Reassessing prediction in the brain: Pre-onset neural encoding during natural listening does not reflect pre-activation

Abstract

Predictive processing theories propose that the brain continuously anticipates upcoming input. However, direct neural evidence for predictive pre-activation during natural language comprehension remains limited and debated. Previous studies using large language model (LLM)-based encoding models with fMRI and ECoG have reported pre-onset signals that appear to encode upcoming words, but these effects may instead reflect dependencies in the stimulus or autocorrelations in neural activity. Here, we re-examined this question by aligning LLM-derived word embeddings with neural activity recorded during naturalistic listening using magnetoencephalography (MEG) and electrocorticography (ECoG). We replicated pre-onset encoding effects previously observed in ECoG across both modalities, and found that they persist even after controlling for stimulus correlations. Crucially, temporal generalization analyses revealed no stable overlap between pre- and post-onset representations, indicating that pre-onset activity does not reflect pre-activation of the next word. Consistent with this, long-range predictive effects previously reported in fMRI did not replicate in our higher-temporal-resolution data. While we found no evidence for predictive pre-activation, we observed clear signatures of postdiction, with neural activity reflecting persistent encoding of prior words. These results suggest that reported apparent predictive signals do not reflect pre-activation of upcoming input. They call for caution in interpreting LLM-based encoding models and highlight the need for a more nuanced understanding of what constitutes "prediction" in language comprehension.