Quantum-like modeling (QLM) in cognitive science and decision theory operates on macroscopic neural structures and their information processing, unlike reductionist quantum brain models. There is a gap between the oscillatory dynamics of neural networks and quantum-like behavioral patterns. To bridge it, generalized probability theory and a pre-quantum classical statistical field theory (PCSFT) are used, enabling a transition from classical 'oscillatory cognition' to a QLM description of decision-making. The problem of mental entanglement—the generation of quantum-like entangled states by classical networks—is solved. An operator-algebraic approach is employed, based on algebras of observables and establishing a tensor structure for the state space, along with a standard method for generating entangled states in spatially separated neural networks. Prospects for experimental detection of mental entanglement using EEG/MEG are discussed.
The brain generates quantum-like states without a single quantum particle. As electrical waves of nerve cells synchronize, they give rise to a mathematical analog of entanglement. Like an orchestra where cellos and flutes, playing separately, suddenly start carrying a single melody, distant brain regions link into a unified network—a veritable galaxy of thought.
This model can be tested: brain spectroscopy (EEG) already picks up traces of quantum-like states. This moves theories of consciousness from philosophical debate into experimental science.
Entropy—a measure of uncertainty—bridges physics and psychology, explaining how sudden insights are born.
🎯 EEG records brain rhythms reminiscent of the twinkling of galaxies. Astrophysicists have long used spectroscopy; now neuroscientists will arm themselves with it to see the quantum effects of thought.