Learnability of morphomic patterns of syncretism

Morphological systems often reuse the same forms in different functions, creating what is known as syncretism. While syncretism varies greatly, certain cross-linguistic tendencies are apparent. Patterns where all syncretic forms share a morphological feature value (e.g., first person, or plural number) are most common cross-linguistically, and this preference is mirrored in results from learning experiments. While this suggests a general bias towards natural (featurally homogeneous) over unnatural (featurally heterogeneous) patterns, little is yet known about gradients in learnability and distributions of different kinds of unnatural patterns. In this paper we assess apparent cross-linguistic asymmetries between different types of unnatural patterns in person-number verbal agreement paradigms and test their learnability in an artificial language learning experiment. We find that the cross-linguistic recurrence of unnatural patterns of syncretism in person-number paradigms is proportional to the amount of shared feature values (i.e., similarity) amongst the syncretic forms. Our experimental results further suggest that the learnability of syncretic patterns also mirrors the paradigm's degree of feature-value similarity. We propose that this gradient in learnability reflects a general bias towards similarity-based structure in morphological learning, which previous literature has shown to play a crucial role in word learning as well as in category and concept learning more generally. Our results thus support a more nuanced view of the natural-unnatural distinction in morphological paradigms, and suggest that a preference for similarity-based structure during language learning might shape the worldwide transmission and typological distribution of syncretic patterns in morphological paradigms.

形态系统往往在不同的功能中重复使用相同的形态，从而形成了所谓的同形异义词现象。尽管同形异义词现象的多样性很大，但在跨语言层面上，某些共同的趋势依然显著。在跨语言层面上，所有同形异义词共享的形态学特征值（例如，第一人称或复数形式）的例子最为常见，这一偏好也在学习实验的结果中得到了体现。尽管这表明了对自然（自然同质）模式相对于非自然（自然异质）模式的普遍偏好，但对于可学习性和不同类型非自然模式分布的梯度，目前所知甚少。在本文中，我们评估了不同类型非自然模式在人称-数一致范畴中的跨语言不对称性，并测试了它们在一个人工语言学习实验中的可学习性。我们发现，在人称-数范畴中，非自然同形异义词模式的跨语言重复出现与同形异义词形式之间共享的特征值（即相似度）成正比。我们的实验结果进一步表明，同形异义词模式的可学习性也反映了范畴特征值相似度的程度。我们提出，这种可学习性的梯度反映了形态学习中对基于相似度结构的普遍偏好，而先前文献已经表明，这种偏好对于词汇学习以及更广泛的类别和概念学习都起着至关重要的作用。因此，我们的结果支持了对形态范畴中自然-非自然区分的更为细腻的观点，并表明在语言学习过程中对基于相似度结构的偏好可能塑造了同形异义词模式在全球范围内的传播和类型分布。