  In human vision objects and their parts can be visually recognized from
purely spatial or purely temporal information but the mechanisms integrating
space and time are poorly understood. Here we show that human visual
recognition of objects and actions can be achieved by efficiently combining
spatial and motion cues in configurations where each source on its own is
insufficient for recognition. This analysis is obtained by identifying minimal
videos: these are short and tiny video clips in which objects, parts, and
actions can be reliably recognized, but any reduction in either space or time
makes them unrecognizable. State-of-the-art deep networks for dynamic visual
recognition cannot replicate human behavior in these configurations. This gap
between humans and machines points to critical mechanisms in human dynamic
vision that are lacking in current models.
