Modeling fMRI time courses with linguistic structure at various grain sizes


Neuroimaging while participants listen to audiobooks provides a rich data source for theories of incremental parsing. We compare nested regression models of these data. These mixed-effects models incorporate linguistic predictors at various grain sizes ranging from part-of-speech bigrams, through surprisal on context-free treebank grammars, to incremental node counts in trees that are derived by Minimalist Grammars. The fine-grained structures make an independent contribution over and above coarser predictors. However, this result only obtains with time courses from anterior temporal lobe (aTL). In analogous time courses from inferior frontal gyrus, only N-grams improve upon a non-syntactic baseline. These results support the idea that aTL does combinatoric processing during naturalistic story comprehension, processing that bears a systematic relationship to linguistic structure.

Proceedings of the 6th Workshop on Cognitive Modeling and Computational Linguistics