Important feature identification for perceptual sex of point-light walkers using supervised machine learning

The present study aimed to elucidate the dynamic features that are highly predictive in the biological and perceptual sex classification of point-light walkers (PLWs) and how these features behave in sex classification using supervised machine learning. Fifteen observers judged the sex of 21 PLWs from a side view. A fast Fourier transform was applied to retrieve the spectral components from the multiphasic hip and shoulder movements. An exhaustive search identified the most important features for biological and perceptual sex classifications. An individual conditional expectation (ICE) with a support vector machine (SVM) model was used to interpret the behavior of each important feature. The observers judged the biological sex from side-view PLWs with an accuracy of 62.9% for 10 male PLWs and of 57.0% for 11 female PLWs. The SVM model for biological sex prediction demonstrated that the third harmonic of hip motion played a dominant role in achieving a high predictive accuracy of 90.5% with few feature interactions. In the model of perceptual sex prediction, however, an accurate prediction of 85.7% was achieved using five spectral components of hip and shoulder motions, where the ICE plots of the features followed heterogeneous courses, suggesting feature interactions. The machine learning model suggests that biological sex classification depends mainly on local cues of the PLW. However, the high-performance model of perceptual sex classification involves interactions of various frequency components of hip and shoulder motions, suggesting more complex processes in sex perception.