Literature DB >> 11540946

Automation bias: decision making and performance in high-tech cockpits.

K L Mosier1, L J Skitka, S Heers, M Burdick.   

Abstract

Automated aids and decision support tools are rapidly becoming indispensable tools in high-technology cockpits and are assuming increasing control of"cognitive" flight tasks, such as calculating fuel-efficient routes, navigating, or detecting and diagnosing system malfunctions and abnormalities. This study was designed to investigate automation bias, a recently documented factor in the use of automated aids and decision support systems. The term refers to omission and commission errors resulting from the use of automated cues as a heuristic replacement for vigilant information seeking and processing. Glass-cockpit pilots flew flight scenarios involving automation events or opportunities for automation-related omission and commission errors. Although experimentally manipulated accountability demands did not significantly impact performance, post hoc analyses revealed that those pilots who reported an internalized perception of "accountability" for their performance and strategies of interaction with the automation were significantly more likely to double-check automated functioning against other cues and less likely to commit errors than those who did not share this perception. Pilots were also lilkely to erroneously "remember" the presence of expected cues when describing their decision-making processes.

Entities:  

Mesh:

Year:  1997        PMID: 11540946     DOI: 10.1207/s15327108ijap0801_3

Source DB:  PubMed          Journal:  Int J Aviat Psychol        ISSN: 1050-8414


  13 in total

Review 1.  Automation bias: a systematic review of frequency, effect mediators, and mitigators.

Authors:  Kate Goddard; Abdul Roudsari; Jeremy C Wyatt
Journal:  J Am Med Inform Assoc       Date:  2011-06-16       Impact factor: 4.497

Review 2.  Individual differences in cognition, affect, and performance: behavioral, neuroimaging, and molecular genetic approaches.

Authors:  Raja Parasuraman; Yang Jiang
Journal:  Neuroimage       Date:  2011-05-03       Impact factor: 6.556

3.  Reduced Verification of Medication Alerts Increases Prescribing Errors.

Authors:  David Lyell; Farah Magrabi; Enrico Coiera
Journal:  Appl Clin Inform       Date:  2019-01-30       Impact factor: 2.342

4.  Understanding human management of automation errors.

Authors:  Sara E McBride; Wendy A Rogers; Arthur D Fisk
Journal:  Theor Issues Ergon Sci       Date:  2014

5.  Dopamine beta hydroxylase genotype identifies individuals less susceptible to bias in computer-assisted decision making.

Authors:  Raja Parasuraman; Ewart de Visser; Ming-Kuan Lin; Pamela M Greenwood
Journal:  PLoS One       Date:  2012-06-27       Impact factor: 3.240

Review 6.  Automation bias and verification complexity: a systematic review.

Authors:  David Lyell; Enrico Coiera
Journal:  J Am Med Inform Assoc       Date:  2017-03-01       Impact factor: 4.497

Review 7.  Research Trends in Artificial Intelligence Applications in Human Factors Health Care: Mapping Review.

Authors:  Onur Asan; Avishek Choudhury
Journal:  JMIR Hum Factors       Date:  2021-06-18

8.  A qualitative investigation of healthcare workers' strategies in response to readmissions.

Authors:  Priyadarshini R Pennathur; Brennan S Ayres
Journal:  BMC Health Serv Res       Date:  2018-02-27       Impact factor: 2.655

9.  Human factors challenges for the safe use of artificial intelligence in patient care.

Authors:  Mark Sujan; Dominic Furniss; Kath Grundy; Howard Grundy; David Nelson; Matthew Elliott; Sean White; Ibrahim Habli; Nick Reynolds
Journal:  BMJ Health Care Inform       Date:  2019-11

10.  Learning From the Slips of Others: Neural Correlates of Trust in Automated Agents.

Authors:  Ewart J de Visser; Paul J Beatty; Justin R Estepp; Spencer Kohn; Abdulaziz Abubshait; John R Fedota; Craig G McDonald
Journal:  Front Hum Neurosci       Date:  2018-08-10       Impact factor: 3.169

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.