Infant Statistical Learning

Causal learning by infants and young children: From computational theories to language practices

Causal reasoning-the ability to reason about causal relations between events-is fundamental to understanding how the world works. This paper reviews two prominent theories on early causal learning and offers possibilities for theory bridging. Both theories grow out of computational modeling and have significant areas of overlap while differing in several respects. Explanation-Based Learning (EBL) focuses on young infants' learning about causal concepts of physical objects and events, whereas Bayesian models have been used to describe causal reasoning beyond infancy across various concept domains. Connecting the two models offers a more integrated approach to clarifying the developmental processes in causal reasoning from early infancy through later childhood. We further suggest that everyday language practices offer a promising space for theory bridging. We provide a review of selective work on caregiver-child conversations, in particular, on the use of scaffolding language including causal talk and pedagogical questions. Linking the research on language practices to the two cognitive theories, we point out directions for further research to integrate EBL and Bayesian models and clarify how causal learning unfolds in real life. This article is categorized under: Psychology > Learning Cognitive Biology > Cognitive Development.

Perceptual Awareness in Human Infants: What is the Evidence?

Perceptual awareness in infants during the first year of life is understudied, despite the philosophical, scientific, and clinical importance of understanding how and when consciousness emerges during human brain development. Although parents are undoubtedly convinced that their infant is conscious, the lack of adequate experimental paradigms to address this question in preverbal infants has been a hindrance to research on this topic. However, recent behavioral and brain imaging studies have shown that infants are engaged in complex learning from an early age and that their brains are more structured than traditionally thought. I will present a rapid overview of these results, which might provide indirect evidence of early perceptual awareness and then describe how a more systematic approach to this question could stand within the framework of global workspace theory, which identifies specific signatures of conscious perception in adults. Relying on these brain signatures as a benchmark for conscious perception, we can deduce that it exists in the second half of the first year, whereas the evidence before the age of 5 months is less solid, mainly because of the paucity of studies. The question of conscious perception before term remains open, with the possibility of short periods of conscious perception, which would facilitate early learning. Advances in brain imaging and growing interest in this subject should enable us to gain a better understanding of this important issue in the years to come.

Establishing neural representations for new word forms in 12-month-old infants

During the first year of life, infants start to learn the lexicon of their native language. Word learning includes the establishment of longer-term representations for the phonological form and the meaning of the word in the brain, as well as the link between them. However, it is not known how the brain processes word forms immediately after they have been learned. We familiarized 12-month-old infants (N = 52) with two pseudowords and studied their neural signatures. Specifically, we determined whether a newly learned word form elicits neural signatures similar to those observed when a known word is recognized (i.e., when a well-established word representation is activated, eliciting enhanced mismatch responses) or whether the processing of a newly learned word form shows the suppression of the neural response along with the principles of predictive coding of a learned rule (i.e., the order of the syllables of the new word form). The pattern of results obtained in the current study suggests that recognized word forms elicit a mismatch response of negative polarity, similar to newly learned and previously known words with an established representation in long-term memory. In contrast, prediction errors caused by acoustic novelty or deviation from the expected order in a sequence of (pseudo)words elicit responses of positive polarity. This suggests that electric brain activity is not fully explained by the predictive coding framework.

Visual statistical learning in preverbal infants at a higher likelihood of autism and its association with later social communication skills

Visual statistical Learning (SL) allows infants to extract the statistical relationships embedded in a sequence of elements. SL plays a crucial role in language and communication competencies and has been found to be impacted in Autism Spectrum Disorder (ASD). This study aims to investigate visual SL in infants at higher likelihood of developing ASD (HL-ASD) and its predictive value on autistic-related traits at 24-36 months. At 6 months of age, SL was tested using a visual habituation task in HL-ASD and neurotypical (NT) infants. All infants were habituated to a visual sequence of shapes containing statistically predictable patterns. In the test phase, infants viewed the statistically structured, familiar sequence in alternation with a novel sequence that did not contain any statistical information. HL-ASD infants were then evaluated at 24-36 months to investigate the associations between visual SL and ASD-related traits. Our results showed that NT infants were able to learn the statistical structure embedded in the visual sequences, while HL-ASD infants showed different learning patterns. A regression analysis revealed that SL ability in 6-month-old HL-ASD infants was related to social communication and interaction abilities at 24-36 months of age. These findings indicate that early differences in learning visual statistical patterns might contribute to later social communication skills.

Learning the Meanings of Function Words From Grounded Language Using a Visual Question Answering Model

Interpreting a seemingly simple function word like "or," "behind," or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network-based visual question answering models apparently can learn to use function words as part of answering questions about complex visual scenes. In this paper, we study what these models learn about function words, in the hope of better understanding how the meanings of these words can be learned by both models and children. We show that recurrent models trained on visually grounded language learn gradient semantics for function words requiring spatial and numerical reasoning. Furthermore, we find that these models can learn the meanings of logical connectives and and or without any prior knowledge of logical reasoning as well as early evidence that they are sensitive to alternative expressions when interpreting language. Finally, we show that word learning difficulty is dependent on the frequency of models' input. Our findings offer proof-of-concept evidence that it is possible to learn the nuanced interpretations of function words in a visually grounded context by using non-symbolic general statistical learning algorithms, without any prior knowledge of linguistic meaning.

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.

Predictability and Variation in Language Are Differentially Affected by Learning and Production

General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory-the component of short-term memory used for temporary storage and manipulation of information. In this study, we consider the relationship between working memory and regularization of linguistic variation. Regularization is a well-documented process whereby languages become less variable (on some dimension) over time. This process has been argued to be driven by the behavior of individual language users, but the specific mechanism is not agreed upon. Here, we use an artificial language learning experiment to investigate whether limitations in working memory during either language learning or language production drive regularization behavior. We find that taxing working memory during production results in the loss of all types of variation, but the process by which random variation becomes more predictable is better explained by learning biases. A computational model offers a potential explanation for the production effect using a simple self-priming mechanism.

Contribution of statistical learning in learning to read across languages

Statistical Learning (SL) refers to human's ability to detect regularities from environment Kirkham, N. Z. (2002) & Saffran, J. R. (1996). There has been a growing interest in understanding how sensitivity to statistical regularities influences learning to read. The current study systematically examined whether and how non-linguistic SL, Chinese SL, and English SL contribute to Chinese and English word reading among native Chinese-speaking 4th, 6th and 8th graders who learn English as a second language (L2). Children showed above-chance learning across all SL tasks and across all grades. In addition, developmental improvements were shown across at least two of the three grade ranges on all SL tasks. In terms of the contribution of SL to reading, non-linguistic auditory SL (ASL), English visual SL (VSL), and Chinese ASL accounted for a significant amount of variance in English L2 word reading. Non-linguistic ASL, Chinese VSL, English VSL, and English ASL accounted for a significant amount of variance in Chinese word reading. Our results provide clear and novel evidence for cross-linguistic contribution from Chinese SL to English reading, and from English SL to Chinese reading, highlighting a bi-directional relationship between SL in one language and reading in another language.

Cultural evolution creates the statistical structure of language

Human language is unique in its structure: language is made up of parts that can be recombined in a productive way. The parts are not given but have to be discovered by learners exposed to unsegmented wholes. Across languages, the frequency distribution of those parts follows a power law. Both statistical properties-having parts and having them follow a particular distribution-facilitate learning, yet their origin is still poorly understood. Where do the parts come from and why do they follow a particular frequency distribution? Here, we show how these two core properties emerge from the process of cultural evolution with whole-to-part learning. We use an experimental analog of cultural transmission in which participants copy sets of non-linguistic sequences produced by a previous participant: This design allows us to ask if parts will emerge purely under pressure for the system to be learnable, even without meanings to convey. We show that parts emerge from initially unsegmented sequences, that their distribution becomes closer to a power law over generations, and, importantly, that these properties make the sets of sequences more learnable. We argue that these two core statistical properties of language emerge culturally both as a cause and effect of greater learnability.

Rules generalization in children with dyslexia

BACKGROUND

Rule learning (RL) is the ability to extract and generalize higher-order repetition-based structures. Children with Developmental Dyslexia (DD) often report difficulties in learning complex regularities in sequential stimuli, which might be due to the complexity of the rule to be learned. Learning high-order repetition-based rules represents a building block for the development of language skills.

AIMS

This study investigates the ability to extract and generalize simple, repetition-based visual rules (e.g., ABA) in 8-11-year-old children without (TD) and with a diagnosis of Development Dyslexia (DD) and its relationship with language and reading skills.

METHOD

Using a forced-choice paradigm, children were first exposed to a visual sequence containing a repetition-based rule (e.g., ABA) and were then asked to recognize familiar and novel rules generated by new visual elements. Standardized language and reading tests were also administered to both groups.

RESULTS

The accuracy in recognizing rules was above chance for both groups, even though DD children were less accurate than TD children, suggesting a less efficient RL mechanism in the DD group. Moreover, visual RL was positively correlated with both language and reading skills.

CONCLUSION

These results further confirm the crucial role of RL in the acquisition of linguistic skills and mastering reading abilities.

Abstract processing of syllabic structures in early infancy

Syllables are one of the fundamental building blocks of early language acquisition. From birth onwards, infants preferentially segment, process and represent the speech into syllable-sized units, raising the question of what type of computations infants are able to perform on these perceptual units. Syllables are abstract units structured in a way that allows grouping phonemes into sequences. The goal of this research was to investigate 4-to-5-month-old infants' ability to encode the internal structure of syllables, at a target age when the language system is not yet specialized on the sounds and the phonotactics of native languages. We conducted two experiments in which infants were first familiarized to lists of syllables implementing either CVC (consonant-vowel-consonant) or CCV (consonant-consonant-vowel) structures, then presented with new syllables implementing both structures at test. Experiments differ in the degree of phonological similarity between the materials used at familiarization and test. Results show that infants were able to differentiate syllabic structures at test, even when test syllables were implemented by combinations of phonemes that infants did not hear before. Only infants familiarized with CVC syllables discriminated the structures at test, pointing to a processing advantage for CVC over CCV structures. This research shows that, in addition to preferentially processing the speech into syllable-sized units, during the first months of life, infants are also capable of performing fine-grained computations within such units.

Playing hide and seek: Contextual regularity learning develops between 3 and 5 years of age

The ability to acquire contextual regularities is fundamental in everyday life because it helps us to navigate the environment, directing our attention where relevant events are more likely to occur. Sensitivity to spatial regularities has been largely reported from infancy. Nevertheless, it is currently unclear when children can use this rapidly acquired contextual knowledge to guide their behavior. Evidence of this ability is indeed mixed in school-aged children and, to date, it has never been explored in younger children and toddlers. The current study investigated the development of contextual regularity learning in children aged 3 to 5 years. To this aim, we designed a new contextual learning paradigm in which young children were presented with recurring configurations of bushes and were asked to guess behind which bush a cartoon monkey was hiding. In a series of two experiments, we manipulated the relevance of color and visuospatial cues for the underlying task goal and tested how this affected young children's behavior. Our results bridge the gap between the infant and adult literatures, showing that sensitivity to spatial configurations persists from infancy to childhood, but it is only around the fifth year of life that children naturally start to integrate multiple cues to guide their behavior.

Object geometry serves humans' intuitive physics of stability

How do humans judge physical stability? A prevalent account emphasizes the mental simulation of physical events implemented by an intuitive physics engine in the mind. Here we test the extent to which the perceptual features of object geometry are sufficient for supporting judgments of falling direction. In all experiments, adults and children judged the falling direction of a tilted object and, across experiments, objects differed in the geometric features (i.e., geometric centroid, object height, base size and/or aspect ratio) relevant to the judgment. Participants' performance was compared to computational models trained on geometric features, as well as a deep convolutional neural network (ResNet-50), none of which incorporated mental simulation. Adult and child participants' performance was well fit by models of object geometry, particularly the geometric centroid. ResNet-50 also provided a good account of human performance. Altogether, our findings suggest that object geometry may be sufficient for judging the falling direction of tilted objects, independent of mental simulation.

Resource-rational account of sequential effects in human prediction

An abundant literature reports on 'sequential effects' observed when humans make predictions on the basis of stochastic sequences of stimuli. Such sequential effects represent departures from an optimal, Bayesian process. A prominent explanation posits that humans are adapted to changing environments, and erroneously assume non-stationarity of the environment, even if the latter is static. As a result, their predictions fluctuate over time. We propose a different explanation in which sub-optimal and fluctuating predictions result from cognitive constraints (or costs), under which humans however behave rationally. We devise a framework of costly inference, in which we develop two classes of models that differ by the nature of the constraints at play: in one case the precision of beliefs comes at a cost, resulting in an exponential forgetting of past observations, while in the other beliefs with high predictive power are favored. To compare model predictions to human behavior, we carry out a prediction task that uses binary random stimuli, with probabilities ranging from 0.05 to 0.95. Although in this task the environment is static and the Bayesian belief converges, subjects' predictions fluctuate and are biased toward the recent stimulus history. Both classes of models capture this 'attractive effect', but they depart in their characterization of higher-order effects. Only the precision-cost model reproduces a 'repulsive effect', observed in the data, in which predictions are biased away from stimuli presented in more distant trials. Our experimental results reveal systematic modulations in sequential effects, which our theoretical approach accounts for in terms of rationality under cognitive constraints.

Rhythmically Modulating Neural Entrainment during Exposure to Regularities Influences Statistical Learning

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

Childhood unpredictability and the development of exploration

Early in development, the process of exploration helps children gather new information that fosters learning about the world. Yet, it is unclear how childhood experiences may influence the way humans approach new learning. What influences decisions to exploit known, familiar options versus trying a novel alternative? We found that childhood unpredictability, characterized by unpredictable caregiving and unstable living environments, was associated with reduced exploratory behavior. This effect holds while controlling for individual differences, including anxiety and stress. Individuals who perceived their childhoods as unpredictable explored less and were instead more likely to repeat previous choices (habitual responding). They were also more sensitive to uncertainty than to potential rewards, even when the familiar options yielded lower rewards. We examined these effects across multiple task contexts and via both in-person (N = 78) and online replication (N = 84) studies among 10- to 13-y-olds. Results are discussed in terms of the potential cascading effects of unpredictable environments on the development of decision-making and the effects of early experience on subsequent learning.

Reading minds or reading scripts? De-intellectualising theory of mind

Understanding the origins of human social cognition is a central challenge in contemporary science. In recent decades, the idea of a 'Theory of Mind' (ToM) has emerged as the most popular way of explaining unique features of human social cognition. This default view has been progressively undermined by research on 'implicit' ToM, which suggests that relevant precursor abilities may already be present in preverbal human infants and great apes. However, this area of research suffers from conceptual difficulties and empirical limitations, including explanatory circularity, over-intellectualisation, and inconsistent empirical replication. Our article breaks new ground by adapting 'script theory' for application to both linguistic and non-linguistic agents. It thereby provides a new theoretical framework able to resolve the aforementioned issues, generate novel predictions, and provide a plausible account of how individuals make sense of the behaviour of others. Script theory is based on the premise that pre-verbal infants and great apes are capable of basic forms of agency-detection and non-mentalistic goal understanding, allowing individuals to form event-schemata that are then used to make sense of the behaviour of others. We show how script theory circumvents fundamental problems created by ToM-based frameworks, explains patterns of inconsistent replication, and offers important novel predictions regarding how humans and other animals understand and predict the behaviour of others.

Realistic and broad-scope learning simulations: first results and challenges

There is a current 'theory crisis' in language acquisition research, resulting from fragmentation both at the level of the approaches and the linguistic level studied. We identify a need for integrative approaches that go beyond these limitations, and propose to analyse the strengths and weaknesses of current theoretical approaches of language acquisition. In particular, we advocate that language learning simulations, if they integrate realistic input and multiple levels of language, have the potential to contribute significantly to our understanding of language acquisition. We then review recent results obtained through such language learning simulations. Finally, we propose some guidelines for the community to build better simulations.

Absolute pitch judgments of familiar melodies generalize across timbre and octave

Most listeners can determine when a familiar recording of music has been shifted in musical key by as little as one semitone (e.g., from B to C major). These findings appear to suggest that absolute pitch memory is widespread in the general population. However, the use of familiar recordings makes it unclear whether these findings genuinely reflect absolute melody-key associations for at least two reasons. First, listeners may be able to use spectral cues from the familiar instrumentation of the recordings to determine when a familiar recording has been shifted in pitch. Second, listeners may be able to rely solely on pitch height cues (e.g., relying on a feeling that an incorrect recording sounds "too high" or "too low"). Neither of these strategies would require an understanding of pitch chroma or musical key. The present experiments thus assessed whether listeners could make accurate absolute melody-key judgments when listening to novel versions of these melodies, differing from the iconic recording in timbre (Experiment 1) or timbre and octave (Experiment 2). Listeners in both experiments were able to select the correct-key version of the familiar melody at rates that were well above chance. These results fit within a growing body of research supporting the idea that most listeners, regardless of formal musical training, have robust representations of absolute pitch - based on pitch chroma - that generalize to novel listening situations. Implications for theories of auditory pitch memory are discussed.

Preventing Excessive Noise Exposure in Infants, Children, and Adolescents

Noise exposure is a major cause of hearing loss in adults. Yet, noise affects people of all ages, and noise-induced hearing loss is also a problem for young people. Sensorineural hearing loss caused by noise and other toxic exposures is usually irreversible. Environmental noise, such as traffic noise, can affect learning, physiologic parameters, and quality of life. Children and adolescents have unique vulnerabilities to noise. Children may be exposed beginning in NICUs and well-baby nurseries, at home, at school, in their neighborhoods, and in recreational settings. Personal listening devices are increasingly used, even by small children. Infants and young children cannot remove themselves from noisy situations and must rely on adults to do so, children may not recognize hazardous noise exposures, and teenagers generally do not understand the consequences of high exposure to music from personal listening devices or attending concerts and dances. Environmental noise exposure has disproportionate effects on underserved communities. In this report and the accompanying policy statement, common sources of noise and effects on hearing at different life stages are reviewed. Noise-abatement interventions in various settings are discussed. Because noise exposure often starts in infancy and its effects result mainly from cumulative exposure to loud noise over long periods of time, more attention is needed to its presence in everyday activities starting early in life. Listening to music and attending dances, concerts, and celebratory and other events are sources of joy, pleasure, and relaxation for many people. These situations, however, often result in potentially harmful noise exposures. Pediatricians can potentially lessen exposures, including promotion of safer listening, by raising awareness in parents, children, and teenagers. Noise exposure is underrecognized as a serious public health issue in the United States, with exposure limits enforceable only in workplaces and not for the general public, including children and adolescents. Greater awareness of noise hazards is needed at a societal level.

The relation between implicit statistical learning and proactivity as revealed by EEG

Environmental events often occur on a probabilistic basis but can sometimes be predicted based on specific cues and thus approached proactively. Incidental statistical learning enables the acquisition of knowledge about probabilistic cue-target contingencies. However, the neural mechanisms of statistical learning about contingencies (SLC), the required conditions for successful learning, and the role of implicit processes in the resultant proactive behavior are still debated. We examined changes in behavior and cortical activity during an SLC task in which subjects responded to visual targets. Unbeknown to them, there were three types of target cues associated with high-, low-, and zero target probabilities. About half of the subjects spontaneously gained explicit knowledge about the contingencies (contingency-aware group), and only they showed evidence of proactivity: shortened response times to predictable targets and enhanced event-related brain responses (cue-evoked P300 and contingent negative variation, CNV) to high probability cues. The behavioral and brain responses were strictly associated on a single-trial basis. Source reconstruction of the brain responses revealed activation of fronto-parietal brain regions associated with cognitive control, particularly the anterior cingulate cortex and precuneus. We also found neural correlates of SLC in the contingency-unaware group, but these were restricted to post-target latencies and visual association areas. Our results document a qualitative difference between explicit and implicit learning processes and suggest that in certain conditions, proactivity may require explicit knowledge about contingencies.

Big number, big body: Jersey numbers alter body size perception

Vision has been shown to be an active process that can be shaped by top-down influences. Here, we add to this area of research by showing a surprising example of how visual perception can be affected by cognition (i.e., cognitive penetration). Observers were presented, on each trial, with a picture of a computer-generated football player and asked to rate the slenderness of the player on an analog scale. The results of two experiments showed that observers perceived athletes wearing small jersey numbers as more slender than those with high numbers. This finding suggests that the cognition of numbers quantitatively alters body size perception. We conjecture that this effect is the result of previously learned associations (i.e., prior expectations) affecting perceptual inference. Such associations are likely the result of implicit learning of the statistical regularities of number and size attributes co-occurrences by the nervous system. We discuss how these results are consistent with previous research on statistical learning and how they fit into the Bayesian framework of perception. The current finding supports the notion of top-down influences of cognition on perception.

Statistical learning across passive listening adjusts perceptual weights of speech input dimensions

Statistical learning across passive exposure has been theoretically situated with unsupervised learning. However, when input statistics accumulate over established representations - like speech syllables, for example - there is the possibility that prediction derived from activation of rich, existing representations may support error-driven learning. Here, across five experiments, we present evidence for error-driven learning across passive speech listening. Young adults passively listened to a string of eight beer - pier speech tokens with distributional regularities following either a canonical American-English acoustic dimension correlation or a correlation reversed to create an accent. A sequence-final test stimulus assayed the perceptual weight - the effectiveness - of the secondary dimension in signaling category membership as a function of preceding sequence regularities. Perceptual weight flexibly adjusted according to the passively experienced regularities even when the preceding regularities shifted on a trial-by-trial basis. The findings align with a theoretical view that activation of established internal representations can support learning across statistical regularities via error-driven learning. At the broadest level, this suggests that not all statistical learning need be unsupervised. Moreover, these findings help to account for how cognitive systems may accommodate competing demands for flexibility and stability: instead of overwriting existing representations when short-term input distributions depart from the norms, the mapping from input to category representations may be dynamically - and rapidly - adjusted via error-driven learning from predictions derived from internal representations.

Learning conversational dependency: Children's response using un in Japanese

This study investigates how Japanese-speaking children learn interactional dependencies in conversations that determine the use of un, a token typically used as a positive response for yes-no questions, backchannel, and acknowledgement. We hypothesise that children learn to produce un appropriately by recognising different types of cues occurring in the immediately preceding turns. We built a set of generalised linear models on the longitudinal conversation data from seven children aged 1 to 5 years and their caregivers. Our models revealed that children not only increased their un production, but also learned to attend relevant cues in the preceding turns to understand when to respond by producing un. Children increasingly produced un when their interlocutors asked a yes-no question or signalled the continuation of their own speech. These results illustrate how children learn the probabilistic dependency between adjacent turns, and become able to participate in conversational interactions.

Network structure influences the strength of learned neural representations

Human experience is built upon sequences of discrete events. From those sequences, humans build impressively accurate models of their world. This process has been referred to as graph learning, a form of structure learning in which the mental model encodes the graph of event-to-event transition probabilities [1], [2], typically in medial temporal cortex [3]-[6]. Recent evidence suggests that some network structures are easier to learn than others [7]-[9], but the neural properties of this effect remain unknown. Here we use fMRI to show that the network structure of a temporal sequence of stimuli influences the fidelity with which those stimuli are represented in the brain. Healthy adult human participants learned a set of stimulus-motor associations following one of two graph structures. The design of our experiment allowed us to separate regional sensitivity to the structural, stimulus, and motor response components of the task. As expected, whereas the motor response could be decoded from neural representations in postcentral gyrus, the shape of the stimulus could be decoded from lateral occipital cortex. The structure of the graph impacted the nature of neural representations: when the graph was modular as opposed to lattice-like, BOLD representations in visual areas better predicted trial identity in a held-out run and displayed higher intrinsic dimensionality. Our results demonstrate that even over relatively short timescales, graph structure determines the fidelity of event representations as well as the dimensionality of the space in which those representations are encoded. More broadly, our study shows that network context influences the strength of learned neural representations, motivating future work in the design, optimization, and adaptation of network contexts for distinct types of learning over different timescales.

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.

Executive function skills account for a bilingual advantage in English novel word learning among low-income preschoolers

The current study compared economically disadvantaged bilingual and monolingual preschoolers' performance on an English novel word learning task and examined whether children's executive function (EF) skills account for differences in novel word learning performance across groups. In total, 39 English monolinguals and 35 Spanish-English bilingual preschoolers from low-income homes completed a battery of EF measures and the Quick Interactive Language Screener to gauge English novel word learning ability. Within a poverty context, bilingual preschoolers performed significantly better on measures of English novel word learning as compared with their monolingual peers. This bilingual advantage in novel word learning ability was mediated by short-term memory, but not inhibition or attention shifting, which indicates that gains in short-term memory may facilitate word learning in English for bilingual preschoolers from economically disadvantaged backgrounds. These findings have important practical implications for interventions designed to promote English vocabulary growth for low-income bilingual children.

Aging effects and feasibility of statistical learning tasks across modalities

Knowledge on statistical learning (SL) in healthy elderly is scarce. Theoretically, it is not clear whether aging affects modality-specific and/or domain-general learning mechanisms. Practically, there is a lack of research on simplified SL tasks, which would ease the burden of testing in clinical populations. Against this background, we conducted two experiments across three modalities (auditory, visual and visuomotor) in a total of 93 younger and older adults. In Experiment 1, SL was induced in all modalities. Aging effects appeared in the tasks relying on an explicit posttest to assess SL. We hypothesize that declines in domain-general processes that predominantly modulate explicit learning mechanisms underlie these aging effects. In Experiment 2, more feasible tasks were developed for which the level of SL was maintained in all modalities, except the auditory modality. These tasks are more likely to successfully measure SL in elderly (patient) populations in which task demands can be problematic.

Little fast, little slow, should I stay or should I go? Adapting cognitive control to local-global temporal prediction across typical development

Adaptive cognitive control (CC), the ability to adjust goal-directed behavior according to changing environmental demand, can be instantiated bottom-up by implicit knowledge, including temporal predictability of task-relevant events. In S1-S2 tasks, either local (trial-by-trial hazard expectation) or global (block-by-block expectation) temporal information can induce prediction, allowing for proactive action control. Recent developmental evidence showed that adaptive CC based on global temporal prediction emerges earlier than when it is based on the local one only. However, very little is known about how children learn to dynamically adjust behavior on the fly according to changing global predictive information. Addressing this issue is nevertheless crucial to unravel the mechanisms underlying adaptive CC flexibility. Here we used a modified version of the Dynamic Temporal Prediction task to investigate how typically developing younger (6-8 years) and older children (9-11 years), adolescents (12-15 years) and adults (21-31 years) use global prediction to shape adaptive CC over time. Specifically, the short-long percentage of S2 preparatory intervals was manipulated list-wide to create a slow-fast-slow-fast fixed block sequence and test how efficiently the response speed adapted accordingly. Overall, results revealed that in all groups behavioral performance is successfully adjusted as a function of global prediction in the late phase of the task (block 3 to 4). Remarkably, only adolescents and adults exhibit an early adaptation of adaptive CC (block 1 to 2), while children younger than 11 show sluggish ability in inferring implicit changes in global predictive rules. This age-related dissociation suggests that, although being present from an early age, adaptive CC based on global predictive information needs more developmental space to become flexible in an efficient way. In the light of a neuroconstructivist approach, we suggest that bottom-up driven implicit flexibility may represent a key prerequisite for the development of efficient explicit cognitive control.

The association of cognitive abilities with language disorder in 8-year-old children: A population-based clinical sample

BACKGROUND

Despite accumulated evidence that language development depends on basic cognitive processes, the balance in contributions of verbal and non-verbal cognitive skills to language abilities is still underexplored. Little is known about which cognitive measures best predict the degree of severity in children with language disorder (LD).

AIMS

To examine the association between verbal and non-verbal cognitive abilities with language abilities in typically developing and language impaired 8-year-old children, as well as which cognitive abilities are most effective in distinguishing LD severity levels.

METHODS & PROCEDURES

Children (N = 509) from the Language-8 Study, which oversampled probable cases of children with LD from a population-based cohort in Norway, were assessed at 8 years. Language skills were assessed using the Norwegian Clinical Evaluation of Language Fundamentals-4 (CELF-4). Children's verbal and non-verbal cognitive abilities were assessed via standardized cognitive measures. An exploratory factor analysis (EFA) was first conducted to uncover the underlying factor structure of the cognitive variables. Using a hierarchical multiple regression analysis, we then examined to what extent the non-verbal cognition factor explained language abilities above and beyond verbal cognition factors. Lastly, multinomial logistic regression was used to examine which cognitive measures best predicted the degree of severity in the children with LD.

OUTCOMES & RESULTS

The EFA resulted in three factors (Verbal Cognition, Processing Speed and Memory, and Non-Verbal Cognition). The hierarchical multiple regression analysis revealed that all three cognitive factors contributed significantly to individual variation in language abilities. Non-Verbal Cognition explained 5.4% variance in language abilities above and beyond that accounted for by Verbal Cognition and Processing Speed and Memory. Results from the multinomial logistic regression analysis indicated that cognitive subtests, including Familiar Sequences, WASI Vocabulary and WASI Similarities, not only distinguished LD from typically developing children, but were also efficient in distinguishing severity of LD symptoms.

CONCLUSIONS & IMPLICATIONS

This study confirms concurrent links between language and non-verbal cognitive skills above and beyond the contribution of verbal cognitive skills. The results provide further evidence that children with LD experience both language and cognitive problems in mid-childhood. Our findings suggest implications for LD intervention and diagnosis. The findings support the importance of measuring both verbal and non-verbal cognitive skills when making an LD diagnosis, and point to the potential of targeting underlying cognitive skills as one strategy to support language abilities.

WHAT THIS PAPER ADDS

What is already known on the subject Language development is dependent on basic cognitive processes. These include both verbal and non-verbal cognitive abilities. Children with LD often experience both language and cognitive problems. There is evidence that performance on cognitive tests may be associated with the degree of severity of LD. What this paper adds to existing knowledge The current results from a large population-based cohort establish that a number of verbal and non-verbal cognitive abilities are tightly linked to variation in language abilities and the degree of severity of LD. Our study confirms concurrent links between language and non-verbal cognitive abilities above and beyond the contribution of verbal cognitive abilities. We also identify specific verbal and non-verbal cognitive tests that distinguish between typical children and children with LD, as well as LD severity. What are the potential or actual clinical implications of this work? Our findings support the importance of measuring both verbal and non-verbal cognitive skills when making an LD diagnosis. Our findings also point to the potential of targeting underlying cognitive skills as one strategy to support language abilities. We suggest that future intervention studies focus on the impact of non-verbal cognitive skills on language development in children with LD.

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.

The Influence of Active and Passive Motion Experience on Infants' Visual Prediction Ability

In the present study we examined whether infants' visual prediction abilities were related to different types of motion experiences. We tested 30 6-month-old infants on a visual-spatial prediction task in which they had to visually anticipate the locational reappearance of temporarily occluded moving objects. We assigned infants to one of three experience groups: active locomotion training, passive motion experience, and a no-training control group. We tested the infants' visual prediction abilities before and after these trainings. We found improved infant predictions at a post-training test only for passively trained infants (p = .015, d = -1.033; Bonferroni corrected). Thus, we conclude that infants' visual-spatial predictions of temporally occluded moving objects was facilitated by mere movement experience, even if passive. Visual information gathered during even passive movement seemed sufficient for visual prediction.

Cascades in language acquisition: Re-thinking the linear model of development

The first 5 years of life are characterized by incredible growth across domains of child development. Drawing from over 50 years of seminal research, this chapter contextualizes recent advances in language sciences through the lens of developmental cascades to explore complexities and connections in acquisition. Converging evidence-both classic and contemporary-points to the many ways in which advances in one learning system can pose significant and lasting impacts on the advances in other learning systems. This chapter reviews evidence in developmental literature from multiple domains and disciplines (i.e., cognitive, social, motor, bilingual language learning, and communication sciences and disorders) to examine the phenomenon of developmental cascades in language acquisition.

A systematic review and Bayesian meta-analysis of the acoustic features of infant-directed speech

When speaking to infants, adults often produce speech that differs systematically from that directed to other adults. To quantify the acoustic properties of this speech style across a wide variety of languages and cultures, we extracted results from empirical studies on the acoustic features of infant-directed speech. We analysed data from 88 unique studies (734 effect sizes) on the following five acoustic parameters that have been systematically examined in the literature: fundamental frequency (f₀), f₀ variability, vowel space area, articulation rate and vowel duration. Moderator analyses were conducted in hierarchical Bayesian robust regression models to examine how these features change with infant age and differ across languages, experimental tasks and recording environments. The moderator analyses indicated that f₀, articulation rate and vowel duration became more similar to adult-directed speech over time, whereas f₀ variability and vowel space area exhibited stability throughout development. These results point the way for future research to disentangle different accounts of the functions and learnability of infant-directed speech by conducting theory-driven comparisons among different languages and using computational models to formulate testable predictions.

Implementation of an Early Communication Intervention for Young Children with Cerebral Palsy Using Single-Subject Research Design

The implementation of an intervention protocol aimed at increasing vocal complexity in three pre-linguistic children with cerebral palsy (two males, starting age 15 months, and one female, starting age 16 months) was evaluated utilising a repeated ABA case series design. The study progressed until the children were 36 months of age. Weekly probes with trained and untrained items were administered across each of three intervention blocks. Successive blocks targeted more advanced protophone production and speech movement patterns, individualised for each participant. Positive treatment effects were seen for all participants in terms of a greater rate of achievement of target protophone categories and speech movement patterns. Tau coefficients for trained items demonstrated overall moderate to large AB phase contrast effect sizes, with limited evidence of generalisation to untrained items. Control items featuring protophones and speech movements not targeted for intervention showed no change across phases for any participant. Our data suggest that emerging speech-production skills in prelinguistic infants with CP can be positively influenced through a multimodal intervention focused on capitalising on early periods of plasticity when language learning is most sensitive.

Updating Contextual Sensory Expectations for Adaptive Behavior

The brain has the extraordinary capacity to construct predictive models of the environment by internalizing statistical regularities in the sensory inputs. The resulting sensory expectations shape how we perceive and react to the world; at the neural level, this relates to decreased neural responses to expected than unexpected stimuli ("expectation suppression"). Crucially, expectations may need revision as context changes. However, existing research has often neglected this issue. Further, it is unclear whether contextual revisions apply selectively to expectations relevant to the task at hand, hence serving adaptive behavior. The present fMRI study examined how contextual visual expectations spread throughout the cortical hierarchy as we update our beliefs. We created a volatile environment: two alternating contexts contained different sequences of object images, thereby producing context-dependent expectations that needed revision when the context changed. Human participants of both sexes attended a training session before scanning to learn the contextual sequences. The fMRI experiment then tested for the emergence of contextual expectation suppression in two separate tasks, respectively, with task-relevant and task-irrelevant expectations. Effects of contextual expectation emerged progressively across the cortical hierarchy as participants attuned themselves to the context: expectation suppression appeared first in the insula, inferior frontal gyrus, and posterior parietal cortex, followed by the ventral visual stream, up to early visual cortex. This applied selectively to task-relevant expectations. Together, the present results suggest that an insular and frontoparietal executive control network may guide the flexible deployment of contextual sensory expectations for adaptive behavior in our complex and dynamic world.SIGNIFICANCE STATEMENT The world is structured by statistical regularities, which we use to predict the future. This is often accompanied by suppressed neural responses to expected compared with unexpected events ("expectation suppression"). Crucially, the world is also highly volatile and context-dependent: expected events may become unexpected when the context changes, thus raising the crucial need for belief updating. However, this issue has generally been neglected. By setting up a volatile environment, we show that expectation suppression emerges first in executive control regions, followed by relevant sensory areas, only when observers use their expectations to optimize behavior. This provides surprising yet clear evidence on how the brain controls the updating of sensory expectations for adaptive behavior in our ever-changing world.

Computational and neural mechanisms of statistical pain learning

Pain invariably changes over time. These fluctuations contain statistical regularities which, in theory, could be learned by the brain to generate expectations and control responses. We demonstrate that humans learn to extract these regularities and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian inference with dynamic update of beliefs. Healthy participants received probabilistic, volatile sequences of low and high-intensity electrical stimuli to the hand during brain fMRI. The inferred frequency of pain correlated with activity in sensorimotor cortical regions and dorsal striatum, whereas the uncertainty of these inferences was encoded in the right superior parietal cortex. Unexpected changes in stimulus frequencies drove the update of internal models by engaging premotor, prefrontal and posterior parietal regions. This study extends our understanding of sensory processing of pain to include the generation of Bayesian internal models of the temporal statistics of pain.

Attentional control in middle childhood is highly dynamic-Strong initial distraction is followed by advanced attention control

The ability to shield against distraction while focusing on a task requires the operation of executive functions and is essential for successful learning. We investigated the short-term dynamics of distraction control in a data set of 269 children aged 4-10 years and 51 adults pooled from three studies using multilevel models. Participants performed a visual categorization task while a task-irrelevant sequence of sounds was presented which consisted of frequently repeated standard sounds and rarely interspersed novel sounds. On average, participants responded slower in the categorization task after novel sounds. This distraction effect was more pronounced in children. Throughout the experiment, the initially strong distraction effects declined to the level of adults in the groups of 6- to 10-year-olds. Such a decline was neither observed in the groups of the 4- and 5-year-olds, who consistently showed a high level of distraction, nor in adults, who showed a constantly low level of distraction throughout the experimental session. Results indicate that distraction control is a highly dynamic process that qualitatively and quantitatively differs between age groups. We conclude that the analysis of short-term dynamics provides valuable insights into the development of attention control and might explain inconsistent findings regarding distraction control in middle childhood. In addition, models of attention control need to be refined to account for age-dependent rapid learning mechanisms. Our findings have implications for the design of learning situations and provide an additional source of information for the diagnosis and treatment of children with attention deficit disorders.

Infants use emotion to infer intentionality from non-random sampling events

Infants use statistical information in their environment, as well as others' emotional communication, to understand the intentions of social partners. However, rarely do researchers consider these two sources of social information in tandem. This study assessed 2-year-olds' attributions of intentionality from non-random sampling events and subsequent discrete emotion reactions. Infants observed an experimenter remove five objects from either the non-random minority (18%) or random majority (82%) of a sample and express either joy, disgust, or sadness after each selection. Two-year-olds inferred the experimenter's intentionality by giving her the object that she had previously selected when she expressed joy or disgust after non-random sampling events, but not when she expressed sadness or sampled at random. These findings demonstrate that infants use both statistical regularities and discrete emotion communication to infer an agent's intentions. In particular, the present findings show that 2-year-olds infer that an agent can intentionally select a preferred or an undesired object from a sample as a function of the discrete emotion. Implications for the development of inferring intentionality from statistical sampling events and discrete emotion communication are discussed.

Adaptive Cognitive Control in Prematurely Born Children: An HD-EEG Investigation

Preterm birth is a neurodevelopmental risk condition often associated with cognitive control (CC) impairment. Recent evidence showed that CC can be implicitly adapted through associative learning. In the present study we investigated the ability to flexibly adjust CC as a function of implicit stimulus-response temporal regularities in preterm (PT; N = 21; mean age 8 ± 1.3 years; gestational age 30 ± 18.5 weeks) and full-term (FT; N = 20; mean age 8 ± 1.3 years) school-age children. All children underwent an HD-EEG recording while undergoing the Dynamic Temporal Prediction (DTP) task, a simple S1–S2 detection task purposely designed to generate local-global temporal predictability of imperative stimuli. The Wisconsin card sorting test (WCST) was administered to measure explicit CC. The PT group showed more premature and slower (DTP) as well as perseverative (WCST) responses than the FT group. Moreover, pre-terms showed poor adaptive CC as revealed by less efficient global response-speed adjustment. This behavioral pattern was mirrored by a reduced and less sensitive to global manipulation anticipatory Contingent Negative Variation (CNV) and by different cortical source recruitment. These findings suggest that implicit CC may be a reliable endophenotypic marker of atypical cognitive development associated with preterm birth.

Studying the Developing Brain in Real-World Contexts: Moving From Castles in the Air to Castles on the Ground

Most current research in cognitive neuroscience uses standardized non-ecological experiments to study the developing brain. But these approaches do a poor job of mimicking the real-world, and thus can only provide a distorted picture of how cognitive operations and brain development unfold outside of the lab. Here we consider future research avenues which may lead to a better appreciation of how developing brains dynamically interact with a complex real-world environment, and how cognition develops over time. We raise several problems faced by current mainstream methods in the field, before briefly reviewing novel promising approaches that alleviate some of these issues. First, we consider research that examines perception by measuring entrainment between brain activity and temporal patterns in naturalistic stimuli. Second, we consider research that examines our ability to parse our continuous experience into discrete events, and how this ability develops over time. Third, we consider the role of children as active agents in selecting what they sample from the environment from one moment to the next. Fourth, we consider new approaches that measure how mutual influences between children and others are instantiated in suprapersonal brain networks. Finally, we discuss how we may reduce adult biases when designing developmental studies. Together, these approaches have great potential to further our understanding of how the developing brain learns to process information, and to control complex real-world behaviors.

Acquiring Complex Communicative Systems: Statistical Learning of Language and Emotion

During the early postnatal years, most infants rapidly learn to understand two naturally evolved communication systems: language and emotion. While these two domains include different types of content knowledge, it is possible that similar learning processes subserve their acquisition. In this review, we compare the learnable statistical regularities in language and emotion input. We then consider how domain-general learning abilities may underly the acquisition of language and emotion, and how this process may be constrained in each domain. This comparative developmental approach can advance our understanding of how humans learn to communicate with others.

Children's thinking about group-based social hierarchies

Wealth, power, and status are distributed unevenly across social groups. A surge of recent research reveals that people being recognizing, representing, and reasoning about group-based patterns of inequity during the first years of life. We first synthesize recent research on what children learn about group-based social hierarchies as well as how this learning occurs. We then discuss how children not only learn about societal structures but become active participants in them. Studying the origins and development of children's thoughts and behavior regarding group-based social hierarchies provides valuable insight into how systems of inequity are perpetuated across generations and how intergroup biases related to wealth, power, and status may be mitigated and reshaped early in development.

Statistical Learning in Vision

Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

How Tone, Intonation and Emotion Shape the Development of Infants’ Fundamental Frequency Perception

Fundamental frequency (ƒ₀), perceived as pitch, is the first and arguably most salient auditory component humans are exposed to since the beginning of life. It carries multiple linguistic (e.g., word meaning) and paralinguistic (e.g., speakers’ emotion) functions in speech and communication. The mappings between these functions and ƒ₀ features vary within a language and differ cross-linguistically. For instance, a rising pitch can be perceived as a question in English but a lexical tone in Mandarin. Such variations mean that infants must learn the specific mappings based on their respective linguistic and social environments. To date, canonical theoretical frameworks and most empirical studies do not view or consider the multi-functionality of ƒ₀, but typically focus on individual functions. More importantly, despite the eventual mastery of ƒ₀ in communication, it is unclear how infants learn to decompose and recognize these overlapping functions carried by ƒ₀. In this paper, we review the symbioses and synergies of the lexical, intonational, and emotional functions that can be carried by ƒ₀ and are being acquired throughout infancy. On the basis of our review, we put forward the Learnability Hypothesis that infants decompose and acquire multiple ƒ₀ functions through native/environmental experiences. Under this hypothesis, we propose representative cases such as the synergy scenario, where infants use visual cues to disambiguate and decompose the different ƒ₀ functions. Further, viable ways to test the scenarios derived from this hypothesis are suggested across auditory and visual modalities. Discovering how infants learn to master the diverse functions carried by ƒ₀ can increase our understanding of linguistic systems, auditory processing and communication functions.

Children Treat Grammatical Errors Differently for Native and Non-Native Speakers

Both children and adults demonstrate biases against non-native speakers. However, in some situations, adults act more generously towards non-native speakers than towards native speakers. In particular, adults judge errors from non-native speakers less harshly, presumably because they expect such errors. In the present study, we asked whether 5-6-year-old children place less weight on errors from speakers with a foreign accent. In Experiment 1, 5- and 6-year-old children (N = 80) listened to pairs of either native or foreign-accented speakers (between-subjects) label objects. For native speaker pairings, children preferred information provided by grammatical speakers over information from speakers who made subject-verb agreement errors. In contrast, children chose between foreign-accented speakers at chance. In Experiment 2 (N = 40), children preferred information from grammatical foreign-accented speakers over information from foreign-accented speakers who produced word-order violations. These findings constitute the first demonstration that children treat speech errors differently based on a speaker’s language background.