Transformers over-extend what humans underlearn: the case of Spanish L-shaped morphome

The cognitive reality of irregular morphological patterns has been debated for decades: do speakers extend them to novel forms, or are they lexical artifacts? A neural network trained on distributional input offers a learnability test: if it recovers the pattern, the pattern is learnable from input statistics alone. We apply this test to the Spanish L-shaped morphome, where the first-person singular indicative stem appears in every present subjunctive cell despite lacking apparent phonological or semantic motivation. We further ask whether the frequency of irregular verbs in the input modulates generalization, evaluating transformers under three frequency conditions (10%, 50%, 90% irregular) and comparing them to human behavioral data. On full-form production from pseudoword inputs all models performed poorly, but all three conditions produced the correct stem more often than humans (43--49% vs. 33%). Response preferences revealed a clear divergence: humans consistently favored regular inflections, whereas models preferred irregular forms more as their proportion in training grew. Models in the naturalistic and balanced conditions were also sensitive to phonological similarity between pseudowords and real Spanish irregular verbs, an effect absent in humans. The L-shaped morphome is thus learnable from distributional input alone, but models generalize it qualitatively differently from humans.