This research is being done in conjunction with IIS-9731990, which is being performed by Michael Pazzani in the Information and Computer Science Department at the University of California, Irvine. The research is concerned with intelligent decision aids that can be developed by data mining techniques. Experience has determined that such systems can learn accurate models, but that experts in areas where those models are used in decision aids are often reluctant to trust them because they do not, for instance, use the same tests intermediate conclusions or abstractions that the experts have grown to trust. Or they do not use certain factors at all that experts feel to be relevant. Experts also want models that are stable under small changes in the data being analyzed. Psychologists have discovered factors that simplify the learning, understanding, and communication of category and process information by humans. This research seeks to explore these psychological principles in light of the output of existing KDD algorithms and then go on to develop and evaluate new KDD algorithms that will provide output that is easy for people to learn , use, and communicate to others. With the results of this research, it should be possible to make such decision aids more "human centered", so that they will be used more often and more effectively in practice.