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.

中文翻译：

---

内容表征的准确性

摘要

非人类动物的表征内容、人类核心认知和知觉不能用自然语言的句子来精确表征。然而，这一事实并不妨碍我们通过自然语言对这些内容进行不精确的刻画。在本文中，我通过将可能世界语义学与集合和测量理论相结合，对内容表征的精度进行了说明。