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:
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.
Noether's work may provide phenomenology at least two things:
A concrete mathematical tool for formalizing invariance relationships in subjective experience, in the form of gauge theory;
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.
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.
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.
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‘.
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:
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.
“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.
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.