Adaptive computational models of fast learning of motion direction discrimination.

In a previous study, we found that subjects' performance in a task of direction discrimination in stochastic motion stimuli shows fast improvement in the absence of feedback and the learned ability is retained over a period of time. We model this learning using two unsupervised approaches: a clustering model that learns to accommodate the motion noise, and an averaging model that learns to ignore the noise. Extensive simulations with the models show performance similar to psychophysical results.