Evidence map›Paper›PMID 32901189›Full record

ArticleIJCAI : proceedings of the conference2020

Human Gaze Assisted Artificial Intelligence: A Review.

Ruohan Zhang, Akanksha Saran, Bo Liu, Yifeng Zhu, Sihang Guo, Scott Niekum, Dana Ballard, Mary Hayhoe

Open access · goldAbstract read
In one paragraph

Article in IJCAI : proceedings of the conference, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 5% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Using Gaze for Behavioural Biometrics.Sensors (Basel, Switzerland) · 2023
    Article
  5. 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

8 authors at 1 institution in 1 country.

Ruohan ZhangThe University of Texas at Austin.
Akanksha SaranThe University of Texas at Austin.
Bo LiuThe University of Texas at Austin.
Yifeng ZhuThe University of Texas at Austin.
Sihang GuoThe University of Texas at Austin.
Scott NiekumThe University of Texas at Austin.
Dana BallardThe University of Texas at Austin.
Mary HayhoeThe University of Texas at Austin.
The University of Texas at Austin · US

Funding

VISUAL SENSITIVITY REGULATION &COLOR CODINGR01EY005729 · NEI · UNIVERSITY OF TEXAS AT AUSTIN · PI HAYHOE, MARY M · 1985 to 2023
$7.0M
NEI NIH HHS R01 EY005729
6 · The paper itself

Abstract

Human gaze reveals a wealth of information about internal cognitive state. Thus, gaze-related research has significantly increased in computer vision, natural language processing, decision learning, and robotics in recent years. We provide a high-level overview of the research efforts in these fields, including collecting human gaze data sets, modeling gaze behaviors, and utilizing gaze information in various applications, with the goal of enhancing communication between these research areas. We discuss future challenges and potential applications that work towards a common goal of human-centered artificial intelligence.

Identifiers

PMID32901189
PMCPMC7476326
OpenAlexW3041498386

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.