4.2 Article

Features matter: the role of number and gender features during the online processing of subject- and object- relative clauses in Italian

Journal

LANGUAGE COGNITION AND NEUROSCIENCE
Volume 38, Issue 6, Pages 802-820

Publisher

ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/23273798.2022.2159989

Keywords

Sentence comprehension; relative clauses; number features; gender features; self-paced reading; relativized minimality

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This study investigates the role of morphosyntactic features, specifically number and gender, in adult online comprehension of Italian subject relative clauses (SRC) and object relative clauses (ORC). The study is inspired by developmental research showing that children struggle more with ORC than SRC, but comprehension improves when the head and the subject of the RC have a mismatch in relevant morphosyntactic features, such as number but not gender in Italian. The findings indicate that Italian adults read ORC verbs slower than SRC verbs, but ORC verbs are read faster when there is a head-subject number mismatch, supporting developmental studies and the featural Relativized Minimality principle (fRM).
In this study, we investigated whether different morphosyntactic features, i.e. number and gender, play a role during the adult online comprehension of subject relative clauses (SRC) and object relative clauses (ORC), in Italian. This study was inspired by developmental studies showing that children struggle with ORC compared to SRC; yet, ORC comprehension improves if the head and the subject of the RC mismatch in relevant morphosyntactic features (e.g. number but not gender in Italian, based on the featural Relativized Minimality principle, fRM). We found that Italian adults read ORC more slowly than SRC verbs; moreover, ORC verbs were read faster in the head-subject number mismatch condition, while there was no facilitation in the head-subject gender mismatch condition, in line with developmental studies and fRM. We conclude that online parsing is feature-sensitive, that features are not all equally relevant, and that current models should be refined to account for these differences.

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