We have several simulators for training medical doctors in cutting edge surgery skills. We use insights regarding biases in mental effort regulation to improve self-training protocols.
Database schema matching is a challenging task that call for improvement for several decades. Automatic algorithms fail to provide reliable enough results. We use human matching to overcome algorithm failures and vice versa. We refer to human and algorithmic matchers as imperfect matchers with different strengths and weaknesses. We use insights from cognitive research to predict human matchers behavior and identify those who can do better than others. We then merge their responses with algorithmic outcomes and get better results.
As a team, we currently work on several projects, with several challenging tasks, including riddle solving, database schema matching, and text design in a word processor. In all cases we aim to predict people’s confidence in their success in the task based on their mouse movements before choosing their response and while rating their confidence on a continuous scale.
