Gluck and Bower (1988) suggested that through the use of the Rescorla-Wagner learning rule, a connectionist network might be able to model the inverse base-rate phenomenon found by Medin and Edelson (1988). I prove that a network of the type that they proposed does not capture this effect. However, one can also prove that with additional assumptions about the encoding of features, the Rescorla-Wagner learning rule can be made to model the inverse base-rate effect. The importance of these assumptions and an outline of how they might be tested are then discussed.