By: Mike Johnson, Andrés Gómez Emilsson, and Sean McGowan

Formalism Lineages:

The brain is very complicated, the mind is very complicated, and the mapping between these two complicated things seems very murky. How can we move forward without getting terribly confused? And what should a formal theory of phenomenology even try to do? These are not easy questions, but the following work seems to usefully constrain what answers here might look like:

is most famous for Marr's Three Levels (along with Tomaso Poggio), which describe "the three levels at which any machine carrying out an information-processing task must be understood":

• Computational theory: What is the goal of the computation, why is it appropriate, and what is the logic of the strategy by which it can be carried out?

• Representation and algorithm: How can this computational theory be implemented? In particular, what is the representation for the input and output, and what is the algorithm for the transformation?

• Hardware implementation: How can the representation and algorithm be realized physically?

This framework sounds simple, but is remarkably important since arguably most of the confusion in neuroscience (and phenomenology research) comes from starting a sentence on one Marr-Poggio level and finishing it on another, and this framework lets people debug that confusion.

offered the first “full-stack" paradigm for formalizing consciousness with Integrated Information Theory (IIT). It's notable as a viable empirical theory of consciousness in its own right, a clear enunciation of what the ‘proper goal' of a theory of consciousness should be (determining a mathematical object isomorphic to the phenomenology of a system), and also as a collection of clever tools for approaching the many sub-problems of consciousness. Tononi's lineage traces back to Nobel laureate Gerald Edelman, a distinction shared by many stars of modern neuroscience such as Karl Friston, Olaf Sporns, and Anil K. Seth.

provided the theoretical basis for formalizing invariants in physical systems through Noether's theorem: ‘every symmetry in a system's equations corresponds to a conserved quantity in that system (and vice-versa).' This formed the seed for modern gauge theory, the mathematical basis for modeling conservation laws for energy, mass, momentum, and electric charge.

Noether's work may provide phenomenology at least two things:

1. A concrete mathematical tool for formalizing invariance relationships in subjective experience, in the form of gauge theory;

2. A research aesthetic for what kinds of approaches have produced particularly powerful formalisms in the past — e.g., a focus on determining the invariants of a system, constructing explanations in terms of the presence or absence of mathematical symmetries, and in general finding things people are already doing implicitly and describing them explicitly. Read more.

Self-Organization Lineages:

Traditionally, neuroscience has been concerned with cataloguing the brain: collecting discrete observations about anatomy, observed cyclic patterns (EEG frequencies), and cell types and neurotransmitters, and trying to match these facts with functional stories. However, it's increasingly clear that these sorts of neat stories about localized function are artifacts of the tools we're using to look at the brain, not of the brain's underlying computational structure.

What's the alternative? Instead of centering our exploration on the sorts of raw data our tools are able to gather, we can approach the brain as a self-organizing system, something which uses a few core principles to both build and regulate itself. As such, if we can reverse-engineer these core principles and use what tools we have to validate these bottom-up models, we can both understand the internal logic of the brain's algorithms — the how and why the brain does what it does — as well as find more elegant intervention points for altering it.

notes that adaptive systems such as the brain must resist a natural tendency to disorder — i.e. they must ‘swim upstream' against the second law of thermodynamics to maintain homeostasis — and argues that systems self-organizing against this constraint will exhibit certain predictable properties. In Friston's words:

In short, the long-term (distal) imperative — of maintaining states within physiological bounds — translates into a short-term (proximal) avoidance of surprise. Surprise here relates not just to the current state, which cannot be changed, but also to movement from one state to another, which can change. This motion can be complicated and itinerant (wandering) provided that it revisits a small set of states, called a global random attractor, that are compatible with survival (for example, driving a car within a small margin of error). It is this motion that the free-energy principle optimizes.

Friston's free-energy principle forms the core of a ‘full-stack' model of how the brain self-organizes, and one with corresponding implications for the computational, structural, and dynamical properties of mind.

has pioneered a new method for interpreting neuroimaging which (unlike conventional approaches) may plausibly measure things directly relevant to phenomenology. Essentially, it's a method for combining fMRI/DTI/MRI to calculate a brain's intrinsic ‘eigenvalues', or the neural frequencies which naturally resonate in a given brain, as well as the way the brain is currently distributing energy (periodic neural activity) between these eigenvalues. Furthermore, it seems a priori plausible that measuring these natural resonances could be a powerful technique for understanding the brain, since (1) they follow nicely predictable mathematical laws, and (2) a system with such harmonics will likely self-organize around them, and thus have a hidden predictability or elegance.

has worked extensively on importing key concepts from statistical physics and information theory into neurobiology. His flagship work, the entropic brain, offers entropy and self-organized criticality as key properties which constrain both the dynamics and quality of conscious states, and discusses the effects of psychedelics on these properties:

At its core, the entropic brain hypothesis proposes that the quality of any conscious state depends on the system's entropy measured via key parameters of brain function. Entropy is a powerful explanatory tool for cognitive neuroscience since it provides a quantitative index of a dynamic system's randomness or disorder while simultaneously describing its informational character, i.e., our uncertainty about the system's state if we were to sample it at any given time-point. When applied in the context of the brain, this allows us to make a translation between mechanistic and qualitative properties.

System entropy, as it is applied to the brain, is related to another current hot-topic in cognitive neuroscience, namely “self-organized criticality" (footnote 3 of “the entropic brain; Chialvo et al., 2007). The phenomenon of self-organized criticality refers to how a complex system (i.e., a system with many constituting units that displays emergent properties at the global-level beyond those implicated by its individual units) forced away from equilibrium by a regular input of energy, begins to exhibit interesting properties once it reaches a critical point in a relatively narrow transition zone between the two extremes of system order and chaos. Three properties displayed by critical systems that are especially relevant to the present paper are: (1) a maximum number of “metastable" or transiently-stable states (Tognoli and Kelso, 2014), (2) maximum sensitivity to perturbation, and (3) a propensity for cascade-like processes that propagate throughout the system, referred to as “avalanches" (Beggs and Plenz, 2003).

What many overlook about Carhart-Harris's work is how his concept of ‘entropic disintegration' (the process by which a large influx of energy overwhelms existing attractors and causes the brain to self-organize around new equilibria) opens the door to sophisticated analogies between the self-organizational dynamics brains exhibit when pushed into high-energy states, and the self-organizational dynamics of metals when heated above their recrystallization temperature. QRI is working to extend Carhart-Harris's work on entropic disintegration under the frame of ‘neural annealing‘.

Phenomenology Lineages:

What are the natural kinds of subjective experience? Are there universal ‘laws of psychodynamics' one can discover from introspection? How would one make tangible progress on formalizing a true science of phenomenology? These are all hard problems, and answers are rare and difficult to validate. However, there are some lineages which seem to have particularly useful, concrete, and systematic ontologies of mind:

has written extensively about how the texture of phenomenological experience is at least as important as its intentional content, how various pharmacological substances can alter phenomenological texture in predictable ways, and how this offers various generative heuristics for future research in both phenomenology and pharmacology. A core thread in David's work is valence realism, or the idea that some experiences really do feel better than others, and that this can bridge a key part of the is-ought distinction. Further reading: Can Biotechnology Abolish Suffering?

is many things. A proponent of indirect realism about perception. A champion of analog neural computation based on principles of harmonic resonance. And one of the most insightful and rational psychonauts of all times. His worldview packs a powerful punch of synergistic ideas, and reading his work is a psychoactive experience on multiple levels. You can read about some of the highlights that made us include Steven Lehar as a QRI lineage in this in-depth overview of his work.

provide an invaluable tool for studying consciousness. Such substances enable researchers to induce altered states with large effect sizes in a reliable manner. These exotic states are crucial for reverse-engineering the underlying formalism for consciousness. Ignoring them, in our view, is analogous to physicists ignoring extreme states such as black holes, plasma, or supercritical fluids when aiming to understand the nature of energy, matter, and the physical world. These substances are useful for phenomenological research because they enable users to study the subtle structure of their experience with unique clarity.

Further, by clarifying our criteria for a “high-quality" phenomenological report, we can gather orders of magnitude more information than from hundreds of mediocre reports. QRI is careful to note the distinction between the “intentional content" (what happened) and the “phenomenal content" (how it felt) of such first-person accounts. For example, noting that one's “tracers followed a control-interrupt frequency of 15Hz" is very different from noting that: "the trees spoke to me". Generally, we value the phenomenal content over the intentional content of such experiences. For specific examples of high-quality reports, see: Self-Locatingly Uncertain Psilocybin Trip Report by an Anonymous Reader.

Overall, synthesizing first-person psychoactive reports, combined with analyzing increasingly large brain-imaging datasets of the key signatures of particular substances allows us to bridge the age-old divide between the 1st person and the 3rd person.

Lastly, though psychoactive substances are useful tools, they are merely one tool in a toolkit of other exotic states we explore, such as: intense meditative states (eg. the jhanas) and other spiritual experiences. For further reading, see: Their Scientific Significance is Hard to Overstate.

is, at its core, an attempt at a complete description of phenomenology and its dynamics, including a description of what suffering is and how it arises, and a recipe for how to disrupt the dynamics which lead to suffering. Or as Bhikkhu Bodhi notes in his introduction to A Comprehensive Manual of Abhidhamma (a core Buddhist text):

“The system that the Abhidhamma Piṭaka articulates is simultaneously a philosophy, a psychology, and an ethics, all integrated into the framework of a program for liberation. The Abhidhamma may be described as a philosophy because it proposes an ontology, a perspective on the nature of the real. … The project starts from the premise that to attain the wisdom that knows things ‘as they really are,' a sharp wedge must be driven between those types of entities that possess ontological ultimacy, that is, the dhammas, and those types of entities that exist only as conceptual constructs but are mistakenly grasped as ultimately real."

There's an enormous amount of skillful phenomenological wisdom in Buddhism, all shaped by a 2600-year evolutionary process toward usefulness and persistence. Although not all of Buddhist theory can be imported to a more mathematical frame as-is, there are surprising parallels between Buddhism's theory of mind and QRI's other research lineages, e.g. the ‘self' as a leaky reification formed from self-reinforcing algorithmic processes, jhanas as resonant modes of the brain, etc. Likewise, the meta-heuristic of “how would Buddha research consciousness and suffering if he were alive today?" may be highly generative.

Integrative Lineages:

— In Principia Qualia (PQ), Michael makes significant contributions to the field of consciousness research that clarify otherwise confusing conceptual puzzles. Michael notes that Tononi’s Integrated Information Theory (IIT) of consciousness is the first to provide a fully mathematical account of consciousness. Michael points out significant issues with the theory, but takes on the spirit of mathematizing consciousness and reifies this goal in a very helpful way. Namely, he realizes that IIT’s goal of taking a physical system and then generating a mathematical object out of it, such that the mathematical features of that object are isomorphic to the phenomenology of the experience the system produces, is not only a sensible goal, but a key goal any good theory of consciousness must have. He defines the thesis that this is possible as “Qualia Formalism” (“for any given conscious experience, there exists – in principle – a mathematical object isomorphic to its phenomenology”, PQ pg. 25). Furthermore, he also defines “Qualia Structuralism”, which makes the additional claim that the mathematical object isomorphic to a conscious experience has a rich mathematical structure. With this kind of analysis it becomes possible to locate consciousness in nature in a way that avoids the fuzziness inherent in non-formalist accounts of consciousness (cf. Andrés’ “being/form boundary”). Importantly, Michael then articulates how formalism allows us to tackle Chalmer’s Hard Problem of Consciousness – which is insoluble as formulated – by taking it apart into conceptually crisp modular subcomponents that are solvable (cf. “breaking down the problem of consciousness”). In particular, he modularizes the problem of consciousness into 8 subproblems: the Reality Mapping, Substrate, Boundary/Binding, Scale, Topology of Information, State Space, Vocabulary, and Translation problems (PQ, pg 21-22), from which many QRI ideas scaffold off of. He then goes on to discuss valence within this framework: he coins the term “Valence Structuralism” to name the hypothesis that “valence has a simple encoding in the mathematical representation of a system’s qualia”. This is a radical departure from historical and contemporary explanations for the nature of valence. Namely, that whether an experience feels good or bad is not a function of what the experience does (i.e. its computational role, in Marr’s levels of analysis), what information is being processed by it (i.e. algorithmic structure, in Marr’s levels of analysis), or implementation details (such as neurotransmitters like serotonin, dopamine, opioids, etc. or functional localization such as “the pleasure centers”). Rather, it is a function of the mathematical structure of a system’s formalism. In turn, he then presents the Symmetry Theory of Valence (STV), which posits that the key mathematical feature that determines the valence of a formalism is its symmetry. While others have pointed out the funny connection between phenomenological and sensorial symmetry on the one hand and regularity and positive valence on the other (e.g. Lehar’s Psycho-Aesthetic Hypothesis), to our knowledge Mike is the first to provide a framework that can faithfully “import to physicalism” in a conceptually clear way that properly navigates the subtleties of Marr’s levels of analysis. As explained by Andrés, it is *easy* to strawman poorly articulated version of STV. But STV as presented in PQ is formulated in a conceptually clear way that is much harder to strawman.

STV as stated in PQ is under-constrained in a number of ways, but its role as a conceptual generator is nonetheless really consequential. Additionally, Michael hypothesized that the phenomenological opposite of symmetry would not be the absence of it, but rather, the presence of anti-symmetry. In concrete terms, this might explain why unpleasant sensations can be in some circumstances very simple: negative valence is not, in this framework, the lack of symmetry, but rather the simultaneous presence of incompatible symmetries. This can conceptually reframe the role of various brain regions. It is not, as obviously makes no sense upon reflection, that the pleasure centers have the “essence of pleasure” in them. Rather, according to STV they would be playing a system-wide role, such as working as “tuning knobs for harmony”, essentially modulating the symmetry of the formalism in an efficient way. Likewise, STV allows us to reinterpret the role of specific neurotransmitters, the way in which noise affects us, and the nature of various neurological disorders.

Since PQ, Michael has also written integrative pieces that bridge the theoretical frameworks of formalism, self-organization, and phenomenology. In Against Functionalism he articulated the myriad paradoxes and contradictions that arise if you identify consciousness at Marr’s algorithmic level of analysis. This is deeply important, in so far as it illuminates the impossibility of digital sentience, and thus, suffering (a matter of great importance in the field of Effective Altruism). In A Future for Neuroscience, Michael discusses how QRI’s lineages, including Atasoy’s Connectome Specific Harmonic Wave analysis of neural activity have the potential to deliver enormous explanatory power. In particular, he discusses the likely relationship between felt-sense and resonance, how personality factors can be described in such theoretical terms by coining “Entrainment Quotient” (EnQ) and “Metronome Quotient” (MQ), and introduces the concept of emotional key signatures. In The Neuroscience of Meditation: Four Models he identified ways in which meditation works as a kind of annealing (a connection Andrés originally had made with respect to psychedelic algorithmic reductions) by canalizing “semantically neutral energy” into the brain’s connectome harmonics and, as a result, kick-starting a process of entropic disintegration and re-organization in order to minimize internal dissonance. And in Neural Annealing: Toward a Neural Theory of Everything he further develops this conceptual framework in order to offer new insights on the mechanism of action for long- and short-term drug effects, sleep, romantic love, Bayesian inference, groupthink, and trauma. Of note, is that he offered a systematization of an analytic style Andrés used to make sense of DMT phenomenology, the psychological effects of art, and transformative festivals. This consists of reasoning about energy sources and sinks, their modulations (e.g. via de-activation, overwhelm, or avoiding in various ways), and the resulting annealing effects of each.*

Michael has also provided insightful observations and theoretical frameworks in a wide range of topics, such as what we believe is genuine progress in making sense of the phenomenology of Free Will (PQ pg. 58), the Simulation Argument (pg. 80), cosmological qualia, and even personal identity. A collection of his works can be found on his personal blog OpenTheory.net.

*Due to their extensive collaboration, Andrés and Mike independently arrived at many of these ideas about neural annealing between conversations, making it difficult to assign a clear originator. Around August 2020, Mike's research focus shifted to pursue new and fresh opportunities elsewhere. Mike's contribution to QRI and the research community as a whole will continue to be worthy of appreciation, respect, and celebration.