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延迟会降低对犯罪嫌疑人在场的检测,但不影响对列队辨认的基于猜测的选择。

Delays reduce culprit-presence detection but do not affect guessing-based selection in response to lineups.

作者信息

Therre Amelie, Bell Raoul, Menne Nicola Marie, Mayer Carolin, Lichtenhagen Ulla, Buchner Axel

机构信息

Department of Experimental Psychology, Heinrich Heine University, Universitätsstraße 1, 40225, Düsseldorf, Germany.

出版信息

Sci Rep. 2025 Aug 4;15(1):28382. doi: 10.1038/s41598-025-13937-w.

DOI:10.1038/s41598-025-13937-w
PMID:40759723
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12322189/
Abstract

Police lineups are conducted with varying delays between the crime and the lineup. Crime-to-lineup delays may adversely affect the detection of the presence and absence of the culprit in the lineup and may potentially affect guessing-based selection. In the present study we examined how these processes change across four crime-to-lineup delays. Participants viewed a staged-crime video and then completed simultaneous photo lineups after no delay or after a delay of one day, one week or one month. The results showed a significant decline in the probability of culprit-presence detection. The form of the decline is best described by a power function with the most rapid decline occurring at short crime-to-lineup delays. Eyewitnesses did not compensate the decline in culprit-presence detection by increasing guessing-based selection, as demonstrated by the fact that the probability of guessing-based selection remained constant across crime-to-lineup delays. The findings underscore the critical importance of conducting lineups as soon as possible after a crime to maximize the probability of memory-based-culprit detection.

摘要

警方列队辨认在犯罪发生与列队辨认之间存在不同的时间间隔。从犯罪到列队辨认的延迟可能会对在列队中识别罪犯是否在场产生不利影响,并可能潜在地影响基于猜测的选择。在本研究中,我们考察了这些过程在四种从犯罪到列队辨认的延迟情况下是如何变化的。参与者观看了一段模拟犯罪视频,然后在无延迟、延迟一天、一周或一个月后完成同步照片列队辨认。结果显示,识别出罪犯在场的概率显著下降。这种下降形式最好用幂函数来描述,在犯罪到列队辨认的短延迟情况下下降最为迅速。正如基于猜测的选择概率在不同的犯罪到列队辨认延迟中保持不变这一事实所表明的那样,目击者并没有通过增加基于猜测的选择来弥补识别罪犯在场概率的下降。这些发现强调了在犯罪后尽快进行列队辨认对于最大限度地提高基于记忆识别罪犯的概率至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea2a/12322189/658503812567/41598_2025_13937_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea2a/12322189/79c392b88903/41598_2025_13937_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea2a/12322189/658503812567/41598_2025_13937_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea2a/12322189/79c392b88903/41598_2025_13937_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea2a/12322189/658503812567/41598_2025_13937_Fig2_HTML.jpg

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本文引用的文献

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Eyewitness Lineup Identity (ELI) database: Crime videos and mugshots for eyewitness identification research.目击者列队辨认身份(ELI)数据库:用于目击者辨认研究的犯罪视频和面部照片。
Behav Res Methods. 2025 Jan 21;57(2):63. doi: 10.3758/s13428-024-02585-z.
2
Lineup position affects guessing-based selection but not culprit-presence detection in simultaneous and sequential lineups.排列位置会影响基于猜测的选择,但不会影响同时和连续列队中的嫌疑存在检测。
Sci Rep. 2024 Nov 12;14(1):27642. doi: 10.1038/s41598-024-78936-9.
3
On the possible advantages of combining small lineups with instructions that discourage guessing-based selection.
关于将小型阵容与不鼓励基于猜测的选择的指示相结合的可能优势。
Sci Rep. 2024 Jun 19;14(1):14126. doi: 10.1038/s41598-024-64768-0.
4
On the advantages of using AI-generated images of filler faces for creating fair lineups.利用 AI 生成的填充物人脸图像创建公平的列队的优势。
Sci Rep. 2024 May 29;14(1):12304. doi: 10.1038/s41598-024-63004-z.
5
The effects of lineup size on the processes underlying eyewitness decisions. lineup 大小对目击者决策背后过程的影响。
Sci Rep. 2023 Oct 11;13(1):17190. doi: 10.1038/s41598-023-44003-y.
6
How to develop, test, and extend multinomial processing tree models: A tutorial.如何开发、测试和扩展多项加工树模型:教程
Psychol Methods. 2023 Jul 27. doi: 10.1037/met0000561.
7
Evaluating the impact of first-yes-counts instructions on eyewitness performance using the two-high threshold eyewitness identification model.运用双高阈限目击者识别模型评估首肯计数指令对目击者表现的影响。
Sci Rep. 2023 Apr 21;13(1):6572. doi: 10.1038/s41598-023-33424-4.
8
Measuring lineup fairness from eyewitness identification data using a multinomial processing tree model.使用多项处理树模型从目击者识别数据衡量阵容公平性。
Sci Rep. 2023 Apr 18;13(1):6290. doi: 10.1038/s41598-023-33101-6.
9
Experimental validation of a multinomial processing tree model for analyzing eyewitness identification decisions.多分类处理树模型分析目击证人辨认决策的实验验证。
Sci Rep. 2022 Sep 16;12(1):15571. doi: 10.1038/s41598-022-19513-w.
10
A validation of the two-high threshold eyewitness identification model by reanalyzing published data.重新分析已发表的数据验证双高阈限目击者辨认模型。
Sci Rep. 2022 Aug 4;12(1):13379. doi: 10.1038/s41598-022-17400-y.