Evidence map›Paper›PMID 32009919›Full record

ArticleFrontiers in human neuroscience2019

Cognitive Fitness Framework: Towards Assessing, Training and Augmenting Individual-Difference Factors Underpinning High-Performance Cognition.

Eugene Aidman

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in human neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Eugene AidmanLand Division, Defence Science & Technology Group, Edinburgh, SA, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this article is to introduce the concept of Cognitive Fitness (CF), identify its key ingredients underpinning both real-time task performance and career longevity in high-risk occupations, and to canvas a holistic framework for their assessment, training, and augmentation. CF as a capacity to deploy neurocognitive resources, knowledge and skills to meet the demands of operational task performance, is likely to be multi-faceted and differentially malleable. A taxonomy of CF constructs derived from Cognitive Readiness (CR) and Mental fitness (MF) literature maps into phases of operational cycle from foundational to advanced, mission-ready and recovery. Foundational cognitive attributes, such as attention, executive control and co-action, were hypothesized to be trainable at the initial Cognitive Gym phase. More advanced training targets at the CR phase included stress and arousal regulation, adaptability, teamwork, situation awareness (including detection, sense-making and prediction) and decision making (de-biasing and confidence calibration). The mission-ready training phase is focused on tolerances (to sleep loss, monotony, pain, frustration, uncertainty) and resistance (to distraction, deception or manipulation). Operational Augmentation phase relies on support tools such as decision aids and fatigue countermeasures, while the Recovery phase employs reflexive (e.g., mindfulness), and restorative practices (e.g., nutrition and sleep hygiene). The periodization of cognitive training in this cycle is hypothesized to optimize both real-time cognitive performance and the resilience that enables life-long thriving. One of the most promising avenues of validating this hypothesis is by developing an expert consensus on the key CF ingredients and their relative importance in high-performance settings.

Indexed as

cognitive fitnessmeasurementoperational readiness cycleRDoC domains cognitive functioningtask performancetrainability

Identifiers

PMID32009919
PMCPMC6971199

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.