A rational model of function learning

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• 作者：Christopher G. Lucas ; Thomas L. Griffiths…
• 关键词：Function learning ; Bayesian modeling
• 刊名：Psychonomic Bulletin & Review
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：22
• 期：5
• 页码：1193-1215
• 全文大小：1,977 KB
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• 作者单位：Christopher G. Lucas (1)
  Thomas L. Griffiths (2)
  Joseph J. Williams (3)
  Michael L. Kalish (4)
  
  1. School of Informatics, University of Edinburgh, 10 Crichton St., Edinburgh, EH8 9AB, UK
  2. Department of Psychology, University of California, Berkeley, USA
  3. HarvardX, Harvard University, Cambridge, USA
  4. Department of Psychology, Syracuse University, Syracuse, USA
  
• 刊物主题：Cognitive Psychology;
• 出版者：Springer US
• ISSN：1531-5320

文摘

Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, which provide a probabilistic basis for similarity-based function learning, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results. Keywords Function learning Bayesian modeling