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Abstract
Much research in cognitive neuroscience supports prediction as a canonical computation
of cognition across domains. Is such predictive coding implemented by feedback from
higher-order domain-general circuits, or is it locally implemented in domain-specific
circuits? What information sources are used to generate these predictions? This study
addresses these two questions in the context of language processing. We present fMRI
evidence from a naturalistic comprehension paradigm (1) that predictive coding in
the brain’s response to language is domain-specific, and (2) that these predictions
are sensitive both to local word co-occurrence patterns and to hierarchical structure.
Using a recently developed continuous-time deconvolutional regression technique that
supports data-driven hemodynamic response function discovery from continuous BOLD
signal fluctuations in response to naturalistic stimuli, we found effects of prediction
measures in the language network but not in the domain-general multiple-demand network,
which supports executive control processes and has been previously implicated in language
comprehension. Moreover, within the language network, surface-level and structural
prediction effects were separable. The predictability effects in the language network
were substantial, with the model capturing over 37% of explainable variance on held-out
data. These findings indicate that human sentence processing mechanisms generate predictions
about upcoming words using cognitive processes that are sensitive to hierarchical
structure and specialized for language processing, rather than via feedback from high-level
executive control mechanisms.