Whether neural networks can serve as cognitive models of morphological learning remains an open question. Recent work has shown that encoder-decoder models can acquire irregular patterns, but evidence that they generalize these patterns like humans is mixed. We investigate this using the Spanish \emph{L-shaped morphome}, where only the first-person singular indicative (e.g., \textit{pongo} `I put') shares its stem with all subjunctive forms (e.g., \textit{ponga, pongas}) despite lacking apparent phonological, semantic, or syntactic motivation. We compare five encoder-decoder transformers varying along two dimensions: sequential vs. position-invariant positional encoding, and atomic vs. decomposed tag representations. Positional encoding proves decisive: position-invariant models recover the correct L-shaped paradigm clustering even when L-shaped verbs are scarce in training, whereas sequential positional encoding models only partially capture the pattern. Yet none of the models productively generalize this pattern to novel forms. Position-invariant models generalize the L-shaped stem across subjunctive cells but fail to extend it to the first-person singular indicative, producing a mood-based generalization rather than the L-shaped morphomic pattern. Humans do the opposite, generalizing preferentially to the first-person singular indicative over subjunctive forms. None of the models reproduce the human pattern, highlighting the gap between statistical pattern reproduction and morphological abstraction.

翻译：神经网络能否作为形态学习的认知模型仍是一个开放问题。近期研究表明编码器-解码器模型能够习得不规则模式，但其是否像人类一样泛化这些模式的证据尚不明确。本研究通过西班牙语中的"L形语素"现象探讨该问题：尽管缺乏明显的音系、语义或句法动因，第一人称单数直陈式（如pongo"我放置"）的词干却与所有虚拟式形式（如ponga、pongas）保持一致。我们比较了五种编码器-解码器Transformer模型，这些模型在两方面存在差异：序列式与位置不变式位置编码，以及原子式与分解式标签表征。实验表明位置编码具有决定性作用：当训练数据中L形动词稀缺时，位置不变模型仍能恢复正确的L形范式聚类，而序列式位置编码模型仅能部分捕获该模式。然而所有模型均未能将此模式有效泛化至新形式：位置不变模型能在虚拟式范畴内泛化L形词干，却无法将其扩展至第一人称单数直陈式，形成基于语气的泛化而非真正的L形语素模式。人类则呈现相反趋势——优先向第一人称单数直陈式而非虚拟式形式泛化。所有模型均未复现人类泛化模式，这凸显了统计模式复现与形态抽象能力之间的本质差异。