NeuroViT: A Brain-Aligned Vision Transformer for Product Image Analysis
Publication Date
7-22-2026
Abstract
Consumer decisions in digital marketplaces are heavily shaped by product images, yet marketers have few tools for understanding why one image outperforms another in terms that connect to how people actually see. This paper develops NeuroViT, a vision transformer trained on large-scale fMRI data to model how the human visual cortex processes images, and uses it to build a brain-aligned framework for image mining. We validate NeuroViT against an original fMRI study of hotel images, showing that its modeled neural activity closely tracks observed brain responses in a marketing-relevant domain. From this model, we derive two interpretable, brain-aligned scores---a retinotopic score capturing perceived visual complexity and a place score capturing navigational affordance---and show both track independent human judgments across diverse image sets. We then demonstrate their managerial value in two applications: a preregistered experiment in which hotel image sets with higher navigational affordance causally increase booking intentions, and a large-scale econometric analysis of Airbnb listings in which both scores correspond to meaningful revenue differences. Brain-aligned embeddings also add predictive value beyond the unaligned backbone. Together, these results offer marketers a scalable, neuroscience-grounded framework for understanding and optimizing the visual content that shapes consumer choice.
Document Type
Article
Keywords
E-Commerce, Image Mining, Machine Learning, Computer Vision, Neuroscience, fMRI
Disciplines
Marketing
Source
SMU Cox: Marketing (Topic)
Language
English
