Many interactive learning environments use some measure of uncertainty to estimate how likely the model output is to be correct. The reliability of these estimates is diminished when changes in the environment cause incoming data to drift away from the data the model was trained on. While interactive learning approaches can use implicit feedback to help tune machine learning models to identify and respond to concept drift more quickly, this approach still requires waiting for user feedback before the problem of concept drift can be addressed. We propose that modeled cognitive feedback can supplement implicit feedback by providing human-tuned features to train an uncertainty model that is more resilient to concept drift. In this paper, we introduce modeled cognitive feedback to support interactive learning, and show that an uncertainty model with cognitive features performs better than a baseline model in an environment with concept drift.