Abstract

The contents of representations in non-human animals, human core cognition, and perception cannot precisely be characterized by sentences of a natural language. However, this fact does not stop us from giving imprecise characterizations of these contents through natural language. In this paper, I develop an account of the precision of content characterizations by appealing to possible-world semantics combined with set and measurement theory.