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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
Dotan, Dror – Cognitive Science, 2023
Reading numbers aloud involves visual processes that analyze the digit string and verbal processes that produce the number words. Cognitive models of number reading assume that information flows from the visual input to the verbal production processes--a feed-forward processing mode in which the verbal production depends on the visual input but…
Descriptors: Reading Processes, Hebrew, Arabic, Visual Stimuli
Igor Bascandziev – Cognitive Science, 2024
The ability to recognize and correct errors in one's explanatory understanding is critically important for learning. However, little is known about the mechanisms that determine when and under what circumstances errors are detected and how they are corrected. The present study investigated thought experiments as a potential tool that can reveal…
Descriptors: Cognitive Processes, Experiments, Schemata (Cognition), Cognitive Science
Eva Viviani; Michael Ramscar; Elizabeth Wonnacott – Cognitive Science, 2024
Ramscar, Yarlett, Dye, Denny, and Thorpe (2010) showed how, consistent with the predictions of error-driven learning models, the order in which stimuli are presented in training can affect category learning. Specifically, learners exposed to artificial language input where objects preceded their labels learned the discriminating features of…
Descriptors: Symbolic Learning, Learning Processes, Artificial Intelligence, Prediction
Erdin Mujezinovic; Vsevolod Kapatsinski; Ruben van de Vijver – Cognitive Science, 2024
A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney,…
Descriptors: Morphology (Languages), Phonology, Morphemes, Cues
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
Perkins, Laurel; Feldman, Naomi H.; Lidz, Jeffrey – Cognitive Science, 2022
Learning in any domain depends on how the data for learning are represented. In the domain of language acquisition, children's representations of the speech they hear determine what generalizations they can draw about their target grammar. But these input representations change over development as a function of children's developing linguistic…
Descriptors: Persuasive Discourse, Language Acquisition, Form Classes (Languages), Verbs
Lutken, C. Jane; Legendre, Géraldine; Omaki, Akira – Cognitive Science, 2020
Previous work has reported that children creatively make syntactic errors that are ungrammatical in their target language, but are grammatical in another language. One of the most well-known examples is "medial wh-question" errors in English-speaking children's wh-questions (e.g., "What do you think who the cat chased?" from…
Descriptors: Syntax, Creativity, Error Patterns, Children
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
Jones, Samuel David; Brandt, Silke – Cognitive Science, 2020
High phonological neighborhood density has been associated with both advantages and disadvantages in early word learning. High density may support the formation and fine-tuning of new word sound memories--a process termed lexical configuration (e.g., Storkel, 2004). However, new high-density words are also more likely to be misunderstood as…
Descriptors: Emergent Literacy, Vocabulary Development, Toddlers, Phonology
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
Dewi, Jasinta D. M.; Bagnoud, Jeanne; Thevenot, Catherine – Cognitive Science, 2021
As a theory of skill acquisition, the instance theory of automatization posits that, after a period of training, algorithm-based performance is replaced by retrieval-based performance. This theory has been tested using alphabet-arithmetic verification tasks (e.g., is A + 4 = E?), in which the equations are necessarily solved by counting at the…
Descriptors: Skill Development, Training, Task Analysis, Learning Theories
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
Rafferty, Anna N.; Jansen, Rachel A.; Griffiths, Thomas L. – Cognitive Science, 2020
Online educational technologies offer opportunities for providing individualized feedback and detailed profiles of students' skills. Yet many technologies for mathematics education assess students based only on the correctness of either their final answers or responses to individual steps. In contrast, examining the choices students make for how…
Descriptors: Computer Assisted Testing, Mathematics Tests, Mathematics Skills, Student Evaluation
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