COLLABORATIVE RESEARCH: Jurors' Use of Scientific Information
COLLABORATIVE RESEARCH: Jurors' Use of Scientific Information
批准号:
1733957
负责人:
Sarah Gervais
金额:
$14.11万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2020-08-31
中文摘要
陪审团在民事和刑事案件中做出决定时,经常依赖和解释基于科学研究的证据。科学信息的有效性和可靠性各不相同,但陪审员通常是非专家,他们在做出决定时不具备区分弱科学信息和强科学信息的必要工具。为了评估法庭上科学证据的解释问题,本项目采用实验方法来考察陪审员解释科学证据和根据科学证据采取行动的能力。为此,该项目包括陪审团模拟,将检查陪审员和陪审团的敏感性强与弱的科学信息在法庭上提出。该项目包括两个陪审团模拟实验,旨在测试是否模糊痕迹理论,在认知科学中的一个发达的理论,适用于陪审团的研究背景下,特别是当相比,其他保障陪审团的决策与科学证据。这种多学科,多方法的研究将检查何时以及如何适当地校准陪审员的推理,以科学信息的强度,是否从决策理论中获得的保障可以改善这种校准,以及与科学信息处理相关的各种措施如何相互关联。该项目解决了人类如何根据相关科学数据的质量进行推理并做出推断和决策的基本问题。
英文摘要
Juries often rely on and interpret evidence based on scientific research when making decisions in civil and criminal cases. The validity and reliability of scientific information varies, yet jurors generally are non-experts who do not possess the necessary tools to differentiate between weak and strong scientific information when making decisions. To assess the issue of interpretation of scientific evidence in court, this project uses an experimental approach to examine jurors' ability to interpret and act on scientific evidence. To do so, this project consists of jury simulations that will examine jurors' and juries' sensitivity to strong versus weak scientific information presented in court. The project includes two jury simulation experiments designed to test whether fuzzy trace theory, a well-developed theory in cognitive science, applies in the context of jury research, particularly when compared to other safeguards for jury decision making with respect to scientific evidence. This multidisciplinary, multi-method research will examine when and how jurors' inferences are appropriately calibrated to the strength of scientific information, whether a safeguard derived from decision-making theory can improve that calibration, and how various measures relevant to the processing of scientific information are related to one another. The project addresses fundamental questions about how humans reason with and make inferences and decisions based on the quality of relevant scientific data.
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