# The optimality of attaching unlinked labels to unlinked meanings

arXiv: https://arxiv.org/abs/1310.5884 · HTML: https://mlsanity.com/paper/1310.5884/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2013.

## Built on by (most cited first, 3 of 3)

- [Swap distance minimization beyond entropy minimization in word order variation](https://mlsanity.com/paper/2404.14192/) (2024)
- [The advent and fall of a vocabulary learning bias from communicative efficiency](https://mlsanity.com/paper/2105.11519/) (2021)
- [Zipf's laws of meaning in Catalan](https://mlsanity.com/paper/2107.00042/) (2021)

## Abstract (arXiv)

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.