Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Optimizing the selection of fillers in police lineups.

ABSTRACT: A typical police lineup contains a photo of one suspect (who is innocent in a target-absent lineup and guilty in a target-present lineup) plus photos of five or more fillers who are known to be innocent. To create a fair lineup in which the suspect does not stand out, two filler selection methods are commonly used. In the first, fillers are selected if they are similar in appearance to the suspect. In the second, fillers are selected if they possess facial features included in the witness's description of the culprit (e.g., "20-y-old white male"). The police sometimes use a combination of the two methods by selecting description-matched fillers whose appearance is also similar to that of the suspect in the lineup. Decades of research on which approach is better remains unsettled. Here, we tested a counterintuitive prediction made by a formal model based on signal detection theory: From a pool of acceptable description-matched photos, selecting fillers whose appearance is otherwise dissimilar to the suspect should increase the hit rate without affecting the false-alarm rate (increasing discriminability). In Experiment 1, we confirmed this prediction using a standard mock-crime paradigm. In Experiment 2, the effect on discriminability was reversed (as also predicted by the model) when fillers were matched on similarity to the perpetrator in both target-present and target-absent lineups. These findings suggest that signal-detection theory offers a useful theoretical framework for understanding eyewitness identification decisions made from a police lineup.

SUBMITTER: Colloff MF

PROVIDER: S-EPMC7923643 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Digital Detectives: Websleuthing Reduces Eyewitness Identification Accuracy in Police Lineups.

Project description:Eyewitnesses to crimes sometimes search for a culprit on social media before viewing a police lineup, but it is not known whether this affects subsequent lineup identification accuracy. The present online study was conducted to address this. Two hundred and eighty-five participants viewed a mock crime video, and after a 15-20 min delay either (i) viewed a mock social media site including the culprit, (ii) viewed a mock social media site including a lookalike, or (iii) completed a filler task. A week later, participants made an identification from a photo lineup. It was predicted that searching for a culprit on social media containing the lookalike (rather than the culprit) would reduce lineup identification accuracy. There was a significant association between social media exposure and lineup accuracy for the Target Present lineup (30% more of the participants who saw the lookalike on social media failed to positively identify the culprit than participants in the other conditions), but for the Target Absent lineup (which also included the lookalike) there was no significant association with lineup identification accuracy. The results suggest that if an eyewitness sees a lookalike (where they are expecting to see the culprit) when conducting a self-directed search on social media, they are less likely to subsequently identify the culprit in the formal ID procedure.

| S-EPMC8081957 | biostudies-literature

Self-extinction through optimizing selection.

Project description:Evolutionary suicide is a process in which selection drives a viable population to extinction. So far, such selection-driven self-extinction has been demonstrated in models with frequency-dependent selection. This is not surprising, since frequency-dependent selection can disconnect individual-level and population-level interests through environmental feedback. Hence it can lead to situations akin to the tragedy of the commons, with adaptations that serve the selfish interests of individuals ultimately ruining a population. For frequency-dependent selection to play such a role, it must not be optimizing. Together, all published studies of evolutionary suicide have created the impression that evolutionary suicide is not possible with optimizing selection. Here we disprove this misconception by presenting and analyzing an example in which optimizing selection causes self-extinction. We then take this line of argument one step further by showing, in a further example, that selection-driven self-extinction can occur even under frequency-independent selection.

| S-EPMC3730061 | biostudies-other

Optimizing cord blood selection.

Project description:Nowadays a donor can be found for virtually all patients in need of an allogeneic stem cell transplantation, and the decision whether to use a matched or mismatched unrelated donor, an unrelated donor for umbilical cord blood transplantation (UCBT), or a haploidentical donor depends not only on the availability of the donor but also on patient-, disease-, and center-related factors. This paper summarizes the recent criteria in the selection of cord blood unit, including the cell dose requirement and the HLA typing for the optimal donor choice. The main strategies to optimize the results of UCBT, the conditioning regimens, and the use of antithymocyte globulin and the other platforms of graft-versus-host disease prophylaxis are discussed. The paper describes the results of UCBT in children and adults with malignant and nonmalignant diseases and the comparative analysis with other donor type and stem cell sources. Emerging strategies, focusing on the different platforms of ex vivo expansion and the new applications using cord blood stem cell, are also examined.

| S-EPMC6913431 | biostudies-literature

Do sequential lineups impair underlying discriminability?

Project description:Debate regarding the best way to test and measure eyewitness memory has dominated the eyewitness literature for more than 30 years. We argue that resolution of this debate requires the development and application of appropriate measurement models. In this study we developed models of simultaneous and sequential lineup presentations and used these to compare these procedures in terms of underlying discriminability and response bias, thereby testing a key prediction of diagnostic feature detection theory, that underlying discriminability should be greater for simultaneous than for stopping-rule sequential lineups. We fit the models to the corpus of studies originally described by Palmer and Brewer (2012, Law and Human Behavior, 36(3), 247-255), to data from a new experiment and to eight recent studies comparing simultaneous and sequential lineups. We found that although responses tended to be more conservative for sequential lineups there was little or no difference in underlying discriminability between the two procedures. We discuss the implications of these results for the diagnostic feature detection theory and other kinds of sequential lineups used in current jurisdictions.

| S-EPMC7403381 | biostudies-literature

Optimal seed solver: optimizing seed selection in read mapping.

Project description:MotivationOptimizing seed selection is an important problem in read mapping. The number of non-overlapping seeds a mapper selects determines the sensitivity of the mapper while the total frequency of all selected seeds determines the speed of the mapper. Modern seed-and-extend mappers usually select seeds with either an equal and fixed-length scheme or with an inflexible placement scheme, both of which limit the ability of the mapper in selecting less frequent seeds to speed up the mapping process. Therefore, it is crucial to develop a new algorithm that can adjust both the individual seed length and the seed placement, as well as derive less frequent seeds.ResultsWe present the Optimal Seed Solver (OSS), a dynamic programming algorithm that discovers the least frequently-occurring set of x seeds in an L-base-pair read in [Formula: see text] operations on average and in [Formula: see text] operations in the worst case, while generating a maximum of [Formula: see text] seed frequency database lookups. We compare OSS against four state-of-the-art seed selection schemes and observe that OSS provides a 3-fold reduction in average seed frequency over the best previous seed selection optimizations.Availability and implementationWe provide an implementation of the Optimal Seed Solver in C++ at: https://github.com/CMU-SAFARI/Optimal-Seed-SolverContacthxin@cmu.edu, calkan@cs.bilkent.edu.tr or onur@cmu.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6363230 | biostudies-literature

Individual differences in eyewitness accuracy across multiple lineups of faces.

Project description:Theories of face recognition in cognitive psychology stipulate that the hallmark of accurate identification is the ability to recognize a person consistently, across different encounters. In this study, we apply this reasoning to eyewitness identification by assessing the recognition of the same target person repeatedly, over six successive lineups. Such repeat identifications are challenging and can be performed only by a proportion of individuals, both when a target exhibits limited and more substantial variability in appearance across lineups (Experiments 1 and 2). The ability to do so correlates with individual differences in identification accuracy on two established tests of unfamiliar face recognition (Experiment 3). This indicates that most observers have limited facial representations of target persons in eyewitness scenarios, which do not allow for robust identification in most individuals, partly due to limitations in their ability to recognize unfamiliar faces. In turn, these findings suggest that consistency of responses across multiple lineups of faces could be applied to assess which individuals are accurate eyewitnesses.

| S-EPMC6091462 | biostudies-literature

The effects of repeated lineups and delay on eyewitness identification.

Project description:A significant problem in eyewitness identification occurs when witnesses view a suspect in one venue such as a mugshot and then later in a lineup where the suspect is the only previously viewed person. Prior research has documented that the witness may select the suspect from the lineup due either to misplaced familiarity from seeing the mugshot or to their prior commitment from identifying the suspect from the mugshot. Two experiments attempted to minimize these biases by using repeated identical lineups, such that both targets and fillers were repeated, to determine if such a procedure could be useful. Across two experiments, we also varied the delay between seeing the event and the first lineup, as well as the delay between lineups. Despite the use of identical lineups, we continued to observe the effects of commitment and misplaced familiarity, so our procedure did not remove these problems. In addition, we also found that both repeated lineups and increasing delays can influence people's tendency to choose and their willingness to maintain their decisions, regardless of accuracy. Most importantly, however, despite the negative effects of repeated lineups and the relatively long delays used in our experiments, we obtained strong relations between confidence and accuracy when using confidence-accuracy characteristic plots. High confidence responses were associated with high accuracy.

| S-EPMC6565795 | biostudies-literature

An embedded gene selection method using knockoffs optimizing neural network.

Project description:BackgroundGene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural network has better fitting ability when dealing with nonlinear data, and it can capture features automatically and flexibly. In this work, we propose an embedded gene selection method using neural network. The important genes can be obtained by calculating the weight coefficient after the training is completed. In order to solve the problem of black box of neural network and further make the training results interpretable in neural network, we use the idea of knockoffs to construct the knockoff feature genes of the original feature genes. This method not only make each feature gene to compete with each other, but also make each feature gene compete with its knockoff feature gene. This approach can help to select the key genes that affect the decision-making of neural networks.ResultsWe use maize carotenoids, tocopherol methyltransferase, raffinose family oligosaccharides and human breast cancer dataset to do verification and analysis.ConclusionsThe experiment results demonstrate that the knockoffs optimizing neural network method has better detection effect than the other existing algorithms, and specially for processing the nonlinear gene expression and phenotype data.

| S-EPMC7510330 | biostudies-literature

Optimizing representations for integrative structural modeling using Bayesian model selection.

Project description:MotivationIntegrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually.ResultsHere, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies.AvailabilityNestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor.

| S-EPMC10760022 | biostudies-literature

Heart Rate Variability Assessment of Land Navigation and Load Carriage Activities in Specialist Police Selection.

Project description:Police tactical group (PTG) personnel are exposed to physical, mental, and emotional stressors. Consequently, PTG selection courses (SCs) impart similar challenges, often resulting in candidate attrition. Holistic assessment may provide additional support to stakeholders given these risks. Heart Rate Variability (HRV) is an objective holistic stress measure that may be applicable in PTG SCs but has not been thoroughly researched. Therefore, this study aimed to report HRV data in an end-user accessible format and determine the relationship between HRV and attrition. A total of 18 qualified Australian State law enforcement officers completed a 1-day physical readiness assessment. Of those, six males progressed to an additional two-day course, on which this study is focused. This two-day selection consisted of additional physical challenges and occupational assessments. HRV was obtained from 2-lead ECGs and defined as the percentage of R-R intervals that varied by ≥50 ms (pRR50). Data were summarized in a heat map of consecutive short-term analyses. Three candidates withdrew. A logistic regression based on heat map data found high HRV was significant for predicting attrition, χ2 (6) = 8.318, p = 0.004. HRV may provide insight for PTG stakeholders monitoring attrition. While the sample size was limited and replication is needed, this study tentatively establishes value for HRV monitoring in PTG SCs.

| S-EPMC10572114 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data