  Understanding the functional organization of higher visual cortex is a
central focus in neuroscience. Past studies have primarily mapped the visual
and semantic selectivity of neural populations using hand-selected stimuli,
which may potentially bias results towards pre-existing hypotheses of visual
cortex functionality. Moving beyond conventional approaches, we introduce a
data-driven method that generates natural language descriptions for images
predicted to maximally activate individual voxels of interest. Our method --
Semantic Captioning Using Brain Alignments ("BrainSCUBA") -- builds upon the
rich embedding space learned by a contrastive vision-language model and
utilizes a pre-trained large language model to generate interpretable captions.
We validate our method through fine-grained voxel-level captioning across
higher-order visual regions. We further perform text-conditioned image
synthesis with the captions, and show that our images are semantically coherent
and yield high predicted activations. Finally, to demonstrate how our method
enables scientific discovery, we perform exploratory investigations on the
distribution of "person" representations in the brain, and discover
fine-grained semantic selectivity in body-selective areas. Unlike earlier
studies that decode text, our method derives voxel-wise captions of semantic
selectivity. Our results show that BrainSCUBA is a promising means for
understanding functional preferences in the brain, and provides motivation for
further hypothesis-driven investigation of visual cortex.
