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In this paper we probe the interaction between sequential and hierarchical learning by investigating implicit learning in a group of school-aged children. We administered a serial reaction time task, in the form of a modified Simon Task in which the stimuli were organised following the rules of two distinct artificial grammars, specifically Lindenmayer systems: the Fibonacci grammar (Fib) and the Skip grammar (a modification of the former). The choice of grammars is determined by the goal of this study, which is to investigate how sensitivity to structure emerges in the course of exposure to an input whose surface transitional properties (by hypothesis) bootstrap structure. The studies conducted to date have been mainly designed to investigate low-level superficial regularities, learnable in purely statistical terms, whereas hierarchical learning has not been effectively investigated yet. The possibility to directly pinpoint the interplay between sequential and hierarchical learning is instead at the core of our study: we presented children with two grammars, Fib and Skip, which share the same transitional regularities, thus providing identical opportunities for sequential learning, while crucially differing in their hierarchical structure. More particularly, there are specific points in the sequence (k-points), which, despite giving rise to the same transitional regularities in the two grammars, support hierarchical reconstruction in Fib but not in Skip. In our protocol, children were simply asked to perform a traditional Simon Task, and they were completely unaware of the real purposes of the task. Results indicate that sequential learning occurred in both grammars, as shown by the decrease in reaction times throughout the task, while differences were found in the sensitivity to k-points: these, we contend, play a role in hierarchical reconstruction in Fib, whereas they are devoid of structural significance in Skip. More particularly, we found that children were faster in correspondence to k-points in sequences produced by Fib, thus providing an entirely new kind of evidence for the hypothesis that implicit learning involves an early activation of strategies of hierarchical reconstruction, based on a straightforward interplay with the statistically-based computation of transitional regularities on the sequences of symbols.

Purpose: Statistical learning research seeks to identify the means by which learners, with little perceived effort, acquire the complexities of language. In the past 50 years, numerous studies have uncovered powerful learning mechanisms that allow for learning within minutes of exposure to novel language input. Method: We consider the value of information from statistical learning studies that show potential for making treatment of language disorders faster and more effective. Results: Available studies include experimental research that demonstrates the conditions under which rapid learning is possible, research showing that these findings apply to individuals with disorders, and translational work that has applied learning principles in treatment and educational contexts. In addition, recent research on memory formation has implications for treatment of language deficits. Conclusion: The statistical learning literature offers principles for learning that can improve clinical outcomes for children with language impairment. There is potential for further applications of this basic research that is yet unexplored.

Interest in statistical learning in developmental studies stems from the observation that 8-month-olds were able to extract words from a monotone speech stream solely using the transition probabilities (TP) between syllables (Saffran et al., 1996). A simple mechanism was thus part of the human infant’s toolbox for discovering regularities in language. Since this seminal study, observations on statistical learning capabilities have multiplied across domains and species, challenging the hypothesis of a dedicated mechanism for language acquisition. Here, we leverage the two dimensions conveyed by speech –speaker identity and phonemes– to examine (1) whether neonates can compute TPs on one dimension despite irrelevant variation on the other and (2) whether the linguistic dimension enjoys an advantage over the voice dimension. In two experiments, we exposed neonates to artificial speech streams constructed by concatenating syllables while recording EEG. The sequence had a statistical structure based either on the phonetic content, while the voices varied randomly (Experiment 1) or on voices with random phonetic content (Experiment 2). After familiarisation, neonates heard isolated duplets adhering, or not, to the structure they were familiarised with. In both experiments, we observed neural entrainment at the frequency of the regularity and distinct Event-Related Potentials (ERP) to correct and incorrect duplets, highlighting the universality of statistical learning mechanisms and suggesting it operates on virtually any dimension the input is factorised. However, only linguistic duplets elicited a specific ERP component, potentially an N400 precursor, suggesting a lexical stage triggered by phonetic regularities already at birth. These results show that, from birth, multiple input regularities can be processed in parallel and feed different higher-order networks. Imagine listening to a language you don't know. When does one word end, and another begin? Human infants face a similar challenge, yet remarkably, they grasp the structure of their mother tongue naturally without receiving any explicit indications. By six months, they recognize some common nouns, and by one year, they start saying their first words. This learning begins from birth, with newborns already sensitive to speech patterns. Previous studies have shown that the likelihood of certain syllables appearing after others allows infants to detect regularity and separate speech into chunks. This is because some syllables are more predictive of what comes next than others. For example, in English, many different syllables can follow ‘the’. However, it is highly likely that ‘brocco’ will be followed by ‘li’. The ability to detect these regularities is known as statistical learning. However, whether this relies on a general mechanism or is restricted to a specific speech component, such as the sequence of syllables, remained unknown. To investigate, Fló et al. measured brain electrical activity of newborns up to 4 days old in response to speech specifically designed to contain certain patterns of syllables or voices. In one experiment, the speech had regular patterns in the syllables, while in a second experiment, the pattern was in the voices, and each voice could utter each syllable. Unlike tracking syllable variation, which can help with learning words, voice changes within a word are unnatural and predicting them is not relevant to real-life speech processing. Therefore, if statistical learning in speech is shaped to promote language acquisition, learning should be restricted to syllable patterns. Instead, if statistical learning is a general mechanism, newborns should also detect the patterns in voice. Analysis revealed that newborns were equally capable of discerning regular patterns in syllables despite voice changes and in voices disregarding the syllable that was pronounced. This suggests that statistical learning is a general learning mechanism that can operate across multiple features. Additionally, pseudo-words (those which resemble a real world but don’t exist in the language) were presented to the newborns after they had been familiarised with speech containing either similar syllable or voice patterns. The researchers observed a specific neural response to the pseudowords only when related to syllable patterns. This neural component suggests that only syllabic structures are considered word candidates and processed by a dedicated neural network from birth. Taken together, the findings of Fló et al. reveal insights into how humans process speech when experience with language is minimal, suggesting that statistical learning may have a broader role in early language acquisition that previously thought.

Statistical learning, the ability to detect and extract statistical regularities from the environment, has been proposed as a key mechanism underlying language, social, and cognitive development. Numerous studies have examined statistical learning abilities in autistic individuals to test the hypothesis that differences contribute to the behavioral presentation of autism spectrum disorder (ASD). Findings have been inconsistent, with variations in methodology, sensory modality, and participant characteristics complicating the interpretation of results. The current study presents a systematic review of statistical, implicit, and procedural learning studies in autism, considering how statistical learning abilities vary across (a) modality (e.g., auditory versus visual), (b) methodology (e.g., behavioral versus neuroimaging), and (c) task design, and considering the influence of language and cognitive abilities. Results across 37 studies in visual and auditory modalities indicate few behavioral differences in statistical learning abilities (with the exception of slowed reaction times in autism), and that learning may benefit from extended exposure and explicit cues. In contrast, neuroimaging findings reveal substantial variability in the neural mechanisms implicated in these tasks, with evidence suggesting compensatory cognitive processing in some autistic samples. Individual differences in language, cognitive abilities, and autism-related traits strongly influence statistical learning outcomes. Significant gaps remain, particularly in the inclusion of minimally verbal individuals and those with intellectual disabilities. Methodological heterogeneity, skewed gender and sociodemographic sample characteristics, and inconsistent neural findings highlight the need for more standardized approaches in future research. Understanding the mechanisms of statistical learning in autism has critical implications for language and cognitive interventions, emphasizing the importance of individualized support strategies.

The goal of the present study was to investigate whether a relationship exists between statistical learning ability and sentence processing ability in adult readers and whether this relationship depends on the participant's exposure to print. Fifty participants read syntactically complex sentences while their eye movements were tracked and answered comprehension questions. The region of interest for the eye fixation analyses was the area where the complexity of the sentence became evident. Participants also completed a visual statistical learning (VSL) task and an author recognition test (ART). There were main effects of statistical learning ability and print exposure, as well as an interaction between the two on both first pass and total reading times. Reading times decreased with increasing VSL scores for participants with higher ART scores, whereas reading times increased with increasing VSL scores for participants with lower ART scores. In addition, participants with better statistical learning ability and greater print exposure had higher scores on the comprehension questions. These results demonstrate that efficient processing of complex syntactic structures depends on both good statistical learning skills and exposure to a large amount of print so that these skills have the opportunity to extract the relevant statistical relationships in the language. (PsycInfo Database Record (c) 2025 APA, all rights reserved).