Ting Fung Ho

This paper explores the structural mismatch problem between physical and phenomenal properties, where the similarity relations we experience among phenomenal properties lack corresponding relations in the physical domain. I introduce a new understanding of this problem via the Uniformity Principle: for any set of dimensions used to determine phenomenal similarities, there must be a consistently applied set of physical dimensions generating the same pattern of similarity relations. I then assess the potential of recent machine learning models, specifically graph neural networks, to resolve this problem, as proposed by Epstein. By examining how these models generate sensory maps, I argue that the dimensions they adopt violate the Uniformity Principle in two ways: impure dimensions, where subjective responses affect the physical dimensions used, and non-uniform dimensions, where the applied dimensions vary inconsistently. These issues show that current machine learning models fail to establish a systematic correspondence between physical and phenomenal properties.