A thermoinformational framework for the description of neuropsychological systems

This work presents a statistical thermodynamics-inspired framework that summarizes multichannel EEG and behavior using macroscopic state variables (entropy, internal energy, temperature, Helmholtz free energy) to quantify stability and reconfiguration in neuropsychological systems. Applied to mother-infant EEG dyads performing the A-not-B task, these variables dissociate neural reconfiguration from behavioral success across a large set of model and feature configurations. Informational heat increases during environmental switches and decision errors, consistent with increased information exchange with the task context. In contrast, correct choices are preceded by lower temperature and higher free energy in the window, and are followed by free-energy declines as the system re-stabilizes. In an independent optogenetic dam-pup paradigm, the same variables separate stimulation conditions and trace coherent trajectories in thermodynamic state space. Together, these findings show that the thermoinformational framework yields compact, physically grounded descriptors that hold in both human and mouse datasets studied here.