Natural Stories

Energy-based transformers as unified predictors of reading difficulty in computational psycholinguistics

A new arXiv preprint introduces energy-based transformer measures as predictors of human reading difficulty, evaluated across three reading-time corpora (Natural Stories, UCL eye-tracking, UCL self-paced reading). The energy measure outperforms surprisal alone and appears to subsume both surprisal and attention entropy effects, suggesting it could serve as a single unified predictor. The work connects transformer language models to Hopfield networks and dense associative memory literature, marking the first application of energy-based transformer measures in computational psycholinguistics.