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Thomas St. Pierre; Jida Jaffan; Craig G. Chambers; Elizabeth K. Johnson – Cognitive Science, 2024
Adults are skilled at using language to construct/negotiate identity and to signal affiliation with others, but little is known about how these abilities develop in children. Clearly, children mirror statistical patterns in their local environment (e.g., Canadian children using "zed" instead of "zee"), but do they flexibly…
Descriptors: Language Usage, Group Membership, Vocabulary Skills, Children
Aislinn Keogh; Simon Kirby; Jennifer Culbertson – Cognitive Science, 2024
General principles of human cognition can help to explain why languages are more likely to have certain characteristics than others: structures that are difficult to process or produce will tend to be lost over time. One aspect of cognition that is implicated in language use is working memory--the component of short-term memory used for temporary…
Descriptors: Language Variation, Learning Processes, Short Term Memory, Schemata (Cognition)
Péter Rácz; Ágnes Lukács – Cognitive Science, 2024
People learn language variation through exposure to linguistic interactions. The way we take part in these interactions is shaped by our lexical representations, the mechanisms of language processing, and the social context. Existing work has looked at how we learn and store variation in the ambient language. How this is mediated by the social…
Descriptors: Foreign Countries, Native Speakers, Hungarian, Language Processing
Cassani, Giovanni; Bianchi, Federico; Marelli, Marco – Cognitive Science, 2021
In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data-driven, scalable way, which is grounded in language use. We show a unique and reliable relation between measures of language change and age of acquisition ("AoA") while controlling for frequency,…
Descriptors: English, Language Usage, Language Acquisition, Computational Linguistics
Raviv, Limor; Meyer, Antje; Lev-Ari, Shiri – Cognitive Science, 2020
Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit…
Descriptors: Social Networks, Language Usage, Language Research, Artificial Languages
Markwalder, Ursina; Saalbach, Henrik; Schalk, Lennart – Cognitive Science, 2022
Prior research indicates that humans adapt their language depending on context. This linguistic sensitivity has been suggested to indicate a natural pedagogy shared by all humans. This sensitivity has, however, only been demonstrated with English-speaking samples thus far. In two studies, we followed the experimental procedure of the original…
Descriptors: Teaching Methods, Cross Cultural Studies, German, Metalinguistics
Ito, Chiyuki; Feldman, Naomi H. – Cognitive Science, 2022
Iterated learning models of language evolution have typically been used to study the emergence of language, rather than historical language change. We use iterated learning models to investigate historical change in the accent classes of two Korean dialects. Simulations reveal that many of the patterns of historical change can be explained as…
Descriptors: Diachronic Linguistics, Sociolinguistics, Comparative Analysis, Models
Cruz Blandón, María Andrea; Cristia, Alejandrina; Räsänen, Okko – Cognitive Science, 2023
Computational models of child language development can help us understand the cognitive underpinnings of the language learning process, which occurs along several linguistic levels at once (e.g., prosodic and phonological). However, in light of the replication crisis, modelers face the challenge of selecting representative and consolidated infant…
Descriptors: Meta Analysis, Infants, Language Acquisition, Computational Linguistics
de Varda, Andrea Gregor; Strapparava, Carlo – Cognitive Science, 2022
The present paper addresses the study of non-arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non-arbitrary phonological patterns across a set of typologically distant languages. Different sequence-processing neural networks are trained in a set…
Descriptors: Learning Processes, Phonology, Language Patterns, Language Classification
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
Atkinson, Mark; Smith, Kenny; Kirby, Simon – Cognitive Science, 2018
Languages spoken in larger populations are relatively simple. A possible explanation for this is that languages with a greater number of speakers tend to also be those with higher proportions of non-native speakers, who may simplify language during learning. We assess this explanation for the negative correlation between population size and…
Descriptors: Adult Learning, Second Language Learning, Difficulty Level, Morphology (Languages)
Liu, Tianyin; Chuk, Tin Yim; Yeh, Su-Ling; Hsiao, Janet H. – Cognitive Science, 2016
Expertise in Chinese character recognition is marked by reduced holistic processing (HP), which depends mainly on writing rather than reading experience. Here we show that, while simplified and traditional Chinese readers demonstrated a similar level of HP when processing characters shared between the simplified and traditional scripts, simplified…
Descriptors: Transfer of Training, Auditory Perception, Orthographic Symbols, Chinese