The optimality of attaching unlinked labels to unlinked meanings
This provides a foundational explanation for vocabulary learning biases in cognitive science, though it is incremental as it builds on existing theories like contrast and mutual exclusivity.
The paper tackles the problem of explaining why children and adults assume new words have distinct meanings from known words, presenting the first mathematical proof that this bias maximizes mutual information between words and meanings within an information-theoretic framework.
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.