Порівняння методів

	Facial EMG	Reverse Correlation Task
Галузь	Соціальна психологія	Соціальна психологія
Родина	Process / pipeline	Process / pipeline
Рік появи	1986	2012
Автор методу	John Cacioppo, Richard Petty and colleagues	Ron Dotsch & Alexander Todorov (social-perception application)
Тип	Psychophysiological affect-measurement method	Data-driven mental-representation method
Основоположне джерело	Cacioppo, J. T., Petty, R. E., Losch, M. E., & Kim, H. S. (1986). Electromyographic activity over facial muscle regions can differentiate the valence and intensity of affective reactions. Journal of Personality and Social Psychology, 50(2), 260-268.	Dotsch, R., & Todorov, A. (2012). Reverse correlating social face perception. Social Psychological and Personality Science, 3(5), 562-571.
Інші назви	Facial Electromyography, EMG Affect Measurement, Corrugator-Zygomaticus EMG	Reverse Correlation Image Classification, Classification Image Technique, Noise-Based Reverse Correlation
Пов'язані	3	3
Підсумок	Facial electromyography (EMG) measures affect by recording the tiny electrical signals produced by facial muscles, providing an objective, continuous index of emotional valence and intensity that can detect reactions too subtle or fleeting to produce a visible expression. Cacioppo, Petty, Losch, and Kim showed in 1986 that activity over two muscle regions differentiates affect: the corrugator supercilii (the brow muscle that furrows in frowning) increases with negative affect, while the zygomaticus major (the cheek muscle that pulls in smiling) increases with positive affect, and amplitudes scale with the intensity of the reaction. Because surface electrodes capture muscle activity even when no overt expression occurs, facial EMG offers a sensitive, hard-to-fake measure of evaluative responses widely used in research on attitudes, emotion, persuasion, and social perception, often paired with reaction-time and self-report measures.	The reverse correlation task is a data-driven method for visualizing the mental representations people hold of social categories and traits, such as what a trustworthy, dominant, or criminal face looks like in the mind's eye. Adapted to social perception by Dotsch and Todorov in 2012, the technique superimposes random visual noise on a base face to create many slightly different images, and asks participants to repeatedly choose, from pairs, the image that best fits a target trait. By averaging the noise patterns from the chosen images, the researcher produces a classification image -- a picture that reveals the visual features the participant's mind associates with the trait, without the experimenter ever specifying those features in advance. Independent raters then judge the classification image to confirm it conveys the intended trait. The method made it possible to render otherwise hidden mental representations and biases as concrete, testable images.