방법 비교

	Mouse-Tracking Paradigm	Reverse Correlation Task
분야	사회심리학	사회심리학
계열	Process / pipeline	Process / pipeline
기원 연도	2010	2012
창시자	Jonathan Freeman & Nalini Ambady	Ron Dotsch & Alexander Todorov (social-perception application)
유형	Process-tracing reaction-method	Data-driven mental-representation method
원전	Freeman, J. B., & Ambady, N. (2010). MouseTracker: Software for studying real-time mental processing using a computer mouse-tracking method. Behavior Research Methods, 42(1), 226-241.	Dotsch, R., & Todorov, A. (2012). Reverse correlating social face perception. Social Psychological and Personality Science, 3(5), 562-571.
별칭	Mouse Tracking, Hand-Trajectory Tracking, MouseTracker Paradigm	Reverse Correlation Image Classification, Classification Image Technique, Noise-Based Reverse Correlation
관련	3	3
요약	The mouse-tracking paradigm, popularized by Freeman and Ambady's 2010 MouseTracker software, uses the continuous trajectory of hand movements during a choice to reveal the real-time dynamics of cognition. Participants begin each trial with the cursor at the bottom of the screen and move it to one of two response options in the upper corners; the software records the streaming x- and y-coordinates of the cursor throughout the movement. Because the hand can begin moving before a decision is fully resolved, the curvature of the trajectory toward the unchosen option indexes the degree to which that alternative was simultaneously activated -- a graded, moment-by-moment signature of competition and conflict that a final button press cannot show. Mouse tracking became a popular, inexpensive process-tracing method in social cognition, used to study the dynamics of categorization, evaluation, stereotyping, and decision making as they unfold.	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.