[Hakkin]

Briefly reading the paper, it seems they trained 2 models (using data from different stages in the visual cortex) to generate latent vectors for both the visual and textual representations of the fMRA data, then feed those into Stable Diffusion. Those are the models that would be overfit in this case, so instead of those models being able to encode features like "toy, animal, fluffy, brown, ears, nose, arms, legs" individually, it's likely just encoding all of those features combined into a generic "teddy bear" because the input dataset is too small. Obviously this is an oversimplification, but hopefully you get what I mean. I didn't mean it was literally an object classifier, but that the nature of a model like this, with a dataset so small, it does not have to ability to extrapolate fine details. With a larger dataset and more training, it may be able to actually do that.