Physiological Emergence of Consciousness

Introduction

The concept of emergence describes the appearance of new properties or structures as a result of complex interactions in a system that are not obviously deducible from the properties of the individual components (Goldstein, 1999). With regard to consciousness, a fundamental question arises: How do the physiological processes in the brain correlate with subjective experiences and unified consciousness?

It is important to emphasize that we are not talking about a causal emergence of consciousness here. Rather, we consider consciousness to be an integral part of neuronal beings, whose dynamics interact with physiological processes in complex ways. Our goal is to investigate the correlations between neural mechanisms and the different aspects and states of consciousness.

This approach avoids the problematic assumption that consciousness “arises” or is “generated” and instead focuses on the question of how the dynamics of consciousness vary with observable physiological processes. This allows for a more nuanced view that better reflects both philosophical questions and empirical research results.

In this article, we explore four promising approaches that we can use to try to make the correlations between physiological mechanisms and the dynamics of consciousness tangible. Finally, we look at how these approaches are complementary to each other and can enable a more comprehensive understanding of consciousness.

1. Network Dynamics and Information Integration

A promising approach is based on the theory of dynamical systems and the idea of information integration. The hypothesis is that certain degrees of complexity of neuronal networking and interaction correlate with specific states of consciousness.

Tononi and Edelman (1998) proposed that consciousness is strongly correlated with the integration of information in thalamocortical systems. Their theory of the “dynamic nucleus” postulates that the activity of a group of neurons that is highly integrated and differentiated is closely related to states of consciousness.

Methods: High-resolution EEG or MEG images could reveal transitions in network dynamics that correlate with changes in the state of consciousness, such as the occurrence of coherent oscillations in specific frequency bands. Varela et al. (2001) have shown that synchronized neuronal oscillations, especially in the gamma frequency range (30–100 Hz), correlate with conscious perception.

Recent research by Mashour et al. (2020) uses network analysis techniques to understand how different brain regions interact during different states of consciousness. They found that certain states of consciousness correlate with increased information exchange between different brain regions, which supports the idea of information integration.

2. Critical Transitions in Neural Systems

The theory of critical transitions offers a concrete model for the study of the dynamics of consciousness. According to this approach, certain states of the brain operating near a critical point correlate with specific states of consciousness. At this point, small changes in neural activity could correlate with large-scale, qualitative changes in consciousness dynamics.

Beggs and Plenz (2003) discovered “neuronal avalanches” in the cortex that follow a power law distribution — a feature of critical systems. They argue that this critical state is optimal for information processing and storage, which may be related to certain states of consciousness.

Methods: The observation of power law distributions in the size and duration of neuronal activity clusters could provide clues to such critical transitions that correlate with changes in the state of consciousness. Hesse and Gross (2014) have developed methods to identify and quantify critical dynamics in brain networks.

Recent studies by Tagliazucchi et al. (2016) show that the human brain operates closer to a critical point during wakefulness than during sleep or under anesthesia. This suggests that the critical state may be closely related to certain states of consciousness.

3. Recursive Processing and Meta-Stability

Another concrete idea is based on the hypothesis that certain states of consciousness correlate with recursive processing loops. Neuronal signals are not only processed forward, but also sent back and forth between different brain regions in complex feedback loops. A certain degree of recursion could correlate with a meta-stable state that could be characteristic of certain states of consciousness.

Lamme and Roelfsema (2000) proposed that recurrent processing in visual areas is necessary for conscious visual perception. Their studies show that the first wave of activation in visual areas does not correlate with conscious perception; only the recurrent feedback loops show a strong correlation with conscious experience.

Methods: The analysis of connectivity patterns and information flows between different brain regions using functional MRI imaging or complex EEG analyses could provide insights into these recursive processes. For example, Boly et al. (2011) have shown that certain states of consciousness correlate with specific patterns of effective connectivity between brain regions.

Recent research by Dehaene and Changeux (2011) as part of their “Global Neuronal Workspace” theory emphasizes the importance of long-range feedback connections for certain states of consciousness.

4. Information Theory Approaches

Based on Giulio Tononi’s Integrated Information Theory (IIT), the dynamics of consciousness could be grasped in terms of information theory. The theory proposes that a high degree of integrated information (Φ) in a neural network is strongly correlated with the occurrence of consciousness (Tononi et al., 2016).

The IIT provides a mathematical framework to quantify how much integrated information a system contains. It makes concrete predictions about which types of systems should correlate with consciousness and to what extent.

Methods: This would be measurable by complex calculations of the integrated information from high-resolution images of brain activity. However, the practical calculation of Φ is still a major challenge for complex systems such as the human brain.

Recent research by Massimini et al. (2015) uses the foundations of IIT to assess states of consciousness in clinical contexts, e.g. in patients with impaired consciousness.

5. Nonlinearity and Dynamics in Biological Systems

A fundamental aspect of biological systems is their inherent nonlinearity. This property has far-reaching consequences for our understanding of emergence in general and consciousness in particular.

In biological systems, including the brain, there are virtually no true linearities. Instead, nonlinear interactions are the norm (Laughlin et al., 2000). This means that the output of such a system is not proportional to its input, and that the behavior of the overall system cannot simply be understood as the sum of its parts.

This nonlinear nature of biological systems supports the idea that consciousness should be considered as an integral part of neural systems, not as something that is “generated” linearly or causally. The complexity and non-linearity of these systems makes it possible to understand qualitative changes in the dynamics of consciousness without having to resort to reductionist explanations.

6. Consciousness as an integral part of neural systems

It is important to emphasize that we are viewing consciousness here as an integral part of neural systems, not as something that is causally “generated”. This perspective allows us to investigate the complex interactions between neural processes and states of consciousness without making problematic ontological assumptions.

This view has important implications for consciousness research. It suggests that we should focus less on finding an “origin” of consciousness and more on how different aspects of consciousness interact and coordinate with neural processes.

It must be added that the entire brain is involved in these dynamics, including the processes that we are not aware of. Studies show that the brain stem also plays a significant role in ‘higher’ cognitive processes.

7. Complementarity of Approaches in Consciousness Research

The approaches discussed in this paper — network dynamics and information integration, critical transitions in neural systems, recursive processing and meta-stability, and information-theoretic approaches — each provide valuable insights into different aspects of consciousness. Rather than trying to squeeze these approaches into a single unified theory, it may be more fruitful to think of them as complementary perspectives, each addressing different areas of consciousness research.

Scopes of the approaches

1. Network Dynamics and Information Integration

• Scope: Large-scale brain organization and information processing
• Relevance: Explains the neural basis of consciousness and is particularly useful for understanding attention and global availability of information

1. Critical Transitions in Neural Systems

• Scope: Dynamics of neuronal activity and state transitions
• Relevance: Provides insights into the flexibility of the brain and explains sudden changes in states of consciousness

1. Recursive processing and meta-stability

• Scope: Emergence of coherent perceptions and thoughts
• Relevance: Explains the temporal dynamics of consciousness and phenomena such as multistability in perception

1. Information Theory Approaches

• Scope: Quantification and characterization of states of consciousness
• Relevance: Provides formal methods for measuring complexity and integration in the brain, useful for clinical applications

Advantages of the complementary approach

1. Multi-level explanation: The approaches address consciousness at different levels, from the micro-level of neuronal dynamics to the macro-level of global brain states.
2. Methodological diversity: The different approaches allow for the use of different research methods, from computational models to empirical measurements.
3. Phenomenological breadth: Together, the approaches cover a wide range of conscious phenomena, from simple perceptions to complex cognitive states.
4. Flexibility in application: Depending on the research question or clinical context, the most appropriate approach can be chosen.
5. Avoidance of reductionism: This perspective respects the complexity of consciousness without forcing it into a single explanatory model.

The complementarity of these approaches opens up new possibilities for consciousness research. Future research could focus on further exploring the connections and overlaps between these approaches, and potentially developing new, integrative methods that combine the strengths of multiple approaches.

Ultimately, acknowledging the complementarity of these approaches can lead to a richer and more nuanced understanding of consciousness that does justice to the complexity of this fascinating phenomenon.

Conclusion and outlook

The approaches presented in this article for the study of consciousness dynamics — from network dynamics and information integration to critical transitions and recursive processing to information-theoretical considerations — offer promising ways to understand this complex phenomenon. As we saw in the last section, these approaches can be considered complementary, with each approach shedding light on specific aspects of consciousness.

These approaches offer concrete ideas on how the dynamics of consciousness in relation to physiological processes could be understood and potentially measured. However, they face enormous empirical and conceptual challenges. The greatest difficulty remains to relate these physiological processes to subjective experience — the so-called “hard problem of consciousness” (Chalmers, 1995).

Acknowledging the fundamental nonlinearity of biological systems and their consequences for the understanding of emergence and consciousness represents a crucial step. It underlines the need to go beyond simplistic, linear explanatory approaches and to put the inherent complexity and dynamics at the center of our understanding.

This perspective makes it clear that the complex dynamics of consciousness should not be understood as an exception, but as an expected result of the nonlinearity of biological systems. It opens up new avenues for the study of consciousness and related phenomena that better do justice to the complexity of the brain and human experience.

Future research will likely require an integration of these different approaches. For example, the combination of network dynamics, critical transitions, and information-theoretic approaches could provide a more comprehensive picture of consciousness dynamics.

New technologies such as optogenetic methods (Deisseroth, 2011) and high-resolution imaging techniques promise deeper insights into the neuronal correlates of consciousness. At the same time, philosophical reflections will continue to play an important role in sharpening the conceptual foundations of our understanding of consciousness.

This approach is not to be understood as a completed theory, but as an offer of explanation. It is based on the observation of complex phenomena in nature and uses scientific concepts to develop an understanding of consciousness that is both naturalistic and appropriate to the complexity of the phenomenon. In doing so, we start from observable phenomena and not from preconceived theoretical positions.

This complementary perspective allows us to grasp the complexity of consciousness in all its breadth while addressing specific research questions using the most appropriate methods. It invites further empirical research, theoretical development and interdisciplinary dialogue, where the strengths of each approach can be exploited.

The complexity and dynamics of consciousness continue to require an open, creative and integrative approach. Only by combining and complementing different perspectives and methods can we hope to get to the bottom of this fascinating phenomenon and perhaps one day answer the question of how subjective experience emerges from the activity of our brain.

Literature:

1. Beggs, J. M., & Plenz, D. (2003). Neuronal avalanches in neocortical circuits. Journal of Neuroscience, 23(35), 11167–11177.
2. Boly, M., Garrido, M. I., Gosseries, O., Bruno, M. A., Boveroux, P., Schnakers, C., … & Friston, K. (2011). Preserved feedforward but impaired top-down processes in the vegetative state. Science, 332(6031), 858–862.
3. Chalmers, D. J. (1995). Facing up to the problem of consciousness. Journal of Consciousness Studies, 2(3), 200–219.
4. Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227.
5. Deisseroth, K. (2011). Optogenetics. Nature Methods, 8(1), 26–29.
6. Goldstein, J. (1999). Emergence as a construct: History and issues. Emergence, 1(1), 49–72.
7. Hesse, J., & Gross, T. (2014). Self-organized criticality as a fundamental property of neural systems. Frontiers in Systems Neuroscience, 8, 166.
8. Lamme, V. A., & Roelfsema, P. R. (2000). The distinct modes of vision offered by feedforward and recurrent processing. Trends in Neurosciences, 23(11), 571–579.
9. Mashour, G. A., Roelfsema, P., Changeux, J. P., & Dehaene, S. (2020). Conscious processing and the global neuronal workspace hypothesis. Neuron, 105(5), 776–798.
10. Massimini, M., Boly, M., Casali, A., Rosanova, M., & Tononi, G. (2015). A perturbational approach for evaluating the brain’s capacity for consciousness. Progress in Brain Research, 177, 201–214.
11. Tagliazucchi, E., Chialvo, D. R., Siniatchkin, M., Amico, E., Brichant, J. F., Bonhomme, V., … & Laureys, S. (2016). Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics. Journal of The Royal Society Interface, 13(114), 20151027.
12. Tononi, G., & Edelman, G. M. (1998). Consciousness and complexity. Science, 282(5395), 1846–1851.
13. Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: from consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461.
14. Varela, F., Lachaux, J. P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2(4), 229–239.
15. Laughlin, R. B., Pines, D., Schmalian, J., Stojkovic, B. P., & Wolynes, P. (2000). The middle way. Proceedings of the National Academy of Sciences, 97(1), 32–37.
16. Strogatz, S. H. (2018). Nonlinear dynamics and chaos: with applications to physics, biology, chemistry, and engineering. CRC press.
17. It must be added that the entire brain is involved in these dynamics, including the processes that we are not aware of. Studies show that the brain stem also plays a significant role in ‘higher’ cognitive processes.