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David Abugaber – ProQuest LLC, 2022
Learning new languages is a complex task involving both explicit and implicit processes (i.e., that do/do not involve awareness). Understanding how these processes interact is essential to a full account of second language (L2) learning, but accounts vary as to whether explicit processes help (e.g., DeKeyser, 2007), hinder (e.g., Ellis &…
Descriptors: Second Language Instruction, Second Language Learning, Artificial Languages, Task Analysis
Dionysia Saratsli – ProQuest LLC, 2022
It is often assumed that cross-linguistically more prevalent distinctions are easier to learn potentially due to their conceptual naturalness. Prior work supports this hypothesis in phonology, morphology and syntax but has not addressed semantics. This work aims to unravel the potential factors that contribute to the learnability and the…
Descriptors: Semantics, Grammar, English, Artificial Languages
Lai, Wei; Rácz, Péter; Roberts, Gareth – Cognitive Science, 2020
How do speakers learn the social meaning of different linguistic variants, and what factors influence how likely a particular social-linguistic association is to be learned? It has been argued that the social meaning of more salient variants should be learned faster, and that learners' pre-existing experience of a variant will influence its…
Descriptors: Language Variation, Second Language Learning, Sociolinguistics, Prior Learning
Poletiek, Fenna H.; Monaghan, Padraic; van de Velde, Maartje; Bocanegra, Bruno R. – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2021
Language is infinitely productive because syntax defines dependencies between grammatical categories of words and constituents, so there is interchangeability of these words and constituents within syntactic structures. Previous laboratory-based studies of language learning have shown that complex language structures like hierarchical center…
Descriptors: Semantics, Syntax, Grammar, Generalization
Ouyang, Long; Boroditsky, Lera; Frank, Michael C. – Cognitive Science, 2017
Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of…
Descriptors: Semiotics, Computational Linguistics, Syntax, Semantics
Hudson Kam, Carla L. – Language Learning and Development, 2018
Adult learners know that language is for communicating and that there are patterns in the language that need to be learned. This affects the way they engage with language input; they search for form-meaning linkages, and this effortful engagement could interfere with their learning, especially for things like grammatical gender that often have at…
Descriptors: Infants, Adult Learning, Grammar, Language Patterns
Taylor, J. S. H.; Plunkett, Kim; Nation, Kate – Journal of Experimental Psychology: Learning, Memory, and Cognition, 2011
Two experiments explored learning, generalization, and the influence of semantics on orthographic processing in an artificial language. In Experiment 1, 16 adults learned to read 36 novel words written in novel characters. Posttraining, participants discriminated trained from untrained items and generalized to novel items, demonstrating extraction…
Descriptors: Semantics, Artificial Languages, Reading Processes, Generalization