In addition to surprisal and contextual entropy, several other information-theoretic measures could enhance the prediction of reading times across languages. One such measure is successor entropy, which quantifies the uncertainty regarding the next word given the current context. This measure can provide insights into how much information a reader anticipates needing to process as they encounter new words. Another relevant measure is information density, which assesses how information is distributed across a sentence or discourse. High information density may lead to increased cognitive load, thereby affecting reading times. Additionally, predictive coding frameworks, which consider the brain's predictions about incoming sensory information, could be integrated into reading time models. This approach posits that discrepancies between expected and actual input (prediction errors) can influence processing times. Lastly, contextual predictability, which evaluates how predictable a word is based on its surrounding context, could also serve as a valuable predictor. By incorporating these additional information-theoretic measures, researchers can develop a more comprehensive understanding of the cognitive processes underlying reading across different languages.