PSYCH OpenIR
High reward enhances perceptual learning
Zhang, Meng-Yuan1; He, Qing1,2; Dosher, Barbara Anne3,4; Lu, Zhong-Lin5,6; Huang, Chang-Bing1,2
摘要Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications.
关键词perceptual learning reward transfer perceptual template model
2018-08-01
语种英语
DOI10.1167/18.8.11
发表期刊JOURNAL OF VISION
ISSN1534-7362
卷号18期号:8页码:20
资助项目National Eye Institute[EY017491] ; National Natural Science Foundation of China ; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Science, Wenzhou Medical University[QTJ16006] ; Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences[Y7CX332008] ; Knowledge Innovation Program of Chinese Academy of Sciences[Y3CX102003] ; National Natural Science Foundation of China[NSFC 31400877] ; National Natural Science Foundation of China[31470983] ; National Natural Science Foundation of China[NSFC 31230032] ; National Natural Science Foundation of China[NSFC 31230032] ; National Natural Science Foundation of China[31470983] ; National Natural Science Foundation of China[NSFC 31400877] ; Knowledge Innovation Program of Chinese Academy of Sciences[Y3CX102003] ; Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences[Y7CX332008] ; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Science, Wenzhou Medical University[QTJ16006] ; National Natural Science Foundation of China ; National Eye Institute[EY017491]
出版者ASSOC RESEARCH VISION OPHTHALMOLOGY INC
WOS关键词PRIMARY VISUAL-CORTEX ; CONTRAST SENSITIVITY ; VERNIER ACUITY ; TEXTURE-DISCRIMINATION ; INTERNAL NOISE ; WORKING-MEMORY ; EXTERNAL NOISE ; GLOBAL-MOTION ; TASK ; ATTENTION
WOS研究方向Ophthalmology
WOS类目Ophthalmology
WOS记录号WOS:000443719900011
资助机构National Natural Science Foundation of China ; Knowledge Innovation Program of Chinese Academy of Sciences ; Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences ; Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Science, Wenzhou Medical University ; National Natural Science Foundation of China ; National Eye Institute
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文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/26856
通讯作者Dosher, Barbara Anne; Lu, Zhong-Lin; Huang, Chang-Bing
作者单位1.Chinese Acad Sci, Inst Psychol, CAS Key Lab Behav Sci, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Chinese Acad Sci, Dept Psychol, Beijing, Peoples R China
3.Univ Calif Irvine, Dept Cognit Sci, Irvine, CA 92717 USA
4.Univ Calif Irvine, Inst Math Behav Sci, Irvine, CA 92717 USA
5.Ohio State Univ, Ctr Cognit & Behav Brain Imaging, Ctr Cognit & Brain Sci, Lab Brain Proc LOBES, Columbus, OH 43210 USA
6.Ohio State Univ, Dept Psychol, Columbus, OH 43210 USA
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GB/T 7714
Zhang, Meng-Yuan,He, Qing,Dosher, Barbara Anne,et al. High reward enhances perceptual learning[J]. JOURNAL OF VISION,2018,18(8):20.
APA Zhang, Meng-Yuan,He, Qing,Dosher, Barbara Anne,Lu, Zhong-Lin,&Huang, Chang-Bing.(2018).High reward enhances perceptual learning.JOURNAL OF VISION,18(8),20.
MLA Zhang, Meng-Yuan,et al."High reward enhances perceptual learning".JOURNAL OF VISION 18.8(2018):20.
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