Disjoint reference in real-time comprehension: Computational and cross-linguistic perspectives

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Language comprehension generally seems effortless for adult speakers in their native language. But this impression masks the fact that a significant amount of subconscious mental work is necessary to understand language in real-time. One difficult task in language comprehension is understanding pronouns, referentially underspecified words like 'he' or 'she' that pick up their meaning in context. In order to understand who 'her' refers to in a simple sentence like 'Ellie likes her,' listeners rely on a complex set of linguistic and extralinguistic cues. The linguistic cues do not seem to be arbitrary: Linguists have identified strong cross-linguistic tendencies in how languages constrain the possible meanings of a pronoun, with a limited amount of variation across languages. This cross-linguistic similarity is generally taken to reflect aspects of human cognition that are shared by speakers of all languages. Thus, studying cross-linguistic similarity and variation in pronoun comprehension offers one window into how the human mind creates meaning using language. A better understanding of how speakers of different languages understand pronouns can in turn help in the treatment of language disorders, as well as in the development of natural language processing systems to allow computers to better approximate human language use.This project will investigate this with a cross-linguistic, psycholinguistic approach. The research team will use eye-tracking to measure how listeners understand pronouns in real-time. In particular, they will investigate how linguistic constraints on pronoun reference are used by listeners in real-time: Does a language’s grammatical constraints on a pronoun's meaning immediately influence comprehension, or does it only restrict interpretation at a later stage? To explore this question, the present project will investigate pronoun comprehension in a number of unrelated languages that vary in key grammatical properties, and use computationally implemented cognitive models to understand the results. This project will involve advanced STEM training for researchers at different career stages, from undergraduates to post-doctoral scholars. The cross-linguistic component of this project will involve training and outreach opportunities for members of underrepresented groups in linguistics. The research team will also develop video materials to make the results of this research accessible to the general public.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.