School of Psychology, Georgia Institute of Technology, Atlanta, GA, United States of America.
School of Computer Science, Georgia Institute of Technology, Atlanta, GA, United States of America.
Cognition. 2024 Sep;250:105861. doi: 10.1016/j.cognition.2024.105861. Epub 2024 Jun 17.
Objectively quantifying subjective phenomena like visual illusions is challenging. We address this issue in the context of the Flashed Face Distortion Effect (FFDE), where faces presented in succession appear distorted and grotesque. We first show that the traditional method of quantifying FFDE - via subjective ratings of the level of distortion - is subject to substantial biases. Motivated by this finding, we develop an objective method for quantifying FFDE by introducing two design innovations. First, we create artificially distorted faces and ask subjects to discriminate between undistorted and objectively distorted faces. Second, we employ both an illusion condition, which includes a succession of 15 face flashes, and a control condition, which includes a single face flash and does not induce an illusion. Using these innovations, we quantify the strength of the face distortion illusion by comparing the response bias for identifying distorted faces between the illusion and control conditions. We find that our method successfully quantifies the face distortion, with subjects exhibiting a more liberal response bias in the illusion condition. Finally, we apply our new method to evaluate how the face distortion illusion is modulated by face eccentricity, face inversion, the temporal frequency of the face flashes, and presence of temporal gaps between consecutive faces. Our results demonstrate the utility of our objective method in quantifying the subjective illusion of face distortion. Critically, the method is general and can be applied to other phenomena that are inherently subjective.
客观地量化像视错觉这样的主观现象具有挑战性。我们在连续呈现的面孔会显得扭曲和怪诞的“闪现人脸失真效应(FFDE)”背景下解决了这个问题。我们首先表明,通过失真程度的主观评分来量化 FFDE 的传统方法存在很大的偏差。受此发现的启发,我们通过引入两个设计创新,开发了一种量化 FFDE 的客观方法。首先,我们创建了人为失真的面孔,并要求受试者在未失真和客观失真的面孔之间进行区分。其次,我们同时采用了错觉条件,包括 15 个面孔的连续闪现,以及控制条件,其中只有一个面孔闪现,不会产生错觉。通过这些创新,我们通过比较错觉和控制条件下识别失真面孔的反应偏差来量化面孔失真错觉的强度。我们发现,我们的方法成功地量化了面孔失真,受试者在错觉条件下表现出更宽松的反应偏差。最后,我们应用我们的新方法来评估面孔失真错觉如何受到面孔的偏心率、面孔的反转、面孔闪现的时间频率以及连续面孔之间的时间间隔的影响。我们的结果表明,我们的客观方法在量化面孔失真的主观错觉方面是有效的。关键是,该方法具有通用性,可以应用于其他固有主观性的现象。