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

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.

Keywords: Bayesian brain; Brain dynamics; Complexity; Consciousness; Criticality; Mental disorders; Metastability.

Fig. 1

The origin of consciousness. CET starts from the assumption that the brain should have primarily evolved as volitional subsystems of organisms from simplest neural reflexes. At a causal (hard) level, those should put a principled psyche-matter divide between organisms, exploiting their stimulus-reactions repertoires freely, and non-living systems, governed completely by cause-effects interactions. On the evolutionary scale, memory and cognition should evolve together, thereby advancing each other. Their volition-driven unconscious cooperation would generate momentary conscious states over the stream at a phenomenal (psyche) level. Likewise, emotions can hardly be dissociated from self-awareness; their (limbic) neural substrates should evolve in parallel with conscious cortex-centered substrates, and motivate cognition by emotional decision-making in the functional integrity of the brain

Fig. 2

The stream of consciousness. a In brain dynamics, every conscious state evolves from the previous one as a schematic bunch of all possible metastable states $x_i^j$, processed within a current SRR in a state-space and collapsed after ∆t to a certain conscious state balanced at criticality. Placing the NFVM into the brainstem responsible for arousal and vigilance guarantees that each conscious state will initially be free from predetermination. b The stream S(τ) is shown as a broken (bold) line, running over bunches of different SRRs, each triggered by the NFVM. A state $\psi \left(\mathcal{N},,,t\right)\to x_i$ emerges instantaneously as the ‘winner-take-all’ coalition that does not transmit information to special NCC. c In binocular rivalry, while the incoming signals remain constant, the percept switches to and fro over a temporal period about 2 s during which many states $x_i$ are to be processed in S(τ). Instead of visually experiencing a confusing picture of two images (a cat and a car) simultaneously, subjects report a perceptual alternation in seeing only one of those at a given time

Fig. 3

The reentrant cognition system in predictive processing. a The Bayes theorem describes how the prior belief B (expectation) based on the brain’s generative model M is transformed into the posterior belief over data acquisition D, all placed into a state space of a given SRR. b Hierarchical predictive processing across three cortical regions with feedforward and feedback information flow (adapted from Friston 2008). c Triplets of successive states, accompanied with self-organized recurrent neural activity across hierarchically distributed brain areas, are connected by the RCS over two ∆t intervals as an unclosed causal loop. Consciousness at the present state $x_i$ (data acquisition) self-refers (blue short arc) to its previous state $x_{i-1}$ (priors) to arrive (red long arc) at the future state $x_{i+1}$ (posteriors). d Here brain dynamics are mapped onto the irreflexive chain (X, <) in a 2-dimensional space of a physical axis t (way of knowing) and a phenomenal axis S(τ) (way of being). The chain evolves by the RCS as the perpetum cogito process, running from a subject’s birth moment. Unlike a statistic description of Bayesian learning above, in dynamics, the priors, posteriors, and data acquisition become a single process. e The schematic of neural activity over the causal, computational, and phenomenal levels of description

Fig. 4

Complexity measures and Cognitive evolution. a When extracted in time series of discretized measurements, $C_{\mathit{LMC}}$ and $C_{\mathcal{N}}$ reflect a mixture of synchronization/desynchronization in brain dynamics with maximal values near criticality between subcritical and supercritical phases, presented here by the 2D Ising model (adapted from Tegmark 2015). b While TE depends on mutual information, CQ can statistically reflect how much new information the brain has gained in time. c The C(X) unfolds by formally summarizing increments ∆C over (X, <). d If $\mathrm{CQ}\to 0$, it makes brain dynamics functionally ‘crystalized’ in a subcritical regime. Conversely, $\mathrm{CQ}\to max$ makes brain dynamics chaotic in a supercritical regime, thereby causing minimal coherency of S(τ)

Fig. 5

Disorders in algorithmic coding. The stream of consciousness can be viewed as the brain’s way of making a difference. The temporal coherency of the stream depends on how the difference is processed in Bayesian learning. If it is big, the brain captures too much information to learn something consistently. If it is small, learning fails. a Attention deficit disorder. Here (X, <) is schematically depicted as a totally disconnected sequence of random letters, each standing for a particular state. While being all NFVM-initiated, the states are badly constrained by the RCS. The stream evolves with CQ extending the upper boundary of optimal cognitive processing. b Obsessive–compulsive disorder. The RCS is rigid by generating monotonically periodic sequences of states with cyclical loops in obsessive–compulsive periods where Bayesian updating fails. CQ is thus reduced below the lover boundary of optimal cognitive processing. c Unresponsive wakefulness syndrome. The RCS is disrupted while the stream is locked in a single SRR with no CQ. Note, a state, repeated many times like a freeze-frame, does not imply causally closed loops. (d) Music therapy. In Parsons (2008) code of melodic contours, a notation identifies movements of the pitches on each pair of consecutive notes as “u” (up) if the second note is higher than the first one, “d” (down) otherwise, and “r” (repeat) if the pitches are equal. While rhythm is completely omitted, a well-connected sequence of tones arises

Fig. 6

A conceptual diagram for a quantitative classification of DoC and MDs. a Conscious states are traditionally compared to levels of arousal ranged from coma to full alertness over UWS, MCS, and LIS. Typically, high conscious levels are associated with an increased range of conscious contents (adapted from Boly et al. 2013). b DSM-5 states that changes in cognition must not occur in states of severely reduced level of consciousness such as coma. Taking into account that there is a continuum of levels of consciousness (arousal), it is more accurate to recognize that it is not possible to determine a threshold for cognitive processing between coma and normal states (European Delirium Association 2014). It makes also impossible to separate DoC from MDs. c DoC are estimated by PCI, based on the analysis of EEG-responses to transcranial magnetic stimulation to distinguish altered states of consciousness (adapted from Bodart et al. 2017). d The coherency of cognitive processing can be quantified by CQ over the stream of conscious states. Coma is placed at the bottom of the diagram with both $C_{\mathcal{N}}$ and CQ tending to zero. Yet, ascribing higher values of CQ to epileptic seizures, accompanied with loss of consciousness, is controversial as if the brain might gain too much information there. The controversy arises due to chaotic brain dynamics in a supercritical regime with which seizures are associated (Meisel et al. ; Jirsa et al. 2014). e Tendencies in long-range temporal correlations between synchronization (integration) and chaos (segregation) balanced near criticality are recorded in different psychological states (adapted from Zimmern 2020). These findings, although not related directly to complexity measures, are indicative of CQ