The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning
The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning
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DOI：
10.1016/j.cognition.2009.02.012
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发表时间：
2009-06-01
期刊：
影响因子：
3.4
通讯作者：
Griffiths, Thomas L.
中科院分区：
文献类型：
--
作者：
Reali, Florencia;Griffiths, Thomas L.
学习者对语言结构的规律化在论证语言习得受到强烈的先天制约方面起到了关键作用，并对语言进化具有重要意义。然而，在没有正式模型的情况下，将学习者的归纳偏向与实验室任务中的规律化行为联系起来可能是具有挑战性的。在本文中，我们探索了如何通过迭代学习从语言进化中产生规则的语言结构，在迭代学习中，一个人的语言输出被用来生成提供给下一个人的语言输入。我们使用贝叶斯代理的迭代学习模型来证明，当学习者具有适当的归纳偏差时，这个过程可以导致正则化。然后，我们提出了三个实验，证明在实验室中模拟语言进化的过程可以揭示出对规则化的偏见，否则这些偏见可能不明显，从而允许弱偏见产生强烈的影响。这些实验的结果表明，人们倾向于将不一致的词义映射正规化，即使是对正则化的微弱偏见也可以通过反复学习通过语言进化产生规则语言。(C)2009爱思唯尔B.V.保留所有权利。
The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this paper we explore how regular linguistic structures can emerge from language evolution by iterated learning, in which one person's linguistic output is used to generate the linguistic input provided to the next person. We use a model of iterated learning with Bayesian agents to show that this process can result in regularization when learners have the appropriate inductive biases. We then present three experiments demonstrating that simulating the process of language evolution in the laboratory can reveal biases towards regularization that might not otherwise be obvious, allowing weak biases to have strong effects. The results of these experiments suggest that people tend to regularize inconsistent word-meaning mappings, and that even a weak bias towards regularization can allow regular languages to be produced via language evolution by iterated learning. (C) 2009 Elsevier B.V. All rights reserved.