660 research outputs found

    Improving the enhanced cognitive interview with a new interview strategy: category clustering recall.

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    Increasing recall is crucial for investigative interviews. The enhanced cognitive interview (ECI) has been widely used for this purpose and found to be generally effective. We focused on further increasing recall with a new interview strategy, category clustering recall (CCR). Participants watched a mock robbery video and were interviewed 48 hours later with either the (i) ECI; (ii) revised enhanced cognitive interview 1 (RECI1) — with CCR instead of the change order mnemonic during the second recall; or (iii) revised enhanced cognitive interview 2 (RECI2) — also with CCR but conjunctly used with ‘eye closure’ and additional open‐ended follow up questions. Participants interviewed with CCR (RECI1 and RECI2) produced more information without compromising accuracy; thus, CCR was effective. Eye closure and additional open‐ended follow up questions did not further influence recall when using CCR. Major implications for real‐life investigations are discussed.N/

    Applied Threat and Error Management: Toward Crew-Centered Solutions

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    For an operator, a high level of understanding regarding procedures enables appropriate defenses to be built into a robust Threat and Error Management (TEM) framework. Currently, airline flightdeck crewmember training and reference information is concentrated heavily on what and how procedures are performed, but not on why they must be performed a standard way. This missing component of certainty invites misinterpretation of the standards and induces error. I propose a Crew-Centered TEM (CC/TEM) approach designed to arm flight crewmembers with more depth of procedural understanding than that currently afforded. A recent accident where human error was identified as a probable cause is used as an example of how a CC/TEM approach may have prevented the occurrence. CC/TEM solutions have further application within other safety-critical domains, such as medicine and emergency response

    Neural Network Prediction of Ultimate Compression After Impact Loads in Graphite-Epoxy Coupons from Ultrasonic C-Scan Images

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    The purpose of this project was to investigate how accurately an artificial neural network could predict the ultimate compressive loads of impact damaged 24-ply graphite-epoxy coupons from ultrasonic C-scan images. The 24-ply graphite-epoxy coupons were manufactured with bidirectional preimpregnated tape and cut into 21 coupons, 4 inches by 6 inches each. The coupons were impacted at known impact energies of 10, 12, 14, 16, 18, and 20 Joules in order to create barely visible impact damage (BVID). The coupons were then scanned with an ultrasonic C-scan system to create an image of the damaged area. Each coupon was then compressed to failure to determine its ultimate compressive load. Numeric values for each pixel were determined from the C-scan image. Since the image was represented as a red-green-blue (RGB) map, each pixel had three numbers associated with it, one for each of the three colors. To make the image readable to the artificial neural network the columns of the resulting matrix were then summed, and these numbers were used as inputs for a backpropagation neural network (BPNN) to generate accurate predictions of the ultimate compressive loads. The BPNN was trained and optimized on 15 of the 21 sample data sets and tested on the remaining 6 sample data sets. The optimized BPNN was able to produce ultimate compression after impact (CAI) load predictions for the BVID composite coupons with a worst case error of -8.98%. This was within the ±10% goal for this research and comfortably within the B-basis allowables commonly applied to composite structures. The ultrasonic C-scan images were then preprocessed using Fast Fourier Transforms (FFTs) in an effort to remove any image noise present. The results of the BPNN that was trained and tested on the green color data only were then compared to the results yielded by the BPNN trained and tested on the images that were processed through the FFT. It was found that the FFT processed images had a worst case BPNN prediction error of 8.65%, which was only slightly lower than the -8.98% error that was generated by the unprocessed green layer only C-scan image data. This improvement suggested that the added work involved in FFT preprocessing of the worst case error was not as productive as had been hoped, leading to a few suggestions for future noise removal research. This also reinforced the notion that BPNNs, being an iterative optimization scheme, can provide accurate predictions in the presence of at least small amounts of noise. Thus, image filtering methods coupled with the iterative optimization technique that comprises a BPNN have demonstrated the ability to generate accurate CAI load predictions in composite coupons that have experienced BVID

    The enhanced cognitive interview: expressions of uncertainty, motivation and its relation with report accuracy

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    The Enhanced Cognitive Interview (ECI) is one of the most widely studied and used methods to interview witnesses. However, ECI research has mainly focused on increasing report size and somewhat overlooked how to improve and evaluate report accuracy. No study evaluated if witnesses’ spontaneous expressions of uncertainty are accurate metacognitive judgments, nor if witnesses’ motivation during the interview affects report accuracy. This study examined how witnesses’ judgments of recall ‘uncertainty’ and their motivation perception could relate to report accuracy. Forty-four psychology students watched a mock robbery video recording and were interviewed 48 hours later with either the Portuguese version of the ECI or a Structured Interview (SI). Afterward, participants’ motivation was assessed and items of information were classified as ‘certainties’ or ‘uncertainties’. Results suggest that our ECI protocol was effective, since participants interviewed with the ECI produced more information without compromising accuracy. ‘Uncertainties’ were less accurate than ‘certainties’, and their exclusion raised overall, ECI, and SI, accuracy. More motivated participants had better recall accuracy. Accounting for witnesses’ motivation and spontaneous verbal expressions of uncertainty may be effective and time-saving procedures to increase accuracy. These are key points that professionals and researchers should consider

    Context reinstatement in recognition: memory and beyond

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    Context effects in recognition tests are twofold. First, presenting familiar contexts at a test leads to an attribution of context familiarity to a recognition probe, which has been dubbed ‘context-dependent recognition’. Second, reinstating the exact study context for a particular target in a recognition test cues recollection of an item-context association, resulting in 'context-dependent discrimination'. Here we investigated how these two context effects are expressed in metacognitive monitoring (confidence judgments) and metacognitive control ('don’t know' responding) of retrieval. We used faces as studied items, landscape photographs as study and test contexts and both free- and forced-report 2AFC recognition tests. In terms of context-dependent recognition, the results document that presenting familiar contexts at test leads to higher confidence and lower rates of 'don’t know responses compared to novel contexts, while having no effect on forced-report recognition accuracy. In terms of context-dependent discrimination, the results show that reinstated contexts further boost confidence and reduce 'don’t know' responding compared to familiar contexts, while affecting forced-report recognition accuracy only when contribution of recollection to recognition performance is high. Together, our results demonstrate that metacognitive measures are sensitive to context effects, sometimes even more so than recognition measures

    Protecting eyewitness evidence: Examining the efficacy of a self-administered interview tool

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    Given the crucial role of eyewitness evidence, statements should be obtained as soon as possible after an incident. This is not always achieved due to demands on police resources. Two studies trace the development of a new tool, the Self-Administered Interview (SAI), designed to elicit a comprehensive initial statement. In Study 1, SAI participants reported more correct details than participants who provided a free recall account, and performed at the same level as participants given a Cognitive Interview. In Study 2, participants viewed a simulated crime and half recorded their statement using the SAI. After a delay of 1 week, all participants completed a free recall test. SAI participants recalled more correct details in the delayed recall task than control participants
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