Literature DB >> 11543300

Automation bias and errors: are crews better than individuals?

L J Skitka1, K L Mosier, M Burdick, B Rosenblatt.   

Abstract

The availability of automated decision aids can sometimes feed into the general human tendency to travel the road of least cognitive effort. Is this tendency toward "automation bias" (the use of automation as a heuristic replacement for vigilant information seeking and processing) ameliorated when more than one decision maker is monitoring system events? This study examined automation bias in two-person crews versus solo performers under varying instruction conditions. Training that focused on automation bias and associated errors successfully reduced commission, but not omission, errors. Teams and solo performers were equally likely to fail to respond to system irregularities or events when automated devices failed to indicate them, and to incorrectly follow automated directives when the contradicted other system information.

Entities:  

Mesh:

Year:  2000        PMID: 11543300     DOI: 10.1207/S15327108IJAP1001_5

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


  7 in total

1.  The Unintended Consequences of Health Information Technology Revisited.

Authors:  E Coiera; J Ash; M Berg
Journal:  Yearb Med Inform       Date:  2016-11-10

2.  Understanding human management of automation errors.

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

3.  Technology, cognition and error.

Authors:  Enrico Coiera
Journal:  BMJ Qual Saf       Date:  2015-07       Impact factor: 7.035

4.  Studying human-AI collaboration protocols: the case of the Kasparov's law in radiological double reading.

Authors:  Federico Cabitza; Andrea Campagner; Luca Maria Sconfienza
Journal:  Health Inf Sci Syst       Date:  2021-02-05

5.  How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection.

Authors:  Maia Jacobs; Melanie F Pradier; Thomas H McCoy; Roy H Perlis; Finale Doshi-Velez; Krzysztof Z Gajos
Journal:  Transl Psychiatry       Date:  2021-02-04       Impact factor: 6.222

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.  From Trust in Automation to Decision Neuroscience: Applying Cognitive Neuroscience Methods to Understand and Improve Interaction Decisions Involved in Human Automation Interaction.

Authors:  Kim Drnec; Amar R Marathe; Jamie R Lukos; Jason S Metcalfe
Journal:  Front Hum Neurosci       Date:  2016-06-30       Impact factor: 3.169

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.