Causal Asymmetry Analysis in the View of Concept-Cognitive Learning by Incremental Concept Tree

Causal asymmetry is an important feature in the description of causality, and it has attracted wide attention in the field of physics and philosophy. It hypothesizes that there is a pervasive and fundamental bias in humans’ understanding of physical causation. However, how to express the causal asymmetry in computer science is still an open, interesting, and important issue. In this paper, we propose a solution to this issue by introducing an incremental concept tree (ICT) representation. The ICT is a structure description method originated from the concept tree and attribute topology methods in the field of concept cognitive learning. It focuses on figuring the cognitive process of human being and has been applied to casual analysis. Firstly, we introduce the concept of “causal asymmetry” into the field of concept-cognitive learning according to the internal unity of attribute topology and causality. Secondly, an Incremental concept tree is designed to represent the incremental evolution of the concepts as time arrows on the basis of attribute topology. Finally, we perform an experimental analysis of the Acute Inflammations data to illustrate the feasibility of the proposed algorithm in visualizing causal asymmetry and compare the ICT to the other structural representations. The experimental results show that the ICT is a promising tool for figuring out the casual asymmetry in the view of concept cognitive learning.