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Autism-spectrum quotient (AQ) Questionnaire and Attention to Detail SubscaleThe AQ questionnaire was developed and validated by Baron-Cohen et al. to test ASD-related traits in the general population. It includes five subscales (Attention to Detail, Attention Switching, Imagination, Communication Skills, Social Skills) tapping different aspects of ASD with 10 items each. Respondents were to rate each item (e.g., “I often notice small sounds when others do not”) on a 4-level Likert scale (Definitely agree, Slightly agree, Slightly disagree, Definitely disagree). The Attention to Detail subscale was made of items 5, 6, 9, 12, 19, 23, 28, 29, 30, 49 from the AQ questionnaire.

Novel scale: proto-XRIndexThe proto-XRIndex contained 20 items conceptually derived from the Attention to Detail scale, focusing on those aspects that may be more relevant to the x-ray screening task (see also “Zooming in on the Attention to Detail scale ( N = 547)” in “Results” Section). It was formulated in such a way that they did not always require positive responses and required careful reading. Like in the Attention to Detail scale, respondents were to rate each item on a 4-level Likert scale (Definitely agree, Slightly agree, Slightly disagree, Definitely disagree). X-ray screening taskThe x-ray screening task was partly modeled on Rusconi et al.’s protocol but used color-coded x-ray images of hand luggage. Color-coding is an enhancement function available on airport security x-ray machines. X-ray machines do not produce colored images but density maps.

However, when projecting density maps onto visual displays, it is possible to automatically assign different colors to different density ranges indicating different materials (e.g., blue for metal, orange for biological, green for alloys). Images of a set of bags with and without threats were created by colleagues from the Home Office Centre for Applied Science and Technology (CAST) in Sandridge (UK).

They were generated at optimal irradiation parameters as determined by the x-ray machine builder with an x-ray machine implementing state-of-the-art technology. The machine was being tested by CAST scientists for mass adoption at major checkpoints in the London 2012 Olympics.From an initial database of 100 images, 15 pairs were selected. Each pair showed the same bag with/without a threat (see an example in Figure ). A further 15 pairs were obtained by mirroring those images around their vertical and then their horizontal meridian. This introduced a balance in the spatial location of visual clusters, by ensuring that in the overall set of 60 images (i.e., 30 pairs of threat/no-threat bags) a threat—and any other object—appeared with the same likelihood in the opposite halves with respect to the vertical and the horizontal meridians.

During the x-ray screening task, the images—which had a white background—were inscribed in a 600 × 600 pixel central square, whereas the rest of the display was black. Testing modeParticipants were tested online and remotely (henceforth: online testing indicates the use of questionnaires on a webpage; remote testing indicates the use of dedicated software downloaded from a website and installed on their own computer). A testing session consisted of two main phases: the first comprising only online questionnaires; the second and more demanding phase including both questionnaires and behavioral tasks administered via dedicated software. When participants were tested remotely the software was to be downloaded and installed directly by every participant on their PC. Phase 2: X-ray screening taskAfter completion of Phase 1 and consenting to take part in Phase 2, participants received a personalized email with detailed instructions on how to complete Phase 2.

In order to access the computer tasks participants were advised they would need a PC with Internet connection. They were directed to a web page from where they could safely download and install the dedicated testing software, Psytools (Delosis), which provided a stable testing environment and ensured that data were collected and retrieved securely from a central server. If participants had any concerns or experienced any problems during the installation, they were encouraged to contact the research team for assistance.With their personalized email participants also received three unique codes: a Player Code, a Player Key and a Password. The Player Code and Key were necessary to enable the installation, task loading and data logging, and the Password was necessary to gain access to the tasks on later occasions. In case participants had installed the software on shared computers and did not complete all the tasks in one go, this prevented other users from accessing the incomplete tasks by simply launching Psytools with a double-click on its Desktop shortcut icon. Installing and loading the tasks from the Delosis homepage required internet connection for just a few seconds to communicate with the Delosis computers.

However, there was no need to be on-line to run the tasks, and participants were informed about this. They also learnt that the programme would send the results back to Delosis over the Internet when they had completed the tasks, taking just a few seconds. All the information that participants sent over the Internet was kept strictly confidential and anonymous. Moreover, personal data, e.g., name and email address, which were provided in Phase 1 for the purpose of identification and to allow the researchers to contact each respondent individually were kept separate from the data collected in Phase 2.On successfully installing the software and entering the user window, a list of tasks, including the x-ray screening task, would appear on a participant’s PC screen, ready to be run. The instruction letter emphasized the importance of answering all of the tasks on one’s own. Crucially, a task could be started once and only once. This was aimed to prevent participants from taking a first look, exit, restart, exit, and so forth until they became familiar with the task before deciding to complete the entire session.

They were thus also made aware that they should start a task only when they knew that nobody would interrupt them and were asked to switch their mobile phone off while completing the testing battery. Finally, instructions mentioned that accuracy and speed of response would be recorded and participants were encouraged to be as fast and accurate as possible. Data were logged on the system and automatically sent to the Psytools central server (therefore being accessible to the experimenter) as soon as an internet connection became available on each participant’s computer.The x-ray screening task comprised two phases: a familiarization phase and a threat detection phase. The threat detection phase entailed a few training trials, followed by test trials. When accessing the x-ray screening task participants were advised they would first be shown images of three groups of threats (see e.g., Figure ).

They were encouraged to try and memorize those threats because they would later be asked to detect their presence in x-rayed bags. It was also made clear that the threats would not necessarily be seen from the same viewpoint as in the familiarization phase. Images showing three collections of x-rayed threats (bullets, a gas grenade and a folding knife; several handguns; different types of knives) were displayed twice for 6 s each in alternated sequence. Six threat-detection practice trials (three bags with a threat, three bags without, in random order) followed the familiarization phase. Example threat display presented in the familiarization phase of the x-ray screening task.For the practice trials and during the entire experiment, participants were instructed to place their right index finger on the P key, their left index finger on the Q key and their thumbs on the spacebar. They were to indicate whether the x-rayed bags contained a threat or not by pressing P as quickly as possible if they had spotted a threat, by pressing Q if they thought there was no threat. Each image would remain on the screen for a maximum time of 6 s, which was also the deadline for response.

In the practice trials, feedback would be provided immediately after a response (or when the maximum available time had elapsed) and threats, if any, highlighted with a red circle on the image (see e.g., Figure ). Participants could take all the time they wanted to inspect the feedback screen and the next trial appeared only when they pressed the spacebar.After completing the six practice trials and receiving feedback (response accuracy, response time and location of the threat) for each, participants started the actual experiment.

The experiment followed the same trial structure as the practice phase, but the feedback in this case was limited to a green V for a correct response and a red X for a wrong response. Cumulative accuracy and average reaction time (RT) were also shown on the same display as the feedback after each trial. When ready to move on to the next trial participants pressed the spacebar and a new x-rayed bag would appear. They were informed by a message on the screen when they had reached the middle of the test.

Overall, the task comprised 60 experimental trials and lasted less than 10 min but required participants’ undivided attention throughout. Data analysisData from Study 1 were used to test the validity of the Attention to Detail scale as a predictor of x-ray screening performance with typical security images, and to fine-tune an ad-hoc self-report scale—the XRIndex.

In this and the following studies, questionnaire scores and behavioral performance indices were treated as interval data, in line with most of the literature. Because these data do not always meet all parametric test assumptions, we either performed non-parametric tests or provided bootstrapped estimates and Bias Corrected and accelerated (BCa) 95% confidence intervals (CIs) alongside the most critical parametric tests (Efron and Tibshirani, ).

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All reported significance levels refer to the two-tailed hypothesis. Bonferroni-Holm corrections for multiple tests were applied in exploratory analyses.A series of Spearman’s correlations was first performed to assess the relative independence of the Attention to Detail traits from other ASD-related traits in healthy adults.

Five indices of performance (three of which resulted significantly related to Attention to Detail in Rusconi et al., ) were extracted from behavioral data in the x-ray screening task: (i) percentage of accurate responses; (ii) Hits minus False Alarms (HFA), (iii) sensitivity ( d’); (iv) criterion ( c); and (v) Reaction Times (RTs). Performance was first explored via ANOVA or t-tests having Threat (threat present, threat absent) and Block (first half, second half) as within-participant factors. Linear regressions were then used to test for the validity of Attention to Detail as a predictor of x-ray screening performance. After formulating the hypothesis that the predictive value of the Attention to Detail scale may reside in specific clusters of items, an ordinal EFA was performed on all of the available data collected by this group with the AQ questionnaire (including Rusconi et al.’s ). This highlighted the possibility to parcellate the construct into three components, only two of which were related with x-ray screening performance—one positively correlated, one negatively correlated with it.

A new predictive index was thus derived from the original Attention to Detail scale by combining the negatively related items with the positively related items. The most strongly associated items from our new pool of items were also included, to create a 10-item scale, which we named the XRIndex. Cronbach’s α was also calculated.

Contoh judul skripsi teknik informatika. A series of hierarchical linear regressions tests was then performed to assess whether the XRIndex can predict performance over and above the Attention to Detail scale. Attention to Detail Predicts Screening Performance with Dual-Energy X-Ray Images ( N = 215)For each AQ subscale, a total score was obtained by counting the number of relevant items for which respondents had selected the ASD-related preference, as in Baron-Cohen et al. Central tendency statistics for the five AQ subscales and the total AQ score are shown in Table. Only two participants slightly exceeded the theoretical threshold of clinical relevance obtaining a total score of 33. Since they reported no clinical history and did not classify as outliers in other measures, they were retained in the final sample. A series of Spearman’s correlations highlighted a significant positive correlation between the Communication subscale and the Social Skill and Attention Switching subscales (ρ = 0.45 and ρ = 0.33 respectively), and between the Social Skill and Attention Switching subscales (ρ = 0.19). Like in Rusconi et al.

, no significant correlation was found between the Attention to Detail subscale and the other subscales contributing to the total AQ. Correlation tests involving the Attention to Detail scale are highlighted with bold font. Soc Skill, Social Skill scale of the AQ questionnaire; Att Switch, Attention Switching scale; AttDett, Attention to Detail scale; Comm, Communication scale; Imag, Imagination scale.significant with Bonferroni-Holm correction (min corrected α = 0.003), N = 215.The main dependent variables considered for the x-ray screening task were: accuracy (% total trials), hits minus false alarms or HFA (%), sensitivity d’ = Z (hit rate)−Z (false alarm rate), criterion c = −0.5 (Z (hit rate) + Z (false alarm rate)) and reaction times (RTs, measured in milliseconds). The first three of these were shown to be significantly related to Attention to Detail by Rusconi et al. , whereas no difference was found in RTs between groups. Two main independent variables (within participants) were included in the exploratory analysis when possible: Block (1 vs. 2) and Threat (present vs.

Absent).Accuracy: On average, participants responded correctly 77% (SE = 0.50) of the times and were more accurate in the presence than in the absence of a threat ( M = 78%, SE = 0.60 and M = 75%, SE = 0.60 respectively, F (1,214) = 12.75, p. Zooming in on the attention to detail scale ( N = 547)On closer inspection, the Attention to Detail scale from the AQ questionnaire contains items that cover a range of contents and aspects of piecemeal attention that may not all be relevant for security x-ray screening. In particular, three subgroups of items could be identified based on their textual content: a subgroup concerning single details (e.g., “I tend to notice details that others do not”; AQ items 5, 12, 28 and 30), a subgroup concerning clusters of information (e.g., numbers, patterns and dates; “I usually notice car number plates or similar strings of information”; AQ items 6, 9, 19 and 23), and a subgroup concerning memory (e.g., “I am not very good at remembering people’s date of birth” (reverse scored item); AQ items 29 and 49). So, although the most parsimonious factorial model of Attention to Detail may be unidimensional (Baron-Cohen et al., ), a higher-resolution model may be better suited to our aims. Indeed, the ability to identify objects in a cluttered image may rest, for example, more on the ability to connect details of information into a meaningful pattern rather than purely in a sharp focus on single details, as that would distract away from meaningful wholes within the bigger picture.To retain as much information as possible here, we used 4-level scoring of the AQ items (Hoekstra et al., ), whereas binary scoring was used in the previous section to allow for a direct comparison with Rusconi et al.’s study. With a 4-level coding, however, scale-performance relations did not substantially differ from those obtained with the original binary scoring. We performed an ordinal 3-factor EFA with Full Information Maximum Likelihood extraction method and Promax rotation with LISREL 8.8.

A factor was retained if at least two items showed their highest loading under that factor and such loading was larger than 0.45. Ambiguous items showing a difference smaller than 0.30 between their highest and second highest loading were pruned. These criteria complied with the guidelines provided by Field and Stevens and enabled us to identify a clear-cut model to guide further research. Each of the three factors comprised two high-loading items and all of the factors were thus retained.

Two ambiguous items and two low-loading items were discarded (AQ items 5, 6, 9 and 28), leaving a final selection of six items (AQ items 12, 19, 23, 29, 30 and 49). Identifying predictive sources in the attention to detail scale ( N = 215)In Table, we report the results of Spearman’s correlation analyses between each factor score, derived scores and the relevant indices of performance in the x-ray screening task. The factor Detail showed no relation with performance in threat detection.

Regularities was positively correlated with performance, whereas Memory tended to be negatively correlated with performance in the threat detection task before correction for multiple tests. An index derived by subtracting the Memory from the Regularities scores was positively correlated with most indexes of performance, and remained so after correction for multiple tests.

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The original Attention to Detail (AttDet Original) scale scores are also shown for comparison purposes. Det, Detail; Reg, Regularities; Mem, Memory items of the Attention subscale as identified in the EFA.significant with Bonferroni–Holm correction (min corrected α = 0.002).So far we have explored the relation between x-ray screening performance and the Attention to Detail score obtained in the context of the AQ questionnaire. However, the same participants were also administered the Attention to Detail subscale in isolation a few months later. We can thus test for repetition and criterion validity of the Regularities− Memory subgroup previously identified. Repetition validity was tested by calculating Spearman’s correlation coefficient between the score obtained at the first administration and the score obtained a few months later. Note that this is likely to underestimate the repetition validity coefficient, due to the change in presentation context between test (Attention to Detail items presented within the AQ questionnaire) and retest (Attention to Detail items presented in isolation).

The test-retest correlation for Regularities− Memory was ρ = 0.65, whereas the test-retest correlation for the Attention to Detail subscale (all with four-level scoring) was ρ = 0.75. This is not unexpected as the Attention to Detail score is based on 10 items, whereas the Regularities− Memory score is based on four items only and may thus be more volatile.Criterion validity was also retested by correlating indices of performance in threat detection and the Detail, Regularities−Memory items from the Attention to Detail scale administered in isolation a few months after its first administration within the AQ questionnaire (see Table ). This supported our previous conclusions, by showing that the Regularities− Memory score had stronger association than the Attention to Detail score with behavioral performance and were significantly correlated with accuracy, HFA and d’ in the x-ray screening test. However, the small number of items used to calculate the Regularities− Memory score makes it a potentially volatile index whose reliability would be difficult to assess. We will address this problem in the following section. Towards a Novel Scale: the XRIndex ( N = 283, N = 338 and N = 215)In addition to the Attention to Detail subscale, participants also responded to a pool of 20 novel items in Phase 1.

The aim of the following analysis is to enable the identification of those new items that could add to the Attention to Detail items which were identified as best predictors of x-ray screening performance (i.e., the mini-scales Regularities and Memory comprising two items each). This should help improve (or maintain) the strength and reliability of association between those selected self-report items and x-ray screening performance. At the same time enabling the inclusion of a sufficient number of items in both Regularities and Memory for calculation of scale reliability indices (e.g., Cronbach’s alpha).Two-hundred and eighty-three participants ( N = 283; 65 males; age: M = 23, SD = 5; education: M = 16, SD = 3) responded to: (1) the AQ questionnaire; (2) the Attention to Detail scale; (3) the new items, and their data and were included in a correlational analysis.

Both the new items and the Attention to Detail items from Phase 1 could be correlated with the best predictor of threat detection performance (i.e., Regularities− Memory) previously identified. The five items (including two Attention to Detail items) that were most positively related and the five items (including two Attention to Detail items) that were most negatively related with the original Regularities− Memory score were retained as part of the novel scale. A novel index, the XRIndex, was then obtained with the following formula, with both Regularities and Memory now including two Attention to Detail items and three new items each, and having a similar number of reverse-coded items and negative/positive sentences: XRIndex = Regularities− Memory.To explore the distribution of the XRIndex scores, we used the entire dataset of 338 participants (118 males) who completed Phase 1—and who had thus responded to both the Attention to Detail scale and the new items.

The median XRIndex score was 0 (IQR = 6), and the mean was 0.11 (SD = 4). Skewness and Kurtosis of the XRIndex distribution were very close to 0 (Skewness = 0.10, SE = 0.13; Kurtosis = 0.18, SE = 0.26) and the Q-Q plot showed a reasonable fit of the data with the ideal normal distribution, especially for the central scores (Figure ).

Cronbach’s α was 0.67, which is well within the typical values reported in the social sciences in general and for the AQ subscales in particular (Baron-Cohen et al.,; Field, ). The left hand panel shows a histogram of the distribution of scores for the XRIndex in 338 participants. The right hand panel shows the Normal Q-Q plot of the XRIndex distribution, which in its central part essentially overlaps with the normal distribution.We then conducted a preliminary criterion validity check and tested the prediction that the XRIndex would perform better than the Attention to Detail scale at predicting threat detection performance, thanks to the relation of its items with the original “ Regularities minus Memory” index. To do that we used the data from 215 participants for whom both threat detection performance and XRIndex scores were available. Linear regression models having the XRIndex as predictor revealed significant trends in overall detection accuracy ( R 2 = 0.07, constant = 77, b = 0.44, p.

Scatterplots with linear regression models for the XRIndex on: (A) overall accuracy; (B) Hits minus False Alarms (HFA); and (C) sensitivity (d’) in Study 1. Each point may represent one or more participants.To obtain a simple statistical comparison between Attention to Detail and XRIndex we then tested a series of hierarchical linear regression models on indices of performance in x-ray screening that we had previously related to Attention to Detail.

With the exception of detection accuracy, the XRIndex outperformed the Attention to Detail scale as a linear predictor across all indexes of performance. Moreover, the XRIndex was still a significant predictor of performance when considering detection accuracy, whereas the Attention to Detail scale did not significantly predict any of the other performance indexes (see Table ). PredictorRR 2Adj R 2SEΔ R 2Δ Fdf1df2p(ΔF)DV: Overall accuracy1. XRIndex + AttDet0.280.080.0770.0.201DV: Overall accuracy1. AttDet + XRIndex0.280.080.0770.06.000DV: Detection Accuracy (threat present items)1. XRIndex + AttDet0.250.060.0680.0.008DV: Detection Accuracy (threat present items)1.

AttDet + XRIndex0.250.060.0680.0.024DV: Rejection accuracy (threat absent items)1. XRIndex + AttDet0.220.050.04110.0.744DV: Rejection accuracy (threat absent items)1. AttDet + XRIndex0.220.050.8512120.001DV: HFA1. XRIndex + AttDet0.270.080.07140.0.186DV: HFA1. AttDet + XRIndex0.270.070.8012120.000DV: d’1. XRIndex + AttDet0.290.080.070.540.0.166DV: d’1. AttDet + XRIndex0.290.080.070.540.06.000.

DiscussionStudy 1 provides evidence for the generality of the association between the Attention to Detail trait and threat detection performance with security x-ray images. The association was first reported with a selected sample of participants and single-energy transmission x-ray images of small vehicles (Rusconi et al., ). Here we re-tested the original hypothesis with a much larger sample of participants and dual-energy transmission x-ray images of hand baggage.Taking the Attention to Detail scale as a starting point, a novel self-report scale was then developed, the XRIndex, which could account for up to 7% of the total variance across indices of performance. With a regression coefficient of 0.87 for HFA, performance will increase 0.87 percentage units in HFA for every unit’s increment in XRIndex score. In other words, for every 100 screened bags, an individual obtaining a score of 5 on the XRIndex scale is likely to correctly assess 8.7 more bags than an individual obtaining a score of −5 on the XRIndex. Considering the large volumes of x-ray checks performed worldwide every year (with over 200 million passengers and two million tonnes of freight handled just in the UK; ), the relevance of this finding for aviation security is unmistakable. Phase 1: web questionnairesPhase 1 included the entire AQ questionnaire (comprising 50 items; Baron-Cohen et al., ), the TIPI (Gosling et al., ) and a 10-item version of our self-report scale (the XRIndex) which built on the evidence collected with the initial protocol.

The online platform used to test participants was SurveyMonkey™.The only procedural difference from Study 1 consisted of an additional option made available to participants at the end of Phase 1. Indeed, on the final page participants were required to select one of three options: (i) agree to proceed with the testing (and thus to be contacted via email with further instructions); (ii) withdraw from the study without allowing use of their web questionnaire data; or (iii) withdraw from the study whilst allowing inclusion of their anonymized questionnaire data in the study database. Data analysisData from Study 2 were used to validate the novel self-report scale with an independent sample of participants and assess possible redundancies with personality testing.A series of Spearman’s correlations was performed to assess whether the individual characteristic measured with the XRIndex may overlap with any of the Big Five as measured with TIPI. Cronbach’s α was calculated again for the XRIndex. Five indices of performance were extracted from behavioral data in the x-ray screening task (accuracy, HFA, d’, c, RT).

Performance was first explored via ANOVA or t-tests having Threat (threat present, threat absent) and Block (first half, second half) as within-participant factors. Linear regression models were then tested to assess the validity of XRIndex as a predictor of x-ray screening performance.

Non-Redundancy Between the XRIndex and the Big Five ( N = 620)Raw scores for each dimension measured by TIPI were calculated by reverse coding half the items and calculating the average of the two items corresponding to a dimension, as detailed in Gosling et al. Our sample of participants obtained a median (IQR) score of 5 (2) for Extraversion, 4.5 (1.5) for Agreeableness, 5 (2) for Conscientiousness, 5 (2.5) for Emotional Stability and 5.5 (1.5) for Openness to Experience. After correcting for multiple comparisons with Bonferroni-Holm (min corrected α = 0.005) a positive correlation between Extraversion and Emotional Stability (ρ = 0.19, p. The XRIndex as Predictor of Performance in Security X-Ray Screening ( N = 249)Data from the 249 participants who completed both the threat detection task and the XRIndex scale were used for behavioral validation in the UK sample. In the x-ray screening task, the same dependent variables as for the Italian sample of participants (Study 1) were considered: accuracy (%), HFA (%), sensitivity d’ = Z (hit rate)−Z (false alarm rate), criterion c = −0.5(Z (hit rate) + Z (false alarm rate) and RTs (ms).

Two main independent variables (within participants) were also included in the exploratory analysis when possible: Block (1 vs. 2) and Threat (present vs. Absent).Accuracy: On average, participants responded correctly 76% (SE = 0.48) of the times and were equally accurate in the presence as in the absence of a threat ( M = 77%, SE = 0.50 and M = 76%, SE = 0.60 respectively). Accuracy increased with practice within a testing session (Block 1: M = 74%, SE = 0.59; Block 2: M = 79%, SE = 0.57; F (1,248) = 75.84, p 0.24).Reaction Times: On average, participants responded correctly in 1531 ms (SE = 28) and were faster in the presence than in the absence of a threat ( M = 1181 ms, SE = 18 and M = 1880 ms, SE = 40 respectively, F (1,248) = 568.61, p. DiscussionIn Study 2 we provide a cross-cultural validation of a novel self-report scale, the XRIndex, aimed to capture baseline individual aptitudes to the security screening job with transmission x-ray images. Study 1 showed that the XRIndex, a scale which improves on Baron-Cohen et al.’s Attention to Detail scale for security x-ray screening and use in job selection settings, provides a reliable predictor of threat detection in security x-ray images.

Its cross-cultural validity and robustness were then tested with an independent sample of participants from the UK. Linear regression models of XRIndex scores on screening performance fully replicated Study 1. In the new sample, the XRIndex accounted for up to 8% of the total variance across indices of performance, with a regression coefficient of 0.86 for HFA, which nicely replicates the findings of Study 1. We also showed that the source of individual variability captured with the XRIndex is not redundant with any of the Big Five traits.

This non-redundancy has important practical implications, given the widespread use of personality testing in job selection settings and theoretical implications, due to the specific pattern of correlations (or lack of) between the XRIndex and the Big Five (see Wakabayashi et al., for a similar conclusion concerning the overlap between traits measured via the AQ questionnaire and the Big Five). ParticipantsNinety-three participants (31 males) from the group who took part in the in-lab testing described in Study 2, returned on average 3 weeks later for a second testing session. Fifteen volunteers (two males) from the initial cohort appeared to drop out due to objective obstacles (e.g., overlap with job shifts or unforeseen commitments), rather than for poor motivation. The mean age of the retest sample was 25 (SD = 7) and they had spent on average 15 (SD = 4) years in education.

All of them reported normal or corrected to normal vision. ResultsOverall, participants showed a median XRIndex score of 0 (IQR = 5) at Time 1 and a median XRIndex of 0 (IQR = 6) at Time 2. A moderate correlation was found between individual scores obtained at Time 1 and Time 2 (ρ = 0.65, p 0.16). The Pearson’s correlation coefficient for overall c at time 1 and time 2 was significant r = 0.56 ( N = 93, p. DiscussionRepetition did not appear to undermine the validity and predictive power of the XRIndex; an important result in view of potential applications to real-world settings of the screening tool. Learning and previous exposure to the testing material do not seem to interfere with this newly described relation between a targeted self-report measure and threat detection performance with x-ray images. Because most applicants for security screening jobs will have already received professional training and obtained a competence certificate it would be most useful if our probe was expertise-proof in addition to being repetition-proof.

A definitive conclusion on this point requires ad hoc testing with professional screeners. However, we can argue that the XRIndex may be very useful in those cases where untrained personnel require training to cover a sharp increase in the required frequency of security checks, such as with the Olympics. The authors would like to thank Rachel Askew, Mark Davison, Kirsten Lindsey and Evelyn Peck for their help with data collection at various stages of the study. Thanks to George Vardulakis, Dick Lacey and Tom Eagleton from the Home Office Centre for Applied Science and Technology (CAST) for providing the testing material. Thanks to Eamon McCrory for his comments on a previous version of the manuscript, to Adrian Furnham, the late Raja Parasuraman and the current reviewers for their insightful suggestions.

This research was funded by EPSRC (Grant References: EP/G037264/1, EP/J501396/1 and EP/K503459/1). Expressions of interest in using the XRIndex for selection or research purposes should be submitted to Elena Rusconi.

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