Griffiths, Christian, and Kalish (this issue) present an iterative-learning paradigm applying a Bayesian model to understand inductive biases in categorization. The authors argue that the paradigm is useful as an exploratory tool to understand inductive biases in situations where little is known about the task. It is argued that a theory developed "only" at the computational level is much like a single-bladed knife that is only useful in highly idealized situations. To be useful as a general tool that cuts through the complex fabric of cognition, we need at least two-bladed scissors that combine both computational and psychological constraints to characterize human behavior. To temper its sometimes expansive claims, it is time to show what a Bayesian model "cannot" explain. Insight as to how human reality may differ from the Bayesian predictions may shed more light on human cognition than the simpler focus on what the Bayesian approach "can" explain. There remains much to be done in terms of integrating Bayesian approaches and other approaches in modeling human cognition. (Contains 1 note.)