COGNITIVE SCIENCE PROGRAMME
IIT DELHI

Preverbal syntactic complexity leads to local coherence effects

Faculty: Samar Husain

Abstract

The effective use of preverbal linguistic cues to make successful clause-final verbal prediction as well as robust maintenance of such predictions has been argued to be a cross-linguistic generalization for SOV languages such as German and Japanese. In this paper, we show that native speakers of Hindi (an SOV language) falter in forming a clause-final structure in the presence of a center-embedded relative clause with a non-canonical word order. In particular, the fallibility of the parser is illustrated by the formation of a grammatically illicit locally coherent parse during online processing. Such a parse should not be formed if the grammatically licit matrix clause final structure was being successfully formed. The formation of a locally coherent parse is further illustrated by probing various syntactic dependencies via targeted questions. We show that the parser’s susceptibility to form such structures is not driven by top-down processing, rather the effect can only be explained through a bottom-up parsing approach. Further, our investigation suggests that while plausibility is essential, presence of overt agreement features might not be necessary for forming a locally coherent parse in Hindi. These results go against top-down proposals to local coherence such as lossy surprisal and are consistent with the good-enough processing model to comprehension while only partially supporting the SOPARSE account to processing. The work highlights how top-down processing and bottom-up information interact during sentence comprehension in SOV languages – prediction suffers with increased complexity of the preverbal linguistic environment.

Preverbal syntactic complexity leads to local coherence effects. Sakshi Bhatia and Samar Husain. Language, Cognition and Neuroscience. 2022. Accepted.