Autonomous morphology, such as inflection class systems and paradigmatic distribution patterns, is widespread and diachronically resilient in natural language. Why this should be so has remained unclear given that autonomous morphology imposes learning costs, offers no clear benefit relative to its absence and could easily be removed by the analogical forces which are constantly reshaping it. Here we propose an explanation for the resilience of autonomous morphology, in terms of a diachronic dynamic of attraction and repulsion between morphomic categories, which emerges spontaneously from a simple paradigm cell filling process. Employing computational evolutionary models, our key innovation is to bring to light the role of `dissociative evidence', i.e., evidence for inflectional distinctiveness which a rational reasoner will have access to during analogical inference. Dissociative evidence creates a repulsion dynamic which prevents morphomic classes from collapsing together entirely, i.e., undergoing complete levelling. As we probe alternative models, we reveal the limits of conditional entropy as a measure for predictability in systems that are undergoing change. Finally, we demonstrate that autonomous morphology, far from being `unnatural' (e.g. \citealt{Aronoff1994}), is rather the natural (emergent) consequence of a natural (rational) process of inference applied to inflectional systems.

翻译：自主形态（如屈折类别系统和范式分布模式）在自然语言中广泛存在且具有历时韧性。鉴于自主形态会增加学习成本、相较于其缺失状态未显现明确优势，且可能被持续重塑它的类比力量轻易消除，其存在原因一直未明。本文提出一种解释自主形态韧性的理论，该理论基于形态学范畴间吸引与排斥的历时动态，这种动态从简单的范式单元填充过程中自发涌现。通过计算演化模型，我们的核心创新在于揭示"解离证据"的作用，即理性推理者在类比推理过程中能够获取的、关于屈折区别性的证据。解离证据产生排斥动态，防止形态学类别完全坍缩（即经历彻底拉平）。在探究替代模型的过程中，我们揭示了条件熵作为变化系统中可预测性度量指标的局限性。最终我们证明，自主形态远非"非自然"现象（如\citealt{Aronoff1994}），而是将自然（理性）推理过程应用于屈折系统所产生的自然（涌现）结果。