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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; Doherty, Laura; Maitreyee, Ramya. - : F1000 Research Ltd, 2022
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2
Efficient adaptation to listener proficiency: The case of referring expressions
In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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3
A cognitive bias for Zipfian distributions? Uniform distributions become more skewed via cultural transmission
In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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A cognitive bias for Zipfian distributions? Uniform distributions become more skewed via cultural transmission ...
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Efficient adaptation to listener proficiency: The case of referring expressions ...
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6
Testing a computational model of causative overgeneralizations: Child judgment and production data from English, Hebrew, Hindi, Japanese and K’iche’
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.
URL: http://livrepository.liverpool.ac.uk/3118614/
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7
The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
Lavi-Rotbain, Ori; Arnon, Inbal. - : PsychArchives, 2020
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8
Data for: The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
Lavi-Rotbain, Ori; Arnon, Inbal. - : PsychArchives, 2020
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9
The learnability consequences of Zipfian distributions: Word Segmentation is Facilitated in More Predictable Distributions ...
Lavi-Rotbain, Ori; Arnon, Inbal. - : PsychArchives, 2020
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10
A learning bias for word order harmony: evidence from speakers of non-harmonic languages
In: ISSN: 0010-0277 ; Cognition, Vol. 204 (2020) P. 104392 (2020)
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11
The crosslinguistic acquisition of sentence structure: Computational modeling and grammaticality judgments from adult and child speakers of English, Japanese, Hindi, Hebrew and K'iche'()
In: Cognition (2020)
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12
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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13
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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14
Processing Non-Concatenative Morphology – A Developmental Computational Model
In: Proceedings of the Society for Computation in Linguistics (2019)
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15
Literate and preliterate children show different learning patterns in an artificial language learning task [<Journal>]
Havron, Naomi [Verfasser]; Raviv, Limor [Verfasser]; Arnon, Inbal [Verfasser]
DNB Subject Category Language
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16
Systematicity, but not compositionality: Examining the emergence of linguistic structure in children and adults using iterated learning ...
Raviv, Limor; Arnon, inbal. - : Open Science Framework, 2018
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17
Do current statistical learning capture stable individual differences in children? An investigation of task reliability across modalities ...
Arnon, inbal. - : PsyArXiv, 2018
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18
Statistical learning, implicit learning and first language acquisition: a critical evaluation of age-invariance and the link to language learning outcomes ...
Arnon, inbal. - : PsyArXiv, 2018
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19
“Piensa” twice: On the foreign language effect in decision making
In: Cognition. - Amsterdam [u.a] : Elsevier 130 (2014) 2, 236-254
OLC Linguistik
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20
Introduction : language acquisition in interaction
In: Language in interaction (Amsterdam, 2014), p. 1-12
MPI für Psycholinguistik
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