Interpreting the Artist's Mind: Using LLMs as a Neural Surrogate

Through mechanistic interpretability, and drawing a parallel to Hebbian learning—the neuroscientific principle that “neurons that fire together, wire together”—this project explores whether an LLM can serve as a neural surrogate to extend an artist's cognitive process and tests if linking this extended thought to a sensory output can create a palpable sense of fulfillment. It ponders two central questions for creators in the age of AI: how can one collaborate with generative models to amplify a unique voice, rather than be lost in the echoes of others? And as AI takes on more of the 'doing' how do we fill the innate human need to tangibly create?