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Seamus Donnelly; Caroline Rowland; Franklin Chang; Evan Kidd – Cognitive Science, 2024
Prediction-based accounts of language acquisition have the potential to explain several different effects in child language acquisition and adult language processing. However, evidence regarding the developmental predictions of such accounts is mixed. Here, we consider several predictions of these accounts in two large-scale developmental studies…
Descriptors: Prediction, Error Patterns, Syntax, Priming
Brehm, Laurel; Cho, Pyeong Whan; Smolensky, Paul; Goldrick, Matthew A. – Cognitive Science, 2022
Subject-verb agreement errors are common in sentence production. Many studies have used experimental paradigms targeting the production of subject-verb agreement from a sentence preamble ("The key to the cabinets") and eliciting verb errors (… "*were shiny"). Through reanalysis of previous data (50 experiments; 102,369…
Descriptors: Sentences, Sentence Structure, Grammar, Verbs
Hoppe, Dorothée B.; Rij, Jacolien; Hendriks, Petra; Ramscar, Michael – Cognitive Science, 2020
Linguistic category learning has been shown to be highly sensitive to linear order, and depending on the task, differentially sensitive to the information provided by preceding category markers ("premarkers," e.g., gendered articles) or succeeding category markers ("postmarkers," e.g., gendered suffixes). Given that numerous…
Descriptors: Discrimination Learning, Computational Linguistics, Natural Language Processing, Artificial Languages
Jacobs, Cassandra L.; Cho, Sun-Joo; Watson, Duane G. – Cognitive Science, 2019
Syntactic priming in language production is the increased likelihood of using a recently encountered syntactic structure. In this paper, we examine two theories of why speakers can be primed: error-driven learning accounts (Bock, Dell, Chang, & Onishi, 2007; Chang, Dell, & Bock, 2006) and activation-based accounts (Pickering &…
Descriptors: Priming, Syntax, Prediction, Linguistic Theory
Johns, Brendan T.; Mewhort, Douglas J. K.; Jones, Michael N. – Cognitive Science, 2019
Distributional models of semantics learn word meanings from contextual co-occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co-occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co-occurrences…
Descriptors: Semantics, Learning Processes, Models, Prediction
Paape, Dario; Avetisyan, Serine; Lago, Sol; Vasishth, Shravan – Cognitive Science, 2021
We present computational modeling results based on a self-paced reading study investigating number attraction effects in Eastern Armenian. We implement three novel computational models of agreement attraction in a Bayesian framework and compare their predictive fit to the data using k-fold cross-validation. We find that our data are better…
Descriptors: Computational Linguistics, Indo European Languages, Grammar, Bayesian Statistics
Nordmann, Emily; Cleland, Alexandra A.; Bull, Rebecca – Cognitive Science, 2013
Despite the fact that they play a prominent role in everyday speech, the representation and processing of fixed expressions during language production is poorly understood. Here, we report a study investigating the processes underlying fixed expression production. "Tip-of-the-tongue" (TOT) states were elicited for well-known idioms…
Descriptors: Language Patterns, Error Analysis (Language), Error Patterns, Language Processing
Ambridge, Ben; Rowland, Caroline F.; Pine, Julian M. – Cognitive Science, 2008
According to Crain and Nakayama (1987), when forming complex yes/no questions, children do not make errors such as "Is the boy who smoking is crazy?" because they have innate knowledge of "structure dependence" and so will not move the auxiliary from the relative clause. However, simple recurrent networks are also able to avoid…
Descriptors: Children, Language Processing, Language Patterns, Linguistic Input