Stronger Alignment between Brain Activity and LLM Embeddings during Code Writing compared to Prose Writing

Abstract

Programming is a critical skill underlying modern software systems, yet the cognitive processes supporting code writing are only beginning to be understood, limiting educational practices and developer tools. At the same time, Large Language Models (LLMs) are increasingly used to assist programming. These models themselves are not well understood and can exhibit undesirable behavior like introducing security vulnerabilities. Given evidence that some cognitive representations may be shared between LLMs and the brain, we seek to improve our understanding on both fronts by relating these two systems to one another. We used Voxelwise Encoding Models (VEMs) to relate LLM embeddings to brain activity measured with functional Magnetic Resonance Imaging (fMRI) during naturalistic writing tasks. Using participants' (n = 23) keystrokes as prompts, we extracted LLM embeddings to predict voxelwise Blood Oxygen Level Dependent (BOLD) signal, quantifying alignment as the correlation between predicted and recorded signal. To assess whether this alignment is specific to programming or generalizes to other generative processes, we compared code writing to prose writing. Alignment was strongest in the right frontal pole, and brain activity was significantly better predicted by LLM embeddings during code writing than prose writing (p < 0.001, FDR-corrected). Within participants, the best-modeled voxel locations for code writing were 66% consistent across LLM layers but varied substantially between participants (39% similarity). Our findings suggest stronger alignment between human and LLM representations during structured code generation, with implications for designing AI systems that predict code generation but support natural language tasks.