Spatio temporal convolutional neural networks explain human neural representations of action recognition
Recognizing the actions of others from complex visual scenes is an essential task for humans. Here we investigate the computational mechanisms that support action recognition in the human visual system. We use a novel dataset of well controlled naturalistic videos of five actions performed by five actors at five viewpoint and extend a class of biologically inspired hierarchical computational models of object recognition to recognize actions from videos. We explore a number of variations within the class of convolutional neural networks and assess classification accuracy on a viewpoint invariant action recognition task as well as a correlation measure with magnetoencephalography (MEG) human brain recordings acquired using the same stimuli. We show that feed forward spatio temporal convolutional neural networks solve the task of invariant action recognition and account for the majority of the explainable variance in the neural data. Furthermore, we show that model features that improve performance on viewpoint invariant action recognition lead to a model representation that better matches the representation encoded by neural data. These results advance the idea that robustness to complex transformations, such as 3D viewpoint invariance, is a specific goal of visual processing in the human brain.