Statistical Learning Is Not Age‐Invariant During Childhood: Performance Improves With Age Across Modality

Non-linear development in statistical learning of visual orthographic regularities

Statistical learning is a core ability for individuals in extracting and integrating regularities and patterns from linguistic input. Yet, the developmental trajectory of visual statistical learning has not been fully examined in the orthographic learning domain. Employing an artificial orthographic learning task, we manipulated three levels of positional consistency of radicals, i.e., high (100%), moderate (80%), and low (60%), embedded in pseudocharacters to investigate visual statistical learning across a wide age range between 4-12-year-olds and adults. The non-linear power-function models indicated that the rates of improvement in acquiring varying positional consistencies increased with age, particularly for high and moderate levels. Specifically, we observed a significant enhancement in statistical learning abilities between the ages of 4-5 years and 5-6 years, followed by a stabilization of performance after 8-9 years. Our findings support the age-dependent perspective that individuals' visual statistical learning ability improves significantly in early childhood and then decelerates its improvement progressively until adulthood.

Taking time: Auditory statistical learning benefits from distributed exposure

In an auditory statistical learning paradigm, listeners learn to partition a continuous stream of syllables by discovering the repeating syllable patterns that constitute the speech stream. Here, we ask whether auditory statistical learning benefits from spaced exposure compared with massed exposure. In a longitudinal online study on Prolific, we exposed 100 participants to the regularities in a spaced way (i.e., with exposure blocks spread out over 3 days) and another 100 in a massed way (i.e., with all exposure blocks lumped together on a single day). In the exposure phase, participants listened to streams composed of pairs while responding to a target syllable. The spaced and massed groups exhibited equal learning during exposure, as indicated by a comparable response-time advantage for predictable target syllables. However, in terms of resulting long-term knowledge, we observed a benefit from spaced exposure. Following a 2-week delay period, we tested participants' knowledge of the pairs in a forced-choice test. While both groups performed above chance, the spaced group had higher accuracy. Our findings speak to the importance of the timing of exposure to structured input and also for statistical learning outside of the laboratory (e.g., in language development), and imply that current investigations of auditory statistical learning likely underestimate human statistical learning abilities.

Early Caregiver Predictability Shapes Neural Indices of Statistical Learning Later in Infancy

Caregivers play an outsized role in shaping early life experiences and development, but we often lack mechanistic insight into how exactly caregiver behavior scaffolds the neurodevelopment of specific learning processes. Here, we capitalized on the fact that caregivers differ in how predictable their behavior is to ask if infants' early environmental input shapes their brains' later ability to learn about predictable information. As part of an ongoing longitudinal study in South Africa, we recorded naturalistic, dyadic interactions between 103 (46 females and 57 males) infants and their primary caregivers at 3-6 months of age, from which we calculated the predictability of caregivers' behavior, following caregiver vocalization and overall. When the same infants were 6-12-months-old they participated in an auditory statistical learning task during EEG. We found evidence of learning-related change in infants' neural responses to predictable information during the statistical learning task. The magnitude of statistical learning-related change in infants' EEG responses was associated with the predictability of their caregiver's vocalizations several months earlier, such that infants with more predictable caregiver vocalization patterns showed more evidence of statistical learning later in the first year of life. These results suggest that early experiences with caregiver predictability influence learning, providing support for the hypothesis that the neurodevelopment of core learning and memory systems is closely tied to infants' experiences during key developmental windows.

Memory representations are flexibly adapted to orthographic systems: A comparison of English and Hebrew

Across languages, speech unfolds in the same temporal order, constrained by the forward flow of time. But the way phonology is spatially mapped onto orthography is language-specific, ranging from left-to-right, right-to-left, and top-to-bottom, among others. While the direction of writing systems influences how known words are visually processed, it is unclear whether it influences learning and memory for novel orthographic regularities. The present study tested English and Hebrew speakers on an orthographic word-referent mapping task in their native orthographies (written left-to-right and right-to-left, respectively), where the onsets and offsets of words were equally informative cues to word identity. While all individuals learned orthographic word-referent mappings significantly above chance, the parts of the word that were most strongly represented varied. English monolinguals false alarmed most to competing foils that began with the same bigram as the target, representing word onsets most strongly. However, Hebrew bilinguals trained on their native orthography showed no difference between false alarm rates to onset and offset competitors, representing the beginning and ends of words equally strongly. Importantly, Hebrew bilinguals tested on English words displayed a more English-like false alarm pattern (although not a full switch), suggesting that memory biases adapt to the opposite directionality of encountered text while retaining traces of native language biases. These findings demonstrate that experience with different writing systems influences how individuals represent novel orthographic words, starting in the earliest stages of learning.

The Domain-Specific Neural Basis of Auditory Statistical Learning in 5-7-Year-Old Children

Statistical learning (SL) is the ability to rapidly track statistical regularities and learn patterns in the environment. Recent studies show that SL is constrained by domain-specific features, rather than being a uniform learning mechanism across domains and modalities. This domain-specificity has been reflected at the neural level, as SL occurs in regions primarily involved in processing of specific modalities or domains of input. However, our understanding of how SL is constrained by domain-specific features in the developing brain is severely lacking. The present study aims to identify the functional neural profiles of auditory SL of linguistic and nonlinguistic regularities among children. Thirty children between 5 and 7 years old completed an auditory fMRI SL task containing interwoven sequences of structured and random syllable/tone sequences. Using traditional group univariate analyses and a group-constrained subject-specific analysis, frontal and temporal cortices showed significant activation when processing structured versus random sequences across both linguistic and nonlinguistic domains. However, conjunction analyses failed to identify overlapping neural indices across domains. These findings are the first to compare brain regions supporting SL of linguistic and nonlinguistic regularities in the developing brain and indicate that auditory SL among developing children may be constrained by domain-specific features.

Sensitive periods in language development: Do children outperform adults on auditory word-form segmentation?

Children are more successful language learners than adults, yet the nature and cause of this phenomenon are still not well understood. Auditory statistical learning from speech has been a prominent focus of research in the field of language development because it is regarded as a fundamental learning mechanism underlying word segmentation in early language acquisition. However, a handful of studies that investigated developmental trajectories for auditory statistical learning found no clear child advantages. The degree to which the learning task measures explicit rather than implicit mechanisms might obscure a potential advantage for younger learners, as suggested by recent findings. In the present study, we compared children aged 7-12 years and young adults on an adapted version of the task that disentangles explicit and implicit contributions to learning. They were exposed to a continuous stream of speech sounds comprising four repeating trisyllabic pseudowords. Learning of the hidden words was tested (a) online through a target-detection task and (b) offline via a forced-choice word recognition test that included a memory judgement procedure. Both measures revealed comparable learning abilities. However, children's performance on the recognition task showed evidence for both explicit and implicit word knowledge while adults appeared primarily sensitive to explicit memory. Since implicit memory is more stable in time than explicit memory, we suggest that future work should focus more on developmental differences in the nature of the memory that is formed, rather than the strength of learning, when trying to understand child advantages in language acquisition.

Unraveling the complex interplay between statistical learning and working memory in Chinese children with and without dyslexia across different ages

The relation between statistical learning and working memory in children with developmental dyslexia (DD) remains unclear. This study employed a distributional and a conditional statistical learning experiment and a working memory task to examine this relation in 651 Chinese 6- to 12-year-olds with and without DD (NDD = 199, 101 females; NwoDD = 452, 227 females; participated 2014-2019). Results showed working memory positively associated with recognizing high-predictable and familiar items in both groups, but negatively associated with recognizing unfamiliar items in the DD group only. These findings uncovered the complex interplay between statistical learning and working memory, demonstrating how different working memory abilities in children with and without DD relate to various statistical learning mechanisms at the item level.

Visual artificial grammar learning across 1 year in 7-year-olds and adults

Acquiring sequential information is of utmost importance, for example, for language acquisition in children. Yet, the long-term storage of statistical learning in children is poorly understood. To address this question, 27 7-year-olds and 28 young adults completed four sessions of visual sequence learning (Year 1). From this sample, 16 7-year-olds and 20 young adults participated in another four equivalent sessions after a 12-month-delay (Year 2). The first three sessions of each year used Stimulus Set 1, and the last session used Stimulus Set 2 to investigate transfer effects. Each session consisted of alternating learning and test phases in a modified artificial grammar learning task. In Year 1, 7-year-olds and adults learned the regularities and showed transfer to Stimulus Set 2. Both groups retained their final performance level over the 1-year period. In Year 2, children and adults continued to improve with Stimulus Set 1 but did not show additional transfer gains. Adults overall outperformed children, but transfer effects were indistinguishable between both groups. The current results suggest that long-term memory traces are formed from repeated sequence learning that can be used to generalize sequence rules to new visual input. However, the current study did not provide evidence for a childhood advantage in learning and remembering sequence rules.

Dissociation Between Linguistic and Nonlinguistic Statistical Learning in Children with Autism

Statistical learning (SL), the ability to detect and extract regularities from inputs, is considered a domain-general building block for typical language development. We compared 55 verbal children with autism (ASD, 6-12 years) and 50 typically-developing children in four SL tasks. The ASD group exhibited reduced learning in the linguistic SL tasks (syllable and letter), but showed intact learning for the nonlinguistic SL tasks (tone and image). In the ASD group, better linguistic SL was associated with higher language skills measured by parental report and sentence recall. Therefore, the atypicality of SL in autism is not domain-general but tied to specific processing constraints related to verbal stimuli. Our findings provide a novel perspective for understanding language heterogeneity in autism.

Of words and whistles: Statistical learning operates similarly for identical sounds perceived as speech and non-speech

Statistical learning is an ability that allows individuals to effortlessly extract patterns from the environment, such as sound patterns in speech. Some prior evidence suggests that statistical learning operates more robustly for speech compared to non-speech stimuli, supporting the idea that humans are predisposed to learn language. However, any apparent statistical learning advantage for speech could be driven by signal acoustics, rather than the subjective perception per se of sounds as speech. To resolve this issue, the current study assessed whether there is a statistical learning advantage for ambiguous sounds that are subjectively perceived as speech-like compared to the same sounds perceived as non-speech, thereby controlling for acoustic features. We first induced participants to perceive sine-wave speech (SWS)-a degraded form of speech not immediately perceptible as speech-as either speech or non-speech. After this induction phase, participants were exposed to a continuous stream of repeating trisyllabic nonsense words, composed of SWS syllables, and then completed an explicit familiarity rating task and an implicit target detection task to assess learning. Critically, participants showed robust and equivalent performance on both measures, regardless of their subjective speech perception. In contrast, participants who perceived the SWS syllables as more speech-like showed better detection of individual syllables embedded in speech streams. These results suggest that speech perception facilitates processing of individual sounds, but not the ability to extract patterns across sounds. Our findings suggest that statistical learning is not influenced by the perceived linguistic relevance of sounds, and that it may be conceptualized largely as an automatic, stimulus-driven mechanism.

Does Nonlinguistic Segmentation Predict Literacy in Second Language Education? Statistical Learning in Ivorian Primary Schools

Statistical learning (SL) is a learning mechanism that does not directly depend on knowledge of a language, but predicts language and literacy outcomes for children and adults. Research linking SL and literacy has not addressed children who first learn to read in their second language (L2), common in primary schools worldwide. Several studies have linked SL with childhood literacy in Australia, China, Europe, and the U.S., and we pre-registered an adaptation for Côte d'Ivoire, where students are educated in French and speak a local language at home. Recruiting 117 sixth-graders from primary schools in several villages, we tested for correlations >0.3 between SL and literacy with 80-90% power. We found no evidence for these correlations, but visual SL was correlated with L2 phonological awareness. Although this finding may suggest a role of SL in emergent L2 skills, it underscores the need to include L2 acquisition contexts in literacy research.

Multisensory perception constrains the formation of object categories: a review of evidence from sensory-driven and predictive processes on categorical decisions

Although object categorization is a fundamental cognitive ability, it is also a complex process going beyond the perception and organization of sensory stimulation. Here we review existing evidence about how the human brain acquires and organizes multisensory inputs into object representations that may lead to conceptual knowledge in memory. We first focus on evidence for two processes on object perception, multisensory integration of redundant information (e.g. seeing and feeling a shape) and crossmodal, statistical learning of complementary information (e.g. the 'moo' sound of a cow and its visual shape). For both processes, the importance attributed to each sensory input in constructing a multisensory representation of an object depends on the working range of the specific sensory modality, the relative reliability or distinctiveness of the encoded information and top-down predictions. Moreover, apart from sensory-driven influences on perception, the acquisition of featural information across modalities can affect semantic memory and, in turn, influence category decisions. In sum, we argue that both multisensory processes independently constrain the formation of object categories across the lifespan, possibly through early and late integration mechanisms, respectively, to allow us to efficiently achieve the everyday, but remarkable, ability of recognizing objects. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Memories of structured input become increasingly distorted across development

Trajectories of cognitive and neural development suggest that, despite early emergence, the ability to extract environmental patterns changes across childhood. Here, 5- to 9-year-olds and adults (N = 211, 110 females, in a large Canadian city) completed a memory test assessing what they remembered after watching a stream of shape triplets: the particular sequence in which the shapes occurred and/or their group-level structure. After accounting for developmental improvements in overall memory, all ages remembered specific transitions, while memory for group membership was only observed in older children and adults (age by test-type interaction η2  = .05). Thus, while young children form memories for specifics of structured experience, memory for derived associations is refined later-underscoring that adults and young children form different memories despite identical experience.

The association between statistical learning and language development during childhood: A scoping review

The statistical account of language acquisition asserts that language is learned through computations on the statistical regularities present in natural languages. This type of account can predict variability in language development measures as arising from individual differences in extracting this statistical information. Given that statistical learning has been attested across different domains and modalities, a central question is which modality is more tightly yoked with language skills. The results of a scoping review, which aimed for the first time at identifying the evidence of the association between statistical learning skills and language outcomes in typically developing infants and children, provide preliminary support for the statistical learning account of language acquisition, mostly in the domain of lexical outcomes, indicating that typically developing infants and children with stronger auditory and audio-visual statistical learning skills perform better on lexical competence tasks. The results also suggest that the relevance of statistical learning skills for language development is dependent on sensory modality.

What sticks after statistical learning: The persistence of implicit versus explicit memory traces

Statistical learning is a powerful mechanism that extracts even subtle regularities from our information-dense worlds. Recent theories argue that statistical learning can occur through multiple mechanisms-both the conventionally assumed automatic process that precipitates unconscious learning, and an attention-dependent process that brings regularities into conscious awareness. While this view has gained popularity, there are few empirical dissociations of the hypothesized implicit and explicit forms of statistical learning. Here we provide strong evidence for this dissociation in two ways. First, we show in healthy adults (N = 60) that implicit and explicit traces have divergent consolidation trajectories, with implicit knowledge of structure strengthened over a 24-h period, while precise explicit representations tend to decay. Second, we demonstrate that repeated testing strengthens the retention of explicit representations but that implicit statistical learning is uninfluenced by testing. Together these dissociations provide much needed support for the reconceptualization of statistical learning as a multi-component construct.

Multiple mechanisms regulate statistical learning of orthographic regularities in school-age children: Neurophysiological evidence

Using event-related potentials (ERPs), this study investigated how the brains of Chinese children of different ages extract and encode relational patterns contained in orthographic input. Ninety-nine Chinese children in Grades 1-3 performed an artificial orthography statistical learning task that comprised logographic components embedded in characters with high (100%), moderate (80%), and low (60%) positional consistency. The behavioral results indicated that across grades, participants more accurately recognized characters with high rather than low consistency. The neurophysiological results revealed that in each grade, the amplitude of some ERP components differed, with a larger P1 effect in the high consistency condition and a larger N170 and left-lateralized P300 effect in the low consistency condition. A smaller N170 effect occurred in Grade 3 than in Grade 1, and a larger P300 effect occurred in Grade 1 than in either Grade 2 or 3. These findings suggest the dynamic nature of statistical learning by showing that neural adaptation associated with N170, and attention and working memory related to P1 and P300, regulate different types of structural input, and that children's abilities to prioritize these mechanisms vary with context and age.

Development of statistical learning ability across modalities, domains, and languages

Statistical learning (SL) is defined as our ability to use statistics (e.g., frequencies or transitional probabilities) to detect implicit regularities in the environment. Limited research has examined the developmental trajectory of SL across domains and modalities, and no previous research has made systematic comparisons across domains, modalities, and languages using comparable tasks. The current study investigated the development of SL ability across 9-, 11-, and 13-year-old native Chinese-speaking children in non-linguistic visual and auditory SL, first-language Chinese visual and auditory SL, and second-language English visual and auditory SL. Results showed that children across the three age groups achieved all types of SL, and they performed better in visual modality than in auditory modality. Furthermore, while visual SL constantly improved from 9- to 11- to 13-year-olds, auditory SL improved only from 11- to 13-year-olds but not from 9- to 11-year-olds, which could be explained by the discrepancy in developmental trajectory between auditory language and working memory. This pattern of age and modality interaction was similar across non-linguistic Chinese and English SL. A significant interaction between modality and language type also showed that better learning was achieved in visual SL as compared with auditory SL in both non-linguistic and English stimuli. However, children performed similarly across the two modalities in Chinese, possibly due to the contribution of tonal information. Together, our findings point to the joint function of age, modality, and language type in SL development.

Experience and maturation: The contribution of co-occurrence regularities in language to the development of semantic organization

With development knowledge becomes organized according to semantic links, including early-developing associative (e.g., juicy-apple) and gradually developing taxonomic links (e.g., apple-pear). Word co-occurrence regularities may foster these links: Associative links may form from direct co-occurrence (e.g., juicy-apple), and taxonomic links from shared co-occurrence (e.g., apple and pear co-occur with juicy). Four experiments (2017-2020) investigated this possibility with 4- to 8-year-olds (N = 148, 82 female) and adults (N = 116, 35 female) in a U.S. city with 58.6% White; 29.0% Black, and 5.8% Asian demographics. Results revealed earlier development of the abilities to form direct (ds > 0.536) than the abilities to form shared co-occurrence-based links (ds > 1.291). We argue that the asynchronous development of abilities to form co-occurrence-based links may explain developmental changes in semantic organization.

Modality, presentation, domain and training effects in statistical learning

While several studies suggest that the nature and properties of the input have significant effects on statistical learning, they have rarely been investigated systematically. In order to understand how input characteristics and their interactions impact statistical learning, we explored the effects of modality (auditory vs. visual), presentation type (serial vs. simultaneous), domain (linguistic vs. non-linguistic), and training type (random, starting small, starting big) on artificial grammar learning in young adults (N = 360). With serial presentation of stimuli, learning was more effective in the auditory than in the visual modality. However, with simultaneous presentation of visual and serial presentation of auditory stimuli, the modality effect was not present. We found a significant domain effect as well: a linguistic advantage over nonlinguistic material, which was driven by the domain effect in the auditory modality. Overall, the auditory linguistic condition had an advantage over other modality-domain types. Training types did not have any overall effect on learning; starting big enhanced performance only in the case of serial visual presentation. These results show that input characteristics such as modality, presentation type, domain and training type influence statistical learning, and suggest that their effects are also dependent on the specific stimuli and structure to be learned.

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. Here, we assessed SL to six minutes of an artificial language in English-speaking children (n = 56, 24 females, M = 9.98 years) and adults (n = 44; 31 females, M = 22.97 years), using explicit and implicit behavioural measures and an EEG measure of neural entrainment. With few exceptions, children and adults showed largely similar performance on the behavioural explicit and implicit tasks, replicating prior work. Children and adults also demonstrated robust neural entrainment to both words and syllables, with a similar time course of word-level entrainment, reflecting learning of the hidden word structure. These results demonstrate that children and adults have similar linguistic SL abilities, even when learning is assessed through implicit performance-based and neural measures.

Linking the neural basis of distributional statistical learning with transitional statistical learning: The paradox of attention

Statistical learning, the process of tracking distributional information and discovering embedded patterns, is traditionally regarded as a form of implicit learning. However, recent studies proposed that both implicit (attention-independent) and explicit (attention-dependent) learning systems are involved in statistical learning. To understand the role of attention in statistical learning, the current study investigates the cortical processing of distributional patterns in speech across local and global contexts. We then ask how these cortical responses relate to statistical learning behavior in a word segmentation task. We found Event-Related Potential (ERP) evidence of pre-attentive processing of both the local (mismatching negativity) and global distributional information (late discriminative negativity). However, as speech elements became less frequent and more surprising, some participants showed an involuntary attentional shift, reflected in a P3a response. Individuals who displayed attentive neural tracking of distributional information showed faster learning in a speech statistical learning task. These results suggest that an involuntary attentional shift might play a facilitatory, but not essential, role in statistical learning.

Explicit Instructions Do Not Enhance Auditory Statistical Learning in Children With Developmental Language Disorder: Evidence From Event-Related Potentials

A current issue in psycholinguistic research is whether the language difficulties exhibited by children with developmental language disorder [DLD, previously labeled specific language impairment (SLI)] are due to deficits in their abilities to pick up patterns in the sensory environment, an ability known as statistical learning (SL), and the extent to which explicit learning mechanisms can be used to compensate for those deficits. Studies designed to test the compensatory role of explicit learning mechanisms in children with DLD are, however, scarce, and the few conducted so far have led to inconsistent results. This work aimed to provide new insights into the role that explicit learning mechanisms might play on implicit learning deficits in children with DLD by resorting to a new approach. This approach involved not only the collection of event-related potentials (ERPs), while preschool children with DLD [relative to typical language developmental (TLD) controls] were exposed to a continuous auditory stream made of the repetition of three-syllable nonsense words but, importantly, the collection of ERPs when the same children performed analogous versions of the same auditory SL task first under incidental (implicit) and afterward under intentional (explicit) conditions. In each of these tasks, the level of predictability of the three-syllable nonsense words embedded in the speech streams was also manipulated (high vs. low) to mimic natural languages closely. At the end of both tasks' exposure phase, children performed a two-alternative forced-choice (2-AFC) task from which behavioral evidence of SL was obtained. Results from the 2-AFC tasks failed to show reliable signs of SL in both groups of children. The ERPs data showed, however, significant modulations in the N100 and N400 components, taken as neural signatures of word segmentation in the brain, even though a detailed analysis of the neural responses revealed that only children from the TLD group seem to have taken advantage of the previous knowledge to enhance SL functioning. These results suggest that children with DLD showed deficits both in implicit and explicit learning mechanisms, casting doubts on the efficiency of the interventions relying on explicit instructions to help children with DLD to overcome their language difficulties.

Learning Words While Listening to Syllables: Electrophysiological Correlates of Statistical Learning in Children and Adults

From an early age, exposure to a spoken language has allowed us to implicitly capture the structure underlying the succession of speech sounds in that language and to segment it into meaningful units (words). Statistical learning (SL), the ability to pick up patterns in the sensory environment without intention or reinforcement, is thus assumed to play a central role in the acquisition of the rule-governed aspects of language, including the discovery of word boundaries in the continuous acoustic stream. Although extensive evidence has been gathered from artificial languages experiments showing that children and adults are able to track the regularities embedded in the auditory input, as the probability of one syllable to follow another syllable in the speech stream, the developmental trajectory of this ability remains controversial. In this work, we have collected Event-Related Potentials (ERPs) while 5-year-old children and young adults (university students) were exposed to a speech stream made of the repetition of eight three-syllable nonsense words presenting different levels of predictability (high vs. low) to mimic closely what occurs in natural languages and to get new insights into the changes that the mechanisms underlying auditory statistical learning (aSL) might undergo through the development. The participants performed the aSL task first under implicit and, subsequently, under explicit conditions to further analyze if children take advantage of previous knowledge of the to-be-learned regularities to enhance SL, as observed with the adult participants. These findings would also contribute to extend our knowledge of the mechanisms available to assist SL at each developmental stage. Although behavioral signs of learning, even under explicit conditions, were only observed for the adult participants, ERP data showed evidence of online segmentation in the brain in both groups, as indexed by modulations in the N100 and N400 components. A detailed analysis of the neural data suggests, however, that adults and children rely on different mechanisms to assist the extraction of word-like units from the continuous speech stream, hence supporting the view that SL with auditory linguistic materials changes through development.

The learnability consequences of Zipfian distributions in language

While the languages of the world differ in many respects, they share certain commonalties, which can provide insight on our shared cognition. Here, we explore the learnability consequences of one of the striking commonalities between languages. Across languages, word frequencies follow a Zipfian distribution, showing a power law relation between a word's frequency and its rank. While their source in language has been studied extensively, less work has explored the learnability consequences of such distributions for language learners. We propose that the greater predictability of words in this distribution (relative to less skewed distributions) can facilitate word segmentation, a crucial aspect of early language acquisition. To explore this, we quantify word predictability using unigram entropy, assess it across languages using naturalistic corpora of child-directed speech and then ask whether similar unigram predictability facilitates word segmentation in the lab. We find similar unigram entropy in child-directed speech across 15 languages. We then use an auditory word segmentation task to show that the unigram predictability levels found in natural language are uniquely facilitative for word segmentation for both children and adults. These findings illustrate the facilitative impact of skewed input distributions on learning and raise questions about the possible role of cognitive pressures in the prevalence of Zipfian distributions in language.

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.

Oscillatory activity and EEG phase synchrony of concurrent word segmentation and meaning-mapping in 9-year-old children

When learning a new language, one must segment words from continuous speech and associate them with meanings. These complex processes can be boosted by attentional mechanisms triggered by multi-sensory information. Previous electrophysiological studies suggest that brain oscillations are sensitive to different hierarchical complexity levels of the input, making them a plausible neural substrate for speech parsing. Here, we investigated the functional role of brain oscillations during concurrent speech segmentation and meaning acquisition in sixty 9-year-old children. We collected EEG data during an audio-visual statistical learning task during which children were exposed to a learning condition with consistent word-picture associations and a random condition with inconsistent word-picture associations before being tested on their ability to recall words and word-picture associations. We capitalized on the brain dynamics to align neural activity to the same rate as an external rhythmic stimulus to explore modulations of neural synchronization and phase synchronization between electrodes during multi-sensory word learning. Results showed enhanced power at both word- and syllabic-rate and increased EEG phase synchronization between frontal and occipital regions in the learning compared to the random condition. These findings suggest that multi-sensory cueing and attentional mechanisms play an essential role in children's successful word learning.

Individual Differences in Learning Abilities Impact Structure Addition: Better Learners Create More Structured Languages

Over the last decade, iterated learning studies have provided compelling evidence for the claim that linguistic structure can emerge from non-structured input, through the process of transmission. However, it is unclear whether individuals differ in their tendency to add structure, an issue with implications for understanding who are the agents of change. Here, we identify and test two contrasting predictions: The first sees learning as a pre-requisite for structure addition, and predicts a positive correlation between learning accuracy and structure addition, whereas the second maintains that it is those learners who struggle with learning and reproducing their input who add structure to it. This prediction is hard to test in standard iterated learning paradigms since each learner is exposed to a different input, and since structure and accuracy are computed using the same test items. Here, we test these contrasting predictions in two experiments using a one-generation artificial language learning paradigm designed to provide independent measures of learning accuracy and structure addition. Adults (N = 48 in each study) were exposed to a semi-regular language (with probabilistic structure) and had to learn it: Learning was assessed using seen items, whereas structure addition was calculated over unseen items. In both studies, we find a strong positive correlation between individuals' ability to learn the language and their tendency to add structure to it: Better learners also produced more structured languages. These findings suggest a strong link between learning and generalization. We discuss the implications of these findings for iterated language models and theories of language change more generally.

Modeling the Influence of Language Input Statistics on Children's Speech Production

We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child-caregiver interactions in English to test whether one proposed statistical learning mechanism-backward transitional probability-is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific "chunks" from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age.

Children automatically abstract categorical regularities during statistical learning

Statistical learning allows us to discover myriad structures in our environment, which is saturated with information at many different levels-from items to categories. How do children learn different levels of information-about regularities that pertain to items and the categories they come from-and how does this differ from adults? Studies on category learning and memory have suggested that children may be more focused on items than adults. If this is also the case for statistical learning, children may not extract and learn the multi-level regularities that adults can. We report three experiments showing that children and adults extract both item- and category-level regularities in statistical learning. In Experiments 1 and 2, we show that both children and adults can learn structure at the item and category levels when they are measured independently. In Experiment 3, we show that both children and adults learn about categories even when exposure does not require this: both are able to generalize their learning from the item to the category level. Results indicate that statistical learning operates across multi-level structure in children and adults alike, enabling generalization of learning from specific items to exemplars from categories of those items that observers have never seen. Even though children may be more focused on items during other forms of learning, they learn about categories from item-level input during statistical learning.

Visual statistical learning is facilitated in Zipfian distributions

Humans can extract co-occurrence regularities from their environment, and use them for learning. This statistical learning ability (SL) has been studied extensively as a way to explain how we learn the structure of our environment. These investigations have illustrated the impact of various distributional properties on learning. However, almost all SL studies present the regularities to be learned in uniform frequency distributions where each unit (e.g., image triplet) appears the same number of times: While the regularities themselves are informative, the appearance of the units cannot be predicted. In contrast, real-world learning environments, including the words children hear and the objects they see, are not uniform. Recent research shows that word segmentation is facilitated in a skewed (Zipfian) distribution. Here, we examine the domain-generality of the effect and ask if visual SL is also facilitated in a Zipfian distribution. We use an existing database to show that object combinations have a skewed distribution in children's environment. We then show that children and adults showed better learning in a Zipfian distribution compared to a uniform one, overall, and for low-frequency triplets. These results illustrate the facilitative impact of skewed distributions on learning across modality and age; suggest that the use of uniform distributions may underestimate performance; and point to the possible learnability advantage of such distributions in the real-world.

Statistical Learning and Language Impairments: Toward More Precise Theoretical Accounts

Statistical-learning (SL) theory offers an experience-based account of typical and atypical spoken and written language acquisition. Recent work has provided initial support for this view, tying individual differences in SL abilities to linguistic skills, including language impairments. In the current article, we provide a critical review of studies testing SL abilities in participants with and without developmental dyslexia and specific language impairment and discuss the directions that this field of research has taken so far. We identify substantial vagueness in the demarcation lines between different theoretical constructs (e.g., “statistical learning,” “implicit learning,” and “procedural learning”) as well as in the mappings between experimental tasks and these theoretical constructs. Moreover, we argue that current studies are not designed to contrast different theoretical approaches but rather test singular confirmatory predictions without including control tasks showing normal performance. We end by providing concrete suggestions for how to advance research on SL deficits in language impairments.

Measuring children's auditory statistical learning via serial recall

Statistical learning (SL) has been a prominent focus of research in developmental and adult populations, guided by the assumption that it is a fundamental component of learning underlying higher-order cognition. In developmental populations, however, there have been recent concerns regarding the degree to which many current tasks reliably measure SL, particularly in younger children. In the current article, we present the results of two studies that measured auditory statistical learning (ASL) of linguistic stimuli in children aged 5-8 years. Children listened to 6 min of continuous syllables comprising four trisyllabic pseudowords. Following the familiarization phase, children completed (a) a two-alternative forced-choice task and (b) a serial recall task in which they repeated either target sequences embedded during familiarization or foils, manipulated for sequence length. Results showed that, although both measures consistently revealed learning at the group level, the recall task better captured learning across the full range of abilities and was more reliable at the individual level. We conclude that, as has also been demonstrated in adults, the method holds promise for future studies of individual differences in ASL of linguistic stimuli.

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Statistical learning, a fundamental skill to extract regularities in the environment, is often considered a core supporting mechanism of the first language development. While many studies of statistical learning are conducted within a single domain or modality, recent evidence suggests that this skill may differ based on the context in which the stimuli are presented. In addition, few studies investigate learning as it unfolds in real-time, rather focusing on the outcome of learning. In this protocol, we describe an approach for identifying the cognitive and neural basis of statistical learning, within an individual, across domains (linguistic vs. non-linguistic) and sensory modalities (visual and auditory). The tasks are designed to cast as little cognitive demand as possible on participants, making it ideal for young school-aged children and special populations. The web-based nature of the behavioral tasks offers a unique opportunity for us to reach more representative populations nationwide, to estimate effect sizes with greater precision, and to contribute to open and reproducible research. The neural measures provided by the functional magnetic resonance imaging (fMRI) task can inform researchers about the neural mechanisms engaged during statistical learning, and how these may differ across individuals on the basis of domain or modality. Finally, both tasks allow for the measurement of real-time learning, as changes in reaction time to a target stimulus is tracked across the exposure period. The main limitation of using this protocol relates to the hour-long duration of the experiment. Children might need to complete all four statistical learning tasks in multiple sittings. Therefore, the web-based platform is designed with this limitation in mind so that tasks may be disseminated individually. This methodology will allow users to investigate how the process of statistical learning unfolds across and within domains and modalities in children from different developmental backgrounds.

How does the brain learn environmental structure? Ten core principles for understanding the neurocognitive mechanisms of statistical learning

Despite a growing body of research devoted to the study of how humans encode environmental patterns, there is still no clear consensus about the nature of the neurocognitive mechanisms underpinning statistical learning nor what factors constrain or promote its emergence across individuals, species, and learning situations. Based on a review of research examining the roles of input modality and domain, input structure and complexity, attention, neuroanatomical bases, ontogeny, and phylogeny, ten core principles are proposed. Specifically, there exist two sets of neurocognitive mechanisms underlying statistical learning. First, a "suite" of associative-based, automatic, modality-specific learning mechanisms are mediated by the general principle of cortical plasticity, which results in improved processing and perceptual facilitation of encountered stimuli. Second, an attention-dependent system, mediated by the prefrontal cortex and related attentional and working memory networks, can modulate or gate learning and is necessary in order to learn nonadjacent dependencies and to integrate global patterns across time. This theoretical framework helps clarify conflicting research findings and provides the basis for future empirical and theoretical endeavors.

The prevalence and importance of statistical learning in human cognition and behavior

Statistical learning, the ability to extract regularities from the environment over time, has become a topic of burgeoning interest. Its influence on behavior, spanning infancy to adulthood, has been demonstrated across a range of tasks, both those labeled as tests of statistical learning and those from other learning domains that predated statistical learning research or that are not typically considered in the context of that literature. Given this pervasive role in human cognition, statistical learning has the potential to reconcile seemingly distinct learning phenomena and may be an under-appreciated but important contributor to a wide range of human behaviors that are studied as unrelated processes, such as episodic memory and spatial navigation.

Assessing Visual Statistical Learning in Early-School-Aged Children: The Usefulness of an Online Reaction Time Measure

Visual statistical learning (VSL) was traditionally tested through offline two-alternative forced choice (2-AFC) questions. More recently, online reaction time (RT) measures and alternative offline question types have been developed to further investigate learning during exposure and more adequately assess individual differences in adults (Siegelman et al., 2017b, 2018). We assessed the usefulness of these measures for investigating VSL in early-school-aged children. Secondarily, we examined the effect of introducing a cover task, potentially affecting attention, on children’s VSL performance. Fifty-three children (aged 5–8 years) performed a self-paced VSL task containing triplets, in which participants determine the presentation speed and RTs to each stimulus are recorded. Half of the participants performed a cover task, while the other half did not. Online sensitivity to the statistical structure was measured by contrasting RTs to unpredictable versus predictable elements. Subsequently, participants completed 2-AFC (choose correct triplet) and 3-AFC (fill blank to complete triplet) offline questions. RTs were significantly longer for unpredictable than predictable elements, so we conclude that early-school-aged children are sensitive to the statistical structure during exposure, and that the RT task can measure that. We found no evidence as to whether children can perform above chance on offline 2-AFC or 3-AFC questions, or whether the cover task affects children’s VSL performance. These results show the feasibility of using an online RT task when assessing VSL in early-school-aged children. This task therefore seems suitable for future studies that aim to investigate VSL across development or in clinical populations, perhaps together with behavioral tasks.

Statistical Learning, Implicit Learning, and First Language Acquisition: A Critical Evaluation of Two Developmental Predictions

The role of distributional information in language learning, and learning more generally, has been studied extensively in both the statistical learning and the implicit learning literatures. Despite the similarity in research questions, the two literatures have remained largely separate. Here, we draw on findings from the two traditions to critically evaluate two developmental predictions that are central to both. The first is the question of age invariance: Does learning improve during development or is it fully developed in infancy? The combined findings suggest that both implicit and statistical learning improve during childhood, contra the age invariance prediction. This raises questions about the role of implicit statistical learning (ISL) in explaining the age-related deterioration in language learning: Children's better language learning abilities cannot be attributed to their improved distributional learning skills. The second issue we examine is the predictive relation to language outcomes: Does variation in learning predict variation in language outcomes? While there is evidence for such links, there is concern in both research traditions about the reliability of the tasks used with children. We present data suggesting that commonly used statistical learning measures may not capture stable individual differences in children, undermining their utility for assessing the link to language outcomes in developmental samples. The evaluation of both predictions highlights the empirical parallels between the implicit and statistical learning literatures, and the need to better integrate their developmental investigation. We go on to discuss several of the open challenges facing the study of ISL during development.