The fields of Cognitive Engineering (CE) and Decision Making (DM) have made exceptional contributions to our understanding of complex sociotechnical systems; however, it can be difficult to apply research and findings from CE work to drive Systems Development (SD). This panel discussion examines the connective tissue between the worlds of CE research and SD practice. Panelists with backgrounds across CE and SD will provide generalizable examples of how to apply specific CE theories, methods, and principles (e.g. naturalistic decision making, macrocognition, and resilience engineering), to generate various SD products (e.g. system requirements and designs). The goal of the panel will be to provide useful examples, and spur discussion about means to more readily translate research into practice.
Get full access to this article
View all access options for this article.
References
1.
BisantzA.M.LeeJ.D.PfautzJ.BurnsC.ElmW.C.PennathurP.R. (2013). Bridging the gap between cognitive systems engineering analysis, design and practice. Proc. of HFES 57thAnn. Mtg., 57(1), 334-338.
2.
BisantzA.MRothE.M. (2008). Analysis of cognitive work. In Boehm-DavisD. (Ed.), Volume 3 of the Annual Review of Human Factors and Ergonomics, pp. 1-43. Human Factors and Ergonomics Society.
3.
BisantzA.M.RothE.M.BrickmanB.LinL.HettingerL.McKinneyJ. (2003). Integrating cognitive analyses in a large scale system design process. International Journal of Human Computer Studies, 58, 177-206.
4.
BisantzA.N.RothE.M.PerottiJ. (2015). Study and analysis of complex cognitive work. In WilsonJ.SharplesS. (Eds.) Evaluation of Human Work, 4thEd. CRC Press.
5.
Boehm-DavisD.DursoF.LeeJ.D. (2015). APA Handbook of Human-Systems Integration. APA Press.
6.
CookR.I.WoodsD.D.MillerC.1998. A Tale of Two Stories: Contrasting Views of Patient Safety. National Patient Safety Foundation, Chicago, IL.
7.
CrandallB.KleinG.HoffmanR. (2006). Working minds. MIT Press.
8.
DominguezC.RayoM.DortonS.MoreyD.NaikarN.RothE. (2021). Cognitive engineering: Will they know our name when we are 40?Proc. of the 2021 Intl. Ann. Mtg. of the HFES, 65(1), 257-261.
9.
DortonS.L. (in press). Supradyadic trust in artificial intelligence. Artificial Intelligence and Social Computing, 28.
10.
DortonS.L.MaryeskiL.R.OgrenL.DykensI.T.MainA. (2020). A wargame-augmented knowledge elicitation method for the agile development of novel systems. Systems, 8(27), 1-15.
11.
DortonS. L.HarperS. (2021). Trustable AI: A critical challenge for naval intelligence. Center for International and Maritime Security (CIMSEC).
12.
DortonS.L.HarperS.H. (2022). A naturalistic investigation of trust, AI, and intelligence work. Journal of Cognitive Engineering and Decision Making. https://doi.org/10.1177/15553434221103718
13.
DortonS.SpauldingR.BurtonP.WetzelA. (2016). Assessing corporate perceptions of a human factors lab using thematic text analysis. Proc. of the Hum. Fac. And Ergo. Soc. Annl. Mtg., 60(1), 2068-2072.
14.
FongA.BehzadS.PruittZ.RatwaniR. M. (2021). A machine learning approach to reclassifying miscellaneous patient safety event reports. Journal of Patient Safety, 17(8), 829-833. https://doi.org/10.1097/PTS.0000000000000731
15.
GuarreraT.McGeorgeN.ClarkL.LaVergneD.HettingerZ.FairbanksR.BisantzA.M. (2015). Cognitive engineering design of an emergency department information system. In BisantzA.M.BurnsC.M.FairbanksR.J. (Eds.), Evaluation of Human Work, 4th Ed. CRC Press.
16.
HegdeS.HettingerA.Z.FairbanksR.J.WreathallJ.KrevatS.A.JacksonC.D.BistanzA.M. (2020). Qualitative findings from a pilot stage implementation of a novel organizational learning tool toward operationalizing the Safety-II paradigm in healthcare. Applied Ergonomics, 82, 102913.
17.
HollnagelE. (2018). Safety-I and Safety-II: The past and future of safety management. CRC Press.
18.
KleinG. A.CalderwoodR.MacgregorD. (1989). Critical decision method for eliciting knowledge. IEEE Transactions on systems, man, and cybernetics, 19(3), 462-472.
19.
MoreyD.A. (2021). Breaking away from brittle machines: Evaluating simultaneous inference and data (SID) displays to facilitate machine fitness assessment. Master’s Thesis, The Ohio State University.
20.
MoreyD.A.LiM.RayoM.F. (in press). From reactive to proactive safety: Joint activity monitoring for infection prevention. Proc. of the 2022 Intl. Symp. of Hum. Fac. and Ergo. in Healthcare.
21.
NaikarN. (2013). Work Domain Analysis: Concepts, guidelines, and cases. CRC Press.
22.
RayoM.F. (2017). Designing for collaborative autonomy: updating user-centered design heuristics and evaluation methods. Proc. of the Hum. Fac. and Ergo. Soc. Ann. Mtg., 61(1), 1091–1095. https://doi.org/10.1177/1541931213601877
23.
RayoM. F.FitzgeraldM. C.GiffordR. C.MoreyD. A.ReynoldsM. E.D’AnnolfoK.JefferiesC. M. (2020). The need for machine fitness assessment: Enabling joint human-machine performance in consumer health technologies. Proc. of the Intl. Symp. of Hum. Fac. and Ergo. in Healthcare, 9(1), 40–42. https://doi.org/10.1177/2327857920091041
24.
RayoM.F.HorwoodC.R.FitzgeraldM.C.GraysonM.R.Abdel-RasoulM.Moffatt-BruceS.D. (in press). Situated visual alarm displays support machine fitness Assessment for non-explainable automation. IEEE Transactions on Human-Machine Systems.
25.
RayoM. F.PattersonE. S.Abdel-RasoulM.Moffatt-BruceS. D. (2019). Using timbre to improve performance of larger auditory alarm sets. Ergonomics, 1–33. doi: 10.1080/00140139.2019.1676473
26.
RothE.BisantzA.WangX.KimT.HettingerA. (2021). A work centered approach to system user-evaluation. Journal of Cognitive Engineering and Decision Making, 15(4), 155-174.
27.
SujanM.A.2015. An organisation without a memory: a qualitative study of hospital staff perceptions on reporting and organisational learning for patient safety. Reliability Engineering & System Safety, 144, 45–52. https://doi.org/10.1016/J.RESS.2015.07.011
YoungI. J. B.LuzS.LoneN. (2019). A systematic review of natural language processing for classification tasks in the field of incident reporting and adverse event analysis. International Journal of Medical Informatics, 132, 103971.
