One goal of cognitive science is to build theories of mentalfunction that predict individual behavior. In this project wefocus on predicting, for individual participants, which specificitems in a list will be remembered at some point in the future.If you want to know if an individual will remember something,one commonsense approach is to give them a quiz or test suchthat a correct answer likely indicates later memory for an item.In this project we attempt to predict later memory without ex-plicit assessments by jointly modeling both neural and behav-ioral data in a computational cognitive model which capturesthe dynamics of memory acquisition and decay. In this paper,we lay out a novel hierarchical Bayesian approach for com-bining neural and behavioral data and present results showinghow fMRI signals recorded during the study phase of a mem-ory task can improve our ability to predict (in held-out data)which items will be remembered or forgotten 72 hours later.