Using category structures to test iterated learning as a method for identifying inductive biases
Using category structures to test iterated learning as a method for identifying inductive biases
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DOI：
10.1080/03640210701801974
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发表时间：
2008-01-01
影响因子：
2.5
通讯作者：
Kalish, Michael L.
中科院分区：
文献类型：
--
作者：
Griffiths, Thomas L.;Christian, Brian R.;Kalish, Michael L.
认知科学中研究的许多问题都是归纳问题，要求人们根据数据来评估假设。成功解决这些问题的关键是拥有正确的归纳偏差——关于世界的假设，使得在与观察到的数据同样一致的假设之间做出选择成为可能。本文探讨了一种新的实验方法来识别引导人类归纳推理的偏见。这种方法背后的思想很简单：本文使用参与者在一次试验中产生的反应来产生他们或另一个参与者将在下一次试验中看到的刺激。对这种“迭代学习”过程的正式分析，基于学习者是贝叶斯代理的假设，预测它应该揭示这些学习者的归纳偏差，正如在假设上的先验概率分布所表达的那样。本文介绍了一系列基于类别结构的刺激实验，证明迭代学习可以用来揭示人类学习者的归纳偏见。
Many of the problems studied in cognitive science are inductive problems, requiring people to evaluate hypotheses in the light of data. The key to solving these problems successfully is having the right inductive biases-assumptions about the world that make it possible to choose between hypotheses that are equally consistent with the observed data. This article explores a novel experimental method for identifying the biases that guide human inductive inferences. The idea behind this method is simple: This article uses the responses produced by a participant on one trial to generate the stimuli that either they or another participant will see on the next. A formal analysis of this "iterated learning" procedure, based on the assumption that the learners are Bayesian agents, predicts that it should reveal the inductive biases of these learners, as expressed in a prior probability distribution over hypotheses. This article presents a series of experiments using stimuli based on a well-studied set of category structures, demonstrating that iterated learning can be used to reveal the inductive biases of human learners.