Complex cognition and thoughts intrinsic to mental health and other psychopathologies cannot be fully characterized without creative and precise behavioral measures. In this dissertation, I describe two projects that aimed to improve parameterization or capture individual variation in behavior measures in general populations. In the first project, a novel task elucidated a spectrum of decision thresholds that are typically set categorically and fit from a model rather than directly measured. This can inform model fitting procedures in a standard technique used across a range of psychological and neuroscience applications. The second project aimed to use the guidelines from the Research Domain Criteria (RDoC), a national initiative from NIH to standardize neuropsychological research, to integrate adaptive, reward decision-making with neuromodulation to associate response to the modulation and subclinical mental health features using model parameters. The results were inconclusive, and the limitations of the design are discussed. Suggestions for future experiments are proposed.