contextual interference bears an intriguing similarity to a phenomenon observed during the training of neural networks called catastrophic interference (mccloskey & cohen, 1989). catastrophic interference occurs when a network is trained to criteria on one set of mappings, and then switched to a new set, at which point it loses access to the initial mappings. in a sense, rather than forming a set of connections that would preserve the knowledge it acquired in the first task, the network optimizes its performance completely to the new task. the solution to this problem is simply to interleave the training sets so that the network is forced to optimize its behavior in a way that is sensitive to both of the tasks and their statistics. while this was not thought to characterize human learning (a supposed weakness of this approach), recent evidence suggests that human learning also exhibits this principle under the right conditions (mirman & spivey, 2001).