Logical Transformation on Reasoning Tasks and Statistical Computation of Cognitive Relevance

by Michinao F. MATSUI

For solving complex problems in our real world, it is important not only to get explicit information, but also to identify appropriate information by selecting it from a huge amount of knowledge stored in memory. The most important process is to select appropriate knowledge which is essential to interpretation of current information, and to ignore inappropriate knowledge which is irrelevant. In Relevance Theory, it is claimed that an optimal relevance gives the most appropriate interpretation by means of deductive inference. This paper provides a computational method and a quantification of the cognitive relevance based on Relevance Theory to ``Wason selection task'', which is one of the most famous experiments of logical problem-solving tasks.