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. Prior knowledge enhanced the neural representation of memory-related features indexing syntactic structures that must remain open for future completion, an effect that generalized across grammatical formalisms. This enhancement was strictly local: only immediately preceding contexts improved neural decoding at both word onset and offset, whereas longer histories either returned decoding performance to baseline at the word level or impaired decoding. 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 structural resolution being implemented as a word-level operation. These dissociable dynamics reveal two concurrent neural computational regimes for syntactic processing: a forward-looking, locally maintained predictive code for pending structure and a backward-looking, integrative code engaged when structure is resolved.