Chunking Constrains Prediction during Language Comprehension

Spoken language is a rapid sequence of auditory information that can incorporate complicated internal structures. Prediction and chunking are two possible mechanisms for the brain to process speech efficiently, and they are often viewed as separate or even opposing mechanisms. Here we investigate whether the two mechanisms interact and hypothesize that the chunk (constituent) structure in spoken language modulates how the brain predicts basic linguistic items, e.g., morphemes, the basic unit of meaning in language. In three magnetoencephalography (MEG) experiments, we characterize the neurally manifested prediction of morphemes using the neural response to morpheme surprisal, and we analyze how this response is modulated by chunks, i.e., the linguistic constituent structure. We demonstrate that the MEG surprisal response is significantly stronger for morphemes within an ongoing chunk than morphemes across a chunk boundary. This chunk-boundary effect on morpheme prediction is further modulated by the certainty of a chunk boundary. The findings are then confirmed by analyzing a dataset of ECoG responses to English narratives. These results establish that the brain employs a chunk-based prediction mechanism and more precisely predicts sequential items within a chunk.