A neurobiologically inspired model of sentence comprehension. - Kröger, Bernd J.; Bekolay, Trevor (Language, Cognition & Neuroscience, 2025)
While AI models of sentence comprehension reach high, human-like performance levels, the architecture and neural functioning of these network models are not biologically plausible. We propose a neurobiologically inspired neural network model for sentence comprehension that includes modules representing a mental lexicon, syntactic processing, and semantic processing. Alongside the hierarchical module structure, the developmental trial-and-error (or iterative engineering) process for building the model resulted in the need for two parallel processing pathways, a content-related path that directly forwards lexical information to semantic sentence processing, and a sequence-related path, unfolding the syntactic structure of the sentence. A semantic processing module integrates the information from both pathways. At the model's highest processing level, the information processed in both pathways allows for thematic role assignment, or semantic event specification, for the sentence.