A Unified Neurocomputational Framework for Closed-Loop Motor Control and Sense of Agency

The process of motor control involves comparing motor commands with sensory feedback in a closed-loop system to correct errors and adapt to disturbances. However, understanding which specific sensory features to select and bind to the relevant motor commands remains unclear.

Sense of agency (SoA), the feeling of causing observed feedback, is closely linked to self-identification. Its impairment is associated with acceptance issues for prosthetic devices and neurological disorders like autism and schizophrenia. SoA also relies on comparing desired and observed outcomes in fronto-parietal brain circuits. Yet, these two phenomena have been studied separately, leaving SoA without a functional explanation and disregarding the subjective aspects in models of motor control.

The authors propose that SoA is the subjective experience of selecting self-caused sensory features for closed-loop control. To test this hypothesis, they conducted a visuomotor task where SoA was manipulated through temporal delays and adaptation to spatial perturbations, serving as a proxy for closed-loop integration. The results showed that delays similarly affected both SoA and closed-loop integration, suggesting these phenomena may emerge from the same underlying process.

To model their findings, the researchers used a Bayesian framework. In this model, the probability of self-causation is inferred from temporal congruence, and this probability jointly modulates the subjective experience of agency and the weight assigned to visual feedback during control.