Evidence map›Paper›PMID 39856463›Full record

ArticleBehavior research methods2025

Combining automation and expertise: A semi-automated approach to correcting eye-tracking data in reading tasks.

Naser Al Madi, Brett Torra, Yixin Li, Najam Tariq

Abstract read
In one paragraph

Article in Behavior research methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Naser Al MadiDepartment of Computer Science, Colby College, 4000 Mayflower Hill, Waterville, 04901, Maine, USA. nsalmadi@Colby.edu.
Brett TorraDepartment of Computer Science, Colby College, 4000 Mayflower Hill, Waterville, 04901, Maine, USA.
Yixin LiDepartment of Computer Science, Colby College, 4000 Mayflower Hill, Waterville, 04901, Maine, USA.
Najam TariqDepartment of Computer Science, Colby College, 4000 Mayflower Hill, Waterville, 04901, Maine, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In reading tasks, drift can move fixations from one word to another or even another line, invalidating the eye-tracking recording. Manual correction is time-consuming and subjective, while automated correction is fast - yet limited in accuracy. In this paper, we present Fix8 (Fixate), an open-source GUI tool that offers a novel semi-automated correction approach for eye-tracking data in reading tasks. The proposed approach allows the user to collaborate with an algorithm to produce accurate corrections faster without sacrificing accuracy. Through a usability study (N = 14) we assess the time benefits of the proposed technique, and measure the correction accuracy in comparison to manual correction. In addition, we assess subjective workload through the NASA Task Load Index, and user opinions through Likert-scale questions. Our results show that, on average, the proposed technique was 44% faster than manual correction without any sacrifice of accuracy. In addition, users reported a preference for the proposed technique, lower workload, and higher perceived performance compared to manual correction. Fix8 is a valuable tool that offers useful features for generating synthetic eye-tracking data, visualization, filters, data converters, and eye-movement analysis in addition to the main contribution in data correction.

Indexed as

Eye MovementsEye-Tracking TechnologyReadingAdultAlgorithmsAutomationFemaleHumansMaleSoftwareDrift correctionEye TrackingGUIOpen sourceReadingTool

Identifiers

PMID39856463
PMCPMC11762023

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.