Scientific induction involves an iterative process of hypothesis formulation, testing, and refinement. People in ordinary life appear to undertake a similar process in explaining their world. W e believe that it is instructive to study rule induction in connectionist systems from a similar perspective. W e propose an approach, called the Connectionist Scientist Game, in which symbolic condition-action rules are extracted from the learned connection strengths in a network, thereby forming explicit hypotheses about a domain. The hypotheses are tested by injecting the rules back into the network and continuing the training process. This extraction-injection process continues until the resulting rule base adequately characterizes the domain. By exploiting constraints inherent in the domain of symbolic string-to-string mappings, w e show how a connectionist architecture called RuleNet can induce explicit, symbolic condition-action rules from examples. RuleNet's performance is far superior to that of a variety of alternative architectures we've examined. RuleNet is capable of handling domains having both symbolic and subsymbolic components, and thus shows greater potential than purely symbolic learning algorithms. The formal string manipulation task performed by RuleNet can be viewed as an abstraction of several interesting cognitive models in the connectionist literature, including case role assignment and the mapping of orthography to phonology-