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Stochastic Dynamics of Lexicon Learning in an Uncertain and Nonuniform World

Rainer Reisenauer, Kenny Smith, and Richard A. Blythe
Phys. Rev. Lett. 110, 258701 – Published 21 June 2013
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Abstract

We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning—whereby information from multiple episodes is combined to eliminate incorrect meanings—can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.

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  • Received 22 February 2013

DOI:https://doi.org/10.1103/PhysRevLett.110.258701

© 2013 American Physical Society

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How to Learn a Language Quickly

Published 21 June 2013

Simulations show that you can learn the meaning of words rapidly if you assume that every object has only one word associated with it.

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Authors & Affiliations

Rainer Reisenauer

  • Physik-Department, Technische Universität München, James-Franck-Strasse 1, 85748 Garching, Germany and SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3JZ, United Kingdom

Kenny Smith

  • Language Evolution and Computation Research Unit, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh EH8 9AD, United Kingdom

Richard A. Blythe

  • SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3JZ, United Kingdom

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Issue

Vol. 110, Iss. 25 — 21 June 2013

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