The article reconciles contentious issues between code biology and biosemiotics. The framework of semantic biology and molecular meaning woven around organic codes by Marcello Barbieri defied classical informational conceptualizations of meaning in the biological realm while the discourse on meaning and agency and their respective (in)dependency on interpretation continues. Whereas the role of codes in agency is often secondary to other aspects, I present here a more consistent picture– integrating codes and artifacts to smoothly transcend from the world of mechanistic (...) molecular meaning to organismic agency. Interpretation is presented as a call-to-action based on intraindividual states, and sense-making as call-to-change based on interindividual communication. (shrink)

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. (shrink)