AbstractsLanguage, Literature & Linguistics

MEMORY AND PREDICTION IN CROSS-LINGUISTIC SENTENCE COMPREHENSION

by Maria S. Lago




Institution: University of Maryland
Department: Linguistics
Year: 2014
Record ID: 2024775
Full text PDF: http://hdl.handle.net/1903/15773


Abstract

This dissertation explores the role of morphological and syntactic variation in sentence comprehension across languages. While most previous research has focused on how cross-linguistic differences affect the control structure of the language architecture (Lewis & Vasishth, 2005) here we adopt an explicit model of memory, content-addressable memory (Lewis & Vasishth, 2005; McElree, 2006) and examine how cross-linguistic variation affects the nature of the representations and processes that speakers deploy during comprehension. With this goal, we focus on two kinds of grammatical dependencies that involve an interaction between language and memory: subject-verb agreement and referential pronouns. In the first part of this dissertation, we use the self-paced reading method to examine how the processing of subject-verb agreement in Spanish, a language with a rich morphological system, differs from English. We show that differences in morphological richness across languages impact prediction processes while leaving retrieval processes fairly preserved. In the second part, we examine the processing of coreference in German, a language that, in contrast with English, encodes gender syntactically. We use eye-tracking to compare comprehension profiles during coreference and we find that only speakers of German show evidence of semantic reactivation of a pronoun's antecedent. This suggests that retrieval of semantic information is dependent on syntactic gender, and demonstrates that German and English speakers retrieve qualitatively different antecedent representations from memory. Taken together, these results suggest that cross-linguistic variation in comprehension is more affected by the content than the functional importance of gender and number features across languages.