Examining cognitive load in human-machine collaborative translation: insights from eye-tracking experiments of Chinese-English translation

This study finds that the cognitive load of translators in the process of human-machine collaborative translation shows phased changes, especially when the output quality of machine translation is poor, translators need more cognitive resources to correct. The impact of complex tasks on cognitive load is even more significant, and human translation requires more cognitive effort on the part of translators. Eye tracking data analysis provides empirical support for understanding the cognitive mechanisms in the translation process. For the first time, this study systematically explored the cognitive load characteristics of human-computer collaborative translation through eye tracking technology, filling the research gap in this field in the existing literature. The results of this study not only provide a theoretical basis for optimizing translation tools and designing more efficient translation processes, but also provide a new perspective for cognitive load management in translation teaching and practice.