Emergence Theory and the Conscious Mind: A Theoretical Framework

Jerry A. Smith, Ph.D. - Managing Director and Computational Neuroscientist - Ankura

Acknowledging the Perils of a Bored Mind and Too Little Sleep

Before we explore consciousness as an emergent phenomenon, let's take a brief moment to acknowledge the unique conditions under which many of us encounter this topic. We’ve all been there: those late nights when the brain oscillates between the peaks of intellectual curiosity and the valleys of sheer exhaustion. A mind deprived of sleep tends to wander, teetering between profound philosophical questions and wondering why your fridge hums at a specific pitch at 3 a.m.

During these moments, consciousness can feel like both an enigma and a paradox. Too little sleep, and suddenly, the simplest tasks—like remembering where you put your keys—can seem like complex puzzles designed by the universe. It's often when we're half-asleep that the most peculiar questions arise: What is consciousness? Why do I exist? Most importantly, do I need to get up in three hours?

As we delve into the scientific and philosophical mysteries of how consciousness emerges from neural networks, remember that the organ we’re trying to understand is prone to these little functional lapses. Whether it's sleep deprivation or boredom, consciousness is never too far from slipping into surreal daydreams. So, as we dive into the deep end of multiscale neural dynamics, let’s appreciate the irony: the very minds capable of pondering their existence also tend to shut down when they miss a few hours of rest.

Abstract

This paper explores the intersection of emergence theory and consciousness studies, proposing a theoretical framework for understanding consciousness as an emergent phenomenon. We define consciousness and emergence theory, then integrate these concepts to develop a multi-scale model of conscious emergence. This model spans molecular-level neural processes to macroscale brain network interactions, offering a novel perspective on how subjective experience might arise from physical processes. We discuss the philosophical implications of this framework and its potential impact on our understanding of mind and brain.

1. Introduction

The nature of consciousness remains one of the most profound mysteries in science and philosophy. Despite significant advances in neuroscience, the gap between our understanding of brain function and the subjective experience of consciousness persists. This explanatory gap, famously termed the "hard problem" of consciousness by Chalmers (1995), continues to challenge our scientific and philosophical frameworks.

We turn to emergence theory to address this challenge, which offers a promising avenue for understanding complex phenomena like consciousness. We can bridge the explanatory gap between neural activity and subjective experience by viewing consciousness through the lens of emergence. This paper aims to: 1. Clearly define consciousness and emergence theory 2. Elucidate the principles of emergence theory as applied to consciousness 3. Propose a theoretical multiscale model of conscious emergence 4. Discuss the philosophical implications of this framework

2. Defining Consciousness and Emergence Theory

2.1 What is Consciousness?

Consciousness is a multifaceted concept that has been extensively debated in philosophy, psychology, and neuroscience. This paper defines consciousness as:

The subjective, first-person experience of awareness encompasses the qualitative feel of sensations (qualia) and the sense of self-awareness.

This definition covers several key aspects of consciousness: 1. Subjective experience: The private, inner feel of what it's like to have experiences. 2. Qualia: The specific qualitative characteristics of experiences, such as the redness of red or the pain of a headache. 3. Self-awareness: Recognizing oneself as a distinct entity with thoughts, feelings, and experiences. 4. Unified field of experience: The integrated nature of conscious experience, combining various sensory inputs, thoughts, and emotions into a coherent whole.

It's important to note that consciousness is not a monolithic concept. It includes various states (e.g., wakefulness, dreaming, deep sleep) and levels (e.g., minimal consciousness, full consciousness). Moreover, consciousness can be altered by various factors, including drugs, meditation, and certain medical conditions.

2.2 What is Emergence Theory?

Emergence theory is a conceptual framework used across various scientific disciplines to explain how complex systems and patterns arise from relatively simple interactions. We define emergence as:

The appearance of novel properties or behaviors in a complex system that are not present in, or predictable from, the system's components.

Critical aspects of emergence theory include: 1. Levels of organization: Emergent phenomena typically involve multiple levels of organization, with higher-level properties emerging from lower-level interactions. 2. Irreducibility: Emergent properties cannot be fully reduced to or explained solely by the properties of the system's components. 3. Novelty: Emergent phenomena exhibit new qualities not present in the system's parts. 4. Unpredictability: The emergent properties often need to be more predictable from knowledge of the system's components alone. 5. Downward causation: In some forms of emergence, the higher-level emergent properties can influence the behavior of the lower-level components.

Emergence can be categorized into two main types: 1. Weak emergence, where the emergent phenomena are unexpected given the properties of the parts but can be predicted through simulation or deep analysis of the system. 2. Strong emergence, where the emergent phenomena are not even in principle predictable from the properties of the parts. Examples of emergent phenomena include the formation of hurricanes from simple air and water vapor interactions, the emergence of consciousness from neural activity, and the appearance of liquidity from molecular interactions in water.

3. Intersection of Consciousness and Emergence Theory

Having defined consciousness and emergence theory, we can now explore their intersection. We propose that consciousness can be understood as an emergent phenomenon arising from the complex interactions of neural processes across multiple scales of brain organization.

This perspective offers several advantages: 1. It provides a framework for understanding how subjective experience could arise from physical processes without reducing consciousness to those processes. 2. It aligns with the observed complexity and integrated nature of conscious experience. 3. It offers a potential resolution to the explanatory gap by suggesting that the seeming irreducibility of consciousness to physical processes is a feature of its emergent nature. 4. It accommodates the various levels and states of consciousness observed in nature and altered by various conditions.

We can conceptualize this mathematically:

Let S be a system composed of elements {e?, e?, ..., e?} with properties {p?, p?, ..., p?}

Let f be a function describing the interactions between elements

Let E be an emergent property of S

We can express emergence as:

E(S) ≠ Σ[f(p?(e?))] for all i, j

Where E(S) represents a property of the system that cannot be reduced to a simple sum or function of the properties of its elements.

In the context of consciousness,

Let N be the set of neurons in a brain.

Let C be a conscious experience.

We propose: C(N) = E[f(N)],

Where f represents the complex interactions between neurons, and E represents the emergent property of consciousness.

This formulation highlights the non-reductive nature of consciousness as an emergent phenomenon. It suggests that conscious experience (C) arises from the complex interactions (f) between neurons (N), but is not reducible to a simple function of these components.

4. Theoretical Multiscale Model of Conscious Emergence

We propose a theoretical model that conceptualizes consciousness as emerging through information integration across multiple scales of brain organization. This multiscale approach is crucial for understanding how the complex phenomenon of consciousness arises from the physical substrate of the brain.

Our model spans four primary scales: 1. Molecular scale: Ion channel dynamics and neurotransmitter interactions 2. Cellular scale: Neural firing patterns and local circuit dynamics 3. Mesoscale: Coordination of neural assemblies and small-world networks 4. Macroscale: Large-scale brain network interactions and global workspace dynamics

At each scale, we posit emergent properties that contribute to the overall phenomenon of consciousness. The integration across these scales is theorized to be facilitated by principles of self-organization, criticality, and information integration. Importantly, we propose that consciousness is not localized to any single scale, but rather emerges from the complex interactions and information flow across all scales.

4.1 Molecular and Cellular Scales

We consider how the complex dynamics of ion channels, neurotransmitters, and individual neurons might give rise to emergent properties at the molecular and cellular scales. Drawing on the work of Tononi and Koch (2015), we propose that even at this level, rudimentary forms of information integration occur, forming the building blocks of conscious experience.

Key aspects at this scale include:

1. Nonlinear dynamics of ion channels and synaptic transmission: The behavior of ion channels is inherently nonlinear, exhibiting complex gating mechanisms that depend on voltage, ligands, and other factors. This nonlinearity is crucial for the emergence of complex neuronal behaviors. With its intricate interplay of neurotransmitters and receptors, synaptic transmission adds another layer of nonlinear dynamics. We propose that this nonlinearity at the molecular level is a fundamental prerequisite for the emergence of consciousness, allowing for the rich, non-deterministic behaviors observed in conscious experience.

2. Complex intracellular signaling cascades: Intricate signaling cascades translate external stimuli into internal cellular responses within neurons. These cascades involve multiple interacting pathways, feedback loops, and amplification mechanisms. We suggest that these cascades contribute to the information integration capacity of individual neurons, allowing them to process and store information in a manner that goes beyond simple input-output relations. This internal complexity may be a crucial factor in the emergence of subjective experience.

3. Emergence of neural firing patterns from molecular interactions: The collective behavior of ion channels, influenced by intracellular processes and synaptic inputs, gives rise to the firing patterns of individual neurons. These patterns, including tonic firing, bursting, and oscillations, represent emergent properties that cannot be fully predicted from the properties of particular molecular components. We propose that these diverse firing patterns form the basic 'vocabulary' of neural information processing, contributing to the rich palette of conscious experiences.

4. Quantum effects in neural microtubules: While controversial, theories proposed by Penrose and Hameroff suggest that quantum coherence in neural microtubules might play a role in consciousness. Although our model does not rely on quantum effects, we acknowledge that quantum phenomena at the molecular level could contribute to the emergent properties of consciousness, potentially explaining some of its non-classical features. The emergence of complex behaviors at this scale sets the stage for higher-level emergent phenomena. We propose that the information integration occurring at molecular and cellular levels forms the foundational layer of conscious experience upon which more complex conscious phenomena are built.

4.2 Mesoscale Organization

At the mesoscale, we consider the formation and dynamics of neural assemblies. Building on the work of Varela et al. (2001), we propose that transient synchronization between neural populations creates dynamic functional units that may correspond to discrete elements of conscious experience.

Key aspects of this scale include:

1. Formation of neural assemblies through synchronous firing: Neurons that fire together tend to wire together, forming assemblies that represent specific information or functions. We propose that these assemblies are the neural correlates of basic conscious percepts or concepts. These assemblies' dynamic formation and dissolution could explain the fluid nature of conscious experience, where different percepts and thoughts seamlessly flow into one another.

2. Small-world network properties of local neural circuits: The brain exhibits small-world network properties, characterized by high local clustering and short average path lengths. This architecture allows for efficient local processing and global integration of information. We suggest that this network structure is crucial for consciousness, enabling the rapid integration of information necessary for coherent conscious experience while maintaining the specificity required for rich, detailed percepts.

3. Emergence of oscillatory patterns and rhythms: Neural populations exhibit various oscillatory patterns, such as gamma, theta, and alpha rhythms. We propose that these rhythms play a crucial role in consciousness by temporally binding distributed neural activities. Different oscillatory patterns may correspond to varying states of consciousness or other types of conscious content. The interplay between these rhythms could explain the multi-faceted nature of conscious experience, where multiple streams of information (e.g., sensory, emotional, cognitive) are simultaneously present yet distinct.

4. Criticality and metastability: Growing evidence suggests that the brain operates near a critical state, balancing order and chaos. This criticality allows for optimal information processing and adaptive behavior. We propose that this critical state is essential for consciousness, enabling the brain to balance stability (necessary for coherent experience) and flexibility (vital to the dynamic nature of consciousness). The concept of metastability, where the brain transiently occupies semi-stable states, could explain how consciousness maintains coherence while constantly evolving.

The mesoscale represents a crucial bridge between microscale neural processes and macroscale brain dynamics. We propose that at this scale, consciousness's first genuinely recognizable features emerge, such as the binding of features into coherent percepts and the formation of momentary conscious states.

4.3 Macroscale Dynamics

At the macroscale, we draw on global workspace theory (Baars, 1988) and integrated information theory (Tononi, 2004) to conceptualize how large-scale brain network dynamics might give rise to the unified field of consciousness. We propose that the dynamic reconfiguration of these networks underlies the fluid nature of conscious experience.

Key aspects at this scale include: 1. Integration of information across distributed brain networks: Consciousness requires integrating diverse types of information processed in different brain regions. We propose that this integration occurs through the dynamic coupling of large-scale brain networks, such as the default mode, salience, and executive control networks. The flexible reconfiguration of these networks could explain how different aspects of consciousness (e.g., sensory awareness, self-awareness, executive control) are brought together into a unified experience.

2. Dynamic core formation and dissolution: Building on the concept of a 'dynamic core' proposed by Edelman and Tononi, we suggest that consciousness arises from forming a constantly changing assembly of synchronized neural populations. This dynamic core would integrate the most relevant information at any given moment, explaining the selective nature of consciousness and its capacity to shift focus rapidly.

3. Emergence of global brain states corresponding to different conscious experiences: ?? Different conscious states (e.g., alert wakefulness, dreaming, meditative states) are associated with distinct patterns of global brain activity. We propose that these global states emerge from the complex interactions of lower-level processes and represent qualitatively different modes of conscious experience. The transitions between these states could explain phenomena such as changes in conscious level (e.g., from wakefulness to sleep) and content (e.g., shifts in attention or mood).

4. Predictive processing and consciousness: Incorporating ideas from predictive coding frameworks, we suggest that consciousness at the macroscale involves the brain's ongoing attempt to predict its inputs and minimize prediction errors. This predictive aspect could explain the constructive nature of conscious perception and the role of expectations in shaping conscious experience.

5. Downward causation and conscious control: While consciousness emerges from bottom-up processes, we propose that it also exerts top-down influences through downward causation. This could explain phenomena such as voluntary attention, decision-making, and the felt sense of conscious will. The mechanism for this downward causation could involve the modulation of lower-level neural processes by the global brain state associated with a particular conscious experience.

4.4 Integration Across Scales

Crucially, our model posits that consciousness cannot be fully understood by considering an isolated scale. Instead, the complex interactions and information flow across all scales give rise to the rich, unified experience of consciousness.

We propose several mechanisms for this cross-scale integration:

1. Scale-free dynamics: Certain patterns of brain activity, such as neuronal avalanches, exhibit similar properties across multiple spatial and temporal scales. These scale-free dynamics could provide a common 'language' for information integration across levels of brain organization.

2. Nested oscillations: Oscillatory brain activity at different frequencies can be nested, with the phase of lower-frequency oscillations modulating the amplitude of higher-frequency oscillations. This nested structure could provide a mechanism for integrating information across different temporal and spatial scales.

3. Metastable dynamics: The concept of metastability, where the brain flexibly transitions between semi-stable states, could apply across multiple scales. This could allow for the coordination of processes at different levels, from molecular fluctuations to large-scale network reconfigurations.

4. Information cascades: Information processed at lower levels increases, influencing higher-level processes. Simultaneously, higher-level states constrain and shape lower-level dynamics through top-down influences. This bidirectional flow of information across scales could be critical to the emergence of a unified conscious experience.

Our multiscale model proposes that consciousness emerges from the complex interactions of brain processes across multiple levels of organization. This emergence is characterized by integrating information, forming dynamic patterns, and the constant interplay between bottom-up and top-down processes. Considering consciousness as a multiscale emergent phenomenon, we can bridge the gap between neural mechanisms and subjective experience, potentially addressing longstanding questions in consciousness research.

5. Computational Neuroscience Implications

Our theoretical multiscale model of conscious emergence opens up a new frontier in computational neuroscience, challenging existing paradigms and offering exciting new directions for research. The implications of this model are far-reaching, touching on every aspect of how we computationally approach the study of consciousness. In this section, we will explore these implications in depth, considering how they might reshape our computational strategies and potentially lead to breakthroughs in our understanding of consciousness.

5.1 Multi-scale Modeling Approaches

The multiscale nature of our consciousness model necessitates a fundamental shift in how we approach the computational modeling of neural systems. Traditional approaches that focus on a single scale of organization – individual neurons, local circuits, or whole-brain dynamics – are insufficient to capture the emergent nature of consciousness as we've described it. Instead, we need to develop new computational architectures that seamlessly integrate processes across multiple scales of brain organization. At the heart of this challenge is the need for hierarchical modeling techniques to capture the bottom-up emergence of complex behaviors while accounting for top-down influences. This isn't simply running separate simulations at different scales and combining the results. Rather, we need to develop models where the dynamics at each scale continuously inform and constrain the dynamics at other scales.

For example, consider how we might model the emergence of a conscious percept. At the molecular scale, we must simulate the intricate dance of neurotransmitters and ion channels that give rise to neural firing patterns. These patterns, in turn, shape the behavior of local neural circuits at the cellular scale. The collective activity of these circuits then influences mesoscale dynamics, such as the formation and dissolution of neural assemblies. Finally, at the macro level, we must model how these assemblies interact across distributed brain networks to create a unified conscious experience.

Developing computational tools to bridge these scales is a formidable challenge. One promising approach uses renormalization group techniques borrowed from physics, which allow us to systematically coarse-grain lower-level dynamics into higher-level models. However, unlike in many physical systems, in the brain, we need to preserve specific fine-grained details that may have outsized effects on higher-level dynamics. This requires the development of adaptive algorithms that can dynamically adjust the level of detail in different parts of the model based on their current relevance to conscious processing.

Another crucial aspect of multi-scale modeling is integrating different physical processes – electrical, chemical, and even quantum mechanical – into a coherent computational framework. Each of these processes operates on various spatial and temporal scales, yet in the brain, they interact in complex ways that may be crucial for the emergence of consciousness. Developing 'multiphysics' models that capture these interactions is a key challenge for future research.

5.2 Information Integration Metrics

Central to our model of conscious emergence is the concept of information integration. While this idea has been explored in frameworks like Integrated Information Theory (IIT), our multi-scale approach necessitates significantly expanding and refining how we quantify and track information integration in complex neural systems.

The challenge here is to measure information integration at each scale and understand how information is integrated across scales. This requires the development of new mathematical formalisms that can capture the unique aspects of cross-scale information propagation in neural systems. For instance, how does information encoded in the molecular-scale dynamics of synapses influence and become integrated with information represented in large-scale brain networks?

One approach to this problem is to extend existing information-theoretic measures, such as transfer entropy and mutual information, to account for multiple scales simultaneously. However, this is not simply a matter of independently applying these measures at different scales. We need to develop metrics to capture how information is transformed and integrated across scales.

5.3 Neural Criticality and Metastability

The concepts of criticality and metastability play a central role in our model of conscious emergence, and this has profound implications for computational neuroscience. These ideas suggest that the brain operates in a delicate balance – not too ordered or chaotic – that allows for both the stability necessary to maintain coherent conscious states and the flexibility required for rapid adaptation to changing circumstances.

From a computational perspective, modeling these critical and metastable dynamics presents significant challenges. Traditional approaches to neural modeling often assume relatively stable dynamics or focus on transitions between distinct, well-defined states. In contrast, our model suggests that consciousness emerges from a system constantly poised on the edge of instability, capable of rapid reconfigurations in response to internal dynamics and external inputs.

5.4 Large-Scale Network Dynamics

Our model emphasizes the crucial role of large-scale brain network dynamics in the emergence of consciousness. This perspective aligns with and extends current trends in neuroscience that increasingly recognize the importance of brain-wide interactions in cognitive function. However, our multi-scale approach suggests that we need to think about these large-scale dynamics in new ways, considering how they emerge from and interact with processes at smaller scales. One of the key computational challenges in this area is modeling the dynamic reconfiguration of functional connections in large-scale brain networks. Consciousness, in our model, isn't associated with a fixed pattern of brain connectivity. Instead, it emerges from the ongoing dance of neural populations, forming and dissolving functional connections moment-to-moment.

5.5 Emergent Conscious-like Behaviors

One of our model's most exciting and challenging implications is the potential for developing computational systems that exhibit emergent behaviors analogous to aspects of consciousness. This isn't about creating a fully conscious artificial system – a goal far beyond our current capabilities and understanding. Instead, it's about modeling specific features or components of consciousness to deepen our knowledge and potentially pave the way for more advanced artificial intelligence.

Self-awareness is one such feature that presents intriguing computational challenges. How might a complex, multi-scale system develop the capacity to model and monitor its states and processes? This isn't simply implementing a separate 'self-monitoring' module. Instead, our model suggests that self-awareness might emerge from the complex interactions of processes across multiple scales.

6. Implications for Future Research

While our multiscale model of conscious emergence is primarily theoretical, it opens up numerous exciting avenues for future empirical research. These research directions promise to deepen our understanding of consciousness and have the potential to bridge the gap between theoretical models and observable phenomena. Please talk about these research directions in detail, considering their implications, challenges, and potential approaches.

6.1 Investigation of Information Integration at Multiple Scales of Brain Organization

Our model posits that consciousness emerges from integrating information across multiple scales of brain organization. This concept presents a rich area for future research, challenging us to develop new methods for measuring and analyzing information flow across these scales.

At the microscale, we need to investigate how information is integrated within individual neurons and small neural circuits. This could involve advanced imaging techniques like optogenetics combined with high-resolution microscopy to observe how information is processed and integrated at the level of dendritic computations and local synaptic interactions.

Moving to the mesoscale, the research could focus on how information is integrated across neural assemblies and functional modules. This might involve the use of multi-electrode arrays or high-density EEG to capture the dynamics of neural populations. Of particular interest would be understanding how information from different sensory modalities is integrated into coherent percepts.

At the macroscale, research could explore how information is integrated across large-scale brain networks. Advanced neuroimaging techniques like fMRI, combined with sophisticated analysis methods such as dynamic causal modeling or multivariate pattern analysis, could be employed to track the flow of information across distributed brain regions during various conscious states.

A key challenge in this research is developing methods to quantify information integration across these scales. This may involve the creation of new mathematical formalisms that can capture cross-scale information flow, building on existing frameworks like integrated information theory but extending them to account for the multiscale nature of neural information processing. Furthermore, this line of research could explore how information integration changes in altered states of consciousness, such as during sleep, under anesthesia, or in meditative states. This could provide valuable insights into the relationship between different modes of information integration and the phenomenology of conscious experience.

6.2 Exploration of the Relationship Between Neural Criticality and Conscious States

Our model suggests a deep connection between neural criticality – the delicate balance between order and chaos in brain dynamics – and conscious states. This presents an intriguing area for future research, with the potential to shed light on the dynamic principles underlying consciousness.

One approach to this research could involve analyzing large-scale neural recordings (e.g., from EEG or MEG) to identify signatures of criticality in brain dynamics. Techniques from statistical physics, such as those used to study phase transitions, could be adapted to analyze these neural data. Researchers could look for correlations between measures of criticality (like the statistics of neural avalanches or the scaling behavior of neural fluctuations) and various aspects of conscious experience.

Another promising direction would be to manipulate neural criticality experimentally and observe the effects on consciousness. This could involve transcranial magnetic stimulation (TMS) or transcranial electrical stimulation (tES) to perturb brain dynamics, pushing them towards or away from critical states. The effects of these perturbations on perception, attention, and other aspects of conscious experience could be measured using psychophysical techniques.

6.3 Development of New Mathematical Formalisms for Describing Emergent Phenomena in Complex Neural Systems

The concept of emergence is central to our model, yet formally describing emergence in complex systems remains a significant challenge. Developing new mathematical formalisms to capture emergent phenomena in neural systems is thus a crucial area for future research. One approach could be to extend and adapt tools from complexity science and dynamical systems theory. For instance, researchers could explore using renormalization group techniques, initially developed in physics, to study critical phenomena and describe how emergent properties arise across scales in neural systems. Another promising direction would be to create new algebraic or topological approaches to describing emergence. Recent work in applied topology, such as persistent homology, has shown promise in capturing complex data structures across scales. These methods could be adapted to describe the emergence of conscious states from lower-level neural dynamics.

6.4 Philosophical and Empirical Investigation of the Relationship Between Emergent Properties at Different Scales and Specific Features of Conscious Experience

This research direction sits at the intersection of the philosophy of mind, cognitive science, and neuroscience, aiming to bridge the gap between emergent neural phenomena and the subjective features of conscious experience. On the philosophical side, researchers could work on developing more refined taxonomies of conscious experiences, breaking down the broad concept of consciousness into more specific, potentially measurable components.

This research could involve phenomenological analyses, drawing on both Western and Eastern philosophical traditions, to create a more nuanced description of the varieties of conscious experience. Empirically, it could involve correlating measures of emergent neural properties at different scales with reports of subjective experience. For instance, studies could explore how the emergence of specific patterns of neural synchrony relates to particular qualia or the sense of self.

7. Conclusion

This paper has presented a theoretical framework for understanding consciousness as an emergent phenomenon, integrating principles from emergence theory, complex systems science, and cognitive neuroscience. Our multiscale model provides a novel perspective on how subjective experience might arise from the physical substrate of the brain, spanning from molecular dynamics to large-scale brain network interactions.

The implications of this work are far-reaching, offering new directions for philosophical inquiries into the nature of the mind, empirical investigations of consciousness, and potentially even the development of artificial systems with conscious-like properties. As we refine our understanding of conscious emergence, we may find ourselves on the brink of a new era in our self-understanding as conscious beings and our scientific approach to studying the mind.

By framing consciousness as an emergent phenomenon, we open new avenues for bridging the explanatory gap between subjective experience and physical processes. While many questions remain, this approach offers a promising direction for future research in consciousness studies, neuroscience, and philosophy of mind.

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