviewed, and a possible future direction for neuroscience research in the area of organizational behavior was identified in this article.

Findings

Findings from neuroscience research works suggest that tools and techniques that are useful in the neuroscience domain are also quite powerful and reliable in the context of organizational behavior research. Here, it should be noted that not all of these are independently powerful. Therefore, in certain cases, it is desirable to use neuroscience techniques in association with existing methods.

Originality/value

Neuroscientific research works in the context of the marketing domain were started with the motivation to identify the neural signaling in association with different marketing initiatives. However, the research works have proceeded much deeper and entered into the field of consumer psychology. Further research shows that neuroscience techniques are quite useful in the understanding of consumer behavior and can be extended in the field of organizational behavior. In this study, the authors have provided the future direction of neuroscience research works in the area of organizational behavior.

Consumer Behaviour through the Eyes of Neurophysiological Measures: State-of-the-Art and Future Trends

The new technological advances achieved during the last decade allowed the scientific community to investigate and employ neurophysiological measures not only for research purposes but also for the study of human behaviour in real and daily life situations. The aim of this review is to understand how and whether neuroscientific technologies can be effectively employed to better understand the human behaviour in real decision-making contexts. To do so, firstly, we will describe the historical development of neuromarketing and its main applications in assessing the sensory perceptions of some marketing and advertising stimuli. Then, we will describe the main neuroscientific tools available for such kind of investigations (e.g., measuring the cerebral electrical or hemodynamic activity, the eye movements, and the psychometric responses). Also, this review will present different brain measurement techniques, along with their pros and cons, and the main cerebral indexes linked to the specific mental states of interest (used in most of the neuromarketing research). Such indexes have been supported by adequate validations from the scientific community and are largely employed in neuromarketing research. This review will also discuss a series of papers that present different neuromarketing applications, such us in-store choices and retail, services, pricing, brand perception, web usability, neuropolitics, evaluation of the food and wine taste, and aesthetic perception of artworks. Furthermore, this work will face the ethical issues arisen on the use of these tools for the evaluation of the human behaviour during decision-making tasks. In conclusion, the main challenges that neuromarketing is going to face, as well as future directions and possible scenarios that could be derived by the use of neuroscience in the marketing field, will be identified and discussed.

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

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.

Time-evolving dynamics in brain networks forecast responses to health messaging

Neuroimaging measures have been used to forecast complex behaviors, including how individuals change decisions about their health in response to persuasive communications, but have rarely incorporated metrics of brain network dynamics. How do functional dynamics within and between brain networks relate to the processes of persuasion and behavior change? To address this question, we scanned 45 adult smokers by using functional magnetic resonance imaging while they viewed anti-smoking images. Participants reported their smoking behavior and intentions to quit smoking before the scan and 1 month later. We focused on regions within four atlas-defined networks and examined whether they formed consistent network communities during this task (measured as allegiance). Smokers who showed reduced allegiance among regions within the default mode and fronto-parietal networks also demonstrated larger increases in their intentions to quit smoking 1 month later. We further examined dynamics of the ventromedial prefrontal cortex (vmPFC), as activation in this region has been frequently related to behavior change. The degree to which vmPFC changed its community assignment over time (measured as flexibility) was positively associated with smoking reduction. These data highlight the value in considering brain network dynamics for understanding message effectiveness and social processes more broadly.

The Application of Mobile fNIRS in Marketing Research-Detecting the "First-Choice-Brand" Effect

Recent research in the field of "neuro-marketing" shows promise to substantially increase knowledge on marketing issues for example price-perception, advertising efficiency, branding and shopper behaviour. Recently, an innovative and mobile applicable neuroimaging method has been proposed, namely functional near-infrared spectroscopy (fNIRS). However, this method is, in the research field of marketing, still in its infancy and is, consequently, lacking substantial validity. Against this background, this research work applied a convergent validity approach to challenge the validity of (mobile) fNIRS in the field of "neuro-marketing" and consumer neuroscience. More precisely, we aim to replicate a robust and well-investigated neural effect previously detected with fMRI-namely the "first-choice-brand" effect-by using mobile fNIRS. The research findings show that mobile fNIRS appears to be an appropriate neuroimaging method for research in the field of "neuro-marketing" and consumer neuroscience. Additionally, this research work presents guidelines, enabling marketing scholars to utilise mobile fNIRS in their research work.

Beyond Self-Report: A Review of Physiological and Neuroscientific Methods to Investigate Consumer Behavior

The current paper investigates the value and application of a range of physiological and neuroscientific techniques in applied marketing research and consumer science, highlighting new insights from research in social psychology and neuroscience. We review measures of sweat secretion, heart rate, facial muscle activity, eye movements, and electrical brain activity, using techniques including skin conductance, pupillometry, eyetracking, and magnetic brain imaging. For each measure, after a brief explanation of the underlying technique, we illustrate concepts and mechanisms that the measure allows researchers in marketing and consumer science to investigate, with a focus on consumer attitudes and behavior. By providing reviews on recent research that applied these methods in consumer science and relevant related fields, we also highlight methodological and theoretical strengths and limitations, with an emphasis on ecological validity. We argue that the inclusion of physiological and neuroscientific techniques can advance consumer research by providing insights into the often unconscious mechanisms underlying consumer behavior. Therefore, such technologies can help researchers and marketing practitioners understand the mechanisms of consumer behavior and improve predictions of consumer behavior.

Neuroforecasting Aggregate Choice

Advances in brain-imaging design and analysis have allowed investigators to use neural activity to predict individual choice, while emerging Internet markets have opened up new opportunities for forecasting aggregate choice. Here, we review emerging research that bridges these levels of analysis by attempting to use group neural activity to forecast aggregate choice. A survey of initial findings suggests that components of group neural activity might forecast aggregate choice, in some cases even beyond traditional behavioral measures. In addition to demonstrating the plausibility of neuroforecasting, these findings raise the possibility that not all neural processes that predict individual choice forecast aggregate choice to the same degree. We propose that although integrative choice components may confer more consistency within individuals, affective choice components may generalize more broadly across individuals to forecast aggregate choice.

Associations between coherent neural activity in the brain's value system during antismoking messages and reductions in smoking

OBJECTIVE

Worldwide, tobacco use is the leading cause of preventable death and illness. One common strategy for reducing the prevalence of cigarette smoking and other health risk behaviors is the use of graphic warning labels (GWLs). This has led to widespread interest from the perspective of health psychology in understanding the mechanisms of GWL effectiveness. Here we investigated differences in how the brain responds to negative, graphic warning label-inspired antismoking ads and neutral control ads, and we probed how this response related to future behavior.

METHOD

A group of smokers (N = 45) viewed GWL-inspired and control antismoking ads while undergoing fMRI, and their smoking behavior was assessed before and one month after the scan. We examined neural coherence between two regions in the brain's valuation network, the medial prefrontal cortex (MPFC) and ventral striatum (VS).

RESULTS

We found that greater neural coherence in the brain's valuation network during GWL ads (relative to control ads) preceded later smoking reduction.

CONCLUSIONS

Our results suggest that the integration of information about message value may be key for message influence. Understanding how the brain responds to health messaging and relates to future behavior could ultimately contribute to the design of effective messaging campaigns, as well as more broadly to theories of message effects and persuasion across domains. (PsycINFO Database Record

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.

A Hitchhiker's Guide to Functional Magnetic Resonance Imaging

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular both with clinicians and researchers as they are capable of providing unique insights into brain functions. However, multiple technical considerations (ranging from specifics of paradigm design to imaging artifacts, complex protocol definition, and multitude of processing and methods of analysis, as well as intrinsic methodological limitations) must be considered and addressed in order to optimize fMRI analysis and to arrive at the most accurate and grounded interpretation of the data. In practice, the researcher/clinician must choose, from many available options, the most suitable software tool for each stage of the fMRI analysis pipeline. Herein we provide a straightforward guide designed to address, for each of the major stages, the techniques, and tools involved in the process. We have developed this guide both to help those new to the technique to overcome the most critical difficulties in its use, as well as to serve as a resource for the neuroimaging community.