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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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Ambridge, Ben; Doherty, Laura; Maitreyee, Ramya; Tatsumi, Tomoko; Zicherman, Shira; Mateo Pedro, Pedro; Kawakami, Ayuno; Bidgood, Amy; Pye, Clifton; Narasimhan, Bhuvana; Arnon, Inbal; Bekman, Dani; Efrati, Amir; Fabiola Can Pixabaj, Sindy; Marroquín Pelíz, Mario; Julajuj Mendoza, Margarita; Samanta, Soumitra; Campbell, Seth; McCauley, Stewart; Berman, Ruth; Misra Sharma, Dipti; Bhaya Nair, Rukmini; Fukumura, Kumiko. - : F1000 Research Ltd, 2021
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Abstract:
How do language learners avoid the production of verb argument structure overgeneralization errors (*The clown laughed the man c.f. The clown made the man laugh), while retaining the ability to apply such generalizations productively when appropriate? This question has long been seen as one that is both particularly central to acquisition research and particularly challenging. Focussing on causative overgeneralization errors of this type, a previous study reported a computational model that learns, on the basis of corpus data and human-derived verb-semantic-feature ratings, to predict adults’ by-verb preferences for less- versus more-transparent causative forms (e.g., *The clown laughed the man vs The clown made the man laugh) across English, Hebrew, Hindi, Japanese and K’iche Mayan. Here, we tested the ability of this model to explain binary grammaticality judgment data from children aged 4;0-5;0, and elicited-production data from children aged 4;0-5;0 and 5;6-6;6 (N=48 per language). In general, the model successfully simulated both children’s judgment and production data, with correlations of r=0.5-0.6 and r=0.75-0.85, respectively, and also generalized to unseen verbs. Importantly, learners of all five languages showed some evidence of making the types of overgeneralization errors – in both judgments and production – previously observed in naturalistic studies of English (e.g., *I’m dancing it). Together with previous findings, the present study demonstrates that a simple discriminative learning model can explain (a) adults’ continuous judgment data, (b) children’s binary judgment data and (c) children’s production data (with no training of these datasets), and therefore constitutes a plausible mechanistic account of the retreat from overgeneralization.
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URL: http://livrepository.liverpool.ac.uk/3118614/
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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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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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