Theory: We present a model of voter campaign learning which is based on Bayesian learning models. This model assumes voters are imperfectly informed and that they incorporate new information into their existing perceptions about candidate issue positions in a systematic manner. Hypothesis: Additional information made available to voters about candidate issue positions during the course of a political campaign will lead voters to have more precise perceptions of the issue positions of the candidates involved. Data and Methods: We use panel survey data from the 1976 and 1980 presidential elections, combined with content analyses of the media during these same elections. Our primary analysis is conducted using random effects panel models. Results: We find that during each of these campaigns many voters became better informed about the positions of candidates on many issues and that these changes in voter information are directly related to the information ow during each presidential campaign.