Facial-Attractiveness Choices Are Predicted by Divisive Normalization. (October 2016) - Furl, Nicholas
Do people appear more attractive or less attractive depending on the company they keep? A divisive-normalization account—in which representation of stimulus intensity is normalized (divided) by concurrent stimulus intensities—predicts that choice preferences among options increase with the range of option values. In the first experiment reported here, I manipulated the range of attractiveness of the faces presented on each trial by varying the attractiveness of an undesirable distractor face that was presented simultaneously with two attractive targets, and participants were asked to choose the most attractive face. I used normalization models to predict the context dependence of preferences regarding facial attractiveness. The more unattractive the distractor, the more one of the targets was preferred over the other target, which suggests that divisive normalization (a potential canonical computation in the brain) influences social evaluations. I obtained the same result when I manipulated faces' averageness and participants chose the most average face. This finding suggests that divisive normalization is not restricted to value-based decisions (e.g., attractiveness). This new application to social evaluation of normalization, a classic theory, opens possibilities for predicting social decisions in naturalistic contexts such as advertising or dating.