Current AI systems excel at pattern recognition but fail at causal reasoning. We argue this is not an engineering limitation but reveals something fundamental about the nature of understanding itself. We propose that causal cognition requires a specific physical architecture: stochastic, coupled oscillators with whole-system coordination. To test this, we analyzed high-density EEG (64 channels, 10 subjects, 500 plus trials) from a P300 target recognition task. We computed the Kuramoto Order Parameter (R) to measure global phase synchronization and compared it to standard voltage (ERP) and coherence (ITC) metrics. Four findings establish the framework. Phase and voltage are globally independent (r of 0.048) yet strongly trial-coupled (r of 0.590), proving R captures hidden cognitive structure. Voltage precedes phase by 293 ms, revealing sequential computation. Frequency decomposition shows Theta (169 ms), Alpha (286 ms), and Beta (777 ms) cascade. Our metric is distinct from standard ITC (r of 0.155). Then we tested the standard assumption. Conventional artifact rejection removes eye movements, muscle activity, and autonomic signals before analysis. The standard model assumes cognition is neural activity plus noise. We ran the identical analysis with and without rejection. Removing artifacts reduced the trial-level correlation threefold (from 0.590 to 0.195). Target discrimination reversed sign (from positive 0.6 percent to negative 0.4 percent). What we discard as noise is 70 percent of the signal. This falsifies the standard model. Cognition is not isolated neural computation. It is whole-body phase synchronization spanning neural, muscular, and autonomic systems. For AI, the implications are direct: embodied sensorimotor integration is not optional. It is the substrate that makes understanding possible.