A recurrent model for perceptual learning

Perceptual learning has received enhanced interest during the last years bo th from theoreticians and experimentalists. Recent experimental results rev eal that mechanisms underlying perceptual learning are more complex than pr eviously expected, thereby ruling out any explanations based on simple neur al network models. These findings do not represent an insignificant excepti on to the rule but are evidence that present models fail to reflect some im portant characteristics of the learning process. A new model is introduced that is able to overcome problems of classical neural networks and that mig ht be viewed as a hybrid between supervised and unsupervised learning. (C) 1999 The Optical Society of America. [S1070-9762(99)01209-9].