Overlapping Cortical Substrate of Biomechanical Control and Subjective Agency

Every movement requires the nervous system to solve a complex biomechanical control problem, but this process is mostly veiled from one's conscious awareness. Simultaneously, we also have conscious experience of controlling our movements-our sense of agency (SoA). Whether SoA corresponds to those neural representations that implement actual neuromuscular control is an open question with ethical, medical, and legal implications. If SoA is the conscious experience of control, this predicts that SoA can be decoded from the same brain structures that implement the so-called "inverse dynamics" computations for planning movement. We correlated human (male and female) fMRI measurements during hand movements with the internal representations of a deep neural network (DNN) performing the same hand control task in a biomechanical simulation-revealing detailed cortical encodings of sensorimotor states, idiosyncratic to each subject. We then manipulated SoA by usurping control of participants' muscles via electrical stimulation, and found that the same voxels which were best explained by modeled inverse dynamics representations-which, strikingly, were located in canonically visual areas-also predicted SoA. Importantly, model-brain correspondences and robust SoA decoding could both be achieved within single subjects, enabling relationships between motor representations and awareness to be studied at the level of the individual.<b>Significance Statement</b> The inherent complexity of biomechanical control problems is belied by the seeming simplicity of directing movements in our subjective experience. This aspect of our experience suggests we have limited conscious access to the neural and mental representations involved in controlling the body - but of which of the many possible representations are we, in fact, aware? Understanding which motor control representations percolate into awareness has taken on increasing importance as emerging neural interface technologies push the boundaries of human autonomy. In our study, we leverage machine learning models that have learned to control simulated bodies to localize biomechanical control representations in the brain. Then, we show that these brain regions predict perceived agency over the musculature during functional electrical stimulation.