Evidence map›Paper›PMID 39948380›Full record

ArticleScientific data2025

Pre-AttentiveGaze: gaze-based authentication dataset with momentary visual interactions.

Junryeol Jeon, Yeo-Gyeong Noh, JooYeong Kim, Jin-Hyuk Hong

Abstract readDataset
In one paragraph

Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Junryeol JeonGwangju Institute of Science of Technology, School of Integrated Technology, Gwangju, 61005, Republic of Korea.
Yeo-Gyeong NohGwangju Institute of Science of Technology, School of Integrated Technology, Gwangju, 61005, Republic of Korea.
JooYeong KimGwangju Institute of Science of Technology, School of Integrated Technology, Gwangju, 61005, Republic of Korea.
Jin-Hyuk HongGwangju Institute of Science of Technology, School of Integrated Technology, Gwangju, 61005, Republic of Korea. jh7.hong@gist.ac.kr.ORCID http://orcid.org/0000-0002-8838-5667

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This manuscript presents a Pre-AttentiveGaze dataset. One of the defining characteristics of gaze-based authentication is the necessity for a rapid response. In this study, we constructed a dataset for identifying individuals through eye movements by inducing "pre-attentive processing" in response to a given gaze stimulus in a very short time. A total of 76,840 eye movement samples were collected from 34 participants across five sessions. From the dataset, we extracted the gaze features proposed in previous studies, pre-processed them, and validated the dataset by applying machine learning models. This study demonstrates the efficacy of the dataset and illustrates its potential for use in gaze-based authentication of visual stimuli that elicit pre-attentive processing.

Indexed as

AttentionEye MovementsFixation, OcularHumansMachine Learning

Identifiers

PMID39948380
PMCPMC11825865

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.