critical period hypothesis and syntax

Critical Period In Brain Development and Childhood Learning

Charlotte Nickerson

Research Assistant at Harvard University

Undergraduate at Harvard University

Charlotte Nickerson is a student at Harvard University obsessed with the intersection of mental health, productivity, and design.

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Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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Key Takeaways

• Critical period is an ethological term that refers to a fixed and crucial time during the early development of an organism when it can learn things that are essential to survival. These influences impact the development of processes such as hearing and vision, social bonding, and language learning.
• The term is most often experienced in the study of imprinting, where it is thought that young birds could only develop an attachment to the mother during a fixed time soon after hatching.
• Neurologically, critical periods are marked by high levels of plasticity in the brain before neural connections become more solidified and stable. In particular, critical periods tend to end when synapses that inhibit the neurotransmitter GABA mature.
• In contrast to critical periods, sensitive periods, otherwise known as “weak critical periods,” happen when an organism is more sensitive than usual to outside factors influencing behavior, but this influence is not necessarily restricted to the sensitive period.
• Scholars have debated the extent to which older organisms can develop certain skills, such as natively-accented foreign languages, after the critical period.

The critical period is a biologically determined stage of development where an organism is optimally ready to acquire some pattern of behavior that is part of typical development. This period, by definition, will not recur at a later stage.

If an organism does not receive exposure to the appropriate stimulus needed to learn a skill during a critical period, it may be difficult or even impossible for that organism to develop certain functions associated with that skill later in life.

This happens because a range of functional and structural elements prevent passive experiences from eliciting significant changes in the brain (Cisneros-Franco et al., 2020).

The first strong proponent of the theory of critical periods was Charles Stockhard (1921), a biologist who attempted to experiment with the effects of various chemicals on the development of fish embryos, though he gave credit to Dareste for originating the idea 30 years earlier (Scott, 1962).

Stockhard’s experiments showed that applying almost any chemical to fish embryos at a certain stage of development would result in one-eyed fish.

These experiments established that the most rapidly growing tissues in an embryo are the most sensitive to any change in conditions, leading to effects later in development (Scott, 1962).

Meanwhile, psychologist Sigmund Freud attempted to explain the origins of neurosis in human patients as the result of early experiences, implying that infants are particularly sensitive to influences at certain points in their lives.

Lorenz (1935) later emphasized the importance of critical periods in the formation of primary social bonds (otherwise known as imprinting) in birds, remarking that this psychological imprinting was similar to critical periods in the development of the embryo.

Soon thereafter, McGraw (1946) pointed out the existence of critical periods for the optimal learning of motor skills in human infants (Scott, 1962).

Example: Infant-Parent Attachment

The concept of critical or sensitive periods can also be found in the domain of social development, for example, in the formation of the infant-parent attachment relationship (Salkind, 2005).

Attachment describes the strong emotional ties between the infant and caregiver, a reciprocal relationship developing over the first year of the child’s life and particularly during the second six months of the first year.

During this attachment period , the infant’s social behavior becomes increasingly focused on the principal caregivers (Salkind, 2005).

The 20th-century English psychiatrist John Bowlby formulated and presented a comprehensive theory of attachment influenced by evolutionary theory.

Bowlby argued that the infant-parent attachment relationship develops because it is important to the survival of the infant and that the period from six to twenty-four months of age is a critical period of attachment.

This coincides with an infant’s increasing tendency to approach familiar caregivers and to be wary of unfamiliar adults. After this critical period, it is still possible for a first attachment relationship to develop, albeit with greater difficulty (Salkind, 2005).

This has brought into question, in a similar vein to language development, whether there is actually a critical development period for infant-caregiver attachment.

Sources debating this issue typically include cases of infants who did not experience consistent caregiving due to being raised in institutions prior to adoption (Salkind, 2005).

Early research into the critical period of attachment, published in the 1940s, reports consistently that children raised in orphanages subsequently showed unusual and maladaptive patterns of social behavior, difficulty in forming close relationships, and being indiscriminately friendly toward unfamiliar adults (Salkind, 2005).

Later, research from the 1990s indicated that adoptees were actually still able to form attachment relationships after the first year of life and also made developmental progress following adoption.

Nonetheless, these children had an overall increased risk of insecure or maladaptive attachment relationships with their adoptive parents. This evidence supports the notion of a sensitive period, but not a critical period, in the development of first attachment relationships (Salkind, 2005).

Mechanisms for Critical Periods

Both genetics and sensory experiences from outside the body shape the brain as it develops (Knudsen, 2004). However, the developmental stage that an organism is in significantly impacts how much the brain can change based on these experiences.

In scientific terms, the brain’s plasticity changes over the course of a lifespan. The brain is very plastic in the early stages of life before many key connections take root, but less so later.

This is why researchers have shown that early experience is crucial for the development of, say, language and musical abilities, and these skills are more challenging to take up in adulthood (Skoe and Kraus, 2013; White et al., 2013; Hartshorne et al., 2018).

As brains mature, the connections in them become more fixed. The brain’s transitions from a more plastic to a more fixed state advantageously allow it to retain new and complex processes, such as perceptual, motor, and cognitive functions (Piaget, 1962).

Children’s gestures, for example, pride and predict how they will acquire oral language skills (Colonnesi et al., 2010), which in turn are important for developing executive functions (Marcovitch and Zelazo, 2009).

However, this formation of stable connections in the brain can limit how the brain’s neural circuitry can be revised in the future. For example, if a young organism has abnormal sensory experiences during the critical period – such as auditory or visual deprivation – the brain may not wire itself in a way that processes future sensory inputs properly (Gallagher et al., 2020).

One illustration of this is the timing of cochlear implants – a prosthesis that restores hearing in some deaf people. Children who receive cochlear implants before two years of age are more likely to benefit from them than those who are implanted later in life (Kral and Eggermont, 2007; Gallagher et al., 2020).

Similarly, the visual deprivation caused by cataracts in infants can cause similar consequences. When cataracts are removed during early infancy, individuals can develop relatively normal vision; however, when the cataracts are not removed until adulthood, this results in substantially poorer vision (Martins Rosa et al., 2013).

After the critical period closes, abnormal sensory experiences have a less drastic effect on the brain and lead to – barring direct damage to the central nervous system – reversible changes (Gallagher et al., 2020). Much of what scientists know about critical periods derives from animal studies , as these allow researchers greater control over the variables that they are testing.

This research has found that different sensory systems, such as vision, auditory processing, and spatial hearing, have different critical periods (Gallagher et al., 2020).

The brain regulates when critical periods open and close by regulating how much the brain’s synapses take up neurotransmitters , which are chemical substances that affect the transmission of electrical signals between neurons.

In particular, over time, synapses decrease their uptake of gamma-aminobutyric acid, better known as GABA. At the beginning of the critical period, outside sources become more effective at influencing changes and growth in the brain.

Meanwhile, as the inhibitory circuits of the brain mature, the mature brain becomes less sensitive to sensory experiences (Gallagher et al., 2020).

Critical Periods vs Sensitive Periods

Critical periods are similar to sensitive periods, and scholars have, at times, used them interchangeably. However, they describe distinct but overlapping developmental processes.

A sensitive period is a developmental stage where sensory experiences have a greater impact on behavioral and brain development than usual; however, this influence is not exclusive to this time period (Knudsen, 2004; Gallagher, 2020). These sensitive periods are important for skills such as learning a language or instrument.

In contrast, A critical period is a special type of sensitive period – a window where sensory experience is necessary to shape the neural circuits involved in basic sensory processing, and when this window opens and closes is well-defined (Gallagher, 2020).

Researchers also refer to sensitive periods as weak critical periods. Some examples of strong critical periods include the development of vision and hearing, while weak critical periods include phenome tuning – how children learn how to organize sounds in a language, grammar processing, vocabulary acquisition, musical training, and sports training (Gallagher et al., 2020).

Critical Period Hypothesis

One of the most notable applications of the concept of a critical period is in linguistics. Scholars usually trace the origins of the debate around age in language acquisition to Penfield and Robert’s (2014) book Speech and Brain Mechanisms.

In the 1950s and 1960s, Penfield was a staunch advocate of early immersion education (Kroll and De Groot, 2009). Nonetheless, it was Lenneberg, in his book Biological Foundations of Language, who coined the term critical period (1967) in describing the language period.

Lennenberg (1967) described a critical period as a period of automatic acquisition from mere exposure” that “seems to disappear after this age.” Scovel (1969) later summarized and narrowed Penfield’s and Lenneberg’s view on the critical period hypothesis into three main claims:

• Adult native speakers can identify non-natives by their accents immediately and accurately.
• The loss of brain plasticity at about the age of puberty accounts for the emergence of foreign accents./li>
• The critical period hypothesis only holds for speech (whether or not someone has a native accent) and does not affect other areas of linguistic competence.

Linguists have since attempted to find evidence for whether or not scientific evidence actually supports the critical period hypothesis, if there is a critical period for acquiring accentless speech, for “morphosyntactic” competence, and if these are true, how age-related differences can be explained on the neurological level (Scovel, 2000).

The critical period hypothesis applies to both first and second-language learning. Until recently, research around the critical period’s role in first language acquisition revolved around findings about so-called “feral” children who had failed to acquire language at an older age after having been deprived of normal input during the critical period.

However, these case studies did not account for the extent to which social deprivation, and possibly food deprivation or sensory deprivation, may have confounded with language input deprivation (Kroll and De Groot, 2009).

More recently, researchers have focused more systematically on deaf children born to hearing parents who are therefore deprived of language input until at least elementary school.

These studies have found the effects of lack of language input without extreme social deprivation: the older the age of exposure to sign language is, the worse its ultimate attainment (Emmorey, Bellugi, Friederici, and Horn, 1995; Kroll and De Groot, 2009).

However, Kroll and De Groot argue that the critical period hypothesis does not apply to the rate of acquisition of language. Adults and adolescents can learn languages at the same rate or even faster than children in their initial stage of acquisition (Slavoff and Johnson, 1995).

However, adults tend to have a more limited ultimate attainment of language ability (Kroll and De Groot, 2009).

There has been a long lineage of empirical findings around the age of acquisition. The most fundamental of this research comes from a series of studies since the late 1970s documenting a negative correlation between age of acquisition and ultimate language mastery (Kroll and De Grott, 2009).

Nonetheless, different periods correspond to sensitivity to different aspects of language. For example, shortly after birth, infants can perceive and discriminate speech sounds from any language, including ones they have not been exposed to (Eimas et al., 1971; Gallagher et al., 2020).

Around six months of age, exposure to the primary language in the infant’s environment guides phonetic representations of language and, subsequently, the neural representations of speech sounds of the native language while weakening those of unused sounds (McClelland et al., 1999; Gallagher et al., 2020).

Vocabulary learning experiences rapid growth at about 18 months of age (Kuhl, 2010).

Critical Evaluation

More than any other area of applied linguistics, the critical period hypothesis has impacted how teachers teach languages. Consequently, researchers have critiqued how important the critical period is to language learning.

For example, several studies in early language acquisition research showed that children were not necessarily superior to older learners in acquiring a second language, even in the area of pronunciation (Olson and Samuels, 1973; Snow and Hoefnagel-Hohle, 1978; Scovel, 2000).

In fact, the majority of researchers at the time appeared to be skeptical about the existence of a critical period, with some explicitly denying its existence.

Counter to one of the primary tenets of Scovel’s (1969) critical period hypothesis, there have been several cases of people who have acquired a second language in adulthood speaking with native accents.

For example, Moyer’s study of highly proficient English-speaking learners of German suggested that at least one of the participants was judged to have native-like pronunciation in his second language (1999), and several participants in Bongaerts (1999) study of highly proficient Dutch speakers of French spoke with accents judged to be native (Scovel, 2000).

Bongaerts, T. (1999). Ultimate attainment in L2 pronunciation: The case of very advanced late L2 learners. Second language acquisition and the critical period hypothesis, 133-159.

Cisneros-Franco, J. M., Voss, P., Thomas, M. E., & de Villers-Sidani, E. (2020). Critical periods of brain development. In Handbook of Clinical Neurolog y (Vol. 173, pp. 75-88). Elsevier.

Colonnesi, C., Stams, G. J. J., Koster, I., & Noom, M. J. (2010). The relation between pointing and language development: A meta-analysis. Developmental Review, 30 (4), 352-366.

Eimas, P. D., Siqueland, E. R., Jusczyk, P., & Vigorito, J. (1971). Speech perception in infants. Science, 171 (3968), 303-306.

Emmorey, K., Bellugi, U., Friederici, A., & Horn, P. (1995). Effects of age of acquisition on grammatical sensitivity: Evidence from on-line and off-line tasks. Applied Psycholinguistics, 16 (1), 1-23.

Knudsen, E. I. (2004). Sensitive periods in the development of the brain and behavior. Journal of cognitive neuroscience, 16 (8), 1412-1425.

Hartshorne, J. K., Tenenbaum, J. B., & Pinker, S. (2018). A critical period for second language acquisition: Evidence from 2/3 million English speakers. Cognition, 177 , 263-277.

Kral, A., & Eggermont, J. J. (2007). What’s to lose and what’s to learn: development under auditory deprivation, cochlear implants and limits of cortical plasticity. Brain Research Reviews, 56(1), 259-269.

Kroll, J. F., & De Groot, A. M. (Eds.). (2009). Handbook of bilingualism: Psycholinguistic approaches . Oxford University Press.

Kuhl, P. K. (2010). Brain mechanisms in early language acquisition. Neuron, 67 (5), 713-727.

Lenneberg, E. H. (1967). The biological foundations of language. Hospital Practice, 2( 12), 59-67.

Lorenz, K. (1935). Der kumpan in der umwelt des vogels. Journal für Ornithologie, 83 (2), 137-213.

Marcovitch, S., & Zelazo, P. D. (2009). A hierarchical competing systems model of the emergence and early development of executive function. Developmental science, 12 (1), 1-18.

McClelland, J. L., Thomas, A. G., McCandliss, B. D., & Fiez, J. A. (1999). Understanding failures of learning: Hebbian learning, competition for representational space, and some preliminary experimental data. Progress in brain research, 121, 75-80.

McGraw, M. B. (1946). Maturation of behavior. In Manual of child psychology. (pp. 332-369). John Wiley & Sons Inc.

Moyer, A. (1999). Ultimate attainment in L2 phonology: The critical factors of age, motivation, and instruction. Studies in second language acquisition, 21 (1), 81-108.

Gallagher, A., Bulteau, C., Cohen, D., & Michaud, J. L. (2019). Neurocognitive Development: Normative Development. Elsevier.

Olson, L. L., & Jay Samuels, S. (1973). The relationship between age and accuracy of foreign language pronunciation. The Journal of Educational Research, 66 (6), 263-268.

Penfield, W., & Roberts, L. (2014). Speech and brain mechanisms. Princeton University Press.

Piaget, J. (1962). The stages of the intellectual development of the child. Bulletin of the Menninger Clinic, 26 (3), 120.

Rosa, A. M., Silva, M. F., Ferreira, S., Murta, J., & Castelo-Branco, M. (2013). Plasticity in the human visual cortex: an ophthalmology-based perspective. BioMed research international, 2013.

Salkind, N. J. (Ed.). (2005). Encyclopedia of human development . Sage Publications.

Scott, J. P. (1962). Critical periods in behavioral development. Science, 138 (3544), 949-958.

Scovel, T. (1969). Foreign accents, language acquisition, and cerebral dominance 1. Language learning, 19 (3‐4), 245-253.

Scovel, T. (2000). A critical review of the critical period research. Annual review of applied linguistics, 20 , 213-223.

Skoe, E., & Kraus, N. (2013). Musical training heightens auditory brainstem function during sensitive periods in development. Frontiers in Psychology, 4, 622.

Slavoff, G. R., & Johnson, J. S. (1995). The effects of age on the rate of learning a second language. Studies in Second Language Acquisition, 17 (1), 1-16.

Snow, C. E., & Hoefnagel-Höhle, M. (1978). The critical period for language acquisition: Evidence from second language learning. Child development, 1114-1128.

Stockard, C. R. (1921). Developmental rate and structural expression: an experimental study of twins,‘double monsters’ and single deformities, and the interaction among embryonic organs during their origin and development. American Journal of Anatomy, 28 (2), 115-277.

White, E. J., Hutka, S. A., Williams, L. J., & Moreno, S. (2013). Learning, neural plasticity and sensitive periods: implications for language acquisition, music training and transfer across the lifespan. Frontiers in systems neuroscience, 7, 90.

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Critical Period in Brain Development: Definition, Importance

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• When Does the Critical Period Begin and End?
• The Critical Period Hypothesis—What It States
• What Happens to the Brain in the Critical Period?
• What Kind of Events Impact the Brain During the Critical Period?
• How Do Adverse Events Impact the Brain?
• What's the Difference Between a Critical Period and a Sensitive Period?
• What Happens to the Brain When the Critical Period Ends?

The critical period in brain development is an immensely significant and specific time frame during which the brain is especially receptive to environmental stimuli and undergoes a series of rapid changes. 

These changes have lifelong effects as essential neural connections and pathways are established, playing a vital role in cognitive, emotional, and social development. 

This article will explore the timeline, impacting events, and subsequent consequences of the critical period on brain development. It also explores the distinction between critical periods and sensitive periods and what happens to the brain once the critical period ends.

When Does the Critical Period Begin and End? 

The starting point of the critical period is at conception. The brain starts to form and develop from the moment you are conceived. During pregnancy, a baby's brain is already beginning to shape itself for the world outside. The brain is gearing up and getting ready to absorb a massive amount of information.

The Early Years of a Child's Life

Once the baby is born, the brain kicks into high gear. The early years of a child's life, from birth to around the age of five, are generally considered the core of the critical period. The brain is incredibly absorbent during these years, taking in information rapidly. Everything from language to motor skills to social cues is being learned and processed extensively.

Different aspects of learning and development have different critical periods. For instance, the critical period for language acquisition extends into early adolescence. This means that while the brain is still very good at learning languages during early childhood, it continues to be relatively efficient at it until the teenage years.

The brain is incredibly absorbent during these years, taking in information rapidly. Everything from language to motor skills to social cues is being learned and processed extensively.

Vision Develops During This Period

On the other hand, for certain sensory abilities like vision, the critical period might end much earlier. This means that the brain is most receptive to developing visual abilities in the first few years of life, and after that, it becomes significantly harder to change or improve these abilities.

The Critical Period Hypothesis—What It States 

The brain has a certain time window when it's exceptionally good at learning new things, especially languages. This window of time is what is referred to as the "critical period."

Younger People Learn Languages Faster Than Older People

Eric Lenneberg, a neuropsychologist, introduced the Critical Period Hypothesis. He was very interested in how people learn languages . Through his observations and research, Lenneberg noticed that younger people were much more adept at learning languages than older people. This observation led him to the idea that there is a specific period during which the brain is highly efficient and capable of absorbing languages.

As You Age, It Becomes More Difficult to Absorb New Information

If the critical period is a wide open window in the early years of life, allowing the brain to take in an abundance of information quickly and efficiently, as time progresses, this window begins to close gradually. As it closes, the brain becomes less capable of easily absorbing languages.

This doesn't mean that learning becomes impossible as you age; it merely indicates that the ease and efficiency with which the brain learns start to decline.

What Happens to the Brain in the Critical Period? 

During the critical period, the brain experiences explosive growth. Let's take a look at some of the changes that happen in the brain during the critical period.

Neurons Form Connections

In the early stages, neurons in the brain start to form connections. These connections are called synapses.

Synapses are bridges that help different parts of the brain communicate with each other. In the critical period, the brain is building these bridges at an incredible pace.

Neuroplasticity Strengthens Brain Connections

As a baby interacts with the world, certain connections strengthen while others weaken. For instance, if a baby hears a lot of music, the parts of the brain associated with sounds and music will become stronger. This process of strengthening certain connections is known as brain plasticity because the brain molds itself like plastic.

Attachment to Primary Caregivers

An essential aspect of the critical period is the development of attachment to caregivers. During the early months and years, babies and toddlers form strong bonds with the people caring for them .

These attachments are critical for emotional development. When a caregiver responds to a baby's needs with warmth and care, the baby learns to form secure attachments . This lays the foundation for healthy relationships later in life.

What Happens When Children Are Not Given Attention?

What if a child is not given the attention and care they need during the critical period? This is a significant concern. Without proper attention and stimulation, the brain doesn't develop as effectively. The bridges or connections that should be built might not form properly. This can lead to various issues, including difficulty forming relationships, emotional problems, and learning difficulties.

When a child is given proper attention, stimulation, and care during the critical period, their brain thrives. The connections form rapidly and robustly. This sets the stage for better learning, emotional regulation, and relationship-building throughout life.

What Kind of Events Impact the Brain During the Critical Period? 

When a child is exposed to a rich, stimulating environment where they can play, explore, and learn, it tremendously impacts the brain. Engaging in interactive learning, being read to, and having supportive relationships with caregivers can significantly contribute to a well-developed brain.

Events such as abuse, neglect, head trauma , or extreme stress—collectively known as adverse childhood experiences (ACEs)—can be detrimental to brain development. These adverse events can impede the formation of neural connections and lead to behavioral, emotional, and cognitive difficulties later in life. "Unfortunately, disruptions to normal brain development due to environmental influences such as poverty, neglect, or exposure to toxins can cause lasting damage. This is why it is so important for children to receive adequate nutrition, stimulation, and parental care during these first few years of life; without it, they may suffer developmental delays and other issues that could potentially be avoided with proper attention,"  Harold Hong, MD , a board-certified psychiatrist says.

How Do Adverse Events Impact the Brain? 

When a child is neglected or abused, stress can impact how their brain develops. The parts of the brain involved in emotions and handling stress might not develop properly. This can make it hard for the person to manage their emotions later in life.

The hippocampus, involved in learning and memory, and the amygdala, which plays a role in emotion processing, are especially vulnerable. 

Similarly, if a child does not have enough food to eat or a safe place to live, the chronic stress of these conditions can impact brain development. The brain might focus on survival instead of other important areas of development, like learning and building relationships.

Even accidents that cause head injuries can impact the brain during the critical period. If a child experiences head trauma, it can affect the brain's development depending on the injury's severity and location.

What's the Difference Between a Critical Period and a Sensitive Period?

It is imperative to distinguish between critical periods and sensitive periods.

• Critical periods are specific windows of time during development when the brain is exceptionally receptive to certain types of learning and experiences. Once this period is over, acquiring those skills or attributes becomes significantly more challenging.
• Sensitive periods are phases in which the brain is more responsive to certain experiences. It's easier to learn or be influenced by specific experiences during sensitive periods, but unlike critical periods, missing this timeframe doesn't make it impossible to acquire those skills or traits later.

For example, while there is a critical period for acquiring native-like pronunciation and grammar, there is also a sensitive period for language learning. Children are more adept at learning new languages when they are young, but even if someone misses this window, they can still learn languages later in life.

One way to visualize the difference is to think of critical periods as a tightly defined window of time with a clear beginning and end, during which certain development must occur. In contrast, sensitive periods are more like a gradual slope, where learning at the beginning is optimal, but the ability doesn't disappear entirely over time.

What Happens to the Brain When the Critical Period Ends? 

It's essential to recognize that the end of the critical period does not mean the end of learning or brain development. Instead, it signifies a shift in how the brain learns and adapts. 

During the critical period, the brain is highly plastic, meaning it can change and form new connections rapidly. As this period ends, the brain doesn't lose this plasticity entirely, but the rate at which it can make new connections slows down. 

According to Hong, although some of these connections can still be altered by experiences later in life, such as learning a new language or practicing a skill, it is much harder to make significant changes after the critical period has ended. This highlights just how important it is for parents to provide proper care and nurture during those first few years.

The Brain Becomes More Specialized Via Adult Plasticity

The brain also becomes more specialized in the skills and information it has acquired as this period ends. During the critical period, the brain forms numerous connections, and as it ends, it starts to use these connections more efficiently for specialized tasks.

Even though the critical period ends, the brain still possesses a degree of plasticity and continues to learn throughout life. This is called adult plasticity.

Adult plasticity is not as robust during the critical period, but it allows for the continuous adaptation and learning necessary for us to navigate the ever-changing demands of life.

The conventional view is that critical periods close relatively tightly. However, research has started to challenge this rigid view. It's more accurate to say that the doors of critical periods close but do not necessarily lock.

While the brain's plasticity decreases after these periods, learning and adaptation can still take place, albeit with more effort and over a longer time. This phenomenon of 'metaplasticity'—the brain's ability to change its plasticity levels—remains an exciting area of ongoing research,  Dr. Ryan Sultan , a neuroscientist, child psychiatrist, and professor of psychiatry at Columbia University, says. 

What This Means For You

The critical period represents an invaluable window during which the foundations for cognitive, emotional, and social abilities are established. The environment, experiences, and attachments formed during this period have far-reaching consequences on a person's life.  Understanding the nuances of the critical period is essential for educators, parents, and policymakers to create nurturing environments that support healthy brain development. Providing support and early interventions for children exposed to adverse experiences is vital for ensuring their potential is not hindered by the circumstances of their early life.

Siahaan F. The critical period hypothesis of sla eric lenneberg’s . Journal of Applied Linguistics . 2022;2(1):40-45.

Nelson CA, Gabard-Durnam LJ. Early adversity and critical periods: neurodevelopmental consequences of violating the expectable environment. Trends in Neurosciences. 2020;43(3):133-143.

Colombo J, Gustafson KM, Carlson SE. Critical and sensitive periods in development and nutrition. Ann Nutr Metab . 2019;75(Suppl. 1):34-42.

Patton MH, Blundon JA, Zakharenko SS. Rejuvenation of plasticity in the brain: opening the critical period. Current Opinion in Neurobiology . 2019;54:83-89.

By Toketemu Ohwovoriole Toketemu has been multimedia storyteller for the last four years. Her expertise focuses primarily on mental wellness and women’s health topics.

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What are the main arguments for and against the critical period hypothesis, and what are alternative explanations?

Why is the critical period hypothesis so heavily disputed, yet widely accepted; what are its major strengths and weaknesses; what other explanations exist for the perceived "critical period", if it does not exist?

• critical-period

Let us start with a simple, relatively informal statement: “in most cases, those who start learning a language as children become ultimately become more proficient in a language than those who start learning it later”. This is uncontroversial, and something I think the vast majority of second-language acquisition researchers would agree on. However, this is not how the Critical Period Hypothesis (CPH) is understood within the field of Second Language Acquisition (SLA). CPH is a large subject, and your question is hard to answer in a few paragraphs. Therefore, I am reusing large fragments of an assignment I wrote on this very topic for an SLA course a few years ago. Let me know if something is unclear or the style is too terse at some points.

Summary (TL;DR)

There is no universally accepted definition of a critical period within linguistics and some of the controversies are caused by the fact that different researchers use different definitions.

There are a few key findings that are not controversial:

• early learners perform consistently well in all aspects of language use,
• as we move the starting age, they perform statistically worse and worse until puberty,
• however, the decrease in performance is not uniform.

An explanation, provided by Bialystok (1997) as an alternative to CPH, is the different learning style of children, compared to late learners.

Paradoxically, the differences (or lack thereof) between those who learn a foreign language as adults is the key factor in deciding whether CPH is true or not, and a controversial one:

• Some studies found correlations between the age adult learners started learning a language and their ultimate attainment. In other words, these studies suggest that if we compare people who have been learning a language for a very long time, the ultimate attainment of those who started at the age of 20 is statistically higher than the ultimate attainment of those who started at the age of 40. These studies argue that there is no CPH in the childhood, but rather that our abilities in learning a new language consistently decrease throughout our whole lives.
• Other studies found no clear correlations between the starting age and the ultimate attainment among adult language learners. They point out that the correlation between the starting age and ultimate attainment is clear for those who started before puberty. Based on that, they argue that there is something qualitatively different about starting to learn in an early age, and therefore conclude that it is an argument for CPH.

Definitions of the critical period used by those who argue against CPH

Controversies with the Critical Period Hypothesis (CPH) are related to the issue of ultimate attainment of early and late language learners, that is, the highest language proficiency level they can attain. The patterns in ultimate attainment may be explained by CPH, but they may also have different explanations. Some researchers support some form of the Critical Period Hypothesis (Johnson and Newport 1989, DeKeyser and Larson-Hall 2005, Patkowsky 1994, Scovel 1988), while others argue against it (Bialystok 1997).

A major problem with the Critical Period Hypothesis is that there appears to be no universally accepted definition of a critical period within linguistics . Bialystok (1997) bases her discussion of the critical/sensitive period (which she takes to be synonymous 1 ) on a specific technical definition used in ethology, which includes 14 essential structural characteristics that describe such a period (Bornstein 1989). She argues that one of these characteristics is especially problematic – the system: “structure or function altered in the sensitive period” (Bornstein 1989:184). In other words, she argues that there is no period where a structure in the brain is modified in a way that makes subsequent language learning harder or impossible. Bialystok does, however, agree that there is an optimal period for language learning – something that can be characterised by the statement “ On average, children are more successful than adults when faced with the task of learning a second language ” (Bialystok 1997:117). Despite the controversy around other issues, this fact is uncontested and has been verified by numerous studies .

Bialystok (1997) rejects the existence of a critical period, because of lack of postulated structure that is modified when the period is over. She postulates that an important factor that causes differences in ultimate attainment between early and late starters is learning style: children prefer accommodation (creating new concepts) over assimilation (extending existing concepts). The question remains: why do they prefer accommodation? She suggests that “[t]his may be because children are in the process of creating new categories all the time as they are learning new information” (Bialystok 1997:132).

Definitions of the critical period used by supporters of CPH

The researchers who support some form of the Critical Period Hypothesis (Johnson and Newport 1989, DeKeyser and Larson-Hall 2005), formulate it in a form that is much weaker than Bialystok's (1997) formulation. What they postulate often resembles what Bialystok calls the optimal age.

Johnson and Newport (1989) reformulated CPH into two alternative hypotheses, in order to fit second language acquisition into the picture:

The exercise hypothesis : “Early in life, humans have a superior capacity for acquiring languages. If the capacity is not exercised during this time, it will disappear or decline with maturation. If the capacity is exercised, however, further language learning abilities will remain intact throughout life.” (Johnson and Newport 1989:64)

The maturational state hypothesis : “Early in life, humans have a superior capacity for acquiring languages. This capacity disappears or declines with maturation.” (Johnson and Newport 1989:64)

We can see that if a critical period was found for second language acquisition, we could be almost sure that it exists for L1 acquisition as well (the maturational state hypothesis). However, we cannot deduce in this way in case of the exercise hypothesis – non-existence of a critical period for L2 acquisition does not exclude in any way a possibility of such period for the first language (Bialystok 1997).

DeKeyser and Larson-Hall (2005) formulate the hypothesis as: “language acquisition from mere exposure (i.e. implicit learning) […] is severely limited in older adolescents and adults”. Their formulation is quite vague, as is the constatation that there is a “qualitative change in language learning capacities somewhere between 4 and 18 years”.

There are also definitions that restrict the Critical Period Hypothesis to specific subareas of the language faculty. The most commonly mentioned area is phonology, see e.g. Patkowsky (1994, cited in Bialystok 1997), Scovel (1988, cited in Bialystok 1997).

Age effects before and after puberty

The current consensus is that early learners perform consistently well in all aspects of language use. As we move the starting age, they perform statistically worse and worse until puberty. The decrease in performance is not uniform, and in some areas (such as phonology) it is particularly visible. Performance on the same level as early bilinguals is possible, but rare.

Probably the most controversial aspect is the performance of adult learners. After puberty there is much bigger variance in the performance, so data are more prone to different interpretations. The results obtained by Derwing and Munro (2013) suggest that comprehensibility and good accent are negatively correlated with the age of arrival, that is, the age when English language immersion started. Johnson and Newport (1989) found no correlation of starting age after puberty with ultimate language proficiency, while Bialystok (1997) re-analysed these data and found some negative correlation. A meta-analysis by DeKeyser and Larson-Hall (2005) downplays the role of post-adolescent correlations. As we can see, the jury is still out on this debate.

1 In neuroscience critical period and sensitive period are two separate concepts, see Knudsen (2004).

Bibliography

• Bialystok, E. 1997. The structure of age: in search of barriers to second language acquisition. Second Language Research 13(2): 116-137.
• Bornstein, M.H. 1989. Sensitive periods in development: structural characteristics and causal interpretations. Psychological Bulletin 105,179–97.
• DeKeyser, R. and J. Larson-Hall. 2005. What does the critical period really mean? In J. F. Kroll and A. M. B. de Groot. 2005. Handbook of bilingualism: Psycholinguistic approaches . Cary, NC: Oxford University Press. Pp. 109–27.
• Derwing, T. M., & Munro, M. J. 2013. The development of L2 oral language skills in two L1 groups: A 7-year study. Language Learning 63, 163-185.
• Johnson, J.S., & Newport, E.L. 1989. Critical period effects in second language learning: The influence of maturational state on the acquisition of English as a second language. Cognitive Psychology , 21, 60-99.
• Knudsen, E. I. 2004. Sensitive periods in the development of the brain and behavior. Journal of Cognitive Neuroscience , 16, 1412-25
• Newport, E. L., & Supalla, T. 1987. A critical period effect in the acquisition of a primary language .
• Patkowsky, M. 1980. The sensitive period for the acquisition of syntax in a second language. Language Learning 30, 449–72
• Scovel, T. 1988. A time to speak: a psycholinguistic inquiry into the critical period for human speech . New York: Newbury House

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In This Article Expand or collapse the "in this article" section Critical Periods

Introduction, general overviews.

• Critical Periods in Language: Background Readings
• First-Language Acquisition (L1A)
• Age and Second-Language Acquisition (L2A)
• Controversy around the Critical Period Hypothesis for Second-Language Acquisition (CPH/L2A)
• Critical Period Geometry and Timing in L2A
• Brain-Based Studies of Critical Periods in L2A
• Bilingualism
• First-Language Attrition
• Sign Language
• Foreign Language Education
• Animal Models of Critical Periods
• Absolute Pitch

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Critical Periods by David P. Birdsong LAST REVIEWED: 12 January 2023 LAST MODIFIED: 12 January 2023 DOI: 10.1093/obo/9780199772810-0139

A critical period is a bounded maturational span during which experiential factors interact with biological mechanisms to determine neurocognitive and behavioral outcomes. In humans, the construct of critical period (CP) is commonly applied to first-language (L1) and second-language (L2) development. Some language researchers hold that during a CP, various mechanisms are at work that result in successful language acquisition and language processing. Outside of the period, other factors and mechanisms are involved, resulting in deficits in acquisition and processing. Many researchers believe that L1 development is constrained by maturationally based CPs. However, this notion is more controversial in L2 acquisition research, where the Critical Period Hypothesis for L2 acquisition (CPH/L2A) is debated on empirical, theoretical, and methodological grounds. Bilingualism researchers study the possibility that CPs may govern the likelihood and degree of loss (attrition) of the L1 among bilinguals as they age. Studies of CPs in L1 acquisition and L2 acquisition have been conducted with learners of spoken languages, signed languages, and artificial languages. CP research is considered in educational policy, particularly in the context of foreign language instruction. On a terminological note, a distinction is sometimes drawn between “critical” and “sensitive” periods, the latter term denoting receptivity of the organism to shaping by experience (or, in certain studies, suggesting relatively mild effects). Some researchers use these terms interchangeably, while others use one but not the other. Here, “critical period” will be used as a cover term unless specific reference is being made to sensitive period.

Lillard and Erisir 2011 describes juvenile CPs in language, imprinting, and vision. The article includes an informative table covering seven levels of neural changes in the brain in juveniles versus adults, with notes on the time course of changes and affected brain areas in animal and human models. The authors observe that changes in neural architecture triggered by early versus late experiences differ in degree more than type, and that the variety of triggering experiences is reduced with age. A second table summarizes neuroanatomical, electrophysiological, and neuroimaging techniques for observing specific types of neuroplasticity. Knudsen 2004 is exceptionally informative with respect to: prerequisites for CPs; the properties, mechanisms, and timing of plasticity; reopening of critical periods; the roles of presence and absence of relevant stimulation; and sensitive periods versus critical periods. Knudsen points out that complex behaviors (which include language use) may be regulated by multiple CPs. Takesian and Hensch 2013 emphasizes the individual-level plasticity of the timing of CP onset, peak, and offset, which may vary according to excitatory/inhibitory circuit balance that is sensitive to drugs, sleep, trauma, and genetic perturbation (see also Werker and Hensch 2015 [cited under First-Language Acquisition (L1A) ). Reh, et al. 2020 examines how plasticity is regulated at multiple timescales during development and provides examples from language processing, mental illness, and recovery from brain injury. Gabard-Durnam and McLaughlin 2020 outlines a set of current approaches to the study of sensitive periods in humans. These approaches include environmental manipulations (deprivation, enrichment, substitution), plasticity manipulations via pharmacological intervention, and computational modeling. Frankenhuis and Walasek 2020 develops an evolutionary model that accounts for sensitive periods that occur beyond the early stages of ontogeny.

Frankenhuis, Willem E., and Nicole Walasek. 2020. Modeling the evolution of sensitive periods . Developmental Cognitive Neuroscience 41:100715.

DOI: 10.1016/j.dcn.2019.100715

Sensitive periods in mid-ontogeny are favored by natural selection as a function of the reliability of relevant environmental cues.

Gabard-Durnam, Laurel, and Katie A. McLaughlin. 2020. Sensitive periods in human development: Charting a course for the future. Current Opinion in Behavioral Sciences 36:120–128.

DOI: 10.1016/j.cobeha.2020.09.003

Figures 1, 2 and 3 and their captions are particularly informative.

Knudsen, Eric I. 2004. Sensitive periods in the development of brain and behavior. Journal of Cognitive Neuroscience 16.8: 1412–1425.

DOI: 10.1162/0898929042304796

Focuses on the role of experience in modifying neural circuits during periods of plasticity, leading to connectivity patterns that become stable and less energy intensive, and making up what Knudsen calls the “stability landscape.”

Lillard, Angeline S., and Alev Erisir. 2011. Old dogs learning new tricks: Neuroplasticity beyond the juvenile period. Developmental Review 31:207–239.

DOI: 10.1016/j.dr.2011.07.008

A largely uncritical review and synthesis of well-known studies.

Reh, Rebecca K., Brian G. Dias, Charles A. Nelson III, et al. 2020. Critical period regulation across multiple timescales . Proceedings of the National Academy of Sciences of the United States of America 117.38: 23242–23251.

DOI: 10.1073/pnas.1820836117

A diverse group of specialists’ account of neurobiological CP mechanisms in animals and humans. Notes that “cortical plasticity is not only influenced by an animal’s life experiences but may also be modified by that of the parents. This occurs via parental behavior during the offspring’s early postnatal life, the in utero environment during gestation, or modification of the parental or fetal germ cells” (p. 23246).

Takesian, Anne E., and Takao K. Hensch. 2013. Balancing plasticity/stability across brain development. Progress in Brain Research 207:3–34.

DOI: 10.1016/B978-0-444-63327-9.00001-1

Illuminates the dynamic between the intrinsic plasticity of CP and the stabilization of neural networks, which limits maladaptive proliferation of circuit rewiring past the CP.

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Critical Period Hypothesis (CPH)

Tom Scovel writes, “The CPH [critical period hypothesis] is conceivably the most contentious issue in SLA because there is disagreement over its exact age span; people disagree strenuously over which facets of language are affected; there are competing explanations for its existence; and, to top it off, many people don’t believe it exists at all” (113). Proposed by Wilder Penfield and Lamar Roberts in 1959, the Critical Period Hypothesis (CPH) argues that there is a specific period of time in which people can learn a language without traces of the L1 (a so-called “foreign” accent or even L1 syntactical features) manifesting in L2 production (Scovel 48). If a learner’s goal is to sound “native,” there may be age-related limitations or “maturational constraints” as Kenneth Hyltenstam and Niclas Abrahamsson call them, on how “native” they can sound. Reducing the impression left by the L1 is certainly possible after puberty, but eliminating that impression entirely may not be possible.

Kenji Hakuta et al. explains that the relationship between age and L1 interference in L2 production is really not up for debate:

“The diminished average achievement of older learners is supported by personal anecdote and documented by empirical evidence….What is controversial, though, is whether this pattern meets the conditions for concluding that a critical period constrains learning in a way predicted by the theory” (31).

Some learners manage to overcome the “constraints” that Scovel believes are “probably accounted for by neurological factors that are genetically specified in our species” (114), but these learners are exceptional rather than the rule. It may be biology; it may be due to something else. The debate will continue, but evidence seems to indicate that the older learners become, the more difficult complete acquisition can be.

“David Birdsong, Looking Inside and Beyond the Critical Period Hypothesis.”  YouTube,  uploaded by IWL Channel, 09 May 2016, https://www.youtube.com/watch?v=9Bo0C4dj7Mw.

Application

Instructors should consider taking the CPH into account when assessing their students’ oral communication in the target language. When “maturational constraints” are a potential concern, it seems more fair for instructors to weight comprehension more heavily than nativeness. A thorough understanding of the CPH can also help instructors to counteract adult learners’ “self-handicapping” by helping the learners understand that, in spite of constraints due to aging, they are still capable of acquiring many–if not most–aspects of the target language.

Bibliography

Hakuta, Kenji, et al. “Critical Evidence: A Test of the Critical-Period Hypothesis for Second-Language Acquisition.”  Psychological Science , vol. 14, no. 1, 2003, pp. 31–38.  JSTOR , www.jstor.org/stable/40063748.

Hyltenstam, Kenneth, and Niclas Abrahamsson. “Comments on Stefka H. Marinova-Todd, D. Bradford Marshall, and Catherine E. Snow’s ‘Three Misconceptions about Age and L2 Learning’: Age and L2 Learning: The Hazards of Matching Practical ‘Implications’ with Theoretical ‘Facts.’”  TESOL Quarterly , vol. 35, no. 1, 2001, pp. 151–170.  JSTOR , www.jstor.org/stable/3587863.

Nemer, Randa. “Critical Period Hypothesis.”  Prezi,  04 Dec. 2013, https://prezi.com/zzuch40ibrlq/critical-period-hypothesis-sla/#.

Scovel, Tom.  Learning New Languages . Heinle & Heinle, 2001.

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

Joshua k. hartshorne.

a Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Building 46, 77 Massachusetts Avenue, MIT, Cambridge, MA 02139, United States

b Department of Psychology, Boston College, McGuinn Hall 527, Chestnut Hill, MA 02467, United States

Joshua B. Tenenbaum

Steven pinker.

c Department of Psychology, Harvard University, William James Hall 970, 33 Kirkland St., Cambridge, MA 02138, United States

Associated Data

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.

1. 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 & Newport, 1989 ; Lenneberg, 1967 ; Morgan-Short & 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 & Molis, 2001 ; Hakuta, Bialystok, & Wiley, 2003 ; Hernandez, Li, & MacWhinney, 2005 ; Johnson & 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 & Molis, 2001 ; Guion, Flege, Liu, & Yeni-Komshian, 2000 ; Hakuta et al., 2003 ; Jia, Aaronson, & Wu, 2002 ; Johnson & Newport, 1989 ; McDonald, 2000 ; Sebastián-Gallés, Echeverría, & Bosch, 2005 ; Vanhove, 2013 ).

1.1. Learning ability vs. ultimate attainment

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

• How does learning ability change with age?
• 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 )?

(A–D) Schematic depictions of four theories of how language learning ability might change with age. (E–H) Schematic depictions of four theories of how ultimate attainment might vary with age of first exposure to the language. Note: While the curves hypothesized for learning ability and ultimate attainment resemble one another, there is little systematic relationship between the two; see the main text.

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, Long, & Scarcella, 1979 ; Snow & 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 & 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 .

Simulation results showing how the mapping between hypothetical changes in underlying learning rate (the left graph in each pair) and empirically measured changes in ultimate attainment is many-to-many. These quantitative predictions were derived from the ELSD model, described below, but the basic point is model-independent.

1.2. The present study

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.

2.1. 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 the New York Times , we developed an Internet quiz we hoped would be sufficiently appealing as to attract large numbers of participants. In order to go viral, the quiz needed to be entertaining and intrinsically motivating while also quick to complete, since Internet volunteers rarely spend more than 10 min on a quiz. At the same time, to yield useful data the quiz had to include a robust, comprehensive measure of syntactic knowledge without an artificial ceiling, as well as elicit demographic data about age and linguistic background. Below, we describe how we addressed these desiderata. Procedures were approved by the Committee on the Use of Humans as Experimental Subjects at Massachusetts Institute of Technology.

2.2. Procedure

Potential subjects were invited to take a grammar quiz ( www.gameswithwords.org/WhichEnglish ), the results of which would allow a computer algorithm to guess their native language and their dialect of English. After providing informed consent, subjects provided basic demographic details (age, gender, education, learning disability) and indicated whether they had taken the quiz before. They then completed the quiz and were presented with the algorithm’s top three guesses of their native language and their dialect, which was based on the Euclidean distance between the vector of the subject’s responses and the vector of mean responses for each language and dialect. Participants found this aspect of the quiz highly engaging, and the quiz was widely shared on social media. For instance, it was shared more than 300,000 times on Facebook.

After seeing the guesses, subjects were invited to help us improve the algorithm by filling out a demographic questionnaire. (Although early answers were used to tune the algorithm, the algorithm’s accuracy quickly plateaued and was not tuned further.) This included all the countries they had lived in for at least 6 months, and all the languages they spoke from birth. 1 Participants who listed multiple countries were asked to indicate their current country. For some countries (such as the USA), additional localizing information was collected. Participants who did not report speaking English from birth were asked at what age they began learning English, how many years they had lived in an English-speaking country, and whether any immediate family members were native speakers of English. Approximately 80% of subjects who completed the syntax questions also completed this demographic questionnaire. The data reported here come from those subjects.

2.3. Participants

All participants gave informed consent. 680,333 participants completed the experiment, excluding repeats. We further excluded participants who gave inconsistent or implausible responses to the demographic questions (listing a current age less than the age of first exposure to English; listing a current age that is less than the number of years spent in an English-speaking country; reporting college attendance and a current age of less than 16, or reporting graduate school attendance and a current age of less than 19), resulting in 669,800 participants. Finally, based on the histogram of ages, we excluded participants younger than 7 and older than 89 as implausible. Note: a number of participants ages 7–10 reported in the comments that their parents helped by reading the quiz to them, adding credibility to those data. The resulting number of participants for the analyses was 669,498.

The sample was demographically diverse ( Fig. 3 ). Thirty-eight languages were represented by at least 1000 native speakers, not counting individuals who had multiple native languages. The most common native languages other than English were Finnish (N = 39,962), Turkish (N = 36,239), German (N = 24,995), Russian (N = 22,834), and Hungarian (N = 22,108).

(A) Current country of residence of participants (excluding participants with multiple residences). (B) Histogram of participants by age of first exposure to English. (C) Native languages of the bilinguals (excluding English). (D) Histogram of participants by current age.

Analyses focused on three subject groups. Monolinguals (N = 246,497) grew up speaking English only; their age of first exposure was coded as 0. Immersion learners (N = 45,067) were either simultaneous bilinguals who grew up learning English simultaneously with another language (age of first exposure = 0), or later learners who learned English primarily in an English-speaking setting (defined as spending at least 90% of their life since age of first exposure in an English-speaking country). Non-immersion learners (N = 266,701) had spent at most 10% of post-exposure life in an English-speaking country and no more than 1 year in total. 2 Subjects with intermediate amounts of immersion (N = 122,068) were not analyzed further.

2.4. Materials

We took a shotgun approach to assessing syntax, using as diverse a set of items as we could fit into a short quiz, addressing such phenomena as passivization, clefting, agreement, relative clauses, preposition use, verb syntactic subcategorization, pronoun gender and case, modals, determiners, subject-dropping, aspect, sequence of tenses, and wh- movement. This broad approach has two advantages. First, it provides a more comprehensive assessment of syntactic phenomena than many prior studies, which focused on a smaller number of phenomena ( Flege, Yeni-Komshian, & Liu, 1999 ; Johnson & Newport, 1989 ; Mayberry & Lock, 2003 ). Second, this diversity provides some robustness to transfer from the first language. That is, while native speakers of some languages may find certain phenomena easier to master than others (e.g., Spanish-speakers may find tense reasonably natural while Mandarin-speakers may find word-order restrictions intuitive), the diversity of items should help wash out these differences (see also discussion below).

2.4.1. Item selection

Items were subjected to several rounds of pilot testing to select a suffficient number of critical items that were diagnostic of proficiency (neither too easy nor too hard) and that represented a wide range of grammatical phenomena, while requiring less than 10 min to complete. These included phenomena known to present difficulties for children, such as passives and clefts, and for non-native speakers, such as tenses and articles. We focused particularly on items known to be difficult for speakers of a variety of first languages: in particular, Arabic, French, German, Hindi, Japanese, Korean, Mandarin, Russian, Spanish, or Vietnamese. Based on previous experiments on gameswithwords.org, we expected these to be among the most common native languages.

In addition to the critical items, we included items designed to distinguish among English dialects drawn from websites describing “Irishisms,” “Canadianisms”, and so on. These items were not used for assessing language proficiency and were not used in the data analyses below, but were important for recruiting subjects (see above). Several rounds of pilot-testing reduced this set to the smallest number of items that could reliably distinguish major English dialects.

As in most previous studies, we solicited grammaticality judgments (e.g., “Is the following grammatical: Who whom kissed ?”). In order to shorten the test and improve the subject experience, where possible we grouped multiple grammaticality judgments into a single multiple-choice question. Because the grammaticality judgment task is time-consuming and unsuitable for probing certain grammatical phenomena, we also included items that required matching a sentence to a picture (e.g., to probe topicalization and the application of linking rules). Several rounds of piloting were used to construct a test that involved items of a range of difficulty.

The final set of 132 items is provided in the Supplementary Materials . Of these, 95 were critical items, defined as items for which the same response was selected by at least 70% of the native English speaking adults 18–70 years old in our full dataset in each of thirteen broadly-defined English dialects (Standard American, African American Vernacular English, Canadian, English, Scottish, Irish, North Irish, Welsh, South African, Australian, New Zealand, Indian, and Singaporean). (For obvious reasons, the exact number of critical items was not known until after the data was collected.) All analyses below are restricted to this set.

Many prior studies classify items according to the syntactic phenomenon they test. While this is straightforward for certain types of tests, such as our sentence-picture matching items, the accuracy of these categorizations for grammaticality judgments is unclear. For instance, in judging a sentence to be grammatical, subjects can hardly be expected to know which syntactic rule the experimenter deliberately did not violate. Likewise, ungrammatical sentences may implicate different rules depending on what the intended message was: I eats dinner could involve an agreement error on the verb or a failure of pronoun selection. Thus, the syntactic violation that catches the subject’s eye may not be the one the experimenter had in mind. Because our goal was merely to have a diverse set of items, an exact count of syntactic phenomena is less important than demonstrating diversity. Thus, we have bypassed these theoretically thorny issues by avoiding categorization and simply providing the entire stimulus set in the Supplementary Materials . As a result, readers can judge for themselves whether the items are sufficiently diverse.

2.4.2. Test reliability

Reliability for the critical items was high across the entire dataset (Chronbach’s alpha = 0.86). Because monolingual subjects were close to ceiling, reliability is expected to be lower for that subset. Reliability is a measure of covariation, and the monolinguals exhibited very little variation (the majority missed fewer than 3 items), exactly as one would expect for a valid test. However, reliability for monolinguals was still well above chance (0.66), indicating that what few errors they made were not randomly distributed (as would be expected from mere sloppiness) nor concentrated on a few “bad” items (in which case, there would be little variance). Thus, our test was sensitive to differences in grammatical knowledge even for monolinguals who were close to ceiling. It is difficult to compare these numbers to prior studies, since most did not report reliability (but see DeKeyser, 2000 ; DeKeyser, Alfi-Shabtay, & Ravid, 2010 ; Granena & Long, 2013 ).

2.4.3. Data

The resulting dataset is available at http://osf.io/pyb8s .

3.1. 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, percentage points are not all of equal value, being more meaningful closer to 0% or 100% than when near 50% ( Jaeger, 2008 ). That is, the difference between 95% and 96% is “larger” than the difference between 55% and 56%. Thus, the use of percentages artificially imposes ceiling effects, inflating both Type I and Type II error rates, particularly for interactions. Similarly, graphing results in terms of percentage correct distorts the results (particularly the shapes of curves), and so we have graphed in terms of log odds. For reference, we have included percent correct on the right-hand side of many of the graphs.

Fig. 4 plots the level of performance against current age in separate curves for participants with different ranges of age of first exposure. It simultaneously reveals the effects of age of first exposure (the differences among the curves) and total years of exposure (the left-to-right position along each curve). Immersion learners—who were less numerous than the other groups—were aggregated into three-year bins for age of exposure, except for the simultaneous bilinguals (age of exposure = 0), who constituted their own bin. Curves were smoothed with a five-year floating window (analyses on non-smoothed data are discussed in the next subsection), and each of the estimated performance curves (described below) was restricted to consecutive ages for which there were at least ten participants in the five-year window, leaving 244,840 monolinguals, 44,412 immersion learners, and 257,998 non-immersion learners.

(A and B) Performance curves for monolinguals and immersion learners (A) and non-immersion learners (B) under 70 years old, smoothed with five-year floating windows. (C and D) Corresponding curves for the best-fitting model. (E) Learning rate for the best-fitting model (black), with examples of the many hypotheses for how learning rate changes with age that were considered in model fitting (grey). For additional detail, see Fig. 7 , S3, and S6 .

In order to estimate how underlying learning ability changes with age, we used a novel computational model to disentangle current age, age of first exposure, and amount of experience. Specifically, we modeled syntax acquisition as a simple exponential learning process:

where g is grammatical proficiency, t is current age, t e is age of first exposure, r is the learning rate, and E is an experience discount factor, modeled separately for simultaneous bilinguals, immigrants, and non-immersion learners, reflecting the fact that they may receive less English input than monolinguals. We modeled a possible developmental change in the learning rate r as a piecewise function in which r is constant from birth to age t c , whereupon it declines according to a sigmoid with shape parameters α and δ (α controls the steepness of the sigmoid, and δ moves its center left or right):

The piecewise structure of this Exponential Learning with Sigmoidal Decay (ELSD) model, and the fact that sigmoid functions can accommodate both flat and steep declines, allows it to capture a very wide range of developmental trajectories, including all of those discussed in the literature. Learning rate may be initially high or low, begin declining at any point in the lifespan (or not at all), decline rapidly or gradually, decline continuously or discontinuously, etc. Examples of the many possibilities encompassed by the model include the different curves shown in Figs. 2 and S2 , as well as the gray lines in Fig. 4E .

The model was fitted simultaneously to the performance curves for monolinguals, immersion learners, and non-immersion learners (cf. Fig. 4A and B ). Parameters were fit with Differential Evolution ( Mullen, Aridia, Gil, Windover, & Cline, 2011 ) and compared using Monte Carlo split-half cross-validated R 2 , which avoids over-fitting. The best-fitting model (R 2 = 0.89) involved a rate change beginning at 17.4 years ( Fig. 4E ). The fit was significantly better than the best fit for alternative models in which learning rate did not change (R 2 = 0.66) or changed according to a step function with no further decline in the learning rate after the initial drop (R 2 = 0.70). Details on these and related models can be found in the supplementary materials .

3.2. Interim discussion

Though the ELSD model is necessarily simplified, the good fit between model and data, and the poorer fit by reasonable alternatives, offers good support for the existence of a critical period for language acquisition, and suggests that our estimate of when the learning rate declines (17.4 years old) is likely to be reasonably accurate.

This age is much later than what is usually found for the offset of the critical period for native-like ultimate attainment of syntax. However, as discussed in the Introduction, because language acquisition takes time, there is no reason to suppose that the last age at which native-like ultimate attainment can be achieved is the same as the age at which underlying ability declines (see also Patkowski, 1980 ). Instead, the relationship between ultimate attainment and critical periods is complex, depending also on how long it takes to learn a language. The ELSD model disentangles these factors. In order to better understand the results of the above analyses, we look at these issues in turn.

3.3. The duration of learning

Little is known about how long it takes learners to reach asymptotic performance. On the one hand, developmentalists have observed that by 3–5 years of age, most children show above-chance sensitivity to many syntactic phenomena ( Crain & Thornton, 2011 ; Pinker, 1994 ). Indeed, our youngest native speakers (~7 years old) were already scoring very well on our quiz ( Fig. 5B ).

(A) Histogram of cutoffs used for minimum years of experience to asymptotic learning in previous studies of syntax ( Abrahamsson, 2012 ; Birdsong & Molis, 2001 ; DeKeyser, 2000 ; DeKeyser et al., 2010 ; Flege et al., 1999 ; Granena & Long, 2013 ; Jia et al., 2002 ; Johnson & Newport, 1989 , 1991 ; Mayberry & Lock, 2003 ; Mayberry, Lock, & Kazmi, 2002 ; McDonald, 2000 ; Weber-Fox & Neville, 1996 ). Papers with multiple studies are included only once, except for McDonald (2000) , which used different cutoffs in two different studies. (B) Accuracy for monolinguals (N = 246,497) and simultaneous bilinguals (N = 30,397). Shadowed area represents ± 1 SE. This highlights information also available in Fig. 4A .

While certainly an important fact about acquisition, this is the wrong standard for research into critical periods. The question has never been “why do non-native speakers not match the competency level of preschooler?” Many of them do. In fact, in our dataset, even non-native immersion learners who began learning in their late 20 s eventually surpassed the youngest native speakers in our dataset ( Fig. 4A ).

Instead, the puzzle driving this entire research domain is why later learners do not reach the same proficiency level of mature native speakers. That is a much higher standard. Many other aspects of syntax continue to develop in the school-age years ( Berman, 2004 , 2007 ; Nippold, 2007 ), and prior studies have not been able to determine the age at which syntactic development concludes. Even for those aspects of syntax that preschoolers are sensitive to, they are rarely at ceiling, and they typically do worse than college-age adults, whether assessed through comprehension, elicited production, or spontaneous production (e.g., Kidd & Bavin, 2002 ; Kidd & Lum, 2008 ; Marcus et al., 1992 ; Messenger, Branigan, McLean, & Sorace, 2012 ; Rowland & Pine, 2000 ). However, while we know that performance continues to improve into the school ages, the literature has little to say about when children attain adult levels of accuracy. Moreover, the common practice of comparing children to college-aged adults necessarily renders undetectable any post-college development.

Even less is known about how long non-native speakers continue to improve on the target language. While a few studies found limited continued improvement for immersion learners after the first five years ( Johnson & Newport, 1989 ; Patkowski, 1980 ), these studies had minimal power to detect continued improvement (see below). Specifically, looking at samples of non-native learners who were selected to have at least three years ( Johnson & Newport, 1989 ) or five years ( Patkowski, 1980 ) of experience, these authors found that while age of first exposure predicted performance, length of experience did not. In contrast, analysis of US Census data suggests that learning continues for decades ( Stevens, 1999 ), though the validity of this self-report data is uncertain. Analysis of foreign language education suggests learning in that context may continue for a couple of decades, though this may merely reflect the slower pace of non-immersion learning ( Huang, 2015 ).

This empirical uncertainty is reflected directly in the ultimate attainment literature. Ultimate attainment analyses require restricting analysis to those subjects who have been learning the target language long enough to have reached asymptote (e.g., Johnson & Newport, 1989 ). In the absence of any clear evidence, researchers have chosen a diverse set of cut-offs, ranging anywhere from three ( Birdsong & Molis, 2001 ; McDonald, 2000 ) to fifteen years ( Abrahamsson, 2012 ) ( Fig. 5A ).

Inspection of Fig. 5B suggests that native speakers did not reach asymptote until around 30 years old, though most of the learning takes place in the first 10–20 years. The results for later learners shown in Fig. 4 similarly suggest a protracted period of learning (for detailed results, see Figs. S21 and S22 in the Supplementary Materials , and surrounding discussion). Note that the increases in performance after the first 15–20 years are modest, which accords with the fact that they are not routinely noticed.

While this prolonged learning trajectory was not anticipated in the language learning literature, it joins mounting evidence that many cognitive abilities continue to develop through adolescence and even adulthood, including working memory, face recognition, magnitude estimation, and various measures of crystalized intelligence ( Germine, Duchaine, & Nakayama, 2011 ; Halberda, Ly, Wilmer, Naiman, & Germine, 2012 ; Hartshorne & Germine, 2015 ).

Thus, even native speakers—who are able to make full use of the critical period—take a very long time to reach mature, native-like proficiency. By implication, someone who started relatively late in the critical period—that is, someone who had limited time to learn at the high rate the critical period provides—would simply run out of time. In order to follow up on this issue and test this implication, we turn to analysis of ultimate attainment.

3.4. Ultimate attainment

Based on the results above, we expect that the last age of first exposure at which native-like attainment is still within reach is likely well prior to 17. Below, we first estimate this age from our own data and then compare that against previous estimates.

Following the usual practice, we first restrict the analysis to those subjects who have been learning English long enough to have reached asymptote (e.g., Johnson & Newport, 1989 ). As described in the previous section, there is no consensus as to how long “long enough” is (see Fig. 5A ). This stems from the fact that, prior to our own study, there was little data to constrain hypotheses (see previous section). Inspection of Figs. 4 and ​ and5 5 suggests 30 years old as a reasonable cutoff.

Thus, to estimate the age at which mastery of a second language is no longer attainable, we analyzed ultimate attainment curves by focusing on the 11,371 immersion learners and 29,708 non-immersion learners who had at least 30 years of experience (ensuring asymptotic learning) and who were at most 70 years old (avoiding age-related decline) ( Fig. 6 ). We fitted these curves using multivariate adaptive regression splines ( Friedman, 1991 ; Milborrow, 2014 ). Immersion learners showed only a minimal decline in ultimate attainment until an age of first exposure of 12 years ( B = −0.009; 0.01 SDs/year), after which the decline became significantly steeper ( B = −0.06; 0.07 SDs/year). Non-immersion learners showed similar results: From 4 years to 9 years, proficiency showed no decline (in fact it increased slightly; B = 0.01; 0.01 SDs/year), followed by a steep decline ( B = −0.06; 0.07 SDs/year). Two other methods of estimating changes in slope provided similar results (see Supplementary Materials ).

Ultimate attainment for monolinguals, immersion learners, and non-immersion learners, smoothed with a three-year floating window. Shadowed areas represent ± 1 SE. Attainment for monolinguals was significantly higher than that of simultaneous bilinguals (immersion learners with exposure age = 0) ( p < .01).

While these analyses employ the standard method of analyzing subjects who have (presumably) already reached ultimate attainment, the density of our data allows a more direct analysis. Fig. 7 re-plots the data in Fig. 4 against years of experience, aligning the curves for the learners who began at different ages at the onset of learning. Inspection reveals that the learning trajectories for immersion learners who began in the first decade of life (the orange curves) are almost indistinguishable ( Fig. 7A ). We see a similar trend for the non-immersion learners ( Fig. 7B ).

Accuracy as a function of years of experience, by age of first exposure for immersion learners (A) and non-immersion learners (B). Color scheme is same as in Fig. 4 . Red: monolinguals. Orange: AoFE < 11. Green: 10 < AoFE < 21. Blue: AoFE > 20. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

We confirmed these observations with permutation analysis. Specifically, we calculated the average difference between each performance curve and the performance curve for the youngest learners of that type (the simultaneous bilinguals for immersion learners, the learners with an age of first exposure of 4 years for the non-immersion learners). A positive score indicated that the performance curve was, on average, below the curve for the earliest learners. We then constructed an empirical distribution by randomly permuting the age of exposure across participants at a given number of years of experience. The curves were again smoothed with five-year floating windows and the difference scores were again calculated. This was repeated 1000 times. The percentage of cases in this distribution in which the difference score for a given performance curve is larger than the actual difference score for that performance curve serves as a one-tailed p -value (all comparisons reported as significant are also significant as two-tailed tests). These analyses revealed that the performance curves for immersion learners with average exposure ages of 2, 5, and 8 years were not significantly different from those of simultaneous bilinguals (exposure age = 0; p s > 0.31), while the curves for later learners were significantly lower ( p s < 0.01). Similarly, non-immersion learners with ages of exposure of 5–11 years were indistinguishable from our earliest non-immersion learners (4 years; ps > 0.31), whereas later learners learned significantly more slowly ( p s < 0.01).

3.4.1. Comparison with previous ultimate attainment results

Both traditional ultimate attainment analyses and permutation analyses indicated that learners must start by 10–12 years of age to reach native-level proficiency. Those who begin later literally run out of time before the sharp drop in learning rate at around 17–18 years of age. For non-immersion learners, the ceiling was lower but the overall story was the same: little difference between learners who start within the first decade of life, with a ceiling that noticeably drops for later learners. These findings are consistent with the protracted trajectory of learning that we observe in our data (see previous section).

However, our results for immersion learners diverge from those of some previous studies (there are no similar studies of non-immersion learners). For instance, Johnson and Newport’s (1989) study of immersion learners found no correlation between ultimate attainment and age of first exposure after an onset age of 16, whereas we see a strong relationship (for review, see Qureshi, 2016 ). In principle, this could be due to differences in subject population or the types of grammar rules tested. Indeed, researchers frequently argue that such differences have large effects on ultimate attainment, based on the fact that studies of different populations or stimuli have produced different results ( Abrahamsson, 2012 ; Birdsong & Molis, 2001 ; DeKeyser, 2000 ; DeKeyser et al., 2010 ; Flege et al., 1999 ; Granena & Long, 2013 ; Hakuta et al., 2003 ; Jia et al., 2002 ; Johnson & Newport, 1989 ; Vanhove, 2013 ; Weber-Fox & Neville, 1996 ).

However, a recent analysis by Vanhove (2013) raised questions about whether these differences are statistically meaningful. Whereas most prior studies had between 50 and 250 subjects, Vanhove demonstrates that precisely measuring how ultimate attainment changes as a function of age of first exposure requires thousands. Only one previous dataset, based on US Census data, reaches sufficient sample size ( Hakuta et al., 2003 ; Stevens, 1999 ). However, this study was based on a self-report of proficiency on a four-point scale, which is unlikely to have much precision. Thus, differences across findings in the literature could reflect nothing more than random noise.

Thus, in order to better understand whether the differences in our findings and those of prior studies are meaningful, we need to consider the precision of these findings. We estimated precision using bootstrapping, simulating running many different studies by resampling with replacement from our own data ( Efron & Tibshirani, 1993 ). The results of each simulation will be slightly different, and so the range of results across simulations simulates the variability we would expect from statistical noise alone. Crucially, we can simulate running studies with different sample sizes. Thus, we can ask whether Johnson and Newport’s (1989) findings are within what we might have found had we used our own methods but tested the same number of subjects (N = 69).

For our simulations, we considered two different sample sizes: N = 69, the size of the classic Johnson and Newport (1989) study, and N = 275, larger than the largest prior study, with the exception of the aforementioned Census studies. For comparison, we also simulated studies with N = 11,371, the number of subjects in our own ultimate attainment results described in the previous section.

We focused on three different analyses that have been reported in a number of prior studies ( Bialystok & Miller, 1999 ; Birdsong & Molis, 2001 ; DeKeyser, 2000 ; DeKeyser et al., 2010 ; Flege et al., 1999 ; Johnson & Newport, 1989 ; Weber-Fox & Neville, 1996 ). First, we considered Johnson and Newport’s finding that the correlation between age of first exposure and ultimate attainment is much stronger before an exposure age of 16 ( r = −0.87) than after ( r = −0.16). This finding has proved controversial, with subsequent studies finding much weaker effects or no effect at all ( Bialystok & Miller, 1999 ; Birdsong & Molis, 2001 ; DeKeyser, 2000 ; Johnson & Newport, 1989 ). All these prior findings are well within what one would expect for N = 69 ( Fig. 8 , upper left). As power increased, the variability in the estimates dropped dramatically, with more highly-powered studies being increasingly unlikely to find any substantial difference in the correlations before and after 16 years old.

We conducted 2500 simulated experiments of monolingual and immersion learners with each of three sample sizes: N = 69 (equivalent to Johnson & Newport, 1989 ), N = 275 (larger than the largest prior lab-based study), and N = 11,371 (equivalent to the present study). Three analyses were considered. Left: Correlation between age of first exposure and ultimate attainment prior to 16 years old minus after 16 years old. Middle: First subgroup of subjects to be significantly worse than monolinguals in a t -test (note: the top graph uses the same age bins as Johnson & Newport, 1989 ). Right: age of first exposure at which performance begins to decline more rapidly, if any. Blue: estimates from Bialystok and Miller (1999) , Birdsong and Molis (2001) , DeKeyser (2000) , DeKeyser et al. (2010) , Flege et al. (1999) , Johnson and Newport (1989) , and Weber-Fox and Neville (1996) . While many other papers addressed similar issues, these papers provide the closest analog to Johnson & Newport in that they used a broad-spectrum test of syntax, defined the onset of learning as the age at immigration, and (crucially) report comparable statistics. Red: estimates from current study. Full details available in Supplementary Materials . (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Second, Johnson and Newport also reported that individuals who began learning English at 8–10 years old failed to reach monolingual-like ultimate attainment, whereas individuals who began earlier did, suggesting that the “optimal period” for language-learning is 0–7 years old. Once again, there has been considerable variability in subsequent studies, and our own study finds that even simultaneous bilinguals do not quite reach monolingual levels. Vanhove (2013) suggested, based on power calculations, that accurately estimating the end of the optimal period requires thousands of subjects. Although a small study can detect very large effects, the differences between learners who began just within the optimal period and those who began just after are relatively small ( Fig. 6 ) and thus undetectable with a low-power study. Our simulations confirm this analysis ( Fig. 8 , middle column): in our simulation of Johnson & Newport ( Fig. 8 , middle column, top), the 95% confidence interval contained almost the entire range. Even with 275 subjects, a wide range of findings would be expected. However, simulations based on our full sample show no variability at all, with learners who began at 1 year of age performing reliably worse than monolinguals ( Fig. 8 , middle column, bottom).

Third, whereas the previous analysis of the optimal period followed Johnson and Newport’s method of using t-tests to compare native speakers to groups of later-learners, subsequent researchers have used instead curve estimation—typically segmented regression with breakpoint estimation—which is argued to be more precise and less prone to false positives ( Birdsong & Molis, 2001 ; Vanhove, 2013 ; but see DeKeyser et al., 2010 ). If there is an optimal period, the slope of the ultimate attainment curve should initially be close to 0, followed by a point where it becomes significantly more negative. By this standard of evidence, most studies have failed to find any evidence of an optimal period ( Birdsong & Molis, 2001 ; Flege et al., 1999 ; Vanhove, 2013 ). Our simulations suggest these prior findings were false negatives due to low power: Like the majority of prior studies, low-power simulations elicited largely null results, whereas high-power simulations suggested an optimal period ending in early or middle childhood ( Fig. 8 , right).

3.4.2. Interim discussion

Two sets of analyses of our data suggest that learners who begin as late as 10–12 years old reach similar levels of ultimate attainment as native bilinguals. After that age, we find a continuous decline in attainment as a function of age of first exposure, with no evidence that this relationship ceases after a particular age (cf. Johnson & Newport, 1989 ; Pulvermüller & Schumann, 1994 ). These findings are consistent with our results for learning rate. Interestingly, these findings held not only for immersion but also non-immersion learners, a population that has not been much studied in this regard.

Our findings do contrast with the conclusions of some prior studies of ultimate attainment in immersion learners. However, as our simulations show, these conclusions were probably overfit to point estimates. That is, conclusions depended on the most probable estimate (the optimal period ends at 8 years of age), ignoring the error bars, which in some cases were likely so large as to encompass the entire possible range ( Fig. 8 ). In contrast, our larger sample size allows for fairly precise estimates ( Fig. 8 ). These simulations support Vanhove’s (2013) contention that thousands of subjects are required to provide reliable conclusions about ultimate attainment. Note that we cannot conclude that differences in stimuli or population do not matter for ultimate attainment, only that studying such effects requires very large datasets. We return to this issue in the General Discussion.

4. 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 native and non-native learners both require around 30 years to reach asymptotic performance, at least in immersion settings. While this question has not been previously addressed, these findings are compatible with what is known about the initial period of learning.

Second, we found that ultimate attainment—that is, the level of asymptotic performance—is fairly consistent for learners who begin prior to 10–12 years of age. We found no evidence that the ultimate attainment curve reaches a floor at around puberty, as has been previously proposed ( Johnson & Newport, 1989 ). While these results differed from the conclusions of some prior studies, our simulations showed that the prior findings were in fact too noisy to provide precise estimates. 3 To provide reliable results about ultimate attainment, a study should have in excess of 10,000 subjects (see also Vanhove, 2013 ). This suggests that the results of those prior studies, all but one of which has fewer than 250 subjects, largely reflect statistical noise. The remaining study had many subjects but uncertain validity (see discussion above).

This set of results is internally consistent, adding credibility to the whole. However, our conclusions—like any conclusions—are only as good as the data supporting them. Below, we address a number of possible concerns. These include both methodological concerns about the data and how they were collected but also more theoretical concerns, like the possibility that results differ across subsets of subjects or items. We then conclude by discussing the implications of our results, should they prove valid and robust.

4.1. Potential concerns and complications

4.1.1. familiarity with the testing procedure.

One possible concern is that differences across subjects were due to age-related differences in familiarity with the Internet. Prior comparisons of Internet-based and offine datasets have found little support for this concern ( Hartshorne & Germine, 2015 ). Similarly, some of the differences between children and adults could conceivably be due to general test-taking ability. In order to better understand interactions between subject age and test method, if any, it would be ideal to gather data from a variety of tests in a variety of modalities.

Crucially, however, most of our analyses did not depend on the current age of the subject but on their age at first exposure, which should weaken any effects of current age. Moreover, we can compare the learning trajectories of learners who started at different ages (see Figs. 4 and ​ and7 7 but especially Figs. S21–S22 in the Supplementary Materials ). If older subjects are substantially better at taking our test, this should appear as more rapid early learning. As inspection of the figures indicates, any such effect is inconsistent and small.

4.1.2. Test modality

Our use of a written comprehension test was dictated by our methodology. Comprehension studies can be scored automatically (which is crucial when there are over half a million subjects), and written tests do not require high-quality audio equipment or sound booths. Nonetheless, one might ask how these choices affected our results.

Certainly, differences between production and comprehension and between written and oral modalities can affect comparisons between native and non-native speakers ( Bialystok & Miller, 1999 ). Listening places high demands on speed and memory (one can re-read but not rehear), and the speech must be analyzed by non-native acoustic phonetics and phonology, which we do not test here. Written tests require literacy. Production allows one to strategically avoid difficult and imperfectly learned words and constructions.

Whether any of these factors affect estimates of a critical period depends on whether they interact with the variables that define critical period effects, namely age at first exposure, current age, and years of experience. While the necessary studies are not currently feasible, this is likely to change as technology improves. (For instance, we are exploring the use of machine learning to characterize the nativeness of a written text.)

Importantly, none of these considerations would make the study of critical periods in written comprehension uninteresting or uninformative, merely complex. Results from any modality must reflect underlying grammatical ability at least to some degree, and reading comprehension is important in its own right, given the importance of reading in many modern societies. (In fact, for many non-native speakers, this may be their primary use for the non-native language.)

4.1.3. Item selection and quiz difficulty

Another potential worry is that our results may depend on smallish differences among subjects who are already near the ceiling (for relevant discussion, see: Abrahamsson & Hyltenstam, 2009 ; Birdsong, 2006 ). Mitigating this concern is that, as we argued in the Introduction, the ceiling is where all the action is. What is remarkable about language is that we are (nearly) all extremely good at it, including adult learners. For reference, we noted that over-regularizations of irregular verbs, which are among the most salient errors in the speech of preschoolers, occur in only 0.75% of their utterances. On a continuum of linguistic ability that includes apes and machines at one end, preschoolers and reasonably diligent late learners are clustered at the other end, near native-speaking adults. Indeed, the question in the critical period literature has never been why adults are incapable of learning a new language—obviously they are—but why adult learners so rarely (if ever) achieve native-like mastery. Likewise, asking whether adult learners can master basic syntax may be theoretically interesting but distracts from the original motivation for this literature: adult learners rarely, if ever, achieve the same level of mastery as those who started in childhood. In order to study that phenomenon, the relevant yardstick is the asymptotic performance of native speakers.

Still, we can ask whether our results hold for both items mastered early in typical development and for items mastered only in adolescence or adulthood. We found no evidence of such a difference: In the best-fitting models of learning, the learning rate began to slow at approximately the same time for the 47 items that are mastered by the youngest monolingual English-speakers in the sample (ages 7–8) as for the 48 items that are mastered only by the older ones: 17.3 years old and 18.2 years old, respectively. Moreover, if there were substantial interactions between item and age of first exposure, we would expect to see substantial differences in terms of which items were more or less difficult for early and late learners. However, item difficulty was strongly correlated across learners regardless of age of first exposure (for details of these analyses, see Supplementary Materials , “Item Effects”).

We might similarly ask whether results vary based on the type of syntactic construction tested. Prior analyses of ultimate attainment have provided conflicting results, likely due to the power issues discussed above ( Coppieters, 1987 ; Flege et al., 1999 ; Johnson & Newport, 1989 , 1991 ; McDonald, 2000 ; Weber-Fox & Neville, 1996 ) and the theoretical issues raised below. Our just-discussed analyses of item difficulty provide some initial evidence against substantial differences across syntactic phenomena. More precise analyses would involve the direct comparison of different types of constructions. Unfortunately, our quiz was designed to cover a wide range of phenomena, and thus we have few items of any given type, making it difficult to distinguish differences between items and differences between item types . In any case, such analyses raise thorny theoretical questions: different theories of syntactic processing categorize phenomena differently, and any given sentence involves many different phenomena. Thus, classifying items by syntactic phenomena is far from trivial and may not even be the right approach. Progress on this question will require a significant amount of further research. 4 If it turns out that different aspects of syntax do indeed have different critical periods, the conclusions presented here would need to be revised. Design of follow-up studies may be informed by comparing items in our dataset, which is available at http://osf.io/pyb8s .

4.1.4. The effect of the first language

Our results are unlikely to be specific to any one language or language family: Participants listed more than 6000 native languages or combinations of them. The best-represented language families among immersion and non-immersion learners were Uralic (N = 54,664), Slavic (N = 41,640), West Germanic (N = 38,385), Romance (N = 40,476), Turkic (N = 29,816), and Chinese (N = 15,161). The remaining 29% of participants either had multiple native languages or had native languages belonging to a different family. Thus, no language contributed more than a small fraction of the immersion or non- immersion learners ( Fig. 3C ). However, this leaves the possibility that our results reflect an epiphenomenal average of very different trajectories for very different types of learners ( Bialystok & Miller, 1999 ; McDonald, 2000 ).

It is uncontroversial that speakers of different native languages make characteristic mistakes when speaking English ( Schachter, 1990 , among others); indeed, the algorithm we used as part of our recruitment strategy depended on this fact (see Section 2.2). However, that is logically distinct from the question as to whether critical periods differ across native languages. Ideally, we would compare the results of our model for speakers of different native languages. However, our samples of individual languages are too small. Specifically, because our data are unevenly distributed across ages and learner conditions, we risk over-fitting certain conditions (such as monolinguals) at the expense of others. As described in the Method, we circumvented this issue by averaging across subjects in each bin prior to running the model. This is not applied easily to subsets of the data: too many bins have few or no subjects. In any case, we lack a computationally tractable method for comparing model fits for different datasets. Thus, we must leave this for future research.

We can, however, address a related question. It could be that speakers of different native languages learn English more or less quickly and to a greater or lesser degree. At best, this would add noise to our analyses. At worst, to the extent that native language is confounded with other variables of interest in our sample (e.g., age of first exposure), it could have distorted our results. Anecdotally, many people perceive that speakers of certain languages are better or worse at English, though it is hard to know how much this is confounded with accent (which likely has a critical period distinct from that of syntax), cultural variation in age at first exposure, and differences in the types of exposure (e.g., songs, movies, tourism, coursework) and instructional methods. For instance, in our dataset, speakers of Chinese and Western Germanic languages tended to start learning English in immersion settings earlier than speakers of Turkic or Uralic languages (5.2 and 5.9 years old vs. 13.4 and 14.8 years old, respectively). More systematically, some studies have suggested different patterns of ultimate attainment for speakers of different native languages ( Bialystok & Miller, 1999 ), though caution is warranted given the extremely low power for such studies (see Fig. 8 and surrounding discussion).

We considered the effect of native language on three different metrics of learning success: the level of ultimate attainment (how well the most advanced learners do), the age at the end of the optimal period (the last age to start learning in order to reach native-like performance), and the shape of the learning curve (performance as a function of years of experience). In keeping with our earlier analyses, ultimate attainment was defined as the average performance for subjects no older than 70 years old and with at least 30 years of experience with English. To increase power, we grouped subjects into Uralic, Slavic, West Germanic, Romance, and Chinese language groups (no other language group had nearly as many speakers at similarly wide ranges of years of experience and ages of first exposure). For each measurement, we assessed the level of evidence that speakers of one language group differed from the others using Bayes Factor model comparison with the BIC approximation ( Wagenmakers, 2007 ). Details for all analyses are provided in the Supplementary Materials , under “Item Effects.”

By looking at ultimate attainment, we can assess whether speakers of different languages have greater or lesser success in learning English, equating for years of experience. In fact, the differences across language groups were small (see Fig. S14 ) and generally not reliable. In most cases, analyses favored the null hypothesis (no difference between the target language and the other languages), and differences across language groups were inconsistent: among learners who began at age 0, the best-performing language group was Romance, for learners beginning at 1–5 years old, it was West Germanic, and for learners who began at 6–10 years old, it was Chinese. Likewise, analysis indicated that the length of the optimal period does not vary across language groups. We found slightly more evidence for differences in learning curves. In particular, simultaneous English-Chinese speakers could be distinguished from the rest, whereas simultaneous bilinguals who spoke Romance or West Germanic languages both matched the overall pattern. However, the actual differences are subtle and seem to reflect slightly faster initial learning by the Chinese speakers ( Fig. S18 ). Most other comparisons were not possible due to insufficiently many subjects (see Supplementary Materials ).

Thus, although speakers of different languages make different mistakes, we find only limited evidence of differences in learning once learning context (immersion vs. non-immersion), years of experience, and age at first exposure are taken into account. That said, power analyses suggest that we only had sufficient subjects to detect relatively large effects, meaning that we cannot rule out more subtle differences (see Supplementary Materials , under “Item Effects”). These power analyses should, however, provide guidance on sample sizes for future research along these lines.

Whatever these analyses say about language-learning in general, they do not provide any evidence that our findings were heavily confounded by differences across the native languages in our sample.

4.2. Implications

The analyses above suggest that our findings are reasonably robust, particularly in comparison to those of previous studies. While this inspires confidence, it should also suggest caution: future work that successfully addresses the limitations of the present study may similarly prompt significant revisions in what we believe to be true. Science is the process of becoming less wrong, and while hopefully the revisions are smaller and smaller after each step, there is no way of knowing that this is the case in advance. Thus, confirmation and extension of the present results is crucial, particularly given the novelty of our questions, methods, models, and results.

Nonetheless, we believe it is useful to consider the implications of the present findings, on the presumption that they prove to be (reasonably) robust:

4.2.1. The nature of the critical period for second language acquisition

On the assumption that the present results apply broadly to syntax acquisition by diverse learners, they have profound theoretical implications. Most importantly, they clarify the shape of the well-attested critical period for second-language acquisition: a plateau followed by a continuous decline. The end of the plateau period must be due to changes in late adolescence rather than childhood, whether they are biological, social, or environmental. Thus the critical period cannot be attributed to neuronal death or syntactic pruning in the first few years of life, nor to hormonal changes surrounding adrenarche or puberty ( Johnson & Newport, 1989 ; Lenneberg, 1967 ; Pinker, 1994 ). Also casting doubt on the effect of hormones is our finding that girls do not show a decline in learning ability before boys do, despite their earlier age of puberty (see Supplementary Materials ). Likewise, the critical period cannot be explained by documented developmental changes in working memory, episodic memory, reasoning ability, processing speed, or social cognition ( Hakuta et al., 2003 ; Hartshorne & Germine, 2015 ; Klindt, Devaine, & Daunizeau, 2017 ; Morgan-Short & Ullman, 2012 ; Newport, 1988 ), to the diminished likelihood that adolescent and adult immigrants will be immersed in an environment of native speakers and identify with the new culture, 5 or to gradually accumulating interference from a first language ( Hernandez et al., 2005 ; Jia et al., 2002 ; Sebastián-Gallés et al., 2005 ).

In short, these data are inconsistent with any hypothesis that places the decline in childhood—which is to say, every prior specific hypothesis that we know of. What, then, could explain the critical period? There are a number of possibilities. For instance, it remains possible that the critical period is an epiphenomenon of culture: the age we identified (17–18 years old) coincides with a number of social changes, any of which could diminish one’s ability, opportunity, or willingness to learn a new language. In many cultures, this age marks the transition to the workforce or to professional education, which may diminish opportunities to learn. Note that causality (if any) could run the other direction: cultures may have chosen this age for certain transitions because of age-dependent changes in neural plasticity. Further traction on these issues could come from cross-cultural comparison, or comparison of individuals within a culture who are on different educational tracks.

Alternatively, the critical period could reflect interference from the first language, so long as this interference is non-linear rather than gradually accumulating. While it has generally been assumed that interference from the first language would be proportional to the amount of first language learned—something inconsistent with our data—we cannot rule out the possibility of non-linear interference. Neural network models, which are capable of showing interference from a first language ( Hernandez et al., 2005 ), can exhibit surprising nonlinearities ( Haykin, 1999 ; Hernandez et al., 2005 ). It remains to be seen whether they can successfully model the nonlinearities we actually observed.

Finally, the end of the critical period might reflect late-emerging neural maturation processes that compromise the circuitry responsible for successful language acquisition (whether specific to language or not). While language acquisition researchers often focus on neural development in the childhood years, the brain undergoes significant changes through adolescence and early adulthood ( Blakemore & Mills, 2014 ; Mills, Lalonde, CLasen, Giedd, & Blakemore, 2014 ; Pinto, Hornby, Jones, & Murphy, 2010 ; Shafee, Buckner, & Fischl, 2015 ; Tamnes et al., 2010 ). While continued develoment of the prefrontal cortex is perhaps the most familiar, changes occur throughout the brain and along multiple dimensions. Drawing on these and other findings, some researchers have suggested that adolescence may involve a number of different biologically-driven critical periods ( Crews, He, & Hodge, 2007 ; Fuhrmann, Knoll, & Blakemore, 2015 ; see also Ghitza & Gelman, 2014 ).

Little is certain about the relationship between neural maturation and behavioral maturation, other than the likelihood it is complex. Current evidence suggests that critical periods in perception involve a complex interplay of neurochemical and epigenetic promoters and brakes for both synaptic pruning and outgrowth ( Werker & Hensch, 2015 ). Given this complexity, and the relative sparseness of the data on neural maturation, it is hard to say whether any of the identified neural maturation processes might correspond to the changes in syntax acquisition that we observed.

Nor can we do much more than speculate as to whether these maturational process (if any) are specific to structures subserving language acquisition. It is notable that language-learning ability is, out of every cognitive ability whose developmental trajectory has been characterized behaviorally, the only one that is stable through childhood and declines sharply in late adolescence ( Hartshorne & Germine, 2015 ). This observation is consistent with the possibility of language-specific maturation. However, the developmental trajectories of some cognitive abilities, such as procedural memory, have not been well characterized ( Fuhrmann et al., 2015 ; Hartshorne & Germine, 2015 ). Moreover, cognitive testing has largely focused on simple abilities that can be measured in a single, short session (e.g., working memory). In contrast, syntax acquisition takes place over much longer intervals and involves learning a complex, interlocking system. Thus, progress on this question will require characterization of a broader range of cognitive abilities, as well as acquisition of other complex systems (e.g., music or chess).

In attempting to gain traction on these issues, there are additional complexities, which future studies should seek to clarify. The duration of the critical period may differ for other aspects of language, like phonology and vocabulary. Moreover, we cannot be certain that syntax learning ability is a unitary construct rather than the combination of multiple factors potentially operating on distinct timelines and affecting different aspects of syntax differently. Second, the exact timing of the critical period may be obfuscated by older learners deploying conscious learning strategies, absorbing explicit instruction, or transferring knowledge from the first language. Some purchase on these issues may come from additional studies, potentially using different methods (e.g., online processing, production, ERP, or longitudinal studies), should obtaining sufficiently many subjects become feasible. Finally, because our dataset consists of people’s performance in a second language, it does not directly address the question of how age affects the learning of a first language. It is possible that exposure to linguistic input delays the atrophy of language learning circuitry, in which case the decline in learning ability we have documented would represent the prolongation of a critical period that terminates sooner in people who have been deprived of all language input ( Curtiss, 1994 ; de Villiers, 2007 ; Mayberry, 1993 ; Newport, 1990 ). Because delayed first-language acquisition is fortunately rare, it would be impossible to achieve a sample size similar to the one here, but our results could be used to guide smaller, targeted studies.

Crucially, the investigation of these issues—all of which have long been of interest but difficult to address—can now be guided by the finding that the ability to learn the grammar of a new language, though indeed compromised in adults compared to children, is largely or entirely preserved up to the cusp of adulthood.

4.2.2. Additional implications

The dataset bears on many issues beyond those discussed in detail above. For instance, the data contain a rich source of information about dialect variation and L1 transfer effects. We briefly mention a few other issues. First, prior work has indicated that simultaneous bilinguals do not reach the same level of proficiency in phonology as individuals with a single first language ( Sebastián-Gallés et al., 2005 ). We extend this finding to syntax, where it is apparent throughout the lifespan Fig. 5B ). ( This finding is consistent with some earlier work suggesting that a sufficiently sensitive test can distinguish even highly proficient bilinguals from monolinguals ( Abrahamsson & Hyltenstam, 2008 , 2009 ). 6 Our model captures this difference as one of exposure, estimating that simultaneous bilinguals receive only 63% as much English input as monolinguals (see Fig. S6 ). Though parsimonious, this is not the only possible explanation; alternatives include the effects of suppression of the non-target language and influences of each language on the other ( Birdsong & Gertken, 2013 ).

Similarly, there are a number of interesting demographic effects. We confirm prior findings of a main effect of education on ultimate attainment, with post-secondary education resulting in higher accuracy (see Supplementary Materials , “Education Differences”) ( Birdsong, 2014 ; Hakuta et al., 2003 ). We likewise find a main effect for gender, with higher accuracy by females (see Supplementary Materials , “Gender Differences”). In neither case do these main effects appear to interact with age at first exposure, and so they are unlikely to be relevant for critical periods. However, they likely have implications for other aspects of language learning.

We have made the data available ( http://osf.io/pyb8s ) in the hopes they will be prove informative for investigation of these and other questions.

Supplementary Material

Supplemental, acknowledgments.

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).

Appendix A. Supplementary material

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.cognition.2018.04.007 .

1 The first several thousand participants were asked to list their “native languages.” Based on participant feedback, this was adjusted to “native languages (learned from birth).”

2 A small proportion of the non-immersion learners (2.7%) reported ages of first exposure between 1 and 3 years. These learners scored quite poorly (the ultimate attainment of those with ages of exposure of 1 year was as poor as those with ages of exposure in their 20 s) and exhibited noisy performance curves that, unlike those of all other learners, failed to show any improvement with age ( Fig. S1 ). While this might be a genuine and surprising finding, it more likely reffects the idiosyncratic histories or questionnaire responses of these learners. Unlike the later non-immersion learners, many of whom cited school instruction as their initial source of their exposure, the early non-immersion learners gave little indication about the nature of their first exposure, and it is possible that they had little formal instruction and had learned primarily through television and movies (frequently cited by non-immersion learners as significant sources of English input). Given this uncertainty, we excluded these participants from the main analyses.

3 We also noted a number of limitations and confounds in prior studies, such as how ultimate attainment was defined, which would have biased results. However, detailed investigation shows that the resulting biases and imprecisions were likely swamped by the effect of low power (see Supplementary Materials , “Effect of Analysis Decisions”).

4 We note a further difficulty. All research in this domain has treated items as fixed effects, averaging across them. This simplifies calculation, but at a cost: such statistical analyses do not directly assess the question of whether the results generalize beyond the items used ( Baayen, Davidson, & Bates, 2008 ; Clark, 1973 ). This problem is mitigated somewhat when using a large and representative set of items—as we do—but is particularly problematic when looking at smaller samples of items. The standard solution currently is to use mixed effects modeling ( Baayen et al., 2008 ). However, mixed effects modeling requires significant computational power. We have so far been unable to identify a tractable method of applying mixed effects modeling to a dataset the size of the present one.

5 Note that while critical period researchers widely assume that there are age-related effects on cultural identification among immigrant groups, this may not in fact be the case ( Chudek, Cheung, & Heine, 2015 ).

6 This finding also has practical consequences for research. Many researchers have argued that if later learners can reach monolingual levels of performance, that would be evidence against critical periods (and conversely, the failure of later learners to match monolinguals would be evidence for critical periods) (e.g., Abrahamsson & Hyltenstam, 2009 ). This standard, in conjunction with our results, leads to the unlikely conclusion that the critical period for syntax closes prior to birth. For additional discussion, see Birdsong and Gertken (2013) .

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Age and the critical period hypothesis

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Christian Abello-Contesse, Age and the critical period hypothesis, ELT Journal , Volume 63, Issue 2, April 2009, Pages 170–172, https://doi.org/10.1093/elt/ccn072

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In the field of second language acquisition (SLA), how specific aspects of learning a non-native language (L2) may be affected by when the process begins is referred to as the ‘age factor’. Because of the way age intersects with a range of social, affective, educational, and experiential variables, clarifying its relationship with learning rate and/or success is a major challenge.

There is a popular belief that children as L2 learners are ‘superior’ to adults ( Scovel 2000 ), that is, the younger the learner, the quicker the learning process and the better the outcomes. Nevertheless, a closer examination of the ways in which age combines with other variables reveals a more complex picture, with both favourable and unfavourable age-related differences being associated with early- and late-starting L2 learners ( Johnstone 2002 ).

The ‘critical period hypothesis’ (CPH) is a particularly relevant case in point. This is the claim that there is, indeed, an optimal period for language acquisition, ending at puberty. However, in its original formulation ( Lenneberg 1967 ), evidence for its existence was based on the relearning of impaired L1 skills, rather than the learning of a second language under normal circumstances.

Furthermore, although the age factor is an uncontroversial research variable extending from birth to death ( Cook 1995 ), and the CPH is a narrowly focused proposal subject to recurrent debate, ironically, it is the latter that tends to dominate SLA discussions ( García Lecumberri and Gallardo 2003 ), resulting in a number of competing conceptualizations. Thus, in the current literature on the subject ( Bialystok 1997 ; Richards and Schmidt 2002 ; Abello-Contesse et al. 2006), references can be found to (i) multiple critical periods (each based on a specific language component, such as age six for L2 phonology), (ii) the non-existence of one or more critical periods for L2 versus L1 acquisition, (iii) a ‘sensitive’ yet not ‘critical’ period, and (iv) a gradual and continual decline from childhood to adulthood.

It therefore needs to be recognized that there is a marked contrast between the CPH as an issue of continuing dispute in SLA, on the one hand, and, on the other, the popular view that it is an invariable ‘law’, equally applicable to any L2 acquisition context or situation. In fact, research indicates that age effects of all kinds depend largely on the actual opportunities for learning which are available within overall contexts of L2 acquisition and particular learning situations, notably the extent to which initial exposure is substantial and sustained ( Lightbown 2000 ).

Thus, most classroom-based studies have shown not only a lack of direct correlation between an earlier start and more successful/rapid L2 development but also a strong tendency for older children and teenagers to be more efficient learners. For example, in research conducted in the context of conventional school programmes, Cenoz (2003) and Muñoz (2006) have shown that learners whose exposure to the L2 began at age 11 consistently displayed higher levels of proficiency than those for whom it began at 4 or 8. Furthermore, comparable limitations have been reported for young learners in school settings involving innovative, immersion-type programmes, where exposure to the target language is significantly increased through subject-matter teaching in the L2 ( Genesee 1992 ; Abello-Contesse 2006 ). In sum, as Harley and Wang (1997) have argued, more mature learners are usually capable of making faster initial progress in acquiring the grammatical and lexical components of an L2 due to their higher level of cognitive development and greater analytical abilities.

In terms of language pedagogy, it can therefore be concluded that (i) there is no single ‘magic’ age for L2 learning, (ii) both older and younger learners are able to achieve advanced levels of proficiency in an L2, and (iii) the general and specific characteristics of the learning environment are also likely to be variables of equal or greater importance.

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The Critical Period Hypothesis: A coat of many colours

• David Singleton

Research on age-related effects in L2 development often invokes the idea of a critical period – the postulation of which is customarily referred to as the Critical Period Hypothesis. This paper argues that to speak in terms of the Critical Period Hypothesis is misleading, since there is a vast amount of variation in the way in which the critical period for language acquisition is understood – affecting all the parameters deemed to be theoretically significant and indeed also relating to the ways in which the purported critical period is interpreted in terms of its implications for L2 instruction. The paper concludes that the very fact that there are such diverse and competing versions of the Critical Period Hypothesis of itself undermines its plausibility.

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The Critical Period Hypothesis in Second Language Acquisition: A Statistical Critique and a Reanalysis

* E-mail: [email protected]

Affiliation Department of Multilingualism, University of Fribourg, Fribourg, Switzerland

• Jan Vanhove

• Published: July 25, 2013
• https://doi.org/10.1371/journal.pone.0069172
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17 Jul 2014: The PLOS ONE Staff (2014) Correction: The Critical Period Hypothesis in Second Language Acquisition: A Statistical Critique and a Reanalysis. PLOS ONE 9(7): e102922. https://doi.org/10.1371/journal.pone.0102922 View correction

In second language acquisition research, the critical period hypothesis ( cph ) holds that the function between learners' age and their susceptibility to second language input is non-linear. This paper revisits the indistinctness found in the literature with regard to this hypothesis's scope and predictions. Even when its scope is clearly delineated and its predictions are spelt out, however, empirical studies–with few exceptions–use analytical (statistical) tools that are irrelevant with respect to the predictions made. This paper discusses statistical fallacies common in cph research and illustrates an alternative analytical method (piecewise regression) by means of a reanalysis of two datasets from a 2010 paper purporting to have found cross-linguistic evidence in favour of the cph . This reanalysis reveals that the specific age patterns predicted by the cph are not cross-linguistically robust. Applying the principle of parsimony, it is concluded that age patterns in second language acquisition are not governed by a critical period. To conclude, this paper highlights the role of confirmation bias in the scientific enterprise and appeals to second language acquisition researchers to reanalyse their old datasets using the methods discussed in this paper. The data and R commands that were used for the reanalysis are provided as supplementary materials.

Citation: Vanhove J (2013) The Critical Period Hypothesis in Second Language Acquisition: A Statistical Critique and a Reanalysis. PLoS ONE 8(7): e69172. https://doi.org/10.1371/journal.pone.0069172

Editor: Stephanie Ann White, UCLA, United States of America

Received: May 7, 2013; Accepted: June 7, 2013; Published: July 25, 2013

Copyright: © 2013 Jan Vanhove. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: No current external funding sources for this study.

Competing interests: The author has declared that no competing interests exist.

Introduction

In the long term and in immersion contexts, second-language (L2) learners starting acquisition early in life – and staying exposed to input and thus learning over several years or decades – undisputedly tend to outperform later learners. Apart from being misinterpreted as an argument in favour of early foreign language instruction, which takes place in wholly different circumstances, this general age effect is also sometimes taken as evidence for a so-called ‘critical period’ ( cp ) for second-language acquisition ( sla ). Derived from biology, the cp concept was famously introduced into the field of language acquisition by Penfield and Roberts in 1959 [1] and was refined by Lenneberg eight years later [2] . Lenneberg argued that language acquisition needed to take place between age two and puberty – a period which he believed to coincide with the lateralisation process of the brain. (More recent neurological research suggests that different time frames exist for the lateralisation process of different language functions. Most, however, close before puberty [3] .) However, Lenneberg mostly drew on findings pertaining to first language development in deaf children, feral children or children with serious cognitive impairments in order to back up his claims. For him, the critical period concept was concerned with the implicit “automatic acquisition” [2, p. 176] in immersion contexts and does not preclude the possibility of learning a foreign language after puberty, albeit with much conscious effort and typically less success.

sla research adopted the critical period hypothesis ( cph ) and applied it to second and foreign language learning, resulting in a host of studies. In its most general version, the cph for sla states that the ‘susceptibility’ or ‘sensitivity’ to language input varies as a function of age, with adult L2 learners being less susceptible to input than child L2 learners. Importantly, the age–susceptibility function is hypothesised to be non-linear. Moving beyond this general version, we find that the cph is conceptualised in a multitude of ways [4] . This state of affairs requires scholars to make explicit their theoretical stance and assumptions [5] , but has the obvious downside that critical findings risk being mitigated as posing a problem to only one aspect of one particular conceptualisation of the cph , whereas other conceptualisations remain unscathed. This overall vagueness concerns two areas in particular, viz. the delineation of the cph 's scope and the formulation of testable predictions. Delineating the scope and formulating falsifiable predictions are, needless to say, fundamental stages in the scientific evaluation of any hypothesis or theory, but the lack of scholarly consensus on these points seems to be particularly pronounced in the case of the cph . This article therefore first presents a brief overview of differing views on these two stages. Then, once the scope of their cph version has been duly identified and empirical data have been collected using solid methods, it is essential that researchers analyse the data patterns soundly in order to assess the predictions made and that they draw justifiable conclusions from the results. As I will argue in great detail, however, the statistical analysis of data patterns as well as their interpretation in cph research – and this includes both critical and supportive studies and overviews – leaves a great deal to be desired. Reanalysing data from a recent cph -supportive study, I illustrate some common statistical fallacies in cph research and demonstrate how one particular cph prediction can be evaluated.

Delineating the scope of the critical period hypothesis

First, the age span for a putative critical period for language acquisition has been delimited in different ways in the literature [4] . Lenneberg's critical period stretched from two years of age to puberty (which he posits at about 14 years of age) [2] , whereas other scholars have drawn the cutoff point at 12, 15, 16 or 18 years of age [6] . Unlike Lenneberg, most researchers today do not define a starting age for the critical period for language learning. Some, however, consider the possibility of the critical period (or a critical period for a specific language area, e.g. phonology) ending much earlier than puberty (e.g. age 9 years [1] , or as early as 12 months in the case of phonology [7] ).

Second, some vagueness remains as to the setting that is relevant to the cph . Does the critical period constrain implicit learning processes only, i.e. only the untutored language acquisition in immersion contexts or does it also apply to (at least partly) instructed learning? Most researchers agree on the former [8] , but much research has included subjects who have had at least some instruction in the L2.

Third, there is no consensus on what the scope of the cp is as far as the areas of language that are concerned. Most researchers agree that a cp is most likely to constrain the acquisition of pronunciation and grammar and, consequently, these are the areas primarily looked into in studies on the cph [9] . Some researchers have also tried to define distinguishable cp s for the different language areas of phonetics, morphology and syntax and even for lexis (see [10] for an overview).

Fourth and last, research into the cph has focused on ‘ultimate attainment’ ( ua ) or the ‘final’ state of L2 proficiency rather than on the rate of learning. From research into the rate of acquisition (e.g. [11] – [13] ), it has become clear that the cph cannot hold for the rate variable. In fact, it has been observed that adult learners proceed faster than child learners at the beginning stages of L2 acquisition. Though theoretical reasons for excluding the rate can be posited (the initial faster rate of learning in adults may be the result of more conscious cognitive strategies rather than to less conscious implicit learning, for instance), rate of learning might from a different perspective also be considered an indicator of ‘susceptibility’ or ‘sensitivity’ to language input. Nevertheless, contemporary sla scholars generally seem to concur that ua and not rate of learning is the dependent variable of primary interest in cph research. These and further scope delineation problems relevant to cph research are discussed in more detail by, among others, Birdsong [9] , DeKeyser and Larson-Hall [14] , Long [10] and Muñoz and Singleton [6] .

Formulating testable hypotheses

Once the relevant cph 's scope has satisfactorily been identified, clear and testable predictions need to be drawn from it. At this stage, the lack of consensus on what the consequences or the actual observable outcome of a cp would have to look like becomes evident. As touched upon earlier, cph research is interested in the end state or ‘ultimate attainment’ ( ua ) in L2 acquisition because this “determines the upper limits of L2 attainment” [9, p. 10]. The range of possible ultimate attainment states thus helps researchers to explore the potential maximum outcome of L2 proficiency before and after the putative critical period.

One strong prediction made by some cph exponents holds that post- cp learners cannot reach native-like L2 competences. Identifying a single native-like post- cp L2 learner would then suffice to falsify all cph s making this prediction. Assessing this prediction is difficult, however, since it is not clear what exactly constitutes sufficient nativelikeness, as illustrated by the discussion on the actual nativelikeness of highly accomplished L2 speakers [15] , [16] . Indeed, there exists a real danger that, in a quest to vindicate the cph , scholars set the bar for L2 learners to match monolinguals increasingly higher – up to Swiftian extremes. Furthermore, the usefulness of comparing the linguistic performance in mono- and bilinguals has been called into question [6] , [17] , [18] . Put simply, the linguistic repertoires of mono- and bilinguals differ by definition and differences in the behavioural outcome will necessarily be found, if only one digs deep enough.

A second strong prediction made by cph proponents is that the function linking age of acquisition and ultimate attainment will not be linear throughout the whole lifespan. Before discussing how this function would have to look like in order for it to constitute cph -consistent evidence, I point out that the ultimate attainment variable can essentially be considered a cumulative measure dependent on the actual variable of interest in cph research, i.e. susceptibility to language input, as well as on such other factors like duration and intensity of learning (within and outside a putative cp ) and possibly a number of other influencing factors. To elaborate, the behavioural outcome, i.e. ultimate attainment, can be assumed to be integrative to the susceptibility function, as Newport [19] correctly points out. Other things being equal, ultimate attainment will therefore decrease as susceptibility decreases. However, decreasing ultimate attainment levels in and by themselves represent no compelling evidence in favour of a cph . The form of the integrative curve must therefore be predicted clearly from the susceptibility function. Additionally, the age of acquisition–ultimate attainment function can take just about any form when other things are not equal, e.g. duration of learning (Does learning last up until time of testing or only for a more or less constant number of years or is it dependent on age itself?) or intensity of learning (Do learners always learn at their maximum susceptibility level or does this intensity vary as a function of age, duration, present attainment and motivation?). The integral of the susceptibility function could therefore be of virtually unlimited complexity and its parameters could be adjusted to fit any age of acquisition–ultimate attainment pattern. It seems therefore astonishing that the distinction between level of sensitivity to language input and level of ultimate attainment is rarely made in the literature. Implicitly or explicitly [20] , the two are more or less equated and the same mathematical functions are expected to describe the two variables if observed across a range of starting ages of acquisition.

But even when the susceptibility and ultimate attainment variables are equated, there remains controversy as to what function linking age of onset of acquisition and ultimate attainment would actually constitute evidence for a critical period. Most scholars agree that not any kind of age effect constitutes such evidence. More specifically, the age of acquisition–ultimate attainment function would need to be different before and after the end of the cp [9] . According to Birdsong [9] , three basic possible patterns proposed in the literature meet this condition. These patterns are presented in Figure 1 . The first pattern describes a steep decline of the age of onset of acquisition ( aoa )–ultimate attainment ( ua ) function up to the end of the cp and a practically non-existent age effect thereafter. Pattern 2 is an “unconventional, although often implicitly invoked” [9, p. 17] notion of the cp function which contains a period of peak attainment (or performance at ceiling), i.e. performance does not vary as a function of age, which is often referred to as a ‘window of opportunity’. This time span is followed by an unbounded decline in ua depending on aoa . Pattern 3 includes characteristics of patterns 1 and 2. At the beginning of the aoa range, performance is at ceiling. The next segment is a downward slope in the age function which ends when performance reaches its floor. Birdsong points out that all of these patterns have been reported in the literature. On closer inspection, however, he concludes that the most convincing function describing these age effects is a simple linear one. Hakuta et al. [21] sketch further theoretically possible predictions of the cph in which the mean performance drops drastically and/or the slope of the aoa – ua proficiency function changes at a certain point.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

The graphs are based on based on Figure 2 in [9] .

https://doi.org/10.1371/journal.pone.0069172.g001

Although several patterns have been proposed in the literature, it bears pointing out that the most common explicit prediction corresponds to Birdsong's first pattern, as exemplified by the following crystal-clear statement by DeKeyser, one of the foremost cph proponents:

[A] strong negative correlation between age of acquisition and ultimate attainment throughout the lifespan (or even from birth through middle age), the only age effect documented in many earlier studies, is not evidence for a critical period…[T]he critical period concept implies a break in the AoA–proficiency function, i.e., an age (somewhat variable from individual to individual, of course, and therefore an age range in the aggregate) after which the decline of success rate in one or more areas of language is much less pronounced and/or clearly due to different reasons. [22, p. 445].

DeKeyser and before him among others Johnson and Newport [23] thus conceptualise only one possible pattern which would speak in favour of a critical period: a clear negative age effect before the end of the critical period and a much weaker (if any) negative correlation between age and ultimate attainment after it. This ‘flattened slope’ prediction has the virtue of being much more tangible than the ‘potential nativelikeness’ prediction: Testing it does not necessarily require comparing the L2-learners to a native control group and thus effectively comparing apples and oranges. Rather, L2-learners with different aoa s can be compared amongst themselves without the need to categorise them by means of a native-speaker yardstick, the validity of which is inevitably going to be controversial [15] . In what follows, I will concern myself solely with the ‘flattened slope’ prediction, arguing that, despite its clarity of formulation, cph research has generally used analytical methods that are irrelevant for the purposes of actually testing it.

Inferring non-linearities in critical period research: An overview

Group mean or proportion comparisons.

[T]he main differences can be found between the native group and all other groups – including the earliest learner group – and between the adolescence group and all other groups. However, neither the difference between the two childhood groups nor the one between the two adulthood groups reached significance, which indicates that the major changes in eventual perceived nativelikeness of L2 learners can be associated with adolescence. [15, p. 270].

Similar group comparisons aimed at investigating the effect of aoa on ua have been carried out by both cph advocates and sceptics (among whom Bialystok and Miller [25, pp. 136–139], Birdsong and Molis [26, p. 240], Flege [27, pp. 120–121], Flege et al. [28, pp. 85–86], Johnson [29, p. 229], Johnson and Newport [23, p. 78], McDonald [30, pp. 408–410] and Patowski [31, pp. 456–458]). To be clear, not all of these authors drew direct conclusions about the aoa – ua function on the basis of these groups comparisons, but their group comparisons have been cited as indicative of a cph -consistent non-continuous age effect, as exemplified by the following quote by DeKeyser [22] :

Where group comparisons are made, younger learners always do significantly better than the older learners. The behavioral evidence, then, suggests a non-continuous age effect with a “bend” in the AoA–proficiency function somewhere between ages 12 and 16. [22, p. 448].

The first problem with group comparisons like these and drawing inferences on the basis thereof is that they require that a continuous variable, aoa , be split up into discrete bins. More often than not, the boundaries between these bins are drawn in an arbitrary fashion, but what is more troublesome is the loss of information and statistical power that such discretisation entails (see [32] for the extreme case of dichotomisation). If we want to find out more about the relationship between aoa and ua , why throw away most of the aoa information and effectively reduce the ua data to group means and the variance in those groups?

Comparison of correlation coefficients.

Correlation-based inferences about slope discontinuities have similarly explicitly been made by cph advocates and skeptics alike, e.g. Bialystok and Miller [25, pp. 136 and 140], DeKeyser and colleagues [22] , [44] and Flege et al. [45, pp. 166 and 169]. Others did not explicitly infer the presence or absence of slope differences from the subset correlations they computed (among others Birdsong and Molis [26] , DeKeyser [8] , Flege et al. [28] and Johnson [29] ), but their studies nevertheless featured in overviews discussing discontinuities [14] , [22] . Indeed, the most recent overview draws a strong conclusion about the validity of the cph 's ‘flattened slope’ prediction on the basis of these subset correlations:

In those studies where the two groups are described separately, the correlation is much higher for the younger than for the older group, except in Birdsong and Molis (2001) [ =  [26] , JV], where there was a ceiling effect for the younger group. This global picture from more than a dozen studies provides support for the non-continuity of the decline in the AoA–proficiency function, which all researchers agree is a hallmark of a critical period phenomenon. [22, p. 448].

In Johnson and Newport's specific case [23] , their correlation-based inference that ua levels off after puberty happened to be largely correct: the gjt scores are more or less randomly distributed around a near-horizontal trend line [26] . Ultimately, however, it rests on the fallacy of confusing correlation coefficients with slopes, which seriously calls into question conclusions such as DeKeyser's (cf. the quote above).

https://doi.org/10.1371/journal.pone.0069172.g002

Lower correlation coefficients in older aoa groups may therefore be largely due to differences in ua variance, which have been reported in several studies [23] , [26] , [28] , [29] (see [46] for additional references). Greater variability in ua with increasing age is likely due to factors other than age proper [47] , such as the concomitant greater variability in exposure to literacy, degree of education, motivation and opportunity for language use, and by itself represents evidence neither in favour of nor against the cph .

Regression approaches.

Having demonstrated that neither group mean or proportion comparisons nor correlation coefficient comparisons can directly address the ‘flattened slope’ prediction, I now turn to the studies in which regression models were computed with aoa as a predictor variable and ua as the outcome variable. Once again, this category of studies is not mutually exclusive with the two categories discussed above.

In a large-scale study using self-reports and approximate aoa s derived from a sample of the 1990 U.S. Census, Stevens found that the probability with which immigrants from various countries stated that they spoke English ‘very well’ decreased curvilinearly as a function of aoa [48] . She noted that this development is similar to the pattern found by Johnson and Newport [23] but that it contains no indication of an “abruptly defined ‘critical’ or sensitive period in L2 learning” [48, p. 569]. However, she modelled the self-ratings using an ordinal logistic regression model in which the aoa variable was logarithmically transformed. Technically, this is perfectly fine, but one should be careful not to read too much into the non-linear curves found. In logistic models, the outcome variable itself is modelled linearly as a function of the predictor variables and is expressed in log-odds. In order to compute the corresponding probabilities, these log-odds are transformed using the logistic function. Consequently, even if the model is specified linearly, the predicted probabilities will not lie on a perfectly straight line when plotted as a function of any one continuous predictor variable. Similarly, when the predictor variable is first logarithmically transformed and then used to linearly predict an outcome variable, the function linking the predicted outcome variables and the untransformed predictor variable is necessarily non-linear. Thus, non-linearities follow naturally from Stevens's model specifications. Moreover, cph -consistent discontinuities in the aoa – ua function cannot be found using her model specifications as they did not contain any parameters allowing for this.

Using data similar to Stevens's, Bialystok and Hakuta found that the link between the self-rated English competences of Chinese- and Spanish-speaking immigrants and their aoa could be described by a straight line [49] . In contrast to Stevens, Bialystok and Hakuta used a regression-based method allowing for changes in the function's slope, viz. locally weighted scatterplot smoothing ( lowess ). Informally, lowess is a non-parametrical method that relies on an algorithm that fits the dependent variable for small parts of the range of the independent variable whilst guaranteeing that the overall curve does not contain sudden jumps (for technical details, see [50] ). Hakuta et al. used an even larger sample from the same 1990 U.S. Census data on Chinese- and Spanish-speaking immigrants (2.3 million observations) [21] . Fitting lowess curves, no discontinuities in the aoa – ua slope could be detected. Moreover, the authors found that piecewise linear regression models, i.e. regression models containing a parameter that allows a sudden drop in the curve or a change of its slope, did not provide a better fit to the data than did an ordinary regression model without such a parameter.

To sum up, I have argued at length that regression approaches are superior to group mean and correlation coefficient comparisons for the purposes of testing the ‘flattened slope’ prediction. Acknowledging the reservations vis-à-vis self-estimated ua s, we still find that while the relationship between aoa and ua is not necessarily perfectly linear in the studies discussed, the data do not lend unequivocal support to this prediction. In the following section, I will reanalyse data from a recent empirical paper on the cph by DeKeyser et al. [44] . The first goal of this reanalysis is to further illustrate some of the statistical fallacies encountered in cph studies. Second, by making the computer code available I hope to demonstrate how the relevant regression models, viz. piecewise regression models, can be fitted and how the aoa representing the optimal breakpoint can be identified. Lastly, the findings of this reanalysis will contribute to our understanding of how aoa affects ua as measured using a gjt .

Summary of DeKeyser et al. (2010)

I chose to reanalyse a recent empirical paper on the cph by DeKeyser et al. [44] (henceforth DK et al.). This paper lends itself well to a reanalysis since it exhibits two highly commendable qualities: the authors spell out their hypotheses lucidly and provide detailed numerical and graphical data descriptions. Moreover, the paper's lead author is very clear on what constitutes a necessary condition for accepting the cph : a non-linearity in the age of onset of acquisition ( aoa )–ultimate attainment ( ua ) function, with ua declining less strongly as a function of aoa in older, post- cp arrivals compared to younger arrivals [14] , [22] . Lastly, it claims to have found cross-linguistic evidence from two parallel studies backing the cph and should therefore be an unsuspected source to cph proponents.

The authors set out to test the following hypotheses:

• Hypothesis 1: For both the L2 English and the L2 Hebrew group, the slope of the age of arrival–ultimate attainment function will not be linear throughout the lifespan, but will instead show a marked flattening between adolescence and adulthood.
• Hypothesis 2: The relationship between aptitude and ultimate attainment will differ markedly for the young and older arrivals, with significance only for the latter. (DK et al., p. 417)

Both hypotheses were purportedly confirmed, which in the authors' view provides evidence in favour of cph . The problem with this conclusion, however, is that it is based on a comparison of correlation coefficients. As I have argued above, correlation coefficients are not to be confused with regression coefficients and cannot be used to directly address research hypotheses concerning slopes, such as Hypothesis 1. In what follows, I will reanalyse the relationship between DK et al.'s aoa and gjt data in order to address Hypothesis 1. Additionally, I will lay bare a problem with the way in which Hypothesis 2 was addressed. The extracted data and the computer code used for the reanalysis are provided as supplementary materials, allowing anyone interested to scrutinise and easily reproduce my whole analysis and carry out their own computations (see ‘supporting information’).

Data extraction

In order to verify whether we did in fact extract the data points to a satisfactory degree of accuracy, I computed summary statistics for the extracted aoa and gjt data and checked these against the descriptive statistics provided by DK et al. (pp. 421 and 427). These summary statistics for the extracted data are presented in Table 1 . In addition, I computed the correlation coefficients for the aoa – gjt relationship for the whole aoa range and for aoa -defined subgroups and checked these coefficients against those reported by DK et al. (pp. 423 and 428). The correlation coefficients computed using the extracted data are presented in Table 2 . Both checks strongly suggest the extracted data to be virtually identical to the original data, and Dr DeKeyser confirmed this to be the case in response to an earlier draft of the present paper (personal communication, 6 May 2013).

https://doi.org/10.1371/journal.pone.0069172.t001

https://doi.org/10.1371/journal.pone.0069172.t002

Results and Discussion

Modelling the link between age of onset of acquisition and ultimate attainment.

I first replotted the aoa and gjt data we extracted from DK et al.'s scatterplots and added non-parametric scatterplot smoothers in order to investigate whether any changes in slope in the aoa – gjt function could be revealed, as per Hypothesis 1. Figures 3 and 4 show this not to be the case. Indeed, simple linear regression models that model gjt as a function of aoa provide decent fits for both the North America and the Israel data, explaining 65% and 63% of the variance in gjt scores, respectively. The parameters of these models are given in Table 3 .

The trend line is a non-parametric scatterplot smoother. The scatterplot itself is a near-perfect replication of DK et al.'s Fig. 1.

https://doi.org/10.1371/journal.pone.0069172.g003

The trend line is a non-parametric scatterplot smoother. The scatterplot itself is a near-perfect replication of DK et al.'s Fig. 5.

https://doi.org/10.1371/journal.pone.0069172.g004

https://doi.org/10.1371/journal.pone.0069172.t003

To ensure that both segments are joined at the breakpoint, the predictor variable is first centred at the breakpoint value, i.e. the breakpoint value is subtracted from the original predictor variable values. For a blow-by-blow account of how such models can be fitted in r , I refer to an example analysis by Baayen [55, pp. 214–222].

Solid: regression with breakpoint at aoa 18 (dashed lines represent its 95% confidence interval); dot-dash: regression without breakpoint.

https://doi.org/10.1371/journal.pone.0069172.g005

Solid: regression with breakpoint at aoa 18 (dashed lines represent its 95% confidence interval); dot-dash (hardly visible due to near-complete overlap): regression without breakpoint.

https://doi.org/10.1371/journal.pone.0069172.g006

https://doi.org/10.1371/journal.pone.0069172.t004

https://doi.org/10.1371/journal.pone.0069172.g007

Solid: regression with breakpoint at aoa 16 (dashed lines represent its 95% confidence interval); dot-dash: regression without breakpoint.

https://doi.org/10.1371/journal.pone.0069172.g008

Solid: regression with breakpoint at aoa 6 (dashed lines represent its 95% confidence interval); dot-dash (hardly visible due to near-complete overlap): regression without breakpoint.

https://doi.org/10.1371/journal.pone.0069172.g009

https://doi.org/10.1371/journal.pone.0069172.t005

https://doi.org/10.1371/journal.pone.0069172.t006

https://doi.org/10.1371/journal.pone.0069172.t007

https://doi.org/10.1371/journal.pone.0069172.t008

In sum, a regression model that allows for changes in the slope of the the aoa – gjt function to account for putative critical period effects provides a somewhat better fit to the North American data than does an everyday simple regression model. The improvement in model fit is marginal, however, and including a breakpoint does not result in any detectable improvement of model fit to the Israel data whatsoever. Breakpoint models therefore fail to provide solid cross-linguistic support in favour of critical period effects: across both data sets, gjt can satisfactorily be modelled as a linear function of aoa .

On partialling out ‘age at testing’

As I have argued above, correlation coefficients cannot be used to test hypotheses about slopes. When the correct procedure is carried out on DK et al.'s data, no cross-linguistically robust evidence for changes in the aoa – gjt function was found. In addition to comparing the zero-order correlations between aoa and gjt , however, DK et al. computed partial correlations in which the variance in aoa associated with the participants' age at testing ( aat ; a potentially confounding variable) was filtered out. They found that these partial correlations between aoa and gjt , which are given in Table 9 , differed between age groups in that they are stronger for younger than for older participants. This, DK et al. argue, constitutes additional evidence in favour of the cph . At this point, I can no longer provide my own analysis of DK et al.'s data seeing as the pertinent data points were not plotted. Nevertheless, the detailed descriptions by DK et al. strongly suggest that the use of these partial correlations is highly problematic. Most importantly, and to reiterate, correlations (whether zero-order or partial ones) are actually of no use when testing hypotheses concerning slopes. Still, one may wonder why the partial correlations differ across age groups. My surmise is that these differences are at least partly the by-product of an imbalance in the sampling procedure.

https://doi.org/10.1371/journal.pone.0069172.t009

The upshot of this brief discussion is that the partial correlation differences reported by DK et al. are at least partly the result of an imbalance in the sampling procedure: aoa and aat were simply less intimately tied for the young arrivals in the North America study than for the older arrivals with L2 English or for all of the L2 Hebrew participants. In an ideal world, we would like to fix aat or ascertain that it at most only weakly correlates with aoa . This, however, would result in a strong correlation between aoa and another potential confound variable, length of residence in the L2 environment, bringing us back to square one. Allowing for only moderate correlations between aoa and aat might improve our predicament somewhat, but even in that case, we should tread lightly when making inferences on the basis of statistical control procedures [61] .

On estimating the role of aptitude

Having shown that Hypothesis 1 could not be confirmed, I now turn to Hypothesis 2, which predicts a differential role of aptitude for ua in sla in different aoa groups. More specifically, it states that the correlation between aptitude and gjt performance will be significant only for older arrivals. The correlation coefficients of the relationship between aptitude and gjt are presented in Table 10 .

https://doi.org/10.1371/journal.pone.0069172.t010

The problem with both the wording of Hypothesis 2 and the way in which it is addressed is the following: it is assumed that a variable has a reliably different effect in different groups when the effect reaches significance in one group but not in the other. This logic is fairly widespread within several scientific disciplines (see e.g. [62] for a discussion). Nonetheless, it is demonstrably fallacious [63] . Here we will illustrate the fallacy for the specific case of comparing two correlation coefficients.

Apart from not being replicated in the North America study, does this difference actually show anything? I contend that it does not: what is of interest are not so much the correlation coefficients, but rather the interactions between aoa and aptitude in models predicting gjt . These interactions could be investigated by fitting a multiple regression model in which the postulated cp breakpoint governs the slope of both aoa and aptitude. If such a model provided a substantially better fit to the data than a model without a breakpoint for the aptitude slope and if the aptitude slope changes in the expected direction (i.e. a steeper slope for post- cp than for younger arrivals) for different L1–L2 pairings, only then would this particular prediction of the cph be borne out.

Using data extracted from a paper reporting on two recent studies that purport to provide evidence in favour of the cph and that, according to its authors, represent a major improvement over earlier studies (DK et al., p. 417), it was found that neither of its two hypotheses were actually confirmed when using the proper statistical tools. As a matter of fact, the gjt scores continue to decline at essentially the same rate even beyond the end of the putative critical period. According to the paper's lead author, such a finding represents a serious problem to his conceptualisation of the cph [14] ). Moreover, although modelling a breakpoint representing the end of a cp at aoa 16 may improve the statistical model slightly in study on learners of English in North America, the study on learners of Hebrew in Israel fails to confirm this finding. In fact, even if we were to accept the optimal breakpoint computed for the Israel study, it lies at aoa 6 and is associated with a different geometrical pattern.

Diverging age trends in parallel studies with participants with different L2s have similarly been reported by Birdsong and Molis [26] and are at odds with an L2-independent cph . One parsimonious explanation of such conflicting age trends may be that the overall, cross-linguistic age trend is in fact linear, but that fluctuations in the data (due to factors unaccounted for or randomness) may sometimes give rise to a ‘stretched L’-shaped pattern ( Figure 1, left panel ) and sometimes to a ‘stretched 7’-shaped pattern ( Figure 1 , middle panel; see also [66] for a similar comment).

Importantly, the criticism that DeKeyser and Larsson-Hall levy against two studies reporting findings similar to the present [48] , [49] , viz. that the data consisted of self-ratings of questionable validity [14] , does not apply to the present data set. In addition, DK et al. did not exclude any outliers from their analyses, so I assume that DeKeyser and Larsson-Hall's criticism [14] of Birdsong and Molis's study [26] , i.e. that the findings were due to the influence of outliers, is not applicable to the present data either. For good measure, however, I refitted the regression models with and without breakpoints after excluding one potentially problematic data point per model. The following data points had absolute standardised residuals larger than 2.5 in the original models without breakpoints as well as in those with breakpoints: the participant with aoa 17 and a gjt score of 125 in the North America study and the participant with aoa 12 and a gjt score of 117 in the Israel study. The resultant models were virtually identical to the original models (see Script S1 ). Furthermore, the aoa variable was sufficiently fine-grained and the aoa – gjt curve was not ‘presmoothed’ by the prior aggregation of gjt across parts of the aoa range (see [51] for such a criticism of another study). Lastly, seven of the nine “problems with supposed counter-evidence” to the cph discussed by Long [5] do not apply either, viz. (1) “[c]onfusion of rate and ultimate attainment”, (2) “[i]nappropriate choice of subjects”, (3) “[m]easurement of AO”, (4) “[l]eading instructions to raters”, (6) “[u]se of markedly non-native samples making near-native samples more likely to sound native to raters”, (7) “[u]nreliable or invalid measures”, and (8) “[i]nappropriate L1–L2 pairings”. Problem No. 5 (“Assessments based on limited samples and/or “language-like” behavior”) may be apropos given that only gjt data were used, leaving open the theoretical possibility that other measures might have yielded a different outcome. Finally, problem No. 9 (“Faulty interpretation of statistical patterns”) is, of course, precisely what I have turned the spotlights on.

Conclusions

The critical period hypothesis remains a hotly contested issue in the psycholinguistics of second-language acquisition. Discussions about the impact of empirical findings on the tenability of the cph generally revolve around the reliability of the data gathered (e.g. [5] , [14] , [22] , [52] , [67] , [68] ) and such methodological critiques are of course highly desirable. Furthermore, the debate often centres on the question of exactly what version of the cph is being vindicated or debunked. These versions differ mainly in terms of its scope, specifically with regard to the relevant age span, setting and language area, and the testable predictions they make. But even when the cph 's scope is clearly demarcated and its main prediction is spelt out lucidly, the issue remains to what extent the empirical findings can actually be marshalled in support of the relevant cph version. As I have shown in this paper, empirical data have often been taken to support cph versions predicting that the relationship between age of acquisition and ultimate attainment is not strictly linear, even though the statistical tools most commonly used (notably group mean and correlation coefficient comparisons) were, crudely put, irrelevant to this prediction. Methods that are arguably valid, e.g. piecewise regression and scatterplot smoothing, have been used in some studies [21] , [26] , [49] , but these studies have been criticised on other grounds. To my knowledge, such methods have never been used by scholars who explicitly subscribe to the cph .

I suspect that what may be going on is a form of ‘confirmation bias’ [69] , a cognitive bias at play in diverse branches of human knowledge seeking: Findings judged to be consistent with one's own hypothesis are hardly questioned, whereas findings inconsistent with one's own hypothesis are scrutinised much more strongly and criticised on all sorts of points [70] – [73] . My reanalysis of DK et al.'s recent paper may be a case in point. cph exponents used correlation coefficients to address their prediction about the slope of a function, as had been done in a host of earlier studies. Finding a result that squared with their expectations, they did not question the technical validity of their results, or at least they did not report this. (In fact, my reanalysis is actually a case in point in two respects: for an earlier draft of this paper, I had computed the optimal position of the breakpoints incorrectly, resulting in an insignificant improvement of model fit for the North American data rather than a borderline significant one. Finding a result that squared with my expectations, I did not question the technical validity of my results – until this error was kindly pointed out to me by Martijn Wieling (University of Tübingen).) That said, I am keen to point out that the statistical analyses in this particular paper, though suboptimal, are, as far as I could gather, reported correctly, i.e. the confirmation bias does not seem to have resulted in the blatant misreportings found elsewhere (see [74] for empirical evidence and discussion). An additional point to these authors' credit is that, apart from explicitly identifying their cph version's scope and making crystal-clear predictions, they present data descriptions that actually permit quantitative reassessments and have a history of doing so (e.g. the appendix in [8] ). This leads me to believe that they analysed their data all in good conscience and to hope that they, too, will conclude that their own data do not, in fact, support their hypothesis.

I end this paper on an upbeat note. Even though I have argued that the analytical tools employed in cph research generally leave much to be desired, the original data are, so I hope, still available. This provides researchers, cph supporters and sceptics alike, with an exciting opportunity to reanalyse their data sets using the tools outlined in the present paper and publish their findings at minimal cost of time and resources (for instance, as a comment to this paper). I would therefore encourage scholars to engage their old data sets and to communicate their analyses openly, e.g. by voluntarily publishing their data and computer code alongside their articles or comments. Ideally, cph supporters and sceptics would join forces to agree on a protocol for a high-powered study in order to provide a truly convincing answer to a core issue in sla .

Supporting Information

Dataset s1..

aoa and gjt data extracted from DeKeyser et al.'s North America study.

https://doi.org/10.1371/journal.pone.0069172.s001

Dataset S2.

aoa and gjt data extracted from DeKeyser et al.'s Israel study.

https://doi.org/10.1371/journal.pone.0069172.s002

Script with annotated R code used for the reanalysis. All add-on packages used can be installed from within R.

https://doi.org/10.1371/journal.pone.0069172.s003

Acknowledgments

I would like to thank Irmtraud Kaiser (University of Fribourg) for helping me to get an overview of the literature on the critical period hypothesis in second language acquisition. Thanks are also due to Martijn Wieling (currently University of Tübingen) for pointing out an error in the R code accompanying an earlier draft of this paper.

Author Contributions

Analyzed the data: JV. Wrote the paper: JV.

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The critical period hypothesis for l2 acquisition: an unfalsifiable embarrassment.

1. Introduction

2. the notion of critical period, 3. cph or cphs, 4. problems with the “scrutinized nativelikeness” yardstick, 5. aptitude.

Although language-learning aptitude might seem to be a relatively stable individual characteristic when compared with other factors, such as motivational orientation and action control mechanisms, there seems to be some converging evidence that certain components of aptitude … might improve in the course of language learning.

6. Age or Opportunity?

7. looking for discontinuity, 8. neurolinguistics: new developments, 9. concluding remarks, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Singleton, D.; Leśniewska, J. The Critical Period Hypothesis for L2 Acquisition: An Unfalsifiable Embarrassment? Languages 2021 , 6 , 149. https://doi.org/10.3390/languages6030149

Singleton D, Leśniewska J. The Critical Period Hypothesis for L2 Acquisition: An Unfalsifiable Embarrassment? Languages . 2021; 6(3):149. https://doi.org/10.3390/languages6030149

Singleton, David, and Justyna Leśniewska. 2021. "The Critical Period Hypothesis for L2 Acquisition: An Unfalsifiable Embarrassment?" Languages 6, no. 3: 149. https://doi.org/10.3390/languages6030149

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The critical period hypothesis is a theory in the study of language acquisition which posits that there is a critical period of time in which the human mind can most easily acquire language. This idea is often considered with regard to primary language acquisition, and those who agree with this hypothesis argue that language must be learned in the first few years of life or else the ability to acquire language is greatly hindered. The critical period hypothesis is also used in secondary language acquisition, regarding the idea of a time period in which a secondary language can be most easily acquired.

With regard to primary language acquisition, which refers to the process by which a person learns his or her first language , the critical period hypothesis is quite dramatic. This idea indicates that a person has only a set period of time in which he or she can learn a first language, usually the first three to ten years of development. During this time, language can be learned and acquired through exposure to language; simply hearing others talking on an ongoing and regular basis is sufficient. Once this time period is over, however, those who agree with the critical period hypothesis argue that primary language acquisition may be impossible or greatly impaired.

There is a great deal of research into human brain development that supports this hypothesis, but it is still difficult to prove. One of the only conclusive ways to prove this hypothesis would be to have a person isolated from infancy until about the age of ten, without exposure to human speech. Such upbringing would be unthinkable, however, so this type of experiment cannot be conducted and the hypothesis remains largely unproven.

Unfortunate situations in which a child has been abused and isolated by his or her caregivers have provided opportunities to support the critical period hypothesis. In at least one instance, medical care and study of the child did demonstrate that full language acquisition was nearly impossible. Though this occurrence did support the hypothesis, secondary factors such as possible brain damage make the evidence flawed.

The critical period hypothesis is also frequently applied to secondary language acquisition, though in a somewhat less dramatic way. With regard to secondary language, many linguists and speech therapists agree that a second language can be acquired more easily when someone is young. Studies of the brain indicate that in youth the brain is still developing more quickly and new linguistic information can be processed and incorporated into the brain more easily. Once this period is over, however, secondary language acquisition is still certainly possible, though it can be more difficult.

• https://www.pbs.org/wgbh/nova/transcripts/2112gchild.html

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Critical period hypothesis

The critical period hypothesis says that there is a period of growth in which full native competence is possible when acquiring a language. This period is from early childhood to adolescence.

The critical period hypothesis has implications for teachers and learning programmes, but it is not universally accepted. Acquisition theories say that adults do not acquire languages as well as children because of external and internal factors, not because of a lack of ability.

Example Older learners rarely achieve a near-native accent. Many people suggest this is due to them being beyond the critical period.

In the classroom A problem arising from the differences between younger learners and adults is that adults believe that they cannot learn languages well. Teachers can help learners with this belief in various ways, for example, by talking about the learning process and learning styles, helping set realistic goals, choosing suitable methodologies, and addressing the emotional needs of the adult learner.

Further links:

https://www.teachingenglish.org.uk/article/how-maximise-language-learning-senior-learners

https://www.teachingenglish.org.uk/article/how-much-do-your-learners-use-english-outside-classroom

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