A Perceptual Scaling Approach to Eyewitness Identification

目击者识别的感知尺度方法

• 批准号: 2044092
• 负责人: Thomas Albright
• 金额: $ 50万
• 依托单位: The Salk Institute for Biological Studies
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044092&HistoricalAwards=false
• 关键词: Perceptual; Scaling; Approach; Eyewitness; Identification

Eyewitness identification has long played an important role in criminal investigations and prosecutions, where identifying the culprit is critical to the criminal justice process. Based on advances in understanding of perception and memory, this project will examine the role of “fillers” in lineups. Fillers are the individuals in a lineup known to be innocent who serve to challenge eyewitness recognition. The research will develop a novel method for filler selection based on a combination of human and machine measures of facial similarity.The project builds upon a new paradigm for the design and conduct of lineups for eyewitness identification. Drawing upon perceptual scaling, well-established in sensory psychology, this approach will be used to estimate the strength of eyewitness memories, and thus to generate large libraries of facial images in which the degree of facial similarity is quantified. This research will help to reduce susceptibility of the eyewitness to decision bias and will provide quantitative measures of the perceived similarity of faces. These measures, in combination with a machine learning algorithm, will be used to create a model witness, which is an algorithm that reproduces similarity judgements of human observers and predicts perceptual similarity for new faces. The project will yield an objective system for selecting fillers in order to optimize eyewitness fairness and performance.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.

目击者识别长期以来在刑事调查和起诉中发挥着重要作用，其中识别罪犯对于刑事司法程序至关重要。基于对感知和记忆的理解的进步，该项目将研究“填充者”在阵容中的作用。该研究将开发一种基于人类和机器面部相似度测量相结合的填充物选择的新方法。该项目建立在一种新的设计范例之上。并进行目击者识别阵容利用感官心理学中成熟的感知模型，该方法将用于估计目击者记忆的强度，从而生成大型面部图像库，其中量化面部相似度。研究将有助于降低目击者对决策偏差的敏感性，并将提供面部感知相似性的定量测量，这些测量与机器学习算法相结合，将用于创建模型证人，这是一种算法再现人类观察者的相似性判断，并预测新面孔的感知相似性。该项目将产生一个用于选择填充物的客观系统，以优化目击者的公平性和表现。该奖项反映了 NSF 的法定使命，并通过使用该奖项的评估被认为值得支持。基金会的智力价值和更广泛的影响审查标准。