SOURCE-LINKED INTELLIGENCE
Beyond Static and Linear: What Attention Constraints Best Fit Human Reading Times?
Transformer-based language models are widely used as models of human language processing, yet their attention mechanisms allow lossless access to the full preceding context, unlike the limited memory systems of humans. We hypothesize that installing memory constraints into transformers' attention mechanisms can improve their fit to human behavioral data. While previous work has explored individual constraints in isolation, we conduct a systematic comparison of multiple attention-based memory mechanisms across different model sizes and training corpora, evaluating both psychometric predictive p
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-24T20:54:29.000Z
First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.