Most theories of concepts focus on first-order similarity—how individual instances share features or occupy nearby regions in multidimensional space. However, many conceptual phenomena hinge on detecting patterns of feature covariation rather than simple overlap (Richardson 2019; Solomon & Schapiro 2024). For example, two animal categories may differ in their average feature values yet share analogous internal covariation structures, supporting analogical reasoning and robust generalization. This talk explores the role of covariation in terms of higher-order similarity, arguing that structural similarity across covariational patterns is a crucial yet underappreciated dimension of conceptual organization. I argue that data analysis techniques such as principal component analysis can help formalize this kind of similarity. This perspective accounts for the formation of overhypotheses and structural phenomena such as consistent contrast and value systematicity (Billman & Davies 2005, Dewar & Xu 2010). By moving beyond first-order similarity to the structure of relations among features, we arrive at a two-tier model of conceptual knowledge: intra-concept coherence for local prediction and inter-concept covariational alignment for efficient generalization. This framework might help explain why some conceptual systems are easier to learn, transfer, and remember.