Evidence map›Paper›PMID 35354816›Full record

ArticleScientific reports2022

Age-related effects of executive function on takeover performance in automated driving.

Qijia Peng, Yanbin Wu, Nan Qie, Sunao Iwaki

Abstract read
In one paragraph

Article in Scientific reports, 2022. 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.

Qijia PengGraduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan.
Yanbin WuHuman-Centered Mobility Research Center (HCMRC), National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan.
Nan QieDepartment of Industrial Engineering, Tsinghua University, Beijing, People's Republic of China.
Sunao IwakiGraduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan. s.iwaki@aist.go.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of highly automated vehicles can meet elderly drivers' mobility needs; however, worse driving performance after a takeover request (TOR) is frequently found, especially regarding non-driving related tasks (NDRTs). This study aims to detect the correlation between takeover performance and underlying cognitive factors comprising a set of higher order cognitive processes including executive functions. Thirty-five young and 35 elderly participants were tested by computerized cognitive tasks and simulated driving tasks to evaluate their executive functions and takeover performance. Performance of n-back tasks, Simon tasks, and task switching were used to evaluate updating, inhibition, and shifting components of executive functions by principal component analysis. The performance of lane changing after TOR was measured using the standard deviation of the steering wheel angle and minimum time-to-collision (TTC). Differences between age groups and NDRT engagement were assessed by two-way mixed analysis of variance. Older participants had significantly lower executive function ability and were less stable and more conservative when engaged in NDRT. Furthermore, a significant correlation between executive function and lateral driving stability was found. These findings highlight the interaction between age-related differences in executive functions and takeover performance; thus, provide implications for designing driver screening tests or human-machine interfaces.

Indexed as

Executive FunctionInhibition, PsychologicalAgedAutomationHumansPrincipal Component AnalysisReaction Time

Identifiers

PMID35354816
PMCPMC8967856

What OpenQuestion holds

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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.