Optimizing Predictive Metrics for Human Language Comprehension

Abstract

Expectation and memory have been found to play crucial roles in human language comprehension. Currently, the effects of both expectation and memory can be estimated using computational methods. Computational metrics of surprisal and semantic relevance, which represent expectation and memory respectively, have been developed to accurately predict and explain language comprehension and processing. However, their efficacy is hindered by their inadequate integration of contextual information. Drawing inspiration from the attention mechanism in transformers and human forgetting mechanism, this study introduces an attention-aware method that thoroughly incorporates contextual information, updating surprisal and semantic relevance into attention-aware metrics respectively. Furthermore, by employing the quantum superposition principle, the study proposes an enhanced approach for integrating and encoding diverse information sources based on the two attention-aware metrics. Metrics that are both attention-aware and enhanced can integrate information from expectation and memory, showing superior effectiveness compared to existing metrics. This leads to more accurate predictions of eye movements during the reading of naturalistic discourse in 13 languages. The proposed approaches are fairly capable of facilitating simulation and evaluation of existing reading models and language processing theories. The metrics computed by the proposed approaches are highly interpretable and exhibit cross-language generalizations in predicting language comprehension. The innovative computational methods proposed in this study hold the great potential to enhance our understanding of human working memory mechanisms, human reading behavior and cognitive modeling in language processing. Moreover, they have the capacity to revolutionize ongoing research in computational cognition for language processing, offering valuable insights for computational neuroscience, quantum cognition and optimizing the design of AI systems.