Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Chang, Vincent Chieh-Ying, Chen, I-Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
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
_version_ 1785042811985330176
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
work_keys_str_mv AT changvincentchiehying translationdirectionalityandtheinhibitorycontrolmodelamachinelearningapproachtoaneyetrackingstudy
AT chenifei translationdirectionalityandtheinhibitorycontrolmodelamachinelearningapproachtoaneyetrackingstudy