The Experts below are selected from a list of 35052 Experts worldwide ranked by ideXlab platform
Philippe F. Lacherez - One of the best experts on this subject based on the ideXlab platform.
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When and Why Threats Go Undetected: Impacts of Event Rate and Shift Length on Threat Detection Accuracy during Airport Baggage Screening
Human Factors, 2016Co-Authors: Renata F.i. Meuter, Philippe F. LacherezAbstract:Objective: We aimed to assess the impact of task demands and individual characteristics on Threat Detection in baggage screeners. Background: Airport security staff work under time constraints to ensure optimal Threat Detection. Understanding the impact of individual characteristics and task demands on performance is vital to ensure accurate Threat Detection. Method: We examined Threat Detection in baggage screeners as a function of event rate (i.e., number of bags per minute) and time on task across 4 months. We measured performance in terms of the accuracy of Detection of Fictitious Threat Items (FTIs) randomly superimposed on X-ray images of real passenger bags. Results: Analyses of the percentage of correct FTI identifications (hits) show that longer shifts with high baggage throughput result in worse Threat Detection. Importantly, these significant performance decrements emerge within the first 10 min of these busy screening shifts only. Conclusion: Longer shift lengths, especially when combined with high baggage throughput, increase the likelihood that Threats go undetected. Application: Shorter shift rotations, although perhaps difficult to implement during busy screening periods, would ensure more consistently high vigilance in baggage screeners and, therefore, optimal Threat Detection and passenger safety.
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When and Why Threats Go Undetected: Impacts of Event Rate and Shift Length on Threat Detection Accuracy During Airport Baggage Screening.
Human factors, 2015Co-Authors: Renata F.i. Meuter, Philippe F. LacherezAbstract:OBJECTIVE: We aimed to assess the impact of task demands and individual characteristics on Threat Detection in baggage screeners. BACKGROUND: Airport security staff work under time constraints to ensure optimal Threat Detection. Understanding the impact of individual characteristics and task demands on performance is vital to ensure accurate Threat Detection. METHOD: We examined Threat Detection in baggage screeners as a function of event rate (i.e., number of bags per minute) and time on task across 4 months. We measured performance in terms of the accuracy of Detection of Fictitious Threat Items (FTIs) randomly superimposed on X-ray images of real passenger bags. RESULTS: Analyses of the percentage of correct FTI identifications (hits) show that longer shifts with high baggage throughput result in worse Threat Detection. Importantly, these significant performance decrements emerge within the first 10 min of these busy screening shifts only. CONCLUSION: Longer shift lengths, especially when combined with high baggage throughput, increase the likelihood that Threats go undetected. APPLICATION: Shorter shift rotations, although perhaps difficult to implement during busy screening periods, would ensure more consistently high vigilance in baggage screeners and, therefore, optimal Threat Detection and passenger safety. Language: en
Elena Rusconi - One of the best experts on this subject based on the ideXlab platform.
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xrindex a brief screening tool for individual differences in security Threat Detection in x ray images
Frontiers in Human Neuroscience, 2015Co-Authors: Essi Viding, Francesca Ferri, Elena Rusconi, Timothy MitchenernissenAbstract:X-ray imaging is a cost-effective technique at security checkpoints that typically require the presence of human operators. We have previously shown that self-reported Attention to Detail can predict Threat Detection performance with small-vehicle x-ray images (Rusconi et al., 2012). Here we provide evidence for the generality of such a link by having a large sample of naive participants screen more typical dual-energy x-ray images of hand luggage. The results show that the Attention to Detail score is a linear predictor of Threat Detection accuracy. We then develop and fine-tune a novel self-report scale for security screening: the XRIndex, which improves on the Attention to Detail scale for predictive power and opacity to interpretation. The XRIndex is not redundant with any of the Big Five personality traits. We validate the XRIndex against security x-ray images with an independent sample of untrained participants and suggest that the XRIndex may be a useful aid for the identification of suitable candidates for professional security training with a focus on x-ray Threat Detection. Further studies are needed to determine whether this can also apply to trained professionals.
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XRIndex: a brief screening tool for individual differences in security Threat Detection in x-ray images
Frontiers in Human Neuroscience, 2015Co-Authors: Elena Rusconi, Francesca Ferri, Essi Viding, Timothy Mitchener-nissenAbstract:X-ray imaging is a cost-effective technique at security checkpoints that typically require the presence of human operators. We have previously shown that self-reported attention to detail can predict Threat Detection performance with small-vehicle x-ray images (Rusconi et al., 2012). Here, we provide evidence for the generality of such a link by having a large sample of naive participants screen more typical dual-energy x-ray images of hand luggage. The results show that the Attention to Detail score from the autism-spectrum quotient (AQ) questionnaire (Baron-Cohen et al., 2001) is a linear predictor of Threat Detection accuracy. We then develop and fine-tune a novel self-report scale for security screening: the XRIndex, which improves on the Attention to Detail scale for predictive power and opacity to interpretation. The XRIndex is not redundant with any of the Big Five personality traits. We validate the XRIndex against security x-ray images with an independent sample of untrained participants and suggest that the XRIndex may be a useful aid for the identification of suitable candidates for professional security training with a focus on x-ray Threat Detection. Further studies are needed to determine whether this can also apply to trained professionals.
Renata F.i. Meuter - One of the best experts on this subject based on the ideXlab platform.
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When and Why Threats Go Undetected: Impacts of Event Rate and Shift Length on Threat Detection Accuracy during Airport Baggage Screening
Human Factors, 2016Co-Authors: Renata F.i. Meuter, Philippe F. LacherezAbstract:Objective: We aimed to assess the impact of task demands and individual characteristics on Threat Detection in baggage screeners. Background: Airport security staff work under time constraints to ensure optimal Threat Detection. Understanding the impact of individual characteristics and task demands on performance is vital to ensure accurate Threat Detection. Method: We examined Threat Detection in baggage screeners as a function of event rate (i.e., number of bags per minute) and time on task across 4 months. We measured performance in terms of the accuracy of Detection of Fictitious Threat Items (FTIs) randomly superimposed on X-ray images of real passenger bags. Results: Analyses of the percentage of correct FTI identifications (hits) show that longer shifts with high baggage throughput result in worse Threat Detection. Importantly, these significant performance decrements emerge within the first 10 min of these busy screening shifts only. Conclusion: Longer shift lengths, especially when combined with high baggage throughput, increase the likelihood that Threats go undetected. Application: Shorter shift rotations, although perhaps difficult to implement during busy screening periods, would ensure more consistently high vigilance in baggage screeners and, therefore, optimal Threat Detection and passenger safety.
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When and Why Threats Go Undetected: Impacts of Event Rate and Shift Length on Threat Detection Accuracy During Airport Baggage Screening.
Human factors, 2015Co-Authors: Renata F.i. Meuter, Philippe F. LacherezAbstract:OBJECTIVE: We aimed to assess the impact of task demands and individual characteristics on Threat Detection in baggage screeners. BACKGROUND: Airport security staff work under time constraints to ensure optimal Threat Detection. Understanding the impact of individual characteristics and task demands on performance is vital to ensure accurate Threat Detection. METHOD: We examined Threat Detection in baggage screeners as a function of event rate (i.e., number of bags per minute) and time on task across 4 months. We measured performance in terms of the accuracy of Detection of Fictitious Threat Items (FTIs) randomly superimposed on X-ray images of real passenger bags. RESULTS: Analyses of the percentage of correct FTI identifications (hits) show that longer shifts with high baggage throughput result in worse Threat Detection. Importantly, these significant performance decrements emerge within the first 10 min of these busy screening shifts only. CONCLUSION: Longer shift lengths, especially when combined with high baggage throughput, increase the likelihood that Threats go undetected. APPLICATION: Shorter shift rotations, although perhaps difficult to implement during busy screening periods, would ensure more consistently high vigilance in baggage screeners and, therefore, optimal Threat Detection and passenger safety. Language: en
M. D. Rutherford - One of the best experts on this subject based on the ideXlab platform.
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The face in the crowd effect: Threat-Detection advantage with perceptually intermediate distractors
Visual Cognition, 2009Co-Authors: Kristen M. Krysko, M. D. RutherfordAbstract:The ability to quickly perceive Threatening facial expressions allows one to detect emotional states and respond appropriately. The anger superiority hypothesis predicts that angry faces capture attention faster than happy faces. Previous studies have used photographic (Hansen & Hansen, 1988) and schematic face images (e.g., Eastwood, Smilek, & Merikle, 2001; Ohman, Lunqvist, & Esteves, 2001) in studying the anger superiority effect, but specific confounds due to the construction of stimuli have led to conflicting findings. In the current study, participants performed a visual search for either angry or happy target faces among crowds of novel, perceptually intermediate morph distractors. A Threat-Detection advantage was evident where participants showed faster reaction times and greater accuracy in detecting angry over happy faces. Search slopes, however, did not significantly differ. Results suggest a Threat-Detection advantage mediated by serial rather than preattentive processing.
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A Threat-Detection advantage in those with autism spectrum disorders.
Brain and cognition, 2008Co-Authors: Kristen M. Krysko, M. D. RutherfordAbstract:Identifying Threatening expressions is a significant social perceptual skill. Individuals with autism spectrum disorders (ASD) are impaired in social interaction, show deficits in face and emotion processing, show amygdala abnormalities and display a disadvantage in the perception of social Threat. According to the anger superiority hypothesis, angry faces capture attention faster than happy faces in individuals with a history of typical development [Hansen, C. H., & Hansen, R. D. (1988). Finding the face in the crowd: An anger superiority effect. Journal of Personality and Social Psychology, 54(6), 917-924]. We tested Threat Detection abilities in ASD using a facial visual search paradigm. Participants were asked to detect an angry or happy face image in an array of distracter faces. A Threat-Detection advantage was apparent in both groups: participants showed faster and more accurate Detection of Threatening over friendly faces. Participants with ASD showed similar reaction time, but decreased overall accuracy compared to controls. This provides evidence for less robust, but intact or learned implicit processing of basic emotions in ASD.
Francesca Ferri - One of the best experts on this subject based on the ideXlab platform.
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xrindex a brief screening tool for individual differences in security Threat Detection in x ray images
Frontiers in Human Neuroscience, 2015Co-Authors: Essi Viding, Francesca Ferri, Elena Rusconi, Timothy MitchenernissenAbstract:X-ray imaging is a cost-effective technique at security checkpoints that typically require the presence of human operators. We have previously shown that self-reported Attention to Detail can predict Threat Detection performance with small-vehicle x-ray images (Rusconi et al., 2012). Here we provide evidence for the generality of such a link by having a large sample of naive participants screen more typical dual-energy x-ray images of hand luggage. The results show that the Attention to Detail score is a linear predictor of Threat Detection accuracy. We then develop and fine-tune a novel self-report scale for security screening: the XRIndex, which improves on the Attention to Detail scale for predictive power and opacity to interpretation. The XRIndex is not redundant with any of the Big Five personality traits. We validate the XRIndex against security x-ray images with an independent sample of untrained participants and suggest that the XRIndex may be a useful aid for the identification of suitable candidates for professional security training with a focus on x-ray Threat Detection. Further studies are needed to determine whether this can also apply to trained professionals.
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XRIndex: a brief screening tool for individual differences in security Threat Detection in x-ray images
Frontiers in Human Neuroscience, 2015Co-Authors: Elena Rusconi, Francesca Ferri, Essi Viding, Timothy Mitchener-nissenAbstract:X-ray imaging is a cost-effective technique at security checkpoints that typically require the presence of human operators. We have previously shown that self-reported attention to detail can predict Threat Detection performance with small-vehicle x-ray images (Rusconi et al., 2012). Here, we provide evidence for the generality of such a link by having a large sample of naive participants screen more typical dual-energy x-ray images of hand luggage. The results show that the Attention to Detail score from the autism-spectrum quotient (AQ) questionnaire (Baron-Cohen et al., 2001) is a linear predictor of Threat Detection accuracy. We then develop and fine-tune a novel self-report scale for security screening: the XRIndex, which improves on the Attention to Detail scale for predictive power and opacity to interpretation. The XRIndex is not redundant with any of the Big Five personality traits. We validate the XRIndex against security x-ray images with an independent sample of untrained participants and suggest that the XRIndex may be a useful aid for the identification of suitable candidates for professional security training with a focus on x-ray Threat Detection. Further studies are needed to determine whether this can also apply to trained professionals.
