Investigating Critical Period Effects in Language Acquisition through Neural Language Models

TL;DR

This study uses neural language models to explore whether critical period effects in human language acquisition are due to innate brain maturation or experience-driven stabilization, finding that models without innate-like maturation do not exhibit CP effects.

Contribution

The paper demonstrates that critical period effects are not solely due to statistical learning, and introduces a method to simulate maturational decrease in plasticity in language models.

Findings

Language models without innate maturation do not show CP effects.

Introducing a regularizer can reverse-engineer CP effects in models.

Results suggest innate mechanisms are involved in human CP effects.

Abstract

Humans appear to have a critical period (CP) for language acquisition: Second language (L2) acquisition becomes harder after early childhood, and ceasing exposure to a first language (L1) after this period (but not before) typically does not lead to substantial loss of L1 proficiency. It is unknown whether these CP effects result from innately determined brain maturation or as a stabilization of neural connections naturally induced by experience. In this study, we use language models (LMs) to test the extent to which these phenomena are peculiar to humans, or shared by a broader class of language learners. We vary the age of exposure by training LMs on language pairs in various experimental conditions, and find that LMs, which lack any direct analog to innate maturational stages, do not show CP effects when the age of exposure of L2 is delayed. Our results contradict the claim that CP effects are an inevitable result of statistical learning, and they are consistent with an innate mechanism for CP effects. We show that we can reverse-engineer the CP by introducing a regularizer partway through training to simulate a maturational decrease in plasticity. All in all, our results suggest that L1 learning on its own may not be enough to induce a CP, and additional engineering is necessary to make language models more cognitively plausible.