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Algorithms for the automated correction of vertical drift in eye-tracking data
A common problem in eye-tracking research is vertical drift—the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye-tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passage...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863743/ https://www.ncbi.nlm.nih.gov/pubmed/34159510 http://dx.doi.org/10.3758/s13428-021-01554-0 |
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author | Carr, Jon W. Pescuma, Valentina N. Furlan, Michele Ktori, Maria Crepaldi, Davide |
author_facet | Carr, Jon W. Pescuma, Valentina N. Furlan, Michele Ktori, Maria Crepaldi, Davide |
author_sort | Carr, Jon W. |
collection | PubMed |
description | A common problem in eye-tracking research is vertical drift—the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye-tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passages, where it is critical that fixations on one line are not erroneously recorded on an adjacent line. Correction is often performed manually by the researcher, but this process is tedious, time-consuming, and prone to error and inconsistency. Various methods have previously been proposed for the automated, post hoc correction of vertical drift in reading data, but these methods vary greatly, not just in terms of the algorithmic principles on which they are based, but also in terms of their availability, documentation, implementation languages, and so forth. Furthermore, these methods have largely been developed in isolation with little attempt to systematically evaluate them, meaning that drift correction techniques are moving forward blindly. We document ten major algorithms, including two that are novel to this paper, and evaluate them using both simulated and natural eye-tracking data. Our results suggest that a method based on dynamic time warping offers great promise, but we also find that some algorithms are better suited than others to particular types of drift phenomena and reading behavior, allowing us to offer evidence-based advice on algorithm selection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-021-01554-0. |
format | Online Article Text |
id | pubmed-8863743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-88637432022-03-02 Algorithms for the automated correction of vertical drift in eye-tracking data Carr, Jon W. Pescuma, Valentina N. Furlan, Michele Ktori, Maria Crepaldi, Davide Behav Res Methods Article A common problem in eye-tracking research is vertical drift—the progressive displacement of fixation registrations on the vertical axis that results from a gradual loss of eye-tracker calibration over time. This is particularly problematic in experiments that involve the reading of multiline passages, where it is critical that fixations on one line are not erroneously recorded on an adjacent line. Correction is often performed manually by the researcher, but this process is tedious, time-consuming, and prone to error and inconsistency. Various methods have previously been proposed for the automated, post hoc correction of vertical drift in reading data, but these methods vary greatly, not just in terms of the algorithmic principles on which they are based, but also in terms of their availability, documentation, implementation languages, and so forth. Furthermore, these methods have largely been developed in isolation with little attempt to systematically evaluate them, meaning that drift correction techniques are moving forward blindly. We document ten major algorithms, including two that are novel to this paper, and evaluate them using both simulated and natural eye-tracking data. Our results suggest that a method based on dynamic time warping offers great promise, but we also find that some algorithms are better suited than others to particular types of drift phenomena and reading behavior, allowing us to offer evidence-based advice on algorithm selection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-021-01554-0. Springer US 2021-06-22 2022 /pmc/articles/PMC8863743/ /pubmed/34159510 http://dx.doi.org/10.3758/s13428-021-01554-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Carr, Jon W. Pescuma, Valentina N. Furlan, Michele Ktori, Maria Crepaldi, Davide Algorithms for the automated correction of vertical drift in eye-tracking data |
title | Algorithms for the automated correction of vertical drift in eye-tracking data |
title_full | Algorithms for the automated correction of vertical drift in eye-tracking data |
title_fullStr | Algorithms for the automated correction of vertical drift in eye-tracking data |
title_full_unstemmed | Algorithms for the automated correction of vertical drift in eye-tracking data |
title_short | Algorithms for the automated correction of vertical drift in eye-tracking data |
title_sort | algorithms for the automated correction of vertical drift in eye-tracking data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8863743/ https://www.ncbi.nlm.nih.gov/pubmed/34159510 http://dx.doi.org/10.3758/s13428-021-01554-0 |
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