کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10457868 921925 2009 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
پیش نمایش صفحه اول مقاله
The evolution of frequency distributions: Relating regularization to inductive biases through iterated learning
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Cognition - Volume 111, Issue 3, June 2009, Pages 317-328
نویسندگان
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