{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T06:04:19Z","timestamp":1790661859707,"version":"4.1.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:p>The application of machine learning to consumer neuroscience has made it possible, in principle, to predict consumer responses from media assets alone. However, the pursuit of model sophistication has outpaced attention to a foundational constraint: the quality of any predictive artificial intelligence (AI) system is bounded by the quality of the neurophysiological and biometric data used to train it, and by the rigor of the modeling procedures applied to those data. This article argues that persistent problems of low metric reliability, poor cross-metric consistency, and the broader replication crisis have left the field building predictive systems on an insecure measurement foundation. Anchored in the framework for neurometric validation established by Rams\u00f8y (2019), the paper offers a conceptual synthesis and agenda-setting framework for the AI era: a five-stage pipeline comprising (1) Metric Mapping, the theory-driven identification and pre-screening of candidate neurophysiological and biometric measures; (2) Psychometric Validation, the formal assessment of each metric\u2019s sensitivity, specificity, validity, and reliability; (3) Discriminatory Ability, an assessment of whether a metric produces meaningful score distributions across real-world commercial stimuli; (4) Principled Data Scaling and Model Development, covering both the large-scale data collection and the modeling and validation procedures required for robust prediction; and (5) Model Governance and Drift Prevention, the ongoing monitoring, retraining, and calibration required to maintain predictive validity over time. The individual components draw on established practice in psychometrics, measurement science, and applied machine learning; the contribution lies in integrating them into a coherent, domain-specific architecture for consumer neuroscience, and in making explicit a criterion\u2014Discriminatory Ability\u2014that is widely assumed but rarely tested in applied advertising research. The pipeline is offered not as a validated standard but as a structured research agenda and a basis for future work on standardization, certification, and governance, with the aim of shifting the field from a model-centric toward a validation-centric culture.<\/jats:p>","DOI":"10.3389\/fnhum.2026.1837923","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T05:46:17Z","timestamp":1790660777000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["From metric to model: a five-stage pipeline for building and sustaining valid AI predictive systems in consumer neuroscience"],"prefix":"10.3389","volume":"20","author":[{"given":"Thomas Zo\u00ebga","family":"Rams\u00f8y","sequence":"first","affiliation":[{"name":"Neurons","place":["Taastrup, Denmark"]},{"name":"International Center for Applied Neuroscience","place":["R\u00f8rvig, Denmark"]},{"name":"Free University of Languages and Communication (IULM)","place":["Milan, Italy"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1057\/s41267-017-0081-0","article-title":"Science\u2019s reproducibility and replicability crisis: international business is not immune","volume":"48","author":"Aguinis","year":"2017","journal-title":"J. Int. Bus. Stud."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.jcps.2011.05.001","article-title":"A neural predictor of cultural popularity","volume":"22","author":"Berns","year":"2012","journal-title":"J. Consum. Psychol."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1509\/jmr.13.0572","article-title":"Brain responses to movie trailers predict individual preferences for movies and their population-wide commercial success","volume":"52","author":"Boksem","year":"2015","journal-title":"J. Mark. Res."},{"key":"ref4","first-page":"77","article-title":"Gender shades: intersectional accuracy disparities in commercial gender classification","volume":"81","author":"Buolamwini","year":"2018","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1037\/h0046016","article-title":"Convergent and discriminant validation by the multitrait-multimethod matrix","volume":"56","author":"Campbell","year":"1959","journal-title":"Psychol. Bull."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"4567","DOI":"10.1038\/ncomms5567","article-title":"Audience preferences are predicted by temporal reliability of neural processing","volume":"5","author":"Dmochowski","year":"2014","journal-title":"Nat. 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Surv."},{"key":"ref12","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1037\/0000262-027","volume-title":"APA Handbook of Consumer Psychology","author":"Genevsky","year":"2022"},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.3758\/s13428-017-0935-1","article-title":"The reliability paradox: why robust cognitive tasks do not produce reliable individual differences","volume":"50","author":"Hedge","year":"2018","journal-title":"Behav. Res. Methods"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1017\/S0140525X0999152X","article-title":"The weirdest people in the world?","volume":"33","author":"Henrich","year":"2010","journal-title":"Behav. Brain Sci."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"100804","DOI":"10.1016\/j.patter.2023.100804","article-title":"Leakage and the reproducibility crisis in machine-learning-based science","volume":"4","author":"Kapoor","year":"2023","journal-title":"Patterns"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.neuron.2006.11.010","article-title":"Neural predictors of purchases","volume":"53","author":"Knutson","year":"2007","journal-title":"Neuron"},{"key":"ref17","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","article-title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","volume":"15","author":"Koo","year":"2016","journal-title":"J. Chiropr. Med."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"351","DOI":"10.3390\/computers14090351","article-title":"Model drift in deployed machine learning models for predicting learning success","volume":"14","author":"Kustitskaya","year":"2025","journal-title":"Computers"},{"key":"ref19","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1093\/brain\/106.3.623","article-title":"Time of conscious intention to act in relation to onset of cerebral activity (readiness-potential)","volume":"106","author":"Libet","year":"1983","journal-title":"Brain"},{"key":"ref20","author":"Mitchell","year":"2019"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"aac4716","DOI":"10.1126\/science.aac4716","article-title":"Estimating the reproducibility of psychological science","volume":"349","year":"2015","journal-title":"Science"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: an updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533378","article-title":"Challenges in deploying machine learning: a survey of case studies","volume":"55","author":"Paleyes","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref24","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1001\/jamapsychiatry.2019.3671","article-title":"Establishment of best practices for evidence for prediction: a review","volume":"77","author":"Poldrack","year":"2020","journal-title":"JAMA Psychiatry"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"281","DOI":"10.2501\/JAR-2019-034","article-title":"Building a foundation for neuromarketing and consumer neuroscience research","volume":"59","author":"Rams\u00f8y","year":"2019","journal-title":"J. Advert. Res."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"138","DOI":"10.3389\/fnins.2018.00138","article-title":"Frontal brain asymmetry and willingness to pay","volume":"12","author":"Rams\u00f8y","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref28","author":"Sculley","year":"2015"},{"key":"ref29","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1037\/0033-2909.86.2.420","article-title":"Intraclass correlations: uses in assessing rater reliability","volume":"86","author":"Shrout","year":"1979","journal-title":"Psychol. Bull."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"2477","DOI":"10.1056\/NEJMc2029240","article-title":"Racial bias in pulse oximetry measurement","volume":"383","author":"Sjoding","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"279","DOI":"10.4300\/JGME-D-12-00156.1","article-title":"Using effect size\u2014or why the p value is not enough","volume":"4","author":"Sullivan","year":"2012","journal-title":"J. Grad. Med. Educ."},{"key":"ref32","year":"2024"},{"key":"ref33","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1080\/00913367.2024.2418109","article-title":"Reliability of EEG metrics for assessing video advertisements","volume":"54","author":"Van Diepen","year":"2024","journal-title":"J. Advert."},{"key":"ref34","doi-asserted-by":"publisher","first-page":"176","DOI":"10.2501\/JAR-55-2-176-191","article-title":"How reliable are neuromarketers\u2019 measures of advertising effectiveness? Data from ongoing research holds no common truth among vendors","volume":"55","author":"Varan","year":"2015","journal-title":"J. Advert. Res."},{"key":"ref35","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.neuroimage.2017.06.061","article-title":"Cross-validation failure: small sample sizes lead to large error bars","volume":"180","author":"Varoquaux","year":"2018","journal-title":"NeuroImage"}],"container-title":["Frontiers in Human Neuroscience"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2026.1837923\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T05:46:19Z","timestamp":1790660779000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2026.1837923\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,29]]},"references-count":35,"alternative-id":["10.3389\/fnhum.2026.1837923"],"URL":"https:\/\/doi.org\/10.3389\/fnhum.2026.1837923","relation":{},"ISSN":["1662-5161"],"issn-type":[{"value":"1662-5161","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,29]]},"article-number":"1837923"}}