Conference item
Prior expectations in linguistic learning: a stochastic model of individual differences
- Abstract:
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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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Bibliographic Details
- Publisher:
- Cognitive Science Society Publisher's website
- Host title:
- Proceedings of the 39th Meeting of the Cognitive Science Society
- Journal:
- Proceedings of the 39th Meeting of the Cognitive Science Society Journal website
- Publication date:
- 2017-01-01
- Acceptance date:
- 2017-04-12
- Event location:
- London
Item Description
- Keywords:
- Pubs id:
-
pubs:695659
- UUID:
-
uuid:8b25fcd2-0823-4198-a8e3-e916a4a1ef46
- Local pid:
- pubs:695659
- Source identifiers:
-
695659
- Deposit date:
- 2017-05-18
Terms of use
- Copyright holder:
- Schumacher and Pierrehumbert
- Copyright date:
- 2017
- Notes:
- This paper was presented at the 9th Annual Meeting of the Cognitive Science Society (CogSci 2017), 26–29 July, 2017 London.
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