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There are now dozens of very different theories of consciousness, each somehow contributing to our understanding of its nature. The science of consciousness needs therefore not new theories but a general framework integrating insights from those, yet not making it a still-born "Frankenstein" theory. First, the framework must operate explicitly on the stream of consciousness, not on its static description. Second, this dynamical account must also be put on the evolutionary timeline to explain the origins of consciousness. The Cognitive Evolution Theory (CET), outlined here, proposes such a framework. This starts with the assumption that brains have primarily evolved as volitional subsystems of organisms, inherited from primitive (fast and random) reflexes of simplest neural networks, only then resembling error-minimizing prediction machines. CET adopts the tools of critical dynamics to account for metastability, scale-free avalanches, and self-organization which are all intrinsic to brain dynamics. This formalizes the stream of consciousness as a discrete (transitive, irreflexive) chain of momentary states derived from critical brain dynamics at points of phase transitions and mapped then onto a state space as neural correlates of a particular conscious state. The continuous/discrete dichotomy appears naturally between the brain dynamics at the causal level and conscious states at the phenomenal level, each volitionally triggered from arousal centers of the brainstem and cognitively modulated by thalamocortical systems. Their objective observables can be entropy-based complexity measures, reflecting the transient level or quantity of consciousness at that moment., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yurchenko.)

Decision-making is described as a natural process, one among others, consuming free energy in the least time. The thermodynamic tenet explains why data associated with decisions display the same patterns as any other data: skewed distributions, sigmoidal cumulative curves, oscillations, and even chaos. Moreover, it is shown that decision-making is intrinsically an intractable process because everything depends on everything else. However, no decision is arbitrary but bounded by free energy, such as resources and propellants, and restricted by mechanisms like molecular, neural, and social networks. The least-time maximation of entropy, equivalent to the minimization of free energy, parallels the optimization of subjective expected utility. As the system attains a state of balance, all driving forces vanish. Then there is no need or use to make further decisions. In general, the thermodynamic theory regards those decisions well-motivated that take into account forces, i.e., causes comprehensively in projecting motions, i.e., consequences., Competing Interests: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Annila.)