A new way to identify if variation in children's input could be developmentally meaningful: Using computational cognitive modeling to assess input across socio-economic status for syntactic islands.

While there are always differences in children's input, it is unclear how often these differences impact language development - that is, are developmentally meaningful - and why they do (or do not) do so. We describe a new approach using computational cognitive modeling that links children's input to predicted language development outcomes, and can identify if input differences are potentially developmentally meaningful. We use this approach to investigate if there is developmentally-meaningful input variation across socio-economic status (SES) with respect to the complex syntactic knowledge called syntactic islands. We focus on four island types with available data about the target linguistic behavior. Despite several measurable input differences for syntactic island input across SES, our model predicts this variation not to be developmentally meaningful: it predicts no differences in the syntactic island knowledge that can be learned from that input. We discuss implications for language development variability across SES.

Rate of speech affects the comprehension of pronouns in children with developmental language disorder.

This study examined whether children with Developmental Language Disorder (DLD) have knowledge of binding principles (i.e., linking pronouns to their structurally licensed antecedent) during real-time sentence processing (cross-modal priming, real-time) and overt comprehension (sentence-picture matching, interpretative) and whether rate of speech impacted access to that knowledge. Fourteen children with DLD participated in two experiments, with sentences presented auditorily at either a regular or slow speech rate. Sentences were matched except to contain a pronoun, reflexive, or noun phrase (control) in the same syntactic position. Experiment (1) used a cross-modal picture priming paradigm to test real-time pronoun-antecedent linking abilities at both rates of speech. Children were instructed to make a binary decision during the uninterrupted auditory presentation of a sentence to a visually presented image (of the antecedent) at the offset of a pronoun, a reflexive, or a control noun. Response times between conditions (e.g., pronoun vs. control noun) were compared to determine whether participants showed evidence of facilitative priming (faster response times in the pronoun than control noun condition) at either speech rate. Experiment (2) used an auditory sentence-picture-matching task to test final comprehension of similar sentences containing a pronoun or reflexive. Accuracy was compared across both speech rates. For Experiment (1), children with DLD did not show evidence of real-time pronoun-antecedent priming at the regular speech rate. However, when sentences were slowed, they showed facilitative priming for the pronoun condition. For experiment (2), children with DLD performed at-chance when interpreting sentences with pronouns regardless of speech rate. While children with DLD have been shown to have difficulty processing sentences containing anaphors (such as pronouns), results suggest that this is not due to loss of intrinsic knowledge of binding principles. By slowing the rate of speech input, we showed that children with DLD do have access to that knowledge and can make the correct link during real-time processing between a pronoun and its structurally licensed antecedent (Experiment 1) but need more time to do so. However, the effect of slowed speech input does not extend to final comprehension (Experiment 2).

Lexically-specific syntactic restrictions in second-language speakers.

In two structural priming experiments, we investigated the representations of lexically-specific syntactic restrictions of English verbs for highly proficient and immersed second language (L2) speakers of English. We considered the interplay of two possible mechanisms: generalization from the first language (L1) and statistical learning within the L2 (both of abstract structure and of lexically-specific information). In both experiments, L2 speakers with either Germanic or Romance languages as L1 were primed to produce dispreferred double-object structures involving non-alternating dative verbs. Priming occurred from ungrammatical double-object primes involving different non-alternating verbs (Experiment 1) and from grammatical primes involving alternating verbs (Experiment 2), supporting abstract statistical learning within the L2. However, we found no differences between L1-Germanic speakers (who have the double object structure in their L1) and L1-Romance speakers (who do not), inconsistent with the prediction for between-group differences of the L1-generalization account. Additionally, L2 speakers in Experiment 2 showed a lexical boost: There was stronger priming after (dispreferred) non-alternating same-verb double object primes than after (grammatical) alternating different-verb primes. Such lexically-driven persistence was also shown by L1 English speakers (Ivanova et al., 2012a) and may underlie statistical learning of lexically-dependent structural regularities. We conclude that lexically-specific syntactic restrictions in highly proficient and immersed L2 speakers are shaped by statistical learning (both abstract and lexically-specific) within the L2, but not by generalization from the L1.

From Syntactic to Semantic Interoperability Using a Hyperontology in the Oncology Domain.

Interoperability is crucial to overcoming various challenges of data integration in the healthcare domain. While OMOP and FHIR data standards handle syntactic heterogeneity among heterogeneous data sources, ontologies support semantic interoperability to overcome the complexity and disparity of healthcare data. This study proposes an ontological approach in the context of the EUCAIM project to support semantic interoperability among distributed big data repositories that have applied heterogeneous cancer image data models using a semantically well-founded Hyperontology for the oncology domain.

What Silent Pauses Can 'Tell' Us About the Storytelling Skills of Autistic Children: Relations Between Pausing, Language Skills and Executive Functions.

Silent pauses may serve communicative purposes such as demarcating boundaries between discourse units in language production. Previous research has shown that autistic children differ in their pausing behavior from typically-developing (TD) peers, however, the factors behind this difference remain underexplored. The current study was aimed at comparing the use of silent pauses in the narrative production of autistic children and age-matched TD children, and also to identify possible relations between pausing behavior and the children's language and executive function abilities. According to the study's findings, the autistic children did not differ from their TD peers in the use of grammatical pauses, however, the former tended to produce significantly less syntactically complex narratives than the TD group, which increased the likelihood that the autistic group would pause appropriately at phrasal boundaries. Though we have found low rates of ungrammatical silent pauses and omitted pauses in obligatory discourse contexts across both groups, autistic children with lower cognitive flexibility tended to use more ungrammatical pauses than their peers with higher cognitive flexibility scores. Also, the autistic group tended to omit obligatory silent pauses more often as their narration became more complex. The results demonstrate that syntactic complexity in narrative production modulated autistic children's pausing behavior, and that structurally simple narrations boosted the autistic group's appropriate use of grammatical pauses. The overall findings also demonstrate the importance of studying silent pauses in the narrative discourse of autistic children, and also highlight the links between silent pauses and the children's syntactic and cognitive skills.

Semantic and syntactic processing of emojis in sentential intermediate positions.

The current study investigated the neuro mechanisms of emoji processing as sentence predicate in written context. In the hybrid textuality which is more cognitively engaging, emojis in sentential intermediate positions were designed as either congruent or incongruent to the context. The results showed that incongruent words led to a robust N400 effect, while incongruent emojis only elicited the P600 effect. It implies that semantics and syntax of words can be separated while those of emojis seem to be integrated together. That is, when the meaning of the emoji is violated to the sentential context, its grammatical role cannot be well interpreted, especially when it is used as a key grammatical component in a sentence, such as the predicate. Thus, it shows that even though the meaning of emojis can be interpreted by readers, their syntactic and semantic functions cannot be clearly separated. In comparison with word processing, the larger amplitude with emojis in the time window of 350-500 ms shows more cognitive efforts in emoji semantic processing, possibly arising from the switch of modalities within the visual channel, that is, the multimodal cognitive load.

Processing causal structure sentences in Mandarin Chinese: an eye movements study.

It remains uncertain whether causal structure prediction can improve comprehension in Chinese sentences and whether the position of the headword mediates the prediction effect. We conducted an experiment to explore the effect of causal prediction and headword position in Chinese sentence reading. Participants were asked to read sentences containing causal connectives with their eye movements recorded. In the experiment, we manipulated the causal structure of the sentence and the position of the headword. We found a promoting effect of causal structure on first-pass reading time and a hindering impact on total reading time. However, the effect was not mediated by the headword position. The results show that causal syntactic prediction facilitated early-stage processing and increased the integration cost in the late stage of Chinese sentence processing. These findings also support the constraint-based approach, which suggests an isolation between semantic and syntactic processing.

Verbal short term memory contribution to sentence comprehension decreases with increasing syntactic complexity in people with aphasia.

Sentence comprehension requires the integration of linguistic units presented in a temporal sequence based on a non-linear underlying syntactic structure. While it is uncontroversial that storage is mandatory for this process, there are opposing views regarding the relevance of general short-term-/working-memory capacities (STM/WM) versus language specific resources. Here we report results from 43 participants with an acquired brain lesion in the extended left hemispheric language network and resulting language deficits, who performed a sentence-to-picture matching task and an experimental task assessing phonological short-term memory. The sentence task systematically varied syntactic complexity (embedding depth and argument order) while lengths, number of propositions and plausibility were kept constant. Clinical data including digit-/ block-spans and lesion size and site were additionally used in the analyses. Correlational analyses confirm that performance on STM/WM-tasks (experimental task and digit-span) are the only two relevant predictors for correct sentence-picture-matching, while reaction times only depended on age and lesion size. Notably increasing syntactic complexity reduced the correlational strength speaking for the additional recruitment of language specific resources independent of more general verbal STM/WM capacities, when resolving complex syntactic structure. The complementary lesion-behaviour analysis yielded different lesion volumes correlating with either the sentence-task or the STM-task. Factoring out STM measures lesions in the anterior temporal lobe correlated with a larger decrease in accuracy with increasing syntactic complexity. We conclude that overall sentence comprehension depends on STM/WM capacity, while increases in syntactic complexity tax another independent cognitive resource.

Neural substrates for regulating self-grooming behavior in rodents. / 啮齿动物自我梳理行为调控的神经基质.

Grooming, as an evolutionarily conserved repetitive behavior, is common in various animals, including humans, and serves essential functions including, but not limited to, hygiene maintenance, thermoregulation, de-arousal, stress reduction, and social behaviors. In rodents, grooming involves a patterned and sequenced structure, known as the syntactic chain with four phases that comprise repeated stereotyped movements happening in a cephalocaudal progression style, beginning from the nose to the face, to the head, and finally ending with body licking. The context-dependent occurrence of grooming behavior indicates its adaptive significance. This review briefly summarizes the neural substrates responsible for rodent grooming behavior and explores its relevance in rodent models of neuropsychiatric disorders and neurodegenerative diseases with aberrant grooming phenotypes. We further emphasize the utility of rodent grooming as a reliable measure of repetitive behavior in neuropsychiatric models, holding promise for translational psychiatry. Herein, we mainly focus on rodent self-grooming. Allogrooming (grooming being applied on one animal by its conspecifics via licking or carefully nibbling) and heterogrooming (a form of grooming behavior directing towards another animal, which occurs in other contexts, such as maternal, sexual, aggressive, or social behaviors) are not covered due to space constraints.

Location-enhanced syntactic knowledge for biomedical relation extraction.

Biomedical relation extraction has long been considered a challenging task due to the specialization and complexity of biomedical texts. Syntactic knowledge has been widely employed in existing research to enhance relation extraction, providing guidance for the semantic understanding and text representation of models. However, the utilization of syntactic knowledge in most studies is not exhaustive, and there is often a lack of fine-grained noise reduction, leading to confusion in relation classification. In this paper, we propose an attention generator that comprehensively considers both syntactic dependency type information and syntactic position information to distinguish the importance of different dependency connections. Additionally, we integrate positional information, dependency type information, and word representations together to introduce location-enhanced syntactic knowledge for guiding our biomedical relation extraction. Experimental results on three widely used English benchmark datasets in the biomedical domain consistently outperform a range of baseline models, demonstrating that our approach not only makes full use of syntactic knowledge but also effectively reduces the impact of noisy words.

Assessing inclusion and representativeness on digital platforms for health education: Evidence from YouTube.

BACKGROUND: Studies confirm that significant biases exist in online recommendation platforms, exacerbating pre-existing disparities and leading to less-than-optimal outcomes for underrepresented demographics. We study issues of bias in inclusion and representativeness in the context of healthcare information disseminated via videos on the YouTube social media platform, a widely used online channel for multi-media rich information. With one in three US adults using the Internet to learn about a health concern, it is critical to assess inclusivity and representativeness regarding how health information is disseminated by digital platforms such as YouTube. METHODS: Leveraging methods from fair machine learning (ML), natural language processing and voice and facial recognition methods, we examine inclusivity and representativeness of video content presenters using a large corpus of videos and their metadata on a chronic condition (diabetes) extracted from the YouTube platform. Regression models are used to determine whether presenter demographics impact video popularity, measured by the video's average daily view count. A video that generates a higher view count is considered to be more popular. RESULTS: The voice and facial recognition methods predicted the gender and race of the presenter with reasonable success. Gender is predicted through voice recognition (accuracy = 78%, AUC = 76%), while the gender and race predictions use facial recognition (accuracy = 93%, AUC = 92% and accuracy = 82%, AUC = 80%, respectively). The gender of the presenter is more significant for video views only when the face of the presenter is not visible while videos with male presenters with no face visibility have a positive relationship with view counts. Furthermore, videos with white and male presenters have a positive influence on view counts while videos with female and non - white group have high view counts. CONCLUSION: Presenters' demographics do have an influence on average daily view count of videos viewed on social media platforms as shown by advanced voice and facial recognition algorithms used for assessing inclusion and representativeness of the video content. Future research can explore short videos and those at the channel level because popularity of the channel name and the number of videos associated with that channel do have an influence on view counts.

A syntactic evidence network model for fact verification.

In natural language processing, fact verification is a very challenging task, which requires retrieving multiple evidence sentences from a reliable corpus to verify the authenticity of a claim. Although most of the current deep learning methods use the attention mechanism for fact verification, they have not considered imposing attentional constraints on important related words in the claim and evidence sentences, resulting in inaccurate attention for some irrelevant words. In this paper, we propose a syntactic evidence network (SENet) model which incorporates entity keywords, syntactic information and sentence attention for fact verification. The SENet model extracts entity keywords from claim and evidence sentences, and uses a pre-trained syntactic dependency parser to extract the corresponding syntactic sentence structures and incorporates the extracted syntactic information into the attention mechanism for language-driven word representation. In addition, the sentence attention mechanism is applied to obtain a richer semantic representation. We have conducted experiments on the FEVER and UKP Snopes datasets for performance evaluation. Our SENet model has achieved 78.69% in Label Accuracy and 75.63% in FEVER Score on the FEVER dataset. In addition, our SENet model also has achieved 65.0% in precision and 61.2% in macro F1 on the UKP Snopes dataset. The experimental results have shown that our proposed SENet model has outperformed the baseline models and achieved the state-of-the-art performance for fact verification.

Continuous Theta-Burst Stimulation on the Left Posterior Inferior Frontal Gyrus Perturbs Complex Syntactic Processing Stability in Mandarin Chinese.

The structure of human language is inherently hierarchical. The left posterior inferior frontal gyrus (LpIFG) is proposed to be a core region for constructing syntactic hierarchies. However, it remains unclear whether LpIFG plays a causal role in syntactic processing in Mandarin Chinese and whether its contribution depends on syntactic complexity, working memory, or both. We addressed these questions by applying inhibitory continuous theta-burst stimulation (cTBS) over LpIFG. Thirty-two participants processed sentences containing embedded relative clauses (i.e., complex syntactic processing), syntactically simpler coordinated sentences (i.e., simple syntactic processing), and non-hierarchical word lists (i.e., word list processing) after receiving real or sham cTBS. We found that cTBS significantly increased the coefficient of variation, a representative index of processing stability, in complex syntactic processing (esp., when subject relative clause was embedded) but not in the other two conditions. No significant changes in d' and reaction time were detected in these conditions. The findings suggest that (a) inhibitory effect of cTBS on the LpIFG might be prominent in perturbing the complex syntactic processing stability but subtle in altering the processing quality; and (b) the causal role of the LpIFG seems to be specific for syntactic processing rather than working memory capacity, further evidencing their separability in LpIFG. Collectively, these results support the notion of the LpIFG as a core region for complex syntactic processing across languages.

Knotify_V2.0: Deciphering RNA Secondary Structures with H-Type Pseudoknots and Hairpin Loops.

Accurately predicting the pairing order of bases in RNA molecules is essential for anticipating RNA secondary structures. Consequently, this task holds significant importance in unveiling previously unknown biological processes. The urgent need to comprehend RNA structures has been accentuated by the unprecedented impact of the widespread COVID-19 pandemic. This paper presents a framework, Knotify_V2.0, which makes use of syntactic pattern recognition techniques in order to predict RNA structures, with a specific emphasis on tackling the demanding task of predicting H-type pseudoknots that encompass bulges and hairpins. By leveraging the expressive capabilities of a Context-Free Grammar (CFG), the suggested framework integrates the inherent benefits of CFG and makes use of minimum free energy and maximum base pairing criteria. This integration enables the effective management of this inherently ambiguous task. The main contribution of Knotify_V2.0 compared to earlier versions lies in its capacity to identify additional motifs like bulges and hairpins within the internal loops of the pseudoknot. Notably, the proposed methodology, Knotify_V2.0, demonstrates superior accuracy in predicting core stems compared to state-of-the-art frameworks. Knotify_V2.0 exhibited exceptional performance by accurately identifying both core base pairing that form the ground truth pseudoknot in 70% of the examined sequences. Furthermore, Knotify_V2.0 narrowed the performance gap with Knotty, which had demonstrated better performance than Knotify and even surpassed it in Recall and F1-score metrics. Knotify_V2.0 achieved a higher count of true positives (tp) and a significantly lower count of false negatives (fn) compared to Knotify, highlighting improvements in Prediction and Recall metrics, respectively. Consequently, Knotify_V2.0 achieved a higher F1-score than any other platform. The source code and comprehensive implementation details of Knotify_V2.0 are publicly available on GitHub.

Creation of Standardized Common Data Elements for Diagnostic Tests in Infectious Disease Studies: Semantic and Syntactic Mapping.

BACKGROUND: It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets. OBJECTIVE: This study's objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials. METHODS: We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs. RESULTS: Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date. CONCLUSIONS: The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element's (variable's) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by "wrapping" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium's Clinical Data Acquisition Standards Harmonization Model.

Semantic and Syntactic Predictions in Reading Aloud: Are Good Predictors Good Statistical Learners?

Recent research suggests that becoming a fluent reader may partially rely on a domain-general statistical learning (SL) mechanism that allows a person to automatically extract predictable patterns from the sensory input. The goal of the present study was to investigate a potential link between SL and the ability to make linguistic predictions. All previous studies investigated quite general levels of reading ability rather than the dynamic process of making linguistic predictions. We thus used a recently developed predictive reading task, which consisted of having participants read aloud words that were preceded by either semantically or syntactically predictive contexts. To measure the componential nature of SL, we used a visual and an auditory SL task (VSL, ASL) and the classic serial reaction time task (SRT). General reading ability was assessed with a reading speed/comprehension test. The study was conducted online on a sample of 120 participants to make it possible to explore interindividual differences. The results showed only weak and sometimes even negative correlations between the various SL measures. ASL correlated positively and predicted general reading ability but neither semantic nor syntactic prediction effects. Similarly, one of the SRT measures was significantly associated with reading level and reading speed but not with linguistic prediction effects. In sum, there is little evidence that domain-general SL is a good predictor of people's ability to make domain-specific linguistic predictions. In contrast, SL shows a weak but significant association with general reading ability.

Neural Networks as Cognitive Models of the Processing of Syntactic Constraints.

Languages are governed by syntactic constraints-structural rules that determine which sentences are grammatical in the language. In English, one such constraint is subject-verb agreement, which dictates that the number of a verb must match the number of its corresponding subject: "the dogs run", but "the dog runs". While this constraint appears to be simple, in practice speakers make agreement errors, particularly when a noun phrase near the verb differs in number from the subject (for example, a speaker might produce the ungrammatical sentence "the key to the cabinets are rusty"). This phenomenon, referred to as agreement attraction, is sensitive to a wide range of properties of the sentence; no single existing model is able to generate predictions for the wide variety of materials studied in the human experimental literature. We explore the viability of neural network language models-broad-coverage systems trained to predict the next word in a corpus-as a framework for addressing this limitation. We analyze the agreement errors made by Long Short-Term Memory (LSTM) networks and compare them to those of humans. The models successfully simulate certain results, such as the so-called number asymmetry and the difference between attraction strength in grammatical and ungrammatical sentences, but failed to simulate others, such as the effect of syntactic distance or notional (conceptual) number. We further evaluate networks trained with explicit syntactic supervision, and find that this form of supervision does not always lead to more human-like syntactic behavior. Finally, we show that the corpus used to train a network significantly affects the pattern of agreement errors produced by the network, and discuss the strengths and limitations of neural networks as a tool for understanding human syntactic processing.

Altered neural oscillations in classical galactosaemia during sentence production.

Classical galactosaemia (CG) is a hereditary disease in galactose metabolism that despite dietary treatment is characterized by a wide range of cognitive deficits, among which is language production. CG brain functioning has been studied with several neuroimaging techniques, which revealed both structural and functional atypicalities. In the present study, for the first time, we compared the oscillatory dynamics, especially the power spectrum and time-frequency representations (TFR), in the electroencephalography (EEG) of CG patients and healthy controls while they were performing a language production task. Twenty-one CG patients and 19 healthy controls described animated scenes, either in full sentences or in words, indicating two levels of complexity in syntactic planning. Based on previous work on the P300 event related potential (ERP) and its relation with theta frequency, we hypothesized that the oscillatory activity of patients and controls would differ in theta power and TFR. With regard to behavior, reaction times showed that patients are slower, reflecting the language deficit. In the power spectrum, we observed significant higher power in patients in delta (1-3 Hz), theta (4-7 Hz), beta (15-30 Hz) and gamma (30-70 Hz) frequencies, but not in alpha (8-12 Hz), suggesting an atypical oscillatory profile. The time-frequency analysis revealed significantly weaker event-related theta synchronization (ERS) and alpha desynchronization (ERD) in patients in the sentence condition. The data support the hypothesis that CG language difficulties relate to theta-alpha brain oscillations.

It's All in the Interaction: Early Acquired Words Are Both Frequent and Highly Imageable.

Prior studies have found that children are more likely to learn words that are frequent in the input and highly imageable. Many theories of word learning, however, predict that these variables should interact, particularly early in development: frequency of a form is of little use if you cannot infer its meaning, and a concrete word cannot be acquired if you never hear it. The present study explores this interaction, how it changes over time and its relationship to syntactic category effects in children acquiring American English. We analyzed 1461 monolingual English-speaking children aged 1;4-2;6 from the MB-CDI norming study (Fenson et al., 1994). Word frequency was estimated from the CHILDES database, and imageability was measured using adult ratings. There was a strong over-additive interaction between frequency and imageability, such that children were more likely to learn a word if it was both highly imageable and very frequent. This interaction was larger in younger children than in older children. There were reliable differences between syntactic categories independent of frequency and imageability, which did not interact with age. These findings are consistent with theories in which children's early words are acquired by mapping frequent word forms onto concrete, perceptually available referents, such that highly frequent items are only acquired if they are also imageable, and vice versa.

Learning verbs in English and Korean: The roles of word order and argument drop.

To learn new words, particularly verbs, child learners have been shown to benefit from the linguistic contexts in which the words appear. However, cross-linguistic differences affect how this process unfolds. One previous study found that children's abilities to learn a new verb differed across Korean and English as a function of the sentence in which the verb occurred (Arunachalam et al., 2013). The authors hypothesized that the properties of word order and argument drop, which vary systematically in these two languages, were driving the differences. In the current study, we pursued this finding to ask if the difference persists later in development, or if children acquiring different languages come to appear more similar as their linguistic knowledge and learning capacities increase. Preschool-aged monolingual English learners (N = 80) and monolingual Korean learners (N = 64) were presented with novel verbs in contexts that varied in word order and argument drop and accompanying visual stimuli. We assessed their learning by measuring accuracy in a forced-choice pointing task, and we measured eye gaze during the learning phase as an indicator of the processes by which they mapped the novel verbs to meaning. Unlike previous studies which identified differences between English and Korean learning 2-year-olds in a similar task, our results revealed similarities between the two language groups with these older preschoolers. We interpret our results as evidence that over the course of early childhood, children become adept at learning from a larger variety of contexts, such that differences between learners of different languages are attenuated.