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A critical period for second language acquisition: Evidence from 2/3 million English speakers - ScienceDirect
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## Cognition

# Original Articles  A critical period for second language acquisition: Evidence from 2/3 million English speakers

Author links open overlay panelJoshua K.Hartshorneab, Joshua B.Tenenbauma, StevenPinkerc

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## Abstract

Children learn language more easily than adults, though when and why this ability declines have been obscure for both empirical reasons (underpowered studies) and conceptual reasons (measuring the ultimate attainment of learners who started at different ages cannot by itself reveal changes in underlying learning ability). We address both limitations with a dataset of unprecedented size (669,498 native and non-native English speakers) and a computational model that estimates the trajectory of underlying learning ability by disentangling current age, age at first exposure, and years of experience. This allows us to provide the first direct estimate of how grammar-learning ability changes with age, finding that it is preserved almost to the crux of adulthood (17.4 years old) and then declines steadily. This finding held not only for “difficult” syntactic phenomena but also for “easy” syntactic phenomena that are normally mastered early in acquisition. The results support the existence of a sharply-defined critical period for language acquisition, but the age of offset is much later than previously speculated. The size of the dataset also provides novel insight into several other outstanding questions in language acquisition.

## Introduction

People who learned a second language in childhood are difficult to distinguish from native speakers, whereas those who began in adulthood are often saddled with an accent and conspicuous grammatical errors. This fact has influenced many areas of science, including theories about the plasticity of the young brain, the role of neural maturation in learning, and the modularity of linguistic abilities (Johnson and Newport, 1989, Lenneberg, 1967, Morgan-Short and Ullman, 2012, Newport, 1988, Pinker, 1994). It has also affected policy, driving debates about early childhood stimulation, bilingual education, and foreign language instruction (Bruer, 1999).

However, neither the nature nor the causes of this “critical period” for second language acquisition are well understood. (Here, we use the term “critical period” as a theory-neutral descriptor of diminished achievement by adult learners, whatever its cause.) There is little consensus as to whether children’s advantage comes from superior neural plasticity, an earlier start that gives them additional years of learning, limitations in cognitive processing that prevent them from being distracted by irrelevant information, a lack of interference from a well-learned first language, a greater willingness to experiment and make errors, a greater desire to conform to their peers, or a greater likelihood of learning through immersion in a community of native speakers (Birdsong, 2017, Birdsong and Molis, 2001, Hakuta et al., 2003, Hernandez et al., 2005, Johnson and Newport, 1989, Newport, 1990, Pinker, 1994). We do not even know how long the critical period lasts, whether learning ability declines gradually or precipitously once it is over, or whether the ability continues to decline throughout adulthood or instead reaches a floor (Birdsong and Molis, 2001, Guion et al., 2000, Hakuta et al., 2003, Jia et al., 2002, Johnson and Newport, 1989, McDonald, 2000, Sebastián-Gallés et al., 2005, Vanhove, 2013).

As noted by Patkowski (1980), researchers interested in critical periods focus on two interrelated yet distinct questions:

- (1)
  How does learning ability change with age?

- (2)
  How proficient can someone be if they began learning at a particular age?

The questions are different because language acquisition is not instantaneous. For example, an older learner who (hypothetically) acquired language at a slower rate could, in theory, still attain perfect proficiency if he or she persisted at the learning long enough.

The question of ultimate attainment (2) captures the most public attention because it directly applies to people’s lives, but the question of learning ability (1) is more theoretically central. Does learning ability decline gradually from birth (Guion et al., 2000, Hernandez et al., 2005), whether from neural maturation, interference from the first language, or other causes (Fig. 1A)? Alternatively, is there an initial period of high ability, followed by a continuous decline (Fig. 1B), or a decline that reaches a floor (Fig. 1C) (Johnson & Newport, 1989)? Or does ability remain relatively constant (Fig. 1D), with adults failing to learn for some other reason such as less time and interest (Hakuta et al., 2003, Hernandez et al., 2005)?

Unfortunately, learning ability is a hidden variable that is difficult to measure directly. Studies that compare children and adults exposed to comparable material in the lab or during the initial months of an immersion program show that adults perform better, not worse, than children (Huang, 2015, Krashen et al., 1979, Snow and Hoefnagel-Höhle, 1978), perhaps because they deploy conscious strategies and transfer what they know about their first language. Thus, studies that are confined to the initial stages of learning cannot easily measure whatever it is that gives children their long-term advantage. (Note that strictly speaking, these studies measure learning rate, not learning ability. While these are conceptually distinct, in practice they are difficult to disentangle, and the distinction has played little role in the literature. In the present paper, we will use the terms interchangeably.)

Thus, although the question of learning ability (1) is more theoretically central, empirical studies have largely probed the more tractable question of how ultimate attainment changes as a function of age of first exposure (2). Here, too, there are a number of theoretically interesting possibilities (Fig. 1E–H). The hope has been that identifying the shape of the ultimate attainment curve might tell us something about the shape of the learning ability curve (cf. Birdsong, 2006, Hakuta et al., 2003, Johnson and Newport, 1989). Unfortunately, this turns out not to be the case. Despite the similarities between the two sets of hypothesized curves (e.g., compare Fig. 1A and E), they bear little relationship to one another: The same ultimate attainment curve (e.g., Fig. 1E) is consistent with many different learning ability curves (Fig. 1A–D).

Here is why learning ability curves (Fig. 1A–D) and ultimate attainment curves (Fig. 1E–H) should not be conflated: If, hypothetically, learning ability plummeted at age 15 but it took 10 years of experience to master a language completely, then ultimate attainment would decline starting at an age of exposure of 5 (since someone who began at 6 years old would learn at peak capacity for only 9 of the 10 years required, someone who began at 7 years old would learn for only 8 of those years, and so on). It would be erroneous, in that case, to conclude that a decline in ultimate attainment starting at age 5 implied that children’s learning ability declines starting at age 5. Conversely, showing that people who began learning at a certain age reached native-like proficiency merely indicates that they learned fast enough, not that they learned as fast as a native speaker, just as the fact that two runners both finished a race indicates only that they both started early enough and ran fast enough, not that they ran at the exact same speed.

As a result, it is impossible to directly infer developmental changes in underlying ability (the theoretical construct of interest) from age-related changes in ultimate attainment (the empirically available measurements). Fig. 2 shows that two very distinct ability curves, one with a steady decline from infancy (2A), the other with a sudden drop in late adolescence (2B), can give rise to indistinguishable ultimate attainment curves. (The curves are generated by our ELSD model, described below, but the point is model-independent.) Conversely, a rapid drop in ultimate attainment beginning at age 10 could be explained by a continuous decline in learning ability beginning in infancy (Fig. 2C) or by a discontinuous drop in learning rate at 15 years old (Fig. 2D). Moreover, quantitative differences in the magnitude of a hypothetical decline in underlying learning ability (which are not specified in existing theories) can give rise to qualitative differences in the empirically measured ultimate attainment curves, such as a gentle decline versus a sudden drop-off: compare Fig. 2A with 2C, and Fig. 2B with 2D.

As we have seen, to understand how language-learning ability changes with age, we must disentangle it from age of exposure, years of experience, and age at testing. Unfortunately, this challenge is insuperable with any study that fails to use sufficiently large samples and ranges, because any imprecision in measuring the effects of amount of exposure on attainment, the effects of age of first exposure on attainment, or both, will render the results ambiguous or even uninterpretable.

Moreover, an underlying ability curve can be ascertained only if the measure of language attainment is sufficiently sensitive: If learners hit an artificial ceiling, any gains from an earlier age of exposure or a greater amount of exposure will be concealed. Indeed, the concept of native proficiency entails extreme levels of accuracy. An error rate that would be considered excellent in other academic or psychological settings, such as 0.75%, represents a conspicuous immaturity in the context of language. For example, over-regularizations of irregular verbs, such as runned and breaked, are among the most frequently noted errors in preschoolers’ speech (Pinker, 1999), despite occurring in only 0.75% of utterances (and on 2.5% of past-marked irregular verbs; Marcus et al., 1992).

These basic mathematical facts raise a significant practical problem: Detecting an error that occurs as little as 0.75% of the time requires a lot of data: A preschooler has to produce 92 utterances to have a better than even chance of producing an over-regularization. Thus, to detect even “conspicuous” errors, such as childhood over-regularization, we need to test many subjects on many items.

Below, we describe a study of syntax that attempts to meet these challenges using novel experimental and analytical techniques. To foreshadow, the age at which syntax-learning ability begins to decline is much later than usually suspected, and it takes both native and non-native speakers longer to reach their ultimate level of attainment than has been previously assumed. While both findings are unexpected, we show that the apparent inconsistencies with prior findings can be explained by the much higher precision afforded by our methods. Indeed, the findings below should not be surprising in retrospect. More importantly, these findings appear robust and emerge in a variety of different analyses.

## Section snippets

## Overview

Initial power calculations suggested that several hundred thousand subjects of diverse ages and linguistic backgrounds would be required to disentangle age of first exposure, age at testing, and years of exposure (we return to issues of power in the discussion, below). The standard undergraduate subject pools are not nearly large or diverse enough to achieve this, nor are crowdsourcing platforms like Amazon Mechanical Turk (Stewart et al., 2015). Inspired partly by Josh Katz’s Dialect Quiz for

## Learning rate

We focus first on the difficult but theoretically important question of the underlying learning rate. We defer the traditional question of level of ultimate attainment to a later section. Note that all analyses are conducted in terms of log-odds (the log-transformed odds of a correct answer, using the empirical logit method to avoid division by zero) rather than percent correct. Although prior work on critical periods has tended to use percent correct, this is problematic. Specifically,

## General discussion

Taken together, the analyses above all point to a grammar-learning ability that is preserved throughout childhood and declines rapidly in late adolescence. This model provided a better fit to the data than did a wide range of alternatives, including models with declines that were earlier or later, faster or slower, sharper or smoother.

In addition to providing the first empirical estimate of how language-learning ability changes with age, we addressed two related issues. First, we found that

## Acknowledgements

We are indebted to David Barner, David Birdsong, Kenji Hakuta, Elissa Newport, Laura-Ann Petitto, and Michael Ullman for comments, to Tanya Ivonchyk and Brandon Benson for help with developing the quiz, and to the hundreds of thousands of volunteers who participated in the study. This research was supported by an NIH NRSA award to JKH (5F32HD072748) and the Center for Minds, Brains, & Machines (NSF STC CCF-1231216).

## Contributions

JKH designed the study, collected the data, and performed the analyses. All three authors contributed to designing the analyses and to writing the paper.

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