A Perceptual Scaling Approach to Eyewitness Identification

目击者身份识别长期以来在刑事调查和起诉中发挥着重要作用，其中确定罪犯对刑事司法程序至关重要。基于对感知和记忆的理解的进步，这个项目将研究阵容中“填充物”的作用。填充者是一个阵容中已知是无辜的人谁服务于挑战目击者的承认。该研究将开发一种基于人脸相似性的人机测量相结合的填充物选择新方法。该项目建立在设计和进行目击者识别的阵容的新范例之上。基于感知缩放，在感官心理学中建立良好，这种方法将被用来估计目击者记忆的强度，从而生成大型面部图像库，其中面部相似性的程度被量化。这项研究将有助于减少决策偏见的目击者的敏感性，并将提供定量的措施，感知相似的面孔。这些措施，结合机器学习算法，将被用来创建一个模型证人，这是一个算法，再现人类观察员的相似性判断，并预测感知相似性的新面孔。该项目将产生一个客观的系统来选择填充物，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，并被认为是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的支持。

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.