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Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K’iche’
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Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K’iche’
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The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'()
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In: Cognition (2020)
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The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'
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The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'.
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Mendoza, Margarita Julajuj; Tatsumi, Tomoko; Pye, Clifton; Bannard, Colin; Efrati, Amir; Pixabaj, Sindy Fabiola Can; Narasimhan, Bhuvana; Nair, Rukmini Bhaya; Fukumura, Kumiko; Sharma, Dipti Misra; Pedro, Pedro Mateo; Doherty, Laura; Samanta, Soumitra; Maitreyee, Ramya; Arnon, Inbal; Pelíz, Mario Marroquín; Bekman, Dani; McCauley, Stewart; Zicherman, Shira; Campbell, Seth; Ambridge, Ben; Berman, Ruth. - 2020
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Abstract:
This preregistered study tested three theoretical proposals for how children form productive yet restricted linguistic generalizations, avoiding errors such as *The clown laughed the man, across three age groups (5-6 years, 9-10 years, adults) and five languages (English, Japanese, Hindi, Hebrew and K'iche'). Participants rated, on a five-point scale, correct and ungrammatical sentences describing events of causation (e.g., *Someone laughed the man; Someone made the man laugh; Someone broke the truck; ?Someone made the truck break). The verb-semantics hypothesis predicts that, for all languages, by-verb differences in acceptability ratings will be predicted by the extent to which the causing and caused event (e.g., amusing and laughing) merge conceptually into a single event (as rated by separate groups of adult participants). The entrenchment and preemption hypotheses predict, for all languages, that by-verb differences in acceptability ratings will be predicted by, respectively, the verb's relative overall frequency, and frequency in nearly-synonymous constructions (e.g., X made Y laugh for *Someone laughed the man). Analysis using mixed effects models revealed that entrenchment/preemption effects (which could not be distinguished due to collinearity) were observed for all age groups and all languages except K'iche', which suffered from a thin corpus and showed only preemption sporadically. All languages showed effects of event-merge semantics, except K'iche' which showed only effects of supplementary semantic predictors. We end by presenting a computational model which successfully simulates this pattern of results in a single discriminative-learning mechanism, achieving by-verb correlations of around r = 0.75 with human judgment data.
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URL: https://www.sciencedirect.com/science/article/pii/S0010027720301293
http://livrepository.liverpool.ac.uk/3095707/
http://livrepository.liverpool.ac.uk/3095707/1/CLASS1_Cognition.pdf
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CLASS: Cross Linguistic Acquisition of Sentence Structure ...
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Children's use of prosody and word order to indicate information status in English noun phrase conjuncts
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In: Proceedings of the Linguistic Society of America; Vol 3 (2018): Proceedings of the Linguistic Society of America; 40:1–9 ; 2473-8689 (2018)
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Satellite- vs. Verb-Framing Underpredicts Nonverbal Motion Categorization: Insights from a Large Language Sample and Simulations
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In: ISSN: 2352-6408 ; EISSN: 2352-6416 ; Cognitive Semantics ; https://halshs.archives-ouvertes.fr/halshs-01667327 ; Cognitive Semantics, Brill, 2017 (2017)
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Satellite- vs. Verb-Framing Underpredicts Nonverbal Motion Categorization: Insights from a Large Language Sample and Simulations
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In: ISSN: 2352-6408 ; EISSN: 2352-6416 ; Cognitive Semantics ; https://halshs.archives-ouvertes.fr/halshs-01667327 ; Cognitive Semantics, Brill, 2017 (2017)
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Satellite- vs. Verb-Framing Underpredicts Nonverbal Motion Categorization: Insights from a Large Language Sample and Simulations
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Satellite- vs. verb-framing underpredicts nonverbal motion categorization: Insights from a large language sample and simulations
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Satellite- vs. verb-framing underpredicts nonverbal motion categorization : insights from a large language sample and simulations
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