Eye Tracking Reveals Where Human Reading and AI Processing Diverge
A new study evaluated whether large language models (LLMs) can accurately account for human reading dynamics. The team compared eye-tracking data from 368 adult readers against predictions generated by over 400 neural network language models across syntactically complex texts, including “garden-path” sentences. The study revealed a critical divergence between human cognitive architecture and transformer-based AI architectures. While LLM next-word prediction models successfully account for the initial processing speed of word recognition during smooth forward reading, they fail to predict the cognitive friction that occurs when humans integrate words into broader context. ….[READ]
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