Working Memory Constraints Scaffold Learning in Transformers under Data Scarcity
We investigate the integration of human-like working memory constraints into the Transformer architecture and implement several cognitively inspired attention variants, including fixed-width windows based and temporal decay based attention mechanisms. Our modified GPT-2 models are trained from scratch on developmentally plausible datasets (10M and 100M words). Performance is evaluated on grammatical judgment tasks (BLiMP) and alignment with human reading time data. Our results indicate that these cognitively-inspired constraints, particularly fixed-width attention, can significantly improve grammatical accuracy especially when training data is scarce. These constrained models also tend to...
| Authors | Pranava Madhyastha, Dagmar Adamcova |
|---|---|
| Primary category | cs.CL |
| Published | 22 April 2026 |
| Last updated at source | 23 April 2026 |
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