School of Computer Science THE UNIVERSITY OF BIRMINGHAM Ghost Machine


(like life on earth)

The Meta-Morphogenesis (M-M) Project
(or Meta-Project?)

Aaron Sloman

How can a cloud of dust give birth to a planet
full of living things as diverse as life on Earth?

Part of the answer:

By starting from a very powerful "fundamental" construction kit, i.e. physics+chemistry, and using natural selection to produce many branching layers of information-processing machinery, required for new forms of reproduction, new forms of development, new forms of intelligence, new forms of social/cultural evolution, via new types of "derived" construction kit.

All this required natural selection to discover (unwittingly) and use (unwittingly) new types of mathematical abstraction, especially abstractions with parameters that could vary -- long before there were any human mathematicians to think about mathematical abstractions.

It also required the Fundamental Construction Kit (FCK) to be capable of producing a (never ending?) branching and merging collection of new, increasingly complex and powerful, construction kits, with new mathematical properties, some of which are adopted, or created, by new more complex organisms with more complex functionalities, and needs.

Some of the requirements are physical: requirements for new kinds of structures, new kinds of energy stores, new forms of locomotion, new kinds of chemical synthesis, new biochemical defence mechanisms, new forms of motion, and many more.

Other changing requirements concern information: new ways of acquiring information (e.g. about the environment, or about internal states and processes), or new kinds of information (e.g. information about information), or new ways of storing information or new ways of processing information, and new ways of communicating information.

A consequence of these ideas is that the common view that mathematics is a product of human minds is back to front. All life forms and all important features of life forms, including human minds, are products of the mathematical generative potential of the physical universe interacting with biological evolution by natural selection. New products continually produce new potential. Natural selection would not suffice without that fountain of potential new products. (Recursion was at work long before humans discovered recursion.)

Because many information processing tasks require construction and modification of complex structures with speeds, variety and complexity that could not be achieved by physical mechanisms, the above processes somehow produced powerful virtual machinery long before human engineers discovered the need for such things (in the 20th Century).

It is remarkable that the FCK had multi-layer self-extending capabilities able to produce physical and virtual machines with information processing capabilities that generate questions about the nature of the universe and its ability to produce life.

But many of the intermediate stages and parallel branches are equally remarkable.

All of this raises questions about whether the current forms of mathematics used by physicists have rich enough generative power to explain the generative power of the FCK, a type of question raised in a simpler context by Schrödinger in What is Life? (1944).

Hints from Alan Turing

As Turing seems to have realised: the forms of information-processing used by evolution were richer and more varied than those developed by computer scientists and engineers so far, and made essential use of chemistry.
"In the nervous system chemical phenomena
are at least as important as electrical"

Alan Turing, in 'Computing machinery and intelligence', Mind, 59, 1950, pp. 433--460

Two years later he published: 'The Chemical Basis Of Morphogenesis', in
Phil. Trans. R. Soc. London B 237, 237, pp. 37--72, 1952,

Two years later Turing was dead.
   What would he have done if he had lived several more decades?

Is this a clue to the answer:

In 1946 Turing wrote a letter urging W. Ross Ashby to use Turing's ACE computer to implement his ideas about modelling brains. He expressed a view that is unfashionable among AI researchers at present (2016), but accords with the aims of this project.
He wrote:
"In working on the ACE I am more interested in the possibility of producing models of the actions of the brain than in the practical applications to computing."

So perhaps Turing would have worked on the Meta-Morphogenesis project, namely

Trying to understand the many steps in evolution of increasingly complex biological information-processing systems: including molecules, microbes, mastodons, magpies and mathematicians.

This could lead to discovery of unnoticed mechanisms in biological brains and minds that explain the many discrepancies between animal intelligence and current AI.

This is the Alan Turing-inspired "Meta-Morphogenesis" research project ( )

The project includes attempting to understand the roles of fundamental and derived construction kits in evolution, discussed in:
(Short form ).


A Protoplanetary Dust Cloud?
Protoplanetary disk

    [NASA artist's impression of a protoplanetary disk, from WikiMedia]

How can a cloud of dust give birth to a planet
full of living things as diverse as life on Earth?
(And their enormously varied products.)


(Updated 19 Dec 2014; 16 Mar 2015; 9 May 2016; 18 Jun 2016); 12 Aug 2016; 27 Oct 2016; 4 Nov 2016)
25 Aug 2014
Some of the contents have been moved to a file containing references:
Publications and references related to the Meta-Morphogenesis Project
Including a section on links to related projects (added 19 Dec 2014).

BACKGROUND: How did this project start?
In Memoriam: S. Barry Cooper (1943-2015)

Added 10 Nov 2015
This project resulted from a miscommunication in 2011 with Barry Cooper, who had asked me to contribute to a book on Alan Turing's life and work, eventually published by Elsevier in 2013, when it won several awards. Information about the book and a table of contents can be found here: Alas, I learnt a few days ago, that Barry had died unexpectedly after a very short illness.
For details and links to early tributes see


My personal debt to Barry Cooper
Barry and I had never met until we were both invited to contribute chapters to a book published in 2011 on Information and Computation, edited by Gordana Dodig-Crnkovic and Mark Burgin, and published (at an exorbitant cost, by World Scientific):

Barry and I reviewed each others' chapters and as a result had some direct communication for the first time, by email -- producing mutual respect. Later, in 2011, out of the blue, he invited me to contribute to a proposed Turing centenary volume originally described here:

After some discussion about how best I could contribute, which involved a change of plan mid-way, I sent him my promised three related short chapters, the first for Part I, of the book (How Do We Compute? What Can We Prove?) and the others for Part III (Building a Brain: Intelligent Machines, Practice and Theory).

However, as a result of the earlier change of plan, which Barry had apparently forgotten, I was still listed as a contributor to Part IV (The Mathematics of Emergence: The Mysteries of Morphogenesis). So he later asked me for my missing contribution to Part IV. His request led me to look at Turing's paper on morphogenesis published in 1952, two years before he died.

That triggered questions about what Turing might have done if he had lived another 30-40 years, instead of only two. So I offered Barry a paper proposing "The Meta-Morphogenesis Project" as a conjectured answer. He accepted it (as the final commentary paper in the book) and ever since then I have been working full-time on the project, for which this page is the main entry point, from which many sub-projects have been launched.

A preprint version of the chapter proposing this project is available here.

Barry later invited me to talk to a workshop at the Royal Society Kavli Centre in June 2012, followed by an invitation to talk to the Logic Seminar in Leeds in October, and then in 2013 invited me to contribute to a book The Incomputable, to be published by Springer, being co-edited by Barry and Mariya Soskova.

My chapter, finished in December 2015, not long after Barry's death, extends the idea of the Meta-Morphogenesis project by discussing requirements for many kinds of construction kit to be used evolution, including construction kits for producing physical components of organisms, information processing systems, and new construction kits. Some of the construction kits are concrete e.g. mechanisms for building components of tree trunks, while others are abstract, e.g. construction kits for reasoning mechanisms implemented as virtual machines.

I also met Barry a few times at workshops or conferences, and I always immensely enjoyed and benefited from our discussions. Originally stimulated by Barry's requests and enormously encouraged by his responses to my drafts, I now expect to go on working on these problems as long as I can.

So he changed my life, by giving me a new research direction, which does not often happen to 75-year old retired academics! (Now nearly five years older.)

I am very sad that we'll not be able to have any more conversations.

Formulating the goals of the Meta-Morphogenesis project made me realise that much of my own work of the last 40 years could be re-cast as a contribution to that project. This document, and a growing set of linked documents is my (messy, changing, inept) attempt to present the project: its questions, some of what we don't know, some of what we do know, some of the ways we can make progress, and some of the overlaps with work of other thinkers. (I suspect Immanuel Kant was attempting to work on these topics, but lacked some important conceptual tools developed in the 20th century, connected with information processing systems.)

I am well aware of the risk, earlier pointed out by Erwin Schrödinger:
"I see no other escape from this dilemma (lest our true aim be lost forever) than that some of us should venture to embark on a synthesis of facts and theories, albeit with second hand and incomplete knowledge of some of them - and at the risk of making fools of ourselves.
So much for my apology."

in What is Life? CUP 1944.

I do not intend to apply for any funds for this project. Others may, if they wish.

Two major themes: Construction kits and mathematics
(Added 5 May 2015 - revised Aug 2015)

In November 2014, while writing notes for talks in Edinburgh and Turin, I realised that a central idea in this project must be the idea of a "construction kit", explored in a sub-project introduced here:

Evolution initially uses the "Fundamental" Construction Kit (FCK) provided by Physics/Chemistry, but repeatedly extends the scope of natural selection by producing ever more complex and powerful "Derived" Construction Kits (DCKs). Some of the DCKs are concrete (physical), some are abstract, and some are a mixture: hybrid construction kits. The processes also require scaffolding: use of temporary constructs. Many of the later construction kits are concerned with production of virtual machinery: about which human engineers have learnt a great deal since the 1950s, though the new knowledge has been largely ignored by philosophers (and neuroscientists?), and the philosophical, psychological, and biological implications have been ignored by most computer scientists, biologists, psychologists, and engineers.

This is a very unfortunate communication gap (not being addressed by the new wave of enthusiasm for teaching computing to children -- a missed opportunity, which may have to wait for a new generation of computing teachers with broader interests and backgrounds).

Note on Virtual Machine Functionalism

A "high level" tutorial introduction to the some of the ideas about virtual machines is here:
Virtual Machine Functionalism (VMF):
The only form of functionalism worth taking seriously in Philosophy of Mind and theories of Consciousness.
Some implications for the study of consciousness are discussed in Sloman and Chrisley 2003.

Construction kits intrinsically have mathematical properties: their generative powers and their constraints/limitations. That's why, to a large extent, it is easy to recognize and distinguish novel objects produced using a Meccano kit, a Lego kit, or a Tinker-toy kit. Because the generative powers, in most cases, are recursive or iterative, in the sense that constructed products of a kit can always be extended using that kit, and all the products of a kit are constrained by properties of the kit, the processes of evolution are deeply mathematical. However, when products of a construction kit CK interact with things that are not solely products of CK, the resulting processes can have features that are not derivable from properties of CK.

The theorems of evolution (the blind mathematician) are primarily theorems about what is possible including construction kits and their products. The proofs of the theorems are implicit in the evolutionary and developmental trails leading to instances of the possibilities.

Evolution also uses theorems about what is impossible: constraints on possibilities, i.e. necessities. But these do not have proofs produced directly by evolution, until some of the late products of evolution begin to notice them, think about them, reason about them, and communicate the results of such processes.

All this is part of a long and complex story that may not become clear for many years.

The need to provide a foundation for all the products of biological evolution may one day turn out to imply previously unnoticed constraints on the Fundamental Construct Kit (FCK), with important, new, implications for theoretical physics. At the very least this may help to resolve disagreements about fundamental physics. Alternatively it may reveal gaps in physical theory that require major changes. I expect new kinds of mathematics will be required, in order to bridge the gaps between fundamental physics and some of the complex products of evolution. (Compare Anderson 1972.)

Limits to reductionism
Although the products of evolution are fully implemented in physical mechanisms, accurate descriptions of the powers and actions of some of the new virtual machines require use of an ontology that is not definable in terms of the ontology of physics (e.g. concepts of winning, losing, attacking, defending, noticing a threat, trying to construct a defence). It follows that some of the properties of the virtual machines, and descriptions of some of their states, cannot be derived logically (or mathematically) from descriptions of the underlying physical machines. That non-derivability is consistent with full implementability. This is the variety of anti-reductionism proposed in The Computer Revolution in Philosophy (1978) That's also one of the main themes of the theory of Virtual Machine Functionalism (VMF) mentioned above.

Related work
An incomplete, messy, growing, collection of references to related work, including related research-projects, is here.
Feel free to suggest items for inclusion, giving your reasons. (Email A.Sloman[AT]

Stuart Wray's sketch of meta-morphogenesis

On 5th Jun 2012, Stuart Wray, after reading a draft conference paper on Meta-morphogenesis and the Creativity of Evolution (before the ideas about construction kits had been added):
produced a sketch of the ideas in the project, reproduced here, with his permission:


NOTE added 12 Mar 2015: A different answer by Andrew Hodges.
I have discovered that in 2002 Andrew Hodges asked exactly the same question about what Turing might have done if he had lived longer. He gave a completely different answer, without mentioning Turing's interest in biology, published here in 2004:

What would Alan Turing have done after 1954?


How can a cloud of dust give birth to a planet full of living things as diverse as life on Earth?

Old questions
Many have asked: what sorts of physical and chemical mechanisms could make that possible, at various stages in evolution, or various stages in individual development (epigenesis) in various types of organism, group or ecosystem. They have also asked: what sorts of morphology (physical structure) and behaviour are needed at various stages of evolution or development, and what sorts of life-supporting chemistry are required. Examples of older questions, and links to further material, can be found on these Wikipedia pages (and many others):

Newer questions

This project asks:
-- What forms of information-processing (computation) and what information-processing mechanisms are required, to make the production and diversification of life forms possible?
-- What features were required in the Fundamental construction kit (FCK) provided by physics and chemistry before life began, that supported all the subsequent extensions and applications produced by natural selection, using many Derived Construction Kits (DCKs) (discussed in more detail here).
-- How do the mechanisms, the forms of representation (encodings), and uses of information all evolve and develop and what new forms of life do they support, or in some cases interfere with?
-- What information contents were or are used by organisms, or parts of organisms, at various stages of evolution, at various stages of individual development, in various group interactions (mating-pairs, fighting pairs, parent-child, predator-prey, colony, culture, ecosystem, economy, ...)

In short, what are the causal roles of information in living things, and how do the information contents and the causal roles change over time, in individuals (at sub-cellular levels upwards), in species, in groups and in larger systems?

What information is, how many varieties there are, what can be done with it, what it can do, what mechanisms are required for these processes, are all complex questions discussed further below. An example: From dinosaurs to Birds (in another document).

Mathematical questions

What mathematical possibilities and necessities enable, constrain and shape the options for natural selection, for epigenesis, for individual competences, for cultures, for ecosystems?

What mathematical constraints? -- Topological, geometrical, physical, chemical, biological, computational, epistemological, linguistic, motivational?

D'Arcy Thompson, Brian Goodwin, and researchers included in a book of tributes to Goodwin, focused mainly on geometric and topological changes and constraints in evolution and development of physical forms (though I have not yet read all the papers carefully).

In contrast, the concerns of the M-M project include mathematical structures and constraints relevant to types of information content, forms of representation of information, modes of reasoning, types of control of behaviour, forms of learning, and other uses of information -- which are much less visible, and leave no fossil records. For these and other reasons, the study of M-M problems is still in its infancy. Far fewer researchers are equipped to think about these questions. At least physics, chemistry, and mathematics are taught to many children in schools.

Perhaps nobody is equipped yet: if some key ideas have not yet been discovered?

Other thinkers -- an incomplete list

If Alan Turing had lived longer he could have taken this project much further than I can. If Immanuel Kant had known what we know about information processing machinery, he would also have put the ideas to deep use. There are probably many other thinkers that I have not yet encountered whose ideas are relevant.

Some who have raised similar questions focus mainly on the evolution of human minds, e.g. Merlin Donald and Peter Gardenfors, among many others (including many I have not read).

There also seem to be overlaps with the work of Stuart Kauffman. Jack Birner (2009) has discussed ideas of Popper and Hayek related to this project.

Many of the writings of Daniel Dennett make points similar to the points made here, including his Kinds of Minds (1996), his writings on free will and others. However, we disagree regarding his claim of the centrality of "The intentional stance" for reference to mental states and processes, and his denial of the existence of the entities variously referred to as "sense-data" or "qualia", which I have argued arise naturally in sufficiently complex virtual machine architectures (e.g. in Sloman and Chrisley 2003 and this discussion of Virtual Machine Functionalism(VMF)).

Dennett and I also seem to disagree on the origins of language -- which I argue must first have evolved (with structural variability and compositional semantics, though not necessarily a "linear format", to meet requirements for information processing within organisms, not between organisms.
Talk 111: Two Related Themes (intertwined)
What are the functions of vision? How did human language evolve?

Terrence Deacon's 2011 book Incomplete Nature: How Mind Emerged from Matter overlaps with the ideas presented here in what seems to me to be a fairly shallow way, insofar as his book does not do justice to what we have learnt about information processing in the last seven decades, including work in Artificial Intelligence, on perception, reasoning, theorem-proving, language understanding, planning, vision, learning, and other topics related to this project. Although I have not yet read all of Deacon's book, he also seems to have no understanding of the achievements of designers of new kinds of multi-functional virtual machinery, as described in

Philip Warren Anderson "More is different"
Added 15 Feb 2015:

Iain Styles drew my attention to a very short but influential paper by the Nobel-prize winning physicist Philip Warren Anderson "More is different" in Science 1972 available on JSTOR, suggesting that there are many levels of organization between sub-atomic physics and phenomena studied in other sciences, including biology and the social sciences. The suggestion that there are layers of construction kits of different sorts (proposed here) appears to be supported by, or at least consistent with, his ideas.

Added 16 Mar 2015:
A separate document provides some notes on

Evelyn Fox Keller
Organisms, Machines, and Thunderstorms:
A History of Self-Organization
Historical Studies in the Natural Sciences,
Vol. 38, No. 1 (Winter 2008), pp. 45-75 and Vol. 39, No. 1 (Winter 2009), pp. 1-31
It surveys literature attempting to use ideas about dynamical systems to explain the emergence of mind, and argues that those ideas are inadequate to the task (a conclusion with which I agree, as explained in the paper on the roles of construction-kits in evolution). See

(Added 29 Oct 2014)
The centrality of information
(Control information, referential information, how-to information, explanatory information, information about information, various kinds of self-directed information, and many more.)

In contrast with the majority of evolutionary research (that I know of), this project focuses on changes in types of information and types of information-processing in evolution. Those changes produce changes in the roles of information in control, reproduction, development, discovery, learning, communication, coordination and other processes, in living things of all sorts. But the examples keep changing, and becoming increasingly complicated, as a result of evolution.

I am not sure whether there is any well-defined upper bound to the complexity, though if there is one it is likely to be far beyond the types of complexity found so far on earth. I shall not discuss the implications of that except to note that there's no reason to believe evolution of information processing has stopped, or is close to stopping, not least because changes in physical designs for organisms, i.e. genomes, are not required for evolutionary changes in information processing: as shown by cultural evolution, including evolution of art and science, and most recently the internet and related technologies.

Is virtually unending change in information processing an inevitable consequence of the existence of the universe? I don't know. It would not happen in empty space. It would not happen on a planet derived only from grains of sand. If a planet, or solar system, or galaxy, or universe has enough diverse chemical components and enough random influences, then perhaps the unending (frequent? infrequent?) initiation of processes of evolution, including various types of meta-morphogenesis, is inevitable -- though I don't know how constrained the set of possible evolutionary trajectories is. Not even natural selection can produce chemically impossible brain mechanisms!

Perhaps, when we have a deeper understanding of the Fundamental Construction Kit provided by physics and chemistry and its role in biology, we'll be better able to discuss whether a different kind of universe might have support life in a comparable variety of forms.

It is not clear whether the variety of forms of information processing currently known to science (and engineering) can support the variety of possibilities required to support all the products of natural selection. The Church-Turing thesis, if true, may turn out to be true only of a class of computations that can be performed on numbers (plus structurally equivalent computations). We'll see that there are many forms of information-processing that are not concerned with numbers, like reasoning about continuous deformation of curved lines on curved surfaces, illustrated here. Perhaps our universe provides a wider variety of information mechanisms than the Turing-equivalent forms of computation can. (REF??? recent evidence in Biology.)

This project (since late 2014) uses the notion of construction kits and evolution of construction kits as a framework for understanding some of evolution's most complex achievements. As far as I know this is a new idea. (If not, please send me links to other work on evolution of construction kits.)

The components, relationships, and forms of composition of a construction kit (e.g. Meccano, Lego Bricks, plasticine, paper and scissors) determines what sorts of entities can be constructed using the resources of the kit. What sort of construction kit had to be available from the earliest stages of this planet, to support not only all the physical forms and behaviours of life forms evolved on this planet, but also all the forms of information-processing, not only the information-processing involved in reproduction and growth, but also all the later forms including science, art, mathematics, engineering, politics, religious superstition, ethical debates, etc? Tibor Ganti Ganti (2003) proposed a minimal set of chemical mechanisms for elementary reproducing life forms. I don't know whether he thought his "Chemotron" idea sufficient also for all forms biological information processing.

A more detailed discussion of construction-kits as explanations of biological possibilities was inserted here in November/December 2014:

A closely related topic is the role of explanations of possibilities in science, discussed briefly here:
A theory that explains possibilities may be deep science without being falsifiable.
(First steps toward a "generative grammar" for varieties of mechanism required for various forms of life and ecosystems, including information-processing mechanisms.)

(8 Nov 2014) Entropy and evolution

Issues about entropy and the second law of thermodynamics also need to be discussed in detail eventually, but for now I have a short comment in a separate document about entropy, chemistry, multi-stable multi-structured dynamical systems and the droguli of Lionel Penrose:

Blind theorem-proving

Is evolution more of a blind theorem prover than a blind watchmaker -- proving theorems about what is possible?

Every time some new physical feature, behaviour, or mechanism arises in a living organism, that constitutes an implicit discovery that that sort of thing is possible, and was possible previously, though the realisation of the possibility may be more or less accessible at different stages of evolution. The evolutionary or developmental history contains an implicit proof that it is possible, but extracting the proof at the right level of abstraction may require sophisticated mathematical abilities that do not evolve till much later.

The meta-cognitive abilities even to notice that such discoveries have been made, which require a highly specialised form of information processing competence, did not evolve till very recently (resulting from a mixture of biological and cultural evolution, among other things).

Yet evolution seems to have noticed some of them "implicitly", insofar as it discovered not only very particular solutions, but also generalised patterns that were then instantiated in diverse particular cases. The "laws of form" (studied by D'Arcy Thompson and others) illustrate this: A genome does not specify the precise shape and size of an organism or its parts, but rather a network of relationships between possibilities that can vary between individuals, but even more remarkably, can vary within each individual during that individual's growth and behavioural development (e.g. learning to control movements while size, shape, weight, weight distribution, needs and opportunities all change).

Another example of evolution discovering and using a collection of powerful mathematical abstractions is use of a basic collection of learning abilities to bootstrap abilities to learn how to use increasingly sophisticated features of the prevailing language or languages: a system that was eventually able to work in several thousand different cultures using different languages.

Moreover, the evolved mechanisms in humans somehow provide transitions between having various competences (possibly recently acquired) and becoming able to think about those competences and help others acquire them.

Those transitions from competences to meta-competences (using late developing genetic mechanisms, or learning), include the processes labelled "Representational Redescription" in Karmiloff-Smith (1992). They also have much in common with mathematical discovery, insofar as both often involve finding new abstractions that have many instances.
(Continued below)

Blind mathematical composition

How do products of evolution combine with one another and with other environmental factors to form niches (sets of requirements) enabling and constraining future products of evolution (future designs partially matching the requirements) in multi-level dynamical systems constantly generating new dynamical systems, with new possible trajectories, and new feedback control mechanisms, in individuals, in social groups, in ecosystems, and now in multiple global villages?

How can the genotype available to a newly born or hatched animal make possible hugely (infinitely?) varied developmental trajectories in different environments, e.g. squirrels in different gardens with (mostly) shared genomes learning to defeat new "squirrel-proof" bird-feeders, and humans learning any one (or any two to four?) of several thousand very different human languages, absorbing whatever culture the child grows up in, acquiring competences relevant to local geographical features, local fauna and flora, local sources of food, shelter and danger, personalities of local conspecifics, etc. and in some cases creatively extending those environments through new inventions, new discoveries, new works of art, new moral teachings, new mathematical proofs, etc.

This is why proposing a behavioural test for intelligence is misguided (as Turing understood): no bounded behavioural test can establish the presence of all that potential. A test that indicates lack of intelligence may simply have been unsuited to the individual's capabilities. See

One common answer is that anything with human-like intelligence must use the same (postulated) general purpose learning mechanism, e.g. Juergen Schmidhuber, (2014). [The "empty mind" hypothesis mentioned below]

Even Turing (who should have known better?) toyed with that answer in his 1950 paper, though mainly in the context of a machine that learns to have text-based interactions. All the general purpose mechanisms I've heard proposed so far operate on compressing bit-strings, or symbol-streams, and don't seem to be capable of learning geometrical or topological facts or skills, including the competences of a squirrel, a weaver bird, or a mathematician studying properties of toroidal surfaces.

(I need to check whether I have missed a more powerful, more successful, general purpose learning engine.)

An attempt to characterise the sort of rich "Evo-Devo" interaction that makes nonsense of many speculations about evolutionary or environmental determination, led to the multi-stage developmental model of Chappell and Sloman (2007) depicted below -- apparently extending Waddington's "epigenetic landscape" idea. (This sort of system must have been the product of a complex evolutionary trajectory: it could not have existed when life first began.)

Do we know enough about information-processing and computation?

Are known forms of computation rich enough to provide such a genotype, or are there still secrets to be uncovered in products of evolution?

What are the (mathematical) properties of physics and chemistry that enable a protoplanetary dust cloud to produce machines that can ask questions like these? (The construction-kit question again.)

Is there something about chemistry that we have not yet understood? Only with the properties of chemistry do we seem to combine three features necessary for life from its earliest stages onwards: energy storage and transformation, mechanical and sensory structures that can act on the environment when appropriate, and mechanisms for storing, using, copying, and transforming information Ganti (2003).

Chemistry builds brains, at least in their early stages, though it remains essential for many brain processes throughout life. Perhaps interacting molecules do much more than we know, long after they have constructed neural mechanisms?

Related questions:
Have evolutionary and developmental processes produced biological machines that are intelligent enough to find the answers to these questions, or understand them if found? How?

Can schools and universities provide the sort of education required for researchers and teachers in this project?

What we've learnt since 1952 - varieties of virtual machinery

The Meta-Morphogenesis project attempts to combine and extend Turing's ideas about morphogenesis and his earlier ideas about discrete computation, in the light of what we've learnt since 1952 from computer science, artificial intelligence, computer systems engineering, biology, neuroscience, linguistics, psychology, chemistry, physics, mathematics, and philosophy.

Most philosophers and scientists seem to be unaware of the deep significance of what we have learnt about many forms of virtual machinery:

NOTE (24 Aug 2014):
Most of the references have been moved to a separate file, which includes documents on this web site relevant to the M-M project:
Publications and references related to the Meta-Morphogenesis Project


The main focus of this project:
Transitions in biological information-processing

A vast amount of research has been and is being done on the production by natural selection of new physical and geometrical forms of organisms, of many sizes and types, and production of new behaviours (e.g. J. Maynard Smith and E. Szathmáry, (1995), (1999), and Pallen (2009), among many others mentioned on Gert Korthof's web site).

The Meta-Morphogenesis (M-M or MM) project focuses instead on production of new types of biological information processing, including information-based control mechanisms, whether used for reproduction, growth, development, metabolism, perception, motor control, learning (including creation of new ontologies and new forms of representation), motive formation, planning, planned or unplanned behaviours, meta-cognition, communication, daydreaming, explaining, theory change, mathematical discovery, mathematical proofs, enjoying and producing art, or anything else. All new forms of computation that arise during evolution, development or interaction with other organisms are included. This requires use of a very general notion of "computation", or "information processing", that is not restricted to use of bit-based computers.

The changes in information processing include (a) what is done (as indicated in the previous paragraph), (b) why it is done, e.g. what benefits, if any, result, (c) what the information used is about (e.g. what it refers to, which can include past, present, future, remote, and non-existent entities, events, etc.) and (d) how all that is done, which refers to types of: information bearer, mechanisms for analysing, transforming, constructing, comparing, storing, retrieving information bearers, types of information processing architecture, combining different forms of information processing in larger wholes, types of self-monitoring, self-modulation, self-repair, self-extension, types of competition, types of conflict resolution, types of interrupt mechanism, use of virtual machinery, including multi-layer machines, distributed information-processing (involving several different individuals, or a whole community) and many more.

As explained below, the ability of natural selection to be a sort of "blind mathematician", discovering and using mathematical structures, seems to be crucial -- refuting philosophical claims that mathematics is a human creation.


Why Meta-Morphogenesis?

.... Because the changes produced by the mechanisms of development and change include modified mechanisms for producing new changes in the mechanisms producing development and change.

Natural selection (or the biosphere) is a bit like a young child that has begun to learn, but has no idea that it is learning, what it is learning, how it is learning, why it is learning, what it will do with what it has learnt, why what it has learnt works and why what it has learnt sometimes proves inadequate, either for individuals or for whole species.

A difference is that over billions of years natural selection modifies its information-processing abilities far more than any child can do in a human lifetime, and modifies them in parallel in different ways. Moreover, at least on one planet, it has recently produced some individuals that have begun to understand some of what the evolutionary mechanisms produce without understanding.

The reproductive mechanisms do not normally produce ready-made full understanders, but individuals empowered to grow their understanding guided by the environment and by what some of their forebears and peers have already understood.

Some of those evolutionary changes bear a high level resemblance to the processes in individual development in animals described as "Representational Redescription" in Karmiloff-Smith (1992). In particular, it seems that increases in competence both in evolution and in individual development involve mechanisms that partition discoveries into domains with mathematical structures that can be discovered by appropriate domain-related mechanisms (not merely the use of universally applicable statistical learning techniques as some have supposed). See also the quote from McCarthy below, and the Chappell-Sloman proposal (below).

The key idea: forms of creativity in evolution
Added 8 Aug 2014; Modified 16 Mar 2015

The key idea: evolution changes evolutionary processes and mechanisms, development changes developmental processes and mechanisms, individual learning changes individual learning processes and mechanisms, cultural evolution changes cultural evolutionary processes and mechanisms. Each of those is an example of meta-morphogenesis.

Moreover, each of these processes and mechanisms of change can impact on the others, over appropriate time-scales. That includes changing what evolution can do, by changing the resources available to natural selection -- e.g. by creating, modifying, and combining construction-kits available for evolution, development, learning and social/cultural processes.

If all that is correct, attempts to characterise any of those processes or mechanisms in a uniform way will lead to erroneous theories, because:

Natural selection may seem to be a uniform process, but what it does depends both on the mechanisms generating options between which selections can be made, and the selection mechanisms, which in turn depend partly on external constraints and opportunities -- niches. The points summarised above imply that both the types of option and the selection mechanisms can change dramatically.

Those modifications include:

One of the most important discoveries of biological evolution was the power of "generative" forms of representation of information: e.g. encoding information using trees and networks of information, whose nodes can be either arbitrary non-decomposable objects, or structured (decomposable) objects composed of other objects, for example trees and networks whose nodes contain trees and networks.

The need for such meaning structures is clear in connection with the contents of complex sentences, pictures and diagrams, with parts that have parts that have parts. The need also exists in percepts, in mathematical formulae and proofs, in complex intentions, in explanatory theories, and in action plans.

The ability to create and operate on such structures has been a pervasive feature of AI programming languages, often described as symbolic programming languages, which typically also provide standard instructions for operating on numbers of various sorts. Without this sort of capability, human language, and, I suspect, powerful animal vision systems, could not have evolved. This is why widely used forms of representation using vectors of scalar values are not sufficient for explaining how organisms work. (They don't even suffice for representing chemical structures and processes.)

This is not intended to be a complete list of information processing novelties produced by natural selection. Extending the list, filling in details, and testing ideas by empirical research into processes and products of evolution, building working models to check the feasibility of the theories, and addressing a variety of closely related philosophical problems, including problems about relations between mind and body, are all among the long term aims of the M-M project -- potentially a huge, long term project.

Achieving such goals will require, among other things, major advances in AI and robotics in order to be able to test theories of how organisms work, and may even require novel forms of physical computing machinery, for instance if some of the functions of chemical information processing, with their mixtures of continuous and discrete changes, cannot be replicated in digital computers; and new kinds of mathematics may be required, for reasoning about how some of the systems work.

In the process we can expect many old philosophical problems to be solved or dissolved and many new ones to emerge.

The remainder of this document expands on some of these points and provides links to other, related documents on this web site and to relevant publications. (A partial list)

Offers of collaboration welcome. I have no funds for this research, and do not intend to apply for funds. Others may do so.

Aaron Sloman (School of Computer Science, University of Birmingham.)

Are babies born with empty minds plus a learning machine?

Some researchers, including (as I understand him) Juergen Schmidhuber, (2014) seem to regard the pinnacle of evolutionary design as a totally general, domain-independent learning mechanism, which allows individuals to learn in any environment by discovering statistical relationships between sensory inputs and motor outputs; whereas there seems to be plenty of evidence that humans have different kinds of learning capabilities, used at different stages of development or for different domains of structures and processes.

Compare the views of Neisser (2007) and John McCarthy who wrote:

"Evolution solved a different problem than that of starting a baby with no a priori assumptions."

"Animal behavior, including human intelligence, evolved to survive and succeed in this complex, partially observable and very slightly controllable world. The main features of this world have existed for several billion years and should not have to be learned anew by each person or animal." McCarthy (1996/2008)

McCarthy's suggestion is consistent with the hypothesis that natural selection produced a variety of different learning mechanisms useful for different stages of development in complex organisms, as depicted below.

One way to make progress on such questions is to try to chart the variety of forms of development of information processing in young animals including humans. A subset of that task forms the investigation into "toddler theorems" (the abilities of pre-school children to make proto-mathematical discoveries, without necessarily being aware of what's happening), described in a separate file:


The link with Alan Turing

(Apologies for repetition: to be removed later.)
The idea of the Meta-Morphogenesis project arose from an invitation from Barry Cooper, co-editor of the award-winning book
"Alan Turing: His Work and Impact".
2013 PROSE Award announcements
Detailed list of contents and contributors.
After submitting my three promised papers I found that I was also expected to contribute to part IV (as a result of a misunderstanding). So I read Turing's 1952 paper on Morphogenesis, about which I previously had only very vague knowledge.

Turing's paper is not an easy read, especially for non-mathematicians, but there is a very readable introduction to the ideas in Margaret Boden's magnum opus Boden (2006). In particular, section 15.iv ("Turing's Biological Turn") gives a summary of Turing's work on chemistry-based morphogenesis (which she had read and admired decades earlier).

The previous section 15iii (Mathematical Biology Begins) summarising work by D'Arcy Thompson is also very relevant. E.g. she writes:

Accordingly, D'Arcy Thompson tried to relate morphology to physics, and to the dynamical processes involved in bodily growth. He suggested that very general physical (as opposed to specific chemical or genetic) constraints could interact to make some biological forms possible, or even necessary, while others are impossible.
Boden (2006) Vol 2, 15.iii.a: "Of growth and form" pp 1256
That is closely connected with the view of evolution as a "blind theorem prover", explained below.

Turing's 1952 paper made a deep impression on me, and led me to wonder what Turing might have done if he had lived longer. My tentative (presumptious?) answer was that he might have worked on what I've called The Meta-Morphogenesis project, summarised here. The proposal for a Meta-Morphogenesis project, was first presented as a chapter (written in 2011) published as part of the Turing volume (published in 2013):

A. Sloman, Virtual Machinery and Evolution of Mind (Part 3) Meta-Morphogenesis: Evolution of Information-Processing Machinery, in
Alan Turing - His Work and Impact, Eds. S. B. Cooper and J. van Leeuwen,
Elsevier, Amsterdam, 2013, pp. 849-856,

A piece of evidence that Turing might have been interested: According to his mother, he had always been interested in living things, as depicted by her in this famous drawing:

Margaret Boden's commentary on Turing's work on morphogenesis provides this additional piece of evidence

For the last few years of his life, Turing's energy went primarily into what he called "my mathematical theory of embryology". Indeed, after writing the first Manchester programming manual in 1950, he neglected his duties in the computing laboratory there as a result of his new interest.
Boden (2006) section "15.iv. Turing's Biological Turn" (page 1261)

Perhaps he would have moved (by analogy with some of his earlier moves) from studying embryology to studying the origins of embryology deep in the evolutionary past: the basis of the M-M project. (Later I'll discuss another link with Boden's work: her ideas on creativity and the varieties of creativity in natural selection, including ontological creativity, required for production of new types of virtual machinery mentioned briefly below.

This is a complex, multi-faceted project, and could take several decades, or even much longer. Some of the main ideas are elaborated below, and in other web pages referred to on a separate page. But at present everything is provisional.

How could all life, and products of life, on Earth come out of a cloud of dust that converged to form a planet?

Alan Turing wrote, in a comment that is almost universally ignored, though it occurred in one of his most widely cited papers:
"In the nervous system chemical phenomena are at least as important as electrical." in 'Computing machinery and intelligence', Mind, 59, 1950, pp. 433--460
I wonder if he had thought about the significance of chemistry for evolution of information processing mechanisms rich enough to support minds in a physical universe.

Steps towards an answer
Evolved information-processing -- in animals and machines.
(A huge, long-term multi-disciplinary project.)

Core questions and ideas
How can natural selection produce minds on a lifeless planet? A full understanding of our origins requires us to combine familiar ideas about natural selection with ideas unavailable to Darwin and Wallace, about evolution of information processing functions and mechanisms, since the simplest organisms in chemical soups billions of years ago.

Many research fields can contribute, including: genetics, microbiology, ethology, developmental psychology, neuroscience, linguistics, anthropology, philosophy of science, philosophy of mind, computer science, Artificial Intelligence and robotics. The research requires us to raise new questions about what evolution achieved and how it did so, including questions about new forms of information, new uses for information, and new mechanisms for processing information. Doing that requires us to investigate new construction kits created, and then used, by processes of development, learning, and natural selection to support those developments.

Explanation by natural selection is not enough

Graham Bell writes in his book Selection: The Mechanism of Evolution

    Living complexity cannot be explained except through selection and does
    not require any other category of explanation whatsoever.
No: adequate explanations need to mention both selection mechanisms and enabling mechanisms, as I am sure Bell is aware.

Without enabling mechanisms, selection processes will not have a supply of new working/viable options to choose from. In that case the selection mechanisms will no longer be able to select new viable options.

Both the selection mechanisms and the enabling mechanisms can change during evolution (partly by influencing each other). As a result, we can think of the initial enabling mechanisms, provided by physics and chemistry, as a form of construction kit that natural selection eventually uses (blindly) to build new mechanisms forming an enriched construction kit. If this happens repeatedly (as has happened spectacularly with computing mechanisms in the last 60 years or so), then the most recently evolved biological construction kits may be unrecognisable to scientists who know only about the initial mechanisms.

So the M-M project requires multi-disciplinary investigations of layers of evolved biological construction kits, some of which have helped to produce new construction kits for use by evolution.

There is a useful web site listing common misconceptions about evolution here:
It does not bring out (or try to bring out) the full variety of types of explanation of evolutionary phenomena. E.g. Computer systems engineers have been discovering or inventing new types of information processing for over half a century -- especially new types of virtual machinery. It appears that biological evolution made use of a similar discovery very much earlier, for good reasons, some of them summarised here.

Relevant discoveries by biologists

Systems biologists are constantly discovering new biological types of informed control (information-based control). However, there may be types of biological enabling mechanisms (e.g. forms of chemical or biological computation) that we have not yet learnt about - and that may prevent us understanding some of the transitions in evolution, e.g. some changes in reasoning powers in our ancestors including changes from which we benefited.

Familiar ideas about natural selection need to be expanded to show how small changes can build up to create increasingly complex mechanisms involved in the processes that repeatedly produce:

  1. new physical and chemical structures and processes supporting reproduction, metabolism, growth, immune responses, neural mechanisms, etc.;
  2. new physical forms and new physical behaviours of organisms, including new types of sensing and acting;
  3. (Added 7 Aug 2014): New information-processing challenges, e.g. to deal with more complex physical phenomena, or more intelligent predators or prey, or to meet new demands on parents because of more sophisticated learning capabilities in offspring. (Challenges or requirements can evolve also, not only solutions. Challenges can come not only from new prey, new predators, new competitors, new physical environments, but also from new learning potential of offspring, or from new capabilities that are not easy to use.)
  4. new information-processing capabilities and mechanisms, including sensory interpretation, motivation, learning, planning, decision making, interrupting, self-monitoring, teaching, etc.;
  5. new evolutionary mechanisms, including new drivers of variation and new selection mechanisms.
Point (e) involves 'recursion': evolutionary morphogenesis changes mechanisms of evolutionary morphogenesis -- hence the label 'meta-morphogenesis'.

The project investigates how increasingly complex products of evolution produce increasingly complex forms of information processing including new mechanisms of evolution -- generalising ideas in Turing's 1952 paper on chemical morphogenesis and also the theory of meta-configured individual cognitive development presented in

Jackie Chappell and Aaron Sloman,
Natural and artificial meta-configured altricial information-processing systems,
International Journal of Unconventional Computing, 3, 3, 2007, pp. 211--239,
which includes an earlier version of this diagram, showing different levels at which information from the genome and from the environment combine (after varying developmental delays):

(Chris Miall helped with the original diagram.)

That theory (and diagram) referred to processes of development in an individual -- processes that change some of the mechanisms of later development in that individual. The M-M project extends that idea to evolution, so that in this new context instead of the diagram referring only to development of individual organisms, it can also refer (loosely) to evolution of a species, or even of a whole ecosystem whose main features, including features affecting further evolution, change over time.

The M-M project has begun to identify many changes in forms of biological information processing, including transitions in mechanisms of reproduction, mechanisms of learning and development, and inter-individual and inter-species forms of information-processing. Examples of distinct types of transition in biological information-processing are being collected here.

An important under-studied transition is evolution of capabilities that led to proofs in Euclidean geometry long before modern mathematics, one of the most important extensions of human minds in the last few millennia. How did abilities to think philosophically evolve? Were the cognitive mechanisms unique to humans or did unnoticed subsets develop in other species? When will our robots begin to acquire these abilities?

The questions raised in the M-M project require long term multi-disciplinary collaborative research, perhaps comparable in scale to the Human Genome project. The relevance to philosophy of mathematics is discussed in a related web page. ____________________________________________________________________________

"Information" -- a key idea for this project
(And for Jane Austen.)

The concept of "information used by organisms or machines or biological processes for various purposes" is central to this project. But it is not the concept unfortunately labelled "information" by the great Claude Shannon and his many admirers. He understood the differences but too many researchers ignore them. In fact many researchers think his is the only concept of "information" we have. But there is a much older one, used in everyday life.

The older concept refers to information that has causal roles in evolution, in animal perception, learning, motivation, acting, interacting, thinking, asking, wondering, being puzzled, finding answers (etc.) This ancient concept was often used explicitly by Jane Austen over a century before Shannon's work, and by many others long before her. Several examples from her novel 'Pride and Prejudice' published in 1813, are presented here:
  Jane Austen's concept of information (contrasted with Claude Shannon's).
However, I am not claiming that Jane Austen had considered all the uses of information relevant to biology. Readers may find it useful to try making a list of the kinds of information they use in a typical day, and what they use those kinds for -- or, more realistically, in a typical hour, such as the first hour after waking, including information used getting light (if needed), deciding whether to get up, getting out of bed, getting dressed, ...

In particular, "information-processing" here does not refer only to bit manipulation, or symbol manipulation, the operation of computers, or the sending and receiving of messages: those are all special sub-cases. In particular, the kind of information we are talking about does not need a sender and a receiver every time there is a user.

Acquiring information is finding out about something that the information refers to (or purports to refer to: it could be false information). Information contents used by an organism can come from many different sources outside or inside the organism, and can play different roles: in questions, intentions, instructions, multi-step branching plans, conditions for doing something, theories, and many more. All organisms, and many parts of organisms, including cells, use information -- and not just for reproduction. Information from external or internal sensors can turn on, turn off or modulate behaviour, which may be internal or external behaviour, or a mixture -- e.g. muscle contractions used for grasping something or for running. Working out a plan for achieving a goal uses information about the intended state of affairs to create a new complex information structure whose parts refer to possible actions, possible contents of perception, conditions for doing things, sources of missing information, and many more.

In the simplest cases, information is acquired and used immediately, with no record kept. For example, many homeostatic mechanisms, and servo-control mechanisms, use one or more sensors that continually record internal or external physical states, while response mechanisms continuously alter their behaviour in accordance with the current record, which is continually being overwritten -- online information processing. In more complex cases information received from sensors, is converted to a form that can be stored and used in multiple ways later on -- offline information processing. (A failure to understand such engineering design distinctions has led to vast amounts of confusion about differences between dorsal and ventral streams in brains.

Biological information is of many kinds, with many types of complexity, using many kinds of mechanism, for many types of purpose or function. The earliest organisms must have been restricted to "online" mechanisms (e.g. using chemotaxis). One of the tasks of the M-M project is to investigate the variety types of "offline" use of information, including uses to refer to the past, to remote (currently unsensed) parts of the environment, to possible future states of affairs, and to possible past or remote entities, about which questions are raised and theories formulated. The variety of types of use of information may be far greater than any theory of information produced so far has proposed. Some of the deep questions are concerned with the extent of use of mathematical structures in biological information processing.

For more on the concept of "information" used here see Sloman (2010) [in a separate web page].


Related Videos (Moved to another file 24 Aug 2014)

Long slide presentation introducing the Meta-Morphogenesis project ____________________________________________________________________________

Return to list of contents

Introductory material (some repeated)
Including evolution and mathematics.

Meta-Morphogenesis: Evolution and Development of Information-Processing Machinery (Including (recursively) mechanisms for changing the mechanisms)

The universe is made up of matter, energy and information, interacting with each other and producing new kinds of matter, energy, information and interaction.

How? How did all this come out of a cloud of dust?

In order to find explanations we first need much better descriptions of what needs to be explained.

This is a multi-disciplinary project attempting to describe and explain the variety of biological information-processing mechanisms involved in the production of new biological information-processing mechanisms, on many time scales, between the earliest days of the planet with no life, only physical and chemical structures, including volcanic eruptions, asteroid impacts, solar and stellar radiation, and many other physical/chemical processes (or perhaps starting even earlier, when there was only a dust cloud in this part of the solar system?).

Evolution can be thought of as a (blind) Theorem Prover (or theorem discoverer).

The "proofs" of discovered possibilities are implicit in evolutionary and/or developmental trajectories.

The proofs demonstrate the possibility of

    development of new forms of development
    evolution of new types of evolution
    learning new ways to learn
    evolution of new types of learning
        (including mathematical learning: by working things out
        without requiring empirical evidence)
    evolution of new forms of development
    development of new forms of learning
        (why can't a toddler learn quantum mechanics?)
    how new forms of learning support new forms of evolution
    how new forms of development support new forms of evolution
        (e.g. postponing sexual maturity until mate-selection mating
        and nurturing can be influenced by much learning)
    .... and ways in which social cultural evolution add to the mix
These processes produce new forms of representation, new ontologies and information contents, new information-processing mechanisms, new sensory-motor morphologies, new forms of control, new forms of social interaction, new forms of creativity, ... and more. Some may even accelerate evolution.

More on connections between natural selection and mathematical discovery:
Updated: 27 Oct 2016
    Biology, Mathematics, Philosophy, and Evolution of Information Processing

Additional mathematics-related material in this directory

Return to list of contents

  • Creativity in evolution/natural selection
    Although the processes start off "blind", the achievements are of a kind that would require highly creative processes of design, implementation, testing, development, debugging, and re-design, if produced by human engineers. In some of the later stages, when animal cognition begins to play a role in evolution, this is a form of conscious, but not yet self-conscious, creativity. Similar remarks can be made about varieties of creativity in development of individuals, discussed further in connection with "toddler theorems". (Compare Margaret Boden on creativity.)

    A growing list of transitions in types of biological information-processing:
       Biology, Mathematics, Philosophy, and Evolution of Information Processing

    Mathematics is at root a biological, not an anthropological, phenomenon (as suggested by Wittgenstein). But its possibility depends on deep features of the universe, some of which evolution had to 'discover':
      An attempt to identify a major type of mathematical reasoning with precursors in
     perception and reasoning about affordances, not yet replicated in AI systems:

    Even in microbes
    I suspect there's much still to be learnt about the varying challenges and opportunities faced by microbes at various stages in their evolution, including new challenges produced by environmental changes and new opportunities (e.g. for control) produced by previous evolved features and competences -- and the mechanisms that evolved in response to those challenges and opportunities.

    Example: which organisms were first able to learn about an enduring spatial configuration of resources, obstacles and dangers, only a tiny fragment of which can be sensed at any one time? What changes occurred to meet that need?

    More examples to be collected here:

    NOTE: For a messy, still growing, collection of examples relating to learning and development see this web page on "Toddler theorems": (including an introduction to the idea of a "Domain").

    What is Meta-Morphogenesis? Draft answer (last revised: Aug 2013):

    The study of meta-morphogenesis (M-M) is the study of


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    Example papers and presentations I have written on this topic over the last five decades (DPhil Thesis was in 1962), especially since the early 1990s. (Currently this list duplicates the list in the Toddler theorems paper.)



    Appendix: Schematic (partial) Summary

    Transitions can occur in parts of organisms, in whole organisms, within a species, in interacting groups of species, in societies, and in environments (though organisms are part of the environment for conspecifics and for others).

    A sample list of types of transition produced by biological mechanisms The mechanisms include evolution by natural selection, individual learning, cultural development and transmission, including changes in genomes as well as changes in factors affecting gene expression.

    1. Change of physical shape (in individual, in species)
    2. Change in physical behaviour (in individual, in species)
    3. Change in information processing (in individual, in species) (including control of growth, metabolism, immune system, processing of perception, motive formation, motive selection, action selection, action control, learning, reasoning, ...)
    4. Change in developmental trajectory (physical, non-physical)
    5. Change in what can be learnt (in individual, in species)
    6. Change in type of interaction between individuals (in same species, across species, within "family unit", prey, predators, others...)
    7. Change in type of social organisation (including forms of collaboration, forms of nurturing, forms of education, forms of competition)
    8. Changes in mechanisms of evolution (evolution of evolvability (Dawkins, 1988))
    9. Changes in mechanisms of development
    10. Changes in mechanisms of learning, including extensions of empirical learning to include non-empirical, e.g. mathematical learning (making use of new meta-cognitive capabilities).
    11. Changes in mechanisms of interaction
    12. Changes in mechanisms of self-monitoring, self-control
    13. Changes in construction kits produced by evolution and its products, especially construction kits for information-processing systems. For more detail see
    14. Introduction of new virtual machines, new forms of representation, new ontologies, new architectures, new sources of motivation, new motivation processing mechanisms, and many more, based on new construction-kits.
    Note added 23 Oct 2012
    An expanded version of the above list of transitions is being created in

    Constant extension of what needs to be explained by science.
    (Added 27 Oct 2014)
    As new, more complex forms of evolution, development, learning, perceiving, reasoning, communicating, collaborating, technology, ... keep arising out of the interactions between things that existed previously, along with natural selection (possibly extended by non-natural selection!), there is a never-ending stream (trickle? flood?) of extensions to the phenomena that science needs to explain. For example, it seems likely that at some stage our evolutionary ancestors lacked some of the mathematical abilities that now exist in humans. How those abilities evolved, what new things they make possible, how they make them possible, are all questions for science that would not necessarily have been thought of by scientists observing those ancestors who lacked our mathematical abilities.

    It was not possible until recently to ask the questions raised in:

    What sort of construction-kit must the physical universe have provided to make it possible for life, mind, ecosystems, cultures, etc. to evolve from a planet formed from a cloud of dust?
    Compare the idea of evolved and acquired construction kits, added to this web site in Nov 2014.

    This leads to the conjecture that the space of possible forms of information processing that need to be explained by science is at least as complex as the space of mathematical problems that arise in the arithmetic of natural numbers. And we know that that space has unending complexity.

    If all this is correct there could never be a time at which all scientific questions will have been answered, not even if all questions about the underlying physical/chemical mechanisms that make life possible have been answered. That would be analogous to having a set of axioms for number theory. One of the great discoveries of the twentieth century, due to Gödel and others, was the infinite supply of unanswered mathematical questions that arise from the basics of arithmetic. Whether only a finite subset of the questions are worth answering looks unlikely.

    These changes can interact and influence one another...

    Types of Meta-Morphogenesis: For any of the above biological changes B1, B2, B3,.. etc. and for any environmental states or changes E1, E2, E3,... there can be influences of the following forms ...

    Meta-Morphogenesis (M-M):
    Things that cause changes can produce new things that cause changes. Old phenomena may be produced in new ways: e.g. both types of information acquired and ways of acquiring and using information can change. Often new mechanisms can produce new biological phenomena

    -- e.g. organisms that can discover what they have learnt.
    -- organisms that make and use mathematical discoveries.
    In particular, most forms of biological information processing that exist now are products of parallel trajectories of biological information processing over many stages of evolution and development, including cultural evolution in the case of humans.

    This is quite unlike use of evolutionary computation (GA, GP, etc.) with a fixed evaluation function, often used to solve engineering problems.
    For example, evaluation in natural evolution keeps changing, as environments, including competitors, prey, symbionts, diseases, etc. change.

    Return to list of contents

    (Most moved to separate document.)

    1. Rodney Brooks on layered architectures and evolution
      In the mid 1980s, after apparently becoming dissatisfied with the state of AI, Brooks wrote a series of very influential papers that recommended a 'layered' approach to AI design, namely build systems that have relatively simple capabilities and then add new more sophisticated capabilities, that run in parallel with and make use of the older capabilities. He also related this to suggestions about biological evolution and the relative information-processing complexity of evolutionarily very old organisms, suggesting that what was added more recently to provide human functionality added relatively little.

      His ideas overlap with (and probably helped to influence) those presented in this project, but there are also deep differences. E.g. I don't claim that we can start building simple organisms with our current technology that provide suitable old, 'lower level' layers on which to add newer, more sophisticated, layers of competence. I am, however, suggesting that what is old in the evolutionary history of existing organisms may have many unobvious features that the M-M project should attempt to uncover.

      I also don't propose that it will suffice to start from multi-cellular organisms like insects, that have already evolved capacities to move around in rich and complex environments, foraging, feeding, mating, building nests, etc. Instead I consider the possibility that even at the single-celled level there may have been forms of information processing that underpin some of the types of information processing that interest us in humans and other animals.

      Brooks' suggestion that the importance of internal representations has been over-rated because the best representation of the world is the world itself, has been highly influential, but is at most relevant to what I've called 'online intelligence' involved in control of movements and manipulations using feedback mechanisms of various sorts. (H.A.Simon made similar points.) For deliberative and meta-semantic competences the slogan is not merely wrong: it has been positively harmful.

      Also the ideas in the CogAff project and the CogAff architecture schema allow for a richer variety of types of architecture than the type of layered subsumption architecture proposed by Brooks, though it's possible that each could be modified to cover more of the features of the other.

      His work had enormous influence in many research and teaching centres. Unfortunately the people influenced were often much less intelligent and less subtle than Brooks, and as a result much of the influence has been bad. Hence my critique.

      David Kirsh wrote a critical review of Brooks' ideas around 1986, published in 1991 (here.). Brooks wrote a reply ('From earwigs to humans') published in Brooks (1997). I wrote a somewhat different critical commentary much later Sloman (2009)

    2. Margaret Boden's work (in another file)

    3. Brian Goodwin (in another file)

    4. Peter Strawson on Descriptive Metaphysics (in another file)

    5. The Meta-Descriptive Metaphysics project (begun 2014).

    Return to list of contents

    Document history

    This web site is
    Also accessible as

    Most of the references have been moved to a separate file:

    A slightly messy PDF version is also available:

    This is one of a set of documents on the meta-morphogenesis project.

    A partial index of a wider collection of discussion notes is in

    This version installed: 21 Oct 2012
    Original version installed: 19 Oct 2011 now here.

    UPDATES (A partial list):
    27 Oct 2016: Extended the analogy of evolution as blind mathematician by characterising the relations between the fundamental construction kit and all derived construction kit as closely analogous to proposed relationships between foundations of mathematics and all derived kinds of mathematics.
    Jan-March 2015: added separate pages on construction-kits, and explanations of possibilities.
    19 Dec 2014: added reference to initial construction kit in introduction. Added link to related projects in this file.
    8 Nov 2014: added link to new paper on entropy and evolution.
    27 Oct 2014: Added a bit at the top about origins of this project. Slightly reorganised and extended various portions.
    Made some relevant additions to the (disorganised) notes on Virtual Machine Functionalism - VMF)
    17 Sep 2014: Added more structure to the introduction, with subheadings
    14 Sep 2014: New experimental top section. Is it too confusing? Does it sound like clap-trap to the uninitiated?
    8 Sep 2014: slight rearrangement. Some new references.
    24-5 Aug 2014: considerable reorganisation, with most references moved to here.
    15 Aug 2014 added Birner's paper on Hayek and Popper;
    7 Aug 2014: minor changes;
    30 Jul 2014: added link to Strawson and meta-descriptive metaphysics moved to another file
    5 Apr 2014 (Doyle and Popper links); 17 May 2014; 12 Jun 2014
    31 Jan 2014: added new introduction and reorganised; 10 Feb 2014: Minor eds;
    12 Nov 2013 (Added comparison with ideas of Rodney Brooks.);19 Nov 2013
    2, 16 Aug 2013; 24 Aug 2013 (re-formatting); 6, 29 Sep 2013; 31 Oct 2013;
    (Adam Ford Video fixed) 24 June 2013;
    2 Feb 2013; 24 Apr 2013; 4 May 2013; 20 May 2013; 17 Jun 2013;
    6 Dec 2012 19 Dec 2012; 21 Oct 2012 (Split in two: other part here.);
    10 May 2012; 22 May 2012; 19 Jun 2012; 29 Jun 2012; 7 Jul 2012; 24 Aug 2012; 13 Oct 2012; 14 Nov 2012;
    20 Oct 2011; 22 Nov 2011; 21 Feb 2012 (Appendix);5 Mar, 19 Mar, 23 Apr 2012;

    Maintained by Aaron Sloman
    School of Computer Science
    The University of Birmingham