Making Sense of Soft Tokens: Brain Captioning with Causally Testable Semantic Channels

Brain captioning models map fMRI responses to continuous soft tokens that a frozen language model consumes to generate natural language. While effective for producing captions, these brain-derived tokens are primarily shaped to fit the language model's output space, without an explicit, text-like semantic interface connecting brain activity to language. Consequently, it remains unclear how semantic information is organized within these tokens, what individual tokens represent, and which representations the language model actually uses. We introduce **MindLens**, a lightweight semantic interface that transforms a subset of LLM-facing soft tokens into named semantic channels. Each channel is assigned a human-interpretable concept group and directly scores its concepts in the LLM's own token-embedding space, requiring only two additional scalar parameters in total and no architectural modification to the language model. By assigning each concept to a single channel, MindLens encourages channels to specialize in coherent yet distinct semantic content, transforming an opaque neural prefix into an explicit and structured semantic interface. Rather than adding information, MindLens realigns the semantic information already encoded in the fMRI tokens into interpretable directions that can be directly read and causally tested. On the Natural Scenes Dataset, MindLens achieves a concept mAP of $0.410$ without a learned probe, closely approaching the 0.420 obtained by a trained probe from the unstructured backbone, while preserving caption quality (CIDEr: 64.80 vs. 64.21). Targeted interventions further show that individual channels selectively control their associated concepts during generation. These results establish MindLens as an interpretable intermediate layer between neural activity and language, enabling brain captioning to move beyond fitting final language outputs toward explicitly representing, validating, and causally testing the semantic information carried by brain-derived tokens.