Neural Networks Track the Logical Complexity of Boolean Concepts.

Abstract

The language of thought hypothesis and connectionism provide two main accounts of category acquisition in the cognitive sciences. However, it is unclear to what extent their predictions agree. In this article, we tackle this problem by comparing the two accounts with respect to a common set of predictions about the effort required to acquire categories. We find that the two accounts produce similar predictions in the domain of Boolean categorization, however, with substantial variation depending on the operators in the language of thought.