Visualising mental representations: a primer on noise-based reverse correlation in social psychology

Abstract

In recent years, social psychologists have adopted the psychophysical method of reverse correlation. Reverse correlation is a data-driven method in which judgments of randomly varying stimuli are used to reconstruct participants' internal representation subtending those judgments. Here, we review the method and its findings within the context of social psychology, discussing its promises, achievements, and validity. Our review suggests that, even in these early stages, the technique has proven to be an invaluable tool in the investigation of social perception, such as perception of race, gender, personality traits, and internal states such as emotions. Integrating the way the technique has been used in social psychology, we suggest that reverse correlation primarily enables researchers to do two things: visualise what visual features are diagnostic for particular social judgments and reveal top down biases on social perception. Given these functions, we argue that reverse correlation can be best understood as visualising priors for social perception within a predictive coding framework.