语音计算中的结构与抽象:在并发习得问题中学习泛化

IF 2.1 0 LANGUAGE & LINGUISTICS Journal of Language Evolution Pub Date : 2017-01-01 DOI:10.1093/JOLE/LZX013
Bill D. Thompson, B. D. Boer
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引用次数: 7

摘要

由于文化的演变,声音系统在其较低层次的细节上变化很大,但系统组织的存在是普遍存在的。为什么在这两个抽象层次上的变化模式不同,这能告诉我们什么关于支撑人类语言习得的认知机制?我们探索了人类学习延伸到,甚至可能专门用于,在抽象的高阶层次上进行推理的进化原理。通过识别结构同质性的特征和预测微妙的例外,从分布线索推断系统性的能力可以引导较低水平的学习,并且不受移动目标问题的影响,移动目标问题是语音认知专业化的主要进化异议。我们通过研究支持并发语音类别归纳问题的概括的代表性假设,从统计角度考察了这一想法。我们提出了一个概率模型,用于共同推断单个健全类和一个系统范围的蓝图,用于平衡这些类之间的共享和特殊结构。这些模型让我们得出了一个进化的猜想:文化推动认知适应在学习的抽象层次上上升
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Structure and abstraction in phonetic computation: Learning to generalise among concurrent acquisition problems
Sound systems vary dramatically in their lower-level details as a result of cultural evolution, but the presence of systematic organisation is universal. Why does variation pattern differently at these two levels of abstraction, and what can this tell us about the cognitive mechanisms that underpin human acquisition of speech? We explore an evolutionary rationale for the proposal that human learning extends to, and is perhaps even specialised for, making inferences at the higher-order level of abstraction. The ability to infer systematicity from distributional cues, by identifying signatures of structural homogeneity and anticipating subtle exceptions, can bootstrap lower-level learning, and is not subject to the moving target problem, a major evolutionary objection to specialisation in speech cognition. We examine this idea from a statistical perspective, by studying the representational assumptions that underpin generalisation among concurrent phonetic category induction problems. We present a probabilistic model for jointly inferring individual sound classes and a system-wide blue-print for the balance of shared and idiosyncratic structure among these classes. These models lead us to an evolutionary conjecture: culture pushes cognitive adaptation up the hierarchy of abstraction in learning
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来源期刊
Journal of Language Evolution
Journal of Language Evolution Social Sciences-Linguistics and Language
CiteScore
4.50
自引率
7.70%
发文量
8
期刊最新文献
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