Title: Aligning the Brain with Language Models Through a Nonlinear and Multimodal Approach
Primary area: applications to neuroscience & cognitive science
Keywords: neuroscience, fMRI, encoding model

Abstract: Self-supervised language and audio models effectively predict brain responses to speech. However, while nonlinear approaches have become standard in vision encoding, speech encoding models still predominantly rely on linear mappings from unimodal features. This linear approach fails to capture the complex integration of auditory signals with linguistic information across widespread brain networks during speech comprehension. Here, we introduce a nonlinear, multimodal prediction model that combines audio and linguistic features from pre-trained models (e.g., Llama, Whisper). Our approach achieves a 17.2% and 17.9% improvement in prediction performance (unnormalized and normalized correlation) over traditional unimodal linear models, as well as a 7.7% and 14.4% improvement over prior state-of-the-art models relying on weighted averaging of linear unimodal predictions. These substantial improvements not only represent a major step towards future robust in-silico testing and improved decoding performance, but also reveal distributed multimodal processing patterns across the cortex that support key neurolinguistic theories including the Motor Theory of Speech Perception, Convergence-Divergence Zone model, and embodied semantics. Overall, our work highlights the often neglected potential of nonlinear and multimodal approaches to speech encoding, paving the way for future studies to embrace these strategies in naturalistic neurolinguistics research.