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

Ben Ambridge*, Laura Doherty, Ramya Maitreyee, Tomoko Tatsumi, Shira Zicherman, Pedro Mateo Pedro, Ayuno Kawakami, Amy Bidgood, Clifton Pye, Bhuvana Narasimhan, Inbal Arnon, Dani Bekman, Amir Efrati, Sindy Fabiola Can Pixabaj, Mario Marroquín Pelíz, Margarita Julajuj Mendoza, Soumitra Samanta, Seth Campbell, Stewart McCauley, Ruth BermanDipti Misra Sharma, Rukmini Bhaya Nair, Kumiko Fukumura

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageAmerican English
Article number1
JournalOpen Research Europe
Volume1
DOIs
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 Ambridge B et al.

Keywords

  • Causative
  • Child language acquisition
  • Discriminative learning
  • English
  • Hebrew
  • Hindi
  • Japanese
  • K'iche'
  • Verb semantics

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