The DOI matches its Crossref record; 33 of the 34 references it cites were found in a registry or an index.
Human Performance Consequences of Automated Decision Aids: The Impact of Degree of Automation and System Experience
Abstract
Two experiments are reported that investigate to what extent performance consequences of automated aids are dependent on the distribution of functions between human and automation and on the experience an operator has with an aid. In the first experiment, performance consequences of three automated aids for the support of a supervisory control task were compared. Aids differed in degree of automation (DOA). Compared with a manual control condition, primary and secondary task performance improved and subjective workload decreased with automation support, with effects dependent on DOA. Performance costs include return-to-manual performance issues that emerged for the most highly automated aid and effects of complacency and automation bias, respectively, which emerged independent of DOA. The second experiment specifically addresses how automation bias develops over time and how this development is affected by prior experience with the system. Results show that automation failures entail stronger effects than positive experience (reliably working aid). Furthermore, results suggest that commission errors in interaction with automated aids can depend on three sorts of automation bias effects: (a) withdrawal of attention in terms of incomplete cross-checking of information, (b) active discounting of contradictory system information, and (c) inattentive processing of contradictory information analog to a “looking-but-not-seeing” effect.
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In the citing paper
“The positive loop is triggered by the experience of automation success, and the negative loop by the experience of automation failures.”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Manzey et al., 2012
In the citing paper
“Second, automation failures engender a much stronger influence on trust adjustment than automation successes: trust is difficult to build but can be lost quickly”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Manzey et al., 2012
In the citing paper
“human operators adjust their trust in automation based on positive and negative feedback loops”
From Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation (2023) · cites this paper as Manzey et al., 2012
In the citing paper
“This is in line with previous research, where users in a simulated process control task reduced the verification of additional parameters over time, but further controlled for necessary parameters (Manzey et al., 2012).”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Manzey et al., 2012
Citations
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“To guide the research on automation-induced performance consequences, different framework models have been proposed that allow for a standardized characterization of automated systems with respect to how functions are distributed between humans and machine (e.g., Endsley & Kaber, 1999; Endsley & Kiris, 1995; Parasuram…”
BackgroundMethodResultInfluentialComplacency 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 2×
“…bias—that is, an inappropriate weighting of different information—but the effect of a bias of information processing characterized by an automation-induced withdrawal or reallocation of attention, which resembles the complacency effect in automation monitoring (cf. Parasuraman & Manzey, 2010).”
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Cited by
Sorted by citations
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921
Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust
Hoff, K. A., & Bashir, M. (2015) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720814547570 · Cited by 2,817
From Here to Autonomy: Lessons Learned From Human–Automation Research
Endsley, M. R. (2017) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720816681350 · Cited by 898
Almost human: Anthropomorphism increases trust resilience in cognitive agents.
de Visser, E. J., Monfort, S. S., McKendrick, R., Smith, M. A. B., McKnight, P. E., Krueger, F., & Parasuraman, R. (2016) · Journal of Experimental Psychology: Applied · doi:10.1037/xap0000092 · Cited by 614
What influences algorithmic decision-making? A systematic literature review on algorithm aversion
Mahmud, H., Islam, A. K. M. N., Ahmed, S. I., & Smolander, K. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121390 · Cited by 601
Metadata
- Authors
- Manzey, D., Reichenbach, J., & Onnasch, L.
- Affiliations
- Technische Universität Berlin (DE)
- Venue
- Journal of Cognitive Engineering and Decision Making
- Publisher
- SAGE Publications
- Published
- 2012-01-13
- Volume
- 6
- Issue
- 1
- Pages
- 57-87
- ISSN
- 1555-3434 (ISSN-L)
- OpenAlex
- W2127333783
- Cited by
- 277
- References
- 34
- Citations by year
- 2026: 20 · 2025: 31 · 2024: 33 · 2023: 28 · 2022: 26 · 2021: 26 · 2020: 22 · 2019: 12 · 2018: 14 · 2017: 24 · 2016: 17 · 2015: 8 · 2014: 11 · 2013: 5
- FWCI
- 11.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Cognitive Engineering and Decision Making (publishedVersion)
- Topics
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Risk and Safety Analysis
- Keywords
- automated decision aids, human-automation interaction, levels of automation, supervisory control, workload, automation bias, complacency, operator experience, commission errors, cross-checking, automation failure
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