Intro Series VI: The Systems View of Life and Enactive Cognition
Self-Organising Criticality, Phase Changes in Complex Systems, and Living (Optimising) versus Machine (Maximisation) Systems
This is the sixth post in an Intro Series where I introduce the key concepts and lay out the intellectual grounding of the Bigger-than perspective I’m developing in this substack. There are a number of interlocking pieces to the Bigger-than approach and I’m going over a sort of minimum viable number of pieces individually in order to be able to start to talk about the interlocking wholeness that starts to emerge from the interweaving of each piece… Here are the links to parts I, II, III, IV, and V in this series…
If you’ve got this far and are still reading, thank you. I’m aware that some of the pieces of the puzzle I’ve spent time developing thus far are a little abstract, wordy, and potentially somewhat removed from what we’d ordinarily consider in the realm of psychology or even cognitive science, especially as applied to an individual’s life. I’m going somewhere with all of this, I assure you. In this post we take a step closer towards setting up the Bigger-than-Self Enactive Wisdom Development approach by taking a look at Capra and Luisi’s (2014) Systems View of Life perspective, how it relates to the enactivist approach to cognitive science, and how an understanding of these can lead us to one aspect of the Intellect-Embodiment gap in relation to the Meta-Crisis that can be fruitfully metabolised.
The Systems View of Life
The Systems View of Life (Capra & Luisi, 2014) is a conceptual framework for understanding life as vast (and microscopic), inter-scalar webs of networks of enormous complexity, interacting with and co-regulating one another ceaselessly. In Capra and Luisi’s (2014) synthesis of many different fields of scientific and cybernetic fields of study, both the self and world can be seen ultimately as being made up not of separate and self-existing objects, subjects, and phenomena, but instead as giant interconnected networks existing within networks, extending down to the basic elementary levels of physical and biological organisation and upwards to highly complex multicellular life forms, society, and the biosphere of the planet itself.
This Systems View of Life is presented as a synthesis of many converging lines of evidence and as representing a paradigm shift in our understanding of life that is occurring in diverse fields from physics to biology to cognitive science, sociology, ecology, and health, as well as presenting a viable way forward in addressing many of the systemic bigger-than-self issues facing humanity in contemporary times (Capra & Luisi, 2014). Moreover, it is a view of life that is already being enacted in many fields, including science, business, health, and education. I will be focusing the most in this Substack on the Systems View as I see it applying within the field of psychology, but the interlinkages are of vital importance as this is what gives the framework such power and relevance in addressing Bigger-than-Self Distress and the Meta-Crisis.
One important feature of living systems is their characteristic self-organising properties. All living systems are autopoietic (Maturana & Varela, 1980), meaning they are able to continuously change their structure (e.g., updating cells, learning, growing, ageing) while maintaining a consistency in functionality (staying alive, retaining learned information and capacity to enact important behavioural repertoires). Living organisms are also self-organising systems, meaning that their behaviour is regulated from within the structure of the system itself. Self-organising systems include but are not limited to individual living organisms. Processes within stars, planets, geo- and bio-spheres, ecologies can all be categorised as self-organising - so self-organising characteristics are not solely restricted to living systems, yet all living systems are self-organising.
Perhaps the most famous example of a self-organising system is that by which the evolution of species unfolds: Darwinian Natural Selection. In this self-organising system, factors that generate diversity, such as reproduction and genetic mutation, are pitted in continual dynamic tension with selective pressures that curtail diversity, such as environmental conditions, mortality, factors limiting an organism’s ability to reproduce. The generative factors and selective pressures interact in such a way that leads to the evolution of new species, particularly at times of rapid environmental change, as discussed in the discussion of punctuated equilibria in Part III of this Intro Series.
The dynamic interaction between generative and selective factors can be seen as an opponent processing relationship. Similar opponent processing relationships can be seen in other self-organising systems, such as the human nervous system, where the sympathetic nervous system creates arousal in response to environmental stimuli (fight-flight response) while the parasympathetic system inhibits arousal and enables the body’s natural rest-and-digest response. In Part IV of this Intro Series, I gave the example of Henrique’s (2003) Behavioural Investment Theory (BIT), which frames Skinnerian behavioural conditioning as a self-organising process for selecting behaviour in animals: with responses that increase a behaviour in dynamic tension with those that decrease a behaviour’s frequency and intensity to select for behaviour of an organism within its environment over time.
In the Systems View of Life, we can start to see living systems, whether they be individual organisms, ecosystems, or any scale of networked living system from the smallest single-celled organism to the largest network of ecosystems or biosphere itself, as operating via similar principles of self-organisation, with different factors in complex webs of dynamic tension with one another, which produces a kind of metastable harmony achieved through mutually co-regulating opponent processes. These patterns are seen within ecosystems constantly: the classic example being of how the re-introduction of wolf populations to the Yellowstone National Park regulated the populations of elk, which changed the populations of many species lower in the food chain, and even the very direction of the rivers.
Enactive Cognition: Self-Organising Criticality
The Systems View of Life, and its historical predecessors, have made several important contributions to cognitive science and psychology since the work of the early cyberneticists in the 1940s (e.g., Wiener, 1948). The work of cybernetics formed the basis of much of modern computing, and informed the application of computing metaphors to the brain that became popular in the 1980s and reigned largely for the next three decades. However, a parallel track was in development through that same period, which would evolve into what has become known as the enactivist cognitive paradigm. This track evolved through the works of Maturana and Varela (1970/1980), Varela, Thompson, & Rosch (1991, as discussed in the previous post in this series), Thompson (2007), and has been continued and developed by the likes of Di Paolo (2009), Clark (2015), and a growing field of enactive cognitive scientists.
As I discussed some of the fundamentals of enactivism in the previous post in this series, I won’t repeat that here, but the basic idea is that cognition is a result of systemic interactions that are not limited to within the brain but are fundamentally 4E: embodied (meaningfully shaped by and interlinked with bodily processes and structure), embedded (shaped by and responsive to the physical environment that the body is situated and evolved within), extended (shaped by and participating in the cultural milieus that the human person is part of, including use of tools, language, narrative frameworks), and enactive (a kind of amalgam of all of the above).
Hopefully it is clear how the enactivist view of cognition is more consonant with the Systems View of Life than earlier computational models of cognition, including more of the complex interactions that make up the dynamic processes of cognition than models that see the brain as a kind of central processing unit that passively receives the world, centrally processes it, and then spits out behavioural responses. In enactivism, many of these rigid boundaries dissolve into a more systemic and dynamical understanding of cognition as process, rather than thing.
Another feature of complex self-organising systems is that of Self-Organising Criticality (SOC; e.g., Bak, 1988). This idea, originally observed in physical systems, has been found within cognitive neuroscientific research to apply to cognitive systems (e.g., Zimmern, 2020; Wilting & Priesemann, 2019). The basic idea is that complex systems tend to occupy certain phase states, inside which behaviour is predictable within certain bounds. Within a phase, inputs and outputs into a system may trigger incremental quantitative changes in response, but not qualitative state changes of the entire system. These more comprehensive shifts in state are the phase changes themselves, and they are preceded by the system reaching a state of what is known as “criticality”.
These phase changes, punctuated by criticality, are illustrated by the classic “sandpile model” (Bak, 1988) used to explain the concept of SOC. In the left-hand pile, where the system is not in a phase of criticality, each additional grain of sand increases the slope of the pile, but is unlikely to change the overall structure of the pile in fundamental ways. In the centre pile, the number of sand grains added has led to the pile being in a state of criticality, where any additional grains of sand added are highly likely to trigger a cascade of responses that leave the system in a new phase state. The right-hand diagram shows the sandpile in this post-critical state, where the cascade has unfolded in its prototypical “avalanche” fashion. Note that in the classic example, the sandpile is not on a limited-width platform, and so the sandpile collapses in the avalanche, but then finds a new equilibrium phase state, again increasing in slope with each additional grain of sand, this time with a broader base.
Criticality is considered to be a self-organising phenomenon in certain complex systems as they naturally evolve into critical states given time: if they did not, they would either not be complex or they would not be stable and self-organising systems (Bak et al., 1988). SOC has been researched in cognitive systems for over 25 years. An early hypothesis, put forward by Dunkelman and Radons (1994), which has come to be known as the neural criticality hypothesis, posited that critical states should be evident and evolutionarily selected for in the brain because they are associated with more optimal computational abilities. There is substantial evidence for the existence of criticality in neuronal systems, but support for the hypothesis that they are always and everywhere associated with more optimal computational abilities is mixed at best (Hesse & Gross, 2014; Wilting & Priesemann, 2019).
Wilting and Priesemann (2019) argued that this controversy could be successfully resolved by a more nuanced understanding of criticality in neuronal systems, suggesting that criticality is not always optimal. They point to evidence suggesting that criticality maximises certain properties such as the number of metastable states attained, the dynamic range of networks, information retention, information processing, information representation, and complexity of information processing. These findings tend to lend support to the neural criticality hypothesis. However, they also discuss how maximising criticality was shown to lead to undesirable neural outcomes, such as reduced efficiency, reliability, and a slowing down of processing leading to longer wait times for being ready for new stimuli.
These seemingly contradictory empirical findings in regard to the neural criticality hypothesis led Wilting and Preisemann (2019) to suggest that maximising certain neural properties is not what comprises optimal neural functioning. That instead of all brain regions tending towards criticality all at the same time, what is optimal is a balance between critical and non-critical states, which allow, for example, for a dynamic ability to balance between quality of representation and processing time, sensitivity and specificity, or stimulus detection and discrimination (Wilting & Preisemann, 2019). They advocate that a reverberating regime, which balances between different phase states, rather than a critical one, is optimal for neural systems and resolves the apparent discrepancy in the evidence on the neural criticality hypothesis.
Maximising versus Optimising & Machine versus Living Systems
A central idea within this concept of reverberating regimes is that of optimality rather than maximisation. This idea is one that recurs throughout the Living Systems View - for example, in the idea of metastability. Metastability is a feature of complex systems whereby there are multiple attractor states that the system switches between without any active input from outside the system (Kelso, 2012). Metastable systems oscillate between states flexibly and eschew extremes (Kelso, 2012). Phase transitions do not imply a breakdown in functioning but are instead integrated into the flow of the organism’s self-organising functional grip with its environment (Kelso, 2012). This results in parts of metastable systems being able to flexibly switch between segregating and expressing their own intrinsic dynamics and integrating and coordinating with other parts to create new emergent properties (Bruineberg et al., 2021).
In metastability, which occurs commonly in living systems, we see that maximisation of any one attribute (e.g., arousal, temperature) is not “desirable” in that it is not selected for by the self-organising dynamics of the system. What living systems have evolved, almost universally so, to select for instead is optimality, which is achieved through processes such as opponent processing or metastable attunement. Capra & Luisi (2014) critique the mechanistic, Cartesian worldview that became predominant in the Enlightenment period. This machine-like worldview, which arose in the wake of thinkers such as Descartes and Newton, has resulted in unprecedented advances in science and technology that allows humanity to predict, control, and manipulate reality in ways that maximise human-desired outcomes.
In the context of the meta-crisis, we can see that this machine-like worldview is out of alignment with how living systems work. Living systems fundamentally tend to operate via principles of optimisation, and the machine-like world (including not only science and technology but the structure of our economic systems, institutions, businesses, and organisational hierarchies) we have constructed especially since the Enlightenment and Industrial Revolution fundamentally tends to operate via principles of maximisation. However, this mis-alignment can only be a temporary state of affairs: in natural living systems, such dynamics do not last. There will be a phase change away from maximisation of energy and resource use by humans, one way or another. The question in play at the moment is of the nature and severity of that phase change and the new metastable state that the global system arrives at once the dust settles - but that is a question for another post.
For now, an important takeaway is the distinction between machine and living systems, and there being a central difference between the two in the form of orientation towards maximisation versus optimisation. Note that optimisation requires a fundamentally different orientation: attunement towards external environmental features and behaviour change in response to this. Optimisation of a living system’s (e.g., cell, organism, species) own self-interest always plays out within this larger picture of attunement to the larger environment that the living system is embedded within.
Importantly, non-human organisms (excluding AI from the discussion for now) lack the intelligence to be able to meaningfully maximise their own self-interest in an outsized way relative to the ecosystems they are encased within. Humans alone have had the capacity to maximise their own self-interest in ways that have dominated the landscapes where they’ve lived. The extent to which they have done so in sustainable or unsustainable ways throughout human history and pre-history is a cultural question, and thus further underlines the importance of culture and cultural evolution in responding adaptively to the meta-crisis.
The understanding of the distinction between machine and living systems is potentially a critical one in approaching such cultural questions. A crucially important way we participate in the development of culture, I argue, is through participating in the development of our own cognition and socio-ecological niche(s). This leads us to one application of the concept introduced in the previous post in the series, the Intellect-Embodiment Gap: intellectually we can perceive the difference between machine and living systems, but such intellectual understanding is only a first step towards identifying or realising ourselves as living systems embedded within other living systems. Actually embodying this gap within our own embodied cognition and embedded socio-ecological niches is no easy feat.
However, if we can participate in the development of our own cognition such that its structure and function more closely resembles living systems and not the machine systems that surround us in the currently dominant culture of the globalist civilisation we live in, we have a chance to co-create environments that are in closer alignment with such living systems. Such socio-ecological niches need not (and I would argue cannot) simply be a return to pre-modern societies: we can bring the best and most relevant of our scientific and technological know-how into the future - but this requires evolution and change in novel directions, as such as a technologically advanced society has arguably never existed in a sustainable way that is aligned with living systems. For some excellent analysis and thinking on phase changes in our civilisation and what they might look like, I recommend Anderson’s (2023) article “Avoiding Existential Threats as a Non-Zero-Sum Game” or the podcast series between Hagens and Schmachtenberger (2023) “Bend not Break”.
For individuals, operating at an individual psychological level and desiring to truly (non-self-deceptively) contribute towards such aligned cultural evolution, the question becomes how to embody such a living systems way of being in a society that is at best largely ignorant of vitally important aspects of living systems and at worst actively hostile to such ways of being? This Substack explores ways of transforming our own embodied cognitive systems first, to be the kind of human that can contribute towards a more aligned future for humanity, whatever that looks like within your own socio-ecological niche. It explores ways we can exapt new ways of being while maintaining some form of still-necessary connection to the machine-like systems of the dominant civilisation. In essence, becoming more and more prepared for phase changes, ready to enact these in one’s life and socio-ecological niche when the opportune moments arise.
Transforming one’s own embodied cognitive system to truly meet the complexity of the world at this time is not easy. What seems to be required, or at least greatly beneficial to this process, is a sufficiently deep, non-self-deceptive system of transformation that can allow one to see and gain insight into mis-aligned aspects of one’s own cognition with this Living Systems View of Bigger-than-Self Reality (and for this to unfold in the context of supportive relationship and aligned community, ideally). That is the role of the Enactive Wisdom Development approach to metabolising bigger-than-self distress that I’ll be expanding on in future posts. This approach builds on and utilises this Systems View of Life, and finds applications of it in both ancient and cutting-edge contemporary psychological approaches to embodied transformation, character development, and wisdom cultivation. Stay tuned…