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 blueprint 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

中文翻译：

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语音计算中的结构和抽象：学习在并发习得问题中泛化

由于文化进化，声音系统的底层细节差异很大，但系统组织的存在是普遍的。为什么在这两个抽象层次上的变异模式不同，这能告诉我们什么是人类获得语音的认知机制？我们探索了人类学习扩展到甚至可能专门用于在高阶抽象级别进行推理的提议的进化原理。通过识别结构同质性的特征和预测细微的异常，从分布线索推断系统性的能力可以引导低级学习，并且不受移动目标问题的影响，这是语音认知专业化的主要进化反对意见。我们通过研究支持并发语音类别归纳问题泛化的表征假设，从统计的角度来检验这个想法。我们提出了一个用于联合推断单个声音类的概率模型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。这些模型将我们引向了一个进化猜想：文化将认知适应推上了学习的抽象层次