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Caroline F. Rowland; Amy Bidgood; Gary Jones; Andrew Jessop; Paula Stinson; Julian M. Pine; Samantha Durrant; Michelle S. Peter – Language Learning, 2025
A strong predictor of children's language is performance on non-word repetition (NWR) tasks. However, the basis of this relationship remains unknown. Some suggest that NWR tasks measure phonological working memory, which then affects language growth. Others argue that children's knowledge of language/language experience affects NWR performance. A…
Descriptors: Vocabulary Development, Comparative Analysis, Computational Linguistics, Language Skills
Huteng Dai – ProQuest LLC, 2024
In this dissertation, I establish a research program that uses computational modeling as a testbed for theories of phonological learning. This dissertation focuses on a fundamental question: how do children acquire sound patterns from noisy, real-world data, especially in the presence of lexical exceptions that defy regular patterns? For instance,…
Descriptors: Phonology, Language Acquisition, Computational Linguistics, Linguistic Theory
Alex Warstadt – ProQuest LLC, 2022
Data-driven learning uncontroversially plays a role in human language acquisition--how large a role is a matter of much debate. The success of artificial neural networks in NLP in recent years calls for a re-evaluation of our understanding of the possibilities for learning grammar from data alone. This dissertation argues the case for using…
Descriptors: Language Acquisition, Artificial Intelligence, Computational Linguistics, Ethics
Trott, Sean; Jones, Cameron; Chang, Tyler; Michaelov, James; Bergen, Benjamin – Cognitive Science, 2023
Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test the viability of the language exposure hypothesis by assessing whether models…
Descriptors: Models, Language Processing, Beliefs, Child Development
Jennifer Hu – ProQuest LLC, 2023
Language is one of the hallmarks of intelligence, demanding explanation in a theory of human cognition. However, language presents unique practical challenges for quantitative empirical research, making many linguistic theories difficult to test at naturalistic scales. Artificial neural network language models (LMs) provide a new tool for studying…
Descriptors: Linguistic Theory, Computational Linguistics, Models, Language Research
Qihui Xu – ProQuest LLC, 2022
How early do children produce multiword utterances? Do children's early utterances reflect abstract syntactic knowledge or are they the result of data-driven learning? We examine this issue through corpus analysis, computational modeling, and adult simulation experiments. Chapter 1 investigates when children start producing multiword utterances;…
Descriptors: Language Acquisition, Speech Communication, Computational Linguistics, Syntax
Aarnes Gudmestad; Thomas A. Metzger – Language Learning, 2025
In this Methods Showcase Article, we illustrate mixed-effects modeling with a multinomial dependent variable as a means of explaining complexities in language. We model data on future-time reference in second language Spanish, which consists of a nominal dependent variable that has three levels, measured over 73 participants. We offer step-by-step…
Descriptors: Second Language Learning, Spanish, Applied Linguistics, Predictor Variables
Mahowald, Kyle; Kachergis, George; Frank, Michael C. – First Language, 2020
Ambridge calls for exemplar-based accounts of language acquisition. Do modern neural networks such as transformers or word2vec -- which have been extremely successful in modern natural language processing (NLP) applications -- count? Although these models often have ample parametric complexity to store exemplars from their training data, they also…
Descriptors: Models, Language Processing, Computational Linguistics, Language Acquisition
Chandler, Steve – First Language, 2020
Ambridge reviews and augments an impressive body of research demonstrating both the advantages and the necessity of an exemplar-based model of knowledge of one's language. He cites three computational models that have been applied successfully to issues of phonology and morphology. Focusing on Ambridge's discussion of sentence-level constructions,…
Descriptors: Models, Figurative Language, Language Processing, Language Acquisition
Lieven, Elena; Ferry, Alissa; Theakston, Anna; Twomey, Katherine E. – First Language, 2020
During language acquisition children generalise at multiple layers of granularity. Ambridge argues that abstraction-based accounts suffer from lumping (over-general abstractions) or splitting (over-precise abstractions). Ambridge argues that the only way to overcome this conundrum is in a purely exemplar/analogy-based system in which…
Descriptors: Language Acquisition, Children, Generalization, Abstract Reasoning
Casillas, Marisa – Child Development Perspectives, 2023
In this article, I advocate for an enriched view of children's linguistic input, with the aim of building sustainable and tangible links between theoretical models of language development and families' everyday experiences. Children's language experiences constrain theoretical models in ways that may illuminate universal learning biases. However,…
Descriptors: Child Language, Linguistic Input, Language Acquisition, Context Effect
Lifeng Jin – ProQuest LLC, 2020
Syntactic structures are unobserved theoretical constructs which are useful in explaining a wide range of linguistic and psychological phenomena. Language acquisition studies how such latent structures are acquired by human learners through many hypothesized learning mechanisms and apparatuses, which can be genetically endowed or of general…
Descriptors: Syntax, Computational Linguistics, Learning Processes, Models
Wilson, Kyra; Frank, Michael C.; Fourtassi, Abdellah – Journal of Cognition and Development, 2023
In order for children to understand and reason about the world in an adult-like fashion, they need to learn that conceptual categories are organized in a hierarchical fashion (e.g., a dog is also an animal). While children learn from their first-hand observation of the world, social knowledge transmission via language can also play an important…
Descriptors: Cues, Linguistic Input, Language Acquisition, Speech Communication
Adger, David – First Language, 2020
The syntactic behaviour of human beings cannot be explained by analogical generalization on the basis of concrete exemplars: analogies in surface form are insufficient to account for human grammatical knowledge, because they fail to hold in situations where they should, and fail to extend in situations where they need to. [For Ben Ambridge's…
Descriptors: Syntax, Figurative Language, Models, Generalization
Portelance, Eva; Duan, Yuguang; Frank, Michael C.; Lupyan, Gary – Cognitive Science, 2023
What makes a word easy to learn? Early-learned words are frequent and tend to name concrete referents. But words typically do not occur in isolation. Some words are predictable from their contexts; others are less so. Here, we investigate whether predictability relates to when children start producing different words (age of acquisition; AoA). We…
Descriptors: Prediction, Vocabulary Development, Word Frequency, Child Development