Journal article
Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K’iche
Open research Europe, Vol.1, p.1
03/24/2021
DOI: 10.12688/openreseurope.13008.2
PMCID: PMC10446094
PMID: 37645154
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.
Details
- Title: Subtitle
- Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K’iche
- Creators
- Ben Ambridge - University of LiverpoolLaura Doherty - University of LiverpoolRamya Maitreyee - University of LiverpoolTomoko Tatsumi - Kobe UniversityShira Zicherman - Hebrew University of JerusalemPedro Mateo PedroAyuno Kawakami - University of LiverpoolAmy Bidgood - University of SalfordClifton Pye - University of KansasBhuvana Narasimhan - University of Colorado BoulderInbal Arnon - Hebrew University of JerusalemDani Bekman - Hebrew University of JerusalemAmir Efrati - Hebrew University of JerusalemSindy Fabiola Can PixabajMario Marroquín PelízMargarita Julajuj MendozaSoumitra Samanta - University of LiverpoolSeth Campbell - University of CalgaryStewart McCauley - University of IowaRuth Berman - Tel Aviv UniversityDipti Misra SharmaRukmini Bhaya NairKumiko Fukumura - University of Stirling
- Resource Type
- Journal article
- Publication Details
- Open research Europe, Vol.1, p.1
- DOI
- 10.12688/openreseurope.13008.2
- PMID
- 37645154
- PMCID
- PMC10446094
- NLM abbreviation
- Open Res Eur
- ISSN
- 2732-5121
- eISSN
- 2732-5121
- Grant note
- DOI: 10.13039/100010661, name: Horizon 2020 Framework Programme, award: 681296; DOI: 10.13039/501100000269, name: Economic and Social Research Council, award: ES/L008955/1
- Language
- English
- Date published
- 03/24/2021
- Date updated
- 01/12/2022
- Academic Unit
- Communication Sciences and Disorders
- Record Identifier
- 9984267726902771
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