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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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“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
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“…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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Diebig, M., Müller, A., & Angerer, P. (2020) · Springer eBooks · doi:10.1007/978-3-030-05031-3_10-1 · Cited by 4
No laughing matter: How autonomous agents can improve well-being and teamwork
Linhardt, R. M., Berger, L., & Salas, E. (2024) · Acta Astronautica · doi:10.1016/j.actaastro.2024.10.039 · Cited by 4
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
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 ›
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