IntroductionBased on such physiological data as pupillometry collected in an eye-tracking experiment, the study has further confirmed the effect of directionality on cognitive loads during L1 and L2 textual translations by novice translators, a phenomenon called “translation asymmetry” suggested by the Inhibitory Control Model, while revealing that machine learning-based approaches can be usefully applied to the field of Cognitive Translation and Interpreting Studies.MethodsDirectionality was the only factor guiding the eye-tracking experiment where 14 novice translators with the language combination of Chinese and English were recruited to conduct L1 and L2 translations while their pupillometry were recorded. They also filled out a Language and Translation Questionnaire with which categorical data on their demographics were obtained.ResultsA nonparametric related-samples Wilcoxon signed rank test on pupillometry verified the effect of directionality, suggested by the model, during bilateral translations, verifying “translation asymmetry” at atextuallevel. Further, using the pupillometric data, together with the categorical information, the XGBoost machine-learning algorithm yielded a model that could reliably and effectively predict translation directions.ConclusionThe study has shown that translation asymmetry suggested by the model was valid at atextuallevel, and that machine learning-based approaches can be gainfully applied to Cognitive Translation and Interpreting Studies.