Is the human mind a similarity machine or a probability engine? Perhaps it’s both – but if so, how do these mechanisms work together? These are perennial questions from the cognitive psychology of rationality. The influential heuristics and biases program treated similarity-based reasoning as an efficient, but ultimately irrational shortcut for probabilistic, or Bayesian reasoning. By contrast, I argue that similarity and probability are complementary principles of reasoning, drawing on the relationship between Conceptual Spaces (CS), a similarity-based framework for conceptual content, and Predictive Processing (PP), a Bayesian account of cognition. While CS and PP may operate at different Marr-levels, I focus on recent advances that show how CS can generate priors for Bayesian reasoning, offering a concrete mechanism for integrating similarity and probability. This approach resists reductionist interpretations, such as Tenenbaum and Griffiths’ account of perceptual categorization, by emphasizing the semantic interpretability that similarity provides for Bayesian models of reasoning.