More Like this

1. New signal detection theory-based framework for eyewitness performance in lineups

   https://doi.org/https://doi.org/10.1037/lhb0000343  

   Lee, J.; Penrod, S. D. (January 2019, Law and human behavior)

   Objectives: Eyewitness research has adapted signal detection theory (SDT) to investigate eyewitness performance. SDT-based measures in yes/no tasks fit well for the measurement of eyewitness performance in show-ups, but not in lineups, because the application of the measures to eyewitness identifications neglects the role of fillers. In the present study, we introduce a SDT-based framework for eyewitness performance in lineups—Multi-d′ Model. Method: The Multi-d′ model provides multiple discriminability measures which can be used as parameters to investigate eyewitness performance. We apply the Multi-d′ model to issues in eyewitness research, such as the comparison of eyewitness discriminability between show-ups and lineups; the influence of lineup bias on eyewitness performance; filler selection methods (match-to-description vs. match-to-suspect); eyewitness confidence; and lineup presentation modes (simultaneous vs. sequential lineups). Results: The Multi-d′ model demonstrates that the discriminability of a guilty suspect from an innocent suspect is a function of discriminability involving fillers; and underscores that the decisions that eyewitnesses make in lineups can be regarded from two perspective—detection and identification. Conclusions: We propose that the Multi-d′ model is a useful tool to understand decisionmakers’ performance in a variety of compound decision tasks, as well as eyewitness identifications in lineups. 

   more » « less
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. 

   more » « less
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. 

   more » « less
4. And like that, they were gone: A failure to remember recently attended unique faces

   https://doi.org/10.3758/s13423-021-01965-2  

   Tam, Joyce; Mugno, Michael K.; O’Donnell, Ryan E.; Wyble, Brad (July 2021, Psychonomic Bulletin & Review)

   null (Ed.)

   Attribute amnesia (AA) is a phenomenon in which participants have difficulty answering an unexpected question about an attended attribute of the most recent target stimulus. A similar situation can occur in cases of real-life eyewitness identification when the eyewitness did not explicitly try to remember the alleged perpetrator’s face despite having attended to it. We found that AA is generalizable to novel faces, such that when participants were unexpectedly asked to identify a face, performance was poor, even though they had just attended to that face seconds ago (N = 40 each in an initial experiment and its replication). This finding shows that unexpected face identification is inaccurate even when the face had just been attended to and suffered minimal decay and interference, implying that AA can explain some cases of failure of eyewitness identification that cannot be attributed to a lack of attention or post-event interference. 

   more » « less
5. A Computational Approach to Understand Mental Health from Reddit: Knowledge-Aware Multitask Learning Framework

   https://doi.org/10.1609/icwsm.v16i1.19322  

   Lokala, Usha; Srivastava, Aseem; Dastidar, Triyasha Ghosh; Chakraborty, Tanmoy; Akhtar, Md Shad; Panahiazar, Maryam; Sheth, Amit (June 2022, Proceedings of the International AAAI Conference on Web and Social Media)

   Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of 150k items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel task-adaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adopt a knowledge-assisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models. Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task. 

   more » « less