Facial emotion recognition (FER) tasks are often digitally altered to vary expression intensity; however, such tasks have unknown psychometric properties. In these studies, an FER task was developed and validated—the Graded Emotional Face Task (GEFT)—which provided an opportunity to examine the psychometric properties of such tasks. Facial expressions were altered to produce five intensity levels for six emotions (e.g., 40% anger). In Study 1, 224 undergraduates viewed subsets of these faces and labeled the expressions. An item selection algorithm was used to maximize internal consistency and balance gender and ethnicity. In Study 2, 219 undergraduates completed the final GEFT and a multimethod battery of validity measures. Finally, in Study 3, 407 undergraduates oversampled for borderline personality disorder (BPD) completed the GEFT and a self-report BPD measure. Broad FER scales (e.g., overall anger) demonstrated evidence of reliability and validity; however, more specific subscales (e.g., 40% anger) had more variable psychometric properties. Notably, ceiling/floor effects appeared to decrease both internal consistency and limit external validity correlations. The findings are discussed from the perspective of measurement issues in the social cognition literature.

面部情绪识别 (FER) 任务通常通过数字方式进行更改以改变表情强度；然而，此类任务具有未知的心理测量特性。在这些研究中，开发并验证了 FER 任务——分级情绪面孔任务 (GEFT)——它提供了检查此类任务的心理测量特性的机会。面部表情被改变以针对六种情绪产生五个强度水平（例如，40% 的愤怒）。在研究 1 中，224 名本科生查看了这些面孔的子集并标记了表情。使用项目选择算法来最大化内部一致性并平衡性别和种族。在研究 2 中，219 名本科生完成了最终的 GEFT 和一系列有效性测量方法。最后，在研究 3 中，407 名因边缘性人格障碍 (BPD) 过度抽样的本科生完成了 GEFT 和自我报告的 BPD 测量。广泛的 FER 量表（例如，总体愤怒）证明了可靠性和有效性的证据；然而，更具体的分量表（例如，40% 的愤怒）具有更多可变的心理测量特性。值得注意的是，上限/下限效应似乎会降低内部一致性并限制外部有效性相关性。从社会认知文献中的测量问题的角度讨论了这些发现。