Advancing Our Understanding of the Link between Statistical Learning and Language Acquisition: The Need for Longitudinal Data

Statistical Learning Among Preschoolers With and Without Developmental Language Disorder: Examining Effects of Language Status, Age, and Prior Learning

PURPOSE

Our goal was to compare statistical learning abilities between preschoolers with developmental language disorder (DLD) and peers with typical development (TD) by assessing their learning of two artificial grammars.

METHOD

Four- and 5-year-olds with and without DLD were compared on their statistical learning ability using two artificial grammars. After learning an aX grammar, participants learned a relatively more complex abX grammar with a nonadjacent relationship between a and X. Participants were tested on their generalization of the grammatical pattern to new sequences with novel X elements that conformed to (aX, abX) or violated (Xa, baX) the grammars.

RESULTS

Results revealed an interaction between age and language group. Four-year-olds with and without DLD performed equivalently on the aX and abX grammar tests, and neither of the 4-year-old groups' accuracy scores exceeded chance. In contrast, among 5-year-olds, TD participants scored significantly higher on aX tests compared to participants with DLD, but the groups' abX scores did not differ. Five-year-old participants with DLD did not exceed chance on any test, whereas 5-year-old TD participants' scores exceeded chance on all grammar learning outcomes. Regression analyses indicated that aX performance positively predicted learning outcomes on the subsequent abX grammar for TD participants.

CONCLUSION

These results indicate that preschool-age participants with DLD show deficits relative to typical peers in statistical learning, but group differences vary with participant age and type of grammatical structure being tested.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.26487376.

Limited but specific engagement of the mature language network during linguistic statistical learning

Statistical learning (SL) is the ability to detect and learn regularities from input and is foundational to language acquisition. Despite the dominant role of SL as a theoretical construct for language development, there is a lack of direct evidence supporting the shared neural substrates underlying language processing and SL. It is also not clear whether the similarities, if any, are related to linguistic processing, or statistical regularities in general. The current study tests whether the brain regions involved in natural language processing are similarly recruited during auditory, linguistic SL. Twenty-two adults performed an auditory linguistic SL task, an auditory nonlinguistic SL task, and a passive story listening task as their neural activation was monitored. Within the language network, the left posterior temporal gyrus showed sensitivity to embedded speech regularities during auditory, linguistic SL, but not auditory, nonlinguistic SL. Using a multivoxel pattern similarity analysis, we uncovered similarities between the neural representation of auditory, linguistic SL, and language processing within the left posterior temporal gyrus. No other brain regions showed similarities between linguistic SL and language comprehension, suggesting that a shared neurocomputational process for auditory SL and natural language processing within the left posterior temporal gyrus is specific to linguistic stimuli.

Rhythmically Modulating Neural Entrainment during Exposure to Regularities Influences Statistical Learning

The ability to discover regularities in the environment, such as syllable patterns in speech, is known as statistical learning. Previous studies have shown that statistical learning is accompanied by neural entrainment, in which neural activity temporally aligns with repeating patterns over time. However, it is unclear whether these rhythmic neural dynamics play a functional role in statistical learning or whether they largely reflect the downstream consequences of learning, such as the enhanced perception of learned words in speech. To better understand this issue, we manipulated participants' neural entrainment during statistical learning using continuous rhythmic visual stimulation. Participants were exposed to a speech stream of repeating nonsense words while viewing either (1) a visual stimulus with a "congruent" rhythm that aligned with the word structure, (2) a visual stimulus with an incongruent rhythm, or (3) a static visual stimulus. Statistical learning was subsequently measured using both an explicit and implicit test. Participants in the congruent condition showed a significant increase in neural entrainment over auditory regions at the relevant word frequency, over and above effects of passive volume conduction, indicating that visual stimulation successfully altered neural entrainment within relevant neural substrates. Critically, during the subsequent implicit test, participants in the congruent condition showed an enhanced ability to predict upcoming syllables and stronger neural phase synchronization to component words, suggesting that they had gained greater sensitivity to the statistical structure of the speech stream relative to the incongruent and static groups. This learning benefit could not be attributed to strategic processes, as participants were largely unaware of the contingencies between the visual stimulation and embedded words. These results indicate that manipulating neural entrainment during exposure to regularities influences statistical learning outcomes, suggesting that neural entrainment may functionally contribute to statistical learning. Our findings encourage future studies using non-invasive brain stimulation methods to further understand the role of entrainment in statistical learning.

Statistical word segmentation: Anchoring learning across contexts

The present experiments were designed to assess infants' abilities to use syllable co-occurrence regularities to segment fluent speech across contexts. Specifically, we investigated whether 9-month-old infants could use statistical regularities in one speech context to support speech segmentation in a second context. Contexts were defined by different word sets representing contextual differences that might occur across conversations or utterances. This mimics the integration of information across multiple interactions within a single language, which is critical for language acquisition. In particular, we performed two experiments to assess whether a statistically segmented word could be used to anchor segmentation in a second, more challenging context, namely speech with variable word lengths. The results of Experiment 1 were consistent with past work suggesting that statistical learning may be hindered by speech with word-length variability, which is inherent to infants' natural speech environments. In Experiment 2, we found that infants could use a previously statistically segmented word to support word segmentation in a novel, challenging context. We also present findings suggesting that this ability was associated with infants' early word knowledge but not their performance on a cognitive development assessment.

The role of the hippocampus in statistical learning and language recovery in persons with post stroke aphasia

Although several studies have aimed for accurate predictions of language recovery in post stroke aphasia, individual language outcomes remain hard to predict. Large-scale prediction models are built using data from patients mainly in the chronic phase after stroke, although it is clinically more relevant to consider data from the acute phase. Previous research has mainly focused on deficits, i.e., behavioral deficits or specific brain damage, rather than compensatory mechanisms, i.e., intact cognitive skills or undamaged brain regions. One such unexplored brain region that might support language (re)learning in aphasia is the hippocampus, a region that has commonly been associated with an individual's learning potential, including statistical learning. This refers to a set of mechanisms upon which we rely heavily in daily life to learn a range of regularities across cognitive domains. Against this background, thirty-three patients with aphasia (22 males and 11 females, M = 69.76 years, SD = 10.57 years) were followed for 1 year in the acute (1-2 weeks), subacute (3-6 months) and chronic phase (9-12 months) post stroke. We evaluated the unique predictive value of early structural hippocampal measures for short-term and long-term language outcomes (measured by the ANELT). In addition, we investigated whether statistical learning abilities were intact in patients with aphasia using three different tasks: an auditory-linguistic and visual task based on the computation of transitional probabilities and a visuomotor serial reaction time task. Finally, we examined the association of individuals' statistical learning potential with acute measures of hippocampal gray and white matter. Using Bayesian statistics, we found moderate evidence for the contribution of left hippocampal gray matter in the acute phase to the prediction of long-term language outcomes, over and above information on the lesion and the initial language deficit (measured by the ScreeLing). Non-linguistic statistical learning in patients with aphasia, measured in the subacute phase, was intact at the group level compared to 23 healthy older controls (8 males and 15 females, M = 74.09 years, SD = 6.76 years). Visuomotor statistical learning correlated with acute hippocampal gray and white matter. These findings reveal that particularly left hippocampal gray matter in the acute phase is a potential marker of language recovery after stroke, possibly through its statistical learning ability.

Language learning in aphasia: A narrative review and critical analysis of the literature with implications for language therapy

People with aphasia (PWA) present with language deficits including word retrieval difficulties after brain damage. Language learning is an essential life-long human capacity that may support treatment-induced language recovery after brain insult. This prospect has motivated a growing interest in the study of language learning in PWA during the last few decades. Here, we critically review the current literature on language learning ability in aphasia. The existing studies in this area indicate that (i) language learning can remain functional in some PWA, (ii) inter-individual variability in learning performance is large in PWA, (iii) language processing, short-term memory and lesion site are associated with learning ability, (iv) preliminary evidence suggests a relationship between learning ability and treatment outcomes in this population. Based on the reviewed evidence, we propose a potential account for the interplay between language and memory/learning systems to explain spared/impaired language learning and its relationship to language therapy in PWA. Finally, we indicate potential avenues for future research that may promote more cross-talk between cognitive neuroscience and aphasia rehabilitation.

Assessing the Sensitivity of EEG-Based Frequency-Tagging as a Metric for Statistical Learning

Statistical learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive electroencephalograph (EEG) recordings of neural activity in humans. Importantly, we used carefully constructed controls to address potential acoustic confounds of the frequency-tagging approach, and compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Comparison of the neural metric to previously established behavioral measures for assessing SL showed a significant yet weak correspondence with performance on an implicit task, which was above-chance in 70% of participants, but no correspondence with the more common explicit 2-alternative forced-choice task, where performance did not exceed chance-level. Given the proposed ubiquitous nature of SL, our results highlight some of the operational and methodological challenges of obtaining robust metrics for assessing SL, as well as the potential confounds that should be taken into account when using the frequency-tagging approach in EEG studies.

Does auditory deprivation impairs statistical learning in the auditory modality?

Early sensory deprivation allows assessing the extent of reorganisation of cognitive functions, well beyond sensory processing. As such, it is a good model to explore the links between sensory experience and cognitive functions. One of these functions, statistical learning - the ability to extract and use regularities present in the environment - is suspected to be impaired in prelingually deaf children with a cochlear implant. However, empirical evidence supporting this claim is very scarce and studies have reported contradictory results. This might be because previous studies have tested statistical learning only in the visual modality and did not make clear distinctions between multiple types of statistical regularities. To overcome these problems, we designed a modified serial reaction time task where cochlear implanted children and normal hearing children had to react to auditory sequences that embed multiple statistical regularities, namely transition probabilities of 0^th, 1^st or 2^nd order. We compared the reaction times of the children with the output of a simple computational model that learns transition probabilities. First, 6-12 years old children were able to learn 0^th and 1^st order transition probabilities but not 2^nd order ones. Second, there were no differences between cochlear implanted children and their normal hearing peers. These results indicate that auditory statistical learning is preserved in congenitally deaf children with cochlear implants. This suggests in turn that early auditory deprivation might not be crucially detrimental for the normal development of statistical learning.

Unraveling the Interconnections Between Statistical Learning and Dyslexia: A Review of Recent Empirical Studies

One important aspect of human cognition involves the learning of structured information encountered in our environment, a phenomenon known as statistical learning. A growing body of research suggests that learning to read print is partially guided by learning the statistical contingencies existing between the letters within a word, and also between the letters and sounds to which the letters refer. Research also suggests that impairments to statistical learning ability may at least partially explain the difficulties experienced by individuals diagnosed with dyslexia. However, the findings regarding impaired learning are not consistent, perhaps partly due to the varied use of methodologies across studies - such as differences in the learning paradigms, stimuli used, and the way that learning is assessed - as well as differences in participant samples such as age and extent of the learning disorder. In this review, we attempt to examine the purported link between statistical learning and dyslexia by assessing a set of the most recent and relevant studies in both adults and children. Based on this review, we conclude that although there is some evidence for a statistical learning impairment in adults with dyslexia, the evidence for an impairment in children is much weaker. We discuss several suggestive trends that emerge from our examination of the research, such as issues related to task heterogeneity, possible age effects, the role of publication bias, and other suggestions for future research such as the use of neural measures and a need to better understand how statistical learning changes across typical development. We conclude that no current theoretical framework of dyslexia fully captures the extant research findings on statistical learning.

Statistical and sequence learning lead to persistent memory in children after a one-year offline period

Extraction of environmental patterns underlies human learning throughout the lifespan and plays a crucial role not only in cognitive but also perceptual, motor, and social skills. At least two types of regularities contribute to acquiring skills: (1) statistical, probability-based regularities, and (2) serial order-based regularities. Memory performance of probability-based and/or serial order-based regularities over short periods (from minutes to weeks) has been widely investigated across the lifespan. However, long-term (months or year-long) memory performance of such knowledge has received relatively less attention and has not been assessed in children yet. Here, we aimed to test the long-term memory performance of probability-based and serial order-based regularities over a 1-year offline period in neurotypical children between the age of 9 and 15. Participants performed a visuomotor four-choice reaction time task designed to measure the acquisition of probability-based and serial order-based regularities simultaneously. Short-term consolidation effects were controlled by retesting their performance after a 5-h delay. They were then retested on the same task 1 year later without any practice between the sessions. Participants successfully acquired both probability-based and serial order-based regularities and retained both types of knowledge over the 1-year period. The successful retention was independent of age. Our study demonstrates that the representation of probability-based and serial order-based regularities remains stable over a long period of time. These findings offer indirect evidence for the developmental invariance model of skill consolidation.

How statistical learning interacts with the socioeconomic environment to shape children's language development

Language is acquired in part through statistical learning abilities that encode environmental regularities. Language development is also heavily influenced by social environmental factors such as socioeconomic status. However, it is unknown to what extent statistical learning interacts with SES to affect language outcomes. We measured event-related potentials in 26 children aged 8–12 while they performed a visual statistical learning task. Regression analyses indicated that children’s learning performance moderated the relationship between socioeconomic status and both syntactic and vocabulary language comprehension scores. For children demonstrating high learning, socioeconomic status had a weaker effect on language compared to children showing low learning. These results suggest that high statistical learning ability can provide a buffer against the disadvantages associated with being raised in a lower socioeconomic status household.

Infants' Learning of Rule-Based Visual Sequences Predicts Language Outcome at 2 Years

The ability to learn and generalize abstract rules from sensory input - i.e., Rule Learning (RL) - is seen as pivotal to language development, and specifically to the acquisition of the grammatical structure of language. Although many studies have shown that RL in infancy is operating across different perceptual domains, including vision, no studies have directly investigated the link between infants' visual RL and later language acquisition. Here, we conducted a longitudinal study to investigate whether 7-month-olds' ability to detect visual structural regularities predicts linguistic outcome at 2 years of age. At 7 months, infants were tested for their ability to extract and generalize ABB and ABA structures from sequences of visual shapes, and at 24 months their lexical and grammatical skills were assessed using the MacArthur-Bates CDI. Regression analyses showed that infants' visual RL abilities selectively predicted early grammatical abilities, but not lexical abilities. These results may provide the first evidence that RL mechanisms are involved in language acquisition, and suggest that RL abilities may act as an early neurocognitive marker for language impairments.

Pupil-Linked Arousal Responds to Unconscious Surprisal

Pupil size under constant illumination reflects brain arousal state, and dilates in response to novel information, or surprisal. Whether this response can be observed regardless of conscious perception is still unknown. In the present study, male and female adult humans performed an implicit learning task across a series of three experiments. We measured pupil and brain-evoked potentials to stimuli that violated transition statistics but were not relevant to the task. We found that pupil size dilated following these surprising events, in the absence of awareness of transition statistics, and only when attention was allocated to the stimulus. These pupil responses correlated with central potentials, evoking an anterior cingulate origin. Arousal response to surprisal outside the scope of conscious perception points to the fundamental relationship between arousal and information processing and indicates that pupil size can be used to track the progression of implicit learning.SIGNIFICANCE STATEMENT Pupil size dilates following increase in mental effort, surprise, or more generally global arousal. However, whether this response arises as a conscious response or reflects a more fundamental mechanism outside the scrutiny of awareness is still unknown. Here, we demonstrate that unexpected changes in the environment, even when processed unconsciously and without being relevant to the task, lead to an increase in arousal levels as reflected by the pupillary response. Further, we show that the concurrent electrophysiological response shares similarities with mismatch negativity, suggesting the involvement of anterior cingulate cortex. All in all, our results establish novel insights about the mechanisms driving global arousal levels, and it provides new possibilities for reliably measuring unconscious processes.

The Role of Statistical Learning in Understanding and Treating Spoken Language Outcomes in Deaf Children With Cochlear Implants

Purpose

Statistical learning-the ability to learn patterns in environmental input-is increasingly recognized as a foundational mechanism necessary for the successful acquisition of spoken language. Spoken language is a complex, serially presented signal that contains embedded statistical relations among linguistic units, such as phonemes, morphemes, and words, which represent the phonotactic and syntactic rules of language. In this review article, we first review recent work that demonstrates that, in typical language development, individuals who display better nonlinguistic statistical learning abilities also show better performance on different measures of language. We next review research findings that suggest that children who are deaf and use cochlear implants may have difficulties learning sequential input patterns, possibly due to auditory and/or linguistic deprivation early in development, and that the children who show better sequence learning abilities also display improved spoken language outcomes. Finally, we present recent findings suggesting that it may be possible to improve core statistical learning abilities with specialized training and interventions and that such improvements can potentially impact and facilitate the acquisition and processing of spoken language.

Method

We conducted a literature search through various online databases including PsychINFO and PubMed, as well as including relevant review articles gleaned from the reference sections of other review articles used in this review. Search terms included various combinations of the following: sequential learning, sequence learning, statistical learning, sequence processing, procedural learning, procedural memory, implicit learning, language, computerized training, working memory training, statistical learning training, deaf, deafness, hearing impairment, hearing impaired, DHH, hard of hearing, cochlear implant(s), hearing aid(s), and auditory deprivation. To keep this review concise and clear, we limited inclusion to the foundational and most recent (2005-2018) relevant studies that explicitly included research or theoretical perspectives on statistical or sequential learning. We here summarize and synthesize the most recent and relevant literature to understanding and treating language delays in children using cochlear implants through the lens of statistical learning.

Conclusions

We suggest that understanding how statistical learning contributes to spoken language development is important for understanding some of the difficulties that children who are deaf and use cochlear implants might face and argue that it may be beneficial to develop novel language interventions that focus specifically on improving core foundational statistical learning skills.

Language Outcomes in Adults with a History of Institutionalization: Behavioral and Neurophysiological Characterization

Impoverished early care environments are associated with developmental deficits in children raised in institutional settings. Despite the accumulation of evidence regarding deficits in general cognitive functioning in this population, less is known about the impact of institutionalization on language development at the level of brain and behavior. We examined language outcomes in young adults and adolescents raised in institutions (n = 23) as compared to their socioeconomic status and age peers raised in biological families (n = 24) using a behavioral language assessment and linguistic event-related potentials (ERPs). Controlling for intelligence, adults with a history of institutionalization demonstrated deficits in lexical and grammatical development and spelling. Analyses of ERP data revealed significant group differences in the dynamic processing of linguistic stimuli. Adults with a history of institutionalization displayed reduced neural sensitivity to violations of word expectancy, leading to reduced condition effects for temporo-spatial factors that tentatively corresponded to the N200, P300/N400, and phonological mismatch negativity. The results suggest that language is a vulnerable domain in adults with a history of institutionalization, the deficits in which are not explained by general developmental delays, and point to the pivotal role of early linguistic environment in the development of the neural networks involved in language processing.

The Promise-and Challenge-of Statistical Learning for Elucidating Atypical Language Development

Statistical learning plays an important role in the acquisition of spoken and written language. It has been proposed that impaired or atypical statistical learning may be linked with language difficulties in developmental disabilities. However, research on statistical learning in individuals with developmental disabilities such as autism spectrum disorder, dyslexia, and specific language impairment, and in individuals with cochlear implants, has produced divergent findings. It is unclear whether, and to what extent, statistical learning is impaired or atypical in each of these developmental disabilities. We suggest that these disparate findings point to several critical issues that must be addressed before we can evaluate the role of statistical learning in atypical child development. While the issues we outline are interrelated, we propose four key points relating to (a) the nature of statistical learning, (b) the myriad of ways in which statistical learning can be measured, (c) our lack of understanding regarding the developmental trajectory of statistical learning, and (d) the role of individual differences. We close by making suggestions that we believe will be helpful in moving the field forward and creating new synergies among researchers, clinicians, and educators to better support language learners.

Distributional Cues to Language Learning in Children With Intellectual Disabilities

Purpose

In typical development, distributional cues-patterns in input-are related to language acquisition processes. Statistical and implicit learning refer to the utilization of such cues. In children with intellectual disability, much less is known about the extent to which distributional cues are harnessed in mechanisms of language learning.

Method

This tutorial presents what is known about the process of language learning in children with language impairments associated with different sources of intellectual disability: Williams syndrome, autism spectrum disorder, Down syndrome, and fragile X syndrome.

Results

A broad view is taken on distributional cues relevant to language learning, including statistical learning (e.g., transitional probabilities) and other patterns that support lexical acquisition (e.g., sensitivities to sound patterns, cross-situational word learning) or relate to syntactic development (e.g., nonadjacent dependencies).

Conclusions

Critical gaps in the literature are highlighted. Research in this area is especially limited for Down syndrome and fragile X syndrome. Future directions for taking learning theories into account in interventions for children with intellectual disability are discussed, with a focus on the importance of language input.

Reading as Statistical Learning

Purpose

The purpose of this tutorial is to explain how learning to read can be thought of as learning statistical regularities and to demonstrate why this is relevant for theory, modeling, and practice. This tutorial also shows how triangulation of methods and cross-linguistic research can be used to gain insight.

Method

The impossibility of conveying explicitly all of the regularities that children need to acquire in a deep orthography, such as English, can be demonstrated by examining lesser-known probabilistic orthographic cues to lexical stress. Detection of these kinds of cues likely occurs via a type of implicit learning known as statistical learning (SL). The first part of the tutorial focuses on these points. Next, studies exploring how individual differences in the capacity for SL relate to variability in word reading accuracy in the general population are discussed. A brief overview of research linking impaired SL and dyslexia is also provided. The final part of the tutorial focuses on how we might supplement explicit literacy instruction with implicit learning methods and emphasizes the value of testing the efficacy of new techniques in the classroom. The basic and applied research reviewed here includes corpus analyses, behavioral testing, computational modeling, and classroom-based research. Although some of these methods are not commonly used in clinical research, the depth and breadth of this body of work provide a compelling case for why reading can be thought of as SL and how this view can inform practice.

Conclusion

Implicit methods that draw on the principles of SL can supplement the much-needed explicit instruction that helps children learn to read. This synergy of methods has the potential to spark innovative practices in literacy instruction and remediation provided by educators and clinicians to support typical learners and those with developmental disabilities.

Infant statistical-learning ability is related to real-time language processing

Infants are adept at learning statistical regularities in artificial language materials, suggesting that the ability to learn statistical structure may support language development. Indeed, infants who perform better on statistical learning tasks tend to be more advanced in parental reports of infants' language skills. Work with adults suggests that one way statistical learning ability affects language proficiency is by facilitating real-time language processing. Here we tested whether 15-month-olds' ability to learn sequential statistical structure in artificial language materials is related to their ability to encode and interpret native-language speech. Specifically, we tested their ability to learn sequential structure among syllables (Experiment 1) and words (Experiment 2), as well as their ability to encode familiar English words in sentences. The results suggest that infants' ability to learn sequential structure among syllables is related to their lexical-processing efficiency, providing continuity with findings from children and adults, though effects were modest.

Cross-situational word learning in aphasia

Human learners can resolve referential ambiguity and discover the relationships between words and meanings through a cross-situational learning (CSL) strategy. Some people with aphasia (PWA) can learn word-referent pairings under referential uncertainty supported by online feedback. However, it remains unknown whether PWA can learn new words cross-situationally and if such learning ability is supported by statistical learning (SL) mechanisms. The present study examined whether PWA can learn novel word-referent mappings in a CSL task without feedback. We also studied whether CSL is related to SL in PWA and neurologically healthy individuals. We further examined whether aphasia severity, phonological processing and verbal short-term memory (STM) predict CSL in aphasia, and also whether individual differences in verbal STM modulate CSL in healthy older adults. Sixteen people with chronic aphasia underwent a CSL task that involved exposure to a series of individually ambiguous learning trials and a SL task that taps speech segmentation. Their learning ability was compared to 18 older controls and 39 young adults recruited for task validation. CSL in the aphasia group was below the older controls and young adults and took place at a slower rate. Importantly, we found a strong association between SL and CSL performance in all three groups. CSL was modulated by aphasia severity in the aphasia group, and by verbal STM capacity in the older controls. Our findings indicate that some PWA can preserve the ability to learn new word-referent associations cross-situationally. We suggest that both PWA and neurologically intact individuals may rely on SL mechanisms to achieve CSL and that verbal STM also influences CSL. These findings contribute to the ongoing debate on the cognitive mechanisms underlying this learning ability.

Nonadjacent Dependency Learning in Cantonese-Speaking Children With and Without a History of Specific Language Impairment

PURPOSE

This study investigated nonadjacent dependency learning in Cantonese-speaking children with and without a history of specific language impairment (SLI) in an artificial linguistic context.

METHOD

Sixteen Cantonese-speaking children with a history of SLI and 16 Cantonese-speaking children with typical language development (TLD) were tested with a nonadjacent dependency learning task using artificial languages that mimic Cantonese.

RESULTS

Children with TLD performed above chance and were able to discriminate between trained and untrained nonadjacent dependencies. However, children with a history of SLI performed at chance and were not able to differentiate trained versus untrained nonadjacent dependencies.

CONCLUSIONS

These findings, together with previous findings from English-speaking adults and adolescents with language impairments, suggest that individuals with atypical language development, regardless of age, diagnostic status, language, and culture, show difficulties in learning nonadjacent dependencies. This study provides evidence for early impairments to statistical learning in individuals with atypical language development.

Visual statistical learning is related to natural language ability in adults: An ERP study

Statistical learning (SL) is believed to enable language acquisition by allowing individuals to learn regularities within linguistic input. However, neural evidence supporting a direct relationship between SL and language ability is scarce. We investigated whether there are associations between event-related potential (ERP) correlates of SL and language abilities while controlling for the general level of selective attention. Seventeen adults completed tests of visual SL, receptive vocabulary, grammatical ability, and sentence completion. Response times and ERPs showed that SL is related to receptive vocabulary and grammatical ability. ERPs indicated that the relationship between SL and grammatical ability was independent of attention while the association between SL and receptive vocabulary depended on attention. The implications of these dissociative relationships in terms of underlying mechanisms of SL and language are discussed. These results further elucidate the cognitive nature of the links between SL mechanisms and language abilities.

The multi-component nature of statistical learning

The central argument presented in this paper is that statistical learning (SL) is an ability comprised of multiple components that operate largely implicitly. Components relating to the stimulus encoding, retention and abstraction required for SL may include, but are not limited to, certain types of attention, processing speed and memory. It is likely that individuals vary in terms of the efficiency of these underlying components, and in patterns of connectivity among these components, and that SL tasks differ from one another in how they draw on certain underlying components more than others. This theoretical framework is of value because it can assist in gaining a clearer understanding of how SL is linked with individual differences in complex mental activities such as language processing. Variability in language processing across individuals is of central concern to researchers interested in child development, including those interested in neurodevelopmental disorders where language can be affected such as autism spectrum disorders (ASD). This paper discusses the link between SL and individual differences in language processing in the context of age-related changes in SL during infancy and childhood, and whether SL is affected in ASD. Viewing SL as a multi-component ability may help to explain divergent findings from previous empirical research in these areas and guide the design of future studies.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.

Individual Differences in Statistical Learning Predict Children's Comprehension of Syntax

Variability in children's language acquisition is likely due to a number of cognitive and social variables. The current study investigated whether individual differences in statistical learning (SL), which has been implicated in language acquisition, independently predicted 6- to 8-year-old's comprehension of syntax. Sixty-eight (N = 68) English-speaking children completed a test of comprehension of four syntactic structures, a test of SL utilizing nonlinguistic visual stimuli, and several additional control measures. The results revealed that SL independently predicted comprehension of two syntactic structures that show considerable variability in this age range: passives and object relative clauses. These data suggest that individual differences in children's capacity for SL are associated with the acquisition of the syntax of natural languages.

Language development of internationally adopted children: Adverse early experiences outweigh the age of acquisition effect

We compared English language and cognitive skills between internationally adopted children (IA; mean age at adoption=2.24, SD=1.8) and their non-adopted peers from the US reared in biological families (BF) at two time points. We also examined the relationships between outcome measures and age at initial institutionalization, length of institutionalization, and age at adoption. On measures of general language, early literacy, and non-verbal IQ, the IA group performed significantly below their age-peers reared in biological families at both time points, but the group differences disappeared on receptive vocabulary and kindergarten concept knowledge at the second time point. Furthermore, the majority of children reached normative age expectations between 1 and 2 years post-adoption on all standardized measures. Although the age at adoption, age of institutionalization, length of institutionalization, and time in the adoptive family all demonstrated significant correlations with one or more outcome measures, the negative relationship between length of institutionalization and child outcomes remained most robust after controlling for the other variables. Results point to much flexibility and resilience in children's capacity for language acquisition as well as the potential primacy of length of institutionalization in explaining individual variation in IA children's outcomes.

LEARNING OUTCOMES

(1) Readers will be able to understand the importance of pre-adoption environment on language and early literacy development in internationally adopted children. (2) Readers will be able to compare the strength of the association between the length of institutionalization and language outcomes with the strength of the association between the latter and the age at adoption. (3) Readers will be able to understand that internationally adopted children are able to reach age expectations on expressive and receptive language measures despite adverse early experiences and a replacement of their first language with an adoptive language.

Statistical Learning Is Not Affected by a Prior Bout of Physical Exercise

This study examined the effect of a prior bout of exercise on implicit cognition. Specifically, we examined whether a prior bout of moderate intensity exercise affected performance on a statistical learning task in healthy adults. A total of 42 participants were allocated to one of three conditions-a control group, a group that exercised for 15 min prior to the statistical learning task, and a group that exercised for 30 min prior to the statistical learning task. The participants in the exercise groups cycled at 60% of their respective V˙O2 max. Each group demonstrated significant statistical learning, with similar levels of learning among the three groups. Contrary to previous research that has shown that a prior bout of exercise can affect performance on explicit cognitive tasks, the results of the current study suggest that the physiological stress induced by moderate-intensity exercise does not affect implicit cognition as measured by statistical learning.

Statistical Learning is Related to Early Literacy-Related Skills

It has been demonstrated that statistical learning, or the ability to use statistical information to learn the structure of one's environment, plays a role in young children's acquisition of linguistic knowledge. Although most research on statistical learning has focused on language acquisition processes, such as the segmentation of words from fluent speech and the learning of syntactic structure, some recent studies have explored the extent to which individual differences in statistical learning are related to literacy-relevant knowledge and skills. The present study extends on this literature by investigating the relations between two measures of statistical learning and multiple measures of skills that are critical to the development of literacy-oral language, vocabulary knowledge, and phonological processing-within a single model. Our sample included a total of 553 typically developing children from prekindergarten through second grade. Structural equation modeling revealed that statistical learning accounted for a unique portion of the variance in these literacy-related skills. Practical implications for instruction and assessment are discussed.

Lexical characteristics of expressive vocabulary in toddlers with autism spectrum disorder

PURPOSE

Vocabulary is a domain of particular challenge for many children with autism spectrum disorder (ASD). Recent research has drawn attention to ways in which lexical characteristics relate to vocabulary acquisition. The current study tested the hypothesis that lexical characteristics account for variability in vocabulary size of young children with ASD, applying the extended statistical learning theory of vocabulary delay in late talkers (Stokes, Kern, & Dos Santos, 2012) to toddlers with ASD.

METHOD

Parents reported the words produced by toddlers with ASD (n = 57; age 21-37 months) or toddlers without ASD (n = 41; age 22-26 months) on the MacArthur-Bates Communicative Development Inventories. The average phonological neighborhood density, word frequency, and word length of each toddler's lexicon were calculated. These lexical characteristics served as predictors of vocabulary size.

RESULTS

Findings differed for toddlers with and without ASD and according to subsamples. Word length was the most consistent predictor of vocabulary size for toddlers with ASD.

CONCLUSIONS

Distinct relationships between lexical characteristics and vocabulary size were observed for toddlers with and without ASD. Experimental studies on distributional cues to vocabulary acquisition are needed to inform what is known about mechanisms of learning in neurodevelopmental disorders.

Statistical learning under incidental versus intentional conditions

Statistical learning (SL) studies have shown that participants are able to extract regularities in input they are exposed to without any instruction to do so. This and other findings, such as the fact that participants are often unable to verbalize their acquired knowledge, suggest that SL can occur implicitly or incidentally. Interestingly, several studies using the related paradigms of artificial grammar learning and serial response time tasks have shown that explicit instructions can aid learning under certain conditions. Within the SL literature, however, very few studies have contrasted incidental and intentional learning conditions. The aim of the present study was to investigate the effect of having prior knowledge of the statistical regularities in the input when undertaking a task of visual sequential SL. Specifically, we compared the degree of SL exhibited by participants who were informed (intentional group) versus those who were uninformed (incidental group) about the presence of embedded triplets within a familiarization stream. Somewhat surprisingly, our results revealed that there were no statistically significant differences (and only a small effect size) in the amount of SL exhibited between the intentional versus the incidental groups. We discuss the ways in which this result can be interpreted and suggest that short presentation times for stimuli in the familiarization stream in our study may have limited the opportunity for explicit learning. This suggestion is in line with recent research revealing a statistically significant difference (and a large effect size) between intentional versus incidental groups using a very similar visual sequential SL task, but with longer presentation times. Finally, we outline a number of directions for future research.

Neurocognitive mechanisms of statistical-sequential learning: what do event-related potentials tell us?

Statistical-sequential learning (SL) is the ability to process patterns of environmental stimuli, such as spoken language, music, or one's motor actions, that unfold in time. The underlying neurocognitive mechanisms of SL and the associated cognitive representations are still not well understood as reflected by the heterogeneity of the reviewed cognitive models. The purpose of this review is: (1) to provide a general overview of the primary models and theories of SL, (2) to describe the empirical research - with a focus on the event-related potential (ERP) literature - in support of these models while also highlighting the current limitations of this research, and (3) to present a set of new lines of ERP research to overcome these limitations. The review is articulated around three descriptive dimensions in relation to SL: the level of abstractness of the representations learned through SL, the effect of the level of attention and consciousness on SL, and the developmental trajectory of SL across the life-span. We conclude with a new tentative model that takes into account these three dimensions and also point to several promising new lines of SL research.

Statistical Learning, Letter Reversals, and Reading

Reversal errors play a prominent role in theories of reading disability. We examined reversal errors in the writing of letters by 5-6-year-old children. Of the 130 children, 92 had a history of difficulty in producing speech sounds, a risk factor for reading problems. Children were more likely to reverse letter forms that face left, such as 〈d〉 and 〈J〉, than forms that face right, such as 〈b〉 and 〈C〉. We propose that this asymmetry reflects statistical learning: Children implicitly learn that the right-facing pattern is more typical of Latin letters. The degree of asymmetry that a child showed was not related to the child's reading skill at Time 2, 2 ¾ years later. Although children who went on to become poorer readers made more errors in the letter writing task than children who went on to become better readers, they were no more likely to make reversal errors.

What influences children's conceptualizations of language input?

PURPOSE

Children learning language conceptualize the nature of input they receive in ways that allow them to understand and construct utterances they have never heard before. This study was designed to illuminate the types of information children with and without specific language impairment (SLI) focus on to develop their conceptualizations and whether they can rapidly shift their initial conceptualizations if provided with additional input.

METHOD

In 2 studies, preschool children with and without SLI were exposed to an artificial language, the characteristics of which allowed for various types of conceptualizations about its fundamental properties. After being familiarized with the language, children were asked to judge test strings that conformed to the input in 1 of 4 different ways.

RESULTS

All children preferred test items that reflected a narrow conceptualization of the input (i.e., items most like those heard during familiarization). Children showed a strong preference for phonology as a defining property of the artificial language. Restructuring the input to the child could induce them to track word order information as well.

CONCLUSION

Children tend toward narrow conceptualizations of language input, but the nature of their conceptualizations can be influenced by the nature of the input they receive.