Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Yamaya, Akito; Topić, Goran; Aizawa, Akiko

doi:10.2197/ipsjjip.25.100

Cited by 3 publications

(6 citation statements)

References 13 publications

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“…FixAlign, an R package developed by Cohen (2013), is currently the most well-established method in the experimental psychology community, although other methods have recently been proposed by Schroeder (2019) and Špakov et al (2019). In addition, there is a disparate body of work from several subfields of computer science, such as biometrics (Abdulin & Komogortsev, 2015), educational technology (Hyrskykari, 2006), and user-interface design (Beymer & Russell, 2005), in which various ad-hoc algorithms have been reported (see also Carl, 2013;Lima Sanches et al, 2015;Lohmeier, 2015;Martinez-Gomez et al, 2012;Mishra et al, 2012;Nüssli, 2011;Palmer & Sharif, 2016;Sibert et al, 2000;Yamaya et al, 2017).…”

Section: Figurementioning

confidence: 99%

Algorithms for the automated correction of vertical drift in eye-tracking data

Carr¹,

Pescuma²,

Furlan³

et al. 2020

Preprint

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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 nine 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.

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Section: Figurementioning

confidence: 99%

Algorithms for the automated correction of vertical drift in eye-tracking data

Carr¹,

Pescuma²,

Furlan³

et al. 2020

Preprint

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show abstract

“…The compare algorithm is directly based on the method reported by Lima Sanches et al (2015) and is very similar to the more complex methods described by Yamaya et al (2017). The fixation sequence is first segmented into "gaze lines" by identifying the return sweeps-long saccades that move the eye from the end of one line to the start of the next.…”

Section: Comparementioning

confidence: 99%

“…Lima Sanches et al (2015) considered three measures of similarity and found dynamic time warping (DTW; Sakoe & Chiba, 1978;Vintsyuk, 1968) to be the best method (we discuss DTW in more detail later in this section). Similarly, Yamaya et al (2017) use the closely related Needleman-Wunsch algorithm (Needleman & Wunsch, 1970).…”

Section: Comparementioning

confidence: 99%

“…Relying only on the x values helps the algorithm overcome vertical drift issues, but it is also problematic because in many standard reading scenarios the lines of text in a passage tend to be horizontally similar to each other; each line tends to contain a similar number of words that are of a similar length, resulting in potential ambiguity about how gaze lines and text lines should be matched up. To alleviate this issue, both Lima Sanches et al (2015) and Yamaya et al (2017) only compare the gaze line to a certain number of nearby text lines (we set this parameter to 3, which is effectively the closest line plus one line above and one line below).…”

Section: Comparementioning

confidence: 99%

“…The child trial is extremely noisy and exhibits not just vertical drift issues but also many natural reading phenomena that will pose challenges to the algorithms community, although other methods have recently been proposed by Schroeder (2019Schroeder ( ) andŠpakov et al (2019. In addition, there is a disparate body of work from several subfields of computer science, such as biometrics (Abdulin & Komogortsev, 2015), educational technology (Hyrskykari, 2006), and user-interface design (Beymer & Russell, 2005), in which various ad-hoc algorithms have been reported (see also Carl, 2013;Lima Sanches et al, 2015;Lohmeier, 2015;Martinez-Gomez et al, 2012;Mishra et al, 2012;Nüssli, 2011;Palmer & Sharif, 2016;Sibert et al, 2000;Yamaya et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Algorithms for the automated correction of vertical drift in eye-tracking data

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Identifying Lines and Interpreting Vertical Jumps in Eye Tracking Studies of Reading Text and Code

Shamy

Feitelson

2023

ACM Trans. Appl. Percept.

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Eye tracking studies have shown that reading code, in contradistinction to reading text, includes many vertical jumps. As different lines of code may have very different functions (e.g. variable definition, flow control, or computation) it is important to accurately identify the lines being read. We design experiments that require a specific line of text to be scrutinized. Using the distribution of gazes around this line, we then calculate how the precision with which we can identify the line being read depends on the font size and spacing. The results indicate that, even after correcting for systematic bias, unnaturally large fonts and spacing may be required for reliable line identification. Interestingly, during the experiments the participants also repeatedly re-checked their task and that they are looking at the correct line, leading to vertical jumps similar to those observed when reading code. This suggests that observed reading patterns may be “inefficient”, in the sense that participants feel the need to repeat actions beyond the minimal number apparently required for the task. This may have implications regarding the interpretation of reading patterns. In particular, reading does not reflect only the extraction of information from the text or code. Rather, reading patterns may also reflect other types of activities, such as getting a general orientation, and searching for specific locations in the context of performing a particular task.

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Vertical Error Correction Using Classification of Transitions between Sequential Reading Segments

Cited by 3 publications

References 13 publications

Algorithms for the automated correction of vertical drift in eye-tracking data

Algorithms for the automated correction of vertical drift in eye-tracking data

Algorithms for the automated correction of vertical drift in eye-tracking data

Identifying Lines and Interpreting Vertical Jumps in Eye Tracking Studies of Reading Text and Code

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