The optimality of attaching unlinked labels to unlinked meanings

TL;DR

This paper provides a mathematical proof that children's bias to assume new words have entirely new meanings is optimal, based on mutual information maximization principles from information theory.

Contribution

It introduces the first formal proof of the optimality of the bias to attach unlinked labels to unlinked meanings using information theoretic frameworks.

Findings

Bias is a case of mutual information maximization.

Optimality proven within a broader information theoretic context.

Links between contrast principles and information theory are established.

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.