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1. New Signal Detection Theory-Based Framework for Eyewitness Performance in Lineups

   https://doi.org/https://doi.org/10.17605/osf.io/c7ufx  

   Lee, J.; Penrod, S. D. (June 2019, Journal of applied research in memory and cognition)

   SDT-based measures in yes/no tasks fit well for the measurement of eyewitness performance in a show-up (a detection task), but not in a lineup (a compound task of detection plus identification). We introduce a new SDT-based framework for eyewitness performance in lineups (Multi-d′ model). The Multi-d′ model demonstrates that the discriminability of a guilty suspect from an innocent suspect is a function of discriminability involving fillers and the differential filler siphoning effect; and that eyewitnesses’ discrimination in lineups can be assessed at two levels—detection and identification levels. We apply the Multi-d′ model to issues in eyewitness research. 

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2. A multi-item signal detection theory model for eyewitness identification

   https://doi.org/10.1186/s41235-025-00652-3  

   Yang, Yueran; Burke, Janice_L; Healy, Justice (August 2025, Cognitive Research: Principles and Implications)

   Abstract How do witnesses make identification decisions when viewing a lineup?Understanding the witness decision-making process is essential for researchers to develop methods that can reduce mistaken identifications and improve lineup practices. Yet, the inclusion of fillers has posed a pivotal challenge to this task because the traditional signal detection theory is only applicable to binary decisions and cannot easily incorporate lineup fillers. This paper proposes a multi-item signal detection theory (mSDT) model to help understand the witness decision-making process. The mSDT model clarifies the importance of considering the joint distributions of suspect and filler signals. The model also visualizes the joint distributions in a multivariate decision space, which allows for the incorporation of all eyewitness responses, including suspect identifications, filler identifications, and rejections. The paper begins with a set of simple assumptions to develop the mSDT model and then explores alternative assumptions that can potentially accommodate more sophisticated considerations. The paper further discusses the implications of the mSDT model. With a mathematical modeling and visualization approach, the mSDT model provides a novel theoretical framework for understanding eyewitness identification decisions and addressing debates around eyewitness SDT and ROC applications. 

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3. Phenotypic mismatch between suspects and fillers but not phenotypic bias increases eyewitness identifications of Black suspects

   https://doi.org/10.3389/fpsyg.2024.1233782  

   Jones, Jennifer M; Katzman, Jacqueline; Kovera, Margaret Bull (April 2024, Frontiers in Psychology)

   IntroductionDespite converging evidence that people more closely associate the construct of criminality with Black people who exhibit a more African facial phenotype than Black people who express a more European phenotype, eyewitness researchers have largely ignored phenotypic bias as a potential contributor to the racial disparities in the criminal legal system. If this form of phenotypic bias extends to eyewitness identification tasks, eyewitnesses may be more likely to identify Black suspects with an African rather than European phenotype, regardless of their guilt status. Further, in cases where the witness’s description of the perpetrator does not contain phenotypic information, phenotypic mismatch between the suspect and the other lineup members may bias identification decisions toward or against the suspect. If witnesses can use elements of the lineup construction to guide their identification decisions rather than relying on their recognition memory, then the lineup should be deemed unfair due to suspect bias. The current study also investigated lineup presentation method as a procedural safeguard, predicting that that when lineups were presented simultaneously, there would be a significant two-way interaction of phenotypic bias and lineup composition, with a larger simple main effect of phenotypic bias when lineups were suspect-biased (i.e., the fillers were a phenotypic mismatch to the suspect) than when all lineup members shared the same phenotype. We expected that this interaction would be significantly smaller or non-significant for sequential lineups. MethodsParticipants watched a mock crime video that contained a Black culprit with either a more African phenotype or a less African phenotype before attempting identifications from a photo array that contained a suspect whose phenotype always matched the culprit viewed in the video, but varied in culprit-presence, phenotypic match of the suspect and fillers, and presentation method. ResultsParticipants did not identify Black suspects with Afrocentric features more often than Black suspects with Eurocentric features. However, witnesses made more identifications of suspects when the fillers did not match the suspect’s phenotype compared to when all lineup members possessed similar phenotypic features. DiscussionIn sum, phenotypic bias did not influence our participant-witnesses’ identification decisions, nor interact with lineup composition and lineup presentation type to affect identifications of suspects, suggesting that phenotypic bias may be less influential in match-to-memory tasks than other types of legal decision-making (e.g., determining guilt and sentencing). However, the suggestiveness created by failing to match fillers’ phenotypes to the suspect’s phenotype can be avoided with proper attention to fair lineup construction. 

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4. Insights into Criteria for Statistical Significance from Signal Detection Analysis

   https://doi.org/10.15626/MP.2018.871  

   Witt, Jessica K. (January 2019, Meta-Psychology)

   What is best criterion for determining statistical significance? In psychology, the criterion has been p < .05. This criterion has been criticized since its inception, and the criticisms have been rejuvenated with recent failures to replicate studies published in top psychology journals. Several replacement criteria have been suggested including reducing the alpha level to .005 or switching to other types of criteria such as Bayes factors or effect sizes. Here, various decision criteria for statistical significance were evaluated using signal detection analysis on the outcomes of simulated data. The signal detection measure of area under the curve (AUC) is a measure of discriminability with a value of 1 indicating perfect discriminability and 0.5 indicating chance performance. Applied to criteria for statistical significance, it provides an estimate of the decision criterion’s performance in discriminating real effects from null effects. AUCs were high (M = .96, median = .97) for p values, suggesting merit in using p values to discriminate significant effects. AUCs can be used to assess methodological questions such as how much improvement will be gained with increased sample size, how much discriminability will be lost with questionable research practices, and whether it is better to run a single high-powered study or a study plus a replication at lower powers. AUCs were also used to compare performance across p values, Bayes factors, and effect size (Cohen’s d). AUCs were equivalent for p values and Bayes factors and were slightly higher for effect size. Signal detection analysis provides separate measures of discriminability and bias. With respect to bias, the specific thresholds that produced maximally-optimal utility depended on sample size, although this dependency was particularly notable for p values and less so for Bayes factors. The application of signal detection theory to the issue of statistical significance highlights the need to focus on both false alarms and misses, rather than false alarms alone. 

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5. Computer Vision-Based Geometry Mapping and Matching of Building Elements for Construction Robotic Applications

   https://doi.org/10.1061/9780784483961.057  

   Lacny, Christopher; Zhang, Jiansong (March 2022, Construction Research Congress 2022)

   Robotic automation of construction tasks is a growing area of research. For robots to successfully operate in a construction environment, sensing technology must be developed which allows for accurate detection of site geometry in a wide range of conditions. Much of the existing body of research on computer vision systems for construction automation focuses on pick-and-place operations such as stacking blocks or placing masonry elements. Very little research has focused on framing and related tasks. The research presented here aims to address this gap by designing and implementing computer vision algorithms for detection and measurement of building framing elements and testing those algorithms using realistic framing structures. These algorithms allow for a stationary RGB-D camera to accurately detect, identify, and measure the geometry of framing elements in a construction environment and match the detected geometry to provided building information modeling (BIM) data. The algorithms reduce identified framing elements to a simplified 3D geometric model, which allows for robust and accurate measurement and comparison with BIM data. This data can then be used to direct operations of construction robotic systems or other machines/equipment. The proposed algorithms were tested in a laboratory setting using an Intel RealSense D455 RGB-D camera, and initial results indicate that the system is capable of measuring the geometry of timber-frame structures with accuracy on the order of a few centimeters. 

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