Left prefrontal and parietal contribution to sentence processing: a neuromodulation approach

Describing a comprehensive neurofunctional model of sentence comprehension has always been a complex challenge. On one hand, disentangling the subprocesses that are necessary for computing the meaning of a sentence and their neural underpinnings is insidious. Each subprocess is closely interconnected with the others, and isolating only one as if it were separable can undermine the investigation of the overall process above. On the other hand, available data on the neural basis of sentence processing are not straightforward. This thesis explores relevant contributions and attempts to highlight open questions regarding the neural basis of two key processes in sentence comprehension, namely morphosyntactic processing and thematic role assignment. It presents and discusses original data resulting from an experiment that, to our knowledge, represents the first investigation of the neural basis of these two processes in the same sentential context. Results demonstrate that morphosyntactic and thematic processing rely on functionally distinct neural correlates in the left hemisphere. Morphosyntactic aspects are mostly processed in a left prefrontal network including the left inferior frontal gyrus (IFG) and the middle frontal gyrus (MFG), whereas thematic role assignment correlates with a left parietal node including the left intraparietal sulcus (IPS). Moreover, it is argued that results support the view that these regions play a language-related rather than domain-general role in human cognition. Finally, two statistical approaches to the analysis of the same TMS language data (ANOVA and Linear Mixed Models – LMMs) are compared. Their outcomes are discussed and an attempt is made at accounting for similarities and differences. Results suggest that the two models should not be considered on a sort of quality hierarchy according to which one has greater or lesser explanatory power than the other. Rather, they both represent legitimate and reliable approaches to account for data variability.