Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 3 complete abstracts (672 words), 9 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
The science of designing, building or equipping mechanical devices or artificial environments to the anthropometric, physiological, or psychological requirements of the people who will use them.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Ergonomic; Human Engineering; Human Factors Engineering; Human Factors Engineerings; Human Factors and Ergonomics; Cognitive Ergonomics; Cognitive Ergonomic; Ergonomic, Cognitive.
Where it fits in the MeSH classification
- Broader topics
- Psychology, Applied; Engineering
- Narrower topics
- Data Display; Man-Machine Systems; Task Performance and Analysis; Time Management
- See also
- Psychology, Industrial
Research landscape
PubMed indexes 64,712 records tagged with the MeSH heading “Ergonomics” (counts retrieved 2026-10-31). 8,108 of them (12.5%) were published from 2020 onward, and the busiest period so far is 2010-2019 with 21,253 records.
Reviews account for 3,875 records (6.0%) and meta-analyses for 342. Randomized controlled trials number 3,046 (4.7%) as a share of all records on the topic.
Show the data
| Period | Records |
|---|---|
| through 1999 | 17,552 |
| 2000-2009 | 17,799 |
| 2010-2019 | 21,253 |
| 2020 onward | 8,108 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 3,875 | 6.0% |
| Meta-analyses | 342 | 0.5% |
| Randomized controlled trials | 3,046 | 4.7% |
Method: PubMed query "Ergonomics"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in psychiatry and psychology
Research on mental health and behaviour relies on questionnaires, interviews and rating scales rather than physical measurements, so how a condition was defined and measured shapes every result. Many studies are observational or small, placebo responses can be large, and findings in one group or culture may not carry over to another. Nothing on this page is advice about diagnosis or treatment; anyone worried about their mental health should talk to a qualified clinician. Check how outcomes were measured, who took part and how long they were followed.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Mastering diverse control tasks through world models | 2025 | Research article Design not recorded in structured metadata | 49 | cc by PMID 40175544 · DOI · PMC12003158 |
| Building compositional tasks with shared neural subspaces | 2026 | Research article Design not recorded in structured metadata | 27 | cc by PMID 41299181 · DOI · PMC12872450 |
| Explainable AI improves task performance in human-AI collaboration | 2024 | Research article Design not recorded in structured metadata | 21 | cc by PMID 39730794 · DOI · PMC11681242 |
Mastering diverse control tasks through world models [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence.
- Closing excerpt
- Our work allows solving challenging control problems without extensive experimentation, making reinforcement learning broadly applicable.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (171 words)
Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement-learning algorithms can be readily applied to tasks similar to what they have been developed for, configuring them for new application domains requires substantial human expertise and experimentation1,2. Here we present the third generation of Dreamer, a general algorithm that outperforms specialized methods across over 150 diverse tasks, with a single configuration. Dreamer learns a model of the environment and improves its behaviour by imagining future scenarios. Robustness techniques based on normalization, balancing and transformations enable stable learning across domains. Applied out of the box, Dreamer is, to our knowledge, the first algorithm to collect diamonds in Minecraft from scratch without human data or curricula. This achievement has been posed as a substantial challenge in artificial intelligence that requires exploring farsighted strategies from pixels and sparse rewards in an open world3. Our work allows solving challenging control problems without extensive experimentation, making reinforcement learning broadly applicable.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40175544 · DOI · PMC12003158
Building compositional tasks with shared neural subspaces [2]
Abstract reading summary — source excerpts
- Opening excerpt
- Cognition is highly flexible-we perform many different tasks1 and continually adapt our behaviour to changing demands2,3.
- Closing excerpt
- In summary, our findings suggest that the brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (198 words)
Cognition is highly flexible-we perform many different tasks1 and continually adapt our behaviour to changing demands2,3. Artificial neural networks trained to perform multiple tasks will reuse representations4 and computational components5 across tasks. By composing tasks from these subcomponents, an agent can flexibly switch between tasks and rapidly learn new tasks6,7. Yet, whether such compositionality is found in the brain is unclear. Here we show the same subspaces of neural activity represent task-relevant information across multiple tasks, with each task flexibly engaging these subspaces in a task-specific manner. We trained monkeys to switch between three compositionally related tasks. In neural recordings, we found that task-relevant information about stimulus features and motor actions were represented in subspaces of neural activity that were shared across tasks. When monkeys performed a task, neural representations in the relevant shared sensory subspace were transformed to the relevant shared motor subspace. Monkeys adapted to changes in the task by iteratively updating their internal belief about the current task and then, based on this belief, flexibly engaging the shared sensory and motor subspaces relevant to the task. In summary, our findings suggest that the brain can flexibly perform multiple tasks by compositionally combining task-relevant neural representations.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41299181 · DOI · PMC12872450
Explainable AI improves task performance in human-AI collaboration [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Artificial intelligence (AI) provides considerable opportunities to assist human work.
- Closing excerpt
- These gains represent a significant improvement in task performance.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (303 words)
Artificial intelligence (AI) provides considerable opportunities to assist human work. However, one crucial challenge of human-AI collaboration is that many AI algorithms operate in a black-box manner where the way how the AI makes predictions remains opaque. This makes it difficult for humans to validate a prediction made by AI against their own domain knowledge. For this reason, we hypothesize that augmenting humans with explainable AI improves task performance in human-AI collaboration. To test this hypothesis, we implement explainable AI in the form of visual heatmaps in inspection tasks conducted by domain experts. Visual heatmaps have the advantage that they are easy to understand and help to localize relevant parts of an image. We then compare participants that were either supported by (a) black-box AI or (b) explainable AI, where the latter supports them to follow AI predictions when the AI is accurate or overrule the AI when the AI predictions are wrong. We conducted two preregistered experiments with representative, real-world visual inspection tasks from manufacturing and medicine. The first experiment was conducted with factory workers from an electronics factory, who performed [Formula: see text] assessments of whether electronic products have defects. The second experiment was conducted with radiologists, who performed [Formula: see text] assessments of chest X-ray images to identify lung lesions. The results of our experiments with domain experts performing real-world tasks show that task performance improves when participants are supported by explainable AI with heatmaps instead of black-box AI. We find that explainable AI as a decision aid improved the task performance by 7.7 percentage points (95% confidence interval [CI]: 3.3% to 12.0%, [Formula: see text]) in the manufacturing experiment and by 4.7 percentage points (95% CI: 1.1% to 8.3%, [Formula: see text]) in the medical experiment compared to black-box AI. These gains represent a significant improvement in task performance.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 39730794 · DOI · PMC11681242
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Functional neuroimaging correlates of finger-tapping task variations: an ALE meta-analysis [4]
- Research article Response inhibition and psychopathology: a meta-analysis of go/no-go task performance [5]
- Research article The contralateral delay activity as a neural measure of visual working memory [6]
- Research article Aging and task switching: a meta-analysis [7]
- Research article Directed acyclic graphs: a tool for causal studies in paediatrics [8]
- Research article Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review [9]
References
- Hafner D, Pasukonis J, Ba J, Lillicrap T. Mastering diverse control tasks through world models. . 2025. PMID 40175544 · DOI · PMC12003158
- Tafazoli S, Bouchacourt FM, Ardalan A, Markov NT, Uchimura M, Mattar MG, et al.. Building compositional tasks with shared neural subspaces. . 2026. PMID 41299181 · DOI · PMC12872450
- Senoner J, Schallmoser S, Kratzwald B, Feuerriegel S, Netland T. Explainable AI improves task performance in human-AI collaboration. . 2024. PMID 39730794 · DOI · PMC11681242
- Witt ST, Laird AR, Meyerand ME. Functional neuroimaging correlates of finger-tapping task variations: an ALE meta-analysis. Neuroimage. 2008. PMID 18511305 · DOI · PMC2592684
- Wright L, Lipszyc J, Dupuis A, Thayapararajah SW, Schachar R. Response inhibition and psychopathology: a meta-analysis of go/no-go task performance. J Abnorm Psychol. 2014. PMID 24731074 · DOI
- Luria R, Balaban H, Awh E, Vogel EK. The contralateral delay activity as a neural measure of visual working memory. Neurosci Biobehav Rev. 2016. PMID 26802451 · DOI · PMC4869985
- Wasylyshyn C, Verhaeghen P, Sliwinski MJ. Aging and task switching: a meta-analysis. Psychol Aging. 2011. PMID 21261411 · DOI · PMC4374429
- Williams TC, Bach CC, Matthiesen NB, Henriksen TB, Gagliardi L. Directed acyclic graphs: a tool for causal studies in paediatrics. Pediatr Res. 2018. PMID 29967527 · DOI · PMC6215481
- Moy AJ, Schwartz JM, Chen R, Sadri S, Lucas E, Cato KD, et al.. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc. 2021. PMID 33434273 · DOI · PMC8068426
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.