A Neural Model of Adaptation in Reading

TL;DR

This paper demonstrates that incorporating a simple adaptation mechanism into neural language models enhances their ability to predict human reading times by adapting to lexical and syntactic context, aligning more closely with human reading behavior.

Contribution

It introduces a neural adaptation mechanism that improves language models' predictions of human reading times by dynamically adjusting to linguistic context.

Findings

Adaptation improves prediction accuracy of human reading times.

Model adapts to both lexical items and syntactic structures.

Enhanced alignment with psycholinguistic experimental data.

Abstract

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.