Implicit statistical learning in language processing: word predictability is the key

Reward-related regions play a role in natural story comprehension

The reward system was shown to be involved in a wide array of processes. Nevertheless, the exploration of the involvement of the reward system during language processing has not yet been directly tested. We investigated the role of reward-processing regions while listening to a natural story. We utilized a published dataset in which half of the participants listened to a natural story and the others listened to a scrambled version of it to compare the functional MRI signals in the reward system between these conditions and discovered a distinct pattern between conditions. This suggests that the reward system is activated during the comprehension of natural stories. We also show evidence that the fMRI signals in reward-related areas might potentially correlate with the predictability level of processed sentences. Further research is needed to determine the nature of the involvement and the way the activity interacts with various aspects of the sentences.

Predictive processing of music and language in autism: Evidence from Mandarin and English speakers

Atypical predictive processing has been associated with autism across multiple domains, based mainly on artificial antecedents and consequents. As structured sequences where expectations derive from implicit learning of combinatorial principles, language and music provide naturalistic stimuli for investigating predictive processing. In this study, we matched melodic and sentence stimuli in cloze probabilities and examined musical and linguistic prediction in Mandarin- (Experiment 1) and English-speaking (Experiment 2) autistic and non-autistic individuals using both production and perception tasks. In the production tasks, participants listened to unfinished melodies/sentences and then produced the final notes/words to complete these items. In the perception tasks, participants provided expectedness ratings of the completed melodies/sentences based on the most frequent notes/words in the norms. While Experiment 1 showed intact musical prediction but atypical linguistic prediction in autism in the Mandarin sample that demonstrated imbalanced musical training experience and receptive vocabulary skills between groups, the group difference disappeared in a more closely matched sample of English speakers in Experiment 2. These findings suggest the importance of taking an individual differences approach when investigating predictive processing in music and language in autism, as the difficulty in prediction in autism may not be due to generalized problems with prediction in any type of complex sequence processing.

Statistical learning at a virtual cocktail party

Statistical learning - the ability to extract distributional regularities from input - is suggested to be key to language acquisition. Yet, evidence for the human capacity for statistical learning comes mainly from studies conducted in carefully controlled settings without auditory distraction. While such conditions permit careful examination of learning, they do not reflect the naturalistic language learning experience, which is replete with auditory distraction - including competing talkers. Here, we examine how statistical language learning proceeds in a virtual cocktail party environment, where the to-be-learned input is presented alongside a competing speech stream with its own distributional regularities. During exposure, participants in the Dual Talker group concurrently heard two novel languages, one produced by a female talker and one by a male talker, with each talker virtually positioned at opposite sides of the listener (left/right) using binaural acoustic manipulations. Selective attention was manipulated by instructing participants to attend to only one of the two talkers. At test, participants were asked to distinguish words from part-words for both the attended and the unattended languages. Results indicated that participants' accuracy was significantly higher for trials from the attended vs. unattended language. Further, the performance of this Dual Talker group was no different compared to a control group who heard only one language from a single talker (Single Talker group). We thus conclude that statistical learning is modulated by selective attention, being relatively robust against the additional cognitive load provided by competing speech, emphasizing its efficiency in naturalistic language learning situations.

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.

Enhancing Syntactic Knowledge in School-Age Children With Developmental Language Disorder: The Promise of Syntactic Priming

PURPOSE

We propose that implicit learning, including syntactic priming, has therapeutic promise to enhance the syntactic knowledge of children with developmental language disorder (DLD).

METHOD

We review the chunk-based learning framework of syntactic learning, the developmental evidence in support of it, and the developmental literature on syntactic priming as an instance of chunk-based statistical learning. We use this framework to help understand the nature of the syntactic learning difficulties of children with DLD. We then briefly review the available explicit treatments for syntactic deficits and end by proposing an implicit training activity that integrates syntactic priming with the principles of statistical learning.

RESULTS

Statistically induced chunk-based learning is a powerful driver of syntactic learning, and syntactic priming is a form of this learning. Repeated priming episodes during everyday language interactions lead children to create abstract and global syntactic representations in long-term memory. We offer some thoughts on an implicit language intervention approach with syntactic priming at its center.

CONCLUSIONS

Children's learning of syntactic structures is influenced by repeated syntactic priming experiences. Including a syntactic priming activity in our language intervention toolbox has the promise to enhance children's syntactic knowledge and sentence comprehension and production abilities.

The dynamics of multiword sequence extraction

Being able to process multiword sequences is central for both language comprehension and production. Numerous studies support this claim, but less is known about the way multiword sequences are acquired, and more specifically how associations between their constituents are established over time. Here we adapted the Hebb naming task into a Hebb lexical decision task to study the dynamics of multiword sequence extraction. Participants had to read letter strings presented on a computer screen and were required to classify them as words or pseudowords. Unknown to the participants, a triplet of words or pseudowords systematically appeared in the same order and random words or pseudowords were inserted between two repetitions of the triplet. We found that response times (RTs) for the unpredictable first position in the triplet decreased over repetitions (i.e., indicating the presence of a repetition effect) but more slowly and with a different dynamic compared with items appearing at the predictable second and third positions in the repeated triplet (i.e., showing a slightly different predictability effect). Implicit and explicit learning also varied as a function of the nature of the triplet (i.e., unrelated words, pseudowords, semantically related words, or idioms). Overall, these results provide new empirical evidence about the dynamics of multiword sequence extraction, and more generally about the role of statistical learning in language acquisition.

Paired-associate versus cross-situational: How do verbal working memory and word familiarity affect word learning?

Word learning is one of the first steps into language, and vocabulary knowledge predicts reading, speaking, and writing ability. There are several pathways to word learning and little is known about how they differ. Previous research has investigated paired-associate (PAL) and cross-situational word learning (CSWL) separately, limiting the understanding of how the learning process compares across the two. In PAL, the roles of word familiarity and working memory have been thoroughly examined, but these same factors have received very little attention in CSWL. We randomly assigned 126 monolingual adults to PAL or CSWL. In each task, names of 12 novel objects were learned (six familiar words, six unfamiliar words). Logistic mixed-effects models examined whether word-learning paradigm, word type and working memory (measured with a backward digit-span task) predicted learning. Results suggest better learning performance in PAL and on familiar words. Working memory predicted word learning across paradigms, but no interactions were found between any of the predictors. This suggests that PAL is easier than CSWL, likely because of reduced ambiguity between the word and the referent, but that learning across both paradigms is equally enhanced by word familiarity, and similarly supported by working memory.

Implicit learning and individual differences in speech recognition: an exploratory study

Individual differences in speech recognition in challenging listening environments are pronounced. Studies suggest that implicit learning is one variable that may contribute to this variability. Here, we explored the unique contributions of three indices of implicit learning to individual differences in the recognition of challenging speech. To this end, we assessed three indices of implicit learning (perceptual, statistical, and incidental), three types of challenging speech (natural fast, vocoded, and speech in noise), and cognitive factors associated with speech recognition (vocabulary, working memory, and attention) in a group of 51 young adults. Speech recognition was modeled as a function of the cognitive factors and learning, and the unique contribution of each index of learning was statistically isolated. The three indices of learning were uncorrelated. Whereas all indices of learning had unique contributions to the recognition of natural-fast speech, only statistical learning had a unique contribution to the recognition of speech in noise and vocoded speech. These data suggest that although implicit learning may contribute to the recognition of challenging speech, the contribution may depend on the type of speech challenge and on the learning task.

Implicit Statistical Learning in L2 Sentence Processing: Individual Cognitive Differences

This study investigates whether statistical learning ability, conceptualized as a cognitive ability to learn regularities implicitly, is a good predictor for L2 learners' online language processing performance. Native-English-speaking adults, as a control group, and native-Korean-speaking adult L2 learners of English participated. They completed: (a) an artificial grammar learning task containing nonadjacent dependencies in sequences of non-words, to test statistical learning ability; and (b) a self-paced English reading task containing relative clauses (RC) in which the "filler" and the "gap" formed a long-distance dependency, to test language processing. Both tasks' stimuli were presented element-by-element to mimic the incremental nature of online language processing. The results for the L1 group show that higher accuracy scores on the artificial grammar learning task did not predict higher sentence comprehension scores. The results for the L2 group, however, show a marginally significant correlation between accuracy scores on the artificial grammar learning task and sentence comprehension scores. For both groups, the reading time difference between grammatical and ungrammatical items in the artificial grammar learning task did predict the speed of reading times for items with RCs with a long-distance dependency in the sentence processing task: Larger differences in RTs in the artificial grammar task correlated with slower reading at the critical region of English RCs. These findings suggest a similar mechanism for online first and second language processing of core syntactic phenomena and for statistical learning ability that involves implicitly tracking distributional relations across elements.

The direction of attention in second language phonological contrast learning

This study attempted to describe why some individuals are more successful when learning to perceive the sounds of a second language by analyzing the role attention plays in perceptual learning. Fifty-seven monolingual English-speaking adults completed the study. The participants underwent a perceptual learning paradigm presenting the novel contrast, the voicing contrast between Thai /b/ and /p/. The experiment consisted of a 40-item pretest, 480-trial learning phase, and 40-item posttest. Approximately half of the participants (n = 30) were given explicit instruction to listen for the specific contrast prior to the learning phase; other participants were not told the nature of the contrast. The Attention Network Test (ANT) from Fan, McCandliss, Sommer, Raz, and Posner [(2002). J. Cogn. Neurosci. 14(3), 340-347] was used to assess attentional networks. Generalized linear models and linear mixed effect models (LME) were fit to predict the participants' post-test scores based on ANT subscores, experimental group, and learning block (LME only). The results showed a correlation between attentional control and the ability to learn non-native phoneme contrasts regardless of instruction. In addition, there was a positive interaction between attentional control and the provision of explicit instructions during the learning process, such that individuals with high attentional control learned better when they received explicit instruction prior to training.

Weight status impacts children's incidental statistical learning

The expanding literature investigating the cognitive effects of childhood weight status has not included examinations of incidental statistical learning, the process by which children unintentionally acquire knowledge about patterns in their environments, despite evidence that it underlies many higher-level information processing capabilities. In the present study, we measured event-related potentials (ERPs) while school-aged participants completed a variation of an oddball task in which stimuli predicted the appearance of a target. Children were asked to respond to the target but were not given any information about the existence of predictive dependencies. We found that children with a healthy weight status had larger P3 amplitudes in response to the predictors that were most meaningful in completing the task, a finding that may suggest optimized learning mechanisms influenced by weight status. These findings offer an important first step to understanding how healthy lifestyle factors may influence incidental statistical learning.

Neural dynamics underlying the processing of implicit form-meaning connections: The dissociative roles of theta and alpha oscillations

Implicit learning plays an important role in the language acquisition. In addition to helping people acquire the form-level rules (e.g., the word order regularities), implicit learning can also facilitate the acquisition of word meanings (i.e., the establishment of connections between the word form and its meanings). Although some behavioral studies have explored the processing of implicit form-meaning connections, the neural dynamics underlying this processing remains unclear. Through examining whether participants could implicitly acquire the literal and metaphorical meanings of novel words, and applying the time-frequency analysis on the electroencephalogram (EEG) data collected in the testing phase, the neural oscillations corresponding to the processing of implicit form-literal and form-metaphorical meaning connections were explored. The results showed that participants in the experimental group could implicitly acquire the form-literal and form-metaphorical meaning connections after training, while participants in the control group who were not trained did not have access to such form-meaning connections. Meanwhile, during the processing of form-literal meaning connections, the greater suppression of alpha oscillations was induced by the testing items that follow the same rules as the training items (i.e., the regular testing items) in the experimental group, whereas the stronger enhancement of theta oscillations was elicited by the regular testing items in the experimental group during the processing of form-metaphorical meaning connections. Our study provides insights for understanding the processing of implicit form-literal and form-metaphorical meaning connections and the neural dynamics underlying the processing.

Reading fluency and statistical learning across modalities and domains: Online and offline measures

The vulnerability of statistical learning has been demonstrated in reading difficulties in both the visual and acoustic modalities. We examined segmentation abilities of Hungarian speaking adolescents with different levels of reading fluency in the acoustic verbal and visual nonverbal domains. We applied online target detection tasks, where the extent of learning is reflected in differences between reaction times to predictable versus unpredictable targets. Explicit judgments of well-formedness were also elicited in an offline two-alternative forced choice (2AFC) task. Learning was evident in both the acoustic verbal and visual nonverbal tasks, both in online and offline measures, but learning effects were larger both in online and offline tasks in the verbal acoustic condition. We haven’t found evidence for a significant relationship between statistical learning and reading fluency in adolescents in either modality. Together with earlier findings, these results suggest that the relationship between reading and statistical learning is dependent on the domain, modality and nature of the statistical learning task, on the reading task, on the age of participants, and on the specific language. The online target detection task is a promising tool which can be adapted to a wider set of tasks to further explore the contribution of statistical learning to reading acquisition in participants from different populations.

Neural and cognitive correlates of performance in dynamic multi-modal settings

The endeavour to understand human cognition has largely relied upon investigation of task-related brain activity. However, resting-state brain activity can also offer insights into individual information processing and performance capabilities. Previous research has identified electroencephalographic resting-state characteristics (most prominently: the individual alpha frequency; IAF) that predict cognitive function. However, it has largely overlooked a second component of electrophysiological signals: aperiodic 1/ƒ activity. The current study examined how both oscillatory and aperiodic resting-state EEG measures, alongside traditional cognitive tests, can predict performance in a dynamic and complex, semi-naturalistic cognitive task. Participants' resting-state EEG was recorded prior to engaging in a Target Motion Analysis (TMA) task in a simulated submarine control room environment (CRUSE), which required participants to integrate dynamically changing information over time. We demonstrated that the relationship between IAF and cognitive performance extends from simple cognitive tasks (e.g., digit span) to complex, dynamic measures of information processing. Further, our results showed that individual 1/ƒ parameters (slope and intercept) differentially predicted performance across practice and testing sessions, whereby flatter slopes and higher intercepts were associated with improved performance during learning. In addition to the EEG predictors, we demonstrate a link between cognitive skills most closely related to the TMA task (i.e., spatial imagery) and subsequent performance. Overall, the current study highlights (1) how resting-state metrics - both oscillatory and aperiodic - have the potential to index higher-order cognitive capacity, while (2) emphasising the importance of examining these electrophysiological components within more dynamic settings and over time.

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.

Kindergarteners Use Cross-Situational Statistics to Infer the Meaning of Grammatical Elements

Many studies demonstrate that detecting statistical regularities in linguistic input plays a key role in language acquisition. Yet, it is unclear to what extent statistical learning is involved in more naturalistic settings, when young children have to acquire meaningful grammatical elements. In the present study, we address these points, by investigating whether statistical learning is involved in acquiring a morpho-syntactic structure from input that resembles natural languages more closely. We exposed 50 kindergarteners (M = 5 years, 5 months) to a miniature language in which they had to learn a grammatical marker that expressed number, and which could only be acquired on the basis of the distributional properties in the input. Half of the children performed an attention check during the experiment. Results show that young children are able to learn this meaning. We found no clear evidence that facilitating attention to the input increases learning performance.

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.

Statistical Learning of Language: A Meta-Analysis Into 25 Years of Research

Statistical learning is a key concept in our understanding of language acquisition. Ample work has highlighted its role in numerous linguistic functions-yet statistical learning is not a unitary construct, and its consistency across different language properties remains unclear. In a meta-analysis of auditory-linguistic statistical learning research spanning the last 25 years, we evaluated how learning varies across different language properties in infants, children, and adults and surveyed the methodological trends in the literature. We found robust learning across stimuli (syllables, words, etc.) in infants, and across stimuli and structures (adjacent dependencies, non-adjacent dependencies, etc.) in adults, with larger effect sizes when multiple cues were present. However, the analysis also showed significant publication bias and revealed a tendency toward using a narrow range of simplified language properties, including in the strength of the transitional probabilities used during training. Bayes factor analyses revealed prevalent data insensitivity of moderators commonly hypothesized to impact learning, such as the amount of exposure and transitional probability strength, which contradict core theoretical assumptions in the field. Methodological factors, such as the tasks used at test, also significantly impacted effect sizes in adults and children, suggesting that choice of task may critically constrain current theories of how statistical learning operates. Collectively, our results suggest that auditory-linguistic statistical learning has the kind of robustness needed to play a foundational role in language acquisition, but that more research is warranted to reveal its full potential.

Individual differences in artificial and natural language statistical learning

Statistical learning (SL) is considered a cornerstone of cognition. While decades of research have unveiled the remarkable breadth of structures that participants can learn from statistical patterns in experimental contexts, how this ability interfaces with real-world cognitive phenomena remains inconclusive. These mixed results may arise from the fact that SL is often treated as a general ability that operates uniformly across all domains, typically assuming that sensitivity to one kind of regularity implies equal sensitivity to others. In a preregistered study, we sought to clarify the link between SL and language by aligning the type of structure being processed in each task. We focused on the learning of trigram patterns using artificial and natural language statistics, to evaluate whether SL predicts sensitivity to comparable structures in natural speech. Adults were trained and tested on an artificial language incorporating statistically-defined syllable trigrams. We then evaluated their sensitivity to similar statistical structures in natural language using a multiword chunking task, which examines serial recall of high-frequency word trigrams-one of the building blocks of language. Participants' aptitude in learning artificial syllable trigrams positively correlated with their sensitivity to high-frequency word trigrams in natural language, suggesting that similar computations span learning across both tasks. Short-term SL taps into key aspects of long-term language acquisition when the statistical structures-and the computations used to process them-are comparable. Better aligning the specific statistical patterning across tasks may therefore provide an important steppingstone toward elucidating the relationship between SL and cognition at large.

The Role of Attention, Language Ability, and Language Experience in Children's Artificial Grammar Learning

PURPOSE

The current study examined the role of attention and language ability in nonverbal rule induction performance in a demographically diverse sample of school-age children.

METHOD

The participants included 43 English-speaking monolingual and 65 Spanish-English bilingual children between the ages of 5 and 9 years. Core Language Index standard scores from the Clinical Evaluation of Language Fundamentals-Fourth Edition indexed children's language skills. Rule induction was measured via a visual artificial grammar learning task. Two equally complex finite-state artificial grammars were used. Children learned one grammar in a low attention condition (where children were exposed to symbol sequences with no distractors) and another grammar in a high attention condition (where distractor symbols were presented around the perimeter of the target symbol sequences).

RESULTS

Overall, performance in the high attention condition was significantly worse than performance in the low attention condition. Children with robust language skills performed significantly better in the high attention condition than children with weaker language skills. Despite group differences in socioeconomic status, English language skills, and nonverbal intelligence, monolingual and bilingual children performed similarly to each other in both conditions.

CONCLUSION

The results suggest that the ability to extract rules from visual input is attenuated by the presence of competing visual information and that language ability, but not bilingualism, may influence rule induction.

Vestibular Evaluation and Management of Children with Sensorineural Hearing Loss

Vestibular dysfunction is the most common comorbidity associated with childhood sensorineural hearing loss. Early identification of vestibular dysfunction enables early intervention to mitigate its impact of motor, behavioral, and neurocognitive deficits of developing children. Screening for vestibular impairment can be achieved in the busy clinical setting.

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.

Implicit and Explicit Memory in Youths with High-Functioning Autism Spectrum Disorder: A Case-Control Study

Individuals with autism spectrum disorder (ASD) usually manifest heterogeneous impairments in their higher cognitive functions, including their implicit memory (IM) and explicit memory (EM). However, the findings on IM and EM in youths with ASD remain debated. The aim of this study was to clarify such conflicting results by examining IM and EM using two comparable versions of the Serial Reaction Time Task (SRTT) in the same group of children and adolescents with ASD. Twenty-five youths with high-functioning ASD and 29 age-matched and IQ-matched typically developing youths undertook both tasks. The ability to implicitly learn the temporal sequence of events across the blocks in the SRTT was intact in the youths with ASD. When they were tested for EM, the participants with ASD did not experience a significant reduction in their reaction times during the blocks with the previously learned sequence, suggesting an impairment in EM. Moreover, the participants with ASD were less accurate and made more omissions than the controls in the EM task. The implications of these findings for the establishment of tailored educational programs for children with high-functioning ASD are discussed.

Language statistical learning responds to reinforcement learning principles rooted in the striatum

Statistical learning (SL) is the ability to extract regularities from the environment. In the domain of language, this ability is fundamental in the learning of words and structural rules. In lack of reliable online measures, statistical word and rule learning have been primarily investigated using offline (post-familiarization) tests, which gives limited insights into the dynamics of SL and its neural basis. Here, we capitalize on a novel task that tracks the online SL of simple syntactic structures combined with computational modeling to show that online SL responds to reinforcement learning principles rooted in striatal function. Specifically, we demonstrate—on 2 different cohorts—that a temporal difference model, which relies on prediction errors, accounts for participants’ online learning behavior. We then show that the trial-by-trial development of predictions through learning strongly correlates with activity in both ventral and dorsal striatum. Our results thus provide a detailed mechanistic account of language-related SL and an explanation for the oft-cited implication of the striatum in SL tasks. This work, therefore, bridges the long-standing gap between language learning and reinforcement learning phenomena.

Statistical learning is the ability to extract regularities from the environment; in the domain of language, this ability is fundamental in the learning of words and structural rules. This study uses a combination of computational modelling and functional MRI to reveal a fundamental link between online language statistical learning and reinforcement learning at the algorithmic and implementational levels.

Statistically Induced Chunking Recall: A Memory-Based Approach to Statistical Learning

The computations involved in statistical learning have long been debated. Here, we build on work suggesting that a basic memory process, chunking, may account for the processing of statistical regularities into larger units. Drawing on methods from the memory literature, we developed a novel paradigm to test statistical learning by leveraging a robust phenomenon observed in serial recall tasks: that short-term memory is fundamentally shaped by long-term distributional learning. In the statistically induced chunking recall (SICR) task, participants are exposed to an artificial language, using a standard statistical learning exposure phase. Afterward, they recall strings of syllables that either follow the statistics of the artificial language or comprise the same syllables presented in a random order. We hypothesized that if individuals had chunked the artificial language into word-like units, then the statistically structured items would be more accurately recalled relative to the random controls. Our results demonstrate that SICR effectively captures learning in both the auditory and visual modalities, with participants displaying significantly improved recall of the statistically structured items, and even recall specific trigram chunks from the input. SICR also exhibits greater test-retest reliability in the auditory modality and sensitivity to individual differences in both modalities than the standard two-alternative forced-choice task. These results thereby provide key empirical support to the chunking account of statistical learning and contribute a valuable new tool to the literature.

Artificial grammar learning is facilitated by distributed practice: Evidence from a letter reordering task

Previous studies have shown that distributed practice-a training strategy that is known to facilitate memory-is likely to result in greater learning than massed practice. This effect has been demonstrated largely in explicit tasks. The purpose of this study was to test whether statistical learning of artificial grammar is affected by the lag between learning sessions overall, and by high and low complexity stimuli (as measure by chunk strength). Two groups (spaced-short and spaced-long) learned strings of letters created according to a set of rules and were required to produce new strings using given letter sets. For the spaced-short group, the two learning sessions, each including training and a test phase, took place sequentially with a 10-min break, whereas for the spaced-long group, learning sessions were distributed across two days (1-day lag). Overall results showed improved performance following spaced-long practice compared to spaced-short practice. The results also indicated that in the low chunk strength strings (indicating high complexity), both groups demonstrated similar improvement from first to second testing, while in the high chunk strength strings (indicating low complexity), improvement in letter reordering performance was significantly higher when the learning sessions were distributed across two days. This pattern of findings suggests that stimuli complexity affects the extent to which distributed practice enhance artificial grammar learning.

Toddlers' Ability to Leverage Statistical Information to Support Word Learning

PURPOSE

This study investigated whether the ability to utilize statistical regularities from fluent speech and map potential words to meaning at 18-months predicts vocabulary at 18- and again at 24-months.

METHOD

Eighteen-month-olds (N = 47) were exposed to an artificial language with statistical regularities within the speech stream, then participated in an object-label learning task. Learning was measured using a modified looking-while-listening eye-tracking design. Parents completed vocabulary questionnaires when their child was 18-and 24-months old.

RESULTS

Ability to learn the object-label pairing for words after exposure to the artificial language predicted productive vocabulary at 24-months and amount of vocabulary change from 18- to 24 months, independent of non-verbal cognitive ability, socio-economic status (SES) and/or object-label association performance.

CONCLUSION

Eighteen-month-olds' ability to use statistical information derived from fluent speech to identify words within the stream of speech and then to map the "words" to meaning directly predicts vocabulary size at 24-months and vocabulary change from 18 to 24 months. The findings support the hypothesis that statistical word segmentation is one of the important aspects of word learning and vocabulary acquisition in toddlers.

Does the cerebellar sequential theory explain spoken language impairments? A literature review

During the past decades, converging evidence from clinical, neuroimaging and neuroanatomical studies has demonstrated the key role of the cerebellum in the processing of non-motor aspects of language. Although more is known about the way in which the cerebellum participates in the mechanisms involved in written language, there is ambiguous information on its role in other aspects of language, such as in non-motor aspects of spoken language. Thus, to contribute additional insight into this important issue, in the present work, we review several original scientific papers focusing on the most frequent non-motor spoken language impairments evidenced in patients affected by cerebellar pathology, namely, verbal working memory, grammar processing and verbal fluency impairments. Starting from the collected data, we provide a common interpretation of the spoken language disorders in cerebellar patients, suggesting that sequential processing could be the main mechanism by which the cerebellum participates in these abilities. Indeed, according to the cerebellar sequential theory, spoken language impairments could be due to altered cerebellar function to supervise, synchronize and coordinate the activity of different functional modules, affecting the correct optimization of linguistic processing.

Tracking statistical learning online: Word segmentation in a target detection task

Despite the essential role of statistical learning in shaping human behavior, there are still controversies concerning its measurement. In this paper, we present a novel online target-detection task in an acoustic word segmentation paradigm, which is able to track the process of learning and does not build on deliberation and decision making. Beside testing the novel online task, we also examined its relationship with two offline measures: the traditional two-alternative forced choice (2AFC) task, and the statistically-induced chunking recall (SICR) task (Isbilen et al., 2017). Participants showed a significant learning effect on the online task, reflected in the decrease of reaction times during training and in the differences between reaction times to predictable versus unpredictable targets. Online learning scores correlated with the 2AFC scores, but this association was only present when participants did not have explicit knowledge about stimuli. SICR scores were not associated with any of the other measures. The internal consistency was higher for online learning measures than for the other two tasks. These findings show that the online target detection task is a good tool for assessing statistical learning, and invite further research on its psychometric properties.

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.

Investigating the compatibility, interchangeability, and clinical usability of BOT2-BF and BOT2-SF in primary school-aged children with and without developmental dyslexia

The aim of our study was to determine the sensitivity and specificity levels of BOT2-BF and BOT2-SF tests applied to children with developmental dyslexia and to examine their clinical usability. This study was designed as a case-controlled study. 82 (study group 41, control group 41) 7-12 year children were included. The study occurred three stages. In the first place, the similarities and differences of BOT2-BF and BOT2-SF tests observed in clinical practices were examined by taking the feedback of experts and test correlations. In the second step, BOT2-BF and BOT2-SF tests were examined with reference to the Core Outcome Set (COS) evaluation criteria. In the last step, in order to examine the sensitivity and specificity levels of the tests, assessments were performed in two groups with 1-week intervals. According to the COS results, the BOT2-BF test was examined better than the BOT2-SF test under cross-cultural validity title. The sensitivity and specificity of BOT2-SF were 0.92 and 0.51, and for BOT2-BF were 0.92 and 0.41, respectively. This study will help clinicians working on children with developmental dyslexia to decide the type of test they will use to evaluate and easy access to materials motor proficiency.

Recognition of vocoded words and sentences in quiet and multi-talker babble with children and adults

A vocoder is used to simulate cochlear-implant sound processing in normal-hearing listeners. Typically, there is rapid improvement in vocoded speech recognition, but it is unclear if the improvement rate differs across age groups and speech materials. Children (8–10 years) and young adults (18–26 years) were trained and tested over 2 days (4 hours) on recognition of eight-channel noise-vocoded words and sentences, in quiet and in the presence of multi-talker babble at signal-to-noise ratios of 0, +5, and +10 dB. Children achieved poorer performance than adults in all conditions, for both word and sentence recognition. With training, vocoded speech recognition improvement rates were not significantly different between children and adults, suggesting that improvement in learning how to process speech cues degraded via vocoding is absent of developmental differences across these age groups and types of speech materials. Furthermore, this result confirms that the acutely measured age difference in vocoded speech recognition persists after extended training.

Neural representation of linguistic feature hierarchy reflects second-language proficiency

Acquiring a new language requires individuals to simultaneously and gradually learn linguistic attributes on multiple levels. Here, we investigated how this learning process changes the neural encoding of natural speech by assessing the encoding of the linguistic feature hierarchy in second-language listeners. Electroencephalography (EEG) signals were recorded from native Mandarin speakers with varied English proficiency and from native English speakers while they listened to audio-stories in English. We measured the temporal response functions (TRFs) for acoustic, phonemic, phonotactic, and semantic features in individual participants and found a main effect of proficiency on linguistic encoding. This effect of second-language proficiency was particularly prominent on the neural encoding of phonemes, showing stronger encoding of “new” phonemic contrasts (i.e., English contrasts that do not exist in Mandarin) with increasing proficiency. Overall, we found that the nonnative listeners with higher proficiency levels had a linguistic feature representation more similar to that of native listeners, which enabled the accurate decoding of language proficiency. This result advances our understanding of the cortical processing of linguistic information in second-language learners and provides an objective measure of language proficiency.

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.

Exploring the "anchor word" effect in infants: Segmentation and categorisation of speech with and without high frequency words

High frequency words play a key role in language acquisition, with recent work suggesting they may serve both speech segmentation and lexical categorisation. However, it is not yet known whether infants can detect novel high frequency words in continuous speech, nor whether they can use them to help learning for segmentation and categorisation at the same time. For instance, when hearing "you eat the biscuit", can children use the high-frequency words "you" and "the" to segment out "eat" and "biscuit", and determine their respective lexical categories? We tested this in two experiments. In Experiment 1, we familiarised 12-month-old infants with continuous artificial speech comprising repetitions of target words, which were preceded by high-frequency marker words that distinguished the targets into two distributional categories. In Experiment 2, we repeated the task using the same language but with additional phonological cues to word and category structure. In both studies, we measured learning with head-turn preference tests of segmentation and categorisation, and compared performance against a control group that heard the artificial speech without the marker words (i.e., just the targets). There was no evidence that high frequency words helped either speech segmentation or grammatical categorisation. However, segmentation was seen to improve when the distributional information was supplemented with phonological cues (Experiment 2). In both experiments, exploratory analysis indicated that infants' looking behaviour was related to their linguistic maturity (indexed by infants' vocabulary scores) with infants with high versus low vocabulary scores displaying novelty and familiarity preferences, respectively. We propose that high-frequency words must reach a critical threshold of familiarity before they can be of significant benefit to learning.

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.

Implicit-statistical learning in aphasia and its relation to lesion location

BACKGROUND

Implicit-statistical learning (ISL) research investigates whether domain-general mechanisms are recruited in the linguistic processes that require manipulation of patterned regularities (e.g. syntax). Aphasia is a language disorder caused by focal brain damage in the left fronto-temporal-parietal network. Research shows that people with aphasia (PWA) with frontal lobe lesions manifest convergent deficits in syntax and ISL mechanisms. So far, ISL mechanisms in PWA with temporal or parietal lobe lesions have not been systematically investigated.

AIMS

We investigated two complementary hypotheses: 1) the anatomical hypothesis, that PWA with frontal lesions display more severely impaired ISL abilities than PWA with posterior lesions and 2) the behavioural hypothesis, that the magnitude of impairment in ISL mechanisms correlates to syntactic deficits in aphasia.

METHODS

We tested 13 PWA, 5 with frontal lesions and 8 with posterior lesions, and 11 non-brain-damaged controls on a visual statistical learning (VSL) task. In addition, all PWA completed several linguistic tasks. Reaction times, obtained in the VSL task, were analyzed using linear mixed-effects model. Correlational statistics were used to assess the relationship between VSL task performance and linguistic measures.

RESULTS AND DISCUSSION

We did not find support for the anatomical hypothesis as patients with spared frontal regions also manifested impaired ISL mechanisms. This is attributed to a) ISL mechanisms being vulnerable to other cognitive dysfunctions and/or b) ISL mechanisms anatomically extending to the posterior brain regions. Notably, ISL mechanisms were impaired, but not absent in aphasia. With regards to the behavioural hypothesis, we provide empirical evidence of correlation between ISL mechanisms and syntactic, but not lexical impairment in aphasia. We discuss both the theoretical contributions to the debate of domain-independence of ISL mechanisms and clinical implications for implicit language therapy.

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.

Changes in the Sensitivity to Language-Specific Orthographic Patterns With Age

How do bilingual readers of languages that have similar scripts identify a language switch? Recent behavioral and electroencephalographic results suggest that they rely on orthotactic cues to recognize the language of the words they read in ambiguous contexts. Previous research has shown that marked words with language-specific letter sequences (i.e., letter sequences that are illegal in one of the two languages) are recognized more easily and faster than unmarked words. The aim of this study was to investigate sensitivity to markedness throughout childhood and early adulthood by using a speeded language decision task with words and pseudowords. A large group of Spanish-Basque bilinguals of different ages (children, preteenagers, teenagers and adults) was tested. Results showed a markedness effect in the second language across all age groups that changed with age. However, sensitivity to markedness in the native language was negligible. We conclude that sensitivity to orthotactics does not follow parallel developmental trend in the first and second language.

Exploring Variation Between Artificial Grammar Learning Experiments: Outlining a Meta-Analysis Approach

Artificial grammar learning (AGL) has become an important tool used to understand aspects of human language learning and whether the abilities underlying learning may be unique to humans or found in other species. Successful learning is typically assumed when human or animal participants are able to distinguish stimuli generated by the grammar from those that are not at a level better than chance. However, the question remains as to what subjects actually learn in these experiments. Previous studies of AGL have frequently introduced multiple potential contributors to performance in the training and testing stimuli, but meta-analysis techniques now enable us to consider these multiple information sources for their contribution to learning-enabling intended and unintended structures to be assessed simultaneously. We present a blueprint for meta-analysis approaches to appraise the effect of learning in human and other animal studies for a series of artificial grammar learning experiments, focusing on studies that examine auditory and visual modalities. We identify a series of variables that differ across these studies, focusing on both structural and surface properties of the grammar, and characteristics of training and test regimes, and provide a first step in assessing the relative contribution of these design features of artificial grammars as well as species-specific effects for learning.

Frontal-midline theta frequency and probabilistic learning: A transcranial alternating current stimulation study

Probabilistic learning is a fundamental cognitive ability that extracts and represents regularities of our environment enabling predictive processing during perception and acquisition of perceptual, motor, cognitive, and social skills. Previous studies show competition between neural networks related to executive function/working memory vs. probabilistic learning. Theta synchronization has been associated with the former while desynchronization with the latter in correlational studies. In the present paper our aim was to test causal relationship between fronto-parietal midline theta synchronization and probabilistic learning with non-invasive transcranial alternating current (tACS) stimulation. We hypothesize that theta synchronization disrupts probabilistic learning performance by modulating the competitive relationship. Twenty-six young adults performed the Alternating Serial Reaction Time (ASRT) task to assess probabilistic learning in two sessions that took place one week apart. Stimulation was applied in a double-blind cross-over within-subject design with an active theta tACS and a sham stimulation in a counter-balanced order between participants. Sinusoidal current was administered with 1 mA peak-to-peak intensity throughout the task (approximately 20 min) for the active stimulation and 30 s for the sham. We did not find an effect of fronto-parietal midline theta tACS on probabilistic learning comparing performance during active and sham stimulation. To influence probabilistic learning, we suggest applying higher current intensity and stimulation parameters more precisely aligned to endogenous brain activity for future studies.

Exploring and Exploiting Uncertainty: Statistical Learning Ability Affects How We Learn to Process Language Along Multiple Dimensions of Experience

While the effects of pattern learning on language processing are well known, the way in which pattern learning shapes exploratory behavior has long gone unnoticed. We report on the way in which individual differences in statistical pattern learning affect performance in the domain of language along multiple dimensions. Analyzing data from healthy monolingual adults' performance on a serial reaction time task and a self-paced reading task, we show how individual differences in statistical pattern learning are reflected in readers' knowledge of linguistic co-occurrence patterns and in their exploration and exploitation of content-specific and task-general information. First, we investigated the extent to which an individual's pattern learning correlates with his or her sensitivity to systematic morphological and syntactic co-occurrences, as evidenced while reading authentic sentences. We found that the stream of morphological and syntactic information has a more pronounced effect on the reading speed of, as we will label them, content-sensitive learners in that the more probable the co-occurrence pattern, the faster their reading of that pattern will be. Next, we investigated how differences in pattern learning are reflected in the ways in which individuals approach the reading task itself and adapt to it. Casting this relation in terms of exploration/exploitation strategies, known from Reinforcement Learning, we conclude that content-sensitive learners are also more likely to initially probe (explore) a wider range of directly relevant patterns, which they can later use (exploit) to optimize their reading performance further. By affecting exploratory behavior, pattern learning influences the information that is gathered and becomes available for exploitation, thereby increasing the effect pattern learning has on language cognition.

Non-adjacent dependency learning in infancy, and its link to language development

To acquire language, infants must learn how to identify words and linguistic structure in speech. Statistical learning has been suggested to assist both of these tasks. However, infants' capacity to use statistics to discover words and structure together remains unclear. Further, it is not yet known how infants'  statistical learning ability relates to their language development. We trained 17-month-old infants on an artificial language comprising non-adjacent dependencies, and examined their looking times on tasks assessing sensitivity to words and structure using an eye-tracked head-turn-preference paradigm. We measured infants' vocabulary size using a Communicative Development Inventory (CDI) concurrently and at 19, 21, 24, 25, 27, and 30 months to relate performance to language development. Infants could segment the words from speech, demonstrated by a significant difference in looking times to words versus part-words. Infants' segmentation performance was significantly related to their vocabulary size (receptive and expressive) both currently, and over time (receptive until 24 months, expressive until 30 months), but was not related to the rate of vocabulary growth. The data also suggest infants may have developed sensitivity to generalised structure, indicating similar statistical learning mechanisms may contribute to the discovery of words and structure in speech, but this was not related to vocabulary size.

A role for incidental auditory learning in auditory-visual word learning among kindergarten children

This study focused on the potential role of incidental, auditory perceptual learning in among children learning new words. To this end, we examined how irrelevant auditory similarities across words, that provide no cues regarding their visual or conceptual attributes, influence pseudo-word learning in a name/picture matching paradigm. Two types of irrelevant auditory similarities were used: shared sequences of vowels or consonants. Learning word-to-picture associations in these two conditions was compared to a baseline condition in which items did not share either sequence. Kindergarten children readily learned items in all conditions, but auditory similarity interfered with learning (odds ratio, 1.12). Individual differences in reasoning and vocabulary did not account for the interference effect. These findings suggest that the sensory properties of words continue to influence language learning during the preschool years through rapid incidental learning, even if the effect is relatively small. Consistent with previous studies in the visual modality, we now suggest that incidental perceptual learning occurs in the auditory modality. Furthermore, the current findings suggest that this learning can interfere with word learning, highlighting the importance of the perceptual structure of words in real-world-like learning environments.

A role for incidental auditory learning in auditory-visual word learning among kindergarten children

This study focused on the potential role of incidental, auditory perceptual learning in among children learning new words. To this end, we examined how irrelevant auditory similarities across words, that provide no cues regarding their visual or conceptual attributes, influence pseudo-word learning in a name/picture matching paradigm. Two types of irrelevant auditory similarities were used: shared sequences of vowels or consonants. Learning word-to-picture associations in these two conditions was compared to a baseline condition in which items did not share either sequence. Kindergarten children readily learned items in all conditions, but auditory similarity interfered with learning (odds ratio, 1.12). Individual differences in reasoning and vocabulary did not account for the interference effect. These findings suggest that the sensory properties of words continue to influence language learning during the preschool years through rapid incidental learning, even if the effect is relatively small. Consistent with previous studies in the visual modality, we now suggest that incidental perceptual learning occurs in the auditory modality. Furthermore, the current findings suggest that this learning can interfere with word learning, highlighting the importance of the perceptual structure of words in real-world-like learning environments.

Statistical Learning and Social Competency: The Mediating Role of Language

The current study sought to examine the contribution of auditory and visual statistical learning on language and social competency abilities as well as whether decreased statistical learning abilities are related to increased autistic traits. To answer these questions, participants' (N = 95) auditory and visual statistical learning abilities, language, social competency, and level of autistic traits were assessed. Although the relationships observed were relatively small in magnitude, our results demonstrated that visual statistical learning related to language and social competency abilities and that auditory learning was more related to autism symptomatology than visual statistical learning. Furthermore, the relationship between visual statistical learning and social competency was mediated by language comprehension abilities, suggesting that impairments in statistical learning may cascade into impairments in language and social abilities.

Statistical learning for vocal sequence acquisition in a songbird

Birdsong is a learned communicative behavior that consists of discrete acoustic elements (“syllables”) that are sequenced in a controlled manner. While the learning of the acoustic structure of syllables has been extensively studied, relatively little is known about sequence learning in songbirds. Statistical learning could contribute to the acquisition of vocal sequences, and we investigated the nature and extent of sequence learning at various levels of song organization in the Bengalese finch, Lonchura striata var. domestica. We found that, under semi-natural conditions, pupils (sons) significantly reproduced the sequence statistics of their tutor’s (father’s) songs at multiple levels of organization (e.g., syllable repertoire, prevalence, and transitions). For example, the probability of syllable transitions at “branch points” (relatively complex sequences that are followed by multiple types of transitions) were significantly correlated between the songs of tutors and pupils. We confirmed the contribution of learning to sequence similarities between fathers and sons by experimentally tutoring juvenile Bengalese finches with the songs of unrelated tutors. We also discovered that the extent and fidelity of sequence similarities between tutors and pupils were significantly predicted by the prevalence of sequences in the tutor’s song and that distinct types of sequence modifications (e.g., syllable additions or deletions) followed distinct patterns. Taken together, these data provide compelling support for the role of statistical learning in vocal production learning and identify factors that could modulate the extent of vocal sequence learning.