aa r X i v : . [ phy s i c s . h i s t - ph ] S e p Physics, Determinism, and the Brain
George F R EllisSeptember 8, 2020
Abstract
This paper responds to claims that causal closure of the underlying microphysicsdetermines brain outcomes as a matter of principle, even if we cannot hope to evercarry out the needed calculations in practice. Following two papers of mine whereI claim firstly that downward causation enables genuine causal powers to occur athigher emergent levels in biology (and hence in the brain) [Ellis 2020a], and thatsecondly causal closure is in reality an interlevel affair involving even social levels[Ellis 2020b], Carlo Rovelli has engaged with me in a dialogue where he forcefullyrestates the reductionist position that microphysics alone determines all, specificallythe functioning of the brain. Here I respond to that claim in depth, claiming that if onefirstly takes into account the difference between synchronic and diachronic emergence,and secondly takes seriously the well established nature of biology in general andneuroscience in particular, my position is indeed correct.
Contents
The Learning Brain: Plasticity and Adaptation 29
After I published articles in
Foundation of Physics on emergence in solid state physics andbiology [Ellis 2020a] and on causal closure in biology and engineering contexts [Ellis 2020b],I have had an interesting exchange with Carlo Rovelli questioning my arguments. Thispaper responds as regards brain function. In Section 1.1 I will give an edited version ofthat interchange. In section 1.2 I outline my response. The key point is that while hisargument does indeed apply in the case of synchronic emergence - emergence at one pointin time - it does not apply in the case of diachronic emergence, that is, emergence as itunfolds over time. The paper is summarised in Section 1.3.
Here is a summary of that dialogue. CR - You list a large number of situations in which we understand phenomena, cause andeffects, and we make sense of reality, using high level concepts. I think this is great.- You point out that nobody is able to account for these phenomena in terms ofmicrophysics. I think this is true.- You emphasize the fact that we *need* these high level notions to understand theworld. I think this is important and is an observation which is underappreciated bymany. I agree.- You point out that to some extent similar points have been made by a number ofbiologists, solid state physicists, etcetera. I definitely think this is true.2 Then, you jump to a conclusion about microphysics, which does not follow fromany of the above. And I think that the large majority of physicists also think thatit does not follow. Given a phenomenon (condensed matter, biology, anything else),happening in a specific situation in a specific context, it is the belief of the nearmajority of physicists today that the initial microphysical data (which we may notknow), determines uniquely the probability distribution of the later outcomes.How does this square with all your examples, and why there is no contradictionbetween all your examples and the points you make, and this? The reason is thatin all your examples the high-level cause is also a microphysics configuration. Forinstance, a crystal configuration determines the motion of the electrons, smokingcauses cancer, my getting excited changes the motion of the electrons in my body.All this is true of course. But a crystal configuration, smoking, my getting excitedare also microphysical states of affairs. Once we fold them in the microphysics, thereis no reason whatsoever for the microphysics not to be sufficient to determine whathappens. There isn’t something “in addition” to the microphysics. It is only thatthe microphysics is far more complicated than what we can manage directly in ourcalculations of simple minded understanding.Where is our disagreement? It is not big. But I think it is crucial, and I thinkit can be summarized in the case of the effect of Jupiter and the drop of wateron the beach of Marseille affected by its gravitational pull. Here we can take themicrophysics to be the Newtonian gravitation of all the small grains of matter inthe Solar system, governed by classical mechanics with Newtonian interactions andshort scale pressure. This is a good level of approximation. Can we compute themotion of the drop of water using the Newton equations and all the forces? Of coursewe cannot. There are far too many grains of matter in the Solar system. But wecan go to a higher level description, where we ignore a huge amount of details andwe represent everything in terms of a few planets in their orbits. ”Planet” is not anotion in the microphysics. It is a simple calculation to account for the tidal forcesdue to the Moon and the Sun and to estimate the small correction due to Jupiter.And we find that the drop of water last Tuesday came a bit higher than expectedbecause of Jupiter.Here is the point: there is nothing I see, in all the examples you mention thatdistinguishes them from this version of“top-down-causation”: The high-level effect ofJupiter affects the motion of a drop of water. I do not see any difference between thisand the crystalline structure affecting motion of electrons in condensed matter, or abiological molecule reacting to an evolutionary pressure, or my excitement affectingthe motion of an electron. GE You are then agreeing that high level causation is real? CR ”Causal” mean all sort of different things for all sort of different people. I am notbeing pedantic, I think it is one of the cruxes of the matter. Instinctively, I am withRussell that notices that there are no ”causes” in physics: there are just regularitiesexpressed by laws. But of course I am aware (with Cartwright ) that we do usecauses heavily and effectively . I think that ”causes” make sense with respect toan agent that can act, and that the agency of the agent is ultimately rooted inentropy growth. If ”causal” concepts are understood in this high level and sort ofnon-fundamental manner, then suddenly your entire project makes sense to me Ido not think that anybody would deny that smoking causes cancer. Therefore, yes,I agree that high level causation is real. I think that ”smoking” denotes a large3nsemble of microphysical configurations, all of which (actually, in this context:many of which), evolving according to microphysical laws without any reference tohigher order notions, evolve into later microphysical configurations that belong tothe ensemble that we call ”a person with cancer”. GE “The reason is that in all your examples the high-level cause is also a microphysicsconfiguration” Yes indeed This sounds very close to Denis Noble’s Principle of Bio-logical relativity, extended to include the physics level, which is what I propose inmy two papers [Ellis 2020a] [Ellis 2020b]. I certainly state that the micro levels areeffective, as you do - there is no disagreement there. Maybe then we are not so farapart. Question: would you agree that you can invert that statement to get: ”in allthese examples the microphysics configuration is also a high-level cause”? CR Here we are back to the ambiguity of the notion of “cause”. I do not know what youmean. The microphysical configurations belonging to “smoking” do evolve into thosebelonging to “cancer”, while the microphysical configurations “almost everything thesame but not smoking”, do not. If this is what we mean by “cause”, I agree. If thereis some other meaning of “cause”, I sincerely do not understand it. I think that thisis what we more more less mean usually, (what else means “smoking causes cancer”if not that by not not smoking we can get less cancer?), then I am happy to use“cause” here. GE This is the key phrase: ”Once we fold them in the microphysics”. This sounds verymuch like what I am saying. The higher level situation is setting the context for themicrophysics to act, and hence shaping the specific outcomes that occur through themicrophysics If that is what you mean, then we agree! Otherwise what does thatphrase mean? CR No, here we disagree, I think. Because there isn’t the microphysics, and then some-thing else that sets the context. The macro-physics is just a way of talking aboutmicrophysical states. ”Smoking” is not something that that is added to the micro-physics: it is that one possible configuration of the microphysics. More precisely:an ensemble of many possible configurations of the microphysics. This is the mainpoint: you cannot have the same microphysics with different ”contexts”. Different”contexts” always require different microphysics. It is the key question: the questionI asked you when we started talking about that at a conference somewhere: supposethere are two Chess games on two different planets: everything looks the same butthe rules of Chess are a bit different on the two planets (in one you can castle afterhaving moved the king, in the other you cannot). Here a very high order difference(different rules of Chess) make the evolution different in the two planets. BUT (hereis the point): could the rules in the two planets be exactly the same if the micro-physics was the same? NO, of course, because different rules of the games meansdifferent memories stored in the players brains, hence different synapses, hence dif-ferent physics. To get a difference, you need different microphysical configurations.You cannot have a high level source of difference without having a microphysics leveldifference that achieves the same result. GE To get full clarity, please clarify two questions:1. Consider one person and the issue of smoking; or (for your neuroscientist friends)one person’s brain states when they play chess. Are you envisaging the total cause oftheir future behaviour being the microphysics configuration of their own body/brain,4r of some larger ensemble of particles? If so, what set? What set does “causalclosure””, in your terms, refer to? CR A larger ensemble of course, because what happens to a person depends on plenty ofexterior influences. GE The second question.2. Do you consider “folding them in the microphysics” (first reply above) as anongoing process that is taking place all the time, or not? CR “Folding then in the microphysics” means recognizing what they are, once translatedinto microphysics. They are ensembles of microphysical states. If you do this atthe start of a time interval and if you include enough degrees of freedom to accountfor anything that matters, then the microscopic evolution does the job, whether ornot we can compute. If this was NOT the case, we would have found cases whetherthese evolution laws fail. When people say that a theory is ”causally closed” theymean that the initial conditions determine the following (of course: in principle. Inpractice we cannot do the calculations, nor know all relevant initial conditions.) GE To be clear, is it a process that takes place once at the start of some interaction (thebrain starts considering a chess problem) and then the physics by itself determinesall outcomes; or is it an ongoing process that is taking place all the time ? CR It is not a process. I can in principle describe the set of events without any referenceto high level concepts, or I can describe the set of events using high level concepts.Both work. The difference is that one may be unmanageable and the other may bemanageable. We have these different levels of description everywhere in life: I canthink of my trip from Verona to Marseille accounting for the instantaneous changesof velocities, or I can represent it just as average constant velocity with some snack-pauses. The first is unmanageable, the second uses high level concepts, is moreuseful. But it has less information than the first, not more. GE Finally, of course I believe the outcomes lies in the space of possibilities allowed bythe microphysics. It could not be otherwise, and the marvel is that that set oflaws is able to produce such complexity. There is no way I am underestimatingwhat the physics allows. In the case of biology, I see it as happening because thephysics allows the existence of the Platonic space of all possible proteins discussedby Andreas Wagner in his marvelous book
Arrival of the Fittest [Wagner 2014]. CR Yes, this is what I meant. I am glad we agree here. This space of possibility isimmense, and too hard to explore theoretically even if we know the microscopic lawsand the fact these laws are not violated and they determine (probabilistic) evolutionunequivocally. Still, we carve it out for our understanding by recognising high levelpatterns and using them. But then why do you need the microphysics to be affectedby something outside it? You do not need it. And we have zero evidence for anythinglike this. The autonomy of the higher level logic that you keep citing is no argumentagainst the autonomy of the microphysics.But you seem to mean more. Do you mean that they select which dynamical historiesare realised and which not? That is: which initial conditions are allowed and whichnot? Or they select which quantum outcomes become actual, over and above thequantum probability amplitudes? Or what else?5t seems to me you confuse the richness of the tools of a physicist, and the complexityof reality, with a statement about lack of causal closure of the microphysics, thatdoes not follow.Finally, a last response: CR Today the burden of the proof is not on this side. Is on the opposite side. Because: (i)
There is no single phenomenon in the world where microphysics has been provenwrong (in its domain of validity of velocity, energy, size...). (ii)
By induction and Occam razor, is a good assumption that in its domain validityit holds. (iii)
There are phenomena too complex to calculate explicitly with microphysics.These provide no evidence against (ii), they only testify to our limited tools.There is much more, but I will not repeat it all. Rather I summarise points of agreement,of misunderstanding, and of disagreement, and then take up that challenge.
Points of agreement • All is based in immutable lower level physical laws, unaffected by context • These laws allow immensely complex outcomes when applied to very complex mi-crostates, such as those that underlie a human brain • Testable higher level laws correctly express dynamics at higher levels • They make processes at that level transparent in a way that is completely hiddenwhen one traces that same dynamics at the lower levels
Point of misunderstanding • Downward causation [ CR ] From your examples it does not follow that our current elementary theories,such GR and the SM, have free parameters that are controlled by something elsethat we understand. That would be a wild speculation unsupported by anything.[ GE ] The theories themselves of course do not have such parameters, and I havenever claimed that they do. But Lagrangians used in specific contexts do.Those theories per se have generic Lagrangians that apply to anything at all, and sosay nothing detailed about anything specific. They do not by themselves determineoutcomes in biology or engineering. A particular context determines the details ofthe terms in the Lagrangian, and that happens in a contextually dependent way:after all, in a particular case it represents a specific context. Once the Lagrangianhas been determined at time t then the next emergent step is indeed fully deter-mined purely at the micro physical level, as Carlo claims. But macro conditionscan then change parameters in the Lagrangian. That is where the downward causaleffects come in. For specific examples, the case of transistors in digital computers isdiscussed in [Ellis and Drossel 2019], and voltage gated ion channels in the brain in[Ellis and Kopel 2019].The key issue is whether downward causation is real, having real causal powers. Iargue that it is; and that this kind of causation does not required any compromisingof the underlying physics. It works by changing constraints [Juarrero 2002].6 oints of disagreementCR (i) In principle, we could entirely deduce what happens even ignoring the high-levelconcepts (ii)
In practice, we use high-level concepts (iii)
In addition, we get a better grasp, a better sense of ”understanding”, a bettercontrol, in terms of higher level notions.Hence: high-level notions are far more relevant for comprehension of the world. Ithink that (ii) and (iii) are very important.( iv ) But if you add the negation of (i), you get everybody disagreeing (me included)and the message about (ii) and (iii) does not go through. GE 1
Causation in biology is an interlevel concept [Mossio 2013]. Physics underlies thisbut does not by itself give causal closure. What Carlo calls “causal closure of physics”is in fact the statement that at its own level, it is a well-posed theory: a completelydifferent affair. Carlo’s statement can be defended in the synchronic case (at a time), but is notalways true for individual brains in the diachronic case (unfolding over time). Statistics don’t cut it. Individual events occur. We have to explain why specificindividual brain events occur, for example leading to the specific words in Carlo’semails. Specific events in individual lives occur and need to be accounted for. Microphysics enables this but does not determine the outcomes. The basic physicsinteractions of course enable all this to happen: they allow incredible complexity toemerge. Higher level organising principles such as Darwin’s theory of evolution thencome into play. That then changes the macro level context and hence the micro levelcontext. This downward process [Campbell 1974] relies on concepts such as ‘living’that simply cannot be represented at the microlevel, but determine outcomes. The reductionist physics view is based in a linear view of causation. Central to theway biology works are the closely related ideas of self cause and circular causation . In the rest of this paper, I give a full response to Carlo’s arguments in the case of thebrain, based in the nature of causation in biology. It rests on three things. First, tak-ing seriously the nature of biology in general [Campbell and Reece 2008] and neuroscience[Kandel 2012] [Kandel et al
Synchronic and diachronic emergence
Carlo’s argument - the microstate uniquelydetermines macro level outcomes - is correct when we consider synchronic emergence.That is what a lot of neuroscience is about. It is not valid however when one considersdiachronic emergence. The issue here is one of timescales.
Synchronic emergence is when the timescale δt := t b − t a of the considered micrody-namic outcomes is very short relative to the timescale δT of change of structures at themicro scale: δT ≫ δt . It is the issue of emergent dynamics when parameters are constant7nd constraints unchanging. In the case of the brain this would for example be the flowof electrons in axons leading to mental outcomes at that time, with this micro structuretaken as unchanging. Electrons and ions flow in a given set of neural connections. Diachronic emergence is when the timescale of micro dynamic outcomes considered δt is of the same order or larger than the timescale δT of change of structure at the microscale: δT ≤ δt , so microdynamics contexts alters significantly during this time. It is thecase when parameters or constraints change because of interactions that are taking place.In the case of the brain this would for example be when something new is learned so thatstrengths of neural connections are altered. Consider first a single brain
Dynamic outcomes at the molecular scale are due to thespecific structures at the cellular scale, neural connectivity for example, and the way thatthey in turn constrain electron and ion activity. Three points arise. • First, the brain is an open system . It is not possible for the initial physical stateto determine later states because of the flood of data incoming all the time. Thelast round of microlevel data does not determine the initial data that applies at thenext round of synchronic emergence. The brain has evolved a set of mechanisms thatenable it to cope with the stream of new data flowing in all the time by perceiving itsmeaning, predicting futures, and planning how to respond. This is what determinesoutcomes rather than evolution from the last round of initial data. • Second, the brain is a plastic brain that changes over time as it learns. Neuralconnections are altered as learning takes place in response to the incoming streamof data. This change in constraints alters future patterns of electron and ion flows.This learning involves higher level variables and understandings such as “A globalCoronavirus pandemic is taking place”, that cannot be characterised at lower levelsand cannot be predicted from the initial brain microdata. • Third, there is a great deal of stochasticity at the molecular level that breaksthe ideal of Laplacian determinism at that level. Molecular machines have beenevolved that take advantage of that stochasticity to extract order from chaos. Froma higher level perspective, this stochasticity enables organisms to select lower leveloutcomes that are advantageous to higher level needs. From a systems perspective,this enables higher level organising principles such as existence of dynamical systembasins of attraction to determine outcomes.This argument applies to all biology, as all biological systems are by their nature opensystems [Peacocke 1989]. The initial physics data for any organism by itself cannot inprinciple determine specific later outcomes because of this openness.The fundamental physical laws are not altered or overwritten when this happens; ratherthe context in which they operate - for example opening or closing of ion channels inaxons - determine what the specific outcomes of generic physical laws will be as alterconfiguration. From a physics viewpoint this is represented by time dependent constraintterms or potentials in the underlying Hamiltonian [Ellis and Kopel 2019].
The whole universe gambit
The ultimate physicalist response is “Yes the brain maybe an open system but the whole universe is not; and the brain is just part of the universe,which is causally complete. Hence brain dynamics is controlled by the microphysics alonewhen one takes this into account, because it determines all the incoming information tothe brain”. However this argument fails for the following reasons:8
Firstly there is irreducible quantum uncertainty in outcomes, which impliesthe lower physics levels are in fact not causally complete. This can get amplifiedto macroscales by mechanisms that change mental outcomes, such as altered geneexpression due to damage by high energy photons. • Secondly, this downward process - inflow of outside information to individual brains- does not uniquely determine brain how microstructures change through memoryprocesses because of multiple realisability . But such uniqueness is required tosustain a claim that the causal closedness of microphysics determines specific brainoutcomes over time. • Thirdly, chaotic dynamics associated with strange attractors occurs, which meansthe emergent dynamics of weather patterns is not in fact predictable even in principleover sufficiently long timescales. This affects decisions such as whether to take anumbrella when going to the shops or not. • Fourthly, microbiome dynamics in the external world affects brain outcomes inunpredictable ways, for example when a global pandemic occurs • Fifthly, this all takes place in a social context where social interactions take placebetween many brains, each of which is itself an open system. Irreducible uncertaintyinfluences such contexts due to the real butterfly effect (weather) and the impossi-bility, due to the molecular storm, of predicting specific microbiome mutations thatoccur (e.g. COVID-19), leading to social policy decisions, that are high level vari-ables influencing macro level brain states. The outcomes then influence details ofsynaptic connections and hence shape future electron and ion flows.This downward causation from the social/psychological level to action potential spikechains and synapse activation is essential to the specific outcomes that occur at the physicallevel of electron and ion flows in individual brains. Causal closure only follows when weinclude those higher level variables in the dynamics.
Section 2 sets the scene by discussing the foundations for what follows, in particular thefact that life is an ongoing adaptive process. In the following sections I discuss the keyissues that support my view.Firstly, an individual brain is an open system, and has been adapted to handle theproblems this represents in successfully navigating the world (Section 3). This rather thanthe initial brain micro data determines outcomes.Secondly, the brain learns: it is plastic at both macro and micro levels, which contin-ually changes the context within which the lower level physics operates (Section 4).Third, the kind of Laplacian view of determinism underlying Carlo’s position is brokenat the molecular level because of the huge degree of stochasticity that happens at that level(Section 5). Biological processes - such as Darwinian evolution, action choices, and thebrain pursuing a line of logical argumentation - are what in fact determine outcomes, takingadvantage of that stochasticity. Biological causation occurs selects preferred outcomesfrom the molecular storm, and the brain selects from action options.In section 6 I counter the whole universe gambit by claiming that this will not workbecause of quantum wave function collapse, macro level chaotic dynamics, multiple realis-ability of macro brain states, and unpredictable microbiome interactions that affect braindynamics both directly and via their social outcomes.9ection 7 consider how higher level organising principles - the effective laws that operateat higher levels - are in fact what shapes outcomes. This is what enables causal closure -an interlevel affair - in practice. I also comment on the issue of freewill (Section 7.2).
As stated above, the premise of this paper is that when relating physics to life, one shouldtake seriously the nature of biology as well as that of physics. I assume the standard un-derlying microphysics for everyday life, based in the Lagrangian for electrons, protons, andnuclei, see [Laughlin and Pines 2000] and [Bishop 2005]. This section sets the foundationfor what follows by discussing the nature of biology and of causation.Section 2.1 discusses the basic nature of biology. Section 2.2 outlines the biologicalhierarchy of structure and function. Section 2.3 discusses the nature of Effective Theoriesat each emergent level L . Section 2.4 discusses the equal validity of each level in causalterms. Section 2.5 discusses the various types of downward causation, and Aristotle’s fourtypes of causes as well as Tinbergen’s ‘Why’ questions. Section 2.6 discusses the importantissue of multiple realisability of higher level structure and function at lower levels. FinallySection 2.7 discusses the key role of Higher Level Organising Principles. All life [Campbell and Reece 2008] is based in the interplay between structure (that is,physiology [Hall 2016] [Rhoades and Pflanzer 1989]) and function. For good functional,developmental, and evolutionary reasons, it is composed (
Table 1 : § AdaptiveModular Hierarchical Structures [Simon 2019] [Booch 2006] based in the underlyingphysics. It comes into being via the interaction between evolutionary and developmental(Evo-Devo) processes [Carroll 2005] [Carroll 2008], and has three key aspects.
1. Teleonomy: function/purpose
Life has a teleonomic nature, where Jacques Monoddefines teleonomy as the characteristic of being ”endowed with a purpose or project”([Monod 1971]:9) He points out the extreme efficiency of the teleonomic apparatus in ac-complishing the preservation and reproduction of the structure. As summarised by NobelPrizewinner Leland Hartwell and colleagues [Hartwell et al
Although living systems obey the laws of physics and chemistry, the notionof function or purpose differentiates biology from other natural sciences. Or-ganisms exist to reproduce, whereas, outside religious belief, rocks and starshave no purpose. Selection for function has produced the living cell, with aunique set of properties that distinguish it from inanimate systems of interact-ing molecules. Cells exist far from thermal equilibrium by harvesting energyfrom their environment. They are composed of thousands of different types ofmolecule. They contain information for their survival and reproduction, in theform of their DNA ”.Function and purpose emerge at the cell level. Francois Jacob says [Jacob 1974] “At each level of organisation novelties appear in both properties and logic. Toreproduce is not within the power of any single molecule by itself. This faculty An excellent introduction to the relevant mechanisms is given in [Noble 2016]. Quoted in [Peacocke 1989]:275. ppears only within the power of the simplest integron deserving to be called aliving organism, that is, the cell. But thereafter the rules of the game change.At the higher level integron, the cell population, natural selection imposes newconstraints and offers new possibilities. In this way, and without ceasing to obeythe principles that govern inanimate systems, living systems become subject tophenomena that have no meaning at the lower level. Biology can neither bereduced to physics, nor do without it. ”
2. Life is a process
Being alive is not a physical thing made of any specific elements.It is a process that occurs at macro levels, in an interconnected way. In the case of humanbeings it involves all levels from L4 (the cellular level) to Level L6 (individual humanbeings), allowing causal closure [Mossio 2013] [Mossio and Moreno 2010] and hence self-causation [Juarrero 2002] [Murphy and Brown 2007]. Life is an ongoing adaptive process involving metabolism, homeosta-sis, defence, and learning in the short term, reproduction, growth,and development in the medium term, and evolution in the longterm. It uses energy, disposes of waste heat and products, and usescontextual information to attain its purposes.
The claim I make is that this process of living has causal power, making things happenin an ongoing way. High level processes take place via an interlevel dialogue betweenlevels [Noble 2008], higher levels continually altering the context of the underlying phys-ical levels in order to carry out these functions [Ellis and Kopel 2019]. Yes of coursethe resulting physical processes can be traced out at the physics level. But my claimwill be that biological imperatives [Campbell and Reece 2008] enabled by physiologicalsystems [Rhoades and Pflanzer 1989] [Hall 2016] shape what happens. Evolutionary pro-cesses [Mayr 2001] [Carroll 2008] have enabled this synergy to occur [Noble 2016].
3. Basic biological needs and functions
In the case of animal life, the basic bio-logical functions are, B1:
Metabolism (acquiring energy and matter, getting rid of waste),
B2:
Homeostasis and defence,
B3:
Reproduction and subsequent development,
B4:
Mobility and the ability to act,
B5:
Information acquisition and processing.They serve as attractors when variation takes places ([Ginsburg and Jablonka 2019]:245).They are the higher level organising principles that evolution discovers and then embodiesin hierarchically structured physiological systems, where the macro functions are supportedat the micro level by metabolic networks, gene regulatory networks, and cell signalling net-works, selected from an abstract space of possibilities and realised through specific proteins[Wagner 2014]. Information is central to what happens [Nurse 2008] [Davies 2019].These principles cannot be described or identified at the underlying microphysical levelsnot just because the relevant variables are not available at that level, but because theirmultiple realisability at lower levels means they do not correspond to specific patterns ofinteractions at the ion and electron level. They correspond to a whole equivalence class ofsuch patterns of interactions (Section 2.6). An ‘Integron’ is each of the units in a hierarchy of discontinuous units formed by integration ofsub-units of the level below [Jacob 1974]:302. See
Table 1 , Section 2.2. Other forms of life share
B1-3. . Interaction networks These processes are realised by means of immensely complex interaction networks at the molecular level [Buchanan et al N1 : Metabolic Networks ([Wagner 2014] §
3) [Noble 2016]
N2:
Gene Regulatory Networks ([Wagner 2014] § N3:
Signalling Networks [Junker & Schreiber 2011] [Buchanan et al
N4:
Protein Interaction Networks [Junker & Schreiber 2011]based in very complex molecular interactions [Berridge 2014] and with higher level designprinciples shaping their structure [Alon 2006], and at the cellular level,
N5:
Neural Networks [Kandel et al
5. Branching causal logic
In order to meet these needs, the dynamics followed ateach level of biological hierarchies is based on contextually informed dynamical branch-ing L that support the functions α of a trait T in a specific environmental context E [Ellis and Kopel 2019]. Thus biological dynamics can be functionally-directed rather thandriven by inevitability or chance: Biological dynamics tends to further the function α of a trait T through contextually informed branching dynamics L (1)where the dynamics L in its simplest form is branching logic of the form [Hoffmann 2012] L: given context C, IF T ( X ) THEN F Y ) , ELSE F Z ) (2)(a default unstated “ELSE” is always to leave the status quo). Here X is a contextualvariable which can have many dimensions, Y and Z are variables that may be the samevariables as X or not. T ( X ) is the truth value of arbitrary evaluative statements dependingon X . It can be any combination of Boolean logical operations (NOT, AND, OR, NOR,etc.) and mathematical operations, while F Y ) and F Z ) are outcomes tending tofurther the function α . Thus they might be the homeostatic response “If blood sugarlevels are too high, release insulin”, or the conscious dynamic “If the weather forecastsays it will rain, take an umbrella”. At the molecular level, these operations are based inthe lock and key molecular recognition mechanism ([Noble 2016]:71), [Berridge 2014]. Thismechanism is how information [Nurse 2008] [Davies 2019] gets to shape physical outcomes.
6. Brain Function
The human brain supports all these activities by a series of higherlevel processes and functions. These are [Purves et al
BR1:
Sensation, perception, classification
BR2:
Prediction, planning, making decisions, and action
BR3:
Experimenting, learning, and remembering
BR4:
Experiencing and responding to emotions
BR5:
Interacting socially, communicating by symbols and language
BR6:
Metacognition, analysis, and reflection, ‘off-line’ exploration of possibilities.It does so via its complex adaptive modular hierarchical structure [Kandel et al
The framework for the following is the hierarchy of structure and function for the biologicalsciences shown in (
Table 1 ), based in the underlying physics.12 iology Levels Processes
Level 8 ( L8 ) Environment Ecological, environmental processesLevel 7 ( L7 ) Society Social processesLevel 6 ( L6 ) Individuals Psychological processes, actionsLevel 5 ( L5 ) Physiological systems Homeostasis, emergent functionsLevel 4 ( L4 ) Cells Basic processes of lifeLevel 3 ( L3 ) Biomolecules Gene regulation, metabolismLevel 2 ( L2 ) Atom, ion, electron Physics Atomic, ionic, electron interactionsLevel 1 ( L1 ) Particle and Nuclear Physics Quark, lepton interactions Table 1 : The hierarchy of structure for biology (left) and corresponding processes (right). L2 is the relevant physics level of emergence, L4 the fundamental biological level, madepossible by L3 (in particular proteins, RNA, DNA), in turn made possible by L2 and so L1 .The first level where the processes of life occur is L4 , the level of cells. At level L6 onefinds the integrated processes of an individual organism. At level L7 one finds sociology,economics, politics, and legal systems. I am assuming that each of these levels exists as a matter of fact - they exist ontologically.The key issue is, if we propose a specific level L exists ontologically, there should be a validEffective Theory ET L applicable at that level which characterizes that level. ‘Valid’ meansit either makes testable predictions that have been confirmed, or at least characterizes thevariables that would enter such a relation. Here following [Ellis 2020a] and [Ellis 2020b],one can characterise an Effective Theory ET L ( a L ) valid at some level L as follows: An Effective Theory ET L ( a L ) at an emergent level L is a reliable relationbetween initial conditions described by effective variables v L ∈ L and outcomes o L ∈ L : ET L ( a L ) : v L ∈ L → ET L ( a L )[ v L ] = o L ∈ L (3) where a L are parameters of the relation, and ET L ( a L ) may be an exact orstatistical law. The parameters a L may be vectorial or tensorial Thus I will define a meaningful level to exist if there is such a relation. Determining thatrelation is in effect epistemology, but what it indicates is the underlying ontology.The effective theory ET L ( a L ) is well posed if for specific choices of the parameters a L it provides a unique mapping (3) from v L to o L . This is the concept one should useinstead or referring to the theory as being causally complete. That is a misnomer becausefirstly, the idea of causality does not apply to the physics laws per se (although effectivetheories do), and secondly because causal completion - the set of conditions that actuallydetermine what outcomes will occur in real-world contexts - is always an interlevel affair,no single level L by itself is causally complete (Section 6.6). Effective Theories representverifiable patterns of causation at the relevant level, not causal closure [Ellis 2020b]. The cautionary note reflects the difficulty in establishing reliable relations at levels L6 - L8 . Thetheories may have to be described in terms of propensities rather than mathematical laws. They are nev-ertheless well established fields of study, for example [Gray and Bjorklund 2018] at Level L6 , [Berger 1963]at Level L7 , and [Houghton 2009] at Level L8 . ffective theory examples It is useful to give some examples of effective theories atdifferent levels. It is my contention, in agreement with [Noble 2012] [Noble 2016], thatreal causal processes are going on at each of these levels, even though this is enabled byunderlying levels, including the physics ones. The relevant effective theories are more thanjust useful descriptions of high level processes. In all but the last two cases this is demon-strated by the fact that evolution has selected genomes that result in them happening.Their causal effectiveness is a driver of evolutionary selection.
1. Gene regulation
The kind of gene regulatory processes discovered by Jacob andMonod [Jacob and Monod 1961] [Monod 1971] represent real causal processes at thecellular level (they require the relevant molecular processes, but can only take placein the context of a functioning cell [Hofmeyer 2018]). Their importance is that theyunderlie the Evo-Devo processes discussed in [Carroll 2005] [Carroll 2008].
2. Action potential propagation
Brain processes are supported at the micro level bypropagation of action potential spikes according to the Hodgkin-Huxley Equations[Hodgkin and Huxley 1952]. This is an emergent phenomenon that cannot be de-duced from the underlying physics per se because they involve constants that arenot fundamental physical constants. [Woodward 2018] defends the view that theexplanation the equations provide are causal in the interventionist sense.
3. The brain
The way the brain works overall [Kandel 2012] [Gray and Bjorklund 2018]is based in the underlying neuroscience [Kandel et al
4. Natural Selection
Natural selection [Mayr 2001] is a meta-principle: it is a process ofdownward causation [Campbell 1974] that allows the others listed above to come intobeing. Because the biological needs listed above are attractor states in the adaptivelandscape [McGhee 2011], evolutionary convergence takes place [McGhee 2006]: thatis, there are multiple ways they can be met. Any physiological implementation in theequivalence class that satisfies the need will do. Thus this is an example of multiplerealisability (Section 2.6), which characterizes topdown causation [Ellis 2016].
5. Smoking, lung cancer, and death
The relation between smoking and lung canceris an established causal link, as discussed in depth in [Pearl and Mackenzie 2018]. Itcan certainly be redescribed at the physics level, but the key concepts in the correla-tion - smoking, cancer - cannot. Therefore, starting off with an initial state describedat the microphysics level, one cannot even in principle determine the probabilitiesof cancer occurring on the basis of those variables alone, let alone when death willoccur as a result of the cancer, because death also cannot be described at that level.Once cancer occurs (at the genetic/cellular levels
L3/L4 ) leading to death (at thewhole organism level L6 ) this will alter physical outcomes at the ion/electron level L2 because the process of life (see above) has ceased. This is a real causal chain,not just a handy redescription of micro physics: smoking causes cancer and thendeath as a matter of fact. The physics allows this of course, but the actual physicaltrajectories and outcomes follows from the essential higher level dynamics of thecessation of being alive. 14 .4 Equal Validity of Levels There is a valid Effective Theory ET L at each level L , each of them represents a causallyvalid theory holding at its level, none more fundamental than the others. This is expressednicely in [Schweber 1993], commenting on Phil Anderson’s views: “Anderson believes in emergent laws. He holds the view that each level has itsown “fundamental” laws and its own ontology. Translated into the language ofparticle physicists, Anderson would say each level has its effective Lagrangianand its set of quasistable particles. In each level the effective Lagrangian - the“fundamental” description at that level - is the best we can do.” None of them can be deemed to be more fundamental than any other, inter alia becausenone of them is the fundamental level, i.e. none is the hoped for Theory of Everything(TOE). This has to be the case because we don’t know the underlying TOE, if there isone, and so don’t - and can’t - use it in real applications. So all the physics laws we usein applications are effective theories in the sense of [Castellani 2002], applicable at theappropriate level. Similarly, there are very well tested effective theories at levels
L3-L5 inbiology: the molecular level, the cellular level, the physiological systems level for example.Whenever there are well established laws at the higher levels (for example the laws ofperception at Level L6 ) the same applies to them too.More fundamentally, this equal causal validity occurs because higher levels are linked tolower levels by a combination of upwards and downwards causation [Noble 2012] [Noble 2016]so no level by itself is causally complete. They interact with each other with each levelplaying a role in causal completeness. Hence ([Noble 2016]:160), The Principle of Biological Relativity : There is no privileged level ofcausation in biology: living organisms are multi-level open stochastic systemsin which the behaviour at any level depends on higher and lower levels andcannot be fully understood in isolation
This is because of circular causality which for example necessarily involves downwardcausation from the whole cell to influence the behaviour of its molecules just as muchas upward causation form the molecular level to the cellular level [Noble 2016]:163-164).This applies to all levels in
Table 1 , i.e. it includes the underlying physics levels as well[Ellis and Kopel 2019] [Ellis 2020b], as has to be the case for physical consistency.In the case of the brain, after having set out in depth the hierarchical structure ofthe brain ([Churchland and Sejnowski 2016]:11,27-48), Churchland and Sejnowski state([Churchland and Sejnowski 2016]:415)“
An explanation of higher level phenomena in terms of lower level phenom-ena is usually referred to as a reduction, though not in the perjorative sensethat implies the higher levels are unreal, explanatorily dismissable, or somehowempirically dubious”, which agrees with the view put here. Brain computational processes have real causalpower [Marr 2010] [Scott 2002] [Churchland and Sejnowski 2016].
Causation can be characterised either in an interventionist or a counterfactual sense, eitherindicating when causation takes place [Pearl 2009] [Pearl and Mackenzie 2018]. The firstkey claim I make is that as well as upward causation, downward causation takes place15Noble 2012] [Ellis 2016]. The second one is that as well as efficient causation, Aristotle’sother forms of causation play a key role in real world outcomes.
Downward causation
Physicists take for granted upward causation, leading to emer-gence through aggregation effects such as coarse graining. However one can claim thereis also downward causation that occurs via various mechanisms [Noble 2008] [Ellis 2012][Ellis 2016], allowing strong emergence [Chalmers 2000] to occur. Carlo agrees downwardcausation takes place, but believes it can be rewritten purely in terms of low level physics,and hence does not represent strong emergence.Downwards effects in a biological system occur because of physiological processes[Noble 2008], [Noble 2012]. These processes [Hall 2016] are mediated at the molecularlevel by developmental systems [Oyama et al 2001] operating through metabolic and generegulator networks [Wagner 2014] and cell signalling networks [Berridge 2014], guided byhigher level physiological needs. They reach down to the underlying physical level L2 via time dependent constraints [Ellis and Kopel 2019]. The set of interactions betweenelements at that level is uniquely characterised by the laws of physics L , but their specificoutcomes are determined by the biological context in which they operate.An example is determination of heart rate. Pacemaker activity of the heart is viacells in the sinoatrial node that create an action potential and so alter ion channel out-comes. This pacemaking circuit is an integrative characteristic of the system as a whole[Fink and Noble 2008] - that is, it is an essentially higher level variable - that acts downto the molecular level [Noble 2012] [Noble 2016]. In the synchronic case - nothing changesat either macro or micro levels - it is correct that one can predict the lower level and hencethe higher level dynamics purely from the lower level initial state. However if the higherlevel state changes - an athlete starts running - the higher level state changes, and thisalters lower level conditions. Nothing about the initial molecular level state of the heartor the underlying physics state could predict this happening. Neither could initial knowl-edge of both the athletes heart and brain micro states determine this outcome, because itdepended on an external event - the firing of the starting gun, another macro level eventwhich the athlete’s initial states cannot determine.Considering the individual athlete, causation at the macro level is real: the firing of thestarting gun led to her leaving the starting post. Downward causation that alters motionof ATP molecules in her muscles via metabolic networks is real: that is a well establishedphysiological process [Rhoades and Pflanzer 1989]. The result is altered electron flows inthe muscles, in a way consistent with the laws of physics but unpredictable from her initialmicrophysical state. Regression to include the brain state of the person firing the gun willnot save the situation, as one then has to include all the influences on his brain state[Noble et al The network in question contains about 28 neurons and serves to drive themuscles controlling the teeth of the gastric mill so that food can be groundup for digestion. The output of the network is rhythmic, and hence the mus-cular action and the grinders movements are correspondingly rhythmic. Thebasic electrophysiological and anatomical features of the neurons have been cat-alogued, so that the microlevel vitae for each cell in the network is impressivelydetailed. What is not understood is how the cells in the network interact toconstitute a circuit that produces the rhythmic pattern. No one cell is a repos- tory for the cells rhythmic output; no one cell is itself the repository for theproperties displayed by the network as a whole. Where then does the rhythmic-ity come from? Very roughly speaking, from the patterns of interactions amongcells and the intrinsic properties of the component cells. The network produces rhythmic patterns in the cells, which produce rhythmic activityin the constitutive electrons and ions. This is a classic example of higher level ordercontrolling both macro and micro level outcomes.
Types of downward causation
The basic type of downward causation are as follows(developed from [Ellis 2012] [Noble 2012] [Noble 2016] [Ellis 2016]):
TD1A Boundary conditions are constraints on particles in a system arising from theenvironment as in the case of a cylinder determining pressure and temperature ofthe enclosed gas, or the shape of tongue and lips determining air vibrations and sospoken words. Structural Constraints are fairly rigid structures that determinepossible micro states of particles that make up the structure, as in the case of acylinder constraining the motion of a piston, or a skeleton that supports a body.
TD1B Channeling and Containing constraints are key forms of contextual causationshaping microbiological and neural outcomes.
Channeling constraints determinewhere reactants or electrical currents can flow, as in blood capillaries in a body, wiresin a computer, or neural axons and dendrites in a brain.
Containing constraints confine reactants to a limited region, so preventing them from diffusing away andproviding the context for reaction networks to function. A key case is a cell wall.
TD2A Gating and signalling constraints
Gating constraints control ingress andegress to a container, as in the case of voltage gated ion channels in axons, or ligandgated ion channels in synapses. They function via conformational changes controlledby voltage differential in the former case, and molecular recognition of ligands in thelatter case, thus underlying cell signalling processes [Berridge 2014].
TD2B Feedback control to attain goals is a cybernetic process where the differencebetween a goal and the actual state of a system generates an error signal that is fedback to a controller and causes corrective action, as in thermostats and engine gover-nors [Wiener 1948]. In biology this is homeostasis , a crucial feature of physiology atall levels [Hall 2016]. Because of this closed causal loop, goals determine outcomes.Changing the goals changes both macro and micro outcomes, as in altering the set-ting on a thermostat. In biology, multilevel homeostatic systems are continuallyresponding to internal changes and external perturbations [Billman 2020].
TD3A Creation of New Elements takes place in two ways.
Creation of newlower level elements occurs in physics when crystal level conditions create quasi-particles such as phonons that play a key role in dynamics at the electron level[Ellis 2020a]. This is what [Gillett 2019] calls a
Downward Constitutive relation .It occurs in biology when genes are read to create proteins, a contextual process[Gilbert and Epel 2009] controlled by gene regulatory networks according to higherlevel needs [Noble 2016].
Creation of new higher level elements restructureslower level relations and so alters lower level dynamics. In engineering this takes Carlo’s example of Jupiter causing tides on Earth fits here: Jupiter is part of the Earth’s environment,causing a detectable gravitational field at Marseilles.
TD3B Deleting or Altering Lower Level elements is the complementary processthat is crucial in biology. In developmental biology, apoptosis (programmed celldeath) plays a key role for example in digit formation (separating fingers and thumbs),while in neural development, synaptic connections are pruned as development takesplace [Wolpert et al
Adaptive selection due to selection criteria which alters eitherthe set of lower level elements by deletion as in Darwinian selection [Campbell 1974]and the functioning of the immune system, or selecting optimal configurations, as inneural network plasticity involved in learning.The higher level types of downward causation:
TD4 (Adaptive selection of goals) and
TD5 (Adaptive selection of selection criteria) build on these ones [Ellis 2012] [Ellis 2016].The key issue is whether any of these types of downward causation are really causallyeffective, or just redescriptions in convenient form of microphysical causation.
Aristotle’s kinds of causation
There is an important further point as regards causa-tion. As Aristotle pointed out [Bodnar 2018], there are four kinds of causation that occurin the real world. This is discussed by ([Juarrero 2002]:2,125-128,143) ([Noble 2016]:176-179) and ([Scott 2002]:298-300) They are • Material Cause : the physical stuff that is needed for an outcome; the stuff out ofwhich it is made, e.g., the bronze of a statue. In biology this is the physical stuff, thechemical elements as characterised by the periodic table, that make biology possible. • Formal Cause : which makes anything what it is and no other; the material causenecessary for some outcome must be given the appropriate form through the wayin which the material is arranged e.g., the shape of a statue. In biology, this is thestructure at each level that underlies function at that level: physiological systems[Hall 2016] and the underlying biomolecules such as proteins [Petsko and Ringe 2009]. • Efficient Cause : The primary source of the change or rest, the force that bringsan action into being; nowadays in the Newtonian case taken to be the effect of forceson inert matter, in the quantum chemistry case, Schr¨odinger’s equation. • Final Cause : the goal or purpose towards which something aims: “that for thesake of which a thing is done”.Physics only considers efficient causes [Juarrero 2002]. Biology however needs material,formal, and efficient causes. [Hofmeyer 2018] gives a careful analysis of how the relationbetween them can be represented and how they are are realised in biology, giving as anexample an enzyme that catalyses a reaction. He explains that a set of rules, a conventionor code, forms an interface between formal and efficient cause.All four kinds of causation are needed to determine specific outcomes in social contexts,which is the context within which brains function. Without taking them all into account,one cannot even account for existence of a teapot [Ellis 2005].18 network of causation is always in action when any specific outcomes occurs. Whenwe refer to ‘The Cause’, we are taking all the others for granted - the existence of theUniverse, of laws of physics of a specific nature, and of the Earth for example.
Tinbergen’s ‘Why’ questions
In biology, an alternative view on causation is providedby Tinbergen’s four ‘Why’ questions. [Bateson and Laland 2013] summarise thus: “Tinbergen pointed out that four fundamentally different types of problem areraised in biology, which he listed as ‘survival value’, ‘ontogeny’, ‘evolution’,and ‘causation’. These problems can be expressed as four questions about anyfeature of an organism: What is it for? How did it develop during the lifetimeof the individual? How did it evolve over the history of the species? And, howdoes it work?”
That is, he raises functional, developmental, evolutionary, and mechanistic issues thatall have to be answered in order to give a full explanation of existence, structure, andbehaviour of an organism.
A key point is that multiple realisability plays a fundamental role in strong emergence[Menzies 2003]. Any particular higher level state can be realised in a multiplicity of waysin terms of lower level states. In engineering or biological cases, a high level need deter-mines the high level function and thus a high level structure that fulfills it. This higherstructure is realised by suitable lower level structures, but there are billions of ways thiscan happen. It does not matter which of the equivalence class of lower level realisations isused to fulfill the higher level need, as long as it is indeed fulfilled. Consequently you can-not even express the dynamics driving what is happening in a sensible way at a lower level.Consider for example the statements
The piston is moving because hot gas on one sideis driving it and
A mouse is growing because the cells that make up its body are dividing .They cannot sensibly be described at any lower level not just because of the billions of lowerlevel particles involved in each case, but because there are so many billions of differentways this could happen at the lower level , this cannot be expressed sensibly at the protonand electron level. The point is the huge number of different combinations of lower levelentities can represent a single higher level variable. Any one of the entire equivalence classat the lower level will do. Thus it is not the individual variables at the lower level thatare the key to what is going on: it is the equivalence class to which they belong. But thatwhole equivalence class can be describer by a single variable at the macro level, so that isthe real effective variable in the dynamics. This is a kind of interlevel duality: { v L ∈ L } ⇔ { v i : v i ∈ E L-1 ( v L-1 ) ∈ (L-1) } (4)where E L-1 ( v L-1 ) is the equivalence class of variables v L-1 at Level
L-1 corresponding tothe one variable v L at Level L. The effective law EF L at Level L for the variables v L atthat level is equivalent to a law for an entire equivalence class E L-1 ( v L-1 ) of variables atLevel
L-1 . It does not translate into an Effective Law for natural variables v L-1 per se atLevel
L-1 .The importance of multiple realisability is discussed in [Menzies 2003] [Ellis 2019] and[Bishop and Ellis 2020]. 19 ssentially higher level variables and dynamics
The higher level conceptsare indispensible when multiple realisability occurs, firstly because they definethe space of data d L relevant at Level L , and secondly because of (4), variablesin this space cannot be represented as natural kinds at the lower level. EffectiveLaws EF L at level L can only be expressed at level L-1 in terms of an entireequivalence class at that level. One can only define that equivalence class byusing concepts defined at level L . To inject reality into this fact, remember that the equivalence class at the lower level istypically characterised by Avagadro’s number.
A key issue in the discussion is the degree to which higher level dynamics depends on thelower level dynamics. As can be seen from the previous subsections, the nature of biologicalcausation is quite unlike the nature of causation at the underlying physical levels. Whatdetermines these outcomes then?
Higher Level Organising Principles
The key idea here is that higher level biologicalOrganising Principles exist that are independent of the underlying lower level dynamics,and shape higher level outcomes. The specific lower level realisation is immaterial, as longas it is in the right equivalence class (Section 2.6). Generically they form attractors thatshape higher level outcomes [Juarrero 2002]152-162; the lower level components come alongfor the ride, with many biological oscillators being examples ([Noble 2016]:76-86,179).
Protectorates
This is parallel to the claim by [Laughlin and Pines 2000] of existenceof classical and quantum protectorates, governed by dynamical rules that characteriseemergent systems as such. They state“
There are higher organising principles in physics, such as localization andthe principle of continuous symmetry breaking, that cannot be deduced frommicroscopics even in principle. ... The crystalline state is the simplest knownexample of a quantum protectorate, a stable state of matter whose generic low-energy properties are determined by a higher organizing principle and nothingelse... they are transcendent in that they would continue to be true and lead toexact results even if the underlying Theory of Everything was changed.
As an example, [Haken 1996] states that profound analogies between different systemsbecome apparent at the order parameter level, and suggest that the occurrence of orderparameters in open systems is a general law of nature. He characterizes this in terms of a slaving principle [Haken and Wunderlin 1988]. [Green and Batterman 2020] develop thisfurther, citing the universality of critical phenomena as a physics case. The Renormalisa-tion Group explanation extracts structural features that stabilize macroscopic phenomenairrespective of changes in microscopic details Biology
In biology, such organising principles can be claimed to govern microbiology,physiology, and neuroscience (Sections 2.1 and 4). The idea is that once life exists andevolutionary processes have started, they are what shape outcomes, rather than the un-derlying physical laws, because they express essential biological needs [Kauffman 1995]. I thank Karl Friston for this comment. allow the outcomes to occur: they lie within the
Possibil-ity Space Ω L of outcomes allowed by the physical laws L , for instance the proteins en-abling all this to occur are characterised by a possibility space of huge dimension, asare the metabolic networks and gene regulatory networks that lead to specific outcomes[Wagner 2014]. But as emergence takes place through developmental processes repeatedmany many times over evolutionary timescales, it is these principles that determine biolog-ical success. Hence [Ginsburg and Jablonka 2019] it is they that determine evolutionaryoutcomes in an ongoing Evo-Devo process [Carroll 2005] [Carroll 2008]. They act as at-tractors for both evolution and for ongoing brain dynamics.This proposal is supported in multiple ways. In functional terms, homeostasis is a central organising principle in all physiologyat multiple scales: “
It is important to note that homeostatic regulation is not merely theproduct of a single negative feedback cycle but reflects the complex interaction of multiplefeedback systems that can be modified by higher control centers ” [Billman 2020]. Also phys-iological functions acting as dynamical systems have attractors that organise outcomes.For example, this happens in the neural dynamics of cell assemblies ([Scott 2002]:244-248):“
In Hopfield’s formulation, each attractor is viewed as a pattern stored non-locally by the net. Each such pattern will have a basin of attraction into whichthe system can be forced by sensory inputs.”
Thus cell assemblies form attractors ([Scott 2002]:287). Also Hopfield neural networksconverge to attractors in an energy landscape [Churchland and Sejnowski 2016]:88-89) andattractor networks are implemented by recurrent collaterals ([Rolls 2016]:75-98).
In developmental terms it can be expressed in terms of Waddington’s epigeneticlandscape [Gilbert 1991] ([Noble 2016]:169,259) which presents much the same idea viacell fate bifurcation diagrams. This is how developmental processes converge on outcomesbased in the same higher level organising principles.
In evolutionary terms, it can be expressed in terms of the adaptive landscape ofSewell Wright [Wright 1932l] [McGhee 2006], showing how evolution converges to adaptivepeaks where these principles are supported to a greater or lesser degree. This viewpointis supported by much evidence for convergent evolution [McGhee 2011].
Neuroscience
There is a huge amount written about neuroscience and biological psy-chology, with a vast amount of detail: [Scott 2002] [Purves et al et al
The Predictive Brain: Brains as open systems
Each human body, and each brain, is an open system. This is where the difference betweensynchronic and diachronic emergence is crucial. It has two aspects: our brains are notmade of the same matter as time progresses (Section 3.1), and new information is coming inall the time and altering our brain states (Section 3.2). The way this is interpreted dependson the fact that our brain is an emotional brain (Section 3.3) and a social brain (Section3.4). Language and symbolism enables abstract and social variables to affect outcomes(Section 3.5). Consequently the microphysical state of a specific person’s brain is unableas a matter of principle to predict their future brain states (Section 3.6) Predictive brainsthat can handle this situation are attractor states for brain evolutionary development.
Because we are open systems [Peacocke 1989], the human body at time t > t is notmade of the same material particles as it was at time t . Thus what happens in life is likethe case of a candle ([Scott 2002]:303):“ As a simple example of an open system, consider the flame of a candle. ..Because the flame is an open system, a relation P → P cannot be written -even “in principle”- for the physical substrate. This follows from the fact thatthe physical substrate is continually changing. The molecules of air and waxvapour comprising the flame at time t are entirely different from those at time t . Thus the detailed speeds and positions of the molecules present at time t are unrelated to those at time t . What remains constant is the flame itself -a process.” Body maintenance : A balance between protein synthesis and protein degradation isrequired for good health and normal protein metabolism. Protein turnover is the re-placement of older proteins as they are broken down within the cell, so the atoms andelementary particles making up the cell change too. Over time, the human body is noteven made up of the same particles: they turn over completely on a timescale of 7 years[Eden et al
The brain
Neuroscientist Terence Sejnowski states: ‘ ‘Patterns of neural activity can indeed modify a lot of molecular machineryinside a neuron. I have been puzzled by my ability to remember my childhood,despite the fact that most of the molecules in my body today are not the sameones I had as a child; in particular, the molecules that make up my brain areconstantly turning over, being replaced with newly minted molecules. ” Metabolic networks ensure the needed replacements take place on a continuous basis,despite stochasticity at the molecular level (Section 5). This is where multiple realisabilityplays a key role (Section 2.6).
Conclusion
Initial data for the specific set of particles making up a specificbrain at time t cannot determine emergent outcomes uniquely for that brainover time, for it is not made of the same set of particles at time t ≫ t . .2 Dealing with New Information: The Predictive Brain That effect of course takes time. The very significant immediate ongoing effect of beingan open system is that incoming sensory information conveys masses of new data on anongoing basis. This new data may contain surprises, for example a ball smashes a window.The brain has to have mechanisms to deal with such unpredictability: the previously storeddata at the microphysics level cannot do so, as it does not take this event into account.
Hierarchical predictive coding
Indeed, the brain has developed mechanisms to makesense of the unpredictable inflow of data and best way react to it [Clark 2013] [Clark 2016][Hohwy 2013] [Hohwy 2016] [Szafron 2019]. Andy Clark explains [Clark 2013]:“
Brains, it has recently been argued, are essentially prediction machines. Theyare bundles of cells that support perception and action by constantly attemptingto match incoming sensory inputs with top-down expectations or predictions.This is achieved using a hierarchical generative model that aims to minimizeprediction error within a bidirectional cascade of cortical processing. Such ac-counts offer a unifying model of perception and action, illuminate the func-tional role of attention, and may neatly capture the special contribution ofcortical processing to adaptive success. This ‘hierarchical prediction machine’approach offers the best clue yet to the shape of a unified science of mind andaction.”
In brief, following up Ross Ashby’s notion that “ the whole function of the brain is summedup in error correction, ” the following takes place in an ongoing cycle:
PB1 Hierarchical generative model
The cortex uses a hierarchical model to generatepredictions of internal and external conditions at time t on the basis of data availableat time t . PB2 Prediction error and attention
During the interval [ t , t ] sensory systems (vi-sion, hearing, somatosensory) receive new information on external conditions andinternal states At time t , nuclei in the thalamus compare the predictions with theincoming data. If it exceeds a threshold, an error signal (‘surprisal’) is sent to thecortex to update its model of the internal and external situation (Bayesian updating),and focus attention on the discrepancy. PB3 Action and outcomes
The updated model is used to plan and implement action.The impact of that action on the external world provides new data that can be usedto further update the model of the external world (active intervention).
This is an interlevel information exchange as described by ([Rao and Ballard 1999]:80):“
Prediction and error-correction cycles occur concurrently throughout the hier-archy, so top-down information influences lower-level estimates, and bottomupinformation influences higher-level estimates of the input signal” .The outcome [Hohwy 2007] is (as quoted in [Clark 2013]),“
The generative model providing the “top-down”predictions is here doing muchof the more traditionally “perceptual” work, with the bottom up driving sig-nals really providing a kind of ongoing feedback on their activity (by fitting,or failing to fit, the cascade of downward-flowing predictions). This procedure ombines“top-down” and “bottom-up” influences in an especially delicate andpotent fashion, and it leads to the development of neurons that exhibit a “selec-tivity that is not intrinsic to the area but depends on interactions across levelsof a processing hierarchy” ([Friston 2003], p.1349). Hierarchical predictive cod-ing delivers, that is to say, a processing regime in which context-sensitivity isfundamental and pervasive ”. Perception
Consequently, perception is a predictive affair [Purves 2010]. Helmholz’sinverse problem (how to uniquely determine a 3-d world from a 2-d projection) is solvedby filling in missing information on the basis of our expectations. ([Kandel 2012]:202-204)gives a overview of how this understanding originated with Helmholz, who called this top-down process of hypothesis testing unconscious inference , and was developed by Gombrichin his book
Art and Illusion [Gombrich 1961]. [Kandel 2012] (pages 304-321) emphasizesthe top-down aspect of this process, and its relation to memory. [Purves 2010](pp.120-124)describes how he came to the same understanding (see also page 221).
Action
The relation to action is given by [Friston 2003] [Friston et al
As strange as it sounds, when your own behaviour is involved, your predic-tions not only precede sensation, they determine sensation. Thinking of goingto the next pattern in a sequence causes a cascading prediction of what youshould experience next. As the cascading prediction unfolds, it generates themotor commands necessary to fulfill the prediction. Thinking, predicting, anddoing are all part of the same unfolding of sequences moving down the corticalhierarchy.” [Seth 2013] summarised the whole interaction thus:“
The concept of Predictive Coding (PC) overturns classical notions of per-ception as a largely ‘bottom-up’ process of evidence accumulation or featuredetection, proposing instead that perceptual content is specified by top-downpredictive signals that emerge from hierarchically organized generative modelsof the causes of sensory signals. According to PC, the brain is continuouslyattempting to minimize the discrepancy or ‘prediction error’ between its inputsand its emerging models of the causes of these inputs via neural computationsapproximating Bayesian inference. Prediction errors can be minimized eitherby updating generative models (perceptual inference and learning; changing themodel to fit the world) or by performing actions to bring about sensory statesin line with predictions (active inference; changing the world to fit the model”
This is a very brief sketch of a very complex program, summarised in Andy Clark’s book
Surfing Uncertainty [Clark 2016] and in [Miller and Clark 2018]. Nothing here contradictsthe mechanisms discussed in depth in texts such as [Purves et al et al et al .3 The emotional brain
A first further crucial aspect of our brains is that they are emotional brains . Theunderstandings and actions enabled by the predictive mechanisms mentioned above arecrucially affected and shaped by affective (emotional) states.The cognitive science paradigm of purely rational choice is not the way the real brainworks. Emotion has key effects on cognition [Damasio 2006] and behaviour [Panksepp 2009][Purves et al
EB1 The emotional brain
Both primary (innate) and secondary (social) emotions playa key role in guiding cognition and focusing attention.
The predictive coding paradigm can be extended ([Clark 2016]:231-237) to include thiscase. [Seth 2013] says the following“
The concept of the brain as a prediction machine has enjoyed a resurgencein the context of the Bayesian brain and predictive coding approaches withincognitive science. To date, this perspective has been applied primarily to extero-ceptive perception (e.g., vision, audition), and action. Here, I describe a predic-tive, inferential perspective on interoception: ‘interoceptive inference’ conceivesof subjective feeling states (emotions) as arising from actively-inferred gener-ative (predictive) models of the causes of interoceptive afferents. The modelgeneralizes ‘appraisal’ theories that view emotions as emerging from cognitiveevaluations of physiological changes ... interoceptive inference involves hier-archically cascading top-down interoceptive predictions that counterflow withbottom-up interoceptive prediction errors. Subjective feeling states - experi-enced emotions - are hypothesized to depend on the integrated content of thesepredictive representations across multiple levels ” [Miller and Clark 2018] develop this crucial emotional relationship to cortical activity indepth, using the predictive coding framework:“
But how, if at all, do emotions and sub-cortical contributions fit into thisemerging picture? The fit, we shall argue, is both profound and potentiallytransformative. In the picture we develop, online cognitive function cannotbe assigned to either the cortical or the sub-cortical component, but insteademerges from their tight co-ordination. This tight co-ordination involves pro-cesses of continuous reciprocal causation that weave together bodily informationand ‘top-down’ predictions, generating a unified sense of what’s out there andwhy it matters. The upshot is a more truly ‘embodied’ vision of the predictivebrain in action.”
As well as influencing immediate functioning of the brain, affect relates crucially to brainplasticity and so to changes in brain micro structure (Section 5.4).
Second, a crucial aspect of our brains is that they are social brains : we are evolvedto live in a social context, which has key influences on our lives and minds as the brainreceives data and responds to the situation around. Sociality appears to be a main driverfor human brain evolution [Dunbar 1998] [Dunbar 2003] and results in social cognition([Purves et al et al he advantage of social brains
Living in cooperative groups greatly enhanced ourancestors survival prospects [Harari 2014] enabling the rise of cooperative farming, culture,and technology, which then was the key to the emergence of civilisation that enabled ourdominance over the planet [Bronowski 2011]. A social brain was needed for social cohesionto emerge: the cognitive demands of living in complexly bonded social groups selectedincreasing executive brain (neocortical) size [Dunbar 1998a] [Dunbar 2014].
The nature of the social brain: Theory of Mind
It is not just a matter of beingcooperative and able to communicate: central to the social brain is the ability known as“theory of mind” (ToM) [Dunbar 1998a]. It is very important that we can read other peo-ples minds (understanding their intentions) - which we do on an ongoing basis [Frith 2013].We all have a theory of mind [Frith and Frith 2005]. Its cortical basis is discussed by[Frith 2007], but additionally it has a key precortical base related to the primary emotionalsystems identified by [Panksepp 2009], namely the very strong emotional need to belong toa group [Panksepp and Biven 2012] [Ellis and Toronchuk 2013] [Stevens and Price 2015]Its evolutionary basis is discussed by [Donald 1991] [Tomasello 2009] [Dunbar 2014].It is summed up by ([Donald 2001]:86-87) as follows:“
Our normal focus is social, and social awareness is highly conscious, thatis, it heavily engages our conscious capacity... Conscious updating is vital tosocial life ... One might even make the case that consciousness- especiallyour lightning fast, up-to-date, socially attuned human consciousness - is theevolutionary requirement for both constructing and navigating human culture.It remains the basis, the sine qua non, for all complex human interactions ”.Michael Tomasello agrees, as is evident in the title of his book
The Cultural Origin ofHuman Cognition [Tomasello 2009].
Relation to predictive coding
The description of the social brain in terms of thepredictive processing paradigm is presented by [Constant et al “ Cognitive niche construction is construed as a form of instrumental intel-ligence, whereby organisms create and maintain cause-effect models of theirniche as guides for fitness influencing behavior. Extended mind theory claimsthat cognitive processes extend beyond the brain to include predictable statesof the world that function as cognitive extensions to support the performanceof certain cognitive tasks. Predictive processing in cognitive science assumesthat organisms (and their brains) embody predictive models of the world thatare leveraged to guide adaptive behavior. On that view, standard cognitivefunctions - such as action, perception and learning - are geared towards theoptimization of the organism’s predictive (i.e., generative) models of the world.Recent developments in predictive processing - known as active inference - sug-gest that niche construction is an emergent strategy for optimizing generativemodels.
Those models include models of social context and of other minds, characterised via cul-tural affordances [Ramstead et al et al See also [Kirchhoff et al et al We argue that human agents learn the shared habits, norms, and expectationsof their culture through immersive participation in patterned cultural practicesthat selectively pattern attention and behaviour. We call this process“ThinkingThrough Other Mind” (TTOM) - in effect, the process of inferring other agents’expectations about the world and how to behave in social context. ”Then downward causation from the social environment changes the brain:“
The brain only has direct access to the way its sensory states fluctuate (i.e.,sensory input), and not the causes of those inputs, which it must learn to guideadaptive action - where ‘adaptive’ action solicits familiar, unsurprising (inte-roceptive and exteroceptive) sensations from the world. The brain overcomesthis problematic seclusion by matching the statistical organization of its statesto the statistical structure of causal regularities in the world. To do so, thebrain needs to re-shape itself, self-organizing so as to expect, and be ready torespond with effective action to patterned changes in its sensory states thatcorrespond to adaptively relevant changes ‘out there’ in the world”
The sociology of this all is discussed by [Berger 1963] and [Berger and Luckmann 1991].Overall, one can summarise as follows:
SB1 The social brain
Because we live in a social world we are very socially aware. Wehave a social brain which shapes our responses to incoming data in crucial ways onthe basis of social understandings, which are continually changing over time .Theory of mind is based in prediction, and is a routine part of everyday life [Frith 2013].
Third, a key feature of the social brain is its ability to engage in spoken and writtenlanguage, and more generally to engage in symbolism. This adds in a whole new categoryof incoming information that the brain has to take into account and respond to.
Language
A key step in evolution of mind is developing language. [Dunbar 1998a]suggests its prime function is to enable exchange of information regarding bonding inthe social group. It is a product of a mind-culture symbiosis ([Donald 2001]:11,202) andforms the basis of culture ([Donald 2001]:274), symbolic technologies ([Donald 2001]:305),as well as cultural learning ([Tomasello 2009]:6) and inheritance ([Tomasello 2009]:13).[Ginsburg and Jablonka 2019] Language enables sharing ideas and information over timeand distance, and enables the social and psychological power of stories [Gottschall 2012].
Abstract and social variables
In evolutionary terms, the transition to the symbolicspecies [Deacon 1997] enabled abstract causation [Ellis and Kopel 2019] to occur, which inter alia involves social interactions and abstract concepts such as the amount of moneyin my bank account and the concept of a closed corporation [Harari 2014]. Thus not allthe relevant variables are physical variables; some are abstract variables resulting fromsocial interactions [Berger 1963] [Berger and Luckmann 1991] which are causally effective.27 igher order predictability
Symbolism and abstract reasoning greatly increases ourpower of prediction: we can simulate situations offline, rather than having to enact them tosee what the consequences are. It also greatly increases the complexity of our responses toincoming social data, which are interpreted in the light of the social context [Berger 1963][Berger and Luckmann 1991] [Donald 2001] [Frith 2013]
SB2 The symbolic brain
Human social interaction is based in language, in turn basedin our symbolic ability. This ability transforms the way our minds interpret muchincoming data, as well as allowing internal cognitive processes that are a major causalfactor in our individual and social lives.
This is the fundamental mechanism by which the brain operates at a macro level, for whichthere is much evidence. Again one can claim that this is the way the brain operates as amatter of fact, it is not just the way we think it operates. Causation at this level is real:the whole of society depends on it.This will play an important role in Section 6 because it relates to the interaction ofthe brain to the outside world.
An individual brain considered as an entity on its own is an open system, and has beenadapted to handle the problems this represents in successfully navigating the world. Thisrather than initial brain micro data determines it specific outcomes as time progresses.
Microphysics data for brain states
Consider a specific individual brain at time t .During a time interval [ t , t ], the initial brain microphysics data D ( t ) is added to bynew data D ext ( t ) coming from the environment after t . The data D ( t ) at a later time t > t is not predictable even in principle from D ( t ). Hence the microphysics evolutionis undetermined by data D ( t ), even in principle. You may for example see a car crashat a time t > t that alters all the future brain states; but your brain did not know thatwas going to happen.Thus the brain as an open system receives unexpected information and handles it in apredictive way. The initial state of the brain obviously cannot determine these outcomesas it has no control over what the incoming data will be. This is the key outcome of thedifference between synchronic and diachronic emergence. The brain is an open system
Initial micro data of a brain state at onemoment cannot possibly determine what it will do at a later time, notjust because new matter comes in and replaces old, but also becausenew information comes in from outside and alters outcomes. Theinitial data at time t cannot know what the initial data at time t willbe and hence cannot determine specific later brain outcomes. Thebrain handles this uncertainty via the predictive brain mechanismsPB1-PB3, EB1, SB1-SB2 outlined above. The physicalist gambit is to say ah yes, but microphysics determines uniquely the evolutionof all the other systems the brain is interacting, so the system as a whole is determinedby the microphysics dynamics alone. I respond to that proposal in Section 6.28 redictive Brain Mechanisms as Attractor states
Evolutionary processes will honein on these predictive brain mechanisms as attractor states. This occurs via the mechanismof exploration and selective stabilisation recognised independently by Changeaux and byEdelman ([Ginsburg and Jablonka 2019]:119-123,247-248).Thus these mechanisms can be claimed to be Higher Order Principles (see Section2.7) for brain structure and function. It is their remarkable properties that shape brainstructure, and its functioning in the face of the unpredictable flow of incoming data.
In carrying out these responses to incoming information, remembering and learning takesplace; indeed this is a pre-requisite for functioning of predictive brain mechanisms. Thisadds a new dimension to the effects just discussed: not only is the new data unpredictable,but also brain structure is changed in ways affected by that inflow of new data. Thus thecontext for microphysics outcomes - the specific set of constraints determining electronand ion flow possibilities - is also different at the later time.Plasticity at the macro level as the brain adapts to its environment, remembers, andlearns [Gray and Bjorklund 2018] is enabled by corresponding changes at the micro levelas neural networks weights change [Kandel et al
Learning takes place by change of connectivity and weights in neural networks at theneuronal level [Kandel et al
Developmental processes
This plasticity occurs particularly when brain developmentis taking place. Random initial connections are refined ([Wolpert et al §
11) andnew experiences can modify the original set of neuronal connections ([Gilbert 1991]:642)while the brain is responding to the surrounding environment ([Purves 2010]: § §
5, 229).
Learning Processes
Erik Kandel explored the mechanism of learning in depth. Heidentified gene regulatory process related to learning [Kandel 2001] “Serotonin acts on specific receptors in the presynaptic terminals of the sensoryneuron to enhance transmitter release. ... during long-term memory storage,a tightly controlled cascade of gene activation is switched on, with memory-suppressor genes providing a threshold or checkpoint for memory storage ...With both implicit and explicit memory there are stages in memory that areencoded as changes in synaptic strength and that correlate with the behavioralphases of short- and long-term memory” he relation to physics These changes alter the context within which the underlyingphysics operates. Changing constraints at the microphysics level is the mechanism ofdownward causation to that level [Ellis and Kopel 2019]. This determines what dynamicsactually takes place at the ion/electron level, which of course the fundamental laws bythemselves cannot do. The outcomes are determined by biological context in this way.
LB1 The Developing and Learning Brain
The brain is plastic at the micro level, asdevelopment and learning takes place. Neural network connections and weights arealtered via gene regulatory processes.
Thus neural network learning [Churchland and Sejnowski 2016] - a real causal process ateach network level - alters electron outcomes and so later psychological level dynamics.
Eric Kandel states “
One of the most remarkable aspects of an animal’s behavior is theability to modify that behavior by learning ” [Kandel 2001], and emphasizes that socialfactors affect this learning. [Kandel 1998] gives five principles for psychotherapy thatmake this clear. For those who are skeptical of psychotherapy, replace that word with‘teaching’ or ‘coaching’ in the following, and its crucial meaning still comes through.
Kandel Principle 1
All mental processes, even the most complex psychological pro-cesses, derive from operations of the brain. The central tenet of this view is thatwhat we commonly call mind is a range of functions carried out by the brain. Theactions of the brain underlie not only relatively simple motor behaviors, such as walk-ing and eating, but all of the complex cognitive actions, conscious and unconscious,that we associate with specifically human behavior, such as thinking, speaking, andcreating works of literature, music, and art.
Kandel Principle 2
Genes and their protein products are important determinants ofthe pattern of interconnections between neurons in the brain and the details oftheir functioning. Genes, and specifically combinations of genes, therefore exerta significant control over behavior. ... the transcriptional function of a gene - theability of a given gene to direct the manufacture of specific proteins in any given cell -is, in fact, highly regulated, and this regulation is responsive to environmental factors... the regulation of gene expression by social factors makes all bodily functions,including all functions of the brain, susceptible to social influences.
Kandel Principle 3
Behavior itself can also modify gene expression. Altered genes donot, by themselves, explain all of the variance of a given major mental illness. Socialor developmental factors also contribute very importantly. Just as combinations ofgenes contribute to behavior, including social behavior, so can behavior and socialfactors exert actions on the brain by feeding back upon it to modify the expressionof genes and thus the function of nerve cells. Learning ...produces alterations in geneexpression.
Kandel Principles 4/5
How does altered gene expression lead to the stable alterationsof a mental process? Alterations in gene expression induced by learning give riseto changes in patterns of neuronal connections. These changes not only contributeto the biological basis of individuality but strengthen the effectiveness of existingpatterns of connections, also changing cortical connections to accommodate newpatterns of actions.... resulting in long-lasting effect on the the anatomical patternof interconnections between nerve cells of the brain.30 he hierarchical predictive coding view
The way this all fits into the predictivecoding viewpoint discussed in the last section is explained by [Rao and Ballard 1999].Overall the outcomes can be summarised thus:
LB2 The Learning Brain
The brain is plastic at the macro level as learning takesplace, supported by plasticity at the micro level. Learning at the macrolevel respondsto social and psychological variables.
The previous section emphasized, in the case of a single brain, that because of incomingdata, the microstate at time t cannot be predicted from initial data at time t < t because it does not include this incoming data. This section emphasizes that in addition,the micro level constraints are changed because neural network wiring or weights will havechanged as the brain adapts at both macro and micro levels to ongoing environmentalevents and changes. So not only is the data different than expected because the brain isan open system, but the dynamical context for the underlying physics is different too. The brain is an adaptive system.
Individual brain structure changesin response to incoming data. As new information comes in, neuralnetwork weights are continually changed via gene regulation. Thischange of context alters constraints in the underlying Lagrangian,and so changes the context for future physical interactions. Noneof this can be determined by the initial brain micro data at time t ,as these changes are shaped by data that has come in since then. This is a further reason why diachronic emergence is crucially different from synchronic.
Adapting and learning brains as attractor states
Evolutionary processes will honealso in on these learning brain mechanisms as attractor states, via the mechanism ofexploration and selective stabilisation recognised independently by Changeaux and byEdelman ([Ginsburg and Jablonka 2019]:119-123,247-248).
A key feature undermining physicalist determinism of brain states is the stochasticity thatoccurs in biology at the molecular level, which uncouples biology from detailed Laplaciandeterminism. Section 5.1 discusses this stochasticity, and Section 5.2 how this opens upthe way for selecting desired low level outcomes that will fulfill higher level purposes - oneof the key forms of downward causation. Section 5.3 discusses how this applies specificallyto the brain. A key way that randomness is used in shaping the brain is Neural Darwinism(Section 5.4). The issue of how agency is possible arises, and this is essentially via multilevel causal closure that takes advantage of this selective process (Section 5.5).This shows how biological stochasticity opens up the way to higher level biologicalneeds acting as attractors that shape brain dynamics, rather than brain outcomes beingthe result purely of deterministic or statistical lower level physical dynamics. All of thiscan again be traced out at the underlying physics level, but it is the biology that is theessential causal factor through setting the context for physical outcomes.31 .1 Biology and stochasticity
There is massive stochasticity at the molecular level in biology. This undoes Laplacian de-terminism at the micro level: it decouples molecular outcomes from details of initial data atthe molecular level. How then does order emerge? By biology harnessing this stochasticityto produce desirable higher level results, as happens for example in the case of molecularmachines [Hoffmann 2012] and the adaptive immune system [Noble and Noble 2018].
Stochasticity and molecular machines
As described in Hoffman’s book
Life’s ratchet:how molecular machines extract order from chaos [Hoffmann 2012] biomolecules live in acell where a molecular storm occurs. Every molecular machine in our cells is hit by afast-moving water molecule about every 10 − seconds. He states“ At the nanoscale, not only is the molecular storm an overwhelming force, butit is also completely random. ”The details of the initial data molecular positions and momenta) are simply lost. Toextract order from this chaos, “ one must make a machine that could ‘harvest’ favorablepushes from the random hits it receives. ” That is how biology works at this level.
Stochasticity in gene expression
Variation occurs in the expression levels of proteins[Chang et al et al
Stochasticity in genetic variation
The processes of genetic variation before selectionare mutation and recombination [Alberts 2007], drift [Masel 2011], and migration. Theyall are subject to stochastic fluctuations. Mutations arise spontaneously at low frequencyowing to the chemical instability of purine and pyrimidine bases and to errors during DNAreplication [Lodish et al et al
The microbiome
A key factor in physiology is that our bodies contain many billions ofmicrobes that affect bodily functioning and health. This is being studied in depth by theHuman Microbiome Project [Peterson etal , in the nose, in the lungs:320 /ml, on the skin: 10 . This leads to infectious diseases (rheumatic fever, hepati-tis, measles, mumps, TB, AIDS, inflammatory bowel disease) and allergic/autoimmunediseases (Asthma, diabetes, multiple sclerosis, Croon’s disease).Because of the stochasticity in gene mutation, recombination, and horizontal genetransfer, and the huge numbers involved, together with the impossibility of setting datato infinite precision ( § This level of stochasticity raises a real problem: how could reliable higher levels of biolog-ical order, such as functioning of metabolic and gene regulatory networks and consequentreliable development of an embryo [Wolpert et al
Organisms can harness stochasticity through which they can generate manypossible solutions to environmental challenges. They must then employ a com-parator to find the solution that fits the challenge. What therefore is un-predictable in prospect can become comprehensible in retrospect. Harnessingstochastic and/or chaotic processes is essential to the ability of organisms tohave agency and to make choices.
This is the opposite of the Laplacian dream of the physical interactions of the underlyingparticles leading to emergent outcomes purely on the basis of the nature of those inter-actions. It is the detailed structure of molecular machines, together with the lock andkey molecular recognition mechanism used in molecular signalling [Berridge 2014], thatenables the logic of biological processes to emerge as effective theories governing dynamicsat the molecular level. They exist in the form they do because of the higher level organis-ing principles that take over. The emergent levels of order appear because they are basedin higher level organising principles characterising emergent protectorates as described by[Laughlin and Pines 2000] (see Section 2.7). For example Friston’s Free Energy Principle[Friston 2010] [Friston 2012] is such a higher level organising principle. It does not followfrom the microphysical laws. In fact all the higher level Effective Theories ET L (Section2.4) characterise such Higher Level Organising principles. STB1 Variation leads to a variety of states, from which outcomes are selected;
States that fulfill biological functions are attractor states for function andhence for evolution and development.
This agrees with ([Ginsburg and Jablonka 2019]:245)“
Biological attractors are usually functional - the mechanisms enabling them tobe reached reliably, in spite of different starting conditions, evolved by naturalselection”.
This is the process exploration and selective stabilisation mechanism that is described in([Ginsburg and Jablonka 2019]:119-123,247-248). The driving of the process by biologicalneeds is the reason that convergent evolution occurs [McGhee 2011].33 .3 The brain and stochasticity
There are various kinds of stochasticity in brain function, apart from the fact that itinvolves necessarily the stochasticity in molecular dynamics just discussed.
Stochasticity in Neural Activity
The neural code is spike chains [Rieke et al et al et al
Brain responses vary considerably from moment to moment, even to identicalsensory stimuli. This has been attributed to changes in instantaneous neuronalstates determining the system’s excitability. Yet the spatio-temporal organiza-tion of these dynamics remains poorly understood. .... criticality may repre-sent a parsimonious organizing principle of variability in stimulus-related brainprocesses on a cortical level, possibly reflecting a delicate equilibrium betweenrobustness and flexibility of neural responses to external stimuli.”
This stochasticity allows higher level organising principles such as attractors to shape neu-ral outcomes in decision making in the brain [Rolls and Deco 2010] [Deco at al stochasticity greatly enhances efficiency in reaching attractor states [Palmer 2020].
Creativity
A key feature of mental life is creativity, which has transformed human lifeboth through inventiveness in science (Maxwell, Turing, Bardeen, Townes, Cormack, andso on) and in commerce (Gates, Jobs, Zuckerberg, Bezos, and so on). It has been proposed([Rolls 2016]:137) that the possibility of creativity is an outcome of stochasticity due torandom spiking of neurons, resulting in a brain state being able to switch from one basinof attraction to another.
The gut-brain axis
The body microbiome (Section 5.1) has a key influence on thebrain. Effects are as follows [Cryan et al
The microbiota and the brain communicate with each other via various routesincluding the immune system, tryptophan metabolism, the vagus nerve and theenteric nervous system, involving microbial metabolites such as short-chainfatty acids, branched chain amino acids, and peptidoglycans. Many factorscan influence microbiota composition in early life, including infection, modeof birth delivery, use of antibiotic medications, the nature of nutritional pro-vision, environmental factors, and host genetics. At the other extreme of life,microbial diversity diminishes with aging. Stress, in particular, can signifi-cantly impact the microbiota-gut-brain axis at all stages of life. Much recentwork has implicated the gut microbiota in many conditions including autism,anxiety, obesity, schizophrenia, Parkinson’s disease, and Alzheimer’s disease.”
It is also involved in neurodegenerative disease [Rosario Iet al 2020]. Because of the un-predictability of how the microbiome will develop, both due to the stochasticity of itsgenetic mutation and the randomness of the microbes imported from the environment,34he specific outcomes of these interactions are unpredictable from initial micro biologicaldata in an individual body, and hence a fortiori from knowledge of the details of the un-derlying physical level. One can however study the statistics of molecular evolution overthe mutational landscape [Gillespie 1984] [Kauffman and Levin 1987].Note that not all the key factors determining outcomes are purely microbiological orphysiological: stress, a mental state, is a key factor in its dynamics.
As well as changing neural network weights [Churchland and Sejnowski 2016] via generegulatory networks[Kandel 2001], neural plasticity during development involves pruningconnections that were initially made randomly [Wolpert et al
Neural Darwinism (orNeuronal Group Selection) [Edelman 1987] [Edelman 1993]. This is a process where neu-ral connections are altered by neuromodulators such as dopamine and serotonin thatare diffusely spread from precortical nuclei to cortical areas via ‘ascending systems’.They then modify weights of all neurons that are active at that time, thus at one shotstrengthening or weakening an entire pattern of activation - a vary powerful mechanism.This mechanism ([Ginsburg and Jablonka 2019]:119-123,247-248) was also discovered by[Changeux and Danchin 1976]. [Seth and Baars 2005] describe these processes thus:“
In the brain, selectionism applies both to neural development and to moment-to-moment functioning. Edelman postulates two overlapping phases of devel-opmental and experiential variation and selection. The first is the formationduring development of a primary repertoire of many neuronal groups by celldivision, migration, selective cell death, and the growth of axons and dendrites.This primary repertoire of neurons is epigenetically constructed through a suiteof genetic and environmental influences, and generates a high level of diversityin the nascent nervous system. The second, experiential, phase involves thedynamic formation from this primary repertoire of a secondary repertoire offunctional neuronal groups, by the strengthening and weakening of synapsesthrough experience and behavior. This phase involves the selective amplifica-tion of functional connectivities among the neurons produced in the first phase,with which it overlaps. In this manner, an enormous diversity of anatomicaland functional circuits is produced ”This provides a key mechanism for experientially based selection of connectivity patterns.
Primary Emotions
An important feature of Edelman’s theory is that the subcorticalnuclei involved, as well as the neuromodulators, are precisely the same as are involvedin Jaak Panksepp’s primary emotional systems [Panksepp 2009] (Section 3.3). Hence thetheory is in fact a theory of
Affective Neural Darwinism [Ellis and Toronchuk 2005][Ellis and Toronchuk 2013], making clear the importance of affect (emotion) for brain plas-ticity and learning.
STB2 Stochasticity and Neural Darwinism
Brain plasticity is affected by neu-romodulators diffusely projected to the cortex from nuclei in subcorti-cal arousal system via ascending systems, selecting neuronal groups forstrengthening or weakening. In this way emotions affect neural plasticity.
TD3B ) that plays a key role in all biology.
Agency clearly takes place at the psychological level. People plan and, with greater orlesser success, carry out those plans [Gray and Bjorklund 2018], thus altering features ofthe physical world. In this way, technological developments such as farming and metallurgyand abstract ideas such as the design of an aircraft or a digital computer have causal power[Ellis 2016] and alter history [Bronowski 2011].The emergent psychological dynamics of the brain demonstrably has real causal powers.So how does such agency occur?
Self-causing systems
Agency is centrally related to the idea of a self-causing sys-tem . The idea of a system is crucial, “an integration of parts into an orderly wholethat functions as an organic unity” ([Juarrero 2002]:108-111). This enables self-causation([Juarrero 2002]:252):“
Complex adaptive systems exhibit true self-cause: parts interact to producenovel, emergent wholes; in turn these distributed wholes as wholes regulate andconstrain the parts that make them up”. ([Murphy and Brown 2007]:85-104) develop the theme further, emphasizing firstly how acomplex adaptive system represents the emergence of a system with a capacity to controlitself. Secondly, agency is related to the variation and selection process emphasized here:the dynamical organisation of a complex adaptive system functions as an internal selectionprocess, established by the system itself, that operates top-down to preserve and enhanceitself. This process is an example of the interlevel causal closure that is central to biology[Mossio 2013] [Ellis 2020b]. It leads to the circularity of the embodied mind [Fuchs 2020]:“
From an embodied and enactive point of view, the mind-body problem hasbeen reformulated as the relation between the lived or subject body on the onehand and the physiological or object body on the other. The aim of the paperis to explore the concept of circularity as a means of explaining the relationbetween the phenomenology of lived experience and the dynamics of organism-environment interactions. .. It will be developed in a threefold way: (1)
As the circular structure of embodiment, which manifests itself (a) in thehomeostatic cycles between the brain and body and (b) in the sensorimotorcycles between the brain, body, and environment. This includes the interdepen-dence of an organism’s dispositions of sense-making and the affordances of theenvironment. (2)
As the circular causality, which characterizes the relation between parts andwhole within the living organism as well as within the organism-environmentsystem. (3)
As the circularity of process and structure in development and learning.Here, it will be argued that subjective experience constitutes a process of sense-making that implies (neuro-)physiological processes so as to form modified neu-ronal structures, which in turn enable altered future interactions. n this basis, embodied experience may ultimately be conceived as the integra-tion of brain-body and body-environment interactions, which has a top-down,formative, or ordering effect on physiological processes.” This is also related to the Information Closure theory of consciousness [Chang et al “We hypothesize that conscious processes are processes which form non-trivial informa-tional closure (NTIC) with respect to the environment at certain coarse-grained scales.This hypothesis implies that conscious experience is confined due to informational closurefrom conscious processing to other coarse-grained scales.”
The predictive coding view
Intentional action is a process of agent selection fromvarious possibilities. This possibility of agency is congruent with the predictive codingview, as three examples will demonstrate. Firstly [Seth et al
We describe a theoretical model of the neurocognitive mechanisms underlyingconscious presence and its disturbances. The model is based on interoceptiveprediction error and is informed by predictive models of agency, general mod-els of hierarchical predictive coding and dopamine signaling in cortex ...Themodel associates presence with successful suppression by top-down predictionsof informative interoceptive signals evoked by autonomic control signals and,indirectly, by visceral responses to afferent sensory signals. The model connectspresence to agency by allowing that predicted interoceptive signals will dependon whether afferent sensory signals are determined, by a parallel predictive-coding mechanism, to be self-generated or externally caused.”
Secondly [Negru 2018] puts it this way:“
The aim of this paper is to extend the discussion on the free-energy principle(FEP), from the predictive coding theory, which is an explanatory theory of thebrain, to the problem of autonomy of self-organizing living systems. From thepoint of view of self-organization of living systems, FEP implies that biologicalorganisms, due to the systemic coupling with the world, are characterized byan ongoing flow of exchanging information and energy with the environment,which has to be controlled in order to maintain the integrity of the organism.In terms of dynamical system theory, this means that living systems have adynamic state space, which can be configured by the way they control the free-energy. In the process of controlling their free-energy and modeling of thestate space, an important role is played by the anticipatory structures of theorganisms, which would reduce the external surprises and adjust the behaviorof the organism by anticipating the changes in the environment. In this way,in the dynamic state space of a living system new behavioral patterns emergeenabling new degrees of freedom at the level of the whole.”
Finally [Szafron 2019] characterizes it thus: “ Using the Free Energy Principle and Active Inference framework, I describe aparticular mechanism for intentional action selection via consciously imaginedgoal realization, where contrasts between desired and present states influenceongoing neural activity/policy selection via predictive coding mechanisms andbackward-chained imaginings (as self-realizing predictions). A radically embod-ied developmental legacy suggests that these imaginings may be intentionally haped by (internalized) partially-expressed motor predictions and mental simu-lations, so providing a means for agentic control of attention, working memory,and behavior.” The overall result is a final higher level organising principle that acts as an attractor stateduring evolution:
STB3 Stochasticity and Agency
Stochasticity at the micro level allows macro level dy-namics to select preferred micro outcomes, thus freeing higher levels from the tyrannyof domination by lower levels. By this mechanism, downward selection of preferredmicro outcomes enables self-causation and agency.
The big picture is that randomness is rife in biology. Evolutionary processes have adaptedbiological systems to take advantage of this [Hoffmann 2012], with higher level processesselecting preferred outcomes from a variety of possibilities at the lower levels, therebyenabling the higher level organising principles characterised in the previous two sections toshape physical outcomes [Noble and Noble 2018]. The underlying physics enables this, butdoes not by itself determine the particular outcomes that occur, for they are contextuallydetermined via time dependent dynamical constraints ([Juarrero 2002]:131-162).
The hardcore reductionist responds to the previous sections by saying yes the brain is anopen system, but the universe as a whole is not. Extend your micro data to include all theparticles in the universe - well, in the region of the universe that is causally connected to us(i.e inside the particle horizon [Hawking and Ellis 1973]) - and it is then causally closed.All the data incoming to the brain is determined by causally complete microphysicalprocesses in this extended domain, hence brain outcomes are determined by them too.The response, denying that this can work, has many levels. Please note that as statedbefore I am concerned with the possibility of physics determining specific outcomes, suchas the words in Carlo’s emails, not just statistical outcomes. His emails did not contain astatistical jumble of letters or words: they contained rational arguments stated coherently.This is what has to be explained. The question is how the underlying physics relates tosuch specific rational outcomes.In order of increasing practical importance the issues are as follows.First, Section 6.1 denies that the micro physical level is in fact causally complete,because of irreducible quantum indeterminism. While this can indeed have an effect onthe brain, its primary importance is to deny that physics at the micro level is in principlecausally complete.Second, Section 6.2 makes the case that even if the incoming data was determineduniquely by microphysics everywhere in the surroundings, they would not determine aunique brain micro state in any individual because of the multiple realisability of macrostates by microstates ( § inter alia becauseit applies to weather patterns and forest fires.Fourth, Section 6.4 points out that there is considerable randomness in the externalworld biology that the mind interacts with at both micro and macro levels. These biologicaloutcomes are not precisely predictable from their micro physics initial data. It has keyimpacts on the mind related in particular to the relations between humans and viruses.38ifth, Section 6.5 points out that because the brain is a social brain ( § Carlo’s argument is that micro data dependence of all outcomes undermines the possibil-ity of strong emergence. To summarise, suppose I am given the initial positions r i andmomenta p i of all particles in the set P everywhere, where P := (protons, neutron, electrons) (5)at a foundational level L1 . At a higher level L2 this constitute an emergent structure S ,such as a neural network. The details of S are determined by the microdata, even thoughits nature cannot be recognised or described at level L1 . The forces between the particlesat level L1 completely determine the dynamics at level L1 . Hence the emergent outcomesat level L2 are fully determined by the data at level L1 , so the emergence of dynamicalproperties and outcomes at level L2 must be weak emergence and be predictable, at leastin principle, from the state (5) of level L1 , even if carrying out the relevant computationsis not possible in practice. This would apply equally to physical, engineering, and bio-logical emergent systems. It is in effect a restatement of the argument from supervenience.There are problems with the argument just stated as regards both microphysics and macro-physics. Quantum physics uncertainty relation
The Heisenberg uncertainty relations under-mine this Laplacian dream because initial data cannot be specified with arbitrary accuracy[Heisenberg 1949]. The standard deviations of position σ x and momentum σ p obeys σ x σ p ≥ ~ / L1 . Conse-quently, outcomes based on standard Lagrangians dependent on x and p are uncertain inprinciple.Essentially the same issue arise in the case of classical physics [Del Santo and Gisin 2019]because data cannot be prescribed to infinite accuracy [Ellis et al § Irreducible uncertainty in quantum outcomes
There is irreducible uncertainty ofquantum outcomes when wave function collapse to an eigenstate takes place, with out-comes only predictable statistically via the Born rule [Ghirardi 2007]. One cannot for “Everywhere” means within the particle horizon [Hawking and Ellis 1973]. et al Biological damage due to cosmic rays
Cosmic rays can alter genes significantlyenough to cause cancer. In particular, galactic cosmic rays lead to significant fatal-ity risks for long-term space missions. This is discussed in [Cucinotta and Cacao 2017][Cucinotta et al et al
Unpredictable brain effects
This obviously can affect the mental processes of thoseundertaking space travel. The brain can be affected crucially by distant events that arein principle unpredictable because they result from quantum decay of excited atoms.The statistics of outcomes is strictly predicted by quantum theory. But in termsof causal completeness of biological events, we wish to know which specific person getsaffected at what specific time, thereby changing individual thought patterns. Detailedmicrophysical initial data everywhere cannot tell us that.This is a situation that only affects a small number of people, but it is importantbecause it establishes that in principle the physicalist whole world gambit does not work(after all, that argument is an in principle argument: no one argues that it can work inpractice in terms of allowing actual predictions of unique biological outcomes).
Incoming sensory data in a real world context affects brain macrostates which then shapemicro level connections via learning. But they do not do so in a unique way: incomingsensory data does determine unique brain microstates because of the multiple realisabilityof higher level states at the physical level.
Mental states and multiple realisability
A given set of incoming data does notresult in a unique brain physical microstate because of multiple realisability of the higherlevel state at the lower level (Section 2.6). This is a key property of brain function.[Silberstein and McGeever 1999] state “Functionalists (and others) claim that mental states are radically multi-realizable,i.e., that mental states like believing that p and desiring that p can be multiplyrealized within individual across time, across individuals of the same species,across species themselves, and across physical kinds such as robots. If thisis true, it raises crucial question: why do these states always have the samebehavioural effects? In general we expect physically similar states to have sim-ilar effects and different ones to have different effects. So some explanationis required of why physically disparate systems produce the same behaviouraleffects. If there is nothing physically common to the ‘realizations’ of a givenmental state, then there is no possibility of any uniform naturalistic explanationof why the states give rise to the same mental and physical outcomes.” Unpredictable brain effects
Unique micro level physical conditions in the brain (thespecific details of constraints in the electron/ion Lagrangian that will determine the ongo-ing brain dynamics) cannot in principle be determined by incoming data from the externalworld because of multiple realisability. Ordered outcomes appear at the brain macro levelaccording to the predictive coding logic outlined in Section 5.3, which then activates anyone of the microstates in the corresponding equivalence class at the micro level (Section2.6). All of this can of course be traced at the microphysical level, both internal to thebrain and externally. But what is driving it is psychological level understandings.
The atmosphere is an open system dynamically driven by the Sun’s radiation, and withvary complex interactions taking place between the atmosphere, subject to winds and con-vection, water (the seas and lakes and clouds and ice), and land [Ghil and Lucarini 2020].These are unpredictable in detail because of chaotic dynamics.
Convection patterns
Consider a higher physical level L3 in the context of a fluid whereconvection patterns take place. Because of the associated chaotic dynamics together withthe impossibility (6) of setting initial data to infinite precision (Section 6.1), macroscopicoutcomes are unpredictable in principle from micro data (5) . Convection patterns are anexample [Bishop 2008]: an extremely small perturbation in a fluid trapped between twolevels where a heat differential is maintained can influence the particular kind of convectionthat arises. [Anderson 2001] puts it this way:“ A fluid dynamicist when studying the chaotic outcome of convection in aBenard cell knows to a gnat’s eyelash the equations of motion of this fluidbut also knows, through the operation of those equations of motion, that thedetails of the outcome are fundamentally unpredictable, although he hopes toget to understand the gross behaviour. This aspect is an example of a verygeneral reality: the existence of universal law does not, in general, producedeterministic, cause-and-effect behaviour. ”The outcome is an emergent layer of unpredictability at both local scales (thunderstorms,tornados, typhoons, and so on) and globally (large scale weather outcomes). The lat-ter are famously characterised by strange attractors [Lorenz 1963], involving instabilityand fractals, but much more importantly interactions between different length scalesthat make prediction impossible in principle [Lorenz 1969], as discussed in depth by[Palmer et al
Large-scale structures arise out of fluid molecules, but they alsodynamically condition or constrain the contributions the fluid molecules can make, namelyby modifying or selecting which states of motion are accessible to the fluid molecules” .41 he Butterfly Effect
Lorenz intended the phrase ‘the butterfly effect’ to describethe existence of an absolute finite-time predictability barrier in certain multi-scale fluidsystems, implying a breakdown of continuous dependence on initial conditions for largeenough forecast lead times [Palmer et al
It is proposed that certain formally deterministic fluid systems which possessmany scales of motion are observationally indistinguishable from indetermin-istic systems. Specifically, that two states of the system differing initially by asmall observational error will evolve into two states differing as greatly as ran-domly chosen states of the system within a finite time interval, which cannotbe lengthened by reducing the amplitude of the initial error .”This happens because of the interactions between the different length scales involved.Palmer’s illuminating paper [Palmer et al does indeed occur - but only for some particular sets of initial data. Nevertheless occur-ring from time to time denies causal closure of physics on this scale in practice. Thatclearly means the underlying physics at the particle level cannot have been causally closedeither. The multiscale weather dynamics studied by [Lorenz 1969] reaches down to influ-ence atomic and electron motions (think thunderstorms) at the lower physics level. Butthis is the crucial point: you cannot predict when those cases will occur. Forest Fires are an example of self-organised critical behaviour [Malamud et al , and secondly the spread of the fire is determined by local winds which arechanged by local convection effects due to the fire. The detailed dynamics of the fire areunpredictable because of these links; even probabilities are tricky [Mata et al Unpredictable brain effects
In terms of the effect on the brain, these random out-comes shape decisions from whether to open an umbrella on a trip to the shops, to farmers’decisions as to when to harvest crops, aircraft pilots decisions about en route flight plan-ning, and homeowners decisions about whether or not to flee a forest fire. It causes anessential unpredictability in mental outcomes. This is a first reason the external world hasan ongoing unpredictable key effect on individual brains.
Biological dynamics in the external world are subject to unavoidable uncertainty becauseof the random nature of molecular level events, already alluded to in Section 5.1.
Interacting microbiomes and viruses
The immense complexity of each individualperson’s microbiome (Section 5.1) interacts, through social events, with other people’smicrobiomes, as do their viruses. Genetic variability is central to the mutation of microbesand viruses in the external world. Detailed physical microstates everywhere determinethe statistics of such variations, but not the specific ones that actually occur, which aredue inter alia to mutation and recombination and horizontal gene transfer in the caseof microbes, mistakes by RNA or DNA polymerases, radiation- or chemical- or host celldefences-induced mutation, and re-assortment in the case of viruses. Predicting mutations Dry Thunderstorms Could Accelerate the California Wildfires,
42s essentially impossible, even for viruses with 10 ,
000 bases like HIV. All you CAN sayis that the known mutation rate for that organism predicts that every single copy of theHIV genome (for example) will have at least one mutation (10 − rate). Unpredictable brain effects
This has crucial effects in our brains that are completelyunpredictable because firstly of the randomness of the genetic mutations leading to thesespecific microbes and viruses, and secondly of the details of the events that lead to theirspread through animal and human populations; this can all be expressed in terms ofrugged adaptive landscapes [Orr 2005]. This firstly directly affects human health andbrain dynamics in each of the set of interacting brains ( § et al Our brain is a social brain (Section 3.4). Information from the external world affectsmental states via ongoing complex social interactions, which have real causal powers.They structure our mental activities in everyday life.
Social understandings
There is an intricate relation between the individual and soci-ety [Berger 1963] [Longres 1990] [Berger and Luckmann 1991] [Donald 2001] and betweenindividuals and institutions in a society [Elder-Vass 2010] [Elder-Vass 2012]. The down-ward effect of the social context on an individual brain is mediated by social interactionsand understandings (Section 3.4). In this social context, a complex interaction takes placeinvolving mind reading, prediction, filling in of missing data, taking social context intoaccount [Longres 1990] [Donald 2001] [Frith 2009]. This nature of the interactions of amany brains, each a self causing open system (Section 5.5), is the main practical day today reason that microphysics everywhere cannot determine unique outcomes in each ofthe brains involved. Downward causation from the social level interactions to individualbrains to the underlying molecular biology and thence physical levels is the causal chain.
Abstract variables have causal powers
This is all enabled by our symbolic ability[Deacon 1997], resulting in our use of spoken and written language, which is the key factorenabling this to happen [Ginsburg and Jablonka 2019] (Section 3.5). This affects ourindividual brain operations as we consider the continually changing detailed implicationsof money, closed corporations, laws, passports, and so on in our lives.
Policy decisions have causal powers
Given this context, social variables have causalpower [Longres 1990] [Harari 2014] and affect brain states; in particular, this applies topolicy decisions. The interaction outcomes are shaped at the social level, which is wherethe real causal power resides, and then affect individual brain states in a downward way.Complex interpretative processes take place shaping psychological level reactions, whichthen shape neural network and synapse level changes in a contextual way on an ongoingbasis as studied by social neuroscience [Cacioppo et al I thank Ed Rybicki for this comment. npredictable brain effects Policy decisions are sometimes based in unpredictableevents such as cyclones or forest fires (Section 6.3) or a global pandemic or local infectiousoutbreak (Section 6.4). Mandatory evacuating of towns in the face of a cyclone or wildfire, going into shelters in the case of a tornado, or policy decisions such as lockdownsin the face of a pandemic will all be unpredictable because their cause is unpredictable,and so will cause unpredictable outcomes in individual brains at macro and micro levels.The causal chain is an unpredictable trigger event, followed by a social policy choice thatthen changes outcomes in individual brains. Detailed physical data everywhere enablesthis to happen by providing the basis for stochastic outcomes that cannot be determineduniquely from that data be because of the real butterfly effect in the case of weather, andits analogue in the case of microbe and viral mutations. Carlo’s view that microphysicsdetermines all brain dynamics in this extended context could hold if it were not for therandom nature of the trigger events. Carlo’s move of bringing into focus the larger context is certainly correct in the followingsense: the way that causal closure takes place in reality involves the whole environment.But that means it is an interlevel affair, for the environment involves all scales.
The real nature of causal closure • From my viewpoint, what is meant by the phrase “causal closure” as used by Carloand other physicists is in fact that one is talking about existence of a well-posedeffective theory EF L that holds at some emergent level L . This means data d L for variables v L at that level L specifies unique outcomes, or the statistics of suchoutcomes, at that level. • However existence of such a theory does by itself not determine any specific physicaloutcomes. It implies that if the right data and boundary conditions are present, and all constraints that hold are specified, then a unique or statistical outcome ispredicted by the physics at that level. • It does not attempt to say where that data, boundary conditions, and constraintscome from. But without them you do not have causal closure in what should betaken to be the real meaning of the term: sufficient conditions are present to causallydetermine real world outcomes that happen.
For example social dynamics are activecausal factors that reach down to affect physics outcomes, as is abundantly clear inthe COVID-19 crisis: policies about face masks affect physical outcomes. • My use of the term, as developed in full in [Ellis 2020b], regards causal closure asinterlevel affair, such as is vital to biological emergence [Mossio 2013]. The conjunc-tion of upward and downward effects must self-consistently determine the boundaryconditions, constraints, and initial data at a sufficient set of levels that unique orstatistical outcomes are in fact determined by the interlocking whole. • When that happens you can of course trace what is happening at whatever physicslevel you choose as a base level L0 . But over time, the later initial data, boundaryconditions, and constraints at that level are dynamically affected by the downwardmechanisms outlined in Section 2.5. Because causation is equally real at each level, L0 , astime proceeds. Higher Level Organising Principles, independent of the lower levelphysics, shape physical outcomes. The state of variables at level L0 at time t uniquely determines the higher levels at that time, but not at a later time t > t . • The freedom for higher levels to select preferred lower level outcomes exists becauseof the stochastic nature of biological processes at the cellular level (Section 5.2). • The illusion of the effective theory at a physical level L0 being causally complete isbecause physicists neglect to take into account their own role in the experiments thatestablish the validity of the effective theory that holds at that level. When you takethat role into account, those experiments involve causal closure of all levels from L0 to the psychological level L6 where experiments are planned and the social level L7 which enables the experimental apparatus to come into being.Another term used for causal closure in this sense is operational closure : the organisationalform of the processes that enable autopoietic self-production and conservation of systemboundaries [Di Paolo and Thompson 2014] [Ramstead et al The predictive coding/free energy viewpoint
My view agrees with the growingpredictive coding consensus, as presented in previous sections. Karl Friston (private com-munication) says the following:“
I imagine that downward causation is an integral part of the free energy for-malism; particularly, its predication on Markov blankets. I say this in thesense that I have grown up with a commitment to the circular causality im-plicit in synergetics and the slaving principle (c.f., the Centre Manifold The-orem in dynamical systems). As such, it would be difficult to articulate anymechanics without the downward causation which completes the requisite cir-cular causality. Practically, this becomes explicit when deriving a renormal-isation group for Markov blankets. We use exactly the same formalism thatHerman Haken uses in his treatments of the slaving principle [Haken 1996][Haken and Wunderlin 1988] to show that Markov blankets of Markov blan-kets constitute a renormalisation group. If existence entails a Markov blanket,then downward causation (in the sense of the slaving principle) must be anexistential imperative.”
The final conclusion of this section is the following
Unique causal outcomes in individual microphysical brain states donot occur when one includes causal effects of the external world.
This does not work (i) because microphysics is not in fact causallyclosed due to quantum wave function collapse, (ii) external informa-tion cannot uniquely determine microphysical states in the brain -multiple realisability makes this impossible, (iii) unpredictable macrolevel chaotic dynamics occurs, (iv) microbiome dynamics that affectbrain states is unpredictable, and (v) the way external states in-fluence brain states is strongly socially determined and can includeevents that are in principle unpredictable.
However it certainly is true that such downward causal effects on individual brains occur.They just do not do so in a way that is uniquely determined by physical effects alone.45
Microphysics Enables but Does Not Determine
In this section I summarise my response (Section 7.1), and comment on the relation of allthe above to the issue of free will (Section 7.2) and to the possibility spaces that are thedeep structure of the cosmos (Section 7.3).
There are a series of key issues that shape my response. • I am concerned with what determines the specific outcomes that occur in real worldcontexts, not just with statistical prediction of outcomes. How does physics underliethe existence of a Ming dynasty teapot? Of the particular digital computer onwhich I am typing this response? Of Einstein’s publication of his paper on GeneralRelativity? Of the election of Donald Trump as President of the United States ofAmerica? • Consider a specific individual brain at a particular time. The difference betweensynchronic and diachronic emergence is key. Carlo’s view can be defended in thesynchronic emergence case, but cannot be correct in the diachronic case, becauseindividual brains are open systems. The initial microphysical state of the brainsimply does not include all the data that determine its outcomes at later times.This is what is discussed in depth in Sections 3 - 5.
The whole universe context
Claiming that this problem is solved by going to a largerscale where causal closure does indeed hold (the cosmological scale), which therefore im-plies that the specific evolution of all subsystems such as individual brains is also uniquelydetermined, does not work for a series of reasons. I list them now with the theoreticallymost important issues first. This is the inverse of the order that matters in terms of de-termining outcomes in practical terms. As far as that is concerned, the most importantissues are the later ones.It does not work because of,1. Irreducible quantum uncertainty at the micro level which affects macro outcomes;this demonstrates that the claim is wrong in principle. It can indeed have macro ef-fects on the brain, but this is not so important at present times because of the shield-ing effect of the earth’s atmosphere. However it has played important role in evolu-tionary history [Percival 1991], as discussed by [Todd 1994] [Scalo and Wheeler 2002]2. The fact that downward effects from that larger context to the brain, which certainlyoccur via neural plasticity and learning, cannot in principle determine a unique brainmicrostate, because of multiple realisability of those detailed physical states whenthis occurs. Unique brain microstates cannot occur in this way.3. Uncertainty in principle at the ecosystem level due to chaotic dynamics and the realbutterfly effect plus the inability to set initial molecular conditions precisely. This hasmajor unpredictable effects on individual brains due to forest fires, thunderstorms,tornadoes, and tropical cyclones.4. Microbiome dynamics that is in principle unpredictable because of the molecularstorm and huge number of molecules involved, plus the inability to set initial molec-ular conditions precisely. This affects individual and social outcomes as evolution46akes place on a rugged adaptive landscape that keeps changing as all the interactingspecies evolve. This crucially affects brain dynamics through the gut-brain axis.5. Social understandings that shape how external signals are interpreted by the brain,when social level policies and choices (which may involve unpredictable events suchas thunderstorm details or pandemic outbreaks) chain down to influence flows ofelectrons in axons. It simply is not a purely physics interaction.Carlo’s vision of the external world as a whole evolving uniquely and thereby determiningunique brain states because they are a part of the whole, may work in some contexts whereirreducible uncertainty 1. and effective uncertainty 3. and 4. do not occur. It cannothowever work when any of these effects come into play, which certainly happens in thereal world. This demonstrates that as a matter of principle, it is the higher level effects -psychological and social variables - that are sometimes calling the tune. But that meansthey are always effectively doing so in the social context which is the habitat of minds.
Causal closure
Real world causal closure is an interlevel affair, with microphysicaloutcomes determined by features ranging from global warming and tropical cyclones toCOVID-19 policy decisions. It simply cannot occur at the microphysics level alone. Someof the effective variables which have changed human history are abstract concepts such asthe invention of arithmetic, the concept of money, and the idea of a closed corporation.These have all crucially affected microphysical outcomes, as have abstract theories suchas the theory of the laser and the concepts of algorithms, compilers, and the internet.Overall, as stated by [Bishop 2012], the situation is that “Whatever necessity the laws of physics carry, it is always conditioned by thecontext into which the laws come to expression.”
So in response to Carlo’s final email (Section 1.1): CR Today the burden of the proof is not on this side. Is on the opposite side. Because: (i)
There is no single phenomenon in the world where microphysics has been provenwrong (in its domain of validity of velocity, energy, size...). GE The view I put respects the microphysics completely. Of course it underlies all emer-gent phenomena, without exception. Microphysics certainly is not wrong.
CR (ii)
By induction and Occam’s razor, is a good assumption that in its domain validityit holds. GE Yes it holds in its domain of validity, which is at the microscale. The question atstake is, How much larger is its domain of validity? I think the comment is meantto say that its domain of validity includes biology and the brain, in the sense that,by itself it fully determines all biological and brain outcomes.However completely new kinds of behaviour emerge in the biological domain. Thekind of causation that emerges is simply different than the kind of statistically de-terminist relation between data and outcomes that holds at the microphysical level.The microphysics allows this emergence: it lies within the space of possibilities deter-mined by that physics. In that sense the higher level outcomes lie within the domainof validity of the microphysics. But the microphysics by itself does not determine themacro level outcomes (see the listed points above). Occam’s razor does not work.47
R (iii)
There are phenomena too complex to calculate explicitly with microphysics.These provide no evidence against (ii), they only testify to our limited tools. GE Carlo only considers efficient causation, because that is what physicists study. AsAristotle pointed out [Bodnar 2018], that is only one of the four kinds of causationthat occur in the real world (Section 2.5). In the real world the other kinds ofcausation play a key role in determining outcomes. All four are needed to determinespecific outcomes.I accept the need to provide the burden of proof. I have done so in the preceding sections.
The implication of Carlo’s argument is that the causal power of microphysics prevents theexistence of free will. This touches on a vast and complex debate. My arguments abovedeny that the underlying physics can disprove existence of free will in a meaningful sense.But that does not dispose of the debate. Does neurobiology/neuroscience deny free will?
Incomplete reductionism: neurobiology
Francis Crick gives a neuroscience basedreductionist argument regarding the brain in
The astonishing hypothesis [Crick 1994]: “You, your joys and your sorrows, your memories and your ambitions, yoursense of personal identity and free will, are in fact no more than the behaviorof a vast assembly of nerve cells and their associated molecules. ”
Now the interesting point is that this a denial of Carlo’s arguments. Crick is assuming thatthe real level of causation is at the cellular and molecular biology levels: that is where theaction is, it is at that level that physical outcomes are determined. The implication is thatthis is what determines what specific dynamical outcomes take place at the underlyingphysical level - which is my position [Ellis and Kopel 2019].
Free will and neurobiology
As to free will itself, does neurobiology and neurosciencedeny its existence? That is a long and fraught debate related to intentionality and agency.Amongst the deeply considered books that argue for meaningful free will are [Donald 2001][Dupr´e 2001] [Murphy and Brown 2007] [Murphy et al free will should be understood as being the primary cause of one’s ownactions; this is a holistic capacity of mature, self-reflective human organisms acting withinsuitable social contexts ”. This is essentially the consensus of the authors just named,Libet’s experiments notwithstanding. Chris Frith expresses it this way [Frith 2009]:“ I suggest that the physiological basis of free will, the spontaneous and intrinsicselection of one action rather than another, might be identified with mechanismsof top-down control. Top-down control is needed when, rather than respondingto the most salient stimulus, we concentrate on the stimuli and actions relevantto the task we have chosen to perform. Top-down control is particularly rele-vant when we make our own decisions rather than following the instructions ofan experimenter. Cognitive neuroscientists have studied top-down control ex-tensively and have demonstrated an important role for dorsolateral prefrontalcortex and anterior cingulate cortex. If we consider the individual in isolation, One of the topmost cited neuroscientists in the world: he had 203,843 citations on 2020/08/01. hen these regions are the likely location of will in the brain. However, indi-viduals do not typically operate in isolation. The demonstration of will evenin the simplest laboratory task depends upon an implicit agreement between thesubject of the experiment and the experimenter. The top of top-down controlis not to be found in the individual brain, but in the culture that is the humanbrain’s environmental niche ”This is a good description of both topdown effects in the brain [Ellis 2018] and interlevelcausal closure [Ellis 2020b]. It is also expressed well by [Baggini 2015]. Free will denialists don’t really believe it
The physicist Anton Zeilinger told methe following story. He was once being harassed by someone who strongly argued that wedo not have free will. Anton eventually in frustration reached out and slapped him in theface. He indignantly shouted, “
Why did you do that? ”, to which Anton responded “
Whydo you ask me that question? You have just been explaining to me at length that I am notresponsible for my actions. According to you, it’s not a legitimate question.”
If you have an academic theory about the nature of causation and free will, it mustapply in real life too, not just when you are engaged in academic argumentation. If not,there is no reason whatever for anyone else to take it seriously - for you yourself do not.
Free will and the possibility of science
The ultimate point is that if we don’t havemeaningful free will, in the sense of the possibility of making rational choices betweendifferent possible understandings on the basis of coherence and evidence, then scienceas an enterprise is impossible. You then cannot in a meaningful way be responsible forassessing theories anyone proposes. The theory that free will does not exist causes thedemise of any process of scientific investigation that is alleged to lead to that theory. Wehad better find a better theory - such as those in the books cited above.
Denial of consciousness or qualia
Finally one should note that many pursuing theview that free will does not exists also deny that consciousness and/or qualia exist andplay any role in brain function. But neuroscience simply does not know how to solvethe hard problem of consciousness [Chalmers 1995]. As stated in [Tallis 2016], neuro-science helps define the necessary conditions for the existence of human consciousnessand behaviour, but not the sufficient conditions. The self-defeating philosophical move ofdenying that consciousness and/or qualia exist (“what one canot explain does not exist”[Tallis 2016]) does not succeed in explanatory terms: how can you deny something if youhave no consciousness? In that case, you do not satisfy the necessary conditions to denyanything: you do not exist in any meaningful sense [Donald 2001]. For more on this seealso [Gabriel 2017] [Dennett and Strawson 2018].But in any event this is a different debate than my debate with Carlo, which in the end isabout physics denying free will.
All the biological effects discussed here are allowed by the underlying physical levels: theydo not violate or alter those generic equations, which apply to all physical interactionswithout exception.A useful way to characterise this is in terms of possibility spaces . In the case of physics,these include phase spaces [Arnold 1989] (classical physics) and Hilbert spaces (quantum49hysics) [Isham 2001]. These are determined by the laws of physics, and are indeed equiv-alent to them: the laws allow the possibilities described by the possibility spaces, and thepossibility spaces characterise the nature of the underlying laws.Now the interesting point is that there are also biological possibility spaces. At the mi-crobiology level they include a space of all possible proteins allowed by the laws of physics[Wagner 2014], of which only some have been realised on Earth by evolutionary processes[Petsko and Ringe 2009]. Similarly there are sets of possible genotype-phenotype maps formetabolism and for gene regulation [Wagner 2014]. There are limitations on physiologicalpossibilities due to the nature of physics [Vogel 2000] and consequent scaling laws for bi-ology [West et al et al
At Home in the Universe [Kauffman 1995].
Acknowledgments
I thank Carlo Rovelli for his patient dialogue with me regarding thisissue. It is a pleasant contrast with the arrogant dismissive comments and ad hominem contemptuous personal attacks that are common in some reductionist circles and writings.I thank Karl Friston, Tim Palmer, and Ed Rybicki for helpful comments.
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