Memory systems constantly confront the challenge of capturing both the shared features that connect experiences together and the unique features that distinguish them. Across two experiments, we leveraged a color memory distortion paradigm to investigate how we handle this representational tension when learning new information. Over a thirty-minute period, participants learned shared and unique features of categories of novel objects, where each feature was assigned a particular color. While participants did not differ in how accurately they remembered these features overall, when inaccurate, participants misremembered the color of shared (relative to unique) features as more similar to the category's average color, suggesting more integration of shared features in memory. This same rapid representational warping manifested in a neural network model trained on the same categories. The work reveals how memories for different features are rapidly and differentially warped as a function of their roles in a category.