We compared neural responses to naturalistic videos and representations in deep network models trained with static and dynamic information. Models trained with dynamic information showed greater correspondence with neural representations in all brain regions, including those previously associated with the processing of static information. Among the models trained with dynamic information, those based on optic flow accounted for unique variance in neural responses that were not captured by Masked Autoencoders. This effect was strongest in ventral and dorsal brain regions, indicating that despite the Masked Autoencoders' effectiveness at a variety of tasks, their representations diverge from representations in the human brain in the early stages of visual processing.