A localist, parallel constraint satisfaction, artificial neural network model is presented that accounts for a broad collection of attitude and attitude-change phenomena. The network represents the attitude object and cognitions and beliefs related to the attitude, as well as how to integrate a persuasive message into this network. Short-term effects are modeled by activation patterns due to parallel constraint satisfaction processes, and long-term effects are modeled by weight changes due to the settling patterns of activation. Phenomena modeled include thought-induced attitude polarization, elaboration and attitude strength, motivated reasoning and social influence, an integrated view of heuristic versus systematic persuasion, and implicit versus explicit attitude change. Results of the simulations are consistent with empirical results. The same set of simple mechanisms is used to model all the phenomena, which allows the model to offer a parsimonious theoretical account of how structure can impact attitude change. This model is compared with previous computational approaches to attitudes, and implications for attitude research are discussed.