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Humans can recognize their whole-body movements even when displayed as dynamic dot patterns. The sparse depiction of whole-body movements, coupled with a lack of visual experience watching ourselves in the world, has long implicated nonvisual mechanisms to self-action recognition. Using general linear modeling and multivariate analyses on human brain imaging data from male and female participants, we aimed to identify the neural systems for this ability. First, we found that cortical areas linked to motor processes, including frontoparietal and primary somatomotor cortices, exhibit greater engagement and functional connectivity when recognizing self-generated versus other-generated actions. Next, we show that these regions encode self-identity based on motor familiarity, even after regressing out idiosyncratic visual cues using multiple regression representational similarity analysis. Last, we found the reverse pattern for unfamiliar individuals: encoding localized to occipitotemporal visual regions. These findings suggest that self-awareness from actions emerges from the interplay of motor and visual processes. 

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, we empirically identify a mathematically specific critical point near which waking cortical oscillatory dynamics operate, which is known as the edge-of-chaos critical point, or the boundary between stability and chaos. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magnetoencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of low-frequency cortical electric oscillations away from this critical point; conversely, we show that psychedelics may increase the information richness of cortical activity by tuning low-frequency cortical oscillations closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC) and show that assessing the proximity of slow cortical oscillatory electrodynamics to the edge-of-chaos critical point may be useful as an index of consciousness in the clinical setting.