Do transformer models generalize morphological patterns like humans do? We investigate this by directly comparing transformers to human behavioral data on Spanish irregular morphomic patterns from \citet{Nevins2015TheRA}. We adopt the same analytical framework as the original human study. Under controlled input conditions, we evaluate whether transformer models can replicate human-like sensitivity to the morphome, a complex linguistic phenomenon. Our experiments focus on three frequency conditions: natural, low-frequency, and high-frequency distributions of verbs exhibiting irregular morphomic patterns. Transformer models achieve higher stem-accuracy than human participants. However, response preferences diverge: humans consistently favor the "natural" inflection across all items, whereas models preferred the irregular forms, and their choices are modulated by the proportion of irregular verbs present during training. Moreover, models trained on the natural and low-frequency distributions, but not the high-frequency distribution, exhibit sensitivity to phonological similarity between test items and Spanish L-shaped verbs, mirroring a limited aspect of human phonological generalization.

翻译：Transformer模型是否像人类一样泛化形态模式？我们通过将Transformer模型与\citet{Nevins2015TheRA}中西班牙语不规则形态模式的人类行为数据直接比较来研究这一问题。我们采用与原始人类研究相同的分析框架。在受控输入条件下，我们评估Transformer模型能否复现人类对形态素（一种复杂语言现象）的敏感性。我们的实验聚焦于三种频率条件：呈现不规则形态模式的动词在自然、低频和高频分布下的表现。Transformer模型在词干准确率上优于人类参与者。然而，响应偏好存在差异：人类在所有测试项中始终偏好"自然"屈折形式，而模型更倾向于不规则形式，且其选择受训练过程中不规则动词比例的影响。此外，在自然分布和低频分布（而非高频分布）上训练的模型，对测试项与西班牙语L形动词之间的音系相似性表现出敏感性，这反映了人类音系泛化能力的某个有限方面。