Testing the Pristine Conditions Hypothesis of the Confidence-Accuracy Relationship in Eyewitness Identification

检验目击者识别中置信度与准确性关系的原始条件假设

• 批准号: 2146757
• 负责人: James Lampinen
• 金额: $ 66.99万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2146757&HistoricalAwards=false
• 关键词: Testing; Pristine; Conditions; Hypothesis; Confidence

It has been estimated that eyewitness identification plays a role in approximately 80,000 criminal cases per year. Eyewitnesses can provide crucial evidence in criminal cases, but eyewitness identifications are not always accurate. Eyewitnesses can make two kinds of mistakes. They can fail to identify guilty individuals from lineups and they can falsely identify innocent people from lineups. Both types of errors present a threat to public safety because they contribute to perpetrators being free to commit additional crimes. In addition, mistaken identifications risk the liberty of innocent suspects. For these reasons, it is important to better understand the situations and procedures that are associated with accurate eyewitness identifications.One important factor that may be used to predict eyewitness accuracy is the witness’s confidence at the time of the identification. A number of studies have found that when a witness identifies a suspect with high confidence that the witness is quite likely to be correct. This is particularly true when ideal procedures are used by police to obtain the identification. However, more recently, some studies have found that there are situations where, even when ideal procedures are used, highly confident witnesses are not always highly accurate. The researchers have proposed a series of studies using virtual reality recreations of crimes to help understand when highly confident witnesses are likely to be highly accurate and when they are likely to be less accurate. Use of virtual reality can create more lifelike and emotionally engaging events than have traditionally been used by eyewitness researchers. The research focuses on testing two ideas. The first idea is that some witnesses might have a better understanding of how their own memory and perception works than other witnesses. Witnesses who are more in tune with how their memory and perception works are likely to only say they are confident when the probability of being correct is high. Witnesses who have a poor understanding of how their memory and perception works, may mistakenly say they are confident, even when the odds of being correct are lower. The second idea is that there may be witnessing conditions that are so poor (e.g., poor lighting, very long distance) that even the best witnesses are unlikely to be very accurate and witnesses may not fully take into account how poor the witnessing conditions are when making their confidence judgments. These hypotheses will be tested through a series of experiments testing both child and adult witnesses and will provide a better understanding of how eyewitness confidence and accuracy are related.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.

据估计，目击者识别每年在大约 80,000 起刑事案件中发挥作用。 目击者可以在刑事案件中提供关键证据，但目击者的身份并不总是准确的。 目击者可能会犯两种错误。 他们可能无法从队列中识别出有罪的人，也可能错误地从队列中识别出无辜者。 这两类错误都会对公共安全构成威胁，因为它们会导致犯罪者随意实施更多犯罪。 此外，错误的身份识别还会危及无辜嫌疑人的自由。出于这些原因，更好地了解与准确目击者识别相关的情况和程序非常重要。可用于预测目击者准确性的一个重要因素是目击者在识别时的信心。 多项研究发现，当证人高度自信地指认嫌疑人时，该证人很有可能是正确的。当警察使用理想的程序来获取身份时尤其如此。 然而，最近的一些研究发现，在某些情况下，即使使用理想的程序，高度自信的证人也并不总是高度准确。研究人员提出了一系列研究，利用犯罪的虚拟现实再现来帮助了解高度自信的证人何时可能高度准确，何时可能不太准确。 与目击者研究人员传统上使用的方式相比，虚拟现实的使用可以创造出更加逼真、更加引人入胜的事件。 该研究的重点是测试两个想法。 第一个想法是，一些目击者可能比其他目击者更好地理解自己的记忆和感知是如何运作的。 那些更了解自己的记忆和感知运作方式的目击者可能只会在正确的可能性很高时才会说他们有信心。 对自己的记忆和知觉运作方式了解甚少的目击者可能会错误地表示自己很自信，即使正确的可能性较低。 第二个想法是，可能存在很差的目击条件（例如，光线差、距离很远），即使是最好的目击者也不太可能非常准确，并且目击者在做出信心判断时可能不会充分考虑到目击条件有多差。这些假设将通过一系列测试儿童和成人证人的实验进行检验，并将更好地理解目击者的置信度和准确性之间的关系。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。