Linguistic structure and probability are jointly encoded in high gamma power

During speech comprehension, the brain dynamically infers a hierarchy of increasingly abstract representations from the sensory input. An important step in the inferential hierarchy is the combination of words to form phrases and sentences. Whether this process is driven primarily by statistical patterns in the linguistic input, or by a mechanism that combines words into hierarchical representations, is a subject of considerable debate that has regained importance with the arrival of large language models. This study investigates whether local cortical activity (high gamma power; 70-150 Hz) from intracranial recordings is jointly modulated by lexical probability and syntactic structure; and whether lexical probability affects the inference of syntactic structure. To this end, an open dataset of electrocorticography recordings is analyzed with multivariate temporal response functions and a model comparison approach. The results indicate that high gamma power is sensitive to multi-word estimates of constituency structure and lexical probability estimates, both in isolation and jointly. The temporal response functions suggest that syntactic structure building depends on interregional communication between regions connected through dorsal- and ventral streams. Furthermore, the study provides evidence that bottom-up syntactic information is less likely to be encoded by neural populations that strongly code for lexical probability measures, while top-down syntactic information shares neural resources with lexical uncertainty. We suggest that lexical uncertainty modulates the weighting of anticipatory structural information. With this, the current study supports models that suggest that cues are leveraged flexibly in a feed-forward and feed-back fashion during speech comprehension.