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Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study
INTRODUCTION: Based 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 b...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187886/ https://www.ncbi.nlm.nih.gov/pubmed/37205087 http://dx.doi.org/10.3389/fpsyg.2023.1196910 |
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author | Chang, Vincent Chieh-Ying Chen, I-Fei |
author_facet | Chang, Vincent Chieh-Ying Chen, I-Fei |
author_sort | Chang, Vincent Chieh-Ying |
collection | PubMed |
description | INTRODUCTION: Based 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. METHODS: Directionality 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. RESULTS: A 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 a textual level. 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. CONCLUSION: The study has shown that translation asymmetry suggested by the model was valid at a textual level, and that machine learning-based approaches can be gainfully applied to Cognitive Translation and Interpreting Studies. |
format | Online Article Text |
id | pubmed-10187886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101878862023-05-17 Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study Chang, Vincent Chieh-Ying Chen, I-Fei Front Psychol Psychology INTRODUCTION: Based 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. METHODS: Directionality 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. RESULTS: A 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 a textual level. 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. CONCLUSION: The study has shown that translation asymmetry suggested by the model was valid at a textual level, and that machine learning-based approaches can be gainfully applied to Cognitive Translation and Interpreting Studies. Frontiers Media S.A. 2023-05-02 /pmc/articles/PMC10187886/ /pubmed/37205087 http://dx.doi.org/10.3389/fpsyg.2023.1196910 Text en Copyright © 2023 Chang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychology Chang, Vincent Chieh-Ying Chen, I-Fei Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study |
title | Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study |
title_full | Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study |
title_fullStr | Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study |
title_full_unstemmed | Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study |
title_short | Translation directionality and the Inhibitory Control Model: a machine learning approach to an eye-tracking study |
title_sort | translation directionality and the inhibitory control model: a machine learning approach to an eye-tracking study |
topic | Psychology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10187886/ https://www.ncbi.nlm.nih.gov/pubmed/37205087 http://dx.doi.org/10.3389/fpsyg.2023.1196910 |
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