The Resonance Principle: Empirical Evidence for Emergent Phase Synchronization in Human Causal Reasoning (2511.10596v1)

Abstract: Current artificial intelligence systems excel at correlational pattern matching but fail to achieve genuine causal understanding, a limitation often described as the "Kepler versus Newton" problem. We argue that this limitation is inherent to deterministic digital architectures. We introduce the Resonance Principle, a theoretical framework proposing that causal understanding emerges only in stochastic, bounded agents with intrinsic cost functions. The agent's substrate is modeled as a network of weakly coupled oscillators, where action proposals arise as stable resonant modes excited by intrinsic noise. We hypothesize that the brain, a stochastic and resonant system, operates according to this principle. To test this, we analyzed high-density EEG data (25 recordings, 500 trials) from a P300 BCI task. We computed the Kuramoto Order Parameter (R) to measure global phase synchronization (resonance) and compared it to the Event-Related Potential (ERP) voltage. Global resonance and voltage were statistically uncorrelated (r = 0.048), yet trial-level analysis revealed a strong correlation (r = 0.590, p < 0.0001). This suggests that resonance is a hidden mechanism coordinating neural firing, giving rise to measurable ERPs. We conclude that phase synchronization is not a byproduct but a fundamental signature of emergent causal understanding.