Abstract
How language users become able to process forms they have never encountered in input is central to our understanding of language cognition. A range of models, including rule-based models, stochastic models, and analogy-based models have been proposed to account for this ability. Despite the fact that all three models are reasonably successful, we argue that productivity in language is more insightfully captured through learnability than by rules or probabilities. Using a combination of computational modelling and behavioural experimentation we show that the basic principle of error-driven learning allows language users to detect relevant patterns of any degree of systematicity. In case of allomorphy, these patterns are found at a level that cuts across phonology and morphology and is not considered by mainstream approaches to language. Our findings thus highlight how a learning-based approach applies to phenomena on the continuum from rule-based over probabilistic to “unruly” and constrains our inferences about the types of structures that should be targeted on a cognitively realistic account of allomorphic representation.
Original language | English |
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Pages (from-to) | 60-83 |
Number of pages | 24 |
Journal | Language, Cognition and Neuroscience |
Volume | 36 |
Issue number | 1 |
Early online date | 24 Sept 2020 |
DOIs | |
Publication status | Published - Jan 2021 |
Bibliographical note
Funding Information:This work was suported by The Leverhulme Trust [grant number RL-2016-001] which funded all authors except JJ who was funded by The Wolfson Foundation. We are greatly indebted to Tamas Rudas for extensive discussions regarding the analysis of categorical data. We are grateful to Neil Bermel, Jim Blevins, Paul O?Neill and the two journal reviewers for detailed comments on earlier versions of this manuscript. We would also like to thank the audiences at conferences and seminars where we presented parts of this study for comments and discussion.
Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.
Keywords
- Productivity
- error-driven learning
- emergentism
- inflectional morphology
- allomorphy
- Experimental and Cognitive Psychology
- Cognitive Neuroscience
- Linguistics and Language
- Language and Linguistics
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Research data supporting 'What is learned from exposure: an error-driven approach to productivity in language'
Milin, P. (Creator), Ez-zizi, A. (Creator), Divjak, D. (Creator) & Józefowski, J. (Creator), University of Birmingham, 31 Jul 2020
DOI: 10.25500/edata.bham.00000525
Dataset