regression hypothesis language development

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• Published: 13 May 2021

Bayonet-shaped language development in autism with regression: a retrospective study

• David Gagnon 1 , 2 ,
• Abderrahim Zeribi 3 , 4 , 5 ,
• Élise Douard 3 , 5 ,
• Valérie Courchesne 6 ,
• Borja Rodríguez-Herreros 7 ,
• Guillaume Huguet 3 , 5 ,
• Sébastien Jacquemont 3 , 5 ,
• Mor Absa Loum 3 , 5 &
• Laurent Mottron   ORCID: orcid.org/0000-0001-5668-5422 1 , 2  

Molecular Autism volume  12 , Article number:  35 ( 2021 ) Cite this article

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Language delay is one of the major referral criteria for an autism evaluation. Once an autism spectrum diagnosis is established, the language prognosis is among the main parental concerns. Early language regression (ELR) is observed by 10–50% of parents but its relevance to late language level and socio-communicative ability is uncertain. This study aimed to establish the predictive value of ELR on the progression of language development and socio-communicative outcomes to guide clinicians in addressing parents’ concerns at the time of diagnosis.

We used socio-communicative, language, and cognitive data of 2,047 autism spectrum participants from the Simons Simplex Collection, aged 4–18 years (mean = 9 years; SD = 3.6). Cox proportional hazard and logistic regression models were used to evaluate the effect of ELR on language milestones and the probability of using complex and flexible language, as defined by the choice of ADOS module at enrollment. Linear models were then used to evaluate the relationship of ELR and non-verbal IQ with socio-communicative and language levels.

ELR is associated with earlier language milestones but delayed attainment of fluent, complex, and flexible language. However, this language outcome can be expected for almost all autistic children without intellectual disability at 18 years of age. It is mostly influenced by non-verbal IQ, not ELR. The language and socio-communicative level of participants with flexible language, as measured by the Vineland and ADOS socio-communicative subscales, was not affected by ELR.

Limitations

This study is based on a relatively coarse measure of ultimate language level and relies on retrospective reporting of early language milestones and ELR. It does not prospectively document the age at which language catches up, the relationship between ELR and other behavioral areas of regression, nor the effects of intervention.

Conclusions

For autistic individuals with ELR and a normal level of non-verbal intelligence, language development follows a “bayonet shape” trajectory: early first words followed by regression, a plateau with limited progress, and then language catch up.

Introduction

One of the first parental concerns leading to an assessment for possible autism is a delay or atypicalities in language and communication [ 1 , 2 ]. The diagnosis of autism spectrum disorder, most commonly given at a preschool age, occurs frequently when the child may be functionally non-verbal [ 3 ]. However, responses to legitimate parental questions related to language outcome, either proximal [ 4 ] or even more so as adults [ 5 ], are difficult to provide. Apart from the limited proportion of autistic children with typical early language development, the language prognosis of non- or minimally verbal autistic preschoolers is hard to predict [ 6 ], even though the proportion of autistic individuals who become fluent by school age is higher than previously thought [ 7 ]. The progression of expressive and receptive language development leading to fluency is often not continuous in autism [ 5 , 8 ] and marked by distinct early phenotypic pathways [ 9 ]. The aim of this study was to help clinicians respond to questions about language prognosis at the time of diagnosis of autism spectrum disorder with early language regression (ELR). After apparently normal language and motor development [ 10 ], from 10 to 50% of parents of children later diagnosed with autism spectrum disorder note a loss of previously acquired words [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. This loss is associated with a plateau in further language development [ 10 , 16 , 19 ]. The regressive event and stagnation of language progress may be considered either as two aspects of the same phenomenon [ 19 , 20 , 21 ] or as two distinct phenomena [ 22 ].

Such early language regression (ELR) is rarely encountered in non-autistic children [ 16 , 23 , 24 ]. It occurs at a mean age of 21 months [ 17 ], a time when typical developing children experience rapid expansion of their spoken vocabulary [ 16 ]. ELR has been suggested to be specific to autism spectrum disorder and is rarely seen in developmental language disorder (DLD) [ 23 ]. DLD and autism are considered to be distinct disorders reflecting different etiological mechanisms [ 25 ], despite sharing delayed early language milestones and progression, being occasionally comorbid and sometimes difficult to differentially diagnose [ 23 , 26 , 27 ].

Developmental regression was initially associated with poorer language outcomes [ 14 , 28 , 29 ], slow and atypical language development, and unattained complete functional language [ 13 , 29 ]. Other studies have found that most children with ELR regain their previous language skills [ 16 , 30 , 31 ] after a delay of 4–26 months [ 11 ], between 3.5 and 5 years of age [ 31 ]. However, it is not clear if ELR affects subsequent language progression or the ultimate level of language attainment.

Large cohort studies support that developmental regression is associated with later higher severity of autism characteristics [ 32 , 33 , 34 ] and lower intellectual quotient (IQ) [ 34 , 35 ]. Both are considered to be predictors of lower language outcome [ 3 , 5 , 7 ]. However, children with ELR generally utter their first words within normal age limits, which is generally not the case for autistic children without ELR [ 16 , 22 , 23 , 33 , 36 ], and similarly for their first phrase, if ELR occurs after the production of the first phrase [ 23 ]. Such early milestones are associated with higher IQ [ 7 ] and language level [ 37 , 38 , 39 ] in the general autistic population.

Previous studies on regression in the Simons Simplex Collection (SSC) focused on the loss of prelinguistic skills and parental beliefs about the origins of the regression [ 30 , 40 ]. The progression of language following ELR has been explored little in large cohorts, which focus on the reported duration of the loss and the global level of language, without consideration of language developmental pathways [ 30 , 33 ]. Studies exploring language outcomes in children with ELR after the age of six years are scarce, with little or no attempt to separate the effect of intellectual disability (ID) from ELR. These studies also did not distinguish between a possible delay in language development and permanent impairment [ 23 , 30 , 41 , 42 ].

In this study, we used the SSC to investigate the predictive value of ELR and the effect of NVIQ on language development and late socio-communicative outcomes. A conservative definition of ELR (loss of 5 words for at least three months) was chosen to increase the validity of our retrospective measures [ 43 , 44 ]. Retrospective information on regression presents a number of limitations: ELR may be less reported by parents of children who quickly regain language [ 16 ]. Data on early milestones are also prone to a telescoping effect [ 16 , 45 ], meaning that the older the child is, the later their milestones are reported. However, caregivers are generally more sensitive to language than other behavioral abnormalities [ 2 , 46 ] and ELR is the most consistently reported type of regression [ 44 , 47 ].

Our first objective was to describe the progression of language of autistic children who experienced ELR and to estimate the proportion of them who finally achieved functional and flexible use of language according to their NVIQ. The language level status was provided by the ADOS module used by the clinician at the time of enrollment in the cohort. ADOS module 3 or 4 was chosen if the child had mastered “fluent speech,” i.e., “spontaneous, flexible use of sentences with multiple clauses that describe logical connections within a sentence,” whereas module 2 was used for children possessing only “some flexible phrase” language [ 48 ]. The progression of language development was estimated based on the cumulative incidence of early language milestones and the increase in the proportion of fluent speakers, as the age at enrollment in the SSC increased.

Our second objective was to assess the respective effect of ELR on further language and socio-communicative development once fluent speech is reached, while considering NVIQ. As the SSC cohort is cross sectional, we ignored whether non-fluent speakers at enrollment would later develop fluent speech. We thus conservatively focused only on participants with fluent speech at the time of enrollment for this objective. This strategy avoided assuming permanent language impairment based on delayed language development.

Participants

Individuals from the Simons Simplex Collection (SSC) came from 12 university-affiliated research clinics under the guidance of the University of Michigan Autism and Communication Disorders Center. All clinicians received proper training for the administration of the ADOS and ADI-R, with at least 4–6 months of practice, and met standard requirements for research reliability [ 49 ]. All individuals included in the SSC were diagnosed with DSM-IV autism, PDD-NOS, or Asperger disorder, based on the clinicians’ best judgment. All participants scored above the threshold on the Autism Diagnostic Observation Schedule (ADOS) and cutoffs in social and communication domains of the Autism Diagnostic Interview-Revised (ADI-R). Participants were between 4 and 18 years of age at enrollment, had no previous additional neurodevelopmental diagnoses, and had a mental age of over 18 months ( www.sfari.org ). Information from ADI-R on first words, first phrases, and ELR was available for 2,047 autistic participants (mean age = 9.0 years; SD = 3.6). Among this group, 1,707 never experienced ELR (No-ELR), whereas 231 participants experienced ELR after their first words (ELR-W) and 109 after their first phrases (ELR-P) (Table 1 ). Among them, 1153 individuals (1017 No-ELR, 136 ELR) were fluent speakers at enrollment (based on the use of ADOS module 3 or 4), and complete information on their socio-communicative and cognitive abilities was available. The relative prevalence of epilepsy, which has been suggested to be associated with ELR [ 50 ], was not different between the ELR and No-ELR groups (Table 1 ).

The ADI-R is a semi-structured, retrospective interview that documents the three behavioral areas relevant for a DSM-IV autism diagnosis [ 51 ]. Language regression was determined from ADI-R question #11, as a loss of five or more words for at least three months, the most commonly accepted definition of ELR when using a retrospective questionnaire [ 17 ]. Questions #17, #19, #9, and #10 were used to determine the age when the language regression occurred, the duration of the loss, the age of the first meaningful word, and the age of the first two-word phrases including a verb, respectively. The total scores from the Verbal Communication Domain and the Reciprocal Social Interaction Domain of the ADI-R algorithm were used to retrospectively evaluate the historical severity of the socio-communicative impairment. Questions #18 and #85 (in combination with SSC medical history form data) were used to determine whether epileptic attacks were associated with the regressive event according to parental reports and whether the participants had been diagnosed with epilepsy.

The Autism Diagnostic Observation Schedule-Generic (ADOS) is a clinician-administrated semi-structured observational assessment [ 48 ]. Modules 1 and 2 of ADOS are used for children who have phrase language at most, but who are not “fluent speakers.” Modules 3 and 4 are used for fluently speaking children or adults. “Fluent speech” is defined as “spontaneous, flexible use of sentences with multiple clauses that describe logical connections within a sentence” [ 48 ]. The ADOS calibrated “social affect domain” scores obtained for module 3 [ 52 ] and 4 [ 53 ] were used to quantify the severity of autism socio-communicative impairment at enrolment of fluent speakers.

The Vineland-Second Edition (VABS) [ 54 ] is a standardized, semi-structured, parent or caregiver interview that evaluates adaptive skills for everyday life functioning: communication skills, daily living skills, and socialization. It is one of the most widely used, supported, and validated adaptive behavior scales [ 55 , 56 ]. It has a high degree of test–retest reliability (internal consistency: 0.72–0.90, inter-rater reliability: 0.78–0.80, test–retest reliability: 0.88–0.92) [ 54 ] and excellent test–retest reliability (0.94) in the communication domain in the autistic pediatric population [ 57 ]. Two subdomains of the communication domains, receptive and expressive language v-scale scores, were used in this study as measures of expressive and receptive language.

The Peabody Picture Vocabulary 4th edition (PPVT-4) [ 58 ] standard score was used as a direct receptive vocabulary measure. In this test, the child has to point to a picture, out of four, corresponding to the word mentioned by the clinician. This measure was used as an additional measure of receptive language focusing on vocabulary knowledge.

The non-word repetition subtest (NWR) standard score from the Comprehensive Test of Phonological Processing [ 59 ] is a well-accepted short-term phonological memory task that is impaired in children with DLD [ 60 ]. Participants must accurately repeat non-words. This task was used to determine whether any distinction between autistic children with and without ELR could be measured by the NWR task and whether this distinction could contribute to differences in the progression of language development.

Verbal IQ (VIQ) and non-verbal IQ (NVIQ) scores were derived from appropriate psychometric tests. The Differential Ability Scales-Second Edition Early Years/School Age [ 61 ], Mullen Scales of Early Learning [ 62 ], Wechsler Abbreviated Scale of Intelligence, First Edition [ 63 ], and Wechsler Intelligence Scale for Children, 4th Edition [ 64 ] were used. For children who failed to complete the age-appropriate test, the IQ was calculated by the formula IQ = (age equivalent score)/chronological age × 100.

The statistical analyses were performed using R software version 3.6.3 [ 65 ] (see Additional file 1 for specific packages).

Demographics

Chi-square, Kruskal–Wallis, or Wilcoxon signed-rank test analyses were used to compare demographic data. An ANOVA was used to test differences in IQ scores between ELR-W, ELR-P, and No-ELR in the whole sample, followed by group differences with Bonferroni’s correction applied for pairwise comparisons between the ELR-W or ELR-P and No-ELR groups.

Language milestones

The time to achieve first words and first phrases and the time between these milestones are presented as Kaplan–Meier plots for each group (ELR-W, ELR-P, No-ELR). The association between groups and the time at which milestones were achieved were analyzed using Cox proportional hazards models. Hazard ratios (HRs) were obtained for each group. Models were adjusted for NVIQ, sex, and age of assessment to control for any telescoping effect [ 45 ]. Logistic-regression analysis was used to estimate the probability of the “fluent speech” status, according to the ADOS module used (fluent speaker: module 3 or 4; not fluent speaker: module 2 or 1), by age, depending on the presence of ELR and the absence of ID (NVIQ ≥ 70) or NVIQ. The interaction between ELR and ID or NVIQ on the probability of being a fluent speaker was also tested. We estimated the elapsed time between the first word/phrase and fluent speech by subtracting the median age of the first word/phrase for each group, from the predicted age at which 50% of each group would be expected to be fluent speakers according to the logistic model.

Effects of ELR and NVIQ on socio-communicative and language levels in verbal autistic children

The effect of ELR on socio-communicative and language measures among fluent speakers was quantified using multiple linear regression analyses. Each linear regression was also corrected for NVIQ, sex, and age of assessment for historically reported measures. The socio-communicative and language scores were standardized within the sample for comparability purposes.

Socio-demographic information on the participants, with or without ELR, for the full sample and those who were fluent speakers at enrollment are presented in Table 1 . Sixteen percent of parents reported ELR (11% after the production of first words—ELR-W, and 5% after the first phrases—ELR-P). These proportions remained constant across age at enrollment and were not associated with missing data on ELR, age of first words, or first phrases (Additional file 2 : Figure S1), indicating a constant recall bias.

IQ characteristics of ELR vs No-ELR autistic children

Overall, participants with ELR had a lower IQ than No-ELR participants (NVIQ: p  = 3.3e−13; ELR-W: 78, ELR-P: 79, No-ELR: 89; VIQ: p  = 1.3e−21; ELR-W: 68, ELR-P: 70, No-ELR: 85), which was also true when restricted to fluent speakers (NVIQ: p  = 0.038; ELR: 92; No-ELR: 96; VIQ: p  = 4.6e−4; ELR: 88, No-ELR: 95). Children with ELR showed a striking discrepancy between the VIQ and NVIQ relative to No-ELR children, as revealed by the VIQ/NVIQ ratio (V/NVIQ ratio: p  = 1.4e−13; ELR-W: 0.84, ELR-P: 0.88, No-ELR: 0.95). This was mainly driven by non-fluent speakers, as the V/NVIQ ratio did not differ between groups for the fluent speakers (V/NVIQ ratio: p  = 0.081; ELR: 0.97, No-ELR 0.99) (see Table 1 ).

Faster language onset for children with ELR

First words emerged earlier in children who experienced ELR, either before (HR = 1.65, 95% CI [1.44–1.89], p  = 1.40e−12) or after (HR = 3.32, 95% CI [2.72–4.05], p  = 2.73e−32) the first phrase. The onset of the first phrase also occurred earlier for autistic children with ELR-P (HR = 4.92 95% CI [4.03–6.01], p  = 9.50e−55), with a shorter time interval between the first words and first phrase than No-ELR children (HR = 2.13, 95% CI [2.07–3.07], p  = 2.14e−20). As expected, the first phrase was delayed for autistic children with ELR-W (HR = 0.66, 95% CI [0.58–0.76, p  = 7.04e−09). There was a much longer time interval between first words and the first phrase for ELR-W than No-ELR children (HR = 0.38, 95% CI [0.33–0.44], p  = 8.92e−41). Overall, ELR occurred in children with earlier initial language onset (see Fig.  1 for descriptive data). Cox models were adjusted for NVIQ, age at enrollment, and sex.

Effect of early language regression (ELR) on language milestones. Proportion of children without language regression (No-ELR), language regression after the production of first words (ELR-W), and language regression after the production of first phrases (ELR-P), achieving language milestones by age/time. a Proportion achieving first words by age. b Proportion achieving first phrases by age. c Proportion achieving first phrase by time, in months, after the first words

Non-verbal or minimally verbal plateau following ELR

We evaluated the progression of language development following the first milestones by stratifying for ID, here defined as a NVIQ < 70, which strongly influences further language development [ 7 , 8 ]. Non-intellectually disabled autistic children who experienced ELR took an average of 21 months to recuperate the language level they had preceding ELR and 50 months between the first phrase and “fluent speech” (Fig.  2 ). This period was twice the duration observed for autistic children without ELR (ELR-W: 50 months, ELR-P: 50 months, No-ELR: 21 months) (See Figs.  2 , 3 a). Our study was not sufficiently powered to conduct the same analysis for intellectually disabled children. As expected, ID was strongly associated with a lower probability of being a fluent speaker (OR = 0.045, 95% CI [0.062–0.031], p  = 1.1e−69). The exclusion of participants with missing information on early language milestones and ELR did not change the magnitude of this effect (Additional file 4 : Table S2). ELR also delayed fluent speech (OR = 0.40, 95% CI [0.29–0.53], p  = 1.16e−9), but to a lesser extent than ID.

“Bayonet-shaped” language progression of autistic children who experienced early language regression (ELR). Schematic representation of language milestones/age range for autistic children. The progression of language of autistic children with ELR followed a three-step “bayonet-shaped” pattern: early typical language progression followed by a minimally verbal period after ELR and a final catch up phase. The achievement of fluent speech is delayed for children with ELR. Typical development milestones are presented for reference [ 84 ]. No-ELR: no-early language regression, ELR-W: early language regression after first words, ELR-P: early language regression after first phrase, ID: intellectual disability (non-verbal IQ < 70)

Probabilities of having achieved “fluent speech’’ status by age. Probabilities were derived from a logistic regression, according to a history of early language regression (ELR) and the presence or not of an intellectual disability (ID), with the 95% IC. a The probability of being a fluent speaker in non-intellectually disabled participants does not differ between the No-ELR group and that with ELR after their first phrases (ELR-P). Autistic children with a history of ELR after their first words (ELR-W) show delayed development of fluent speech, but still have the same language prognosis at the age of 18. b Intelligence explained the fluent speech status considerably more than a history of ELR. Almost all autistic children without intellectual disability (with a non-verbal intellectual quotient ≥ 70) will have achieved fluent speech before the age of 18, whether they have had a history of ELR or not

ELR does not lead to a poor language prognosis

More than 97% of non-intellectually disabled autistic children of 18 years of age (ELR: 0.98, 95% CI [0.97–0.99], No-ELR: 0.99 95% CI [0.989–0.996]) are expected to be fluent speakers according to the logistic model, whether they experienced ELR or not. Higher NVIQ was strongly and positively associated with the probability of being a fluent speaker (NVIQ: OR = 1.08, 95% CI [1.07–1.09], p  = 7.92e−89) (Fig.  3 b). The speech status (fluent/not fluent) at the time of enrollment was explained by the NVIQ, rather than a history of ELR (Fig.  3 b). There was no interaction effect between ELR and NVIQ or ID on the probability of being a fluent speaker (NVIQ: p  = 0.19; ID: p  = 0.30).

Expressive and receptive language levels of fluent speakers, as reported by parents, were not associated with ELR (z-score VABS expressive : ß = −0.14, 95% CI [− 0.31 to 0.026], p  = 0.099; z-score VABS receptive : ß = 0.025, 95% CI [−0.15 to 0.20], p  = 0.774). This was also true for social-communicative ability, directly assessed by the clinician (z-score ADOS social affect : ß = 0.13, 95% CI [−0.052 to 0.30], p  = 1.6e−1). However, children with ELR were characterized by a history of more socio-communicative impairments than No-ELR children (z-score ADI-R social : ß = 0.32, 95% CI [0.14–0.50], p  = 3.7e−4; z-score ADI-R communication : ß = 0.43, 95% CI [0.25–0.61], p  = 2.2e−6). Receptive vocabulary, measured by the PPVT, but not NWR ability, was lower in fluent speakers with ELR than those with no ELR (z-score PPVT: ß = −0.25, 95% CI [−0.38 to −0.11], p  = 5.1e−4; z-score NWR: ß = 0.038, 95% CI [−0.13 to 0.21], p  = 0.65). Analyses were adjusted for NVIQ, sex, and age of assessment for all retrospective measures (see Fig.  4 a, Additional file 5 : Table S3).

Effects of ELR and non-verbal intelligence quotient on socio-communicative and language measures in fluent speakers. Outcome measures were standardized within the sample to show effect sizes side by side. a Effect of early language regression (ELR). ELR did not have a significant effect on the expressive and receptive communicative levels measured by the Vineland (VABS) nor when socio-communicative competence was directly assessed by clinicians (ADOS social affect). Historical measures of impaired communication and social ability, retrospectively reported by parents, are associated with ELR. Lexical knowledge, measured by the PPVT, is negatively affected by ELR. The non-word repetition task score (NWR) is not significantly associated with ELR. Analyses were adjusted for NVIQ, sex, and age of assessment for historical measures. b Effect of non-verbal intelligence quotient (NVIQ). NVIQ was positively associated with levels of expressive and receptive language measured by the VABS. Higher NVIQ is protective against the severity of socio-communicative deficit measured by the ADOS. Historical measures of communication are negatively associated with NVIQ, but not social ability when measured retrospectively by the ADIR-R. Lexical knowledge and NWR are both positively associated with NVIQ. Analyses were adjusted for ELR, sex, and age of assessment for historical measures a Effect on the standardized log transformed outcome score

NVIQ is associated with socio-communicative and language levels

NVIQ was positively associated with expressive and receptive language level of fluent speakers (z-score VABS expressive : ß = 0.019, 95% CI [0.016–0.022], p  = 3.39e−36; z-score VABS receptive : 0.013, 95% CI [0.010–0.016], p  = 2.35e−18) and negatively associated with directly assessed socio-communicative impairment (z-score ADOS social affect : ß = −0.0079, 95% CI [−0.011 to −0.0049], p  = 2.8e−7). NVIQ was negatively associated with past communication impairment (z-score ADI-R communication : ß = −0.0060, 95% CI [−0.0091 to −0.0030], p  = 9.89e−5), but not social impairment (z-score ADI-R social : ß = −0.0029, 95% CI [−0.0059 to 0.00016], p  = 6.29e−2). NVIQ was also positively associated with receptive vocabulary and the NWR score for fluent speakers (z-score PPVT: ß = 0.033, 95% CI [0.031–0.035], p  < 1e−50; z-score NWR: ß = 0.019 [0.016–0.022], p  = 5.7e−37). The analyses were adjusted for ELR, sex, and age of assessment for all retrospective measures (see Fig.  4 b, Additional file 5 : Table S3).

We used cross-sectional data on a large cohort of autistic children of up to 18 years of age to clarify the effect of ELR on language milestones and the probability of becoming a fluent speaker. ELR occurs in children with earlier language onset, as previously shown [ 23 ], and delays the achievement of fluent speech. However, it does not affect the probability of their having fluent speech by the age of 18 years, nor undermine the attained expressive and receptive language level measured by the Vineland or socio-communicative ability measured by the ADOS calibrated social affect domain.

Our measurements of language milestones and outcomes were based on parent questionnaires, such as the ADI-R and Vineland, and direct assessment by the PPVT, NWR, and ADOS. Parental reports are prone to recall bias: ELR is suspected to be less reported by parents whose children quickly regain language [ 16 ]. Although some have reported the same bias for older children [ 66 ], this was not true for our SSC sample. Retrospective measurements, such as the age of milestones or ELR, are also prone to a telescoping effect [ 16 , 45 ], meaning that the older the child is, the later the milestones are reported. Analyses were corrected for age at assessment to reduce this effect, which in the context of our study was likely a conservative bias. Conversely, knowledge about the diagnosis [ 67 ], as well as the parents’ beliefs about the causes [ 40 ], could push parents to wrongly report ELR. Despite their suboptimal precision, reports of language regression are the most reliably reported type of regression over time [ 44 ] and the most confirmed type of regression when using home-videos [ 43 ].

The association of ELR with earlier language onset is mitigated by the fact that only children who have developed language will be able to lose it. However, there was no upper limit for the age at which we considered ELR as such. Hence, one child could have been a “late onset” speaker and still show language regression, but the opposite was found. The presence and timing of regression influenced the duration of the language acquisition plateau, but not the language outcome, extending previous results on the language outcome of ELR [ 23 , 33 ]. Despite a smaller receptive vocabulary, these children reached the expressive and receptive language level of their peers, according to Vineland parent-reported measures, and to their social communication in a clinical setting (ADOS score). Moreover, ELR was not associated with differences in the NWR task, which usually reveals deficits in DLD [ 60 ]. Such a language development pathway and outcome question the interpretation of early language measurements in ELR children as permanent impairment [ 10 ]. As suggested previously [ 37 ], the slope of language development may be more informative on the language outcome than the level of language observed during the second year of life.

Fluent speakers with ELR presented a more “severe” autistic phenotype in the area of social communication, according to parental recall. This is consistent with the observation that social ability usually regresses parallel to language [ 31 , 68 ]. It also suggests that the progress in language development in these children may not rely on social ability, which is not required for language development, to the same extent as for typically developing children [ 69 , 70 ].

A flexible and complex language outcome can be expected for almost all individuals of normal non-verbal intelligence, the achievement of fluent speech being highly associated with NVIQ. This finding is consistent with intelligence being a better indicator than regression to estimate the further needs of autistic children [ 35 ]. Conversely, this may also explain why ELR is prospectively associated with greater gains in IQ with age [ 71 ], as children who are more verbal become more accessible to intelligence testing. The VIQ was lower than the NVIQ in ELR children in our entire sample. However, the VIQ and NVIQ tended to equalize when evaluated only in fluent speakers, consistent with an initial language onset delay followed by a “catch up” in adolescence [ 5 ]. Overall, this emphasizes the importance of assessing non-verbal intelligence, regardless of how challenging this may be in autistic preschool-age children [ 72 ]. Although both language and intelligence are related, the two measures are still relatively independent [ 73 , 74 ].

Language progression before and after ELR follows a three-step, “bayonet-shaped” developmental process: (1) learning first words at an early or typical age [ 11 ]; (2) a “plateau” of several years, which doubles the typical time between the first phrase and fluent speech; and (3) catching up to the expressive and receptive language level of their non-regressive autistic peers. Although such a catch-up phase is in agreement with the results of previous studies directly measuring expressive and receptive language in competent talkers [ 23 , 75 ], it is at odds with those including children over six years of age, which generally showed a lower communicative or language level for autistic children who had had language regression [ 42 , 76 ]. However, these studies did not consider NVIQ, which significantly influences language development [ 5 ].

Prospective declarations concerning the prognosis for language at the time of diagnosis should underline that most non-intellectually disabled autistic children are fluent speakers by the age of eight [ 7 , 8 ] and ELR does not change their final prognosis. This possibility remains open until at least nine years of age [ 5 ]. Finally, our results document the outcome of the plateau in language development that occurs in autistic children when preceded by regression. Regression and plateau may belong to a continuum, the extreme of which is marked by frank regression of language [ 10 , 16 , 19 , 20 , 21 , 68 ]. The prevalence of early regression is highly dependent on the definition used [ 17 , 77 ]. It increases when using more fine-grain questionnaires [ 30 , 31 , 78 , 79 ] and reaches 86% when socio-communicative loss/stagnation is used as a criterion in prospective studies, in a population with an elevated likelihood of having ASD [ 80 ]. By using a restrictive definition of regression, we classified participants who exhibited subtle forms of regression as non-regressive. ELR retrospectively reported by parents may represent only the most visible fraction of the behavioral or language losses prospectively found in most autistic children [ 80 ]. The “bayonet-shaped” language progression curve may represent the more general progression of autistic language development, of which the milder form would be observed in autistic children with subthreshold ELR or a language plateau without evident ELR. This could be verified by further research to ascertain whether the pattern of language development of the two situations is similar or not [ 11 ].

Longitudinal studies on children with an elevated likelihood of having ASD [ 80 ], such as those using and comparing different measures and definitions of ELR on the progression of language development, are needed to build mechanistic models of language development in autism.

Our study had several limitations. The SSC is composed of participants of simplex families, for whom ELR may be distinct from that of individuals from multiplex families. It is composed of autistic individuals with moderate to severe autistic symptomatology, more representative of autistic children without ID than of those with ID [ 49 ]. The study is based on a measure of speech level which does not allow us to detail the difficulties that may persist even in fluent speakers. The predictions made about fluent speech cannot be generalized above and beyond the age range of our sample. We conservatively used a restrictive definition of ELR that relies on parental recall. This may not have identified the entire regressive population, contributing to the low frequency of ELR in this study [ 17 ]. The definition of ELR used in this study is representative of the usual information that clinicians have to rely on at the time of diagnosis. There is an inevitable balance between the validity of the measure and the ratio of autistic population concerned by this measure. However, the generalization of our findings to children with ELR identified through other methods should be made with caution. Also, this study did not include control groups with typical children or those with other conditions (e.g., DLD). This limits our ability to contextualize the language developmental profile of autistics with ELR to other "late bloomers" seen in other conditions [ 81 ]. Given the cross-sectional nature of this study, NVIQ levels were those measured at the time of enrollment. Thus, this study did not consider the evolution of NVIQ over time. The NVIQ measured at enrollment is at risk of being underestimated in younger children without ID [ 82 , 83 ]. The NVIQ may also have been affected by numerous factors, such as ELR, which tends to be associated with an underestimation of IQ at a young age [ 71 ]. Retrospective data on socio-communicative impairment, extracted from the ADI-R, document the most intense level of abnormalities presented or at the age at which abnormalities were the most obvious. Such measures are thus subject to temporal imprecision. Finally, our study does not provide information on the relationship between language regression and other behavioral areas of regression, nor on atypical pre-regression “early-onset’’ features. It also does not document the effects of intervention or their absence in the developmental course of language.

Responses to parents’ concerns relating to language outcome should highlight the fact that ELR does not affect the late language prognosis, but that it may delay its progression. The language development associated with ELR follows a ''bayonet” shape: early first words, regression, plateau, and language catch-up. Regardless of the etiological relationship between intelligence and ELR, NVIQ is strongly associated with fluent speech. Without NVIQ assessment, ELR should not be used as a predictor for a poor language prognosis, and a strong autistic phenotype in the socio-communicative domain at a young age does not necessarily overlap with a poorer language prognosis. Characterizing the progress, as well as the quality of the language “plateau,” with prospective studies may lead to a better understanding of language acquisition in autism. Finally, the “catching up” of language abilities after a plateau, if intrinsic to the development of language in autism, represents a confounding variable to be considered in any measurement of the effect of intervention on later language level.

Availability of data and materials

The dataset used for the current study is available for approved researchers by applying at https://base.sfari.org .

Abbreviations

Autism diagnostic interview-revised

Autism diagnostic observation schedule

Developmental language disorder

Early language regression

Early language regression after first words

Early language regression after first phrases

Intellectual disability

Intelligence quotient

Non-verbal intelligence quotient

Non-word repetition task

Peabody picture vocabulary test

Simons simplex collection

Vineland adaptive behavior scales

Verbal intelligence quotient

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Acknowledgements

We appreciate having been able to obtain access to the phenotypic data in the SFARI base. We thank the reviewers for their substantial comments and suggestions which we used to improve the manuscript.

This research was supported by a research grant from the “Programme de Formation en Recherche en Autisme Québec of the RTSA/TACC” and by the Chaire de Recherche Marcel & Rolande Gosselin en Neurosciences Cognitives et Autisme de l’Université de Montréal.

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David Gagnon and Abderrahim Zeribi have contributed equally to this work. Mor Absa Loum and Laurent Mottron have contributed equally to this work

Authors and Affiliations

Research Center of the CIUSSS-NIM, Hôpital Rivière-Des-Prairies, 7070, Boul. Perras, Montreal, QC, H2E 1A4, Canada

David Gagnon & Laurent Mottron

Department of psychiatry, University of Montreal, 2900 Boul. Édouard-Montpetit, Montreal, QC, H3T 1J4, Canada

University of Montreal, 2900, Boul. Édouard-Montpetit, Montreal, QC, H3T 1J4, Canada

Abderrahim Zeribi, Élise Douard, Guillaume Huguet, Sébastien Jacquemont & Mor Absa Loum

University of Sherbrooke, 2500, Boul. de L’Université, Sherbrooke, QC, J1K 2R1, Canada

Abderrahim Zeribi

Sainte-Justine Research Center, 3175, Chemin de La Côte-Sainte-Catherine, Montreal, QC, H3T 1C5, Canada

Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, 3801 University Street, Montreal, QC, H3A 2B4, Canada

Valérie Courchesne

Centre Cantonal Autisme, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Avenue de Beaumont 23, 1011, Lausanne, Switzerland

Borja Rodríguez-Herreros

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DG, AZ, ED, and MAL were involved in the conception, design, and data processing, analyses, and interpretation. GH, SJ, and LM were involved in the conception, design, and data analyses and interpretation. VC and BRH were involved in the data processing and analyses. MAL was the statistical expert. All authors contributed to writing the manuscript or made substantial revisions to it. They also all read and approved the final manuscript.

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Correspondence to Laurent Mottron .

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Supplementary Information

Additional file 1.

. List of R packages used for the analyses.

Additional file 2

. Figure S1. Prevalence and relative prevalence of early language regression (ELR) in the original sample.

Additional file 3

. Table S1. Socio-demographic data of non-fluent participants with or without ELR.

Additional file 4

. Table S2. Effect of intellectual disability and age on the prediction of being a fluent speaker.

Additional file 5

. Table S3. Effects of ELR, NVIQ, age, and sex on standardized socio-communicative and language levels in fluent speakers.

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Gagnon, D., Zeribi, A., Douard, É. et al. Bayonet-shaped language development in autism with regression: a retrospective study. Molecular Autism 12 , 35 (2021). https://doi.org/10.1186/s13229-021-00444-8

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15 Language in Adulthood

• Published: April 2006
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It is tempting to postulate that language development across the lifespan is U-shaped such that language “regression” mirrors language acquisition. Typically, this regression hypothesis is put forth to account for aphasic disorders and has also been applied to language loss with regard to the discontinued use of a first language and the decline of language in dementia. Both strong and weak forms of the regression hypothesis are proposed: the strong form holds that language regression is the mirror image of language acquisition at all levels of analysis, whereas the weak form suggests parallels while acknowledging different mechanisms or principles. Within the cognitive aging framework, there has been an emphasis on four contrasting accounts of age-related changes to language: cognitive slowing, inhibitory deficits, the effects of working-memory limitations, and language-specific effects on word retrieval. This chapter considers each account with regard to a salient phenomenon: older adults' use of simplified speech register resulting from an age-related decline in the syntactic complexity of oral and written language.

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• Research article
• Open access
• Published: 28 August 2014

The developmental relationship between language and motor performance from 3 to 5 years of age: a prospective longitudinal population study

• Mari V Wang 1 ,
• Ratib Lekhal 2 ,
• Leif E Aaro 1 ,
• Arne Holte 1 , 3 &
• Synnve Schjolberg 1  

BMC Psychology volume  2 , Article number:  34 ( 2014 ) Cite this article

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Previous research has found that language and motor skills are closely interrelated developmental areas. This observation has led to questions about the specificity of these domains, and the nature of the associations. In this study, we investigated the longitudinal relationship between language and gross and fine motor performance from 3 to 5 years of age.

We tested the prediction across and within developmental domains using cross-lagged panel models. In addition, estimates of specificity for each domain were calculated. Analyses were performed using parental reports in a sample of 11 999 children from a prospective population study.

Structural equation modelling revealed unique positive predictions from early language performance to later fine and gross motor skills. Neither gross nor fine early motor skills uniquely predicted later language performance. Both language and motor skills were stable from 3 to 5 years of age. Motor skills were more stable in boys than in girls. Boys had lower scores than girls on fine motor performance, but gender differences in cross-lagged associations between language and motor performance were non-significant. The variance specific to language performance decreased from 68% to 46% in relation to fine motor skills and from 61% to 46% in relation to gross motor skills from 3 to 5 years of age.

From 3 to 5 years of age the stability within each developmental area is high, and unique prediction from one domain to the other is weak. These results implicate stable and correlated developmental pathways at this age.

Peer Review reports

Associations between language and motor skills have frequently been recognized. The developmental pathways within each domain have been described in terms of rapid changes, plateaus, as well as wide variability (Iverson [ 2010 ]) and common traits between domains have been found (Hill [ 1998 ]). Consequently, it has been difficult to disentangle the associations. Most previous research on this association has focused one-sidedly on motor profiles in children with Specific Language Impairment (SLI) (Iverson and Braddock [ 2011 ]). A growing literature investigates the interrelatedness of these developmental domains (Iverson [ 2010 ]; Alcock and Krawczyk [ 2010 ]). However, previous literature has been dominated by focus on one out of three perspectives, rather than combining them. These three perspectives are; 1) co-occurrence of difficulties, 2) stability of each domain across time, and 3) predictive power from one domain to another across time. Most previous studies are hampered by small sample sizes and are often limited to clinical rather than population based samples, mainly with Specific language impairment (SLI) or Developmental coordination disorder (DCD) (Hill [ 1998 ]; Iverson and Braddock [ 2011 ]). The purpose of the present study is to gain new knowledge about the developmental relationship between language and motor performance across age by combining the three perspectives described above in one population based longitudinal study.

Several theories suggest links between motor development and specific aspects of language. The development of gestures is the foremost example of this. Motor skills influence the performance of gestures and studies have shown that children with language delays very often have a history of problems with gestures (Iverson and Goldin-Meadow [ 2005 ]; Zambrana et al. [ 2012a ]). Further, theories of motor cognition, i.e. the notion that cognition is embedded in actions, suggest that perception and action share common computational codes and underlying neural architectures. This idea has been further developed in the study of mirror-neurons. It has been suggested that the mirror-neuron system is the basic neural mechanism from which language has developed, and that this system represents a strong link between language and action representation (Rizzolatti and Arbib [ 1998 ]). Theories of embodied cognition argue that motor resonance enhances language comprehension (Glenberg and Kaschak [ 2002 ]; Fischer and Zwaan [ 2008 ]). These theories suggest that a broader developmental focus should be employed both in research and in clinical practice when investigating language and motor development.

Lately, researchers have questioned the specificity of several developmental disorders (Goorhuis-Brouwer and Wijnberg-Williams [ 1996 ]; Snowling [ 2012 ]). The frequent overlap in symptoms across domains in developmental disorders as well as co-morbid diagnoses suggests less clear distinction between clinical groups, especially in children, than suggested by the diagnostic systems. When comparing children diagnosed with SLI or DCD to children with no previously suspected disorder but with low standard scores on language or motor skills, researchers found that diagnosed children were more pervasive underachievers on a large set of measures of developmental difficulties additional to those corresponding to their diagnosis compared to those with low standard scores (Dyck and Piek [ 2010 ]). This observation suggests that a broader developmental focus should be employed both in research and in clinical practice.

Arguments have been proposed for grouping neurodevelopmental disorders together, such as language and motor difficulties (Viholainen et al. [ 2006 ]; Andrews et al. [ 2009 ]). These disorders have several common features (Rutter et al. [ 2006 ]). They involve similar neural structures, and the development is characterized by a delay/deviance rather than a remission or relapse (Jancke et al. [ 2007 ]). Both of these disorders involve some degree of cognitive impairment and have a marked male preponderance (Rutter et al. [ 2006 ]). The genetic influences on individual differences in both domains are quite strong (Fox et al. [ 1996 ]; Spinath et al. [ 2004 ]). Language difficulties have been found to be highly hereditary (Spinath et al. [ 2004 ]), and children with DCD have been found to have neurological similarities to children with SLI, such as frequent rolandic spikes during sleep, suggesting a genetic component (Scabar et al. [ 2006 ]). More research is needed on potential common genetic factors influencing development of both skills. Factors such as socio-economic status (Payne et al. [ 1994 ]), parental history of difficulties (Choudhury & Benasich [ 2003 ]), or low birth weight (Ribeiro et al. [ 2011 ]) are known to influence both language and motor skills. Thus, a child with slow development in one of the domains will also be at risk of developmental delay in the other.

Motor skills are often divided into gross and fine motor skills. These are described as overlapping but different (Hill [ 2001 ]). Some studies have found that language skills were associated only with gross and not fine motor skills (Piek et al. [ 2008 ]; Alcock and Krawczyk [ 2010 ]), but an overall finding in literature concerning children with language delays is that they are characterised by deficits in both gross and fine motor skills (Hill [ 2001 ]; Noterdaeme et al. [ 2002 ]).

Studying at risk populations, two literature reviews have concluded that contrary to the definition of SLI, people with SLI may exhibit non-linguistic problems, such as impairments of gross and fine motor skills, and other functional problems (Hill [ 2001 ]; Ullman and Pierpont [ 2005 ]). These findings are consistent with the results from a meta-analysis of 14 clinical studies indicating an association between gross and fine motor delay and language delay in children (Rechetinikov and Maitrat [ 2009 ]). Comparing language profiles in children with DCD or SLI to controls, results showed that the language profiles of children with either DCD or SLI are similar in the majority of cases (Archibald and Alloway [ 2008 ]). Also, research comparing motor profiles in children with SLI or DCD shows that both groups are significantly lower than controls on motor scores (Hill [ 1998 ]). Few longitudinal studies have investigated developmental stability of language and motor skills in general populations and results from these are inconsistent. However, the prospective longitudinal study Early Language in Victoria Study (ELVS) (Reilly et al. [ 2009 ]) showed that about half of late talkers catch up with their peers, and a Finnish follow up study (Cantell et al. [ 2003 ]) suggested that about half of children with motor delay also catch up with their peers.

Symptoms of delayed or deviant language development are related to a variety of different developmental outcomes such as ADHD, emotional and behavioural problems (Toppelberg and Shapiro [ 2000 ]; Beitchman et al. [ 1996 ]). Likewise, impaired motor function early in life has been found to be a precursor of problems with language acquisition later on (AmielTison et al. [ 1996 ]; Cantell et al. [ 1994 ]). Only a few studies have analysed the relationship between language and motor development longitudinally in community samples [Rechetinikov and Maitrat [ 2009 ]; Archibald and Alloway [ 2008 ]. Piek and colleagues (Piek et al. [ 2008 ]) studied the relationship of early motor development and school age motor and cognitive development in 33 typically developing children. They demonstrated that parent-reported scores on the Ages and Stages Questionnaire (ASQ), measuring gross motor skills during infancy, predicted later motor and cognitive performance. The same association was not found for fine motor skills (Piek et al. [ 2008 ]). These results are consistent with the claims that early locomotor experiences are an essential agent for developmental change (Iverson [ 2010 ]; Campos et al. [ 2000 ]). However, the association was limited to working memory and speed of processing only and no association was found between early gross motor skills and later verbal comprehension (Campos et al. [ 2000 ]). Another study of typical language development in 102 children between 9 and 23 months demonstrated large variability in both gross and fine motor skills within each child across age, between the children at each age level and across the developmental domains (Darrah et al. [ 2003 ]). Further, one study on 21 month old children (Alcock and Krawczyk [ 2010 ]), investigated various motor skills, including oral movements, in association with language production, comprehension, and complexity. Results showed no residual associations between gross and fine motor performance and measured aspects of language development when controlling for oral motor movements. These studies do not support a clear predictive power from one domain to the other. Some studies support language and motor skills as separate domains while others suggest that motor skills are a prerequisite for language development (Iverson [ 2010 ]) or that language predicts motor performance (Webster et al. [ 2005 ]).

In a previous study, we investigated the relationship between language and motor skills in typically developing children from 18 months to 3 years of age (Wang et al. [ 2012 ]). The study explored the association between language and motor skills (a distinction between gross and fine motor skills was not made in this study) both concurrently and over time. The results showed that whereas both skills were quite stable across age, early motor performance was an equally strong predictor of later language performance as early language performance was. Early language performance did not predict later motor performance. At 18 months of age typically developing children are in the beginning of rapid changes in development in both language and motor performance (Darrah et al. [ 2003 ]). At the age of 3, however, most children are able to use and understand basic language, and are also able to move around and manipulate their physical environment (Campos et al. [ 2000 ]). It is therefore important to see whether findings based on development from 18 months to 3 years can be replicated at older ages.

In the present study we investigate the co-occurrence, stability, and change in language and gross and fine motor performance from 3 to 5 years of age in a large, prospective longitudinal population study. This study is based on the same sample as in our previous study. Our main aim is to scrutinize the developmental relationship between language and fine and gross motor performance across age. More specifically we hypothesise; there are cross-sectional correlations – language and motor performance are associated at both 3 and 5 years of age; language and motor performance are both stable from 3 to 5 years of age, language performance at 3 years of age predicts change in motor performance from 3 to 5 years of age, and motor performance at 3 years of age predicts change in language performance from 3 to 5 years of age; there are gender differences – boys have poorer skills in both language and motor domains, and we explore whether there are gender differences also in associations within and across domains over time. Finally, we hypothesize that associations are similar for both gross and fine motor performance. We also investigate the specificity of each developmental domain.

Participants

The Norwegian Mother and Child Cohort Study (MoBa) is a prospective population-based pregnancy cohort study conducted by the Norwegian Institute of Public Health (Magnus et al. [ 2006 ]). Participants were recruited from all over Norway from 1999–2008. A total of 38.5% of invited women consented to participate. Informed written consent was obtained from all participants. The cohort now includes 109 018 children. Follow-up is conducted by questionnaires at regular intervals and by linkage to national health registries. The study was approved by the Regional Committee for Medical Research Ethics and the Norwegian Data Inspectorate.

By June 2011 (data release version 5), 25 474 children had turned 5 years of age and were thus eligible for the present study. Data from three waves of data collection were used; 17 weeks (Q1), 3 years (Q6), and 5 years (Q5yr). We also used data from the Medical Birth Registry of Norway (MBRN). For inclusion in this study, mothers must have answered both the 3-year questionnaire and the 5-year questionnaire. A total of 12 383 children satisfied this criterion. A total of 384 children were excluded because of serious physical malformations, cerebral palsy, Down’s syndrome, cleft palate or because of missing information on MBRN data. This gave a total number of 11 999 participants (6 025 boys and 5 974 girls), corresponding to 47% of the eligible 5 year olds.

Demographic, health-, pregnancy- and birth-related variables have previously been examined to investigate potential self-selection bias in MoBa. Despite risk prevalence differences between the sample and the population, estimates of risk exposure and child developmental outcomes were not significantly different when MoBa participants were compared with the entire population of Norwegian mothers (Nilsen et al. [ 2009 ]).

Language skills

Language skills were assessed through maternal ratings on selected items from the Ages and Stages Questionnaire (ASQ) (Squires et al. [ 1999 ]) included in the MoBa questionnaires. The ASQ has been validated in a Norwegian sample and found to be a successful diagnostic tool for developmental difficulties (Richter and Janson [ 2007 ]). At 3 years, language was measured by six ASQ items, and at 5 years, by seven ASQ items. All items had three response categories (yes, sometimes, and not yet). Because the ASQ originally was intended as a screening tool, most items had skewed distribution across response categories. One item at 5 years singled out with 99.5% responding “yes”, meaning that virtually all children mastered the skill and was excluded (Question 3: Does your child use four- and five- word sentences? For example, does your child say, “I want the car”?). More information on the items is presented in Additional file 1 .

Motor skills

Fine and gross motor skills at 3 years were assessed through maternal ratings on four items from the ASQ. All items had three response categories (yes, sometimes, and not yet). At 5 years motor skills were measured by ten items (five items on gross and five on fine motor skills) from Child Development Inventory (CDI) (Ireton et al. [ 1977 ]). At 5 years one item indicating gross motor skills was excluded because of low factor loadings to the latent variable (< .40) (question 5: Rides a two-wheeled bike, with or without training wheels). The distribution of responses to CDI-items was also skewed (See Additional file 1 for further information).

Information on the child’s APGAR scores five minutes after birth, birth weight, and gestational age, was retrieved from MBRN. Information on parents’ age, income, education and Norwegian language background was gathered during pregnancy (Q1). Information about maternal psychological distress (anxiety and depression) was assessed using a five-item short version of the Hopkins Symptom Checklist-5 (SCL-5), at both 3 and 5 years. The short version used has been shown to have good construct validity (Strand et al. [ 2003 ]). Information about the child’s age at return of the questionnaires was included as covariate at both 3 and 5 years.

The relationships among latent variables were examined with cross-lagged panel models. The models specified associations between language performance and motor performance at 3 years, auto-regression coefficients for each of the factors, cross-lagged regression coefficients, and association between language performance and motor performance at 5 years (see Figures 1 and 2 ).

Results from cross-lagged panel analysis. Correlations, auto-regressive-, and cross, lagged correlations between language and fine motor performance at 3 and 5 years of age.

Results from cross-lagged panel analysis. Correlations, auto-regressive-, and cross, lagged correlations between language and gross motor performance at 3 and 5 years of age.

The structural equation model (SEM) analyses were done using Mplus 6 (Muthén and Muthén [ 2007 ]). Because of the non-normal distribution of several variables in the study, estimation procedures robust to deviations from the normal distribution were utilized in all SEM analyses. Weighted least square parameter estimates using a diagonal weight matrix with standard errors and mean- and variance adjusted chi-square tests, using a full weight matrix (WLSMV) (Muthén and Muthén [ 2007 ]) were applied.

Models including control for communication and motor skills at 18 months of age were also estimated, but did not alter the associations between language and motor skills from 3 to 5 years of age in a noteworthy manner. Results from analyses of this relationship from 18 months to 3 years of age are presented elsewhere (Wang et al. [ 2012 ]). Finally, analyses were done to calculate the percentage of shared and specific variance for the latent factors at 3 and 5 years.

Missing data

WLSMV estimation works in four steps and uses a procedure for handling of missing with elements from maximum likelihood estimation and pairwise present deletion. This procedure was used for outcome measures. Missing value analysis (MVA) and an expectation-maximization (EM) algorithm were used to impute missing values for co-variates using SPSS (Inc. S [ 2008 ]).

Measurement models

Exploratory factor analyses showed that language and motor measures represented two distinct domains at each point in time. The items clustered as expected on all latent variables, except for fine motor skills at 5 years, where one item loaded on both fine and gross motor skills (question 1: Puts together a puzzle with nine or more pieces). Responses on this item were also severely skewed across response categories, and the item was excluded from the subsequent analyses. Next, we conducted confirmatory factor analyses (CFA) on the two waves of data to validate the factor structure of the latent variables language at 3 and 5 years and gross and fine motor skills at 5 years. CFA conducted for language at 3 years of age showed that the standard estimates ranged from .71 to .88 for the six items (Comparative fit index (CFI) = .994, Tucker Lewis Index (TLI) = .989, root mean square error of approximation (RMSEA) = .024). At 5 years the standard estimates for the six items indicating language at 5 years ranged from .64 to .87, (CFA = .988, TLI = .981, RMSEA = .029). The standard estimates for the four items indicating gross motor skills at 5 years ranged from .52 to .92, (CFA = .992, TLI = .977, RMSEA = .032) whereas the standard estimates for the four items indicating fine motor skills ranged from .74 to .83 (CFA = .997, TLI = .991, RMSEA = .034). Two items were available for indicating fine, and two for gross motor skills at 3 years. The standard estimates for these items were fixed to be equal.

Before including the latent variables in structural models, correlation estimates between all latent variables, and the observed variables for gross and fine motor skills at 3 years (see Table 1 ), were computed independently of each other. All correlations were highly significant.

Cross-lagged panel models

The latent variables from the measurement models were included in two two-wave cross-lagged panel models. The models allowed all structural parameters to be freely estimated, providing good model fit both when including measures of fine (CFI = .983, TLI = .981, RMSEA = .011), and measures of gross motor skills (CFI = .965, TLI = .960, RMSEA = .015). The first model produced χ 2 ( N  = 11483) = 885.894, p  < .001 with 354 degrees of freedom, whereas the second produced χ 2 ( N  = 11483) = 1225.438, p  < .001 with 354 degrees of freedom. The structural models are presented in Figures 1 and 2 .

Language and fine motor skills

At 3 years, children’s language was positively associated with fine motor performance, with the correlation between language and fine motor skills being .44. The regression coefficient for language from 3 to 5 years was .79, and the regression coefficient for fine motor performance from 3 to 5 years was .43. A Wald chi-square test showed that these regression coefficients were significantly different ( p < .001 ). The cross-lagged coefficient for language on fine motor performance was .24 ( p < .001 ), indicating that language performance at 3 years predicted fine motor performance at 5 years. The cross-lagged coefficient for fine motor on language performance was .00 (ns). A Wald test showed that the cross-lagged coefficients were significantly different ( p < .001 ), indicating a weaker prediction from early fine motor performance to later language performance than from early language to later fine motor performance. A Wald test comparing the regression coefficients of early language and fine motor performance on later language performance showed a significant difference ( p < .001 ), indicating that early language is better than early fine motor performance at predicting later language performance. A Wald test comparing the regression coefficients of early language and fine motor performance on later fine motor performance was significant ( p < .001 ), indicating that early fine motor performance were a better predictor of later fine motor performance than was early language performance.

Language and gross motor skills

The correlation coefficient for language and gross motor skills at 3 years were .30, and .11 at 5 years. The regression coefficient for language from 3 to 5 years was .80, and for gross motor the regression coefficient was .56. These coefficients were not significantly different. The cross-lagged coefficient for early language on later gross motor skills was .13, and was significantly different from the cross-lagged coefficient for early gross motor on later language skills -.03 ( p < .001 ). Language at 3 years of age was a significantly better predictor of later language performance than gross motor skills ( p < .001 ) and gross motor skills at 3 years of age was a significantly better predictor of later gross motor skills than language performance at 3 years of age ( p < .001 ).

Longitudinal domain specificity

In addition a significant increase over time of shared variance with both fine and gross motor development was found for language development (Table 2 ). In relation to fine motor skills, the variance specific to language decreased from 68% to 46%, whereas in relation to gross motor the decrease was from 61% to 46% from 3 to 5 years of age. For fine motor skills the variance specific to this domain increased from 43% at 3 years to 53% at 5 years, and for gross motor skills the variance specific to this domain increased from 33 to 59% from 3 to 5 years of age.

Gender differences

Girls performed better than boys on all indicators both for language and motor skills at both ages. The largest differences were found in fine motor skills at 5 years (see Additional file 1 ). These differences were not significance tested. However, to investigate whether there were significant gender differences in the relationships between the latent variables in the final model a multi-group analysis was performed to compare boys and girls on all relevant parameters. Confidence intervals on parameters for boys and girls were compared. Non-overlap between confidence intervals was only found on the covariance between language and gross motor skills at 3 years, with girls having a higher covariance than boys (Table 3 ). In contrast to the model including both genders, the regression coefficient for early language skills on later gross motor skills was not significant for boys. The difference between boys and girls on this parameter was, however, not significant.

A decomposition of variance similar to the one shown in Table 2 was also done for girls and boys separately (table not shown). No gender differences proved significant, except for a decrease in shared variance with language for gross motor skills in boys.

The aim of this study was to examine the development of language and motor performance in children from 3 to 5 years of age and associations between the two domains cross-sectionally as well as longitudinally. Our results were consistent with the hypothesis that motor and language development are associated developmental pathways. We found that the auto-correlations for both language and motor performance are high and stable over time. However, the predictive power from one domain to the across age other found by earlier research (Webster et al. [ 2005 ]) was weak in our study when controlling for stability within each domain.

Earlier studies, mainly with clinical samples have shown that a large proportion of children with impairments in one area also have impairments in the other (Archibald and Alloway [ 2008 ]). Our results support this assumption in finding strong cross-sectional associations between language and both gross and fine motor skills. However, our results indicate that between 3 and 5 years of age in the general population the stability within domains is much higher than the effect one domain has on the other. Similar to what has been found by others (AmielTison et al. [ 1996 ]; Cantell et al. [ 2003 ]) we find significant developmental associations across domains. However, this was only true for zero order correlations, and the associations disappeared when controlling for stability, except for the association between early language and later fine and gross motor skills. Language also had a significant increase in shared variance with both gross and fine motor performance from 3 to 5 years of age. This means that language at 3 years of age was associated with later fine and gross motor performance over and above what was explained by the correlation between domains at 3 years and the stability of each domain from 3 to 5 years of age. This finding is supported by the overall most common finding in previous literature, that as many as half of the children with language delays in pre-school years later develop motor difficulties (Webster et al. [ 2005 ]). Early language development thus seems to have a unique contribution to later fine and gross motor development.

The previous study on this population (from 18 months to 3 years of age) also adjusted for stability when investigating the developmental relationship across these domains (Wang et al. [ 2012 ]). The main results from the current study were consistent with the earlier results with some exceptions. Language did not predict motor development from 18 months to 3 years of age, whereas from 3 to 5 years of age, this association was significant. In the previous study there was a significant association from 18 months to 3 years between motor skills and later language performance, but neither gross nor fine motor performance at 3 years of age predicted language at 5. Wide individual variability in typical language development at 18 months makes defining late development more problematic. In contrast, defining a late developer at 3 is easier. In motor development, however, more observable milestones such as independent walking occur early. At 3 years of age the easiest assessable milestones are reached (Luinge et al. [ 2006 ]), and the variation in performance no longer predicts performance in language skills at 5. Thus, it seems that development before the age of 3 is different from development after 3 years of age in both domains.

As expected (Zambrana et al. [ 2012b ]), we found that boys had lower scores on the measures of language and motor performance than girls. The correlation between language and gross motor skills at 3 years of age was also higher for boys. This implies that in addition to differences in performance level, the developmental relationship of language and fine and gross motor skills is mainly similar across gender.

Conclusions from these results should be considered in light of the strengths and limitations of the study. A major strength of the current study is the prospective-longitudinal design and the community-based sample (Sonuga-Barke [ 2012 ]). Another strength is the examination of the relationship in a cross-lagged panel model where relations between domains are controlled for development within each domain (Selig and Little [ 2012 ]). Most previous findings on the association between language and motor performance come from studies using clinical samples and have, therefore, been subject to help seeking biases (Cohen and Cohen [ 1984 ]). Disorders in both domains have their onsets in early to late childhood. When doing research on clinical groups, some cases might be left out or, as shown by Dyck and Piek, (Dyck and Piek [ 2010 ]) children seen by specialists have more severe symptoms than undiscovered cases. Furthermore, if there is in fact an association between these domains, children seen by specialists are already at risk of cognitive problems because of their motor problems or vice versa (Wassenberg et al. [ 2005 ]). Thus, population based samples are needed in order to identify developmental relationships between these domains not limited to the extreme ends of poor performance.

Some limitations should also be considered. First, since a large scale study makes it difficult to assess each child on clinical measures, questionnaires must serve as the source of information. When observation is not possible, measures of children’s skills and performances must be based on mother’s reports. Mothers have been found to be reliable raters of their child’s language skills (Rydz et al. [ 2006 ]). However, we must also concider the possibility that some of the shared variance found in this study could be due to reliance on verbal instructions on the motor tasks in the both the ASQ and the CDI. Second, different measures are used across different studies, and this can make it difficult to compare results from one study to the other. In the current study different measurements are used across time. Since children’s language and motor performance usually develop between 3 and 5 years of age, what we measure are slightly different phenomena at the different ages. This might lead to underestimation of stability across age (for more information on included items, see Additional file 1 ). It is also important to be aware of the possible consequences of including only two items for measures of fine and gross motor skills respectively at 3 years of age. However, the large sample in this study compensate to some degree for possible measurement errors in the assessment of fine and gross motor skills at 3 years of age. Further, even though there is variation in both domains, there is a ceiling effect, especially for girls. Thus, the variability captured in this study might best show variability around the performance levels expected for the late developers and show less variability in the normal range of language and motor performance.

The clinical implication of findings in the current study is that identification of difficulties at one point in time alone does not necessarily tell anything about potential future difficulties. Our results suggest that the development of language and motor skills change to become more interrelated over time. Assessing both domains more than once is recommended if a child is encountered with problems in any one domain. There is always a risk of one problem overshadowing the other unless specifically assessed. Whereas motor performance at 18 months predicted both language and motor performance at 3 (Wang et al. [ 2012 ]), neither gross nor fine motor skills predicted language performance from 3 to 5 years of age. The opposite was true for language performance. This shows that the cross-correlations were different between the two studies. However, we found high stability within each domain, and a strong association between the two at all time-points. Additionally we found an increase in the variance motor skills share with language skills over time.

Our results are consistent with the idea of stable and associated developmental pathways for language and motor performance from 3 to 5 years of age. This study is among the first population based studies to investigate the developmental relationship between the two domains during childhood. The trend in research has turned from focusing on specific motor and/or language impairments to conceptualizing problems co-occurring in developmentally disordered children. Children with highly specific deficits are the exception rather than the rule (Andrews et al. [ 2009 ]). This finding can be further nuanced by results from the current study. In general, our results confirm what has been found earlier, namely that the two domains are related but the picture seems to be more complex. First, our results indicate that the relationship is dependent of age. We clearly see a developmental relationship of language and motor performance but the relationship changes from early to later preschool years. Second, when comparing boys and girls we find that for boys, early language performance does not significantly predict later gross motor skills. Third, we found that controlling for the direct effects over time within each domain uncover a different relationships across these two domains, than when considering unadjusted correlations. Finally, both domains show stability outperforming the prediction from one domain to the other from 3 to 5 years of age.

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Acknowledgements

The Norwegian Mother and Child Cohort Study is supported by the Norwegian Ministry of Health and the Ministry of Education and Research, NIH/NIEHS (contract no NO-ES-75558), NIH/NINDS (grant no.1 UO1 NS 047537–01), and the Norwegian Research Council/FUGE (grant no. 151918/S10). We would like to thank Eivind Ystrom for contribution to the statistical analyses. We are grateful to all the participating families in Norway who take part in this ongoing cohort study. This study is supported by EXTRA funds from the Norwegian Foundation for Health and Rehabilitation (2009/0348).

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Mari V Wang, Leif E Aaro, Arne Holte & Synnve Schjolberg

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MVW performed the statistical analyses and drafted the manuscript. RL, LEA, AH, and SS contributed to design and interpretation of results, and helped to draft or critically revise the manuscript. All authors read and approved the final manuscript.

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Additional file 1:Additional data distribution in answers on all items indicating language and motor performance at 3 and 5 years, divided by gender. Description of data: The data in the additional file describes the distribution across response categories on the items indicating language and motor performance at 3 and 5 years of age. The data are presented separately for boys and girls. Distribution in answers on items available in the data that were excluded from the analyses is also presented. (DOC 82 KB)

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Wang, M.V., Lekhal, R., Aaro, L.E. et al. The developmental relationship between language and motor performance from 3 to 5 years of age: a prospective longitudinal population study. BMC Psychol 2 , 34 (2014). https://doi.org/10.1186/s40359-014-0034-3

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Received : 04 March 2013

Accepted : 20 August 2014

Published : 28 August 2014

DOI : https://doi.org/10.1186/s40359-014-0034-3

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Types and Hypotheses of Language Attrition

2016, TESOL Arabia

The phenomenon of language attrition has been an issue of interest to researchers within the field of applied linguistics. This paper aims to examine what is meant by language attrition, whether it is language modification, shift, or simply loss. This paper also highlights four language attrition types: (1) loss of L1 in L1 environment; (2) loss of L1 in L2 environment; (3) loss of L2 in L1 environment; and (4) loss of L2 in L2 environment. Furthermore, the paper sheds light on the key hypotheses that attempt to explain how language is lost: the regression hypothesis, the activation threshold hypothesis, and the inter-language or interference hypothesis.

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The current paper presents a case of language attrition in Delhi. In 1947, many Saraiki speaking families moved to India from Pakistan. The current study analyzes some of the phonemes of L1 consonant inventory of the Saraiki speakers of Delhi. 118 participants of both male and female gender participated in this study. Half of them were Pakistan-born migrants and the remaining half were Delhi-born progeny of the migrants. In a production task, the participants of were asked to produce a set stimuli carrying the target sounds i.e. [ɳ ɲ ŋ nh lh ɳh] in word medial and final position. The productions were recorded and the recordings were evaluated by four native speakers of northern Saraiki from Pakistan on a Likert scale. The results show that the participants who were born in Saraiki speaking monolingual environment of Pakistan, were slow in convergence than their sons and daughters who were born in a bilingual environment of Delhi. A strong role of function load and dominant language (Hindi) but a minor effect of markedness, gender and attitude was identified in attrition of L1 of the participants.

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In this review, we elaborated on the main issues in the literature related to language attrition in general and second language attrition in particular. This review includes two parts. The first section outlines the general hypotheses concerning the nature of second language attrition and then reviews generally agreed-upon findings regarding the linguistic and sociolinguistic effects of second language attrition. In the course of the review we compared the findings with a subject named Gerannaz who had experienced attrition some years ago and was able return to its initial state to some extent through prolonged practice. Many of the research findings were supported, however variations were perceived.

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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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The Role of First Language Attrition in Persian Idiomatic Expressions

• Published: 28 March 2020
• Volume 49 , pages 607–629, ( 2020 )

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• Fatemeh Mehdiabadi 1 ,
• Nina Maadad 1 &
• Ali Arabmofrad 2  

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In recent years, despite the fact that many researchers have devoted much of their attention to second language attrition, not much focus has been given to first language attrition (FLA) specifically among Iranian immigrants. The present study attempts to describe FLA in the semantic domain of idiomatic expression and effect of length of residence among Persian native speakers who live in Iran as well as those who migrate to English-speaking countries. The present study explores language attrition in three migrant populations (Persians in the United States, Australia, and Canada). The participants were selected through convenience sampling. Furthermore, to find the impact of length of residence, the immigrants were divided into two groups comprising short- and long-term residence groups. The instrument applied by the researchers for data collection included a researcher-devised idiomatic expression test to assess immigrants’ level of idiom comprehension and demographic information questionnaire to have a better understanding of immigrants’ background characteristics. Results revealed that the immigrants underwent FLA and the rate of attrition was higher in long-term immigrants. The results are in harmony with the Activation Threshold Hypothesis showing the language attrition among fewer L1 users. The finding of this study sheds new light on the understanding of the concept of first language attrition in migration studies.

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School of Education, The University of Adelaide, Adelaide, Australia

Fatemeh Mehdiabadi & Nina Maadad

Department of English Language and Literature, Golestan University, Gorgān, Iran

Ali Arabmofrad

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Appendix 1: Idiomatic Expression Test in Persian

The English translation of the Idiomatic Expression Test is as follow:

Read the below sentences and choose the best answer:

The meaning of “everything remains calm and quiet” is…

not having any peace

nothing happened

The meaning of “back to square one” is …

being disappointed

getting failed and go back to the beginning

going back to the first place

The meaning of “behind the eight ball” is …

being in a complicated situation…

being in a hard situation

falling in a whole

The meaning of “come out into the open” is …

appearing in public

showing off

showing yourself a good person

The meaning of “being like a cat on hot bricks” is …

being lament

getting angry

The meaning of “not have a penny to one’s name” is …

being wretch

having no food to eat

The meaning of “cut off someone’s security” is …

making someone angry

making someone bored

making someone feel helpless

The meaning of “call someone’s bluff” is …

to show somebody up

to speak candidly

The meaning of “can’t stand the sight of somebody” is …

being blind

to be very jealous of somebody

not having time to see anyone

The meaning of “being as white as a sheet” is …

being excited

being shocked

The meaning of “giving someone what for” is …

paying wage

to punish someone

The meaning of “biting the bullet” is …

to endanger yourself

The meaning of “adding fuel to the flame” is…

to act naughty

The meaning of “never wake a sleeping lion” is …

don’t bother a lion

don’t play with a lion

don’t ask for trouble

The meaning of “looking down your nose at someone” is …

to be proud

The meaning of “making free with (something or someone)” is …

to give away

to donate from someone’s pocket

The meaning of “flying off the handle” is …

being anxious

to evade your responsibilities

The meaning of “setting the world on fire” is …

hunting the dangerous animals

getting into trouble

to do wonderful things

The meaning of “it’s all up with (somebody)” is …

The meaning of “making somebody’s mouth water” is …

to get sick

drinking water

feeling hungry

The meaning of “it’s my cup of tea” is …

to benchmark something

something preferred or desired

The meaning of “seeing the error of someone’s ways” is …

being successful

getting failed

finding an important thing

The meaning of “putting someone’s best foot forward” is …

to work for free

to try as hard as you can

to work with full power

The meaning of “beating the air” is …

having no power

attempt too much

doing attempt with no purpose

The meaning of “turning someone off” is …

to kill someone

not having more endurance and patience

annoying someone

≪I sincerely thank you for your cooperation≫

Appendix 2: Demographic Information Questionnaire

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Mehdiabadi, F., Maadad, N. & Arabmofrad, A. The Role of First Language Attrition in Persian Idiomatic Expressions. J Psycholinguist Res 49 , 607–629 (2020). https://doi.org/10.1007/s10936-020-09699-3

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Published : 28 March 2020

Issue Date : August 2020

DOI : https://doi.org/10.1007/s10936-020-09699-3

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