Our aim in this article is to elaborate the role of training in representational change theory (RCT), particularly in terms of Ohlsson's (2011) spread of activation explanation (named redistribution theory), and to develop novel training manipulations that effect the re-encoding mechanism proposed by RCT (Ohlsson, 1992). Two experiments are reported that aim to help solve verbal insight problems and to enable some constraint to be relaxed. In Experiment 1, participants were trained to use heuristics to solve unseen problems from the same category that shared the same representational obstacle, namely, ambiguous word and ambiguous name problems. Concurrent verbal protocols were collected and analyzed in terms of the hypotheses proposed by participants. Training improved solution rate of unseen problems from the trained categories and, as expected, positive transfer was specific to the trained category of problem. Analysis of the incorrect hypotheses proposed during problem solving provided supplementary evidence of the effectiveness of training at inducing representation change. In Experiment 2, a similar approach to training was developed to help solve functional fixedness problems. Solution rate increased with training, although transfer was specific to the trained category of problem. Theoretical and methodological issues are discussed.