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Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K’iche’

Ambridge, Ben and Doherty, Laura and Maitreyee, Ramya and Tatsumi, Tomoko and Zicherman, Shira and Mateo Pedro, Pedro and Kawakami, Ayuno and Bidgood, Amy and Pye, Clifton and Narasimhan, Bhuvana and Arnon, Inbal and Bekman, Dani and Efrati, Amir and Fabiola Can Pixabaj, Sindy and Marroquín Pelíz, Mario and Julajuj Mendoza, Margarita and Samanta, Soumitra and Campbell, Seth and McCauley, Stewart and Berman, Ruth and Misra Sharma, Dipti and Bhaya Nair, Rukmini and Fukumura, Kumiko (2022) 'Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K’iche’.' Open Research Europe, 1. p. 1. ISSN 2732-5121

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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 (and an expanded version with multiple hidden layers) 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 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 acquisition of verbs’ argument structure restrictions.

Item Type: Article
Divisions: Faculty of Science and Health
Faculty of Science and Health > Health and Social Care, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 21 Feb 2022 20:43
Last Modified: 21 Feb 2022 20:43
URI: http://repository.essex.ac.uk/id/eprint/32361

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