Evidence map›Paper›PMID 28744209›Full record

ReviewFrontiers in human neuroscience2017

Performance Monitoring Applied to System Supervision.

Bertille Somon, Aurélie Campagne, Arnaud Delorme, Bruno Berberian

Abstract readReview
In one paragraph

Review in Frontiers in human neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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.

Bertille SomonONERA, Information Processing and Systems DepartmentSalon Air, France.
Aurélie CampagneUniv. Grenoble Alpes, CNRS, LPNC UMR 5105Grenoble, France.
Arnaud DelormeCentre de Recherche Cerveau & Cognition, Pavillon Baudot, Hopital Purpan, BP-25202Toulouse, France.
Bruno BerberianONERA, Information Processing and Systems DepartmentSalon Air, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nowadays, automation is present in every aspect of our daily life and has some benefits. Nonetheless, empirical data suggest that traditional automation has many negative performance and safety consequences as it changed task performers into task supervisors. In this context, we propose to use recent insights into the anatomical and neurophysiological substrates of action monitoring in humans, to help further characterize performance monitoring during system supervision. Error monitoring is critical for humans to learn from the consequences of their actions. A wide variety of studies have shown that the error monitoring system is involved not only in our own errors, but also in the errors of others. We hypothesize that the neurobiological correlates of the self-performance monitoring activity can be applied to system supervision. At a larger scale, a better understanding of system supervision may allow its negative effects to be anticipated or even countered. This review is divided into three main parts. First, we assess the neurophysiological correlates of self-performance monitoring and their characteristics during error execution. Then, we extend these results to include performance monitoring and error observation of others or of systems. Finally, we provide further directions in the study of system supervision and assess the limits preventing us from studying a well-known phenomenon: the Out-Of-the-Loop (OOL) performance problem.

Indexed as

error detectionerror-related negativityfeedback-related negativitymind-wanderingneuroergonomicsOut-of-the-loopPerformance monitoringsystem monitoring

Identifiers

PMID28744209
PMCPMC5504305

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.