The aim of neurocognitive psychometrics is to measure individual differences in parameters of cognitive processes. Often, these parameters are measured as performances indicators (e.g., response times or accuracies) in tasks supposedly engaging one specific cognitive process. This approach presumes that a specific task provides a process-pure measure of a single cognitive process—an assumption that is often violated as most cognitive tasks do not measure one specific cognitive process, but rather a combination of several cognitive processes. In this talk, I will discuss latent change and bifactor models as modeling approaches that may help to overcome this conceptual problem by modeling interindividual differences in intraindividual differences between experimental conditions. Special consideration will be given to recent studies suggesting negligible variance and substantial interindividual heterogeneity in these intraindividual experimental effects (Frischkorn, Schubert, & Hagemann, 2019; Gärtner & Strobel, 2019; Rey-Mermet, Gade, & Oberauer, 2018; Rouder & Haaf, 2019; Rouder, Kumar, & Haaf, 2019). Moreover, I will demonstrate how mathematical models of cognition and neural correlates of cognitive processes can be used to directly quantify individual differences in specific cognitive processes without relying on the assumption of pure insertion. I will highlight several cognitive models that may be of particular interest for cognitive psychometrics and discuss particularities of the psychometric modeling of model parameters and neural data, such as their hierarchical nature and their typically low-to-moderate consistencies.