Detecting Memory-Based Interaction Obstacles with a Recurrent Neural Model of User Behavior

A memory-based interaction obstacle is a condition which impedes human memory during Human-Computer Interaction, for example a memory-loading secondary task. In this paper, we present an approach to detect the presence of such memory-based interaction obstacles from logged user behavior during system use. For this purpose, we use a recurrent neural network which models the resulting temporal sequences. To acquire a sufficient number of training episodes, we employ a cognitive user simulation. We evaluate the approach with data from a user test and on which we outperform a non-sequential baseline by more than 23%.