title: Adaptive computational models of fast learning of motion direction discrimination.
abstract: 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.
authors.affiliation: Intelligent Systems Laboratory, Boston University, MA 02215, USA.
authors.forename: V
authors.initials: V
authors.lastname: Sundareswaran
authors.forename: L M
authors.initials: LM
authors.lastname: Vaina
mesh_terms: D000818:Animals; D003198:Computer Simulation; D004192:Discrimination, Psychological; D000882:Haplorhini; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D008960:Models, Psychological; D009039:Motion Perception; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology; D011597:Psychomotor Performance; D017603:Signal Detection, Psychological; D013702:Temporal Lobe / Q000502:physiology; D013997:Time Factors