Learning Direction in Global Motion: Two Classes of Psychophysically-Motivated Models
Abstract
Perceptual learning is defined as fast improvement in performance and retention of the learned ability over a period of time. In a set of psy(cid:173) chophysical experiments we demonstrated that perceptual learning oc(cid:173) curs for the discrimination of direction in stochastic motion stimuli. Here we model this learning using two approaches: a clustering model that learns to accommodate the motion noise, and an averaging model that learns to ignore the noise. Simulations of the models show performance similar to the psychophysical results.
Cite
Text
Sundareswaran and Vaina. "Learning Direction in Global Motion: Two Classes of Psychophysically-Motivated Models." Neural Information Processing Systems, 1994.Markdown
[Sundareswaran and Vaina. "Learning Direction in Global Motion: Two Classes of Psychophysically-Motivated Models." Neural Information Processing Systems, 1994.](https://mlanthology.org/neurips/1994/sundareswaran1994neurips-learning/)BibTeX
@inproceedings{sundareswaran1994neurips-learning,
title = {{Learning Direction in Global Motion: Two Classes of Psychophysically-Motivated Models}},
author = {Sundareswaran, V. and Vaina, Lucia M.},
booktitle = {Neural Information Processing Systems},
year = {1994},
pages = {917-924},
url = {https://mlanthology.org/neurips/1994/sundareswaran1994neurips-learning/}
}