방법 비교

	Mere Exposure Paradigm	Reverse Correlation Task
분야	사회심리학	사회심리학
계열	Process / pipeline	Process / pipeline
기원 연도	1968	2012
창시자	Robert Zajonc	Ron Dotsch & Alexander Todorov (social-perception application)
유형	Experimental paradigm for attitude formation	Data-driven mental-representation method
원전	Zajonc, R. B. (1968). Attitudinal effects of mere exposure. Journal of Personality and Social Psychology, 9(2, Pt.2), 1-27.	Dotsch, R., & Todorov, A. (2012). Reverse correlating social face perception. Social Psychological and Personality Science, 3(5), 562-571.
별칭	Mere Exposure Effect, Familiarity-Liking Paradigm, Exposure-Attitude Paradigm	Reverse Correlation Image Classification, Classification Image Technique, Noise-Based Reverse Correlation
관련	3	3
요약	The mere exposure paradigm, established by Robert Zajonc in 1968, shows that simply being repeatedly exposed to a stimulus, with no reinforcement or even conscious recognition, increases liking for it. In the canonical procedure, participants are exposed to novel stimuli -- unfamiliar ideographs, foreign words, faces, or melodies -- different numbers of times, and then rate how much they like each one; liking rises as exposure frequency rises, typically following a positive, decelerating curve. The effect occurs even when stimuli are presented subliminally and participants cannot recognize them, indicating that familiarity breeds liking through an affective rather than cognitive route. Zajonc's demonstration that 'mere' repeated exposure suffices to shape attitudes became foundational for theories of preference formation, the affect-cognition relationship, and applications from advertising to interpersonal attraction.	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.