TY - JOUR
T1 - The crosslinguistic acquisition of sentence structure
T2 - Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'
AU - Ambridge, Ben
AU - Tatsumi, Tomoko
AU - Doherty, Laura
AU - Maitreyee, Ramya
AU - Bannard, Colin
AU - Samanta, Soumitra
AU - McCauley, Stewart
AU - Arnon, Inbal
AU - Zicherman, Shira
AU - Bekman, Dani
AU - Efrati, Amir
AU - Berman, Ruth
AU - Narasimhan, Bhuvana
AU - Sharma, Dipti Misra
AU - Nair, Rukmini Bhaya
AU - Fukumura, Kumiko
AU - Campbell, Seth
AU - Pye, Clifton
AU - Pedro, Pedro Mateo
AU - Pixabaj, Sindy Fabiola Can
AU - Pelíz, Mario Marroquín
AU - Mendoza, Margarita Julajuj
N1 - Funding Information:
Ben Ambridge is Professor in the International Centre for Language and Communicative Development (LuCiD) at The University of Liverpool. The support of the Economic and Social Research Council [ ES/L008955/1 ] is gratefully acknowledged. This project has received funding from the European Research Council (ERC) under the European Union's research and innovation programme (grant agreement no 681296: CLASS).
Funding Information:
Ben Ambridge is Professor in the International Centre for Language and Communicative Development (LuCiD) at The University of Liverpool. The support of the Economic and Social Research Council [ES/L008955/1] is gratefully acknowledged. This project has received funding from the European Research Council (ERC) under the European Union's research and innovation programme (grant agreement no 681296: CLASS).
Publisher Copyright:
© 2020 The Author(s)
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
KW - Causative
KW - Child language acquisition
KW - English
KW - Entrenchment
KW - Hebrew
KW - Hindi
KW - Japanese
KW - K'iche
KW - Preemption
KW - Verb semantics
UR - http://www.scopus.com/inward/record.url?scp=85086877259&partnerID=8YFLogxK
U2 - 10.1016/j.cognition.2020.104310
DO - 10.1016/j.cognition.2020.104310
M3 - Article
C2 - 32623135
AN - SCOPUS:85086877259
SN - 0010-0277
VL - 202
JO - Cognition
JF - Cognition
M1 - 104310
ER -