Non-adjacent dependency learning in infancy, and its link to language development

Rebecca L.A Frost,Andrew Jessop,Samantha Durrant,Michelle S Peter,Amy Bidgood,Julian M Pine,Caroline F Rowland,Padraic Monaghan

doi:10.1016/j.cogpsych.2020.101291

Abstract

To acquire language, infants must learn how to identify words and linguistic structure in speech. Statistical learning has been suggested to assist both of these tasks. However, infants’ capacity to use statistics to discover words and structure together remains unclear. Further, it is not yet known how infants’ statistical learning ability relates to their language development. We trained 17-month-old infants on an artificial language comprising non-adjacent dependencies, and examined their looking times on tasks assessing sensitivity to words and structure using an eye-tracked head-turn-preference paradigm. We measured infants’ vocabulary size using a Communicative Development Inventory (CDI) concurrently and at 19, 21, 24, 25, 27, and 30 months to relate performance to language development. Infants could segment the words from speech, demonstrated by a significant difference in looking times to words versus part-words. Infants’ segmentation performance was significantly related to their vocabulary size (receptive and expressive) both currently, and over time (receptive until 24 months, expressive until 30 months), but was not related to the rate of vocabulary growth. The data also suggest infants may have developed sensitivity to generalised structure, indicating similar statistical learning mechanisms may contribute to the discovery of words and structure in speech, but this was not related to vocabulary size.

Highlights

To reach linguistic proficiency, infants must master two critical tasks; identifying words in speech, and discovering the constraints that shape the way those words are used
We investigated the relationship between statistical learning ability and vocabulary development over time
Demonstrating that infants share this same capacity for statistical learning of words and structure would provide critical insight into the nature of the processes that may underlie these tasks in natural language learning, and the time-course in which they may operate

Summary

Introduction

Infants must master two critical tasks; identifying words in speech, and discovering the constraints that shape the way those words are used. Speech contains no absolute cues to word boundaries (Aslin, Woodward, LaMendola, & Bever, 1996) or grammatical structure (Monaghan, Christiansen, & Chater, 2007), it is replete with distributional information that could assist with these tasks: regular co-occurrence of particular syllables provides a helpful description of what constitutes specific words in a language, whereas information about how words are used in combination helps illustrate how that language operates in terms of its grammatical structure. At around the same age, infants can discover simple distributional structure in artificial speech (i.e., an ABA or AAB structure e.g., Marcus, Vijayan, Rao, & Vishton, 1999; Gerken, 2006; 2010), with this capacity potentially increasing in sophistication over development (see e.g., Gómez, 2002; Gómez & Gerken, 1999; Gómez & Maye, 2005; Lany & Gómez, 2008; Lany, Gómez, & Gerken, 2007; Marchetto & Bonatti 2013; 2015, for work on infants’ developing ability to track dependencies between adjacent and non-adjacent items)

Methods

Results

Conclusion