Trust, self-confidence, and operators' adaptation to automation

Lee, John D.; Moray, Neville

doi:10.1006/ijhc.1994.1007

Cited by 750 publications

(612 citation statements)

References 19 publications

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“…However, these claimed benefits may only occur if automated vehicles are successfully implemented into road traffic and trust in this technology is a vital precondition for this. Ghazizadeh, Lee, and Boyle (2012) stated in their Automation Acceptance Model that trust is a crucial contributor to an individual's acceptability of automation technology and several previous studies have empirically shown that trust is a key determinant for reliance on automated systems (Bailey & Scerbo, 2007;Muir & Moray, 1996), adoption of automation (Gefen, Karahanna, & Straub, 2003;Lee & Moray, 1994), and the intention to use autonomous vehicles (Choi & Ji, 2015). In other words, operators tend to use automation that they trust while rejecting automation that they do not (Pop, Shrewsbury, & Durso, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…This degree does not have to exclusively concern a system as a whole but may be specific to particular functions of it. For example, in a study on a supervisory process control task, trust was distinct to the specific automatic controller (Lee & Moray, 1994) and an automation failure did not cause trust to decline in the remaining similar, but independent automatic controllers (Lee & Moray, 1992;Muir & Moray, 1996). In contrast, Keller and Rice (2009) found that when a completely reliable aid was presented with an unreliable aid, operators tended to rate both aids the same in a global, system-wide trust rating rather than treating them as different systems with different reliabilities (component-specific trust).…”

Section: Introductionmentioning

confidence: 99%

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Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and takeover performance. Forty participants encountered three situations in a 17-minute highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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“…The experiment therefore gathered data on real world use of automation in contrast to much of the literature in the area which tends to use artificial simulations and/or non-expert participants (e.g. Lee & Moray, 1994;Sauer, Nickel & Wastell, 2013;Röttger, Bali, & Manzey, 2009;Endsley & Kaber, 1999;Bagheri & Jamieson, 2004;Beck, Dzindolet, & Pierce, 2007;Muir & Moray, 1996;Johnson et al, 2002;Meyer, Feinshreiber, & Parmet, 2003). Other studies which did use real world systems and expert operators have typically been more exploratory (e.g.…”

Section: Effects Of Automationmentioning

confidence: 99%

Impact of automation: Measurement of performance, workload and behaviour in a complex control environment

2015

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This paper describes an experiment that was undertaken to compare three levels of automation in rail signalling; a high level in which an automated agent set routes for trains using timetable information, a medium level in which trains were routed along pre-defined paths, and a low level where the operator (signaller) was responsible for the movement of all trains. These levels are described in terms of a rail automation model based on previous automation theory (Parasuraman, Sheridan, & Wickens, 2000). Performance, subjective workload, and signaller activity were measured for each level of automation running under both normal operating conditions and abnormal, or disrupted, conditions. The results indicate that perceived workload, during both normal and disrupted phases of the experiment, decreased as the level of automation increased and performance was most consistent (i.e. showed the least variation between participants) with the highest level of automation. The results give a strong case in favour of automation, particularly in terms of demonstrating the potential for automation to reduce workload, but also suggest much benefit can achieved from a mid-level of automation potentially at a lower cost and complexity. Impact StatementResearch in the area of automation, and in particular in the examination of human interaction with different levels of automation, has normally been undertaken in Balfe et al, Impact of Automation: Measurement of Performance, Workload and Behaviour in a Complex Control Environment 2 laboratory settings using simple tasks and naïve participants where the level of automation can be easily manipulated. This research was undertaken with expert participants using complex simulation of three ecologically valid levels of automation and provides empirical field validation of some of the results found in laboratory studies.

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“…On the other hand, relying on heuristics in the decision to rely on an automated system may not be appropriate in any situational context. The demand for well-calibrated trust that matches the true capabilities of a system gains its importance in this point (Lee & Moray, 1994;Lee & See, 2004;Muir, 1987Muir, , 1994Sheridan & Hennessy, 1984). Wellcalibrated trust is characterised by high resolution and high specificity (Cohen, Parasuraman, & Freeman, 1999;Lee & See, 2004).…”

Section: Trust In Automationmentioning

confidence: 99%

“…Whereas Muir and Moray (1996) found that operators' trust was a very good predictor of their reliance, others (De Vries et al, 2003;Lee & Moray, 1992, 1994Lewandowsky et al, 2000) found that operators' reliance could be more appropriately predicted by the relationship between their trust in the automation and their self-confidence in manually controlling the system. Specifically, Lee and Moray (1994) found that participants' reliance could be predicted by a logit function of the difference between trust and self-confidence, whereby in general operators tend to rely more on automation when their trust in the automation exceeds their self-confidence, and tend to engage in manual control when trust was lower than their self-confidence. Furthermore, by fitting a time series model to the data, Lee and Moray (1994) could show that participants' reliance depended not only upon the difference between trust and self-confidence, but also on previous reliance on automation (reliance on automation in one trial generally led to reliance on automation in the next trial) and upon individual biases (some participants consistently preferred manual over automatic control regardless of their ratings of trust and self-confidence, and vice versa).…”

Section: The Influence Of Automation Reliability On Trust and Reliancementioning

confidence: 99%

Drivers’ Reliance on Lane-Keeping Assistance Systems as a Function of the Level of Assistance

Popken

Krems

2015

Z. Arb. Wiss.

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Trust, self-confidence, and operators' adaptation to automation

Cited by 750 publications

References 19 publications

Introduction matters: Manipulating trust in automation and reliance in automated driving

Introduction matters: Manipulating trust in automation and reliance in automated driving

Impact of automation: Measurement of performance, workload and behaviour in a complex control environment

Drivers’ Reliance on Lane-Keeping Assistance Systems as a Function of the Level of Assistance

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