Better Models of Eyewitness Identification Across the Lifespan
Better Models of Eyewitness Identification Across the Lifespan
批准号:
1911758
负责人:
Ryan McAdoo
金额:
$13.8万
依托单位:
依托单位国家:
美国
项目类别:
Fellowship Award
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-15 至 2022-02-28
中文摘要
该奖项是国家科学基金会社会、行为和经济科学(SBE)博士后研究奖学金(SPRF)计划和SBE法律和社会科学计划的一部分。SPRF计划的目标是为学术界、工业界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奖励包括在知名科学家的赞助下进行为期两年的培训,并鼓励博士后进行独立研究。美国国家科学基金会寻求促进科学界各阶层的科学家,包括那些未被充分代表的群体的科学家,参与其研究项目和活动;博士后阶段被认为是实现这一目标的一个重要的专业发展阶段。每个博士后必须解决各自学科领域的重要科学问题。在锡拉丘兹大学(Syracuse University)的大卫·卡伦(David Kellen)博士的赞助下,该博士后奖学金奖支持一位早期职业科学家研究整个生命周期的目击者识别,包括未被研究的和不断增长的老年人。特别是,在目击者识别方面的研究很少受到基本识别记忆文献中经常使用的复杂建模的指导。此外,在这一领域中,老年人的独特和不断增长的人口并没有像年轻人那样得到很好的研究,这在文献中留下了一个不幸的空白。拟议的研究试图通过利用复杂的建模方法来了解整个生命周期的目击者识别研究来填补这些空白。本研究旨在促进国家科学基金会的目标,推动识别和目击者记忆的基础研究,并通过调查执法人员管理阵容识别的更好方法和实践来服务于公共福利。本研究主要有理论建构和实证研究两大路径。该提案的理论构建部分将数十年的知识建模基本识别记忆和置信度决策应用于复杂的目击者识别任务,使用现代,复杂的建模方法,如层次贝叶斯方法。这项工作将促进对支持目击者识别决策的过程的基本理解,从而允许识别可以增强目击者记忆的因素。随着理论大楼的建设,将从年轻人和老年人中收集数据,通过各种方法完成目击者识别任务。在实证调查中,研究人员将在理论构建中使用他们从这些新数据中学到的东西,以更好地理解和预测这些人群在做出改变生活的目击者识别决定方面的差异。要研究的具体现象包括年轻人和老年人在准确性、反应偏差(从一组人中选择任何人的意愿)和在许多不同队列条件下的置信度-准确性校准方面的差异(如果他们确实存在差异)。研究人员旨在通过确定:(1)同时排列是否比展示和顺序排列对老年人产生更好的表现,(2)老年人是否表现出强烈的信心-准确性关系,以及(3)老年人是否表现出自己的种族偏见来填补文献中目击者识别的空白。这些现象已经在年轻人中进行了常规研究,但没有证实它们适用于老年人。此外,研究人员将运用他们在拟议研究的理论构建部分所学到的知识来解释产生这些实证调查中观察到的结果的机制。最后要考虑的是个体差异对年轻人和老年人目击者记忆的影响。例如,研究人员将对低功能和高功能老年人在目击者识别任务中的表现有何不同感兴趣。其他变量,包括识字,年龄,社会经济地位,工作记忆容量和注意力控制,将使用尖端的分层建模技术进行检查和分析。最终,拟议的研究将通过提供新的数据来扩展老年人如何作为目击者的小知识领域,更重要的是,确定改进老年人如何进行目击者阵容识别的方法。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。
英文摘要
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 Social Sciences 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. David Kellen at Syracuse University, this postdoctoral fellowship award supports an early career scientist investigating eyewitness identification across the lifespan, including the understudied and growing population of older adults. In particular, little research in eyewitness identification has been guided by the sophisticated modeling often employed in the basic recognition memory literature. Furthermore, the unique, and growing population of aging adults is not as well studied within this domain as younger adults, leaving an unfortunate gap in the literature. The proposed research seeks to fill these gaps by utilizing sophisticated modeling approaches to understand eyewitness identification research across the lifespan. This research aims to promote the goals of the NSF by advancing basic research in recognition and eyewitness memory and serving the public welfare through investigation of better methods and practices for law enforcement officials administering lineup identifications. The proposed research has two main tracks: Theory Building and Empirical Investigation. The Theory Building portion of the proposal applies decades of knowledge modeling basic recognition memory and confidence decisions to the complex eyewitness identification task, using modern, sophisticated approaches to modeling such as hierarchical Bayesian methods. This work will advance fundamental understanding of the processes that underpin eyewitness identification decisions, and thereby allow for the identification of factors that can enhance eyewitness memory. As the Theory Building is underway, data will be collected from younger and aging adults completing eyewitness identification tasks under a variety of methods. In the Empirical Investigation, researchers will use what they learn from these new data in Theory Building to better understand and predict how these populations differ in making life-altering eyewitness identification decisions. Specific phenomena to be investigated include how younger and older adults differ (if indeed they do) in terms of accuracy, response bias (willingness to choose anyone from a lineup), and confidence-accuracy calibration under a number of different lineup conditions. The researchers aim to fill gaps in the literature in eyewitness identification by determining: (1) whether simultaneous lineups produce better performance than showups and sequential lineups for older adults, (2) whether older adults display a strong confidence-accuracy relationship, and (3) whether older adults exhibit an own-race bias. These phenomena have been routinely studied in younger adults, but with no verification that they generalize to an older adult population. Furthermore, the researchers will apply what they learn in the Theory Building portion of the proposed research to explain the mechanisms that give rise to the findings observed in these Empirical Investigations. One final consideration will be the impact of individual differences on eyewitness memory in younger and older adults. For example, the researchers will be interested in how low- and high-functioning older adults differ in how they perform in eyewitness identification tasks. Other variables, including literacy, age, socioeconomic status, working memory capacity, and attentional control, will be examined and analyzed using cutting-edge hierarchical modeling techniques. Ultimately, the proposed research will extend the small sphere of knowledge regarding how older adults function as eyewitnesses by providing new data and, more importantly, identifying the ways to improve how older adults make eyewitness lineup identifications.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.
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国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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项目类别:合作创新研究团队
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资助金额:--
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批准年份:2024
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负责人:姚韬
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依托单位:
新型手性NAD(P)H Models合成及生化模拟
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批准号:20472090
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项目类别:面上项目
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资助金额:23.0万元
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批准年份:2004
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负责人:王乃兴
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依托单位: