critical period hypothesis 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.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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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.

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Critical Period in Brain Development: Definition, Importance

Catherine Falls Commercial / Getty Images

• 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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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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Research Article

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
• Reader Comments

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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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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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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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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Introduction: Ultimate attainment and the critical period hypothesis for second language acquisition

• Theo Bongaerts

1. Preliminaries

For several decades the topic of age effects on ultimate attainment has been high on the agenda of many second language acquisition researchers. A first major evaluation of research in this area was published by Long (1990), who summarized the findings of studies conducted since Penfield and Roberts (1959) and Lenneberg (1967) developed their versions of a critical period hypothesis for language acquisition. Long (1990: 280) argued that the combined findings of the studies conducted to date warranted the conclusion that

The ability to attain native-like phonological abilities in an SL begins to decline by age 6 in many individuals and to be beyond anyone beginning later than age 12, no matter how motivated they might be or how much opportunity they might have. Native-like morphology and syntax only seem to be possible for those beginning before age 15.

In line with most proponents of a critical period for SLA, Long (1990: 280) posited that the decline in abilities is due to incremental (and presumably irreversible) losses of neural plasticity due to brain maturation.

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

Jan vanhove.

Department of Multilingualism, University of Fribourg, Fribourg, Switzerland

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

Associated Data

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.

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.

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

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).

It can then straightforwardly be deduced that, other things equal, the aoa – ua correlation in the older group decreases as the ua variance in the older group increases relative to the ua variance in the younger group (Eq. 3).

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).

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 ​ and4 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.

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

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.

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.

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

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.

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.

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 .

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

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

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

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

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.

Funding Statement

No current external funding sources for this study.

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What Is the Critical Period Hypothesis?

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.

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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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