A Neural Model of Adaptation in Reading
This work addresses the challenge of understanding human cognitive adaptation in reading for psycholinguistics and computational modeling, but it is incremental as it builds on existing claims about adaptation.
The paper tackled the problem of modeling human adaptation in reading by adding a simple adaptation mechanism to a neural language model, resulting in improved predictions of human reading times compared to a non-adaptive model, with performance analyzed on controlled psycholinguistic materials.
It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context. We provide further support to this claim by showing that the addition of a simple adaptation mechanism to a neural language model improves our predictions of human reading times compared to a non-adaptive model. We analyze the performance of the model on controlled materials from psycholinguistic experiments and show that it adapts not only to lexical items but also to abstract syntactic structures.