How can we divide a complex mental process into meaningful parts? In this paper, I explore an approach in which processes are divided into parts that are modular in the sense of being separately modifiable. Evidence for separate modifiability is provided by an instance of selective influence: two factors F and G (usually experimental manipulations) such that part A is influenced by F but invariant with respect to G, while part B is influenced by G but invariant with respect to F. Such evidence also indicates that the modules are functionally distinct. If we have pure measures MA and MB, each of which reflects only one of the parts, we need to show that MA is influenced by F but not G, while MB is influenced by G but not F. If we have only a composite measure MAB of the entire process, we usually also need to confirm a combination rule for how the parts contribute to MAB. I present a taxonomy of separate-modifiability methods, discuss their inferential logic, and describe several examples in each category. The three categories involve measures that are derived pure (based on different transformations of the same data; example: separation of sensory and decision processes by signal detection theory), direct pure (based on different data; example: selective effects of adaptation on spatial-frequency thresholds), and composite (examples: the multiplicative-factor method for the analysis of response rate; the additive-factor method for the analysis of reaction time). Six of the examples concern behavioral measures and functional processes, while four concern brain measures and neural processes. They have been chosen for their interest and importance; their diversity of measures, species, and combination rules; their illustration of different ways of thinking about data; the questions they suggest about possibilities and limitations of the separate-modifiability approach; and the case they make for the fruitfulness of searching for mental modules.