Critical period for first language acquisition may be shorter in autistic children than in typically developing children

doi:10.21203/rs.3.rs-5312615/v1

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Critical period for first language acquisition may be shorter in autistic children than in typically developing children

https://doi.org/10.21203/rs.3.rs-5312615/v1

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The goal of this study was to differentiate between two hypotheses regarding syntactic-language comprehension deficits in autistic adults. One hypothesis suggests a persistent, age-independent barrier, such as sound hypersensitivity or social avoidance, which may hinder acquisition of syntax throughout life. Another hypothesis proposes an age-dependent factor, such as a shortened critical period for language acquisition. These hypotheses predict distinct trajectories for language learning-rates. The first hypothesis expects autistic individuals to consistently exhibit a slower learning-rate compared to neurotypical individuals across all ages. In contrast, the second hypothesis predicts that autistic individuals will initially acquire language at a rate comparable to their neurotypical peers but will experience an earlier decline in learning-rate. To test these predictions, we analyzed language learning-rates in 15,183 autistic and 138 neurotypical individuals, 2 to 22 years-of-age. At age 2, both groups showed comparable learning-rates. In neurotypical individuals, this rate remained stable from ages 2 to 7. However, in autistic individuals, the learning-rate began to decline exponentially starting as early as 2.3 years, with an earlier onset of decline observed in those with more severe autism. These findings strongly support the second hypothesis, indicating that language deficits in autism may be caused by a shortened critical period.

Health sciences/Diseases/Neurological disorders/Neurodevelopmental disorders

Health sciences/Diseases/Neurological disorders/Neurodevelopmental disorders/Autism spectrum disorders

first language acquisition

recursive language

grammatical language

syntactic language

language acquisition

Critical periods, also known as sensitive periods, are key intervals in brain development during which neural circuits are particularly receptive to being shaped by experience. If crucial experiences are missed during these periods, the resulting deficits can be profound and lifelong. Some of the most well-studied examples of strong critical periods include the following observations. 1) Filial imprinting in birds: chicks permanently imprint on the first moving object they see shortly after hatching; since the first moving object is usually their mother, imprinting improves chicks’ survival by enabling them to learn and follow their mother) ¹. 2) Post-childbirth mammalian mother-infant bonding within a few hours of birth ². 3) Monaural occlusion: e.g., plugging one ear in owls during the first two months after birth leads to a permanent inability to localize sounds ³. 4) Song learning in white-crowned sparrows: male white-crowned sparrows can only acquire song dialect within approximately the first 100 days of their lives by learning from older males ⁴. 5) The vestibulo-ocular reflex: e.g., in fish and tadpoles the reflex can only be acquired within the initial 10 days of life ⁵. 6) Monocular deprivation: e.g., cats that had one eye sewn shut from birth until approximately three months of age acquire vision only in the open eye; the mere act of closing one eye for the critical period can significantly impact the physical structure of the brain, leading to a permanent loss of vision through that eye ⁶. Neural circuits that exhibit robust critical periods evolved to be influenced by experiences only during specific early postnatal phases, with subsequent development becoming unattainable. Mechanisms mediating the closure of critical periods include myelination that suppresses axon sprouting and synaptogenesis via inhibitory proteins ⁷, as well as the level of the inhibitory neurotransmitter GABA and the maturation of specific inhibitory circuits ^8,9.

For most people, a familiar example of how learning ability declines with age is the challenge of studying a foreign language. Hartshorne et al. ¹⁰ and later Chen et al. ¹¹ explored the critical period for second language (L2) syntax acquisition in over 1 million participants and concluded that the ability to learn syntax declines exponentially after 17.4 years of age. First language (L1) syntax acquisition was hypothesized to have even shorter and stronger critical period ^12,13. Lenneberg conjectured that the closure of the critical period occurs at about five years of age based on a few cases of childhood traumatic aphasia and hemispherectomy. When the left hemisphere is surgically removed before the age of five (to treat cancer or epilepsy), patients often attain normal language (using the one remaining hemisphere). Conversely, removal of the left hemisphere after five years of age often results in significant impairment of language ^14–18. Other studies suggested an even shorter critical period. Deaf individuals communicating using formal sign language or fitted with a hearing device from an early age, develop normal language comprehension. However, in the absence of early syntactic exposure, deaf individuals show a lifelong measurable deficit in the maximum attained syntactic L1 ^19–22. Friedmann and Szterman’s studies showed that children who had their hearing devices fitted before one year of age had significantly better syntactic L1 comprehension than those who received hearing aids when they were older than one year of age suggesting that plasticity for syntactic L1 starts to decline as early as one year of age ²³.

We aimed to investigate the L1 syntax learning-rate in autistic individuals using a study design similar to that of Hartshorne et al. ¹⁰ and Chen et al. ¹¹. Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by a range of social, communicative, and behavioral challenges ²⁴. Among the various cognitive and linguistic difficulties faced by individuals with ASD ^25–27, one prominent area of interest is syntactic language acquisition ^28–34. Many individuals with ASD exhibit atypical language development, particularly in the acquisition and use of syntax ^35,36. In this article, the term “syntactic language comprehension” is used broadly to distinguish the understanding of complex/syntactic structures from the more basic processes involved in comprehending simple commands and vocabulary ³⁵. While typically developing children grasp L1 syntax effortlessly, many autistic children experience significant challenges, resulting in lifelong deficits in syntactic L1 comprehension for approximately 40% of individuals diagnosed with ASD ³⁶. These deficits often contribute to lower functionality and difficulties in achieving independent living ³⁷.

Language acquisition deficits and the resulting lower L1 performance observed in some autistic adults can be explained by two distinct hypotheses. The first suggests that autistic individuals consistently exhibit a slower rate of L1 acquisition compared to typically developing individuals throughout their development (Fig. 1A). Alternatively, autistic individuals may initially acquire L1 at a similar rate to their neurotypical peers, but this rate could decline earlier, resulting in lower overall proficiency (Fig. 1C). The first hypothesis implies a persistent, age-independent barrier, such as sound hypersensitivity or avoidance of social interactions, which may prevent full mastery of L1 syntax. In contrast, the second hypothesis points to an age-dependent process, like a shortened critical period for L1 syntax acquisition in autistic children, where language learning opportunities diminish earlier than in neurotypical development ^38–40. To differentiate between these two hypotheses, we examined the developmental trajectory of L1 syntax acquisition in a large cohort of 15,183 autistic individuals and 138 neurotypical individuals, ranging from 2 to 22 years old.

Understanding how autistic individuals acquire syntactic language is essential for both theoretical and practical reasons. On a theoretical level, studying syntactic development in autism can provide insights into language acquisition mechanisms. Practically, it can guide families of autistic children toward earlier language intervention. Multiple studies have demonstrated that early intervention, particularly language exercises, significantly improves children's outcomes and reduces the societal cost of caring for autistic individuals ^41–47. Some parents, however, tend to dismiss their child’s language acquisition deficits as a temporary delay or attribute them to a disagreeable character. As a result, children may miss out on language therapy during the critical period when it is most effective. If the time course of L1 syntax acquisition was known, many parents could be persuaded to prioritize their child’s language therapy much earlier.

Participants with ASD

Participants with ASD were users of a language therapy app that was made available gratis at all major app stores in September 2015 ^48–52. Once the app was downloaded, caregivers were asked to register and to provide demographic details, including the child’s diagnosis and age. Caregivers provided informed consent to participate in the study and completed the language comprehension assessment called the Mental Synthesis Evaluation Checklist (MSEC) ^26,53. The first evaluation was administered approximately one month after the download. The subsequent evaluations were administered at approximately three-month intervals for up to three years. To enforce regular evaluations, the app became unusable at the end of each three-month interval and parents were required to complete an evaluation to regain its functionality.

Inclusion criteria

Inclusion criteria were identical to our previous studies of this population ^49,54–60. Specifically, we selected participants based on the following criteria

1) Consistency: Participants’ caregivers must have filled out at least three evaluations, and the interval between the first and the last evaluation was six months or longer. Among the selected participants, the average number of evaluations was 5.3 ± 3.4 (range 3–75). The average number of days between the 1st and the last evaluation was 624 ± 425 (range 160–2,696).

2) Diagnosis: Parent-reported ASD diagnosis at the end of the study. Children without ASD diagnosis were excluded from the study. Autism level (mild/Level 1, moderate/Level 2, or severe/Level 3) was reported by caregivers. Pervasive Developmental Disorder and Asperger Syndrome were combined with mild autism for analysis as recommended by DSM-5 ²⁴. A good reliability of parent-reported diagnosis was demonstrated previously ⁶¹.

Exclusion criteria:

1) Maximum age: Participants older than 22 years of age at the time of their first evaluation were excluded from this study.

2) Minimum age: Participants younger than two years of age at the time of their first evaluation were excluded from this study.

After excluding participants that did not meet these criteria, there were 15,183 total participants. 78% were males.

Syntactic language comprehension measure

Assessing syntactic language comprehension is more challenging than evaluating language production ⁶². Clinicians face time constraints during assessments, and a child may fail to respond or respond only half-heartedly, making it difficult to gauge their true comprehension abilities. While clinicians can only spend a few hours with a child, parents’ interaction with a child is a continuous behavioral experiment. Parents might say “Tomorrow we will go to the playground.” A child who does not understand verb tenses can react to the word “playground” and immediately brings sneakers, implying that s/he expects to go to the playground right now. Conversely, a child who understands verb tenses may show an immediate disappointment on his face indicating that s/he understands that s/he has to wait for the playground until tomorrow. Similar situations can occur about a child’s favorite food, a child’s favorite movie, visiting a relative, and so on. When cleaning a child’s room, parents can ask the child to put pencils on the table, while leaving balls and dolls under the table; to put the socks inside the dirty clothes bin, while not putting crayons into the bin; and so on. A child’s performance in these tasks provides unambiguous cues about his/her comprehension of spatial prepositions. Parents play with their children using toys. Any two animal toys (a horse and a lion) can be arranged into “a horse carrying a lion” or “a lion carrying a horse,” “a horse riding a lion” or “a lion riding a horse,” thus, providing information on the child’s understanding of the change in meaning when the order of words is changed. Parents normally read books to their children. Books commonly require children to imagine novel situations. For example, Dr. Seuss’ “Hop on Pop” book details two situations: “Mouse on house” and “House on mouse,” with pictures representing both arrangements. It is only natural for a parent to interact with their child by asking “Show me: mouse on house,” “Show me: house on mouse.” The child’s answers would unambiguously demonstrate his/her “understanding of the change in meaning when the order of words is changed.”

Therefore, day-to-day conversations, repeated activities, common play, and reading fairy tales aloud collectively provide an ample opportunity to observe a child’s behavior in response to sentences involving spatial prepositions, syntactic structures, verb tenses, and other complex grammatical sentences. These observable behaviors can be used by parents for Bayesian learning of their child’s abilities and can be reported in response to a survey. Accordingly, over a decade ago we developed a parent-reported survey that assesses language comprehension both directly, through items such as “[my child] understands elaborate fairy tales that are read aloud,” “[my child] understands several modifiers in a sentence,” “[my child] understands spatial prepositions” (Table 1: items 1, 2, 6–12, and 20), and indirectly, through items that are strongly related with the syntactic-language-comprehension-phenotype ³⁵. Two related items assess representational drawing (Table 1, items 3 and 4), which has been shown to be associated with the syntactic-language-comprehension-phenotype ⁶³. One item evaluates pretend play (Table 1, item 5), which is a known precursor to syntactic language; lack of pretend play in children with ASD is a strong indicator of challenges in acquisition of the syntactic-language-comprehension-phenotype ^64–67. Additionally, seven items measure understanding of complex recursion through arithmetic (Table 1, items 13–19). Arithmetic items extend the MSEC instrument into a range of complex recursion abilities that share the combinatorial nature of syntactic language while being familiar to parents ^68,69. At an early level, arithmetic is an extension of syntactic-language. Interpretation of syntactic sentences requires a degree of reasoning that is similar to that of arithmetic. Compare the following two sentences: 1) “The lion lives under the monkey, who lives under the dog,” and 2) “Mom had five flowers; she gave two flowers to Dad; how many flowers does Mom have now?” While the first sentence could come from a fairy tale and the second from an arithmetic book, the two instructions involve the same executive function that can be characterized as reasoning, syntactic logic, or interpreting complex recursive sentences. In other words, the level of arithmetic abilities serves as a proxy for the ability to comprehend complex recursive sentences. For several reasons, a parent survey could not ask about the child’s complex recursive abilities directly. First, most parents do not understand the concept of recursion. Second, even if examples of recursive sentences were provided—such as “The lion lives under the monkey, who lives under the dog”—these sentences are not commonly encountered in everyday activities, and parents would likely not know if their children understood them. Third, the goal of MSEC was to assess comprehension of recursive complexity at multiple levels. This is practically impossible to achieve in a parent-survey directly, but easily accomplished in the arithmetic domain, since most parents are well aware of their child’s arithmetic skills. Therefore, seven arithmetic questions were added to the MSEC to measure the child’s combinatorial recursive abilities: 1) Understands NUMBERS (i.e. two apples vs. three apples); 2) Can perform simple arithmetic: 2 + 3 = ?; 3) Can add larger numbers: 7 + 6 = ?; 4) Can perform simple subtraction: 3–2 = ?; 5) Can subtract larger numbers: 15–7 =?; 6) Can perform simple multiplication: 2 × 2 = ?; 7) Can multiply larger numbers: 6 × 7 =?

The possible answers to each MSEC item are: not true (2 points), somewhat true (1 point), very true (0 points). MSEC consists of 20 questions and a score ranges from 0 to 40 points; a lower MSEC score indicates a better developed language comprehension.

The psychometric quality of MSEC was tested with 3,715 parents of ASD children ⁵³. Internal reliability of MSEC was excellent (Cronbach’s alpha = 0.93). MSEC exhibited adequate test–retest reliability, good construct validity, and good known group validity as reflected by the difference in MSEC scores for children of different ASD severity levels. Another study of 143 autistic children 2 to 22 years of age also demonstrated excellent internal consistency of MSEC (Cronbach’s alpha = 0.96). The Exploratory Factor Analysis and Confirmatory Factor Analysis demonstrated MSEC unidimensionality and suggested that all 20 MSEC items were related to a single underlying factor ⁷⁰: (1) A single factor explained 71% of the total variance. (2) The off-diagonal fit value of 0.95 suggested an adequate single-factor model fit for the MSEC assessment. (3) The Comparative Fit Index (CFI) was 0.998, and the Tucker-Lewis Index (TLI) was 0.986, indicating a good model fit. (4) The Root Mean Square Error of Approximation (RMSEA) was 0.075, and the Standardized Root Mean Square Residual (SRMR) was 0.124. (5) All items had significant loadings onto the latent factor (p < 0.01). Confirmation of MSEC's unidimensionality is crucial for validating the inclusion of both 'pre-syntactic' items, such as pretend play, and 'post-syntactic' items, such as arithmetic, in the survey.

Multiple studies demonstrated MSEC’s ability to provide information complementary to the expressive language subscale ^54,56,59. In one longitudinal study, MSEC was the only outcome measure out of five demonstrating the negative effect of prolonged video and television watching ⁵⁵. In another longitudinal study, MSEC was the only outcome measure demonstrating the positive effect of meat, eggs, and vegetables consumption as well as gluten-free diet ⁶⁰. In other studies, MSEC was significantly more sensitive than the expressive language scale to improvements associated with pretend play and joint engagement ^58,59.

MSEC norms have been reported earlier ²⁶.

Table 1

**Mental Synthesis Evaluation Checklist (MSEC)** ⁷¹. **The answers choices were: not true (2 points), somewhat true (1), very true (0). The subscale score ranges from 0 to 40 points. A lower score indicates better language comprehension ability.**
1. Understands simple stories that are read aloud
2. Understands elaborate fairy tales that are read aloud (i.e. stories describing FANTASY creatures)
3. Draws a VARIETY of RECOGNIZABLE images (objects, people, animals, etc.)
4. Can draw a NOVEL image following YOUR description (e.g. a three-headed horse)
5. Engages in a VARIETY of make-believe activities (such as: playing house, playing with toy soldiers, building forts and castles, etc.)
6. Understands some simple modifiers (i.e. green apple vs. red apple or big apple vs. small apple)
7. Understands several modifiers in a sentence (i.e. small green apple)
8. Understands size (can select the largest/smallest object out of a collection of objects)
9. Understands possessive pronouns (i.e. your apple vs. her apple)
10. Understands spatial prepositions (i.e. put the apple ON TOP of the box vs. INSIDE the box vs. BEHIND the box)
11. Understands verb tenses (i.e. I will eat an apple vs. I ate an apple)
12. Understands the change in meaning when the order of words is changed (i.e. understands the difference between 'a cat ate a mouse' vs. 'a mouse ate a cat')
13. Understands NUMBERS (i.e. two apples vs. three apples)
14. Can perform simple arithmetic: 2 + 3 = ?
15. Can add larger numbers: 7 + 6 = ?
16. Can perform simple subtraction: 3–2 = ?
17. Can subtract larger numbers: 15–7 = ?
18. Can perform simple multiplication: 2 × 2 = ?
19. Can multiply larger numbers: 6 × 7 = ?
20. Understands explanations about people, objects or situations beyond the immediate surroundings (e.g., “Mom is walking the dog,” “The snow has turned to water”)

Statistical approach

The MSEC assessment measures the absolute L1 score. L1 learning-rate corresponds to the derivative of the MSEC score over time. Accordingly, L1 learning-rate can be calculated as the difference between each two consecutive MSEC scores divided by the number of days between the assessments normalized by 365 days. Note that L1 learning-rate can always be converted back into absolute L1 score by calculating the area under the curve.

L1 learning-rate was modeled by a piecewise function in which L1 learning-rate r(t) is a constant from birth to age t_c, whereupon it declines exponentially with a time constant τ (this formula was simplified from Hartshorne et al. piecewise sigmoidal function, that was found to best describe L2 learning-rate ¹⁰):

where t is age measured in years, t_c is age after which learning-rate follows an exponential decline (a critical inflection point ^10,11) measured in years, τ is an exponential decline time constant measured in years that controls the steepness of the exponent, and r₀ is a constant measured in MSEC units change per year. Thus, r(t) = r₀, at t = t_c; r(t) = r₀ * e ^− 1, at t = t_c + τ; r(t) = r₀ * e ^− 2, at t = t_c + 2τ; and so on.

The variability among participants was mathematically reconciled using the following R functions: nlme (Nonlinear Mixed-Effects) from the nlme package ⁷² and nlsLM (Nonlinear Least-Squares) from the minpack.lm package ⁷³. The nlme function is considered superior since it allows combining fixed and random effects, where fixed effects are assumed to represent those parameters that are the same for the whole population, while random effects are group dependent variables assumed to consider the variance in the data explained over time and subject ⁷⁴. However, the nlme function is very sensitive to the choice of starting values for the model parameters (r₀, t_c, and τ). This sensitivity can result in a complete failure to fit the model (no convergence) when starting values for the model parameters are suboptimal. In order to facilitate the discovery of the optimal starting values for the model parameters we employed the nlsLM function. The nlsLM function is also sensitive to the choice of starting values for the model parameters and this sensitivity can result in no convergence, but the nlsLM function is recognized for its robustness even for poorly chosen starting parameters ⁷⁵.

L1 learning-rate in typically developing children

A convenience sample of 138 neurotypical participants was obtained by approaching parents of young children on a parent community online site and asking if they would be willing to complete a Google form. The data presented in this manuscript includes everyone who agreed to participate and indicated that their child was “Normally Developing” (other diagnostic options included: Mild Language Delay, Attention Deficit Disorder, Autism Spectrum Disorder, Asperger Syndrome, Social Communication Disorder, Specific Language Impairment, Apraxia, Sensory Processing Disorder, Down Syndrome, and Other). All caregivers consented to anonymized data analysis and publication of the results. The mean age of participants was 4.8 ± 1.8 (range, 2–10.6) years, and 47% of them were male. Neurotypical children reach the ceiling MSEC score by around 8 years of age ²⁶, making it unfeasible to assess L1 learning-rate using MSEC in typically developing children older than 7 years of age.

Syntactic language abilities were reported longitudinally by parents of 15,183 autistic individuals of 2 to 22 years of age by completing the MSEC survey within an app. The average interval between assessments, 155 ± 157 days, was primarily driven by the recurrent 3-month reminder to complete a new assessment. The average number of assessments per participant was 5.3 ± 3.4 (range of 3 to 75).

L1 learning-rate was calculated as MSEC score change per year, estimated as the difference between consecutive assessment scores divided by the number of days between the assessments. This procedure resulted in 61,096 L1 learning-rate data points (4.0 ± 3.0 data points per participant; range: 1 to 41). The thin black line in Fig. 2A shows L1 learning-rate average calculated in each 0.1-year age bin (positive learning-rate corresponds to L1 improvement). L1 learning-rate decreases from a maximum of 6 points per year at 2 years of age to approximately 1 point per year around puberty, eventually reaching zero points per year in the twenties. Rising noise with increasing age is the result of fewer data points in this interval. The histogram of data points over age is shown in Figure S1.

Learning-rate in autistic individuals was compared to neurotypical children (Fig. 2B). Unlike the exponential reduction in learning-rate observed in autistic children, neurotypical children exhibited a constant learning-rate up to around 7 years. The exponential decrease in learning-rate observed in older participants is driven by the ceiling effect and has no physiological meaning. The histogram of data points over age is shown in Figure S2.

In order to understand the trajectory of L1 learning-rate, it was necessary to discover a mathematical formula that most accurately captured the underlying physiological process. The L1 acquisition time course has never been studied in a large population and therefore never modeled by a mathematical function. Only the L2 time course was investigated previously in a large population. In a study of nearly one million individuals Hartshorne et al. showed that L2 learning-rate is best described by a constant r₀ from birth to age t_c = 17.4 years (the critical inflection point), whereupon it declines according to a sigmoid with shape parameters τ and δ (τ controls the steepness of the sigmoid, and δ moves its center left or right) ¹⁰. The authors wrote that “though the ELSD [Exponential Learning with Sigmoidal Decay] model is necessarily simplified, the good fit between model and data, and the poorer fit by reasonable alternatives, offers good support for the existence of a critical period for language acquisition, and suggests that our estimate of when the learning-rate declines (17.4 years old) is likely to be reasonably accurate” ¹⁰. The best-fitting ELSD model reported by Hartshorne et al. has a significant downside of being discontinuous at t = t_c. Accordingly, we borrowed the main ideas from the ELSD model – describing learning-rate by a constant r₀ from birth to age t_c, whereupon it declines according to an exponential – but chose a continuous function r(t) described in methods. The r(t) formula has an additional benefit of being simpler than ELSD, as it only uses three parameters (r₀, t_c, and τ), while keeping the main ideas of ELSD.

The best-fitting parameters of r(t) in the ASD group were determined by modeling unaveraged 61,096 L1 learning-rate data points with the R function nlsLM ⁷³. The nlsLM function is sensitive to starting values for the model parameters. This sensitivity can result in differences in the model outcomes for the same dataset, or a complete failure to fit the model. The nlsLM model converged within a wide range of physiologically meaningful starting values (Table S1, Fig. 2A thick blue line). Importantly, the converging models resulted in a narrow range of output parameters: t_c = 2.24 to 2.30 years (SE = 1.9 to 2.84), τ = 4.82 to 4.84 years (SE = 0.57 to 0.58), and r₀ = 5.90 to 5.99 (SE = 1.70 to 3.27). The model bias was assessed by plotting a histogram of residuals (Figure S3). Equal distribution of the histogram around zero suggests absence of bias in the model.

Additionally, we modeled L1 learning-rate data in the ASD group by the R function nlme ⁷² using the same formula r(t). The nlme function is deemed superior to the nlsLM function since it allows random effects that consider the variance in the data explained over time and subject. All nlme model parameters (t_c, τ, and r₀) were modeled as the random effects ⁷⁴⁠. Similar to the nlsLM function, the nlme function requires input of starting values for the model parameters. The nlme model also converged within a wide range of physiologically meaningful starting values (Table S2). Again, the converging models resulted in a narrow range of output parameters: t_c = 2.24 to 2.29 years (SE = 2.1 to 2.71), τ = 4.75 to 4.99 years (SE = 0.57 to 0.60), and r₀ = 5.80 to 5.92 (SE = 2.51 to 3.27). The model bias was assessed by plotting a histogram of residuals (Figure S4). Equal distribution of the histogram around zero suggests absence of bias in the model. The good fit between model and data, similar results generated by both the nlsLM and the nlme models, models’ good stability to starting values for the model parameters, and lack of model bias all offer good support to the models’ results.

Next, we investigated whether L1 learning-rate differed between children diagnosed with mild (level 1, N = 5,095), moderate (level 2, N = 5,255), and severe (level 3, N = 4,833) ASD. To make an unambiguous comparison, the number of model parameters was reduced to one, the critical inflection point t_c. The other two parameters of r(t) were set to r₀ = 5.9 and τ = 4.9, as determined by the best-fitting model of the complete dataset. The nlsLM model converged within a wide range of physiologically meaningful starting values of t_c (Table S3, Figures S5, S6, S11) resulting in the following output values: mild ASD t_c = 3.15 years (SE = 11.5), moderate ASD t_c = 2.00 years (SE = 6.63), and severe ASD t_c = 1.35 years (SE = 0.43). The model bias was assessed by plotting a histogram of residuals (Figures S6, S9, S12).

The nlme model also converged within a wide range of physiologically meaningful starting values of t_c (Table S4). The models resulted in a narrow range of output: mild ASD t_c = 3.13 to 3.20 years (SE = 0.27), moderate ASD t_c = 1.97 to 2.01 years (SE = 0.30), and severe ASD t_c = 1.33 to 1.38 years (SE = 0.43). The model bias was assessed by plotting a histogram of residuals (Figures S7, S10, S113). Figure 3A shows the results of L1 learning-rate modeling in mild, moderate, and severe ASD, while Fig. 3B illustrates the area under the curve representing the L1 “growth curve,” labeled as “Language score.”

In modeling neurotypical participants, the nlsLM function converged within a wide range of starting values (Fig. 2B thick blue line) and resulted in the following output parameters independent of starting values: t_c = 7.14 years (SE = 0.09), τ = 0.67 years (SE = 0.19), and r₀ = 6.05 (SE = 0.13). The bias was assessed by plotting a histogram of residuals (Figures S14). Equal distribution of the histogram around zero suggests absence of bias.

In the largest longitudinal study to date (N = 15,183) on first language (L1) syntax acquisition in autistic children, we aimed to differentiate between two hypotheses. The first posits a persistent age-independent barrier to L1 acquisition, such as sound hypersensitivity or social withdrawal, which would lead to a consistently slower rate of L1 learning compared to typically developing individuals. The second hypothesis suggests an age-dependent process, such as a shortened critical period for L1 syntax acquisition in autistic children, where the initial learning-rate is similar to that of typically developing peers but declines earlier, resulting in lower overall proficiency (Fig. 1).

A parent-reported MSEC evaluation ^26,53, collected via an app, was used to measure L1 acquisition. L1 learning-rate was assessed as the yearly change in MSEC score. The results indicate that autistic individuals exhibit the highest L1 learning-rate at the earliest measurement point, around 2 years of age. After this point, the learning-rate declines asymptotically, approaching zero (Fig. 2A). At 2 years of age, L1 learning-rates were comparable between autistic and typically developing children (5.9 and 6.1 MSEC units per year, respectively). However, the trajectories of L1 learning-rates diverged significantly over time. While autistic children showed an exponential decline in L1 learning-rate, typically developing children maintained a nearly constant rate until around 7 years of age, at which point they reached the ceiling MSEC score (Fig. 2B). Regardless of the mathematical models used to reconcile variability among participants, these findings unambiguously support the second hypothesis, which predicts a shorter critical period for L1 syntactic acquisition in autistic children.

This result is consistent with the neuroanatomical evidence of cortical surface area over-expansion between 6 months and 1 year of age and the following brain volume overgrowth observed between 1 and 2 years of age in autistic children ^76,77. This overgrowth is likely associated with disruption of the process of refinement of neural circuit connections, leading to a shortened critical period for language acquisition. Moreover, our findings align with the accelerated prefrontal cortex (PFC) development reported by Liu et al. in autistic individuals ⁷⁸. Their study found that the expression of synaptic genes in the PFC peaks before the age of two (the earliest measured time point), while in typically developing individuals, this peak occurs around six years of age. Since syntactic language comprehension depends on executive functions of the PFC (that includes the anterior Broca’s area) ⁷⁹, accelerated PFC development may be responsible for reducing the critical period for L1 acquisition. Critical period plasticity has been also reported to be altered in multiple animal models of autism ^80–83. The combined evidence from our language acquisition data, brain imaging data, gene expression research, and animal studies suggests that accelerated PFC development, and the associated reduction in the critical period for syntactic L1 acquisition, could be a significant factor contributing to language deficits in autistic individuals.

In hindsight, it is unsurprising that children fall along a spectrum when it comes to the duration of their critical period for syntactic L1 acquisition. Like most physiological and psychological traits —such as height, weight, and IQ—the critical period for syntactic L1 acquisition is a variable characteristic. Consequently, it is expected that the duration of this critical period would follow a Gaussian distribution, with natural variation across individuals.

Consistent with this view, separate modeling of participants with mild (level 1), moderate (level 2), and severe (level 3) ASD revealed different values of the critical inflection point t_c. The longest t_c of 3.2 years was observed in the mild ASD group; intermediate t_c of 2.0 years was found in the moderate ASD group; and the shortest t_c of 1.4 years was seen in the severe ASD group (Fig. 3). These findings align with the severity of syntactic L1 deficits typically associated with each ASD level ^84–86.

It's important to note that we did not formally define the duration of the critical period for L1 acquisition, and this was intentional. The model parameter t_c (the inflection point) most closely corresponds to the concept of the end of the critical period (Fig. 3A). However, autistic children continue to acquire L1 syntax well into their twenties, long after their t_c (Fig. 3B). Therefore, the end of the critical period should not be viewed as the cessation of learning, but rather as the point where opportunities for language acquisition begin to diminish.

This study is limited to parent reports and parents may yield to wishful thinking, overestimating their children's abilities ⁸⁷. However, parents possess a deep, nuanced understanding of their children, which is particularly valuable for assessing language comprehension—a skill that can be difficult to accurately evaluate in a clinical setting ⁶². Additionally, several previous studies have shown that parent reports of language skills do not significantly differ from direct assessments by clinicians ^88,89. Furthermore, analyses of our own database suggest that parent reports are both consistent and reliable ^55,56,61. Even if some degree of overestimation were present, it would not affect the study’s findings, as the L1 learning rate was calculated based on the difference between consecutive assessments.

Another potential bias could arise from parents intentionally inflating their child's progress by giving higher scores than in previous assessments. If this were the case, one might expect that parents who used the app more frequently would be more likely to report improvement. To address this concern, we previously calculated the correlation between app usage (measured in days per week) and improvements in various domains, including language comprehension (r = − 0.01), expressive language (r = − 0.06), sociability (r = − 0.04), cognitive awareness (r = − 0.01), and health (r = 0.01) ⁴⁹. The low absolute values of these correlation coefficients, combined with the mixed directions of the correlations (positive for health, negative for other subscales), do not support the idea that increased app usage was associated with inflated ratings of improvement. Moreover, even if parents had a conscious or unconscious tendency to rate their child as improving, this would have been difficult. Parents were blinded to their previous responses, and given that each evaluation consisted of 133 questions, each with 3 to 6 answer options, it is highly unlikely they could remember their prior answers after a three-month interval between assessments. Therefore, we conclude that it is unlikely that evaluation bias influenced the outcomes of this study.

Exploring the developmental trajectories of autistic children through a language therapy app offers a significant advantage for data collection. It is notoriously difficult to identify and study 2-year-old autistic children in clinical settings, as they are typically diagnosed with ASD around 4 years of age ⁹⁰. However, parents often notice language deficits well before the formal diagnosis and may independently initiate language therapy using the app. This proactive use of the app generates a rich source of developmental data. Once children receive an official ASD diagnosis, they are included in the study cohort, and their previously recorded data is incorporated into the analysis, allowing us to capture early language patterns that might otherwise be missed.

Another concern raised regarding the MSEC survey pertains to its focus on syntactic language. The survey’s goal was to assess acquisition of complex language comprehension as opposed to basic commands and vocabulary. Previous research suggests that the comprehension of complex language—such as syntactic structures and modifiers—involves different underlying mechanisms compared to the understanding of simple commands³⁵. Throughout this article, we use the term "syntactic language" to emphasize the survey's focus on complex language comprehension. Our findings regarding the shorter critical period are specifically related to complex (syntactic) language comprehension; in contrast, the comprehension of commands and vocabulary expansion may either have a longer critical period or may not exhibit a critical period at all.

Clinical implications

There is a broad scientific consensus that early and intensive language therapy has the greatest promise of significantly improving outcomes for children with language deficits ⁹². Numerous studies have demonstrated that early language intervention can lead to substantial improvements in children’s language skills and overall development ^41–45. However, a simple and effective explanation of the importance of early language intervention remains elusive. A shorter critical period could serve as a straightforward way to convey the urgency of early language therapy to parents and educators. It is not uncommon for parents to overlook their child’s language acquisition deficits. Yet, in children with a shorter critical period, the opportunity for syntactic L1 learning diminishes significantly by the time they start kindergarten at age 6. This early decline in learning potential increases the risk of never achieving full syntactic language proficiency ^93,94 and, consequently, facing challenges in living independently ³⁷. Pediatricians are generally aware of the critical period for L1 acquisition and recommend early intervention, but they often struggle to convey the sense of urgency to parents due to the ambiguous nature of the concept of a critical period. Parents are more familiar with the critical period in the context of learning a second language (L2). While learning an L2 beyond early childhood is more challenging, it is not impossible ¹⁰. Few parents understand that learning L1 is crucially different from learning L2 ¹². Without a clear explanation, many parents fall back on their intuition from foreign language learning and conclude that L1 can as well be acquired at a later point. In some autistic children, a lifelong syntactic language deficit may be the result of parents’ “wait and see” approach until the child enters kindergarten. This study’s results can help pediatricians communicate to parents the concept of L1 critical period, and the ensuing urgency of early intensive language therapy. Additionally, this study may promote research into pharmacological agents that could extend L1 critical period for autistic children ^95,96.

Funding

This research received no external funding.

Acknowledgements

We wish to thank all participants’ caregivers who found time to complete children’s assessments. The authors are very grateful to Dr. Petr Ilyinskii for his scrupulous editing of this manuscript and Dr. Natalya Markuzon and Misha Tselman for the advice on the study design and statistical analysis. The language therapy app used to collect the data presented in this manuscript was made possible by the contributions of Rita Dunn, Alexander Faisman, Jonah Elgart, Lisa Lokshina, and Yulia Dumov.

Author contributions

AV designed the study. AM, SB, AT, RV, AT, SM, SU, EP, and AV analyzed the data. AV, AT, EK, and EP wrote the paper.

Competing Interests

Authors declare no competing interests.

Informed Consent

Caregivers have provided informed consent to anonymized data analysis and publication of the results. The study was conducted in compliance with the Declaration of Helsinki ⁹⁷.

Compliance with Ethical Standards

Using the Department of Health and Human Services regulations found at 45 CFR 46.101(b)(4), the Biomedical Research Alliance of New York LLC (BRANY) Institutional Review Board (IRB) determined that this research project is exempt from IRB oversight.

Data Availability

De-identified raw data from this manuscript are available from the corresponding author upon reasonable request.

Code availability statement

Code is available from the corresponding author upon reasonable request.

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No competing interests reported.

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Critical period for first language acquisition may be shorter in autistic children than in typically developing children

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Introduction

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Participants with ASD

Syntactic language comprehension measure

Statistical approach

L1 learning-rate in typically developing children

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Clinical implications

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