Biosemiosis is a process of choice-making between simultaneously alternative
options. It is well-known that, when sufficiently young children encounter a
new word, they tend to interpret it as pointing to a meaning that does not have
a word yet in their lexicon rather than to a meaning that already has a word
attached. In previous research, the strategy was shown to be optimal from an
information theoretic standpoint. In that framework, interpretation is
hypothesized to be driven by the minimization of a cost function: the option of
least communication cost is chosen. However, the information theoretic model
employed in that research neither explains the weakening of that vocabulary
learning bias in older children or polylinguals nor reproduces Zipf's
meaning-frequency law, namely the non-linear relationship between the number of
meanings of a word and its frequency. Here we consider a generalization of the
model that is channeled to reproduce that law. The analysis of the new model
reveals regions of the phase space where the bias disappears consistently with
the weakening or loss of the bias in older children or polylinguals. The model
is abstract enough to support future research on other levels of life that are
relevant to biosemiotics. In the deep learning era, the model is a transparent
low-dimensional tool for future experimental research and illustrates the
predictive power of a theoretical framework originally designed to shed light
on the origins of Zipf's rank-frequency law.

通过信息理论和深度学习，本研究考察了生物符号学中的选择过程，特别是儿童词汇学习偏见的弱化和 Zipf 词汇分布定律的影响因素。研究结果也为未来在生物符号学其它方面的实验研究提供了参考。