CoChat
Sign Up
Cited by 19Human-Automation Interaction and SafetySocial Robot Interaction and HRIUsability and User Interface Design

Trust with increasing and decreasing reliability

Open access
Rittenberg, Benjamin S. P., Holland, Christopher W., Barnhart, Grace E., Gaudreau, Sierra M., Neyedli, Heather F.·2024·Human Factors: The Journal of the Human Factors and Ergonomics Society
DOI 10.1177/00187208241228636PMC PMC11487872
Open this paper in CoChat — free account

Abstract

Objective: The primary purpose was to determine how trust changes over time when automation reliability increases or decreases. A secondary purpose was to determine how task-specific self-confidence is associated with trust and reliability level. Background: Both overtrust and undertrust can be detrimental to system performance; therefore, the temporal dynamics of trust with changing reliability level need to be explored. Method: Two experiments used a dominant-color identification task, where automation provided a recommendation to users, with the reliability of the recommendation changing over 300 trials. In Experiment 1, two groups of participants interacted with the system: one group started with a 50% reliable system which increased to 100%, while the other used a system that decreased from 100% to 50%. Experiment 2 included a group where automation reliability increased from 70% to 100%. Results: Trust was initially high in the decreasing group and then declined as reliability level decreased; however, trust also declined in the 50% increasing reliability group. Furthermore, when user self-confidence increased, automation reliability had a greater influence on trust. In Experiment 2, the 70% increasing reliability group showed increased trust in the system. Conclusion: Trust does not always track the reliability of automated systems; in particular, it is difficult for trust to recover once the user has interacted with a low reliability system. Applications: This study provides initial evidence into the dynamics of trust for automation that gets better over time suggesting that users should only start interacting with automation when it is sufficiently reliable.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1177/00187208241228636

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In Rittenberg et al. (2024), participants' performance tracked reliability level, with the increasing group performing better over the 300 trials and the decreasing group performing worse.

    “Interestingly, in both groups, participants’ performance tracked reliability level with participants in the increasing group performing better over the course of the 300 trials and vice versa for the decreasing group.”

    From Calibrating Trust, Reliance and Dependence in Variable-Reliability Automation (2024) · cites this paper as Rittenberg et al. (2024)

  • The direction of continuous changes in system reliability interacts with user trust and self-confidence.

    “Rittenberg et al. (2024) showed that the direction of continuous changes in system reliability interact with user trust and self-confidence.”

    From Calibrating Trust, Reliance and Dependence in Variable-Reliability Automation (2024) · cites this paper as Rittenberg et al. (2024)

  • Although automation trust was declining in both groups in Rittenberg et al. (2024), participants were still depending on the automation.

    “This seems to indicate that although automation trust was declining in both groups, participants were still depending on the automation.”

    From Calibrating Trust, Reliance and Dependence in Variable-Reliability Automation (2024) · cites this paper as Rittenberg et al. (2024)

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 39

  1. Abbass H. A. Petraki E. Merrick K. Harvey J. Barlow M. (2016). Trusted autonomy and cognitive cyber symbiosis: Open challenges. Cognitive Computation, 8(3), 385– 408. 10.1007/s12559-015-9365-5 27257440 PMC4867784 · DOI
  2. Akash K. Hu W.-L. Reid T. Jain N. (2017). Dynamic modeling of trust in human-machine interactions. 2017 American Control Conference (ACC) (pp. 1542– 1548), Seattle, WA, USA, 24–26 May 2017. 10.23919/ACC.2017.7963172 · DOI
  3. Barnhart G. Knocton S. Hunger A. Dithurbide L. Neyedli H. (2023). Interpersonal and human-automation trust in an underwater mine detection task. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 67(1), 145– 150. 10.1177/21695067231192560 · DOI
  4. Bartlett M. L. McCarley J. S. (2019). No effect of cue format on automation dependence in an aided signal detection task. Human Factors, 61(2), 169– 190. 10.1177/0018720818802961 30335518 · DOI
  5. Bhat S. Lyons J. B. Shi C. Yang X. J. (2022). Clustering trust dynamics in a human-robot sequential decision-making task. IEEE Robotics and Automation Letters, 7(4), 8815– 8822. 10.1109/LRA.2022.3188902 · DOI
  6. Boubin J. G. Rusnock C. F. Bindewald J. M. (2017). Quantifying compliance and reliance trust behaviors to influence trust in human-automation teams. Proceedings of the Human Factors and Ergonomics Society - Annual Meeting, 61(1), 750– 754. 10.1177/1541931213601672 · DOI
  7. Chancey E. T. Bliss J. P. Proaps A. B. Madhavan P. (2015). The role of trust as a mediator between system characteristics and response behaviors. Human Factors, 57(6), 947– 958. 10.1177/0018720815582261 25917611 · DOI
  8. Chancey E. T. Bliss J. P. Yamani Y. Handley H. A. H. (2017). Trust and the compliance-reliance paradigm: The effects of risk, error bias, and reliability on trust and dependence. Human Factors, 59(3), 333– 345. 10.1177/0018720816682648 28430544 · DOI
  9. Chen J. Y. C. Barnes M. J. (2014). Human–agent teaming for multirobot control: A review of human factors issues. IEEE Transactions on Human-Machine Systems, 44(1), 13– 29. 10.1109/THMS.2013.2293535 · DOI
  10. Chong L. Zhang G. Goucher-Lambert K. Kotovsky K. Cagan J. (2022). Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of AI advice. Computers in Human Behavior, 127(1), 107018. 10.1016/j.chb.2021.107018 · DOI · Paper page
  11. Clark J. H. (1924). The Ishihara test for color blindness. American Journal of Physiological Optics, 5, 269– 276.
  12. de Visser E. J. Peeters M. M. Jung M. F. Kohn S. Shaw T. H. Pak R. Neerincx M. A. (2019). Towards a theory of longitudinal trust calibration in human–robot teams. International Journal of Social Robotics, 12(2), 459– 478. 10.1007/s12369-019-00596-x · DOI · Paper page
  13. Du N. Huang K. Y. Yang X. J. (2020). Not all information is equal: Effects of disclosing different types of likelihood information on trust, compliance and reliance, and task performance in human-automation teaming. Human Factors, 62(6), 987– 1001. 10.1177/0018720819862916 31348863 · DOI
  14. Endsley M. R. (1987). The application of human factors to the development of expert systems for advanced cockpits. Proceedings of the Human Factors Society Annual Meeting, 31(12), 1388– 1392. 10.1177/154193128703101219 · DOI
  15. Guznov S. Lyons J. Nelson A. Woolley M. (2016). The effects of automation error types on operators’ trust and Reliance. In: Lackey S. Shumaker R. (eds) Virtual, Augmented and Mixed Reality. VAMR 2016 (pp. 116– 124). Springer. 10.1007/978-3-319-39907-2_11 · DOI
  16. Henninger F. Shevchenko Y. Mertens U. Kieslich P. J. Hilbig B. E. (2022). lab.js: A free, open, online experiment builder (v22.0-beta1). Zenodo. 10.5281/ZENODO.597045 PMC9046347 34322854 · DOI
  17. Holland C. (2022). Trust and automation reliability (V1 ed.). Borealis. 10.5683/SP3/BYDQF9 · DOI
  18. Huegli D. Merks S. Schwaninger A. (2020). Automation reliability, human–machine system performance, and operator compliance: A study with airport security screeners supported by automated explosives detection systems for cabin baggage screening. Applied Ergonomics, 86, 103094. 10.1016/j.apergo.2020.103094 32342885 · DOI
  19. Hussein A. Elsawah S. Abbass H. A. (2020). Trust mediating reliability–reliance relationship in supervisory control of human–swarm interactions. Human Factors, 62(8), 1237– 1248. 10.1177/0018720819879273 31590574 · DOI
  20. Hutchinson J. Strickland L. Farrell S. Loft S. (2022). The perception of automation reliability and acceptance of automated advice. Human Factors, 65(8), 001872082110629. 10.1177/00187208211062985 34979821 · DOI · Paper page
  21. Lafond D. Tremblay S. Banbury S. (2013). Cognitive shadow: A policy capturing tool to support naturalistic decision making. 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (pp. 139– 142). CogSIMA. 10.1109/CogSIMA.2013.6523837 · DOI
  22. Lee J. Moray N. (1992). Trust, control strategies and allocation of function in human-machine systems. Ergonomics, 35(10), 1243– 1270. 10.1080/00140139208967392 1516577 · DOI · Paper page
  23. Lee J. D. Moray N. (1994). Trust, self-confidence, and operators’ adaptation to automation. International Journal of Human-Computer Studies, 40(1), 153– 184. 10.1006/ijhc.1994.1007 · DOI · Paper page
  24. Marois A. Labonté K. Lafond D. Neyedli H. F. Tremblay S. (2023). Cognitive and behavioral impacts of two decision-support modes for judgmental bootstrapping. Journal of Cognitive Engineering and Decision Making, 17(3), 215– 235. 10.1177/15553434231153311 · DOI
  25. Merritt S. M. Lee D. Unnerstall J. L. Huber K. (2015b). Are well-calibrated users effective users? Associations between calibration of trust and performance on an automation-aided task. Human Factors, 57(1), 34– 47. 10.1177/0018720814561675 25790569 · DOI · Paper page
  26. Merritt S. M. Sinha R. Curran P. G. Ilgen D. R. Ilgen D. R. (2015a). Attitudinal predictors of relative reliance on human vs. automated advisors. International Journal of Human Factors and Ergonomics, 3(3/4), 327– 345. 10.1504/ijhfe.2015.072982 · DOI
  27. Moray N. Inagaki T. Itoh M. (2000). Adaptive automation, trust, and self-confidence in fault management of time-critical tasks. Journal of Experimental Psychology: Applied, 6(1), 44– 58. 10.1037/1076-898X.6.1.44 10937311 · DOI
  28. Parasuraman R. Riley V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors: The Journal of the Human Factors and Ergonomics Society, 39(2), 230– 253. 10.1518/001872097778543886 · DOI · Paper page
  29. Ross J. M. Szalma J. L. Hancock P. A. Barnett J. S. Taylor G. (2008). The effect of automation reliability on user automation trust and reliance in a search-and-rescue scenario. Proceedings of the Human Factors and Ergonomics Society - Annual Meeting, 52(19), 1340– 1344. 10.1177/154193120805201908 · DOI
  30. Rovira E. McGarry K. Parasuraman R. (2007). Effects of imperfect automation on decision making in a simulated command and control task. Human Factors, 49(1), 76– 87. 10.1518/001872007779598082 17315845 · DOI
  31. Saeidi H. Wang Y. (2019). Incorporating trust and self-confidence analysis in the guidance and control of (semi)autonomous mobile robotic systems. IEEE Robotics and Automation Letters, 4(2), 239– 246. 10.1109/LRA.2018.2886406 · DOI
  32. Sanchez J. Rogers W. A. Fisk A. D. Rovira E. (2014). Understanding reliance on automation: Effects of error type, error distribution, age and experience. Theoretical Issues in Ergonomics Science, 15(2), 134– 160. 10.1080/1463922x.2011.611269 25642142 PMC4307024 · DOI
  33. Seppelt B. D. Lee J. D. (2019). Keeping the driver in the loop: Dynamic feedback to support appropriate use of imperfect vehicle control automation. International Journal of Human-Computer Studies, 125(1), 66– 80. 10.1016/j.ijhcs.2018.12.009 · DOI
  34. Shrauger J. S. Schohn M. (1995). Self-confidence in college students: Conceptualization, measurement, and behavioral implications. Assessment, 2(3), 255– 278. 10.1177/1073191195002003006 · DOI
  35. Strickland L. Boag R. J. Heathcote A. Bowden V. Loft S. (2023). Automated decision aids: When are they advisors and when do they take control of human decision making? Journal of Experimental Psychology: Applied, 29(4), 849– 868. 10.1037/xap0000463 36877467 · DOI
  36. Voss A. Rothermund K. Voss J. (2004). Interpreting the parameters of the Diffusion Model: An empirical validation. Memory & Cognition, 32(7), 1206– 1220. 10.3758/bf03196893 15813501 · DOI
  37. Wickens C. D. Helton W. S. Hollands J. G. Banbury S. (2021). Engineering psychology and human performance (5th ed.). Routledege.
  38. Xu A. Dudek G. (2015). OPTIMo: Online probabilistic trust inference model for asymmetric human-robot collaborations. Proceedings of the tenth annual ACM/IEEE international conference on human-robot interaction (pp. 221– 228), Portland, OR, USA, 02–05 March 2015. 10.1145/2696454.2696492 · DOI
  39. Yu K. Berkovsky S. Taib R. Conway D. Zhou J. Chen F. (2017). User trust dynamics: An investigation driven by differences in system performance. Proceedings of the 22nd international conference on intelligent user interfaces (pp. 307– 317). ACM. 10.1145/3025171.3025219 · DOI · Paper page

Cited by

Sorted by citations

Authors
Rittenberg, B. S. P., Holland, C. W., Barnhart, G. E., Gaudreau, S. M., & Neyedli, H. F.
Affiliations
Dalhousie University (CA)
Corresponding author
Neyedli, Heather F.
ORCID
Venue
Human Factors: The Journal of the Human Factors and Ergonomics Society
Publisher
SAGE Publications
Published
2024-03-06
Volume
66
Issue
12
Pages
2569-2589
ISSN
  • 0018-7208 (ISSN-L)
  • 1547-8181
DOI
10.1177/00187208241228636
PMC
PMC11487872
OpenAlex
W4392507839
Cited by
19
References
9
Citations by year
2026: 11 · 2025: 7 · 2024: 1
FWCI
7.10
Type
journal article
Language
en
Open access
Licence
Locations
  • Human Factors The Journal of the Human Factors and Ergonomics Society (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
Canadian Department of National Defense (Innovation for Defence Excellence and Security)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
  • Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
Keywords
trust in automation, automation reliability, trust dynamics, overtrust, human-automation interaction, trust repair, recommender systems
MeSH terms
Adult, Automation, Female, Humans, Male, Man-Machine Systems, Task Performance and Analysis, Reproducibility of Results, Trust, Young Adult

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 ›

Are you an author, or do you see a problem with this page? Request a review