Abstract
Language learners must learn the meanings of many thousands of words, despite those words occurring in complex environments in which infinitely many meanings might be inferred by the learner as a word’s true meaning. This problem of infinite referential uncertainty is often attributed to Willard Van Orman Quine. We provide a mathematical formalisation of an ideal cross-situational learner attempting to learn under infinite referential uncertainty, and identify conditions under which word learning is possible. As Quine’s intuitions suggest, learning under infinite uncertainty is in fact possible, provided that learners have some means of ranking candidate word meanings in terms of their plausibility; furthermore, our analysis shows that this ranking could in fact be exceedingly weak, implying that constraints which allow learners to infer the plausibility of candidate word meanings could themselves be weak. This approach lifts the burden of explanation from ‘smart’ word learning constraints in learners, and suggests a programme of research into weak, unreliable, probabilistic constraints on the inference of word meaning in real word learners.
Original language | English |
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Pages (from-to) | 18-27 |
Journal | Cognition |
Volume | 151 |
Issue number | 6 |
Early online date | 27 Feb 2016 |
DOIs | |
Publication status | Published - Jun 2016 |
Keywords
- word learning
- cross-situational learning
- Quine's problem
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Dive into the research topics of 'Word learning under infinite uncertainty'. Together they form a unique fingerprint.Datasets
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Word learning under infinite uncertainty - Simulation codes and sample output
Blythe, R. (Creator), Smith, K. (Creator) & Smith, A. D. (Creator), Edinburgh DataShare, 15 Feb 2016
DOI: 10.7488/ds/1345, http://arxiv.org/abs/1412.2487
Dataset
Profiles
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Richard Blythe, SFHEA
- School of Physics and Astronomy - Personal Chair of Complex Systems
Person: Academic: Research Active
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Kenny Smith
- School of Philosophy, Psychology and Language Sciences - Personal Chair of Evolutionary Linguistics
Person: Academic: Research Active