  To model behavioral and neural correlates of language comprehension in
naturalistic environments researchers have turned to broad-coverage tools from
natural-language processing and machine learning. Where syntactic structure is
explicitly modeled, prior work has relied predominantly on context-free
grammars (CFG), yet such formalisms are not sufficiently expressive for human
languages. Combinatory Categorial Grammars (CCGs) are sufficiently expressive
directly compositional models of grammar with flexible constituency that
affords incremental interpretation. In this work we evaluate whether a more
expressive CCG provides a better model than a CFG for human neural signals
collected with fMRI while participants listen to an audiobook story. We further
test between variants of CCG that differ in how they handle optional adjuncts.
These evaluations are carried out against a baseline that includes estimates of
next-word predictability from a Transformer neural network language model. Such
a comparison reveals unique contributions of CCG structure-building
predominantly in the left posterior temporal lobe: CCG-derived measures offer a
superior fit to neural signals compared to those derived from a CFG. These
effects are spatially distinct from bilateral superior temporal effects that
are unique to predictability. Neural effects for structure-building are thus
separable from predictability during naturalistic listening, and those effects
are best characterized by a grammar whose expressive power is motivated on
independent linguistic grounds.
