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Humans can effortlessly recognize others' actions in the presence of complextransformations, such as changes in viewpoint. Several studies have located theregions in the brain involved in invariant action recognition, however, theunderlying neural computations remain poorly understood. We usemagnetoencephalography (MEG) decoding and a dataset of well-controlled,naturalistic videos of five actions (run, walk, jump, eat, drink) performed bydifferent actors at different viewpoints to study the computational steps usedto recognize actions across complex transformations. In particular, we ask whenthe brain discounts changes in 3D viewpoint relative to when it initiallydiscriminates between actions. We measure the latency difference betweeninvariant and non-invariant action decoding when subjects view full videos aswell as form-depleted and motion-depleted stimuli. Our results show nodifference in decoding latency or temporal profile between invariant andnon-invariant action recognition in full videos. However, when either form ormotion information is removed from the stimulus set, we observe a decrease anddelay in invariant action decoding. Our results suggest that the brainrecognizes actions and builds invariance to complex transformations at the sametime, and that both form and motion information are crucial for fast, invariantaction recognition.

Fast, invariant representation for human action in the visual system