A systematic approach to brain dynamics: cognitive evolution theory of consciousness.

Consciousness emerges from three hierarchical levels of neural activity: a causal (hard) level, a computational unconscious (soft) level, and a phenomenal conscious (psyche) level. The cognitive evolution theory (CET) proposes that brains evolved primarily as volitional subsystems, not prediction machines. Consciousness arises near critical points and unfolds as a discrete stream of momentary states, each volitionally driven by subcortical arousal systems. This stream enables the brain to make a difference through predictive (Bayesian) processing. Objective observables include complexity measures reflecting levels of consciousness and dynamical coherency, indicating knowledge gain. CET offers a quantitative classification for disorders of consciousness and mental disorders within this framework.

Study at a glance

Abstract

The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' level respectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.