A Comparative Analysis of the Cognitive Processes in Machine Translation and Human Translation

Abstract

With the rapid advancement of artificial intelligence technology, machine translation systems have demonstrated significant advantages in handling multilingual information conversion. However, the fundamental differences between machine translation and human translators at the cognitive level have not yet been systematically explained. Based on a cognitive science framework, this paper conducts an in-depth analysis of their essential differences in cognitive mechanisms. The study finds that machine translation is built upon computational models and data-driven approaches, characterized by formal processing and probabilistic optimization. In contrast, human translation relies on the complex cognitive system of the biological brain, possessing advanced cognitive capabilities such as contextual understanding, dynamic knowledge management, and creative decision-making. By constructing a systematic comparative analysis framework, this research elucidates the fundamental differences from three dimensions: language comprehension, knowledge application, and decision-making processes. Furthermore, based on cognitive complementarity, a new model of human-machine collaborative translation is proposed. This research not only deepens the theoretical understanding of the cognitive processes in translation but also provides a crucial foundation for developing efficient human-machine collaborative translation systems.