Early Identification of Language Disorders Using Natural Language Processing and Machine Learning: Challenges and Emerging Approaches.

Purpose: Recent advances in artificial intelligence provide opportunities to capture and represent complex features of human language in a more automated manner, offering potential means of improving the efficiency of language assessment. This review article presents computerized approaches for the analysis of narrative language and identification of language disorders in children.

Method: We first describe the current barriers to clinicians' use of language sample analysis, narrative language sampling approaches, and the data processing stages that precede analysis.

Children extract a new linguistic rule more quickly than adults.

Children achieve better long-term language outcomes than adults. However, it remains unclear whether children actually learn language more quickly than adults during real-time exposure to input-indicative of true superior language learning abilities-or whether this advantage stems from other factors. To examine this issue, we compared the rate at which children (8-10 years) and adults extracted a novel, hidden linguistic rule, in which novel articles probabilistically predicted the animacy of associated nouns (e.

Early nap cessation in young children as a correlate of language and psychosocial outcomes: Evidence from a large Canadian sample.

Objectives: Most children stop napping between 2 and 5years old. We tested the association of early nap cessation (ie, children who stopped before their third birthday) and language, cognition functioning and psychosocial outcomes.

Methods: Data were from a national, longitudinal sample of Canadian children, with three timepoints.

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.

Separate but not independent: Behavioral pattern separation and statistical learning are differentially affected by aging.

Our brains are capable of discriminating similar inputs (pattern separation) and rapidly generalizing across inputs (statistical learning). Are these two processes dissociable in behavior? Here, we asked whether cognitive aging affects them in a differential or parallel manner. Older and younger adults were tested on their ability to discriminate between similar trisyllabic words and to extract trisyllabic words embedded in a continuous speech stream.

Dentate Gyrus Integrity Is Necessary for Behavioral Pattern Separation But Not Statistical Learning.

Pattern separation, the creation of distinct representations of similar inputs, and statistical learning, the rapid extraction of regularities across multiple inputs, have both been linked to hippocampal processing. It has been proposed that there may be functional differentiation within the hippocampus, such that the trisynaptic pathway (entorhinal cortex > dentate gyrus > CA3 > CA1) supports pattern separation, whereas the monosynaptic pathway (entorhinal cortex > CA1) supports statistical learning. To test this hypothesis, we investigated the behavioral expression of these two processes in B.

Learning words without trying: Daily second language podcasts support word-form learning in adults.

Spoken language contains overlapping patterns across different levels, from syllables to words to phrases. The discovery of these structures may be partially supported by statistical learning (SL), the unguided, automatic extraction of regularities from the environment through passive exposure. SL supports word learning in artificial language experiments, but few studies have examined whether it scales up to support natural language learning in adult second language learners.

No statistical learning advantage in children over adults: Evidence from behaviour and neural entrainment.

Explicit recognition measures of statistical learning (SL) suggest that children and adults have similar linguistic SL abilities. However, explicit tasks recruit additional cognitive processes that are not directly relevant for SL and may thus underestimate children's true SL capacities. In contrast, implicit tasks and neural measures of SL should be less influenced by explicit, higher-level cognitive abilities and thus may be better suited to capturing developmental differences in SL.

Simple statistical regularities presented during sleep are detected but not retained.

In recent years, there has been growing interest and excitement over the newly discovered cognitive capacities of the sleeping brain, including its ability to form novel associations. These recent discoveries raise the possibility that other more sophisticated forms of learning may also be possible during sleep. In the current study, we tested whether sleeping humans are capable of statistical learning - the process of becoming sensitive to repeating, hidden patterns in environmental input, such as embedded words in a continuous stream of speech.

Musical instrument familiarity affects statistical learning of tone sequences.

Most listeners have an implicit understanding of the rules that govern how music unfolds over time. This knowledge is acquired in part through statistical learning, a robust learning mechanism that allows individuals to extract regularities from the environment. However, it is presently unclear how this prior musical knowledge might facilitate or interfere with the learning of novel tone sequences that do not conform to familiar musical rules.

Optimizing steady-state responses to index statistical learning: Response to Benjamin and colleagues.

Neural entrainment refers to the tendency of neural activity to align with an ongoing rhythmic stimulus. Measures of neural entrainment have been increasingly leveraged as a tool to understand how the brain tracks different types of regularities in sensory input. However, the methods used to quantify neural entrainment are varied, with numerous analytic decision points whose consequences have not been well-characterized.

Preverbal Infants Discover Statistical Word Patterns at Similar Rates as Adults: Evidence From Neural Entrainment.

The discovery of words in continuous speech is one of the first challenges faced by infants during language acquisition. This process is partially facilitated by statistical learning, the ability to discover and encode relevant patterns in the environment. Here, we used an electroencephalogram (EEG) index of neural entrainment to track 6-month-olds' ( = 25) segmentation of words from continuous speech.

Understanding the Neural Bases of Implicit and Statistical Learning.

Both implicit learning and statistical learning focus on the ability of learners to pick up on patterns in the environment. It has been suggested that these two lines of research may be combined into a single construct of "implicit statistical learning." However, by comparing the neural processes that give rise to implicit versus statistical learning, we may determine the extent to which these two learning paradigms do indeed describe the same core mechanisms.

Statistical learning of speech regularities can occur outside the focus of attention.

Statistical learning, the process of extracting regularities from the environment, plays an essential role in many aspects of cognition, including speech segmentation and language acquisition. A key component of statistical learning in a linguistic context is the perceptual binding of adjacent individual units (e.g.

Vocabulary learning benefits from REM after slow-wave sleep.

Memory reactivation during slow-wave sleep (SWS) influences the consolidation of recently acquired knowledge. This reactivation occurs spontaneously during sleep but can also be triggered by presenting learning-related cues, a technique known as targeted memory reactivation (TMR). Here we examined whether TMR can improve vocabulary learning.

Rapid Statistical Learning Supporting Word Extraction From Continuous Speech.

The identification of words in continuous speech, known as speech segmentation, is a critical early step in language acquisition. This process is partially supported by statistical learning, the ability to extract patterns from the environment. Given that speech segmentation represents a potential bottleneck for language acquisition, patterns in speech may be extracted very rapidly, without extensive exposure.

Online neural monitoring of statistical learning.

The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use.

Neural Measures Reveal Implicit Learning during Language Processing.

Language input is highly variable; phonological, lexical, and syntactic features vary systematically across different speakers, geographic regions, and social contexts. Previous evidence shows that language users are sensitive to these contextual changes and that they can rapidly adapt to local regularities. For example, listeners quickly adjust to accented speech, facilitating comprehension.

Phase of Spontaneous Slow Oscillations during Sleep Influences Memory-Related Processing of Auditory Cues.

Unlabelled: Slow oscillations during slow-wave sleep (SWS) may facilitate memory consolidation by regulating interactions between hippocampal and cortical networks. Slow oscillations appear as high-amplitude, synchronized EEG activity, corresponding to upstates of neuronal depolarization and downstates of hyperpolarization. Memory reactivations occur spontaneously during SWS, and can also be induced by presenting learning-related cues associated with a prior learning episode during sleep.

Functional differences between statistical learning with and without explicit training.

Humans are capable of rapidly extracting regularities from environmental input, a process known as statistical learning. This type of learning typically occurs automatically, through passive exposure to environmental input. The presumed function of statistical learning is to optimize processing, allowing the brain to more accurately predict and prepare for incoming input.

Sleep-based memory processing facilitates grammatical generalization: Evidence from targeted memory reactivation.

Generalization-the ability to abstract regularities from specific examples and apply them to novel instances-is an essential component of language acquisition. Generalization not only depends on exposure to input during wake, but may also improve offline during sleep. Here we examined whether targeted memory reactivation during sleep can influence grammatical generalization.

Implicit and explicit contributions to statistical learning.

Statistical learning allows learners to detect regularities in the environment and appears to emerge automatically as a consequence of experience. Statistical learning paradigms bear many similarities to those of artificial grammar learning and other types of implicit learning. However, whether learning effects in statistical learning tasks are driven by implicit knowledge has not been thoroughly examined.

Sleep facilitates learning a new linguistic rule.

Natural languages contain countless regularities. Extraction of these patterns is an essential component of language acquisition. Here we examined the hypothesis that memory processing during sleep contributes to this learning.