领域：学术论文 · 社会科学

Automated decision support systems are one type of automation that supports human operators by providing information about a particular state of the world (Mosier and Manzey 2020). Typical examples are alarm systems or detection systems (Rieger, Heilmann, and Manzey 2021) in security contexts (Goh, Wiegmann, and Madhavan 2005; Hättenschwiler et al. 2018; Huegli, Merks, and Schwaninger 2020), process control (Chavaillaz, Wastell, and Sauer 2016, Chavaillaz et  al. 2019; Chavaillaz and Sauer 2017; Sauer, Chavaillaz, and Wastell 2016), or medical screening tasks (Alberdi et  al. 2004; Drew, Cunningham, and Wolfe 2012; Xiao et al. 2021). One function of decision support systems in visual inspection tasks is to provide direct cues that operators must resolve. Direct cues attract the attention of human operators by indicating the exact location of a possible target (Chavaillaz et  al. 2018; Darnell and Lamy 2022; Posner, Snyder, and Davidson 1980)Such direct cues have two goals: First, they should support the operator’s search during a visual inspection task (Chavaillaz et al. 2018; Goh, Wiegmann, and Madhavan 2005; Wiegmann et al. 2006) by guiding attention towards the cued area (Wolfe 2021); and second, they should improve the operator’s decision making regarding the detection of critical events or targets (Wickens et  al. 2015). However, the reliability of most decision support systems is imperfect, and system designers must decide how to set their decision criterion. 
Usually, the priority is to set a more liberal decision criterion (Sorkin and Woods 1985) to detect more targets (automation hits). But a liberal decision criterion comes at the cost of producing more alarms where no target is present (automation false alarms) or producing automation miscues. Based on previous studies (Chavaillaz et  al. 2020; Goh, Wiegmann, and Madhavan 2005), we define automation miscues as alarms in the wrong location when a target is located elsewhere in the image. Because of the decision support system’s imperfect reliability, operators should assess the validity of its alarms and act appropriately. Such behaviour would improve human–machine system performance (Meyer and Kuchar 2021). Unfortunately, operators often do not react optimally to the decision support system’s presence, resulting in insufficient human–machine system performance (Bartlett and McCarley 2017, 2019; Boskemper, Bartlett, and McCarley 2022). Although direct cues can improve human–machine system performance (Chavaillaz et  al. 2018), they can also have adverse effects when false alarms and miscues become frequent. It has been well-researched that operators will ignore automation alarms if they experience many false alarms, a so-called ‘cry wolf’ effect (Bliss, Gilson, and Deaton 1995; Dixon and Wickens 2006; Parasuraman and Wickens 2008; Zirk, Wiczorek, and Manzey 2020). Several studies have found this phenomenon and described it as a lack of operator compliance (Bliss, Gilson, and Deaton 1995; Huegli, Merks, and Schwaninger 2020, 2023; Parasuraman, Sheridan, and Wickens 2000; Zirk, Wiczorek, and Manzey 2020). We define compliance operationally as the proportion of target-present responses given by operators on trials with automation alarms—regardless of whether true or false (Dixon and Wickens 2006; Manzey, Gérard, and Wiczorek 2014). Compliance has been described as a behavioural expression of trust in automation (Hoff and Bashir 2015; Lee and See 2004). Although there is some evidence that compliance somehow relates to subjective trust in automation (Avril et  al. 2022; Chancey et  al. 2017; Lee and Moray 1992), subjective trust and objective compliance do not always correlate strongly (Chavaillaz, Wastell, and Sauer 2016, Chavaillaz et  al. 2019; Rovira, McGarry, and Parasuraman 2007). Eye-tracking research has shown that the marked areas are the ones that are mainly inspected by operators (Drew, Cunningham, and Wolfe 2012), which implies strong attentional guidance (Wolfe 2021) by direct cues. However, if the marked area is a miscue, this can become a problem if operators get distracted by it and visual search for prohibited articles in other areas of the X-ray image is impaired. Alberdi et al. (2004) and Kunar et al. (2017) analysed the performance data of radiologists detecting breast cancer cells with the help of an automated decision support that provided direct cues. Radiologists benefitted from the alarms when they located cancer cells correctly, but less when miscues were given.
Goh, Wiegmann, and Madhavan (2005) tested student participants in a simulated baggage X-ray screening task in which they had to detect knives in greyscale images with the help of a decision support system. Participants benefitted from direct cues, especially when automation reliability was high, but they missed most of the targets when miscues were present. We extend this research by using highly realistic colourised X-ray images with professional airport security officers supported by an explosive detection system (EDSCB) for cabin baggage screening. Moreover, whereas previous studies investigated whether miscues impair the detection of the same type of target located elsewhere in the X-ray image, we investigated spill-over effects from a cueing system for one type of target (EDSCB for explosive) on detection of different types of targets (guns and knives). X-ray baggage screening at airports aims to prevent passengers from bringing prohibited articles (e.g. guns, knives, bombs) onto an aircraft (Harris 2002; Petrozziello and Jordanov 2019). Bombs are the most dangerous prohibited articles in passenger baggage and are technically called improvised explosive devices (IEDs). Screeners can detect IEDs when they are trained well (Halbherr et al. 2013; Koller et  al. 2008, Koller, Drury, and Schwaninger 2009; Schuster et al. 2013; Schwaninger and Hofer 2004) and when they are supported with explosives detection systems for cabin baggage (EDSCB) screening (Hättenschwiler et al. 2018; Huegli, Merks, and Schwaninger 2020, 2023). EDSCB support screeners by providing direct cues in areas in the X-ray image that might contain explosive material. When the EDSCB is used as a decision support system in primary screening, the screeners conduct on-screen alarm resolution. That is, the screeners visually inspect the X-ray image and decide whether the EDSCB alarm is correct or incorrect (Hättenschwiler et  al. 2018;Schwaninger and Merks 2019). When the screeners decide that the bag could contain a prohibited article, the bag is sent to secondary screening where another screener inspects the bag by applying explosives trace detection or manual baggage opening (Sterchi and Schwaninger 2015). An alternative to on-screen alarm resolution in primary screening is automated decision: Bags on which EDSCB has alarmed are sent directly to secondary screening for further inspection (Hättenschwiler et  al. 2018; Huegli, Merks, and Schwaninger 2020). State-of-the-art multi-view EDSCB technologies (EDSCB of Standard C2) achieve automation hit rates in the range of 75%–90% and automation false alarm rates of 6%–20% (Hättenschwiler et  al. 2018). 
Because such false alarm rates can impair checkpoint efficiency (Sterchi and Schwaninger 2015), some countries still use EDSCB with on-screen alarm resolution. EDSCB false alarms can either occur on target-absent images not containing prohibited articles (genuine false alarms) or on images containing other prohibited articles such as guns or knives (miscues). The goal of the present study was to investigate whether the failure proneness of a decision support system affects operator performance during a visual inspection task. More specifically, we wanted to examine spill-over effects from a cueing system for one type of target (EDSCB for explosives) on human detection of different types of targets (guns and knives). Besides the theoretical relevance of the study, we also wanted to answer the practically relevant question whether miscues are a problem that needs to be considered when conducting EDSCB on-screen alarm resolution in primary screening. 
The present study used highly realistic X-ray images in colour with professional airport security officers supported by EDSCB with a realistic automation reliability. The screeners had to detect prohibited articles (guns, knives, and IEDs). Screeners were tested in three experimental conditions: a false alarm prone condition where all EDSCB alarms were false alarms, a miscue prone condition where all EDSCB alarms were miscues, and a multiple failures condition where EDSCB false alarms and miscues appeared equally often. 
The dependent variables were performance measures (hit rate, false alarm rate, response time), behavioural trust (operator compliance), and subjective trust perception. Basic research on visual search has shown that salient cues capture attention, which can result in lower detection of targets, particularly in difficult visual search tasks (Gaspelin, Ruthruff, and Lien 2016; Luck et  al. 2021; Ruthruff et  al. 2020). Therefore, we expected lowest performance in the miscue prone condition, because screeners get distracted by the miscue and visual search for prohibited articles in other areas of the X-ray image gets impaired. Because knives are more difficult to detect than guns (Halbherr et  al. 2013; Koller et al. 2008, Koller, Drury, and Schwaninger 2009), we expected to find the negative effect of miscues for detecting knives and less, if at all, for detecting guns. Because well trained screeners achieve a high detection of IEDs (Halbherr et  al. 2013; Koller et  al. 2008, Koller, Drury, and Schwaninger 2009), we expected that screeners would comply more with correct EDSCB alarms than with incorrect EDSCB alarms. That is, they should recognise such automation failures.

翻译考点：
考点1 ：“ visual search tasks ”推荐译为"视觉搜索任务 "
考点2：" direct cues "需要译为"直接提示"
考点3：" hit rates " 推荐译为"击中率"
考点4："false alarm rates "推荐译为 "虚报率 "
考点5：" validity "推荐译为"效度"
考点6：" Eye-tracking research "应译为"眼动追踪 "
考点7：" secondary screening"推荐译为"二次扫描 "
考点8：" multi-view"推荐译为"多视图 "
考点9："response time"推荐译为"反应时 "
考点10："miscues"推荐译为" 错误线索"