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，目击者辨认每年在大约8万起刑事案件中起作用。目击证人可以在刑事案件中提供关键证据，但目击证人的指认并不总是准确的。目击者会犯两种错误。他们可能无法从队列中识别出有罪的人，也可能从队列中错误地识别出无辜的人。这两种类型的错误都对公共安全构成威胁，因为它们有助于犯罪者自由地犯下更多的罪行。此外，错误的识别会危及无辜嫌疑人的自由。由于这些原因，重要的是要更好地了解与准确的目击者鉴定有关的情况和程序。可以用来预测目击证人的准确性的一个重要因素是证人在辨认时的信心。许多研究发现，当一个证人以很高的信心指认一个嫌疑人时，这个证人很可能是正确的。当警察使用理想的程序来获得身份证明时，情况尤其如此。然而，最近的一些研究发现，在某些情况下，即使使用了理想的程序，高度自信的证人并不总是高度准确的。研究人员提出了一系列使用虚拟现实重现犯罪的研究，以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。使用虚拟现实可以创造出比传统的目击者研究人员所使用的更逼真、更有情感吸引力的事件。这项研究的重点是测试两个想法。第一个观点是，一些证人可能比其他证人更了解自己的记忆和知觉是如何运作的。那些与自己的记忆和感知方式更合拍的证人，只有在正确的可能性很高的时候才会说自己有信心。那些对自己的记忆和感知机制理解不佳的证人，可能会错误地说自己很自信，即使正确的几率更低。第二种观点是，可能存在目击条件很差的情况（例如，光线不足，距离很远），即使是最好的证人也不太可能非常准确，证人在做出信心判断时可能没有充分考虑到目击条件有多差。这些假设将通过测试儿童和成人证人的一系列实验来检验，并将更好地理解目击者的信心和准确性是如何相关的。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。