Inverting Foundation Models of Brain Function with Simulation-Based Inference

We explore whether foundation models of brain activity can work in reverse: recovering stimulus properties from synthetic brain responses. Using TRIBEv2, LLM-generated news headlines, and simulation-based inference, we show that latent linguistic parameters can be decoded from predicted brain maps, motivating future work in neural decoding and inverse stimulus design.