Huge amounts of money are spent every year on unlearning programs--in drug-treatment facilities, prisons, psychotherapy clinics, and schools. Yet almost all of these programs fail, since recidivism rates are high in each of these fields. Progress on this problem requires a better understanding of the mechanisms that make unlearning so difficult. Much cognitive neuroscience evidence suggests that an important component of these mechanisms also dictates success on categorization tasks that recruit procedural learning and depend on synaptic plasticity within the striatum. A biologically detailed computational model of this striatal-dependent learning is described (based on Ashby & Crossley, 2011). The model assumes that a key component of striatal-dependent learning is provided by interneurons in the striatum called the tonically active neurons (TANs), which act as a gate for the learning and expression of striatal-dependent behaviors. In their tonically active state, the TANs prevent the expression of any striatal-dependent behavior. However, they learn to pause in rewarding environments and thereby permit the learning and expression of striatal-dependent behaviors. The model predicts that when rewards are no longer contingent on behavior, the TANs cease to pause, which protects striatal learning from decay and prevents unlearning. In addition, the model predicts that when rewards are partially contingent on behavior, the TANs remain partially paused, leaving the striatum available for unlearning. The results from 3 human behavioral studies support the model predictions and suggest a novel unlearning protocol that shows promising initial signs of success.