Nonmonotonic extrapolation in function learning
Nonmonotonic extrapolation in function learning
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DOI：
10.1037/0278-7393.30.1.38
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发表时间：
2004-01-01
影响因子：
2.6
通讯作者：
Heit, E
中科院分区：
文献类型：
--
作者：
Bott, L;Heit, E
本文报告了一项解决函数学习中外推的实验结果，特别是参与者是否可以以非单调方式外推的问题。现有的功能学习模型，包括功能学习的外推联想模型（EXAM; E。L.德洛什Busemeyer，& M. A. McDaniel，1997）不能解释这种类型的外推模式。我们提出了一个实验的结果，在该实验中，参与者被示出了一系列成对的刺激反应幅度，这2个维度之间的关系符合一个循环函数。参与者被证明是从这些训练数据中推断的非单调的方式，相反的预测，从ESTA。一个新的功能学习模型，它预测的反应比ESTA更准确。
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.