The optimality of attaching unlinked labels to unlinked meanings

Abstract

Vocabulary learning by children can be characterized by many biases. When encountering a new word, children as well as adults, are biased towards assuming that it means something totally different from the words that they already know. To the best of our knowledge, the 1st mathematical proof of the optimality of this bias is presented here. First, it is shown that this bias is a particular case of the maximization of mutual information between words and meanings. Second, the optimality is proven within a more general information theoretic framework where mutual information maximization competes with other information theoretic principles. The bias is a prediction from modern information theory. The relationship between information theoretic principles and the principles of contrast and mutual exclusivity is also shown.

Little mathematical errors have been corrected; clarity, English and format have been improved. In press in Glottometrics

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• The origins of Zipf's meaning-frequency law
• Optimization models of natural communication
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• The polysemy of the words that children learn over time
• Parallels of human language in the behavior of bottlenose dolphins
• A commentary on "The now-or-never bottleneck: a fundamental constraint on language", by Christiansen and Chater (2016)
• Zipf's laws of meaning in Catalan