The lineup construction process and eyewitness identifications

阵容构建过程及目击者识别

• 批准号: 2203796
• 负责人: Melisa Akan
• 金额: $ 13.8万
• 依托单位: Akan, Melisa
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203796&HistoricalAwards=false
• 关键词: lineup; construction; process; eyewitness; identifications

This award was provided as part of NSF's Social, Behavioral and Economic Sciences (SBE) Postdoctoral Research Fellowships (SPRF) program and SBE's Law and Science program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Jeffrey Starns at the University of Massachusetts Amherst, this postdoctoral fellowship award supports an early career scientist investigating the lineup construction process. Lineups are frequently used to gather evidence from eyewitnesses. A lineup typically consists of a photo of the suspect, who may or may not be guilty, presented alongside photos of innocent individuals, referred to as fillers. The fillers are usually selected by police officers, often based on similarity to the suspect. Eyewitness identifications from lineups constitute an important source of evidence in criminal investigations, and in many cases, eyewitnesses provide the only available evidence. Unfortunately, eyewitness misidentifications of innocent suspects are significant contributors to wrongful convictions, and are known to be plagued with disparities, with potentially devastating consequences for individuals and communities. This project will investigate the process of lineup construction and the effects of the compositional structure of lineups on the accuracy of eyewitness identifications, with a focus on a particularly error-prone identification scenario. By taking a novel approach to the analysis of lineup composition, this project will elucidate factors contributing to lineup construction and identify the characteristics of a lineup that support more accurate identifications. The project will address a significant gap in the field by addressing an under-researched task (lineup construction) using multidimensional scaling (MDS), an advanced statistical tool that has had limited use in the field. The analytical approach and findings will inform policy on lineup construction and contribute to theory development.The project has three main components: (1) similarity judgments; (2) construction of lineups by perceivers; and (3) eyewitness identification using the lineups from (2). Multidimensional scaling (MDS) analyses will be applied to the similarity judgments from (1), generating two multidimensional face spaces that map out how faces are represented by perceivers. The psychological face spaces will be used to reveal the similarity structure of lineups constructed by perceivers. These lineups will then be tested in an eyewitness identification study, using a separate, large sample of participants. This research effort will provide the grounds for theory development and computational modeling of lineup memory and decision-making processes. These precise measurements of lineup properties will deepen our understanding of the relevant issues and shed light on inconsistent findings in the literature. The data, as well as the stimuli set and the analysis code, will be made available for other researchers via public repositories, which will support research infrastructure and spur further research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项是NSF的社会，行为和经济科学（SBE）博士后研究奖学金（SPRF）计划和SBE的法律和科学计划的一部分。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在马萨诸塞州阿默斯特大学的Jeffrey Starns博士的赞助下，该博士后奖学金支持一位早期职业科学家调查阵容构建过程。列队作证经常被用来从目击者那里收集证据。一个阵容通常包括嫌疑人的照片，谁可能是或可能不是有罪的，旁边的照片无辜的个人，被称为填料。填充物通常由警察选择，通常基于与嫌疑人的相似性。目击者从列队辨认中指认是刑事调查中的一个重要证据来源，在许多情况下，目击者提供了唯一可用的证据。不幸的是，目击者对无辜嫌疑人的错误指认是错误定罪的重要因素，众所周知，这种情况存在差异，对个人和社区可能造成破坏性后果。该项目将研究阵容构建的过程以及阵容的组成结构对目击者识别准确性的影响，重点关注特别容易出错的识别场景。通过采取一种新颖的方法来分析阵容构成，该项目将阐明影响阵容构建的因素，并识别支持更准确识别的阵容特征。该项目将通过使用多维缩放（MDS）解决一项研究不足的任务（阵容构建）来解决该领域的重大差距，MDS是一种在该领域使用有限的先进统计工具。分析方法和研究结果将为阵容构建的政策提供信息，并有助于理论发展。该项目有三个主要组成部分：（1）相似性判断;（2）感知者构建阵容;（3）使用（2）的阵容进行目击者识别。多维尺度（MDS）分析将应用于（1）中的相似性判断，生成两个多维人脸空间，映射出感知者如何表示人脸。心理面孔空间将被用来揭示由感知者构建的阵容的相似性结构。然后，这些阵容将在一项目击者身份鉴定研究中进行测试，使用一个单独的大样本参与者。这项研究工作将提供理论发展和计算建模的阵容记忆和决策过程的基础。这些精确测量的阵容属性将加深我们对相关问题的理解，并阐明在文献中不一致的结果。这些数据，以及刺激集和分析代码，将通过公共存储库提供给其他研究人员，这将支持研究基础设施并刺激进一步的研究。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。