Consumer neuroscience and neuromarketing
Consumer neuroscience and neuromarketing use brain and physiology measures to understand how people attend to, feel about, and act on digital content such as websites, social media, and online ads. Across studies, these tools reveal subconscious drivers of attention, emotion, memory, and behaviour that traditional self-reports often miss.
Core mechanisms and methods
Neural and physiological tools
Common tools include EEG, fMRI, fNIRS, eye tracking, GSR, facial coding, EMG, HRV, and ECG to measure attention, arousal, emotion, and memory during exposure to marketing stimuli.
Alsharif 2022 Dejene 2023 Song 2025These tools target perception, attention, emotions, memory, and decision-making, all central to digital consumer behaviour.
Cenizo 2025 Harris 2018Attention, emotion, and online responses
On web platforms, eye tracking and brain measures help optimize interface design and UX, but links between visual attention, brain activity, and emotions in complex online shopping remain underexplored.
Cenizo 2025 Cenizo 2025Health and social cause video ads on digital and social media: EEG theta/alpha patterns indicating attention and episodic memory encoding related to increased donations and ad liking, especially when raw emotion and vulnerability are shown.
Harris 2019 Harris 2019In online consumer reviews, physiological and EEG data show emotional contagion: readers' arousal aligns with the reviewer; negative reviews can trigger arousal with experienced pleasure, while positive reviews may reduce arousal and be experienced as less pleasant.
Herrando 2022 Herrando 2022fMRI work shows that anticipatory affect in reward and aversion regions at video onset predicts both individual viewing choices and aggregate YouTube views and watch time, outperforming conventional metrics.
Tong 2020 Tong 2020Examples across digital contexts
| Context / platform | Key neuromarketing insight | Citation |
|---|---|---|
| Websites / e‑commerce | Eye tracking + EEG reveal attention and cognitive load; multimodal measures (EEG, GSR, EMG, HRV) proposed to capture unconscious emotion and stress. | Cenizo 2025 Chiang 2022 |
| Social media ads / feeds | Sponsored content subtly disrupts ongoing affective flow rather than strongly amplifying emotion; biometric arousal and valence did not predict clicks or engagement. | Hübner 2025 Hübner 2025 |
| Instagram and visual platforms | Visual aesthetics, emotional content, personalization, and user‑generated content strengthen engagement, emotional bonds, memory, and purchase decisions. | — |
Neurophysiological measures can predict emotional response to Instagram ads, especially arousal and attention, but not perfectly. Instagram ad studies and broader advertising research point to three linked facets: which signals matter, what they predict, and where prediction is limited.
Rúa-Hidalgo 2021 Byrne 2022 Ramaswamy 2024Neurophysiological prediction of Instagram ads
| Evidence strength | Claim |
|---|---|
| Strong (8/10) | Multimodal measures predict emotional response better than self-report alone, because physiological and neural signals capture real-time, unconscious reactions that retrospective ratings often miss. Pozharliev 2021 Li 2018 |
| Moderate (7/10) | In Instagram contexts, GSR, eye tracking, and facial coding detect differences in arousal, attention, and implicit valence across ad formats and content features, including GIFs, influencer imagery, and product-versus-model emphasis. Rúa-Hidalgo 2021 Mañas-Viniegra 2020 |
| Moderate (6/10) | EEG markers such as frontal alpha asymmetry and LPP appear to be the most consistent neural indicators of positive versus negative responses to marketing stimuli, and prediction improves when EEG is combined with eye tracking or facial analysis. Byrne 2022 |
Signal types
EEG is the most studied neural tool for ad-response prediction because it is practical for video-based marketing experiments, and frontal/prefrontal alpha features are repeatedly used for emotion recognition. FAA tends to separate positive from negative consumer responses, while LPP tracks conscious emotional evaluation.
Rawnaque 2020 Byrne 2022 Byrne 2022Peripheral measures capture different parts of emotion. GSR indexes autonomic arousal, HR contributes valence and arousal when paired with GSR, EMG and facial coding track expressive valence, and eye tracking captures where emotional attention is allocated.
Cartocci 2017 Alsharif 2024- GSR: best for arousal peaks during ad viewing. Alsharif 2024 Li 2018
- Eye tracking: shows attention allocation, not emotion by itself. Lim 2020 Mañas-Viniegra 2020
- Facial coding / EMG: estimates valence and expression more directly than GSR. Rúa-Hidalgo 2021 Alsharif 2024
What they predict
In Instagram-specific studies, these measures predict moment-to-moment emotional intensity better than brand outcomes alone. Influencer posts with idealized or highly imperfect body imagery produced strong GSR peaks, and adolescents showed more attention and emotional intensity to influencer body appeal than to brands.
Mañas-Viniegra 2020GIF studies on Instagram found a consistent gap between implicit and explicit emotion: comments and declared feelings looked more positive than biometric signals indicated, which means physiology can reveal muted or conflicting emotional reactions that self-report hides.
Rúa-Hidalgo 2021A recent Instagram ad case study also found high attention and multiple GSR peaks, yet concluded the viewer focused more on the model or video flow than on the brand itself.
Zeng 2023Limits and best use
Prediction is strongest for arousal, attention, and broad positive-versus-negative response, but weaker for fine-grained purchase behaviour or universal emotion labels. Reviews of EEG neuromarketing report mixed consistency across individual measures, even though FAA and LPP are the most robust overall.
Byrne 2022Three limits matter most:
- Social context matters on platforms like Instagram, but most studies still test viewers in isolation. Pozharliev 2017
- Order and stimulus context can change measured arousal and engagement. Balconi 2023
- Multimodal fusion is promising, but aligning signals across modalities remains a major technical challenge. Ramaswamy 2024
Neurophysiological measures predict emotional response to Instagram ads best when EEG, GSR, eye tracking, and facial measures are combined, because each captures a different component of emotion. They are useful for detecting unconscious arousal and attention during Instagram ad exposure, but evidence is still moderate rather than definitive for predicting full downstream behaviour.
Neuromarketing for social media ads uses brain and physiological signals to estimate attention, emotion, memory, and sharing potential that surveys often miss. In this literature, EEG is the most established tool for ad-related emotional and cognitive responses, while heart rate and facial measures add complementary information about engagement and valence.
Rawnaque 2020 Alsharif & Isa 2024 Rawnaque 2020Attention and emotion
EEG is widely used in video advertising because its high time resolution can track rapid attention and emotional shifts during digital media exposure. Frontal and prefrontal alpha activity, especially frontal alpha asymmetry, is a common marker of approach versus withdrawal responses to marketing stimuli. Reviews of EEG neuromarketing find that frontal alpha asymmetry and the late positive potential are the most consistent EEG markers of emotional response, preference, and purchase intention, although results remain mixed across studies.
Rawnaque 2020 Alsharif & Isa 2024 Rawnaque 2020 Byrne 2022 Byrne 2022Heart rate contributes a different signal: it tends to reflect attention and emotional engagement, and in some ad studies inter-beat interval relates to valence and recognition memory. Negative advertising content can draw more attention than positive content, as shown by heart rate changes during ad exposure. Facial EMG and facial expression analysis are most useful for moment-to-moment valence, with zygomatic activity indexing more positive responses and corrugator activity indexing more negative responses.
Alsharif & Khraiwish 2024 Baldo 2022 Bolls 2001 Bolls 2001 Sato 2021Forecasting ad outcomes
| Signal | What it best forecasts | Example finding |
|---|---|---|
| EEG | Approach, preference, purchase intention | Frontal alpha asymmetry is the most reliable EEG preference marker in reviews. Byrne 2022 |
| Heart rate | Attention, recognition, engagement | Inter-beat interval predicts ad recognition and brand recognition components. Baldo 2022 |
| Facial EMG | Valence, liking, scene-level affect | Average, peak, and end EMG responses correlate with post-viewing ad attitude. Li 2019 |
| Multimodal combinations | Better overall prediction | Combined signals improve prediction of ad liking, preference, or effectiveness over single modes. Han 2025 Masui 2019 |
Social media ad relevance
For social media ads, these measures are most useful with video and in-feed content, where rapid scene changes and emotional cues unfold over time. EEG research in marketing increasingly targets advertising and is described as useful for predicting ad success and emotional engagement. Facial and heart-rate signals are attractive in digital settings because they are more scalable and can sometimes be collected remotely or with wearable systems.
Alsharif & Isa 2024 Masui 2019 Alsharif & Isa 2024 Marques 2024 Masui 2019The strongest pattern across studies is that no single signal is sufficient. EEG is better for covert attention and approach, heart rate for engagement and recognition-related processing, and facial EMG for immediate positive or negative affect. Machine-learning studies also show that combining physiological features can improve prediction accuracy for ad preference and purchase-related outcomes.
Baldo 2022 Alsharif & Khraiwish 2024 Marques 2024 Byrne 2022Neuromarketing for social media ads works best as a multimodal forecasting approach: EEG estimates approach and preference, heart rate tracks attention and engagement, and facial EMG captures moment-to-moment valence. Across the literature, combining these signals generally forecasts ad affect and downstream effectiveness better than any one measure alone.
Influencer–follower relationships center on three linked processes: attachment, perceived intimacy, and identification, and the evidence shows each helps explain why influencers shape attitudes and behavior. Across the literature, these bonds are usually described as parasocial or trans-parasocial rather than fully reciprocal, but social media interactivity makes them feel unusually close and consequential.
Ki 2020 Hoffner & Bond 2022 Lou 2021 Yuan & Lou 2020Attachment
Attachment to influencers appears to grow from credibility, similarity, and social presence rather than from exposure alone.
Kim & Kim 2022 Aggarwal & Shah 2024- Source credibility, homophily, and fairness all strengthen parasocial attachment or relationship quality. Yuan & Lou 2020 Vu 2024
- Inspiration, enjoyability, similarity, and informativeness help followers see influencers as need-fulfilling human brands, which intensifies attachment. Ki 2020
- Stronger attachment predicts loyalty, ad credibility, lower ad resistance, and stronger endorsement acceptance. Kim & Kim 2022 Ki 2020
Perceived intimacy
Perceived intimacy in social media contexts is mainly built through self-disclosure and responsiveness, which create familiarity and emotional closeness.
Zhang & Mac 2023- Emotional closeness, perceived similarity, and positive feelings increase parasocial interaction with influencers. Ferreira 2024
- Repeated interaction can make followers treat influencers like part of their social circle, increasing checking behavior and stickiness. Vu 2024
- These bonds can improve connection and support, but they also sometimes coincide with loneliness and problematic engagement. Liu & Lee 2024 Farivar 2022
Identification
Identification is a distinct but related mechanism: followers do not only feel close to influencers, they also see themselves in them or align with their communities.
Yin 2025 Wei 2022- Cognitive and affective identification both increase parasocial relationships with virtual influencers, with affective identification showing the stronger effect. Yin 2025
- Personal and social identification mediate how parasocial interaction translates into consuming, contributing, and creating brand-related content. Wei 2022
- Wishful identification and perceived influencer type shape parasocial relationship formation, especially for virtual or human-like influencers. Liu & Wang 2025 Masuda 2022
Overall, influencer–follower attachment, perceived intimacy, and identification are tightly connected and usually reinforce one another. In social media contexts, they help explain both marketing effectiveness and some relational risks when one-sided closeness becomes unusually strong.
Damaj & Nofal 2026 Hoffner & Bond 2022Online social post performance can often be predicted before posting, but accuracy depends on the outcome, features, and platform. Pre-post models using text, metadata, timing, media type, and account history consistently outperform simple baselines, while comments and conversational responses remain harder to forecast than likes or broader engagement counts.
Moro 2016 Kennedy 2021 Kim & Hwang 2025Predictive signal
Studies converge that post-level features such as content type, timing, media format, topic, and prior interaction history carry useful signal for engagement prediction. Text also matters: transformer embeddings improved pre-post forecasting in recent studies, and combining human-designed post features with BERT-derived features produced the best results in a 16,063-post marketing dataset.
Madnure & Kada 2025 Son & Park 2023 S 2025 Madasu 2025 Dai & Wang 2021Visual and multimodal features add further value. Instagram engagement was associated with specific caption language and image structure, while a large Facebook advertising study found image-plus-text fusion outperformed less integrated approaches for predicting user behavior.
Son & Park 2023 Ali 2025Model comparisons
| Model family | Typical strength | Example result | Main caveat |
|---|---|---|---|
| Linear models | Simple baseline | Logistic regression was below average on X/Twitter and Reddit. Hasanuzzaman 2025 | Misses complex patterns |
| Tree ensembles | Strong on tabular features | XGBoost led one multi-model comparison and reached 94.73% accuracy in a Facebook retail study. Hasanuzzaman 2025 Theodoridis & Gkikas 2025 | Depends on engineered inputs |
| Neural and transformer models | Strong on text-rich or combined features | Neural nets outperformed classic ML in engagement-choice prediction; transformer models beat traditional NLP baselines. Dai & Wang 2021 Madasu 2025 | Can need more data |
| Graph neural networks | Capture network structure | GNN methods improved engagement prediction on Facebook and reached 92% precision for high-engagement posts on X. Tengteng 2024 Arazzi 2026 | More complex to deploy |
| Temporal sequence models | Best for evolving engagement | LSTMs were best for sequential prediction, and IC-Mamba improved early engagement forecasting by 4.72% over prior methods. Madnure & Kada 2025 Tian 2025 | Often need early post history |
What is easiest to predict
Likes and aggregate interactions appear easier to predict than comments or behavior choice. In one multi-target study, likes reached R² = 0.98 while comments reached R² = 0.41, suggesting conversational engagement depends on additional unobserved factors. Classic Facebook work achieved around 27% mean absolute percentage error on two performance metrics, showing useful but imperfect forecasting even in structured brand-page settings.
Kim & Hwang 2025 Moro 2016Early post signals can greatly improve prediction when the goal is not strictly pre-post forecasting. First-hour comments predict overall engagement, and interval-censored temporal models can forecast growth within the first 15–30 minutes after posting with strong performance.
Li 2022 Tian 2025Practical patterns
- Content type is repeatedly important, with one Facebook study assigning it 36% relevance and finding status posts drew roughly twice the attention of links, photos, or videos. Moro 2016
- Timing matters, with evening posting recommended for Instagram in one travel-agency dataset and specific time slots linked to more clicks and revenue in content publishing. Son & Park 2023 Li 2022
- Reach and promotion variables matter alongside content, including post reach, paid promotions, and short video views in Facebook-based studies. Theodoridis & Gkikas 2025 Toer 2024
- Follower count alone is insufficient; one Instagram study found it useful but challenging as a sole predictor because audience behavior and platform algorithms vary. Kolhe 2025
Predicting online social post performance is feasible and often practically useful, especially for likes, total interactions, and engagement classes. The literature is less confident for comments, cross-platform generalisation, and settings where only minimal pre-post features are available.
S 2025 Kolhe 2025 Toer 2024