Irruption-Absorption Theory (IAT) proposes that the mind influences brain dynamics during decision-making through two processes: irruption, where mental activity introduces noise or fluctuations that alter brain activity, and absorption, which stabilizes or fixes chosen content. The Haken-Kelso-Bunz (HKB) model, which simulates phase transitions in the brain—such as shifts from randomness to stability—can incorporate these concepts after refinement (IAT*). This framework helps explain experimental results requiring decision-making, suggesting that the mind's selective focus may guide brain dynamics in a way that aligns with physical causality without disrupting it.
Integrated information theory (IIT) proposes a quantitative measure, Φ, to estimate whether a physical system is conscious, its degree of consciousness, and the complexity of its experienced qualia. The theory models a physical system as a probabilistic causal graph of interconnected elements with input-output functions. This paper presents a random search algorithm that optimizes Φ to investigate how graph structure changes with increasing numbers of nodes to achieve higher Φ. The authors also discuss why more complex black-box search methods like Bayesian optimization or metaheuristics face difficulties for this problem and suggest future research directions to improve the search for maximal Φ.