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丹佛疼痛真实性刺激集(D-PASS)。

Denver pain authenticity stimulus set (D-PASS).

机构信息

Department of Psychology, University of Denver, 2155 South Race Street, Denver, CO, 80210, USA.

Psychiatry Residency Program, University of Colorado, Denver, CO, USA.

出版信息

Behav Res Methods. 2024 Apr;56(4):2992-3008. doi: 10.3758/s13428-023-02283-2. Epub 2023 Nov 22.

Abstract

We introduce the Denver Pain Authenticity Stimulus Set (D-PASS), a free resource containing 315 videos of 105 unique individuals expressing authentic and posed pain. All expressers were recorded displaying one authentic (105; pain was elicited via a pressure algometer) and two posed (210) expressions of pain (one posed expression recorded before [posed-unrehearsed] and one recorded after [posed-rehearsed] the authentic pain expression). In addition to authentic and posed pain videos, the database includes an accompanying codebook including metrics assessed at the expresser and video levels (e.g., Facial Action Coding System metrics for each video controlling for neutral images of the expresser), expressers' pain threshold and pain tolerance values, averaged pain detection performance by naïve perceivers who viewed the videos (e.g., accuracy, response bias), neutral images of each expresser, and face characteristic rating data for neutral images of each expresser (e.g., attractiveness, trustworthiness). The stimuli and accompanying codebook can be accessed for academic research purposes from https://digitalcommons.du.edu/lsdl_dpass/1/ . The relatively large number of stimuli allow for consideration of expresser-level variability in analyses and enable more advanced statistical approaches (e.g., signal detection analyses). Furthermore, the large number of Black (n = 41) and White (n = 56) expressers permits investigations into the role of race in pain expression, perception, and authenticity detection. Finally, the accompanying codebook may provide pilot data for novel investigations in the intergroup or pain sciences.

摘要

我们介绍了丹佛疼痛真实性刺激集(D-PASS),这是一个免费资源,包含 105 位个体表达真实和虚假疼痛的 315 个视频。所有表达者都是在展示真实疼痛(105 次;疼痛通过压力测痛仪引起)和两种虚假疼痛(210 次)时被记录下来的,其中一种虚假疼痛(105 次)是在真实疼痛表情之前记录的(未排练的虚假疼痛),另一种是在真实疼痛表情之后记录的(排练的虚假疼痛)。除了真实和虚假疼痛视频外,该数据库还包括一个配套代码本,其中包含在表达者和视频层面评估的指标(例如,控制表达者中性图像的每个视频的面部动作编码系统指标)、表达者的疼痛阈值和疼痛耐受值、观看视频的天真观察者的平均疼痛检测性能(例如,准确性、反应偏差)、每个表达者的中性图像,以及每个表达者中性图像的面部特征评分数据(例如,吸引力、可信度)。刺激物和配套代码本可通过 https://digitalcommons.du.edu/lsdl_dpass/1/ 访问,用于学术研究目的。相对较多的刺激物可以考虑在分析中表达者层面的变异性,并支持更高级的统计方法(例如,信号检测分析)。此外,大量的黑人(n=41)和白人(n=56)表达者允许研究种族在疼痛表达、感知和真实性检测中的作用。最后,配套代码本可为跨群体或疼痛科学领域的新研究提供初步数据。

相似文献

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Denver pain authenticity stimulus set (D-PASS).丹佛疼痛真实性刺激集(D-PASS)。
Behav Res Methods. 2024 Apr;56(4):2992-3008. doi: 10.3758/s13428-023-02283-2. Epub 2023 Nov 22.
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本文引用的文献

2
Automatic Detection of Pain from Facial Expressions: A Survey.自动检测面部表情的疼痛:综述。
IEEE Trans Pattern Anal Mach Intell. 2021 Jun;43(6):1815-1831. doi: 10.1109/TPAMI.2019.2958341. Epub 2021 May 11.
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Black and White Lies: Race-Based Biases in Deception Judgments.黑白谎言:欺骗判断中的基于种族的偏见。
Psychol Sci. 2017 Aug;28(8):1125-1136. doi: 10.1177/0956797617705399. Epub 2017 Jun 16.

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