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Prior expectations in linguistic learning: a stochastic model of individual differences

Abstract:

When learners are exposed to inconsistent input, do they reproduce the probabilities in the input (probability match- ing), or produce some variants disproportionately often (regularization)? Laboratory results and computational models of artificial language learning both argue that the learning mechanism is basically probability matching, with regularization arising from additional factors. However, these models were fit to aggregated experimental data, which can exhibit probability matching...

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Publication status:
Not published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Sub department:
Oxford e-Research Centre
Role:
Author
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Name:
John Templeton Foundation
Grant:
36617/
Publisher:
Cognitive Science Society
Host title:
Proceedings of the 39th Meeting of the Cognitive Science Society
Journal:
Proceedings of the 39th Meeting of the Cognitive Science Society More from this journal
Publication date:
2017-01-01
Acceptance date:
2017-04-12
Event location:
London
Keywords:
Pubs id:
pubs:695659
UUID:
uuid:8b25fcd2-0823-4198-a8e3-e916a4a1ef46
Local pid:
pubs:695659
Source identifiers:
695659
Deposit date:
2017-05-18

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