Perceptual learning, roving and the unsupervised bias
نویسندگان
چکیده
منابع مشابه
Perceptual learning, roving and the unsupervised bias
Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervised neural network models can cope easily with roving conditions. What makes humans so inferior compared to these models? As experimental and c...
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In perceptual learning, performance usually improves when observers train with one type of stimulus, for example, a bisection stimulus. Roving denotes the situation when, instead of one, two or more types of stimuli are presented randomly interleaved, for example, a bisection stimulus and a vernier. For some combinations of stimulus types, performance improves in roving situations whereas for o...
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In perceptual learning, performance often improves within a short time if only one stimulus variant is presented, such as a line bisection stimulus with one outer-line-distance. However, performance stagnates if two bisection stimuli with two outer-line-distances are presented randomly interleaved. Recently, S. G. Kuai, J. Y. Zhang, S. A. Klein, D. M. Levi, and C. Yu, (2005) proposed that learn...
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ژورنال
عنوان ژورنال: Vision Research
سال: 2012
ISSN: 0042-6989
DOI: 10.1016/j.visres.2011.11.001