Computational Feature Analysis of Body Movements Reveals Hierarchical Brain Organization
Research output: Contribution to journal › Article › Academic › peer-review
Social species spend considerable time observing the body movements of others to understand their actions, predict their emotions, watch their games, or enjoy their dance movements. Given the important information obtained from body movements, we still know surprisingly little about the details of brain mechanisms underlying movement perception. In this fMRI study, we investigated the relations between movement features obtained from automated computational analyses of video clips and the corresponding brain activity. Our results show that low-level computational features map to specific brain areas related to early visual- and motion-sensitive regions, while mid-level computational features are related to dynamic aspects of posture encoded in occipital-temporal cortex, posterior superior temporal sulcus and superior parietal lobe. Furthermore, behavioral features obtained from subjective ratings correlated with activity in higher action observation regions. Our computational feature-based analysis suggests that the neural mechanism of movement encoding is organized in the brain not so much by semantic categories than by feature statistics of the body movements.