🤖 AI Summary
This study investigates the neural alignment between brain activity during natural language comprehension and hierarchical representations from multimodal pre-trained models—specifically, auditory (wav2vec 2.0) and vision-language (CLIP) models. Using high-density EEG recorded during auditory sentence processing, we systematically evaluated the predictive power of layer-wise model embeddings for neural responses via ridge regression and contrastive decoding. Our key contribution is a novel “multimodal + layer-aware” representation strategy that integrates cross-model and cross-layer features through concatenation and summation across layers. Results demonstrate that multimodal joint representations significantly outperform unimodal or shallow-layer baselines—particularly in higher-order semantic regions—yielding an average 18.7% increase in explained variance (R²). This provides the first empirical evidence of a neurobiological hierarchy mapping low-level auditory processing onto progressively abstract, cross-modal semantic representations during language understanding.
📝 Abstract
When we hear the word "house", we don't just process sound, we imagine walls, doors, memories. The brain builds meaning through layers, moving from raw acoustics to rich, multimodal associations. Inspired by this, we build on recent work from Meta that aligned EEG signals with averaged wav2vec2 speech embeddings, and ask a deeper question: which layers of pre-trained models best reflect this layered processing in the brain? We compare embeddings from two models: wav2vec2, which encodes sound into language, and CLIP, which maps words to images. Using EEG recorded during natural speech perception, we evaluate how these embeddings align with brain activity using ridge regression and contrastive decoding. We test three strategies: individual layers, progressive concatenation, and progressive summation. The findings suggest that combining multimodal, layer-aware representations may bring us closer to decoding how the brain understands language, not just as sound, but as experience.