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.
- Received 22 February 2013
DOI:https://doi.org/10.1103/PhysRevLett.110.258701
© 2013 American Physical Society
Focus
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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