How Data Drive Early Word Learning: A Cross-Linguistic Waiting Time Analysis

Francis Mollica,Steven T Piantadosi

doi:10.1162/opmi_a_00006

Abstract

The extent to which word learning is delayed by maturation as opposed to accumulating data is a longstanding question in language acquisition. Further, the precise way in which data influence learning on a large scale is unknown—experimental results reveal that children can rapidly learn words from single instances as well as by aggregating ambiguous information across multiple situations. We analyze Wordbank, a large cross-linguistic dataset of word acquisition norms, using a statistical waiting time model to quantify the role of data in early language learning, building off Hidaka ( 2013 ). We find that the model both fits and accurately predicts the shape of children’s growth curves. Further analyses of model parameters suggest a primarily data-driven account of early word learning. The parameters of the model directly characterize both the amount of data required and the rate at which informative data occurs. With high statistical certainty, words require on the order of ∼ 10 learning instances, which occur on average once every two months. Our method is extremely simple, statistically principled, and broadly applicable to modeling data-driven learning effects in development.

Highlights

Evidence for a data-driven view of the timing of language learning comes from studies showing the importance of linguistic input for early learning (Hoff, 2003; Huttenlocher, Haight, Bryk, Seltzer, & Lyons, 1991; Shneidman, Arroyo, Levine, & Goldin-Meadow, 2013; Weisleder & Fernald, 2013)
Our analysis of empirical learning curves strongly suggests that data accumulation begins very early, that production may be delayed due to maturational factors, and that typical words take on the order of ∼ 10 effective learning instances (ELIs) to learn, not hundreds of occurrences and not a single occurrence or two
The model suggests that the informative data points for word learning occur relatively infrequently, about once every two months, and that these occurrences are not strongly related to a word’s overall frequency

Summary

Introduction

Evidence for a data-driven view of the timing of language learning comes from studies showing the importance of linguistic input for early learning (Hoff, 2003; Huttenlocher, Haight, Bryk, Seltzer, & Lyons, 1991; Shneidman, Arroyo, Levine, & Goldin-Meadow, 2013; Weisleder & Fernald, 2013). There are complications for the view that data are all that matters. Maturational constraints are often thought to play an important role in language learning (Borer & Wexler, 1987; Newport, 1990). Many words like function words (e.g., “the”) and number words (e.g., “two”) are learned surprisingly late for their frequenc

Methods

Results

Conclusion