  Self-supervised models create representation spaces that lack clear semantic
meaning. This interpretability problem of representations makes traditional
explainability methods ineffective in this context. In this paper, we introduce
a novel method to analyze representation spaces using three key perceptual
components: color, shape, and texture. We employ selective masking of these
components to observe changes in representations, resulting in distinct
importance maps for each. In scenarios, where labels are absent, these
importance maps provide more intuitive explanations as they are integral to the
human visual system. Our approach enhances the interpretability of the
representation space, offering explanations that resonate with human visual
perception. We analyze how different training objectives create distinct
representation spaces using perceptual components. Additionally, we examine the
representation of images across diverse image domains, providing insights into
the role of these components in different contexts.
