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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 9 of 9 1 Effects of healthy aging and left hemisphere stroke on statistical language learning ... Fama, Mackenzie E.; Schuler, Kathryn D.; Newport, Elissa L.. - : Taylor & Francis, 2022 BASE Show details 2 Effects of healthy aging and left hemisphere stroke on statistical language learning ... Fama, Mackenzie E.; Schuler, Kathryn D.; Newport, Elissa L.. - : Taylor & Francis, 2022 BASE Show details 3 Adults regularize variation when linguistic cues suggest low input reliability Chen, Yiran; Schuler, Kathryn In: Proceedings of the Linguistic Society of America; Vol 7, No 1 (2022): Proceedings of the Linguistic Society of America; 5293 ; 2473-8689 (2022) BASE Show details 4 Distributional learning of recursive structures Li, Daoxin; Schuler, Kathryn In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021) BASE Show details 5 Distributional learning of recursive structures ... 43rd Annual Meeting of the Cognitive Science Society 2021; Li, Daoxin; Schuler, Kathryn. - : Underline Science Inc., 2021 Abstract: Languages differ regarding the depth, structure, and syntactic domains of recursive structures. Even within a language, some structures allow infinite self-embedding while others are more restricted. For example, English allows infinite free embedding of the prenominal genitive -s, whereas the postnominal genitive of is largely restricted to one level and to a limited set of items. Therefore, speakers need to learn from experience which specific structures allow free embedding and which do not. One effort to account for the underlying learning mechanism, the distributional learning proposal, suggests the recursion of a structure (e.g. X1’s- X2) is licensed if X1 position and X2 position are productively substitutable in the input. A series of corpus studies have confirmed the availability of such distributional cues in child directed speech. The present study further tests the distributional learning proposal with an artificial language learning experiment. ... Keyword: Cognitive Linguistics; Cognitive Science; Computational Linguistics; Phonetics; Phonology; Psycholinguistics; Semantics; Semiotics; Syntax; Translation Studies URL: https://dx.doi.org/10.48448/pgzf-cn08 https://underline.io/lecture/26880-distributional-learning-of-recursive-structures BASE Hide details 6 Distributional learning of recursive structures ... 43rd Annual Meeting of the Cognitive Science Society 2021; Li, Daoxin; Schuler, Kathryn. - : Underline Science Inc., 2021 BASE Show details 7 Does Learner’s Preference Match the Typological Pattern of Animacy Hierarchy in Morphological Marking? Schuler, Kathryn; Chen, Yiran In: University of Pennsylvania Working Papers in Linguistics (2021) BASE Show details 8 The role of gender in the acquisition of the Serbian case system Pophristic, Stefan; Schuler, Kathryn In: Proceedings of the Linguistic Society of America; Vol 6, No 1 (2021): Proceedings of the Linguistic Society of America; 896–905 ; 2473-8689 (2021) BASE Show details 9 The effect of Zipfian frequency variations on category formation in adult artificial language learning Schuler, Kathryn D.; Reeder, Patricia A.; Newport, Elissa L.. - 2017 BASE Show details

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