The Effect of Viewing Distance on Empirical Discriminability and the Confidence–Accuracy Relationship for Eyewitness Identification

The distance from which an eyewitness views a perpetrator is a critical factor for eyewitness identification, but has received little research attention. We presented three mock-crime videos to participants, varying distance to three perpetrators (3, 10, or 20 m). Across two experiments, increased distance reduced empirical discriminability in the form of a mirror effect, such that correct identifications decreased while false identifications increased. Moreover, high confidence identifications were associated with high accuracy at 3 m (Experiment 1 and 2) and 10 m (Experiment 2), but not at 20 m. We conclude that eyewitnesses may be less likely to identify a perpetrator viewed at a distance, and also more likely to falsely identify an innocent suspect. Furthermore, there may be certain boundary conditions associated with distance and the impact it has on the confidence-accuracy relationship. More research is needed to elucidate the effect of estimator variable manipulations on the confidence-accuracy relationship

The Weapon Focus Effect: Testing an Extension of the Unusualness Hypothesis

The weapon focus effect (WFE) occurs when a weapon distracts eyewitnesses, harming memory for the perpetrator and other details. One explanation is that weapons are unusual in most contexts, and unusual objects distract eyewitnesses. We extended this unusualness hypothesis to include typical objects used in a distinctive manner, as criminals often make use of a typical object as a weapon (e.g., tire iron, beer bottle). Undergraduates (N = 963) viewed a video depicting a man with a handgun, distinctive object, typical object and action, or typical object used as a weapon. Only the handgun reduced eyewitness identification accuracy relative to the typical object and action, replicating the WFE. Importantly, participants who reported high confidence after choosing from a lineup tended to be highly accurate, regardless of condition

ROCs in Eyewitness Identification:Instructions versus Confidence Ratings

From the perspective of signal-detection theory, different lineup instructions may induce different levels of response bias (Clark, 2005). If so, then collecting correct and false identification rates across different instructional conditions will trace out the ROC – the same ROC that, theoretically, could also be traced out from a single instruction condition in which each eyewitness decision is accompanied by a confidence rating. We tested whether the two approaches do in fact yield the same ROC. Participants were assigned to a confidence rating condition or to an instructional biasing condition (liberal, neutral, unbiased, or conservative). After watching a video of a mock crime, participants were presented with instructions followed by a 6-person simultaneous photo lineup. The ROCs from both methods were similar, but they were not exactly the same. These findings have potentially important policy implications for how the legal system should go about controlling eyewitness response bias

ROCs in Eyewitness Identification: Instructions vs. Confidence Ratings

From the perspective of signal-detection theory, different lineup instructions may induce different levels of response bias (Clark, 2005). If so, then collecting correct and false identification rates across different instructional conditions will trace out the ROC – the same ROC that, theoretically, could also be traced out from a single instruction condition in which each eyewitness decision is accompanied by a confidence rating. We tested whether the two approaches do in fact yield the same ROC. Participants were assigned to a confidence rating condition or to an instructional biasing condition (liberal, neutral, unbiased, or conservative). After watching a video of a mock crime, participants were presented with instructions followed by a 6-person simultaneous photo lineup. The ROCs from both methods were similar, but they were not exactly the same. These findings have potentially important policy implications for how the legal system should go about controlling eyewitness response bias

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Rails: Up and Running

In just a matter of days, you can develop powerful web applications with Rails that once took weeks or months to produce with other web frameworks. Find out how with Rails: Up and Running. Perfect for beginning web developers, this new edition teaches the basics of installing and using Rails 2.1 and the Ruby scripting language, and covers just about everything you need to develop your own Rails applications quickly