School of Computer Science THE UNIVERSITY OF BIRMINGHAM CoSy project CogX project
(like life on earth)

The Meta-Morphogenesis (MM) Project (or Meta-Project?)
(Combining and extending Turing's ideas about morphogenesis
and his earlier ideas about discrete computation.)

The focus of this project

The focus here is not on creation by natural selection of new physical and
geometrical forms, or production of new behaviours, but on creation of new types
of biological information processing, including information-based control
mechanisms, whether used for reproduction, 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, or anything else, -- i.e. new forms of computation (in a
very general sense of "computation" that is not restricted to use of bit-based

The changes comprise both what is done (as indicated in the previous
paragraph) and how it is done, which refers to types of information
bearers, mechanisms for analysing, transforming, constructing, comparing,
storing, retrieving information bearers, types of information processing
architectures, combining different forms of information processing in larger
wholes, types of self-monitoring, self-modulation, self-repair, self-extension,
types of competition that can occur, 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.

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. And, eventually, at least on one planet, it has produced some
individuals that have begun to understand some of what the evolutionary
mechanisms can produce without understanding.

Interestingly, 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.[*]

The key idea (Added 8 Aug 2014)

Another way of expressing 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.
Moreover, each of these processes and mechanisms of change can impact on the
others over appropriate time-scales. If all that is correct, attempts to
characterise any of those processes or mechanisms in a uniform way will
lead to erroneous theories.

For example, 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. The points summarised above imply
that both the types of options and the selection mechanisms can change

Those modifications include: changes in physical and chemical structures and processes
(that require, and also make possible, more complex information processing), changes
in reproductive machinery, changes in genome-driven or partly genome-driven patterns
of individual development (epigenesis) both across generations and within an
individual's development, changes in the relative contributions of genome and
environment and the stages at which they interact in individual development, changes
in forms of adaptation and learning by individuals, changes in forms of sensing,
perceiving and acting, changes in modes of communication and control between
subsystems in an organism, changes in information-processing architectures within
which diverse subsystems can interact, communicate, cooperate, compete and develop,
changes in modes of communication and control between organisms, changes in types of
cooperative or symbiotic processing, changes in requirements for and forms of
competition, changes in abilities to acquire and use information about oneself and
about other individuals (requiring two different but related forms of meta-cognition),
changes in how parents influence offspring in their learning and development,
changes in how groups of individuals acquire, use and transmit information,
changes in how societies and cultures interact, including interactions involving
new technologies, changes in the ways in which the physical environment produces
new challenges and opportunities for information-processing in organisms of
different kinds, including humans (sometimes as a result of biological
processes, or as a result of other processes, e.g. geological events, asteroid
impacts, tides, etc.) and changes in the ways all these processes influence one

One of the most important discoveries of biological evolution was the power of
"generative" forms of representation of information: e.g. encoding information
withing using trees and networks of information, whose nodes can be structured
objects including trees and networks. Without this, 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
inadequate for explaining how organisms work. That doesn't even suffice for
representing chemical structures and processes.

This is not intended to be a complete list. Extending it, 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 MM project.

That will require 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. (Mostly not yet inserted.)

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.

[*]NOTE (Added 17 May 2014)
Some of those changes seem to bear a high level resemblance to the processes in
individual development in animals described as "Representational Redescription" in
Karmiloff-Smith (1992)

Moreover, some researchers 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 McCarthy (1996/2008):

"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."
[*Go Back]


The idea of a Meta-Morphogenesis project arose from a misunderstanding I had with
the editors of the award-winning book "Alan Turing: His Work and Impact"
Detailed list of contents and contributors.
2013 PROSE Award announcements

The proposal for a Meta-Morphogenesis project, partly inspired by Turing's 1952 paper
on Chemical Morphogenesis, was first presented as a chapter in that book:

    Aaron 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,
The ideas are elaborated below, and in other web pages referred to below.

This version installed: 21 Oct 2012
Previous (longer) version installed: 19 Oct 2011 now here.
7 Aug 2014: minor changes; 15 Aug 2014 added Birner's paper on Hayek and Popper.
30 Jul 2014: added link to Strawson and meta-descriptive metaphysics here.
5 Apr 2014 (Doyle and Popper links); 17 May 2014; 12 Jun 2014
31 Jan 2014: added new introduction and reorganised a bit; 10 Feb 2014: Minor eds;
12 Nov 2013 (Added section on comparison with ideas of Rodney Brooks.) ;19 Nov 2013
2 Aug 2013; 16 Aug 2013; 24 Aug 2013 (some re-formatting); 6 Sep 2013; 29 Sep 2013; 31 Oct 2013;
2 Feb 2013; 24 Apr 2013; 4 May 2013; 20 May 2013; 17 Jun 2013; (Video fixed) 24 June 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 2012; 19 Mar 2012; 23 Apr 2012;




The Question:
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 thought about the significance of chemistry for evolution of mind in a physical universe.
A Protoplanetary Dust Cloud?
Protoplanetary disk
[NASA artist's impression of a protoplanetary disk, from WikiMedia]

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? To fully understand our
origins we must 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,
raising new questions about what evolution achieved and how it did so.

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 cannot
select new viable options.

Both the selection mechanisms and the enabling mechanisms can change during evolution
(partly by influencing each other).

There is a useful web site listing common misconceptions about evolution here:
faq.php faq.php

However 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. There are good reasons for thinking
that biological evolution made use of a similar discovery very much earlier, for good
reasons, some of them summarised here.

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 both ideas in Turing's 1952 paper on chemical morphogenesis and 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.)

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.

Additional overview materials
A 57 minute video interview at AGI 2012 in Oxford introduces some of the ideas.
With a transcript here (thanks to Dylan Holmes):
A longer video recording of a tutorial on the Meta-Morphogenesis project is
referenced below.

One 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:


Document history

This web site is
Also accessible as
A slightly messy PDF version is also available:

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

A draft speculative paper on the nature of mathematics and evolution of mathematicians (Sept 2013):
And an extended abstract for a seminar on this topic on 1st Nov 2013:
"From Molecules to Mathematicians"

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

Introductory/Overview Materials

The main concept of information used for this project
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
that is the only concept of "information" we have. But there is a much older one.

The concept of information whose role in evolution, in animal perception, learning,
motivation, acting, interacting, thinking, asking, wondering, being puzzled, finding
answers (etc.) I am referring to, was already known to Jane Austen over a century before
Shannon's work, and to 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 (As opposed to Claude Shannon's)
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, ...
For more in the concept of "information" used here see Sloman (2010).

Further information about the Meta-Morphogenesis project:
Long PDF slide presentation introducing the Meta-Morphogenesis project
(Also flash version on

See also: Abstract for Meta-Morphogenesis tutorial
At: AGI 2012 -- Dec 11th Oxford
St Anne's College Oxford

Related Videos


A growing collection of related papers and discussion notes:

Return to list of contents

Introductory material

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

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.

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:

On 5th Jun 2012, Stuart Wray produced this sketch of the ideas (click to view):
after reading a draft paper on Meta-morphogenesis and the Creativity of Evolution:

For a (very) compressed history of information processing on our planet see
Evolution, Life and Mind: Some Startling Facts

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").
Related Talks
Related talks (PDF) can be found here:

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

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


Presentation By Penrose, Manchester 2012
Added 12 Aug 2012
Roger Penrose seems to partially agree with one of the ideas here

At the Alan Turing centenary conference in Manchester (June 2012),
Roger Penrose gave the final keynote lecture, which was open to the public. His
lecture (The Problem of Modelling the Mathematical Mind) was recorded on video
and is now available online:

Questions from the audience were also recorded. Near the end of the video (at
approximately 1 hour 26 minutes from the start) I had a chance to suggest that
what he was trying to say about human consciousness and its role in mathematical
discovery might be expressed (perhaps more clearly) in terms of the kinds of
meta-cognitive functions required in animals, children, and future robots, as
well as mathematicians. The common process is first gaining expertise in some
domain (or micro-domain!) of experience and then using meta-cognitive mechanisms
that inspect the knowledge acquired so far and discover the possibility of
reorganising the information gained into a deeper, more powerful, generative
form. The best known example of this sort of transition is the transition in
human language development to use of a generative syntax. (At one point I
mistakenly referred to a "generative theorem" when I meant "generative theory".)

I suggested that something similar must have happened when early humans made the
discoveries, without the aid of mathematics teachers, that provided the basis of
Euclidean geometry (later systematised through social processes). I have
proposed that there are many examples, that have mostly gone unnoticed, of young
children discovering what I call "Toddler theorems", some of them probably also
discovered by other animals, as discussed in

This is also related to the ideas about "Representational Re-description" in the
work of Annette Karmiloff-Smith, presented in her 1992 book Beyond Modularity
discussed in

Penrose seemed to agree with my suggestion, and to accept that it might also
explain why the basis of some mathematical competences are biologically
valuable, which he had previously said he was doubtful about. I don't know
whether he realised he was agreeing to a proposal that instead of thinking of
consciousness as part of the explanation of human mathematics, we can switch to
thinking of the biological requirement for mathematical thinking as part of the
explanation of important kinds of human (and animal) consciousness.

This is also connected with the need to extend J.J.Gibson's theory
of perception of affordances discussed in

Return to list of contents




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.)



CLOSELY RELATED (To be expanded and re-ordered)

  1. Richard Dawkins, 'The Evolution of Evolvability',
    in Artificial Life: Proceedings of an Interdisciplinary Workshop on the Synthesis
    and Simulation of Living Systems,

    Ed. Chris G. Langton, Addison-Wesley, 1988, pp. 201--220.
    Dawkins' paper is entirely about evolution of physical form, and of procedures
    for producing physical forms. The idea of meta-morphogenesis includes evolution
    of behaviours, evolution of information processing (including mechanisms for
    producing and controlling behaviour), evolution of forms of learning, learning,
    evolution of mechanisms of development of new information-processing
    capabilities, evolution of abilities to alter the evolvability of all of those.
    Dawkins paper is a useful introduction to the basic idea, with informative toy
    Popper's Darwin Lecture: (Linked here 5 Apr 2014)
    Natural Selection and the Emergence of Mind
    Delivered at Darwin College, Cambridge, November 8, 1977
    Kindly made available on Bob Doyle's remarkable web site which is full of relevant pages:
    (I learnt about this for the first time on 4th Apr 2014. Google should have introduced us sooner!)

  3. 15 Aug 2014: Jack Birner drew my attention to his paper:
    Jack Birner (2009).
    "From Group Selection to Ecological Niches: Popper's rethinking of evolutionary
    theory in the light of Hayek's theory of culture." In
    Z. Parusnikova & R.S. Cohen (eds.), Rethinking Popper, Springer.
    PDF available here.
    From the Abstract:
    Hayek's The Sensory Order contains a physicalistic identity theory of
    the mind. Popper criticized it, saying that it could not explain the higher
    functions of language. Hayek took up that challenge in a manuscript but failed
    to refute Popper's arguments. Drawing upon the same manuscript, Hayek developed
    a theory of behavioural rules and cultural evolution. Despite his criticism of
    the theory of mind on which this evolutionary theory was based, Popper adopted
    Hayek's idea of group selection. He transformed it into a theory of the
    selective power of ecological niches. This became a central element of Popper's
    theory of evolution. The chapter traces the influence Popper and Hayek had on
    each other in the fields of the philosophy of mind and evolutionary theory. ...

    NOTE: an online PDF version of Hayek's The Sensory Order is available here:
    (The 'bw' version is smaller and slightly more readable.)

  4. Annette Karmiloff-Smith
    Beyond Modularity,
      A Developmental Perspective on Cognitive Science,

    MIT Press (1992) --Informally reviewed in

  5. Kenneth Craik's 1943 book (The Nature of Explanation), written nearly
    70 years ago makes some major contributions to the meta-morphogenesis project by
    drawing attention to previously unnoticed problems about biological information
    processing in intelligent animals.
    For a draft incomplete discussion of his contribution, see

  6. Natural and artificial meta-configured altricial information-processing systems
    Jackie Chappell and Aaron Sloman
    International Journal of Unconventional Computing, 3, 3, 2007, pp. 211--239,
    The full variety of powerful information-processing mechanisms 'discovered' by
    evolution has not yet been re-discovered by scientists and engineers. By
    attending closely to the diversity of biological phenomena, we may gain new
    insights into
      (a) how evolution happens,
      (b) what sorts of mechanisms, forms of representation, types of learning
          and development and types of architectures have evolved,
      (c) how to explain ill-understood aspects of human and animal intelligence,
      (d) new useful mechanisms for artificial systems.
    We analyse trade-offs common to both biological evolution and engineering
    design, and propose a kind of architecture that grows itself, using, among other
    things, genetically determined meta-competences that deploy powerful symbolic
    mechanisms to achieve various kinds of discontinuous learning, often through
    play and exploration, including development of an 'exosomatic' ontology,
    referring to things in the environment - in contrast with learning systems that
    discover only sensorimotor contingencies or adaptive mechanisms that make only
    minor modifications within a fixed architecture.
  7. Margaret Boden's work
    There is much relevant content in Margaret Boden's work, e.g. on purposive
    explanation in psychology, on achievements and limitations of AI, on creativity,
    her theoretical work on biology (especially the relations between life and mind)
    and her outstanding historical analyses of various aspects of the development of
    Cognitive Science:
    Mind As Machine: A history of Cognitive Science (Vols 1--2) (2006)

  8. 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 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
    (ref below). Brooks wrote a reply, cited below, published in 1997. I wrote a somewhat
    different critical commentary much later, partly based on the unpublished note on
    requirements, cited below.
    David Kirsh, Today the earwig, tomorrow man?, in
    Artificial Intelligence, 47, 1, 1991, pp. 161--184,
    Rodney A. Brooks, From earwigs to humans, in
    Robotics and Autonomous Systems, 20, 1997, pp. 291 - 304
    Aaron Sloman,
    Some Requirements for Human-like Robots:
    Why the recent over-emphasis on embodiment has held up progress, in
    Creating Brain-like Intelligence,
    Eds. B. Sendhoff, E. Koerner, O. Sporns, H. Ritter and K. Doya, Springer-Verlag, 2009, pp. 248--277,

  9. Peter Strawson on Descriptive Metaphysics (Added 30 Jul 2014)
    The Meta-Descriptive Metaphysics project.
    A note on Strawson's notion of "Descriptive Metaphysics", which claims:
    "There is a massive central core of human thinking which has no history -
    or none recorded in histories of thought; there are categories and concepts
    which, in their most fundamental character, change not at all."

    Perhaps that core actually has a history, in the evolution of human minds and
    some of their precursors, and perhaps slightly different cores have evolutionary
    histories along different lineages. This suggests a new project: investigation of
    Meta-Descriptive Metaphysics described in

  10. Closely related papers (a small subset):
    Aaron Sloman,
    Requirements for a Fully Deliberative Architecture (Or component of an architecture),
    Unpublished Research Note, COSY-DP-0604,
    School of Computer Science, University of Birmingham, UK, May, 2006,
    Aaron Sloman, 2011,
    What's information, for an organism or intelligent machine?
        How can a machine or organism mean?,
    In, Information and Computation, Eds. G. Dodig-Crnkovic and M. Burgin,
    World Scientific, New Jersey, pp.393--438,

  11. Brian Goodwin, whom I met and talked to occasionally at Sussex University
    expressed ideas in conversation (and in his publications which I did not read,
    mainly because I could not keep up with the mathematical details), had ideas
    about natural selection being only part of the story of how evolution works: he
    used to talk about "Laws of Form" constraining the possibilities for growth in
    ways that did not require genetic control. In retrospect I think some of the
    ideas behind the M-M project may have come from him, and before him from D'Arcy
    Thompson, Goethe and others. See Boden (2006)
    Sections 15x(b-d), Vol 2

    However, some of the "laws of form" are concerned with forms of information
    processing and how possibilities are enabled and constrained by (a) the physical
    mechanisms in which the information processing machinery (even virtual
    machinery) has to be implemented and (b) the environments with which organisms
    need to interact in order to develop, learn, live their lives and reproduce --
    some of which include other information processors: friends, foes, food,
    playmates, and things to observe or be observed by.

  12. Stuart Kauffman's work, e.g. see this useful overview by Gert Korthof

    Kauffman's 1995 book is very approachable:
    At home in the universe: The search for laws of complexity

  13. Ideas of David Deutsch. See his old and new web sites:
    (Not working when I last looked)

  14. Two books by Jack Cohen (biologist) and Ian Stewart (mathematician)
    The Collapse of Chaos (1994)
    Figments of Reality: The Evolution of the Curious Mind (1997)

  15. Immanuel Kant's Critique of Pure Reason (1781)
    has relevant ideas and questions, but he lacked our present understanding of
    information processing (which is still too limited)

  16. Much of Jean Piaget's work is also relevant, especially his last two
    (closely related) books written with his collaborators:
      Possibility and Necessity
        Vol 1. The role of possibility in cognitive development (1981)
        Vol 2. The role of necessity in cognitive development (1983)
        Tr. by Helga Feider from French in 1987
    Like Kant, he had deep observations but lacked an understanding of information
    processing mechanisms, required for explanatory theories.

  17. John McCarthy's 1996 paper "The Well Designed Child" is very relevant:
    (Later published in the AI Journal, 172, 18, pp 2003--2014, 2008)
  18. Ulric Neisser wrote in Cognition and Reality, W.H. Freeman., 1976.
     "... we may have been lavishing too much effort on hypothetical models of the
     mind and not enough on analyzing the environment that the mind has been shaped
     to meet."
  19. Steve Burbeck's web site:

  20. Daniel Dennett's very readable little book is very relevant:
    Kinds of minds: towards an understanding of consciousness,
    Weidenfeld and Nicholson, London, 1996,

    This book, like much of what Dennett has written is mostly consistent with my
    own emphasis on the need to understand "the space of possible minds" if we wish
    to understand human minds. Simply trying to study human minds while ignoring all
    others is as misguided as trying to do chemistry by studying one complex
    molecule (e.g. haemoglobin) and ignoring all others.

  21. Dennett and I have also written similar things about how to think about
    discussions of "free will" in the light of changes produced by Biological
    evolution. Dennett
      D.C. Dennett,
      Elbow Room: the varieties of free will worth wanting,
      Oxford: The Clarendon Press, 1984,
      (See also his later book Freedom Evolves)
      A. Sloman, 'How to Dispose of the Free-Will Issue,'
      In AISB Quarterly, No 82, 1992, pp. 31--32,,
      (Originally posted to Usenet some time earlier.)
      Also used (with my permission) as the basis for Chapter 2 of
          Stan Franklin,
          Artificial Minds, MIT Press, 1995,
          (Franklin expanded my notes.)
    Our main difference is that I don't regard what Dennett calls "the intentional
    stance" as a requirement for a science of mind, since reference to mental states
    and processes is not merely a sort of useful explanatory fiction: those states
    and processes, and qualia exist and their existence can be explained in terms of
    the operation of virtual machinery that is a product of biological evolution
    rather than human engineering. However, Dennett sometimes also seems to
    hold that view.

  22. Noam Chomsky's early work deeply influenced my thinking, especially the idea of
    generative forms of representation able to cope with arbitrary
    (essentially infinite) variation in structure (not just values of a fixed size
    vector, so popular in much current AI). See his three notions of 'adequacy',
    observational, descriptive and explanatory adequacy, in Aspects of the theory
    of syntax

  23. Merlin Donald's book
    A Mind So Rare: The Evolution of Human Consciousness (1992)
    Is very relevant. It is spoilt especially near the beginning, by excessive rants
    against reductionism, which originally put me off reading the rest of the book. So it
    lay in a pile of books to be read for several years before I returned to it. Despite
    the complaints about reductionism much of the book attempts to relate empirical
    claims about the capabilities of humans and other animals to requirements for
    explanatory information processing mechanisms. The author does not seem to be well
    informed about achievements of AI and the nature of symbolic computation, so his
    sketchy ideas about explanatory mechanisms can mostly be ignored. But the book gives
    a superb introduction to many of the evolutionary transitions that involve
    information-processing, e.g. Chapter 4.

    In particular, much of what Merlin Donald has written about evolution of consciousness
    is relevant to this project, though it is not clear that he appreciates the
    importance of virtual machinery, as outlined in
    Aaron Sloman
    How Virtual Machinery Can Bridge the ``Explanatory Gap'', In Natural and Artificial Systems,
    Invited talk at SAB 2010, Paris, in
    Proc. SAB 2010, LNAI 6226, Eds. S. Doncieux and et al., Springer, 2010, pp. 13--24,

  24. Peter Gardenfors
    How Homo Became Sapiens: On the evolution of thinking
    Oxford University Press, 2003

  25. The Only Way is Up
    On A Tower of Abstractions for Biology
    Jasmin Fisher, Nir Piterman, and Moshe Y. Vardi
    17th International Symposium on Formal Methods, LNCS 6664, pp. 3-11, 2011
     We draw an analogy between biology and computer hardware systems and argue for
     the need of a tower of abstractions to tame complexity of living systems. Just
     like in hardware design, where engineers use a tower of abstractions to produce
     the most complex man-made systems, we stress that in reverse engineering of
     biological systems; only by using a tower of abstractions we would be able to
     understand the "program of life".

Others -- to be added

I know there are lots more related books and papers -- most of them not yet read by
me. I would welcome a volunteer collaborator (or a group of collaborators) to help
setting up an annotated online bibliography of notes, books, papers, discussions,
videos, etc. relevant to meta-morphogenesis, whether the label is used or not,
especially freely available open access documents, for reasons given here.

Appendix: Schematic 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. Introduction of new virtual machines, new forms of representation, new
    ontologies, new architectures
Note added 23 Oct 2012 An expanded version of the above list of transitions is being created in

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 (MM):
Things that cause changes can produce new things that cause changes.
Old phenomena may be produced in new ways
   e.g. 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

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.

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


















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