Not Every Metric is Equal: Cognitive Models for Predicting N400 and P600 Components During Reading Comprehension

In recent years, numerous studies have sought to understand the cognitive dynamics underlying language processing by modeling reading times and ERP amplitudes using computational metrics like surprisal. In the present paper, we examine the predictive power of surprisal, entropy, and a novel metric based on semantic similarity for N400 and P600. Our experiments, conducted with Mandarin Chinese materials, revealed three key findings: 1) expectancy plays a primary role for N400; 2) P600 also reflects the cognitive effort required to evaluate linguistic input semantically; and 3) during the time window of interest, information uncertainty influences the language processing the most. Our findings show how computational metrics that capture distinct cognitive dimensions can effectively address psycholinguistic questions.