The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Human–automation interaction for multiple robot control: the effect of varying automation assistance and individual differences on operator performance
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In keeping with the distinction between automation and autonomous agents, we view human–automation teams as exhibiting a degree of interdependence among humans and automation with LOA below Level 5 on Parasuraman et al. (2000) continuum (because this is the automation–autonomy break point; Wright et al., 2018).”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Wright et al., 2018
In the citing paper
“Indeed, according to Wright et al. (2018, p. 1034), below Level 5 and above Level 6 offer natural break points:”
From Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature (2022) · cites this paper as Wright et al. (2018)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research
(1988) · Advances in Psychology · doi:10.1016/s0166-4115(08)62386-9 · Cited by 15,003
The attention system of the human brain
Posner, M. I., & Petersen, S. E. (1990) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.13.030190.000325 · Cited by 8,070 · cited 1×
“Individuals with low attentional control have difficulty disengaging from threatening stimuli, as such, it is possible that the presence of a target detection task affected them more than their high attentional control counterparts (Posner 1980; Posner and Petersen 1989).”
BackgroundHumans and automation: Use, misuse, disuse, abuse
Parasuraman, R., & Riley, V. (1997) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/001872097778543886 · Cited by 4,554
A model for types and levels of human interaction with automation
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans · doi:10.1109/3468.844354 · Cited by 4,195 · cited 2×
“Parasuraman, Sheridan, and Wickens (2000) defined automation as ‘the full or partial replacement of a function previously carried out by the human operator’, and identified ten LOAs, in conjunction with four stages of human information processing (information acquisition, analysis, decisions and…”
BackgroundAction video game modifies visual selective attention
Green, C. S., & Bavelier, D. (2003) · Nature · doi:10.1038/nature01647 · Cited by 2,540
Complacency and Bias in Human Use of Automation: An Attentional Integration
Parasuraman, R., & Manzey, D. H. (2010) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720810376055 · Cited by 1,710 · cited 1×
“This is important because there is evidence that if the operator is out-of-the loop, they may become compla-cent, which can lead to untoward consequences (Kaber and Endsley 2004; Parasuraman and Manzey 2010”
BackgroundKang, M. J., Hsu, M., Krajbich, I. M., Loewenstein, G., McClure, S. M., Wang, J. T.-Y., & Camerer, C. F. (2009) · Psychological Science · doi:10.1111/j.1467-9280.2009.02402.x · Cited by 882 · cited 1×
“Increase in pupil diameter (PD) has been found to be positively correlated with increased mental workload (Iqbal, Zheng, and Bailey 2004; Van Orden, Jung, and Makeig 2000; Van Orden et al. 2001), and heightened interest (Kang et al. 2009).”
Performance Consequences of Automation-Induced 'Complacency'
Parasuraman, R., Molloy, R., & Singh, I. L. (1993) · The International Journal of Aviation Psychology · doi:10.1207/s15327108ijap0301_1 · Cited by 875 · cited 1×
“…benefit all users equally, becoming ineffectual or even detrimental to performance for some users, possibly leading to automation-induced complacency (Parasuraman, Molloy, and Singh 1993). attentional control; however, Non-Gamers’ A ′ scores fell in the VS+R condition compared to the VS condition.”
ResultHuman Performance Consequences of Stages and Levels of Automation: An Integrated Meta-Analysis
Onnasch, L., Wickens, C. D., Li, H., & Manzey, D. (2014) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720813501549 · Cited by 524
Task-evoked pupillary response to mental workload in human-computer interaction
Iqbal, S. T., Zheng, X. S., & Bailey, B. P. (2004) · CHI '04 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/985921.986094 · Cited by 317
Manzey, D., Reichenbach, J., & Onnasch, L. (2012) · Journal of Cognitive Engineering and Decision Making · doi:10.1177/1555343411433844 · Cited by 277
Lathan, C. E., & Tracey, M. (2002) · Presence: Teleoperators and Virtual Environments · doi:10.1162/105474602760204282 · Cited by 123 · cited 14×
“There was no difference between High and Low SpA participants on A ′ in either the Manual or VS+R conditions, however, the High SpA participants had significantly higher A ′ in the VS condition ( d = 0.74), indicating higher sensitivity to target presence than the Low SpA participants (Figure 2).”
BackgroundInfluential
Research works printed but not linked: 52
- Adams E., 2013, Fundamentals of Game Design
- Barber D., 2008, The Mixed Initiative Experimental (MIX) Testbed for Human Robot Interactions with Varied Levels of Automation
- Barnes M. J., 2014, Designing for Humans in Autonomous Systems: Military Applications
- 10.1016/0010-0285(82)90007-X · DOI · Paper page
- 10.1177/1555343411399069 · DOI
- 10.1177/154193121005401927 · DOI
- 10.1177/0018720811435843 · DOI · Paper page
- 10.1080/00140139.2012.689013 · DOI
- 10.1109/THMS.2013.2293535 · DOI · Paper page
- 10.1109/TSMCC.2010.2056682 · DOI · Paper page
- 10.1080/08995600802115904 · DOI
- 10.1080/08995600802565785 · DOI
- 10.1080/1463922X.2017.1315750 · DOI · Paper page
- 10.1080/00140130802680773 · DOI
- 10.1016/j.apergo.2012.08.002 · DOI
- Choi, J. J., Y. Kim, and S. S. Kwak. 2014. “The Autonomy Levels and the Human Intervention Levels of Robots: The Impact of Robot Types in Human-robot Interaction.” In Robot and Human Interactive Communication, 2014 RO - MAN: The 23rd IEEE International Symposium on , 1069–1074. IEEE.
- 10.1177/0018720810368674 · DOI
- Davies R. D., 1982, The Psychology of Vigilance
- Defense Science Board, 2016, Summer Study on Autonomy
- 10.1037/0021-843X.111.2.225 · DOI · Paper page
- Ehmke, C., and S. Wilson. 2007. “Identifying Web Usability Problems from Eye-tracking Data.” Paper presented at the 21st British HCI Group Annual Conference on People and Computers: HCI. but Not as We Know It, Lancaster, UK, September 3–7.
- Endsley, M. R. 2000. “Direct Measurement of Situation Awareness: Validity and use of SAGAT.” Chapter 8 In Situation Awareness Analysis and Measurement , edited by M. R. Endsley and D. J. Garland, 10. Mahwah: Lawrence Erlbaum Associates. · DOI · Paper page
- 10.1109/SMC.2017.8122717 · DOI
- 10.1016/S0169-8141(98)00068-7 · DOI · Paper page
- 10.1016/j.cognition.2005.10.004 · DOI · Paper page
- 10.1037/1076-898X.10.2.75 · DOI
- 10.1080/00140139.2013.816375 · DOI · Paper page
- 10.1002/wcs.116 · DOI
- 10.1177/1555343417737203 · DOI
- 10.1080/1463922021000054335 · DOI · Paper page
- 10.1177/0018720810366859 · DOI
- 10.4324/9781410611147 · DOI · Paper page
- 10.1177/154193120805200421 · DOI
- 10.3357/ASEM.2958.2011 · DOI
- Mentzer, C. I., and E. R. Johnson. 2014. “Dismounted Soldier Autonomy Tools (DSAT) – From Conception to Deployment.” Paper presented at 6th annual National Defence Industrial Association Ground Vehicle Systems Engineering & Technology Symposium, Novi, Michigan, August 12–14.
- 10.1080/08995600902768800 · DOI · Paper page
- 10.1111/j.1469-8986.1974.tb01114.x · DOI
- 10.1080/00335558008248231 · DOI · Paper page
- 10.1016/j.ssci.2016.05.002 · DOI
- 10.1111/1467-9280.00309 · DOI · Paper page
- 10.1006/ijhc.1999.0285 · DOI
- 10.1080/00221309.1993.9711153 · DOI
- Snyder, M. G., Z. Qu, J. Y. C. Chen, and M. J. Barnes. 2010. “RoboLeader for Reconnaissance by a Team of Robotic Vehicles.” Paper presented at 2010 International Symposium on Collaborative Technologies and Systems (CTS), Chicago, Illinois, May 17–21.
- SAE, 2014, Taxonomy and Definitions for Terms Related to On-road Motor Vehicle Automated Driving Systems (Standard J3016_201401)
- 10.1080/00140139.2010.489969 · DOI
- 10.1016/j.ijpsycho.2003.12.005 · DOI · Paper page
- 10.1037/a0024170 · DOI
- 10.1016/S0301-0511(99)00043-5 · DOI
- 10.1518/001872001775992570 · DOI · Paper page
- 10.1126/scirobotics.aam8638 · DOI · Paper page
- Wright J. L., 2017, Agent Reasoning Transparency: The Influence of Information Level on Automation-induced Complacency
- Wright J. L., 2013, The Effects of Level of Autonomy on Human-Agent Teaming for Multi-Robot Control and Local Security Maintenance
Cited by
Human–Autonomy Teaming: A Review and Analysis of the Empirical Literature
O’Neill, T., McNeese, N., Barron, A., & Schelble, B. (2022) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820960865 · Cited by 695
Metadata
- Authors
- Wright, J. L., Chen, J. Y. C., & Barnes, M. J.
- Affiliations
- U.S. Army Research Laboratory, Orlando, FL, USA
- U.S. Army Research Laboratory, Ft. Huachuca, AZ, USA
- ORCID
- Wright, Julia L.
- Venue
- Ergonomics
- Publisher
- Informa UK Limited
- Published
- 2018-04-03
- Volume
- 61
- Issue
- 8
- Pages
- 1033-1045
- ISSN
- 0014-0139
- 1366-5847
- Cited by
- 58
- References
- 12
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.