Tuning to Real-Life Language Statistics: Online Processing of Multiword Sequences in Native and Non-Native Speakers Across Language Registers
Elma Kerz, Daniel Wiechmann, Felicity F. Frinsel, Morten H. Christiansen · 2020
A large body of research over the past two decades has demonstrated that children and adults are equipped with statistical learning mechanisms that facilitate their language processing and boost their acquisition. However, this research has been conducted primarily using artificial languages that are highly simplified relative to real language input. Here, we aimed to determine to what extent adult native and non-native speakers show sensitivity to real-life language statistics obtained from large-scale analyses of authentic language use. Through a within-subject design, we conducted a series of behavioral experiments geared towards assessing the sensitivity to two types of distributional statistics (frequency and entropy) during online processing of multiword sequences across four registers of English (spoken, fiction, news and academic language). Our results show that both native and non-native speakers are able to `tune to' multiple distributional statistics inherent in different types of real language input.