Prior knowledge reveals two computational regimes for syntactic processing in the human brain

The human brain rapidly transforms continuous speech into structured, meaningful linguistic representations, yet how prior knowledge constrains this process remains unclear. To characterize this influence, we combined MEG recordings acquired during audiobook listening with corpus-derived transition probabilities over syntactic features defined within both phrase-structure and dependency-based grammars. Across grammatical formalisms, prior knowledge selectively sharpened the neural representation of memory-related features indexing syntactic structures that must remain open for future completion. This enhancement was strictly local: immediately preceding contexts improved neural decoding at both word onset and offset, whereas longer histories produced either a return to baseline at the word level or a deterioration in decoding performance. By contrast, integration-related features indexing the completion of syntactic operations showed no benefit from prior knowledge and were represented most strongly at word offset, consistent with their dependence on word-level structural resolution. These dissociable dynamics reveal two concurrent neural computational regimes for syntactic processing: a forward-looking, locally maintained predictive code for pending structure and an integrative code engaged when structure is resolved. More broadly, our findings impose a mechanistic constraint on neural theories of language processing and on accounts that equate prediction in human language comprehension with the comparatively unconstrained operations of large language models (LLMs).