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函数学习中的非单调外推

Nonmonotonic extrapolation in function learning.

作者信息

Bott Lewis, Heit Evan

机构信息

Department of Psychology, University of Warwick, Coventry, United Kingdom.

出版信息

J Exp Psychol Learn Mem Cogn. 2004 Jan;30(1):38-50. doi: 10.1037/0278-7393.30.1.38.

Abstract

This article reports the results of an experiment addressing extrapolation in function learning, in particular the issue of whether participants can extrapolate in a nonmonotonic manner. Existing models of function learning, including the extrapolation association model of function learning (EXAM; E. L. DeLosh, J. R. Busemeyer, & M. A. McDaniel, 1997), cannot account for this type of extrapolation pattern. We present the results of an experiment in which participants were shown a series of paired stimulus-response magnitudes where the relationship between these 2 dimensions conformed to a cyclic function. Participants were shown to extrapolate from these training data in a nonmonotonic way, contrary to predictions from EXAM. A new model of function learning is presented, which predicts responses more accurately than EXAM.

摘要

本文报告了一项关于函数学习中推断问题的实验结果,特别是参与者是否能够以非单调方式进行推断的问题。现有的函数学习模型,包括函数学习的推断关联模型(EXAM;E.L.德洛什、J.R.布西梅尔和M.A.麦克丹尼尔,1997),无法解释这种类型的推断模式。我们展示了一项实验的结果,在该实验中,向参与者展示了一系列成对的刺激-反应量值,其中这两个维度之间的关系符合循环函数。结果表明,参与者会以非单调的方式从这些训练数据中进行推断,这与EXAM的预测相反。本文提出了一种新的函数学习模型,该模型比EXAM能更准确地预测反应。

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