1. A system for modeling probability matching and loss sensitivity in human subjects, the system comprising: one or more processors and a non-transitory computer-readable medium having executable instructions encoded thereon such that when executed, the one or more processors perform operations of: extracting a set of features related to probability matching and loss sensitivity from collected human responses; processing the set of features from the collected human responses with a genetic algorithm to fit the collected human responses with a set of neural network model instances, wherein the set of neural model instances are derived from a neural model which captures interactions between neuromodulators and prefrontal cortex for decision making; generating a set of model parameters from the genetic algorithm; generating a model of human behavior based on the set of model parameters, the model being at least one of an explanatory model and a predictive model; using the model, determining a bias of a subject's data towards probability matching and loss sensitivity; and automatically generating and outputting a customized instruction to intervene against the bias of the subject's data.