A magnitude-independent neural code for linguistic information during sentence production.

Humans are the only species with the ability to convey an unbounded number of novel thoughts by combining words into sentences. This process is guided by complex semantic and abstract syntactic representations. Despite their centrality to human cognition, the neural mechanisms underlying these systems remain obscured by inherent limitations of non-invasive brain measures and a near total focus on comprehension paradigms. Here, we address these limitations with high-resolution neurosurgical recordings (electrocorticography) and a controlled sentence production experiment. We uncover distinct cortical networks encoding word-level, semantic, and syntactic information. These networks are broadly distributed across traditional language areas, but with focal sensitivity to syntactic structure in middle and inferior frontal gyri. In contrast to previous findings from comprehension studies, these networks are largely non-overlapping, each specialized for just one of the three linguistic constructs we investigate. Most strikingly, our data reveal an unexpected property of higher-order linguistic information: it is encoded independent of neural activity levels. We propose that this "magnitude-independent coding" scheme represents a novel mechanism for encoding information, reserved for higher-order cognition more broadly.