A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.
Neuroscience of consciousness January 1, 2026 Annie G Bryant, Christopher J Whyte
A family of functional connectivity measures based on tracking the 'center of mass' between two brain signals outperforms other measures at decoding conscious visual perception from magnetoencephalography data. These measures generalize across brain regions central to both Integrated Information Theory and Global Neuronal Workspace Theory. Neural mass models simulating each theory's hypothesized dynamics showed that both the GNWT-based model (featuring delayed ignition dynamics) and the IIT-based model (relying on synchronous sensory dynamics) captured the observed connectivity patterns. However, the presence of ignition dynamics independent of task-demand conditions contradicts IIT predictions, lending tentative support to GNWT. The work introduces a framework for systematically identifying and testing neural correlates of conscious vision.