Evidence map›Paper›PMID 42451226›Full record

ArticleSensors (Basel, Switzerland)2026

Eye-Tracking-Based Evaluation of Cognitive Style and Driving Task Effects on AR-HUD Navigation Interfaces.

Jing Li, Xinyu Feng, Min Lin, Hua Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. 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.

Jing LiCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.
Xinyu FengCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.
Min LinCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.
Hua ZhangCollege of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.

Funding

Jiangsu Provincial Social Science Fund Project No. 22SHC012
6 · The paper itself

Abstract

As augmented reality head-up display (AR-HUD) becomes increasingly integrated into intelligent vehicles, inappropriate interface designs may increase drivers' cognitive workload and delay hazard responses. This study investigates how cognitive style, driving task type, and AR-HUD navigation design jointly influence drivers' behavioral performance and visual attention. A total of 55 participants were recruited and screened using the Group Embedded Figures Test, with 38 drivers finally selected for a 2 × 4 × 2 driving-simulation experiment comparing world-fixed (WF) and screen-fixed (SF) interfaces across goal-directed and stimulus-driven tasks. Reaction times and eye-tracking indicators were analyzed using generalized linear models. Results show that stimulus-driven tasks significantly increased reaction times, with rear-vehicle scenarios producing the longest responses (mean = 1.420). During lane-change tasks, WF displays significantly reduced fixation duration (

Indexed as

Augmented RealityAutomobile DrivingCognitionEye-Tracking TechnologyAdultAttentionEye MovementsFemaleHumansMaleReaction TimeTask Performance and AnalysisUser-Computer InterfaceYoung AdultAR-HUDbehavioral performancecognitive styledriving taskeye trackingnavigation design

Identifiers

PMID42451226
PMCPMC13364102

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.