Natural body movements arise in form of temporal sequences of individual actions. In order to realize a visual analysis of these actions, the visual system must accomplish a temporal segmentation of such action sequences. Previous work has studied in detail the segmentation of sequences of piecewise linear movements in the two-dimensional plane. In our study, we tried to compare statistical approaches for segmentation of human full-body movement with human responses. Video sequences were generated by synthesized sequences of natural actions based on motion capture, using appropriate methods for motion blending. Human segmentation was assessed by an interactive adjustment paradigm, where participants had to indicate segmentation points by selection of the relevant frames. We compared this psychophysical data against different segmentation algorithms, which were based on the available 3D joint trajectories that were used for the synthesis of the motion stimuli. Simple segmentation methods, e.g. based on discontinuities in path direction or speed, were compared with an optimal Bayesian action segmentation approach from machine learning. This method is based on a generative probabilistic model. Transitions between classes (types of actions) were modelled by resetting the feature priors at the change points. Change point configurations were modelled by Bayesian binning. Applying optimization within a Bayesian framework, number and the length of individual action segments were determined automatically. Performance of this algorithmic approach was compared with human performance.