语音锁存网络

IF 0.6 0 LANGUAGE & LINGUISTICS Biolinguistics Pub Date : 2021-03-25 DOI:10.5964/bioling.9159
Joe Stephen Bratsvedal Collins
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引用次数: 1

摘要

本文分析了一种吸引子神经网络模型——语音闭锁网络。该模型似乎再现了某些典型的语音现象,尽管没有任何这些语音行为编程或教模型。相反,同化、片段ocp和声音排序似乎是由一些基本的类似大脑的成分与类似音系的特征系统结合而自发产生的。这一意义可以从两个角度来解释:首先,该模型自发地产生被证实的自然语言模式的事实可以作为该模型在神经和心理上的合理性的证据;其次,它为为什么这些模式在自然语言语法中频繁出现提供了一个潜在的解释。也就是说,它们是大脑中锁存动态的结果。
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The Phonological Latching Network
This paper gives an analysis of an attractor neural network model dubbed the Phonological Latching Network. The model appears to reproduce certain quintessentially phonological phenomena, despite not having any of these phonological behaviours programmed or taught to the model. Rather, assimilation, segmental-OCP, and sonority sequencing appear to emerge spontaneously from the combination of a few basic brain-like ingredients with a phonology-like feature system. The significance of this can be interpreted from two angles: firstly, the fact that the model spontaneously produces attested natural language patterns can be taken as evidence of the model’s neural and psychological plausibility; and secondly, it provides a potential explanation for why these patters appear to frequently in natural language grammars. Namely, they are a consequence of latching dynamics in the brain.
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来源期刊
Biolinguistics
Biolinguistics LANGUAGE & LINGUISTICS-
CiteScore
1.50
自引率
0.00%
发文量
5
审稿时长
12 weeks
期刊最新文献
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