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

arXiv: https://arxiv.org/abs/2507.21556 · HTML: https://mlsanity.com/paper/2507.21556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Findings of the BabyLM Challenge: Sample-Efficient Pretraining on Developmentally Plausible Corpora](https://mlsanity.com/paper/2504.08165/) (2025)
- [Cognitive Science in the era of Artificial Intelligence: A roadmap for reverse-engineering the infant language-learner](https://mlsanity.com/paper/1607.08723/) (2016)
- [The SIGMORPHON 2019 Shared Task: Morphological Analysis in Context and Cross-Lingual Transfer for Inflection](https://mlsanity.com/paper/1910.11493/) (2019)
- [The CoNLL--SIGMORPHON 2018 Shared Task: Universal Morphological Reinflection](https://mlsanity.com/paper/1810.07125/) (2018)
- [Language acquisition: do children and language models follow similar learning stages?](https://mlsanity.com/paper/2306.03586/) (2023)
- [CoNLL-SIGMORPHON 2017 Shared Task: Universal Morphological Reinflection in 52 Languages](https://mlsanity.com/paper/1706.09031/) (2017)
- [SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection](https://mlsanity.com/paper/2006.11572/) (2020)
- [Recurrent Neural Networks in Linguistic Theory: Revisiting Pinker and Prince (1988) and the Past Tense Debate](https://mlsanity.com/paper/1807.04783/) (2018)
- [(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models' Performance](https://mlsanity.com/paper/2108.05682/) (2021)
- [Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals](https://mlsanity.com/paper/2005.08826/) (2020)
- [Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models](https://mlsanity.com/paper/2104.06483/) (2021)
- [Frequency matters: Modeling irregular morphological patterns in Spanish with Transformers](https://mlsanity.com/paper/2410.21013/) (2024)
- [Is deeper always better? Replacing linear mappings with deep learning networks in the Discriminative Lexicon Model](https://mlsanity.com/paper/2410.04259/) (2024)

## Abstract (arXiv)

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.