The Duration of Automation Bias in a Realistic Setting

Abstract

Whereas in most studies conducted previously the effect of automation bias has been investigated in terms of an instantaneous decision, this study is aimed at quantifying its duration. Automation bias is modeled as a stochastic process using a unimodal log-log probability distribution. To validate the model, an experiment using an Airbus A320 fixed base flight simulator with a malfunction on the auto throttle was executed with 35 licensed pilots. The effect of pilot experience is investigated; results show that less experienced pilots are on average less sensitive to automation bias but have more variation in performance than more experienced pilots.

Models of Automation Surprise: Results of a Field Survey in Aviation

“…Thirdly, the integrated model implies that the cause of automation surprise is attributed to too much trust in automation and a lack of situational awareness [11] (p. 406), whereas in the case of the contextual control loop, the predominant cause is expected to be lack of knowledge about the automation in relation to the current operational context [12] (p. 635). Note that a number of recent studies that have identified the "looking-but-not-seeing" phenomenon as a precursor for automation surprise [30][31][32][33][34] do not invalidate the integrated model. Although Parasuraman & Manzey do not make this explicit, their model can accommodate this phenomenon as a special case of "attentional bias".…”

Case‐based decision support for fault diagnosis: Do salient ratings discourage situation analysis?

“…People with higher expertise in a domain have been found to rely less on automation and detect automation failures more readily (de Boer, Heems, & Hurts, ; Fan et al., ; Sanchez et al., ). For instance, experienced pilots detected an unexpected automation failure more quickly and more reliably than pilots who had just finished flight school (de Boer et al., ).…”

“…People with higher expertise in a domain have been found to rely less on automation and detect automation failures more readily (de Boer, Heems, & Hurts, ; Fan et al., ; Sanchez et al., ). For instance, experienced pilots detected an unexpected automation failure more quickly and more reliably than pilots who had just finished flight school (de Boer et al., ). Also, medical practitioners with less job experience were more likely to change their initial diagnosis in response to the suggestion of a decision support system, even when this suggestion was wrong (Goddard, Roudsari, & Wyatt, ).…”

“…People with higher expertise in a domain have been found to rely less on automation and detect automation failures more readily (de Boer, Heems, & Hurts, 2014;Fan et al, 2008;Sanchez et al, 2014).…”

Models of Automation Surprise: Results of a Field Survey in Aviation

“…Thirdly, the integrated model implies that the cause of Automation Surprise is attributed to too much trust in automation and a lack of situational awareness [11, p. 406], whereas in the case of the contextual control loop, the predominant cause is expected to be lack of knowledge about the automation in relation to the current operational context [12, p. 635]. Note that a number of recent studies that have identified the 'looking-but-not-seeing' phenomenon as a precursor for Automation Surprise [30,31,32] do not invalidate the integrated model. Although Parasuraman & Manzey do not make this explicit, their model can accommodate this phenomenon as a special case of "attentional bias".…”