In spite of more than 100 years of research, the mechanisms underlying visual masking are still unknown. In recent publications, we introduced an unmasking paradigm involving the fusion of features that revealed interesting spatial characteristics. Here, we investigate the temporal aspects of this paradigm showing very long lasting effects that impose serious restrictions on models of masking. We used a simple feed-forward neural network model to explain these results.