Skip to main content
F1000Research logoLink to F1000Research
. 2026 Aug 29;14:1132. Originally published 2025 Oct 17. [Version 4] doi: 10.12688/f1000research.168220.4

The Neuroscience of Marketing: Noninvasive Neuromarketing Methods for Understanding Consumer Choice and Priorities

Pradeep K 1,a, Sathya Prasad PV 1, Rajalakshmi S A 1,2, Chriso Ricky Gill J 3, Jisha V G 4
PMCID: PMC13579028  PMID: 42755463

Version Changes

Revised. Amendments from Version 3

This article has been updated to present a clarified and strengthened version of the work. The revisions were undertaken in response to reviewer feedback and editorial guidance, ensuring that the manuscript now reflects greater precision, rigor, and scholarly contribution. The methodology has been refined to improve clarity and accuracy, with all selection‑related queries raised during the review process carefully addressed. Reviewer‑suggested points on Ethical Governance and Neuromarketing have been integrated in detail, offering a more comprehensive and balanced perspective on these critical areas. Furthermore, emerging directions and research gaps identified by the reviewers have been freshly included, thereby expanding the scope and relevance of the study. These corrections and additions not only respond to the reviewers’ constructive suggestions but also enhance the article’s value for readers and researchers. By incorporating methodological refinements, ethical considerations, and new insights into neuromarketing, the revised version provides a stronger foundation for future inquiry and practical application. We believe this updated article represents a meaningful contribution to the field and will serve as a more reliable and insightful resource for the academic community as well as the wider public.

Abstract

Neuromarketing and consumer neuroscience aim to enhance self-report and behavioral techniques by assessing neurophysiological and psychophysiological reactions that are frequently challenging to quantify. This study consolidates non-invasive tools frequently employed to examine consumer reactions to marketing stimuli, emphasizing the metrics each method assesses, the marketing inquiries it may reliably address, and the potential dangers of inference involved. We systematically compile evidence pertaining to constructs fundamental to consumer choice, including attention, affective arousal, memory encoding, and valuation, while comparing methodologies such as EEG, fMRI, TMS, Steady State Topography and MEG. The article proposes a method selection matrix that associates common research and applied inquiries (e.g., advertising attention dynamics, packaging assessment, pricing, and value indicators) with suitable measurement strategies, highlighting trade-offs concerning temporal/spatial resolution, ecological validity, cost, and interpretability. Finally, examine methodological challenges and ethical obligations. The scoping review concludes with research gaps where multimodal and more ecologically valid designs are most likely to advance the field.

Keywords: Neuroscience of Marketing, Neuromarketing, Consumer behaviour, Marketing, non-invasive techniques

Introduction

Marketing decisions today depend on consumers’ quick, automatic responses to complex stimuli, such as short-form video ads, interactive shopping interfaces, and optimized product packaging ( Lin et al., 2018; Morin, 2011). Traditional self-reported methods, including surveys, interviews, and focus groups, are crucial for comprehending expressed beliefs and preferences. However, these methods are constrained by recall bias, demand characteristics, and consumers' limited introspective access to the cognitive and emotional processes that influence attention, emotion, and decision-making ( Pradeep et al., 2022). As a result, critical aspects of consumer decision-making often remain inaccessible through introspective methods alone.

Consumer neuroscience and its applied extension, neuromarketing, have emerged over the past two decades as a complementary framework aimed at addressing these limitations by integrating behavioural paradigms with noninvasive neurophysiological and psychophysiological measurements ( Lin et al., 2018; Clithero, Carter & Huettel, 2024). Techniques such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), eye tracking, and peripheral autonomic measures enable the examination of attention allocation, memory encoding, affective arousal, and valuation as they unfold in real-time ( Rawnaque et al., 2020). These methods are increasingly employed in marketing research because they can uncover consumer responses that frequently occur beyond conscious awareness. They offer time-sensitive and objective indicators of the underlying cognitive processes ( Mohd Isa & Anuar, 2024). Notably, neuromarketing is not intended to replace traditional methods; instead, it serves as a complementary approach that enhances the explanatory power through triangulation by integrating subjective reports, observed behaviour, and physiological responses ( Morin, 2011).

Despite its growth and increasing visibility, neuromarketing research still faces conceptual, methodological, and ethical challenges that hinder its overall impact. Previous reviews have often followed a technique-by-technique approach, providing descriptive overviews of individual tools without offering sufficient synthesis across methods or marketing-relevant constructs ( Clithero et al., 2024). Persistent concerns regarding reverse inference, ecological validity, reproducibility, and ethical governance further complicate interpretation and application ( Fisher et al., 2010; Gonçalves et al., 2024). Addressing these gaps, the present review focuses explicitly on noninvasive neuromarketing methods and contributes by (i) proposing a clear typology of commonly used neurophysiological and psychophysiological measures; (ii) offering an integrative comparison linking key marketing questions, such as attention capture, emotional engagement, memory formation, and valuation, to appropriate tools and their limitations; and (iii) synthesising evidence to distinguish what is currently robust from what remains uncertain. By adopting this construct-centred and method-comparative perspective, this review aims to advance both scientific rigor and responsible application in contemporary marketing research ( Lin et al., 2018; Rawnaque et al., 2020). This review explores neuromarketing as a developing interdisciplinary method aimed at elucidating consumer choices and priority development. Further, demonstrates how the integration of neurophysiological evidence with marketing theory and ethical standards can improve the comprehension of automatic and context-dependent decision-making processes in modern consumer environments.

Integrative review trajectories in consumer decision studies

This article pursued a narrative scoping review methodology to synthesise evidence on non-invasive neuromarketing methods and their applications in understanding consumer decision-making processes. The review aims to map the evolution of neuromarketing techniques, examine their associations with key consumer neuroscience constructs including attention, emotion, memory, and valuation, and identify methodological opportunities and limitations reported in the literature. Relevant publications were identified through systematic searches of major academic databases such as Scopus, Web of Science, PubMed, and Google Scholar. Literature published from 2000 to 2026 was included to capture both the emergence and recent advancements in neuromarketing research. Search terms comprised combinations of “neuromarketing,” “consumer neuroscience,” “EEG,” “fMRI,” “MEG,” “TMS,” “Steady-State Topography,” “eye tracking,” “psychophysiology,” “consumer decision-making,” “ttention,” “emotion,” “memory,” and “valuation.” The research were included if they investigated neuroscientific, physiological, or behavioural measurement approaches within marketing-related contexts, such as advertising evaluation, branding, product assessment, pricing decisions, and consumer choice. Studies focusing solely on clinical neuroscience or unrelated cognitive applications were excluded. The selected literature was analysed comparatively to develop an integrated framework that links measurement approaches with consumer-related constructs, practical marketing applications, and associated interpretive limitations.

Understanding the consumer behaviour through neuroimaging paradigm

Currently, various sophisticated technologies are employed to map neural responses related to consumer behavior and other cognitive functions. Among these, two primary neuroimaging techniques functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) are commonly used in neuroscience to visualize brain activity by assessing changes in blood flow. These methods are grounded in the neurophysiological principle that mental activity increases the demand for oxygen and glucose in activated brain regions a demand that is met through increased cerebral blood flow ( Bault & Rusconi, 2020; Kim, H. E. et al., 2022; Dwivedi & Sharma, 2024). fMRI detects brain activity by measuring changes in blood oxygen levels. When neurons become active, blood flow increases, delivering more oxygenated blood to the region. This influx reduces deoxygenated hemoglobin concentration, enhancing the signal and allowing high spatial resolution mapping of neural activity. The Positron emission tomography, on the other hand, involves the injection of radioactive tracers that emit positrons. These tracers such as oxygen-15 and fluorodeoxyglucose (18F-FDG), accumulate in regions of the brain exhibiting increased neural activity, which corresponds to elevated blood flow ( Rawnaque, Rahman, & Anwar, 2020; Alsharif & Mohd Isa, 2024). The resulting radiotracer emissions are detected by PET scanners to produce high-resolution images that reflect patterns of regional glucose metabolism an essential marker of brain function. Alterations in glucose metabolism often indicate underlying neurological or cognitive conditions. Rong, Lulong & Zhihua (2024) observed that medical imaging technologies are advancing rapidly, with molecular imaging emerging as a particularly promising field. The novel innovation in this area have sparked interest not only in clinical neuroscience but also in interdisciplinary applications such as neuromarketing. These innovations offer deeper insights into brain function and cognitive processes in consumer behavior ( Guixeres et al., 2017; Garczarek-Bąk, 2019; Russo, Clement, Jin, Liu, & Zito, 2022). Despite the availability of various neuroimaging methods, PET and fMRI remain the most prominent tools specifically utilized in neuromarketing research due to their ability to directly measure brain activity associated with emotional and decision-making processes.

Neuromarketing seeks to uncover the subconscious neural mechanisms influencing consumer behavior traditional marketing relies on consumers’ self-reported data, while neuromarketing uncovers deep insights into consumer decision-making by parsing brain activity and non-conscious responses; the core differences between these two approaches form the starting point of research in this field. Scholars including Nemorin et al. propose that neuromarketing can bypass consumers’ conscious cognitive filters to obtain more authentic decision-making information ( Romanowski, 2019; Goncalves et al., 2024). Over the past five years, neuromarketing research has focused on two categories of stimuli: product cues and promotional materials, each presented either with or without price information. In these studies, the term “product” covers both physical goods (such as beverage tasting trials) and conceptual presentations (such as 3D visual images). As a core experimental variable, price is set in two ways: as an independent standalone variable, or integrated with product or promotional content. Relevant foundational studies include the 2016 work by Nemorin and the 2015 study by Taqwa et al. Empirical research based on event-related potentials (ERPs) ( Nemorin, 2016; Gorin et al., 2025) shows that price promotions can increase the perceived value of high-priced yet affordable luxury goods, thereby boosting consumers’ purchase willingness.

The brain centric techniques, especially those utilizing advanced neuroimaging tools, are transforming how brands decode and influence consumer behaviour in today’s attention-driven economy. Beyond merely supplementing traditional methods, these approaches reveal real-time neural responses associated with emotional engagement, brand perception, and decision heuristics offering marketers evidence-based pathways to optimize campaign effectiveness ( Plassmann et al., 2019). The various application of advanced techniques allows marketers to pinpoint the neural correlates of attention, memory encoding, reward processing, and emotional engagement ( Petrovic, Petersson, & Ingvar, 2021; Venkatraman et al., 2012; Hasnaoui, Benabdallah, & Djebbari, 2023). These approaches are not only scientifically rigorous but also practically valuable, enabling firms to refine product designs, price strategies, and promotional content in ways that resonate deeply with target audiences ( Chen & Hsu, 2023). For instance, fMRI research has been used to anticipate the success of advertising campaigns and music singles before their public release, indicating the strong real world applicability of neuromarketing ( Berns & Moore, 2012; Dwivedi & Sharma, 2024). While concerns about ethical application and methodological rigor are valid, the field has made considerable strides in standardizing protocols and addressing biases. As such, leveraging diverse and validated neuromarketing techniques remains a promising and legitimate pathway to achieving strategic marketing goals provided that methods are carefully selected and ethically implemented ( Rawnaque, Rahman & Anwar, 2020; Morin, 2011). Studies reveal that consumer attitudes and behavioural responses are shaped by unconscious neural developments, often inaccessible through self-reports ( Hasnaoui, Benabdallah, & Djebbari, 2023). The advance configurated techniques like fMRI, EEG and other which enhance prediction accuracy for campaign effectiveness. These intellectual insights enable marketers to tailor strategies more precisely, bridging gaps left by traditional methods and improving engagement consequences.

Conventional to sophisticated insights of neuromarketing

The conventional approach to market research and data collection, aimed at fulfilling business requirements, proves inadequate in delivering the desired outcomes. Consequently, the adoption of novel techniques and methodologies for acquiring reliable evidence has become authoritative. To offset the substantial costs of advertising campaigns often undertaken by companies to promote new products or increase sales of existing one’s financial returns or measurable market impact are typically required to justify the investment ( Şik & Soba, 2021; Yang, Zhang, Qian, & Wang, 2022). It is widely acknowledged that the success of advertisements cannot be fully captured through verbal self-reports alone. Attempting to interpret emotional responses purely through cognitive or linguistic frameworks often leads to incomplete or misleading conclusions ( Plassmann et al., 2015). Emotions are not solely grounded in language they are complex affective states that may elude conscious articulation. As such, accurately assessing emotional engagement requires moving beyond verbal expression to incorporate neurocognitive and physiological measures, which offer deeper insights into how consumers truly respond to advertising stimuli. Furthermore, such evaluations are subject to criticism for failing to capture the relationship between the influence of advertisements and subsequent consumer behavior ( Romanowski, 2019). Consumer preferences, decisions, and choices are frequently influenced through Neuromarketing techniques. Despite businesses being hesitant to disclose the utilization of Neuromarketing tactics to enhance their marketing efforts, there have been a limited number of extensively documented research studies in this field. Previous studies by Hubert and Kenning (2008) and Khushaba et al. (2019) point out that appealing celebrity advertisements can activate specific brain regions in consumers, driving consumers to complete product recognition and build inherent trust in products. Gholami (2024) further found that consumers also proactively link specific advertising elements to corresponding scenarios, generate strong responses, and ultimately complete purchases. Such studies can help enterprises screen and adjust advertisements that fit consumer needs and can be retained in audiences’ memories, accurately identify factors that trigger neural responses, and reasonably leverage elements including language, visuals, sound effects, and music to create targeted advertisements. The core decisions of marketing managers rely on two types of information sources: traditional research technologies and neuromarketing technologies. The use of psychophysiological estimations allows participants to form preferences and make decisions through conscious thought, as opposed to relying on self-reported estimations of emotional states and unconscious cognitive processes that influence consumer responses in marketing studies. In other words, this approach goes beyond conscious aspects of human behavior and can identify influences on behavior even when consumers are not aware of them ( Zaltman, 2003; Ko, Kim, & Lee, 2021). This made significant contribution to consumer research, considering that approximately 95% of our cognitive and emotional processing occurs at unconscious levels.

Currently, state-of-the-art eye-tracking systems and electrooculography (EOG) a technique that records eye movements by measuring the electrical potential between the front and back of the eye enable the detection of subtle changes in visual attention and ocular behavior. These technologies offer high temporal resolution, often within just a few milliseconds, specifically in tracking eye movement dynamics allowing researchers to precisely map attention patterns in real time ( Duchowski, 2007; Khairunnisa & Sari, 2023). This is particularly useful for studying the effects of momentarily stimulating marketing stimuli and monitoring transient shopping behavior without impeding the decision-making process ( Solnais, Andreu-Perez, Sanchez-Fernandez & Andreu-Abela, 2013). Electroencephalography (EEG) can be integrated with other advanced real-time determination techniques to provide additional insights into customer attention and interest ( Hasnaoui, Benabdallah, & Djebbari, 2023). It can also be utilized in combination with other high time resolution technologies to provide more comprehensive information about viewer attention and arousal during advertising. For example, by synchronizing data from EEG recordings, it is possible to identify the precise focus of the member’s attention at a given moment in time, as well as the specific elements of the scene that triggered activation of the left frontal hemisphere ( Nemorin, 2016). The overall benefits of these technologies contribute to the increasing popularity of Neuromarketing and consumer neuroscience for advertising testing. Neuromarketing utilizes state-of-the-art resources in brain scanning to understand the customer’s purchasing behavior. Schneider & Woolgar (2012) assert that Neuromarketing is the latest tool employed by marketing researchers to comprehend consumer behavior. In fact, understanding consumer behavior, particularly the decision-making processes involved in purchasing, emerges as one of the most frequently cited objectives in neuromarketing research ( Eser et al., 2011; Suhendra, Hermita, & Darmayantie, 2015; Yang, Zhang, Qian, & Wang, 2022). Lee et al. (2007) also states that Neuromarketing has become a popular technology for determining the likelihood and non-probability of purchasing decisions, which has also been identified as a means of shaping companies’ marketing strategies ( Lee et al., 2007; Eser et al., 2011, Nemorin, 2016). Additionally, it helps to eliminate elements that should not be present in the communication, such as elements that lead to consumer aversion to the products. It also assists in the selection of visual and auditory features, as well as the timing and choice of appropriate media. Neuromarketing also has the ability to identify consumers’ needs and, consequently, develop more meaningful and comprehensive consumer marketing strategies.

Non-invasive phenomena for understanding consumer behaviours

Recent advancements in neuroimaging and cognitive neuroscience have enabled the application of non-invasive techniques, including EEG, fMRI, MEG, and eye-tracking, alongside other biometric tools, to decode consumer behavior with notable precision. These methodologies uncover unconscious emotional and cognitive processes, thereby complementing traditional approaches by offering deeper insights and establishing a scientific basis for evidence-based marketing interventions ( Khushaba et al., 2019; Yang, Zhang, Qian, & Wang, 2022; Hsu & Yoon, 2023). Central to this paradigm shift is the use of non-invasive brain imaging techniques, which provide a robust means of identifying neural activity in response to marketing stimuli. This capability allows researchers and practitioners to decode consumer prioritization, evaluation, and selection of products within complex environments. Among these techniques, EEG is particularly prominent due to its temporal resolution, portability, and cost-effectiveness ( Raiesdana & Mousakhani, 2022; Vecchiato et al., 2011). EEG facilitates detailed observation of cognitive and emotional processing through metrics such as event-related potentials (ERPs) and frontal asymmetry ( Vecchiato et al., 2011; Mileti, Guido, & Prete, 2016). Notably, increased activity in the left prefrontal cortex is associated with approach behavior and positive evaluations, serving as a critical biomarker for consumer preferences. As highlighted by Khushaba et al. (2013), EEG proves particularly effective in scenarios where consumers' conscious reasoning does not fully explain their purchase decisions, such as during exposure to emotionally charged advertisements or branding content.

Further expanding the scope of consumer behavior analysis, explore the theoretical and ethical boundaries of neuromarketing in comparison to “mind-reading.” They emphasize that while non-invasive methods cannot access private thoughts, they can reliably capture automatic reactions that correlate with preference and motivation ( Booth & Freeman, 2014). In particular, neurophysiological responses to product packaging, advertisement narrative arcs, or price visibility provide marketers with cues about what drives attention and valuation, thereby aiding in more precise campaign tailoring. Complementing the neurophysiological focus, Medina et al. (2020) employed neuroimaging tools to compare prosocial and non-prosocial consumer responses during pricing decisions. This study found that the insular and prefrontal regions show differentiated activation depending on ethical and prosocial dispositions. The implication here is that consumer priorities are not merely rational or economic but also neurologically modulated by identity-related values and social cognition, and these can be decoded using non-invasive methods like fMRI and EEG in combination.

An emerging and increasingly significant development within neuromarketing is the use of real-time neural data to drive highly personalized marketing strategies, which Mileti et al. (2016) describe as an advanced form of neuroadaptive marketing a specialized subset that dynamically tailors advertising content based on immediate neural responses. The neuroscience technique leverages consumer micro-signals, such as moment-to-moment emotional fluctuations detected by facial coding or eye-tracking, in conjunction with EEG signals to anticipate behavioral outcomes ( Mileti et al., 2016; Yang, Zhang, Qian, & Wang, 2022). This hyper-individualized approach has profound implications for e-commerce and dynamic pricing models, allowing for the adaptation of product offers in milliseconds based on neural correlates of interest or aversion. The broader economic relevance of integrating neuromarketing into innovation management becomes particularly apparent when addressing uncertain consumer preferences in new product development ( Romanowski, 2019). Further, non-invasive tools allow for early testing of prototypes, packaging, or pricing strategies, thereby reducing market failure risk. The strategic integration of neuromarketing within product innovation pipelines represents a methodological leap, embedding consumer intuition directly into corporate decision-making. The extensive non-invasive neuromarketing tools provide a scientifically grounded and ethically feasible method for uncovering the latent processes that shape consumer priorities. They circumvent the biases inherent in self-reported data, provide an objective basis for segmentation, targeting, and positioning. However, minor challenges remain in standardizing protocols, ensuring data interpretability, and maintaining consumer consent and transparency. Nonetheless, the field’s trajectory supported by a growing body of evidence signals a development toward becoming an indispensable facet of modern consumer insight generation.

Neuroscience approaches for mapping consumer behaviour and choices

Traditional marketing tools often fail to capture the subconscious cognitive and emotional mechanisms that shape consumer behavior ( Pradeep et al., 2022; Genevsky & Knutson, 2020). In contrast, neuromarketing leverages advanced non-invasive techniques such as EEG, fMRI, MEG etc. to observe neural responses in real-time, without requiring conscious effort from participants. These methodologies enable researchers to identify specific brain regions activated during brand exposure, decision-making, and emotional engagement, thereby providing detailed insights that exceed those derived from self-reported data. These techniques substantially improve the predictive validity of consumer responses, allowing marketers to develop more effective strategies grounded in neurobiological evidence ( Morin et al., 2023).

Neuromarketing is a highly influential contemporary marketing strategy. A 2017 study by Stanton et al. shows that the academic community has adopted different methods to explore the feasibility and impacts of this type of technology ( Stanton et al., 2017). Neuroimaging, the core supporting technology rooted in neuroscience that underpins neuromarketing, can verify hypotheses, enrich existing knowledge, and analyze the impacts of marketing stimuli on consumers’ brains. Neuromarketing can be divided into three categories: technologies that record brain metabolic activity, technologies that record electrical brain activity, and technologies that explore brain activity without recording. All three categories serve as important parameters for uncovering core marketing-influencing factors such as consumers’ intent and hesitation.

Electroencephalography

Electroencephalography (EEG) is a highly effective and cost-efficient technique for investigating the dynamics between brain activity and behavior ( Bell & Cuevas, 2012). Notably, the examination of various spectral bands, including Delta (0– Hz), Theta (3–7 Hz), Alpha (8–12 Hz), Beta (13–30 Hz), and Gamma (30–0 Hz), has been employed to analyze consumers’ cognitive and affective processes in response to marketing stimuli ( Mostafa, 2012). The versatility of this method allows researchers to explore an extensive range of fields, enabling a comprehensive understanding of the temporal sequencing of neural events ( Light et al., 2010). The recording of EEG signals is conducted by placing multiple electrodes on the participant’s scalp in order to enhance the conduction of impulses to the electrodes. The electric current generated by the typical human brain is on the order of a few microvolts. These voltage fluctuations are a result of ionic currents flowing between the brain and nerve cells. Nerve cells communicate with each other through electrical impulses ( Mileti, Guido, & Prete, 2016; Yang, Zhang, Qian, & Wang, 2022; Hsu & Yoon, 2023). The electrical potential detected by an individual neuron is extremely weak and therefore not detectable. Each electrode reflects the combined activity of thousands or millions of neurons with the same spatial orientation, thus the EEG measures the overall synchronous activity of a large number of neurons present in the brain. It is highly valuable in evaluating emotional stability and monitoring emotional conditions ( Hsu & Yoon, 2023; Kline, 2000). This device is a portable tool that aids in assimilating and synchronizing stimuli. The only limitation of this method is that it can only record apparent electrical signals and cannot differentiate deep brain structures or exchange information. Consequently, the EEG provides greater temporal resolution and less spatial resolution in the field of neuroscience. The electroencephalogram technology, frequently utilized in neuromarketing, offers insightful data on brain activity and is both portable and reasonably priced.

Advanced wireless electroencephalogram device represents a significant breakthrough in neuromarketing to enabling real-time, mobile, and ecologically valid monitoring of brain activity in naturalistic environments. These devices offer high temporal resolution and improved comfort, allowing researchers to track consumers’ neural responses to advertisements, retail layouts, and digital content outside laboratory constraints ( Ma et al., 2022; Luis-Alberto, & Sanchez-Fernandez, 2022). The effectiveness in capturing emotional engagement, cognitive load, and attention metrics during real-world consumer experiences. As wearable EEG technology becomes more sophisticated, it opens new possibilities for continuous consumer insight, adaptive advertising, and personalized marketing strategies based on neurophysiological feedback.

Functional magnetic resonance imaging

Functional Magnetic Resonance Imaging (fMRI) is widely regarded as one of the primary methods for investigating brain activity. This technique enables the analysis of intricate and minute brain structures; however, it is more expensive than many other methods. Moreover, the 6–10 second delay in recording neural activity represents a limitation when measuring responses to marketing stimuli ( Ariely & Berns, 2010). There are two main techniques used in functional MRI: the Blood Oxygen Level Dependent (BOLD) technique and the dynamic exogenous technique ( Hare et al., 1998). The BOLD technique is preferred as it does not require intravenous contrast. Chow et al. (2017) highlight that the majority of fMRI studies utilize BOLD contrast imaging, which involves mapping active regions of the brain based on changes in blood oxygen. Blood flow in the brain is locally regulated in response to oxygen and carbon dioxide levels in cortical tissue. This method has garnered significant attention in the field of Neuromarketing, while fMRI provides excellent spatial resolution, its temporal resolution is relatively poor. In Neuromarketing, fMRI is employed to measure various factors such as memory encoding, emotional valence, sensory perception, brand loyalty and trust, brand preference, and brand recall.

Recent advancements have employed fMRI to investigate neural valuation systems, with particular focus on activity in the ventromedial prefrontal cortex (vmPFC) and nucleus accumbens, which serve as reliable indicators of purchasing intention and product desirability ( Enax et al., 2015; Plassmann et al., 2021). Additionally, emerging fMRI-based research examines the brain's response to sustainability messaging and ethical branding, demonstrating increased activation in regions associated with moral reasoning and social cognition, such as the temporoparietal junction and dorsomedial prefrontal cortex ( Karmarkar, Bollinger, 2022). This study introduces a novel perspective on consumer behaviour, highlighting that decision-making is influenced not only by price and utility but also by values, emotional resonance, and ethical alignment. Despite, advance in fMRI technology with enhanced models and machine learning integration, it provides deeper insights into the subconscious processes that drive consumer preferences in increasingly complex marketplaces.

Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is a non-invasive neuroscience technique used to stimulate targeted areas of the cerebral cortex which enables researchers to establish causal links between specific brain regions and behavioural responses to inducing temporary changes in neural activity. The devise placed to the scalp, TMS generates a magnetic field that stimulates underlying brain tissue, unsettling or enhancing cortical processing in localized areas ( Sliwinska, Vitilo & Devlin, 2014). This permits for precise manipulation of cognitive functions and provide insights into the timing and role of neural processes in real-time decision-making. In addition, consumer research, TMS provides a powerful technique to explore how the brain responds to marketing stimuli. By selectively activating or inhibiting regions associated with emotion, attention, and decision-making, researchers can examine how these changes affect purchasing attitudes and behavior. Recent studies using TMS on the dorsolateral prefrontal cortex (DLPFC) have shown that modulating this region can significantly alter consumer assessment and risk preferences during purchasing decisions ( Gupta et al., 2021). Moreover, this technique has been effectively used to study the influence of affective priming on product judgments, indicating that temporary disruption of emotion-processing circuits can modulate brand attitudes and consumer trust ( Zhang & Li, 2022). Research findings support the growing role of TMS in neuromarketing, where it helps decode, the neural mechanisms underlying consumer attention, preference formation, and decision-making, enabling marketers to develop empirically informed engagement strategies.

Indeed, TMS can directly influence consumption-related behaviours, such as delaying gratification or shifting preference toward serviceable versus hedonic products ( Ramsoy et al., 2021). Furthermore, emerging TMS applications in neuromarketing explore the modulation of empathy and social cognition through ethical consumption decisions, revealing that stimulating the right temporoparietal junction can alter perceived brand authenticity and trust ( Cian et al., 2022). Through effectively use these techniques is not merely a validation tool for neural correlates but a mechanism for probing and reshaping the cognitive architecture of consumer thought itself. This opens promising avenues for designing interventions in areas such as compulsive buying, financial risk behavior, and ethical advertising.

Steady State Topography

Steady-State Topography (SST), introduced by Richard Silberstein in 1990, is a cutting-edge neurophysiological method for mapping neural activity in response to external stimuli. Initially developed for cognitive neuroscience, this device supports significant applications in neuromarketing and consumer neuroscience, especially in evaluating consumer perception, emotive engagement, and brand communication ( Maran, Moonisha, Patel, Mari Muthu, & Anbazhagan, 2021). In SST experiments, participants are exposed to audiovisual stimuli while a dim sinusoidal flicker is presented in their peripheral vision, causing a steady-state visually evoked potential (SSVEP). This neural response is measured across cortical areas to assess variations in cognitive and emotional processing. The specific concern of SST, particularly in neuromarketing, is its sensitivity to changes in latency, the delay between stimulus and neural response, which reflects long-term memory encoding, sustained attention, and emotional intensity ( Silberstein & Nield, 2021). By analysing latency shifts and cortical activation patterns over time, SST enables a more nuanced understanding of how consumers perceive advertising messages, process branding cues, and form durable attitudes toward products. Steady-State Topography-based new research demonstrated its ability to predict advertising effectiveness and product recall with high precision, making it an advanced tool for capturing subconscious consumer behaviour in real-world media environments.

Magnetoencephalography

Magnetoencephalography (MEG) is a technique that measures brain activity by detecting potential magnetic fields on the scalp using sensitive detectors housed in a helmet placed on the head. Unlike other methods, MEG is not influenced by tissue type, such as blood, brain matter, or bone, and can provide spatial and temporal resolution to determine the depth of a point in the brain. The MEGs are capable of monitoring population neuronal activity in the brain, but the costs associated with their use are significant due to the requirement for a magnetic field-free environment. This device records electrical activity in the brain, offering high temporal resolution to trace the consumers’ responses to marketing stimuli in real time.

The Magnetoencephalography offer unique advantage in neuromarketing method through providing millisecond-level temporal resolution and superior spatial localization of brain activity compared to Electroencephalography, while remaining non-invasive unlike functional magnetic Resonance. This method is very magnificent to captures magnetic fields generated by neuronal activity which allowing precise tracking of consumer responses to marketing stimuli such as advertisements and product designs ( Lee et al., 2021; Morinaga et al., 2022; Dikker & Pineda, 2023). Undouble this unique method effectively maps cognitive processes like brand recall, emotional arousal, and decision-making pathways. This real-time, high-fidelity insight supports profound consumer profiling and enhances predictive modelling in marketing strategies.

Current evidence and knowledge gaps in core consumer neuroscience constructs

The current state of consumer neuroscience, particularly through the lens of neuromarketing, sheds light on four fundamental constructs influencing consumer decision-making: attention, emotion, memory, and valuation. Despite significant advancements, each construct presents unique evidence alongside notable knowledge gaps and methodological constraints that temper the predictive power of these insights when applied singularly to real-world consumer behavior.

Attention

Neuromarketing leverages eye-tracking and electroencephalography (EEG) to provide granular data on consumers’ visual engagement and attentional focus toward marketing stimuli such as advertisements, product packaging, and digital content. These methods effectively capture moment-to-moment cognitive engagement, revealing how attentional resources are allocated during exposure to marketing material. However, attention primarily denotes an initial cognitive process, which while critical does not independently forecast purchase decisions or long-term brand loyalty. This limitation arises because attentional engagement measures lack the capacity to account for the complex, multifaceted determinants of actual consumer behavior beyond stimulus exposure ( Adhikari, 2023; Alsharif et al., 2021).

Emotion

Neurophysiological and peripheral psychophysiological tools such as EEG, facial electromyography, galvanic skin response, and heart rate variability have enriched the understanding of the emotional dimension of consumer interactions with brands and advertising. These approaches measure implicit affective responses that are often inaccessible through self-report. Nevertheless, emotional interpretation remains nuanced and complicated by individual differences including personal traits, cultural influences, and situational factors, which can modulate the valence and intensity of emotion elicited. Consequently, emotional response data although invaluable must be contextualized within these moderating variables to avoid misinterpretation or overgeneralization ( Alsharif et al., 2021, 2025; Dwivedi & Sharma, 2024).

Memory

Techniques such as functional magnetic resonance imaging (fMRI), EEG, and Steady-State Topography offer insights into the neural substrates of brand encoding, recognition, and recall processes. This neural-level understanding is vital for capturing how consumers process and store brand-related information. However, laboratory-based neuroimaging measures of memory activation do not consistently translate into long-term brand loyalty or predict actual purchasing behavior in the marketplace. This gap underscores a critical limitation: neural correlates of memory activation do not directly equate to behavioral outcomes influenced by diverse external and internal factors beyond memory alone ( Alsharif et al., 2021; Yao & Wang, 2024).

Valuation

Neuromarketing neuroimaging studies have made notable progress in mapping brain regions associated with reward processing, value assignment, and preference formulation during consumer choice tasks. These findings underscore the neural valuation signals as core cognitive components underpinning decision making. Yet, such valuation signals represent only part of a broader panorama; consumer purchase behaviors are additionally shaped by social contexts, cultural norms, economic considerations, and environmental influences, limiting the scope of prediction based solely on neural valuation ( Alsharif et al., 2021; Yao & Wang, 2024).

Integration and methodological considerations

The evidence collectively suggests that while neuromarketing methods offer rich, complementary insights into consumer cognition and affect, they should not be regarded as standalone indicators for predicting consumer behavior. Instead, neural data must be integrated with behavioral measures, consumer self-reports, and real-world sales or market outcomes to enhance the ecological validity and predictive accuracy of findings. Importantly, many neuromarketing studies face challenges including limited sample sizes, high costs, potential confounds, and issues related to reverse inference the problem of inferring specific mental states merely from observed neural activation patterns. Such limitations urge cautious interpretation and a multi-methodological approach that bridges neural data with longitudinal and contextually grounded behavioral research ( Gupta et al., 2025; Song et al., 2025; Yao & Wang, 2024).

Emerging Directions and Knowledge Gaps

Future research should focus on:

Integrative frameworks are being developed to merge neural, behavioral, and contextual data throughout the entire consumer decision-making process, aiming to clarify how affective, cognitive, and motivational processes interact. Advanced computational methods, including machine learning and artificial intelligence, are utilized to interpret complex, multivariate neuromarketing data, enhancing the prediction of overall consumer behavior. Ethical issues in neuroscience-based marketing, such as privacy, data security, and informed consent, are being addressed to ensure the responsible use of consumer neural data. Longitudinal and field studies are being expanded to connect neural markers with real purchasing behaviors and sustained brand engagement, thus addressing the shortcomings of short-term laboratory assessments. Additionally, there is an exploration of less-studied areas like cross-modal interactions and diverse market segments to develop more universally applicable neuromarketing models ( Alsharif et al., 2025; Gupta et al., 2025; Yao & Wang, 2024).

The consumer neuroscience through neuromarketing has substantially advanced the comprehension of attention, emotion, memory, and valuation processes in consumer decision-making. However, the predictive power and practical application hinge upon appropriately integrating multimodal neural metrics with real-world behavioral indicators while carefully navigating limitations and ethical complexities inherent in this emerging field. Thus, the synthesis of current evidence indicates a promising yet nuanced landscape for neuromarketing, highlighting both its transformative potential and the critical need for sophisticated, ethically guided research designs moving forward ( Alsharif et al., 2021, 2025; Dwivedi & Sharma, 2024; Yao & Wang, 2024).

Conclusion

Neuromarketing is most valuable when used as a complement to established behavioral and self-report methods, rather than as a tool for directly ‘reading’ consumers’ minds. By integrating neurophysiological insights with traditional marketing research, neuromarketing provides a deeper understanding of unconscious consumer responses, such as emotional engagement, attention, and memory encoding, which are often inaccessible through conventional methods alone. Techniques like EEG, fMRI, and TMS offer real-time, objective data that can refine marketing strategies, enhancing the precision of advertisements, pricing, and product designs. However, while these techniques have proven effective in capturing the intricacies of consumer behavior, they also face notable challenges. These include limitations related to spatial and temporal resolution, as well as ethical concerns surrounding privacy, consent, and potential manipulation. The future of neuromarketing depends on addressing these issues through the development of multimodal, longitudinal, and cross-cultural research, which can provide a more holistic view of consumer behavior. Furthermore, aligning neuromarketing with ethical standards and marketing theory will be crucial in ensuring its responsible and sustainable application. Ultimately, the long-term success of neuromarketing hinges on balancing scientific rigor with ethical responsibility, offering marketers not just deeper insights into consumer behavior, but also a path to more effective and ethically sound marketing practices.

Acknowledgement

We acknowledge the support to this publication from the Indian Council of Social Science Research (ICSSR), F.No. 02/130/2022-23/ICSSR/RP/MJ/OBC.

Funding Statement

The author(s) declared that no grants were involved in supporting this work.

[version 4; peer review: 1 approved

Data availability

All data are publicly available and cited in the manuscript. No new data were generated, and no DOI applies.

References

  1. Adhikari K: Application of selected neuroscientific methods in consumer sensory analysis: A review. J. Food Sci. 2023;88(S1):53–A64. 10.1111/1750-3841.16526 [DOI] [PubMed] [Google Scholar]
  2. Alsharif AH, Md Salleh NZ, Baharun R, et al. : Neuromarketing research in the last five years: A bibliometric analysis. Cogent Bus. Manag. 2021;8(1):1978620. 10.1080/23311975.2021.1978620 [DOI] [Google Scholar]
  3. Alsharif A, Khraiwish A: Tools in marketing research: Exploring emotional responses to stimuli. Sci. Ann. Econ. Bus. 2024;71(2):173–192. 10.47743/saeb-2024-0009 [DOI] [Google Scholar]
  4. Alsharif AH, Mohd Isa S: Revolutionizing consumer insights: the impact of fMRI in neuromarketing research. Futur Bus J. 2024;10:79. 10.1186/s43093-024-00371-z [DOI] [Google Scholar]
  5. Alsharif AH, Wang J, Isa SM, et al. : The synergy of neuromarketing and artificial intelligence: A comprehensive literature review in the last decade. Futur. Bus. J. 2025;11: Article 170. 10.1186/s43093-025-00591-x [DOI] [Google Scholar]
  6. Andreassi JL: Psychophysiology: Human behavior and physiological response. 2007; 5th ed. Mahwah, NJ: Lawrence Erlbaum Associates. [Google Scholar]
  7. Ariely D, Berns GS: Neuromarketing: the hope and hype of neuroimaging in business. Nat. Rev. Neurosci. 2010;11(4):284–292. 10.1038/nrn2795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bault N, Rusconi E: The art of influencing consumer choices: a reflection on recent advances in decision neuroscience. Front. Psychol. 2020;10:3009. 10.3389/fpsyg.2019.03009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bell MA, Cuevas K: Using EEG to study cognitive development: Issues and practices. J. Cogn. Dev. 2012;13(3):281–294. 10.1080/15248372.2012.691143 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Berns GS, Moore SE: A neural predictor of cultural popularity. J. Consum. Psychol. 2012;22(1):154–160. 10.1016/j.jcps.2011.05.001 [DOI] [Google Scholar]
  11. Booth DA, Freeman RPJ: Mind-reading versus neuromarketing: How does a product make an impact on the consumer? J. Consum. Mark. 2014;31(3):177–189. 10.1108/jcm-08-2013-0674 [DOI] [Google Scholar]
  12. Chen YJ, Hsu L: How online travel agencies’ logo design promotes purchase intention: A behavioral and neuroscientific interpretation of consumers’ construal level. Asia Pac. J. Tour. Res. 2023;28(1):19–35. 10.1080/10941665.2023.2187701 [DOI] [Google Scholar]
  13. Chow HM, Mar RA, Xu Y, et al. : Personal experience with narrated events modulates functional connectivity within visual and motor systems during story comprehension. Hum. Brain Mapp. 2017;35(12):5985–5999. 10.1002/hbm.22589 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cian L, Krishna A, Elder RS: The neural basis of brand authenticity: Insights from TMS on empathy-related processing. J. Mark. Res. 2022;59(1):145–160. 10.1177/00222437211053796 [DOI] [Google Scholar]
  15. Clement J, Smith V, Zlatev J, et al. : Assessing information on food packages. Eur. J. Mark. 2017;51:219–237. 10.1108/EJM-09-2013-0509 [DOI] [Google Scholar]
  16. Clithero JA, Carter RM, Huettel SA: Methodological best practices and interpretive challenges in decision neuroscience. J. Consum. Psychol. 2024;34(1):3–20. 10.1002/jcpy.1362 [DOI] [Google Scholar]
  17. Cordeiro R, Reis A, Ferreira BM, et al. : Neuromarketing: Decoding the Role of Emotions and Senses and Consumer Behavior. Cognitive Behavioral Neuroscience in Organizational Settings. IGI Global;2024; p.18. 10.4018/979-8-3693-1858-4.ch005 [DOI] [Google Scholar]
  18. Dikker S, Pineda JA: Understanding real-time audience engagement using MEG hyperscanning. Journal of Consumer Neuroscience. 2023;6(2):101–113. 10.1016/j.jcns.2023.02.003 [DOI] [Google Scholar]
  19. Duchowski AT: Eye tracking methodology: Theory and practice. 2007; 2nd ed. London: Springer. [Google Scholar]
  20. Dwivedi A, Sharma V: Exploring the role of neuromarketing in consumer decision-making. ShodhKosh: J. Vis. Perform. Arts. 2024;5(7):253–260. 10.29121/shodhkosh.v5.i7.2024.2443 [DOI] [Google Scholar]
  21. Enax L, Weber B, Ahlers M, et al. : Food packaging cues influence taste perception and increase effort provision. Front. Psychol. 2015;6. 10.3389/fpsyg.2015.00882 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Eser, et al. : Indicate that neuromarketing uses stateof-the-art resources in brain scanning to understand the consumer buying process. 2011. 10.5296/jmr.v6i2.5446 [DOI] [Google Scholar]
  23. Fisher CE, Chin L, Klitzman RL: Defining neuromarketing: Practices and professional challenges. Harv. Rev. Psychiatry. 2010;18(4):230–237. 10.3109/10673229.2010.496623 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Garczarek-Bąk U: An Overview to Neuromarketing Research (Ed.): Managing Economic Innovations – Methods and Instruments, Poznań 2019. 2019. 978-83-7986-277-1. 10.12657/9788379862771-4 [DOI]
  25. Genco SJ, Pohlmann AP, Steidl P: Neuromarketing for Dummies. John Wiley & Sons;2013. [Google Scholar]
  26. Genevsky A, Knutson B: Decoding consumer valuation using fMRI and machine learning. Front. Neurosci. 2020;14:278. 10.3389/fnins.2020.00278 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gholami M: Neuroscience in Marketing: A Review of Neuromarketing Techniques. Business, Marketing, and Finance Open. 2024;1(1):27–39. 10.1017/bmfopen.2024.3 [DOI] [Google Scholar]
  28. Goncalves M, Hu Y, Aliagas I, et al. : Neuromarketing algorithms’ consumer privacy and ethical considerations: challenges and opportunities. Cogent Bus. Manag. 2024;11(1): 2333063. 10.1080/23311975.2024.2333063 [DOI] [Google Scholar]
  29. Gorin A, Kuznetsova E, Kislov A, et al. : Neural correlates of the non-optimal price: an MEG/EEG study. Front. Hum. Neurosci. 2025;19: 1470662. 10.3389/fnhum.2025.1470662 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Guixeres J, Bigne E, Ausin Azofra JM, et al. : Consumer neuroscience-based metrics predict recall, liking and viewing rates in online advertising. Front. Psychol. 2017;8:1808. 10.3389/fpsyg.2017.01808 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Gunawan CN, Chen Y, Hsu J, et al. : How online travel agencies’ logo design promotes purchase intention: A behavioral and neuroscientific interpretation of consumers’ construal level. Asia Pac. J. Tour. Res. 2023;28(1):19–35. 10.1080/10941665.2023.2187701 [DOI] [Google Scholar]
  32. Gupta R, Venkatraman V, Carter RM: Neuromodulation of consumer valuation: A transcranial magnetic stimulation study of risk and reward in marketing contexts. J. Consum. Neurosci. 2021;4(1):23–35. 10.1016/j.jcns.2021.01.003 [DOI] [Google Scholar]
  33. Gupta R, Kapoor AP, Verma HV: Neuro-insights: A systematic review of neuromarketing perspectives across consumer buying stages. Front. Neuroergon. 2025;6:1542847. 10.3389/fnrgo.2025.1542847 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hare HV, Guenther B, Haughton VM, et al. : Functional magnetic resonance imaging: Clinical applications. Neurosurgery. 1998;42(4):824–836. 10.1097/00006123-199804000-00099 [DOI] [Google Scholar]
  35. Hasnaoui LH, Benabdallah A, Djebbari A: Investment of biomedical applications in marketing: Electroencephalogram-based consumer decision prediction. Biomedical Engineering: Applications, Basis and Communications. 2023;35(04):2350013. 10.4015/S1016237223500138 [DOI] [Google Scholar]
  36. Hsu CT, Yoon C: The neuroscience of consumer decision making. Annu. Rev. Psychol. 2023;74:473–499. 10.1146/annurev-psych-032720-122607 [DOI] [Google Scholar]
  37. Hubert M, Kenning P: A current overview of consumer neuroscience. J. Consum. Behav. 2008;7(4–5):272–292. 10.1002/cb.251 [DOI] [Google Scholar]
  38. Jones T, Townsend DW: History and future technical innovation in positron emission tomography. J. Med. Imaging. 2017;4(1): 011013. 10.1117/1.JMI.4.1.011013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Karmarkar UR, Bollinger B: Neural responses to ethical branding: An fMRI study on the moral dimension of consumer choice. NeuroImage: Reports. 2022;2(1):100053. 10.1016/j.ynirp.2022.100053 [DOI] [Google Scholar]
  40. Khairunnisa G, Sari H: Eye tracking-based analysis of customer interest on the effectiveness of eco-friendly product advertising content. J. Optimasi Sist. Ind. 2023;22(2):153–164. 10.25077/josi.v22.n2.p153-164.2023 [DOI] [Google Scholar]
  41. Khushaba RN, Kodagoda S, Mian A: Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Neurocomputing. 2013;100:142–150. 10.1016/j.neucom.2012.09.016 [DOI] [Google Scholar]
  42. Khushaba RN, Wise C, Kodagoda S, et al. : Predicting advertisement success with EEG and eye-tracking data: A multimodal neuromarketing study. Front. Neurosci. 2019;13:102. 10.3389/fnins.2019.00102 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Kim HE, Kwon JH, Kim J-J: Did It Change Your Mind? Neural Substrates of Purchase Intention Change and Product Information. Front. Neurosci. 2022;16: 871353. 10.3389/fnins.2022.871353 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Kline JP: The contributions of electroencephalography to the study of emotion. Biol. Psychol. 2000;55(1):19–33. 10.1016/S0301-0511(00)00069-8 [DOI] [Google Scholar]
  45. Ko E, Kim EY, Lee SH: The neural basis of luxury branding: Evidence from fMRI. Front. Psychol. 2021;12:638318. 10.3389/fpsyg.2021.638318 [DOI] [Google Scholar]
  46. Lee N, Broderick AJ, Chamberlain L: What is ‘neuromarketing’? Eur. Bus. Rev. 2007;19(4):339–346. 10.1108/09555340710760130 [DOI] [Google Scholar]
  47. Lee Y, Kim H, Chung J: Tracking brand recall using MEG-based neural decoding. Front. Neurosci. 2021;15:667892. 10.3389/fnins.2021.667892 [DOI] [Google Scholar]
  48. Light GA, Williams LE, Minow F, et al. : Electroencephalography (EEG) and event-related potentials (ERPs) for the characterization of neural oscillations in schizophrenia: A practical guide. Biological Psychiatry. 2010;69(2):112–120. 10.1016/j.biopsych.2010.09.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Lin M-H(J), Cross SNN, Jones WJ, et al. : Applying EEG in consumer neuroscience. Eur. J. Mark. 2018;52(1–2):66–91. 10.1108/EJM-12-2016-0805 [DOI] [Google Scholar]
  50. Liu D, Wang N, Song M, et al. : Global glucose metabolism rate as diagnostic marker for disorder of consciousness of patients: quantitative FDG-PET study. Front. Neurol. 2025;15: 1425271. 10.3389/fneur.2024.1425271 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Luis-Alberto C, Sanchez-Fernandez J: Advances in Neuroscience and Marketing: Analyzing Tool Possibilities and Research Opportunities. Spanish Journal of Marketing - ESIC. 2022;ahead-of-print(ahead-of-print). 10.1108/SJME-10-2021-0196 [DOI] [Google Scholar]
  52. Ma X, Wang Y, Zhang H: Applications of wearable EEG in real-time consumer behavior analysis: A neuromarketing perspective. Front. Neurosci. 2022;16:853097. 10.3389/fnins.2022.853097 [DOI] [Google Scholar]
  53. Maran K, Moonisha K, Patel DS, et al. : Application of neuromarketing techniques in FMCG products advertising. Elementary Education Online. 2021;20(6):6471–6477. Reference Source [Google Scholar]
  54. McClure SM, Li J, Tomlin D, et al. : Neural correlates of behavioral preference for culturally familiar drinks. Neuron. 2004;44(2):379–387. 10.1016/j.neuron.2004.09.019 [DOI] [PubMed] [Google Scholar]
  55. Medina CAG, Guerrero LA, Viedmadel-Jesús M, et al. : The processing of price during purchase decision making: Are there neural differences among prosocial and non-prosocial consumers? J. Clean. Prod. 2020;271:122648. 10.1016/j.jclepro.2020.122648 [DOI] [Google Scholar]
  56. Mileti A, Guido G, Prete MI: Nanomarketing: A new frontier for neuromarketing. Psychology & Marketing. 2016;33(8):664–674. 10.1002/mar.20907 [DOI] [Google Scholar]
  57. Mohd Isa S, Anuar NNA: Neuromarketing cues: an eye-tracking study on mothers’ visual attention to organic vegetable advertisements. Neuroscience Research Notes. 2024;7(4):363.1–363.7. 10.31117/neuroscirn.v7i4.363 [DOI] [Google Scholar]
  58. Morin C: Neuromarketing: The new science of consumer behavior. Symposium: Consumer Culture in Global Perspective. 2011;48(2):131–135. 10.1007/s12115-010-9408-1 [DOI] [Google Scholar]
  59. Morin C, Hsu M, Yoon C: The new frontier of neuromarketing: Innovations, applications, and ethical considerations. Front. Neurosci. 2023;17:1191052. 10.3389/fnins.2023.1191052 [DOI] [Google Scholar]
  60. Morinaga M, Takahashi H, Nishimoto S: MEG signatures of consumer choice in visual marketing tasks. NeuroImage: Reports. 2022;2(1):100055. 10.1016/j.ynirp.2022.100055 [DOI] [Google Scholar]
  61. Mostafa MM: Brain processing of food brands using an electroencephalogram (EEG): A neuromarketing study. Journal of Food Products Marketing. 2012;18(2):90–109. 10.1080/10454446.2012.653782 [DOI] [Google Scholar]
  62. Nemorin S: Neuromarketing and the “poor in world” consumer: how the animalization of thinking underpins contemporary market research discourses. Consum. Mark. Cult. 2016. 10.1080/10253866.2016.1160897 [DOI] [Google Scholar]
  63. Ome T, Pacula RL: Eye tracking and neural measures in consumer research. J. Neurosci. Psychol. Econ. 2011;4(2):61–70. [Google Scholar]
  64. Petrovic P, Petersson KM, Ingvar M: Neural correlates of consumer decisions in multi-option contexts using MEG. Front. Hum. Neurosci. 2021;15:638426. 10.3389/fnhum.2021.638426 [DOI] [Google Scholar]
  65. Plassmann H, Varga M, Tusche A, et al. : Predicting sales of new consumer packaged products with fMRI, behavioral, survey, and market data. Marketing Science Institute Working Paper Series, 21-139. 2015. Retrieved from MSI Working Papers. [Google Scholar]
  66. Plassmann H, Venkatraman V, Huettel S, et al. : Consumer neuroscience: Applications, challenges, and possible solutions. Front. Hum. Neurosci. 2019;13:101. 10.3389/fnhum.2019.00101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Plassmann H, Venkatraman V, Huettel SA, et al. : Consumer neuroscience: Applications, challenges, and possible solutions. J. Mark. Res. 2021;58(2):325–340. 10.1177/0022243720965062 [DOI] [Google Scholar]
  68. Pradeep K, Solomon ML, Kumar D, et al. : Locating the viability of approach and applications in neuromarketing. Neuroquantology. 2022;20(17):568–576. 10.14704/Nq.2022.20.17.Nq88070 [DOI] [Google Scholar]
  69. Raiesdana S, Mousakhani M: Consumer preference analysis of an electric car using EEG signals. Comput. Intell. Neurosci. 2022:1875673. 10.1155/2022/9002101 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Ramsoy TZ, Larsen T, Skov M: Neuromodulation of impulsive choice: TMS effects on delay discounting in consumer decision-making. J. Consum. Psychol. 2021;31(3):529–542. 10.1002/jcpy.1208 [DOI] [Google Scholar]
  71. Rawnaque FS, Rahman KM, Anwar SF: Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inf. 2020;7:10. 10.1186/s40708-020-00109-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Romanowski R: Managing Economic Innovations – Methods and Instruments. Bogucki Wyd. Nauk., Poznań. 2019;2019. 978-83-7986-277-1. 10.12657/9788379862771-4 [DOI] [Google Scholar]
  73. Rong L, Lulong L, Zhihua D: Mentor or partner: Matching effect of para-social interaction and product type on purchase intention in live-streaming commerce. Electron. Commer. Res. Appl. 2024. 10.1016/j.elerap.2024.101076 [DOI] [Google Scholar]
  74. Russo V, Ma Q, Clement J, et al. : Editorial: Neuromanagement and Neuromarketing. Front. Psychol. 2022;13:864566. 10.3389/fpsyg.2022.864566 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Schneider T, Woolgar S: Technologies of ironic revelation: Enacting consumers in neuromarkets. Consumption Markets & Culture. 2012;15(2):169–189. 10.1080/10253866.2012.654959 [DOI] [Google Scholar]
  76. Şik E, Soba M: Eyes do not lie: Investigation of the effectiveness of outdoor advertising instruments—An applied neuromarketing research with eye-tracking technique. Acta Bioethica. 2021;27(2):235–246. 10.4067/S1726-569X2021000200235 [DOI] [Google Scholar]
  77. Silberstein RB, Nield GE: Steady-state topography as a tool for measuring cognitive and emotional engagement in real-time consumer neuroscience. Journal of Consumer Neuroscience. 2021;5(1):17–29. 10.1016/j.jcns.2021.01.004 [DOI] [Google Scholar]
  78. Sliwinska MW, Vitello S, Devlin JT: Transcranial magnetic stimulation for investigating causal brain-behavioral relationships and their time course. J. Vis. Exp. 2014;89:e51735. 10.3791/51735 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Spence C: Cognitive influence on the evaluation of wine: The impact and assessment of price. Food Res. Int. 2024;187:114411. 10.1016/j.foodres.2024.114411 [DOI] [PubMed] [Google Scholar]
  80. Solnais C, Andreu-Perez J, Sánchez-Fernández J, et al. : The contribution of neuroscience to consumer research: A conceptual framework and empirical review. J. Econ. Psychol. 2013;36:68–81. 10.1016/j.joep.2013.02.011 [DOI] [Google Scholar]
  81. Song G, Gazi MAI, Waaje A, et al. : The neuromarketing: Bridging neuroscience and marketing for enhanced consumer engagement. IEEE Access. 2025;13:40331–40353. 10.1109/ACCESS.2025.3545742 [DOI] [Google Scholar]
  82. Stanton SJ, Sinnott-Armstrong W, Huettel SA: Neuromarketing: Ethical implications of its use and potential misuse. J. Bus. Ethics. 2017;144(4):799–811. 10.1007/s10551-016-3059-0 [DOI] [Google Scholar]
  83. Suhendra A, Taqwa T, Hermita M, et al. : Implementation of Naive Bayes method for product purchasing decision using neural impulse actuator in neuromarketing. In 2015 International Conference on Information & Communication Technology and Systems (ICTS)(pp.181–186).2015; IEEE. 10.1109/ICTS.2015.7379879 [DOI] [Google Scholar]
  84. Taqwa T, Suhendra A, Hermita M, et al. : Implementation of Naïve Bayes method for product purchasing decision using neural impulse actuator in neuromarketing, International Conference on Information & Communication Technology and Systems (ICTS) Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inf. 2015;7:10. 10.1186/s40708-020-00109-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Vaquero JJ, Kinahan P: Positron emission tomography: Current challenges and opportunities for technological advances in clinical and preclinical imaging systems. Annu. Rev. Biomed. Eng. 2015;17:385–414. 10.1146/annurevbioeng-071114-040723 [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Vecchiato G, Toppi J, Astolfi L, et al. : Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements. Medical & Biological Engineering & Computing. 2011;49(5):579–583. 10.1007/s11517-011-0747 [DOI] [PubMed] [Google Scholar]
  87. Venkatraman V, Dimoka A, Pavlou PA, et al. : Predicting advertising success beyond traditional measures: New insights from neurophysiological methods and market response modeling. J. Mark. Res. 2012;52(4):436–452. 10.1509/jmr.13.0593 [DOI] [Google Scholar]
  88. Yang Y, Zhang T, Qian W, et al. : EEG-based affective computing on brand preference: A deep learning approach. Front. Comput. Neurosci. 2022;16:837653. 10.3389/fncom.2022.837653 [DOI] [Google Scholar]
  89. Yao X, Wang Y: Using neural data to forecast aggregate consumer behavior in neuromarketing: Theory, metrics, progress, and outlook. J. Consum. Behav. 2024;23(4):2142–2159. 10.1002/cb.2324 [DOI] [Google Scholar]
  90. Zaltman G: How customers think: Essential insights into the mind of the market. 2003; Boston, MA: Harvard Business School Press. [Google Scholar]
  91. Zhang Y, Li H: Transcranial magnetic stimulation reveals the neural basis of affective priming in consumer trust. Front. Neurosci. 2022;16:867421. 10.3389/fnins.2022.867421 [DOI] [Google Scholar]
F1000Res. 2026 Sep 15. doi: 10.5256/f1000research.207864.r516873

Reviewer response for version 4

Rupali Gill 1

  1. Clarify the definition and classification of “non-invasive” methods, particularly PET.

  2. Clearly identify the review type and describe the literature-search methodology.

  3. Strengthen the method-selection matrix using concrete marketing applications.

  4. Expand discussion of reverse inference, predictive validity, and ecological validity.

  5. Add a practical ethical governance checklist/table.

  6. Clarify the methodological role of TMS.

  7. Perform comprehensive language editing and remove repetition.

  8. Check technical terminology, frequency bands, figures/tables, and references.

Is the review written in accessible language?

Partly

Are all factual statements correct and adequately supported by citations?

Partly

Are the conclusions drawn appropriate in the context of the current research literature?

Yes

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes

Reviewer Expertise:

Neuromarketing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

F1000Res. 2026 Sep 14. doi: 10.5256/f1000research.207864.r515812

Reviewer response for version 4

Ramachandran K K 1

No futher comments. Approved.

Is the review written in accessible language?

Partly

Are all factual statements correct and adequately supported by citations?

Yes

Are the conclusions drawn appropriate in the context of the current research literature?

Partly

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes

Reviewer Expertise:

Machine Learning,Artificial Intelligence,Learning Algorithms,Business,Deep Learning,Use Of Artificial Intelligence,Artificial Intelligence Technology,Consumer Behavior,Integration Of Artificial Intelligence,Internet Of Things,Artificial Intelligence Systems,Big Data

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

References

  • 1. : Augmented and Virtual Reality (AR/VR) in Marketing: Developing Immersive Client Experiences to Increase Engagement. https://ieeexplore.ieee.org/document/10434889 .
  • 2. : Examining the Integration of Artificial Intelligence and Marketing Management to Transform Consumer Engagement. https://ieeexplore.ieee.org/document/10545209/authors#authors .
  • 3. : Utilising AI to Transform Marketing Management and Enhance Consumer Interaction Strategies. https://ieeexplore.ieee.org/document/10545081 .
F1000Res. 2026 Sep 10. doi: 10.5256/f1000research.207864.r515815

Reviewer response for version 4

Singgih Santoso 1

Thank you for the revisions made to the manuscript. I have reviewed Version 4 in relation to my previous report. Several of my earlier concerns have been satisfactorily addressed, particularly the clarification of the review methodology and the strengthened synthesis of the method-selection matrix and knowledge gaps.

However, two issues remain and would benefit from further clarification.

First, the conceptual consistency regarding the term “noninvasive” still requires attention. Although PET has been removed from the abstract list of noninvasive methods, PET continues to be discussed in the main text despite its use of radioactive tracers. The manuscript should explicitly clarify that PET is not a noninvasive technique and explain why it is retained in the discussion or alternatively revise the relevant section to maintain consistency with the stated scope of the article. A clearer typology of the methods would also help resolve this issue.

Second, the discussion of methodological and ethical limitations has been improved substantially, including issues related to privacy, data security, informed consent, and the limitations of predicting real-world consumer behavior. However, the ethical governance discussion would benefit from a clearer practical statement or brief checklist covering informed consent, privacy, data use, data security, and the appropriate limits of neuroadaptive marketing.

Overall, Version 4 represents a substantial improvement and addresses most of the concerns raised in my previous review.

I recommend Approved with Reservations, with the expectation that these two remaining issues be clarified in the next revision.

Is the review written in accessible language?

Partly

Are all factual statements correct and adequately supported by citations?

Yes

Are the conclusions drawn appropriate in the context of the current research literature?

Partly

Is the topic of the review discussed comprehensively in the context of the current literature?

Partly

Reviewer Expertise:

Marketing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

F1000Res. 2026 Jun 29. doi: 10.5256/f1000research.198451.r468284

Reviewer response for version 3

Singgih Santoso 1

Article makes use of non-invasive neuromarketing techniques to assess how consumers make purchasing decisions. Such technologies include EEG, fMRI, MEG, TMS, Steady-State Topography, eye-tracking and peripheral psychophysiology, and are related to the core constructs of attention, emotion, memory and valuation. And the article also includes a method-selection matrix which links common marketing-related questions (e.g. ad testing, packaging, pricing) with applicable techniques and describes the trade-offs involved for both cost and temporal/spatial resolution versus ecological validity.

comments

First, the scope and method of the review are still not clearly described. I would recommend that the authors state explicitly that this is a narrative/scoping review and briefly describe how they identified the relevant literature (e.g., the main databases searched, the years covered, the keywords used, and the basic criteria for inclusion). This will provide clarity regarding what is meant by “comprehensive” coverage and will respond to earlier concerns about the transparency of the review process.

Second, the method- selection matrix is a nice addition; however, the synthesis of the matrix can be improved. It would be helpful for readers if the authors provided some specific examples to help them understand why certain methods or combinations of methods are better suited for use than others (e.g., pre-testing TV ads, evaluating sustainable branding, optimizing pricing). In addition, it would be helpful for the authors to include a short subsection that summarizes what we know and don’t know for each of the four main constructs (i.e., attention, emotion, memory, and valuation) at the end of the conclusions to help ground the conclusions in evidence from the literature review.

third, the lack of consistency in terms and concept relating to ‘non-invasive’ measures is still a major issue. Those who previously reviewed research in this area pointed out that PET is one example that has complicating factors. The author should clarify the use of the term “non-invasive” by developing a typology (metabolic imaging, electrophysiology, neuromodulation, peripheral psycho-physiological, and behavioural / implicit).

Fourth, a welcome addition to the paper is the inclusion of discussion about methodical and ethical challenges; although they were not explored in detail. The paper could benefit from a more rigorous examination of the issues raised by breaking down the issues using examples of situations where neuromarketing outcomes could not predict reality or where low sample sizes/inverse inference have led to conclusions that have been overgrown. I also propose developing a checklist of ethical practices and principles for practitioners regarding consent, privacy, use of data, and limitations of neuroadaptive marketing and including them in a table in this article to increase the practical relevance of the article.

Overall recommendation

After reviewing the article and its revisions, I recommend it as approved, but with reservations. After clarifying their review method, providing more integration around their method selection matrix, strengthening their critique of methodology and ethical considerations, and improving the clarity of the language used to describe these issues, this paper will provide an important addition to the literature.

NOTE:

The Turnitin test shows that the article has a similarity score of 97% and an AI writing score of 57%, which proves that the article was generated by AI and contains plagiarism. In order for it to be published, please reduce the similarity score and ensure the AI writing score is BELOW 30%.

Is the review written in accessible language?

Partly

Are all factual statements correct and adequately supported by citations?

Yes

Are the conclusions drawn appropriate in the context of the current research literature?

Partly

Is the topic of the review discussed comprehensively in the context of the current literature?

Partly

Reviewer Expertise:

Marketing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

F1000Res. 2026 Mar 30. doi: 10.5256/f1000research.195165.r468285

Reviewer response for version 2

Ramachandran K K 1

The manuscript presents a narrative review of non-invasive neuromarketing methods employed to examine consumer behavior. It discusses key techniques such as EEG, fMRI, MEG, TMS, eye-tracking, and Steady-State Topography, and connects them to core constructs including attention, emotion, memory, and valuation. The authors seek to move beyond purely descriptive accounts by providing a more comparative and integrative framework that incorporates a method-selection matrix and an analysis of trade-offs among cost, ecological validity, and temporal versus spatial resolution. In addition, it outlines potential directions for future research, particularly emphasizing the importance of multimodal approaches and more ecologically valid study designs.

Strength:

  1. The paper successfully links neuroscientific methods with core marketing constructs such as attention, emotion, memory, and valuation.

  2. It incorporates recent literature, reflecting current developments in the field.

  3. A well-written article that is appropriate to the journal.

Weakness:

  1. The readability is partially effective as several sections contain errors in grammar and phrasing and are frequently dense and repetitive. This makes it difficult to follow the arguments.

  2. Ensure consistency

Comments:

The manuscript addresses a relevant and emerging topic and offers a valuable integrative perspective on non-invasive neuromarketing methods. The effort to connect techniques with core consumer behavior constructs and to propose a comparative framework is a notable strength . To avoid redundancy, simplify the sentence structure to make it easier to follow.

The manuscript presents a meaningful contribution to the field and is suitable for indexing with a moderate revision addressing clarity, and consistency.

Is the review written in accessible language?

Partly

Are all factual statements correct and adequately supported by citations?

Yes

Are the conclusions drawn appropriate in the context of the current research literature?

Partly

Is the topic of the review discussed comprehensively in the context of the current literature?

Yes

Reviewer Expertise:

Marketing, Management, AI, Retail Management

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

F1000Res. 2025 Dec 29. doi: 10.5256/f1000research.185388.r432458

Reviewer response for version 1

Fitri Aprilianty 1,2

1. Is the topic of the review discussed comprehensively in the context of the current literature? ( Partly)

The paper covers many key tools and cites a broad range of literature, including recent work on EEG, fMRI, MEG, TMS, eye-tracking, and neuromarketing ethics.

However, the discussion is mostly descriptive and technique-by-technique, with limited synthesis across studies, and there is no explicit method for selecting or delimiting the reviewed literature. Important dimensions such as cross-modal comparisons, application domains (e.g. branding, pricing, digital platforms), and known controversies (e.g. reverse inference, effect sizes, replicability) are touched on only briefly, if at all.

  • Clarify the scope and method by state explicitly that this is a narrative/scoping review rather than a systematic review, and briefly describe how the authors identified and selected the literature 

  • Add at least one integrative section or figure (e.g. a table or matrix) that:
    • compares techniques on temporal/spatial resolution, cost, ecological validity, and typical marketing questions they address; and
    • summarises “what we know” after two decades of consumer neuroscience (e.g. robust findings on value coding, attention, memory, and predictive validity for real-world outcomes).
  • Expand the “big picture” integration in the conclusion: what are the main areas of consensus/disagreement in the literature, and where are the most promising gaps (e.g. multi-modal approaches, longitudinal designs, cross-cultural neuromarketing)?

2. Are all factual statements correct and adequately supported by citations?

Non-invasive” and invasive inconsistency. The title and abstract frame the review as focusing on non-invasive methods, listing PET alongside fMRI, EEG, etc. Later, the PET section correctly notes that PET involves injection of radioactive tracers and is therefore invasive and less commonly used. This is internally inconsistent and conceptually misleading. Either (a) remove PET from the “non-invasive” framing in the title/abstract and clearly state that PET is minimally invasive and now rarely used in consumer work, or (b) broaden the title/scope beyond “non-invasive” and clearly distinguish invasive vs non-invasive methods throughout. 

The paper sometimes groups very different tools under “non-invasive neuromarketing techniques” (e.g. PET, TMS, implicit association tests, biometric sensors) without clearly distinguishing neuroimaging / electrophysiology from behavioural / psychophysiological measures.

Use a consistent typology (e.g. metabolic imaging (fMRI, PET), electrophysiological (EEG, MEG, SST), neuromodulation (TMS), peripheral psychophysiology (GSR, HR, facial EMG), behavioural implicit measures).

4. Are the conclusions drawn appropriate in the context of the current research literature? 

However, the conclusions are quite general and do not always flow directly from the preceding sections. They would be more impactful if they more clearly summarised what the reviewed evidence actually shows and what remains uncertain.

Anchor conclusions to specific evidence. For example, summarise key robust findings (e.g. value coding in vmPFC/NAcc, predictive power of certain neural markers for campaign success, reliability of frontal alpha asymmetry for approach–avoidance tendencies).

Differentiate short-term promise vs long-term challenges

Is the review written in accessible language?

Yes

Are all factual statements correct and adequately supported by citations?

Partly

Are the conclusions drawn appropriate in the context of the current research literature?

Partly

Is the topic of the review discussed comprehensively in the context of the current literature?

Partly

Reviewer Expertise:

Consumer Behavior, marketing, neuromarketing

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

F1000Res. 2025 Dec 11. doi: 10.5256/f1000research.185388.r425175

Reviewer response for version 1

Jesús Jaime Moreno Escobar 1

The proposal advocates for the use of non-invasive neuromarketing techniques—such as fMRI, EEG, PET, MEG, and eye-tracking—to understand the subconscious drivers of consumer behavior. This approach aims to overcome the limitations of traditional methods like surveys and focus groups by directly measuring brain activity and physiological responses.

Advantages:

  1. Access to Subconscious Processes: The primary advantage is the ability to bypass the limitations of self-reporting. These techniques can reveal unconscious emotional, cognitive, and decision-making processes that consumers cannot or will not articulate, providing more authentic insights.

  2. Objective and Rich Data: Neuroimaging provides objective, biological data on consumer responses to marketing stimuli (ads, products, brands). This allows for a deeper understanding of the neural mechanisms behind attention, emotional engagement, memory encoding, and preference formation.

  3. Improved Predictive Power: Studies show that neural data can predict market outcomes, such as advertising success and product desirability, more accurately than traditional methods. This allows companies to refine product designs, pricing, and promotional content with greater confidence, potentially reducing market failure.

  4. Real-Time and High-Resolution Insights: Techniques like EEG and MEG offer very high temporal resolution (milliseconds), allowing researchers to track consumer responses in real time. This is crucial for understanding the immediate impact of fast-paced marketing stimuli.

  5. Application to Innovation and Personalization: Neuromarketing supports new product development by testing prototypes and can enable hyper-personalized, "neuroadaptive" marketing where content is dynamically tailored based on real-time neural feedback.

Disadvantages:

  1. High Cost and Complexity: Many of these tools, particularly fMRI and MEG, are extremely expensive to acquire and operate. They also require highly specialized expertise in both neuroscience and data analysis, limiting their accessibility.

  2. Methodological Limitations: Each technique has inherent trade-offs. fMRI has excellent spatial resolution but poor temporal resolution and a high cost. EEG is portable and has great temporal resolution but poor spatial resolution and cannot probe deep brain structures.

  3. Ethical and Privacy Concerns: The field raises significant ethical questions about "mind-reading," consumer manipulation, and data privacy. There is public apprehension about the potential for brands to exploit subconscious biases and vulnerabilities.

  4. Interpretation Challenges: Linking specific neural signals to complex psychological constructs like "brand loyalty" or "purchase intent" is inferentially complex. The data requires careful interpretation, and standardized protocols are still evolving, which can lead to variability in results.

  5. Limited Adoption and Transparency: The high barriers to entry have slowed widespread adoption in market research. Furthermore, businesses are often hesitant to disclose their use of neuromarketing, which fuels skepticism about its application.

    Dear Editor,

    Thank you for the opportunity to review this manuscript. I have evaluated it based on standard scholarly criteria. My overall recommendation is to  reject the manuscript in its current form, but with encouragement for a significant resubmission. The manuscript addresses a topic of considerable interest but requires substantial revisions to meet the journal's standards. Below is my detailed evaluation.

    1. Originality / Novelty

    The topic of applying non-invasive neuromarketing techniques is inherently relevant. However, the manuscript's originality is limited. It functions primarily as a descriptive catalog of established methods (fMRI, EEG, PET, MEG) and their general advantages and disadvantages, rather than presenting a novel synthesis, a new conceptual framework, or a critical gap analysis that would distinguish it from other existing reviews in the field. The authors have not clearly demonstrated how their approach or synthesis offers a new perspective compared to the extant literature.

    2. Significance of Content

    The content is of significant potential interest to the fields of marketing, consumer behavior, and applied neuroscience. Understanding the subconscious drivers of consumer decision-making is a central challenge, and a well-executed review that critically appraises the value and limitations of these tools would be highly valuable. The potential for impact is present, but it is not fully realized in the current draft.

    3. Quality of Presentation

    This is a major weakness of the manuscript. The authors  do not use a consistently readable level of English. Grammatical errors, awkward phrasing, and unclear syntax are prevalent throughout the text, which significantly hinders comprehension and detracts from the scholarly tone. Furthermore, the logical structure is problematic; the justification for the review and the clear articulation of the main problem it aims to solve are not effectively exposed, making the manuscript's core contribution difficult to discern.

    4. Scientific Soundness

    The scientific soundness is compromised by several factors. While the reference list is relevant, it is not sufficiently current, with only 45% of the 88 references from the last five years. This undermines the manuscript's claim to represent the state-of-the-art in a rapidly evolving field. More critically, the review lacks a balanced critical perspective. It lists disadvantages (e.g., cost, ethical concerns) but does not engage with them in a substantive way that would provide a rigorous appraisal of the field's validity and reliability. The manuscript describes rather than critiques.

    5. Interest to the Readers

    A robust and well-written review on this topic would be of high interest to the diverse readership of  F1000Research, including neuroscientists, marketing professionals, and behavioral economists. The subject matter aligns well with interdisciplinary research. However, the current presentation issues significantly reduce its accessibility and, therefore, its potential impact.

    6. Overall Merit and Critical Concern

    The manuscript has a strong foundational premise and covers a valuable topic. However, its current overall merit is low due to the critical issues in presentation, originality, and scientific rigor. The  originality report shows a 19% similarity index, which is acceptable, but also flags that 71% of the content has a high probability of being AI-generated. This is a severe concern. The stilted language, lack of a coherent narrative voice, and descriptive rather than critical analysis are consistent with this finding. For a scholarly publication, the intellectual synthesis and critical perspective must be demonstrably the product of the authors' expertise.

    Recommendation:

    I recommend  rejecting the current manuscript but encouraging a resubmission after the authors undertake a thorough, top-to-bottom revision. The authors should:

  6. Completely rewrite the manuscript to ensure clarity, fluency, and a strong, critical authorial voice, decisively addressing the concerns of AI-assisted writing.

  7. Restructure the paper to clearly state its unique contribution, define a specific gap in the literature, and provide a compelling justification for the review.

  8. Update the literature review significantly to include more recent publications (post-2019).

  9. Move beyond description to provide a critical, balanced analysis that deeply engages with both the advantages and the methodological and ethical challenges of neuromarketing.

  10. The core topic has great potential, and I would be interested in reviewing a fundamentally revised version that demonstrates genuine scholarly engagement and critical thought.

Is the review written in accessible language?

Yes

Are all factual statements correct and adequately supported by citations?

Yes

Are the conclusions drawn appropriate in the context of the current research literature?

Yes

Is the topic of the review discussed comprehensively in the context of the current literature?

No

Reviewer Expertise:

COmputer Vision, Data Analitycs

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Data Availability Statement

    All data are publicly available and cited in the manuscript. No new data were generated, and no DOI applies.


    Articles from F1000Research are provided here courtesy of F1000 Research Ltd

    RESOURCES