Spurious reconstruction from brain activity.

Advances in brain decoding, particularly in visual image reconstruction, have sparked discussions about the societal implications and ethical considerations of neurotechnology. As reconstruction methods aim to recover visual experiences from brain activity and achieve prediction beyond training samples (zero-shot prediction), it is crucial to assess their capabilities and limitations to inform public expectations and regulations. Our case study of recent text-guided reconstruction methods, which leverage a large-scale dataset (Natural Scenes Dataset, NSD) and text-to-image diffusion models, reveals critical limitations in their generalizability, demonstrated by poor reconstructions on a different dataset. UMAP visualization of the text features from NSD images shows limited diversity with overlapping semantic and visual clusters between training and test sets. We identify that clustered training samples can lead to "output dimension collapse," restricting predictable output feature dimensions. While diverse training data improves generalization over the entire feature space without requiring exponential scaling, text features alone prove insufficient for mapping to the visual space. Our findings suggest that the apparent realism in current text-guided reconstructions stems from a combination of classification into trained categories and inauthentic image generation (hallucination) through diffusion models, rather than genuine visual reconstruction. We argue that careful selection of datasets and target features, coupled with rigorous evaluation methods, is essential for achieving authentic visual image reconstruction. These insights underscore the importance of grounding interdisciplinary discussions in a thorough understanding of the technology's current capabilities and limitations to ensure responsible development.