Evidence map›Paper›PMID 34434758›Full record

ArticleMethodsX2021

Cognitive task analysis and workload classification.

Benjamin M Knisely, Janell S Joyner, Monifa Vaughn-Cooke

Abstract read
In one paragraph

Article in MethodsX, 2021. 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

3 authors.

Benjamin M KniselyDepartment of Mechanical Engineering, University of Maryland, College Park, MD 20740, United States.
Janell S JoynerDepartment of Mechanical Engineering, University of Maryland, College Park, MD 20740, United States.
Monifa Vaughn-CookeDepartment of Mechanical Engineering, University of Maryland, College Park, MD 20740, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automation can be utilized to relieve humans of difficult and repetitive tasks in many domains, presenting the opportunity for safer and more efficient systems. This increase in automation has led to new supervisory roles for human operators where humans monitor feedback from autonomous systems and provide input when necessary. Optimizing these roles requires tools for evaluation of task complexity and resulting operator cognitive workload. Cognitive task analysis is a process for modeling the cognitive actions required of a human during a task. This work presents an enhanced version of this process: Cognitive Task Analysis and Workload Classification (CTAWC). The goal of developing CTAWC was to provide a standardized process to decompose cognitive tasks in enough depth to allow for precise identification of sources of cognitive workload. CTAWC has the following advantages over conventional CTA methodology:•Integrates standard terminology from existing taxonomies for task classification to describe expected operator cognitive workload during task performance.•Provides a framework to evaluate adequate cognitive depth when decomposing cognitive tasks.•Provides a standard model upon which to build an empirical study to evaluate task complexity.

Indexed as

AutonomousHuman-machine systemHuman performancePupillometryWorkstation

Identifiers

PMID34434758
PMCPMC8374157

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.