Changes in abilities and ontologies include (in no significant order): Ontologies required for learnable abilities including: abilities to label, abilities to describe, abilities to predict, abilities to manipulate directly or to control indirectly, abilities to perform actions of varying complexity and difficulty, abilities to classify, abilities to explain, abilities to evaluate, abilities to appreciate, abilities to plan, abilities to carry out plans, including modifying or extending them during execution, abilities to design things, abilities to make things to satisfy a need, abilities to prove things, make inferences, calculate or reason, abilities to discover things, abilities to communicate (using language or other media), abilities to understand communications, abilities to teach or assist others, abilities to collaborate as leader or subordinate or equal, abilities to empathise, abilities to introspect in various ways, abilities to resolve conflicts, within oneself or between individuals, and many more related abilities.

All those abilities are generic and may have sub-cases that have to be learnt separately, and in some cases the learning can include increasing speed, fluency, reliability and accuracy of performance.

Intelligent abilities require use of knowledge about types of things that can exist or happen, i.e. knowledge of an ontology. A simple homeostatic controller, e.g. a thermostat, may use a very simple ontology perhaps including contents of a form of sensory input (e.g. temperatures) and contents of a form of output e.g. 'raise' or 'lower' signals to a heater or cooler.

Organisms (and future robots) with multiple modes of sensing and acting on a complex independently existing environment need ontologies that straddle modes of perception and action, for instance the ability to express where something is or how it is moving, irrespective of how the object's location is sensed or changed.

If we had a better understanding of how various ontologies used for various purposes in organisms evolved, and how they develop in individuals, we might be better able to design machines with intelligence that matches those of animals, including humans. Instead we can now only produce machines with very shallow and restricted abilities, that often turn out to be very brittle when dealing with novelty. For more on changes in ontologies and associated forms of representation, see:
http://www.cs.bham.ac.uk/research/projects/cogaff/misc/simplicity-ontology.html

E. Bremer
"Synthesis and uptake of compatible solutes as a microbial defence against osmotic
and temperature stress",
http://www.uni-marburg.de/fb17/fachgebiete/mikrobio/mpireport_bremer.pdf

It seems very likely that during evolution of some species several changes of continuous control mechanisms occur, either directly through alterations in the genome, or indirectly through improved learning mechanisms. However this document is more concerned with intelligent deliberation where control is not continuous. [To be expanded]

(There are very many biological mechanisms that do this, some relatively simple, e.g. phototropism, geotropism, hydrotropism??, others much more complex, e.g. carnivores seeking prey that can move, or animals seeking mates.

Note that in general change detection requires more complex mechanisms than detection: e.g. it may require storage of previous information to be compared with new information. So puzzlement about change blindness is mis-directed: change detection, not non-detection, is what primarily requires explanation.)

  -- combined to detect more complex phenomena
  -- used to detect unrelated phenomena in parallel, leading
     to possible conflicts in reactions (e.g. choosing what
     to consume, what to avoid).

• selection from multiple categories (apple, orange, plum, ...),
• use of ordered categories, e.g.
  • infant, child, teenager, adult, elder, ...,
  • unpleasant, harmful, dangerous, fatal
• use of ordered (comparable) intervals between ordered categories
• use of scalar values that can vary continuously (e.g. pressure, temperature, chemical concentration, illumination,...)
• Use of vectors of values (collections of labels) to describe an entity, e.g. "big, red, heavy, soft, immobile, ...."

Compare S.S. Stevens' "scales" of measurement (1946).

• Developing abilities to chunk portions of a range of variation into different categories, e.g. tiny, small, medium, big, huge, or different degrees of loudness, or different stages in development (infant, toddler, child, youth, adult, elder ...), or perhaps colour categories;
• Developing abilities to produce "fuzzy chunking" (discretization) of continuous changes, e.g. into phases of expansion, contraction, pulsation, translation, rotation (etc.) of some or all of an object or surface.
• Developing ability to produce "fuzzy chunking" (in L. Zadeh's sense?) of continuous surfaces, into sub-surfaces, e.g. bumps, dents, grooves, ridges and other extended surface features that can be straight, curved, wiggly, spiral-shaped, etc.

Some of these kinds of discretization will be done by individuals separately, while others may depend on developing a consensus among members of a community, and others may result from species differentiation -- producing different ways of dividing a continuum (plants and their pollinators?).

Far more relations are important in interacting with a complex environment than unary predicates, yet very many writers seem to assume that all or most concept formation is formation of unary concepts (predicates), e.g. straight, square, box, shoe, house, dog, etc. Contrast different ways of generating new concepts:

• use of ordered collections of simultaneous values (vectors of values)
• use of structural descriptions involving relationships of many kinds between objects, their parts, their features, their composition, their capabilities, etc.

  Unfortunately current AI students seem not to learn about some of the deep pioneering work done in the late 1960s and early 1970s in which use of structural descriptions, including comparisons of structural descriptions was central, for example the work by T.G. Evans on a geometric analogy program which inspired work by P. Winston on learning structural descriptions from examples, and G.J. Sussman on A Computational Model of Skill Acquisition, referring to thinking skills, not physical skills (both of which are important in humans and many other species).

• use of forms of representation allowing structural variability, and varying complexity (e.g. lists, trees, graphs, sentences), possibly based on a generative grammar (which could be implicit in the ways certain mechanisms work).
• use of representations of sub-regions in a continuous space of topologically equivalent structures E.g. string, sausage, horseshoe, hook, toroid, sphere, ellipse, bowl, spiral, helix, and many more. (E.g. seeing leaves, petals, sand-dunes or hairless torsos.)

One of the deep questions related to this is how the differences are represented between

• X is occurring (e.g. rain is falling);
• X is occurring but might not have occurred (e.g. rain might not have fallen now);
• X is not occurring but could occur (e.g. it might have been raining but isn't)

and similar examples relating to past and future, or different locations (what could or could not happen here and there).

It is sometimes proposed that such information contents require use of a modal logic, that adds operators such as "possible", "impossible", "necessary" and "contingent" to a formalism for expressing facts. But that presumes that all information is represented propositionally (in a form expressible in sentences), but, for many reasons, including observations about mathematical competences below I suspect the ability to think and reason about counterfactuals uses architectural extensions (illustrated by the work of John Barnden and Mark Lee on counterfactuals and ATT-Meta).

• http://www.cs.bham.ac.uk/research/projects/cogaff/misc/triangle-theorem.html
• http://www.cs.bham.ac.uk/research/projects/cogaff/misc/triangle-sum.html
• http://www.cs.bham.ac.uk/research/projects/cogaff/misc/torus.html
• http://www.cs.bham.ac.uk/research/projects/cogaff/misc/knots

Frank Guerin informed me that his three and a half year old son asked at a restaurant "Why didn't we get them last time?" when the restaurant provided wet serviettes for wiping children's hands. This requires

• the ability to store information about previous events
• the ability to compare stored and new events,
• the ability to consider possibilities that have not occurred, and to ask "Why not?" (Mentioned above.)

There are anecdotes about other animals being able to remember past events involving individuals who have helped or harmed them. [REFs needed.]

Compare: toddler theorems about numbers and numerosity (often confused by researchers).

Some ideas about this are presented in this discussion of the speed, power, and flexibility of human visual processing:
  A Multi-picture Challenge for Theories of Vision
  http://www.cs.bham.ac.uk/research/projects/cogaff/talks/#talk88

How do you know that if a vertex of a planar triangle moves along a median away from the opposite face the area of the triangle must increase, no matter what the size, shape, orientation, colour or location of the triangle? Compare the two cases (a) and (b) in the figure below? I suggest this uses deep functions of animal vision that have mostly been ignored.

Abilities to perceive and reason about possibilities and constraints on possibilities in a mathematical context are deeply connected with the ability to perceive and reason about affordances, which must have evolved earlier. This sort of requirement is one among many aspects of cognition that are blindly ignored (as opposed to being temporarily postponed) by most researchers on "embodied" or "enactive" cognition. More examples are e.g. here, here and here. Compare "toddler theorems" about how what's visible through a doorway to another room changes as you move your location relative to the doorway in various directions.

For example, I know of no animal whose visual system uses a lens to project light onto a regular rectangular grid of optical sensors, as almost all artificial visual systems do. The physical and functional design features of biological eyes may provide deep clues to the information processing functions and mechanisms of natural vision systems that have largely been ignored.

An overview of some of the functions of vision in humans is under construction here and http://www.cs.bham.ac.uk/research/projects/cogaff/misc/vision

A larger project is to identify major transitions in biological visual information-processing since the earliest forms. I have many online papers and presentations related to functions of vision, and will later attempt to organise them. One way to do that is in terms of the 3x3 CogAff Schema grid, (outlined here), which combines three columns of functionality:

• perception,
• central processing, and
• action

and three layers of functionality, listed here from the bottom up:

• evolutionarily old reactive mechanisms,
• newer deliberative mechanisms (able to represent and reason about things that are not currently perceived and also possible alternatives to things that are perceived or known to exist
• still newer meta-semantic, meta-cognitive and meta-management mechanisms (able to represent and reason about things that represent and reason, including other agents and oneself.

Many evolutionary and developmental transitions are concerned with either adding new kinds of functionality within these layers or columns, or connecting functionality in different parts of the grid, across columns or layers, to develop more complex systems, e.g. producing social actions (such as smiling, beckoning, teaching, that involve not just the low level motor control system but also meta-semantic competences generating intentions and actions, and visually interpreting actions and responses of other agents.

Because of the nature of this grid there are many possible sequences in which particular competences can be added by evolutionary and developmental processes.

Although the CogAff grid/schema provides a useful framework for thinking about design alternatives it must be considered as a very crude approximation, especially the obviously inadequate implication that there are only 9 major subdivisions among types of information processing.

In particular, he attempts to explain how human vision (and presumably also vision in some other animals), can use the information sent to the primary visual cortex which is constantly changing because of saccades and other eye movements as well as head movements and movements of the whole body. His "retinoid" theory proposes a constantly changing mapping between the retinal information in V1 and the enduring information structure that encodes what is seen.

I would summarise this by saying that instead of regarding F1 as the first level of visual processing in the usual manner, we should regard it as an extension of the hardware evolved for collecting visual information (collecting photons).

It is part of a "sampling" mechanism for rapidly sampling different portions of the optic array (Gibson), using saccades, with the samples immediately "forwarded" to several other subsystems for further processing and for absorption into various enduring information structures holding different sorts of information about contents of the environment, not contents of retinal stimulation -- which would be of far less importance to many subsystems in an intelligent animal or machine. Since the main sampling is done by the high resolution fovea, and the fovea will not find any gaps in the optic array, the other subsystems do not obtain information about gaps that one might suppose the "blind spot" on the retina would produce.

A corollary is that theories that associate contents of self-consciousness (contents of visual self-awareness) with contents of V1 (in humans) may be completely misguided, since useful meta-cognitive mechanisms for inspecting current contents of visual processing will normally need to know now what the raw, unprocessed data are, but what various results of intermediate data are. (Compare the sort of visual self-awareness needed by a good artist drawing or painting realistic pictures.)

Whether all the details are correct (including Trehub's proposal that information is stored in regular grid structures, which I doubt) a theory of that general sort has several merits in addition to explaining why no "blind spot" is perceived, even during monocular vision. There are still many unanswered questions about forms of representation used and types of processing associated with various aspects of visual intelligence.

We may be in a better position to answer them after the meta-morphogenesis project has uncovered many more intermediate stages in the evolution of current animal vision systems. [REF DISCUSSION PAPER IN PREPARATION]

Much is known about physical, chemical, and morphological aspects of many kinds of eyes, and also about their functions, which typically depend on the needs of the organism, its optical sensor morphology, the features of the environment (including available food, predators, mate-features), the actions of which the organism is capable, and the available types of information processing mechanism.

Several animals have two or more eyes, and in some cases these seem to operate as independent sensors. But humans, and various other animals, including primates, hunting mammals, and many birds seem to be able to use two eyes pointing roughly in the same direction to drive two collaborating streams of information processing to compute distances of perceived objects by triangulation.

Unfortunately, Julesz and others discovered that humans are able to see 3-D structures in random dot stereograms, and this led many researchers to assume that the methods required for doing that, by first finding low level correspondences in the images, are used for all stereo vision. However, most natural scenes do not produce random dot patterns on the retina, and it is easy to confirm that a great deal of 3-D structure can be seen monocularly (e.g. try wearing an eye-shield for a few hours). So it is at least possible that animal systems use the results of monocular perception to identify corresponding locations in the left and right percepts and use those correspondences to perform triangulation. I offer this merely as an illustration of how easily an experimental discovery leading to a large tranche of computational modelling can distract research attention away from an important biological function.

Conjecture: binocular depth perception first occurred as a transition from monocular perception that made use of monocular structure to find corresponding items in left and right visual fields to use for triangulation. Later, additional mechanisms evolved to deal with cases like textured surfaces or sand dunes, where the monocular percepts do not provide sharp points of comparison. Normally the two mechanisms function in parallel. If this conjecture is correct it could lead to much improved stereo vision systems in robots, primarily using the results of monocular vision. (Perhaps that has already been done.)

Some references on evolution of binocular vision (Added 2 Nov 2014)

Both of these miss the requirement to identify 3-D features of shapes that may or may not be visible from all views, and which may or may not be relevant to possible uses and behaviours of the object, and may take account of various combinations of topological properties of the objects, metrical properties of the object, qualitative semi-metrical properties, e.g. constancy, increase or decrease of curvature of part of a surface, or "phase transitions" in orientation or curvature, e.g. regions where curvature changes from concave to convex or vice versa, regions where curvature is constant, and many more.

--

1. In what ways can the objects be grouped perceptually?
   -- As regards spatial structure (topological, metrical)
   -- As regards kinds of curvature change in various parts
   -- As regard planes and axes of symmetry
   -- As regards optical properties of the material
   -- As regards height above the supporting surface
   -- As regards distance from the viewer
   -- As regards surface markings
   -- As regards affordances (possible uses, possible actions: e.g. graspability by viewer's left or right hand, use for pouring contents into a narrow container, use for drinking out of, ...)
2. Which objects have been moved between the two photographs, and in what ways?
3. How have the viewpoint and viewing direction changed?
4. In the scene depicted on the right, which objects would be easier to pick up with the viewer's left hand and which would be easier with the right?

When can a typical human infant use vision to take in the information required to answer such questions? Which other species can do it? What forms of representation and capabilities had to evolve to make such tasks possible?

• Use of "external memories" (e.g. stigmergy)
• Use of "internal memories" (various kinds of "cognitive maps")
• Use of "social memories" (collaborative route finding and following)

Many mathematically well educated researchers assume that animals (and intelligent machines) must express all those spatial properties and relationships using an ontology that assumes an all encompassing space, with global metrics for length, area, volume, curvature, orientation, angle, etc.

However, it is far from obvious that many animals (or any animals) can do that, and moreover there are many unsolved problems about how to derive such information from visual and other sensor data. Perhaps that is a poor analysis of the problems evolution and its products solved.

For various reasons, to be explained later, I suspect that the ability to think about, make use of, or acquire information expressed in terms of such global metrics, e.g. using a global cartesian coordinate frame, or a global polar coordinate frame, is a very late, relatively sophisticated achievement (only developed in 1637 and thereafter by Descartes, Fermat, and their successors -- without which Newton's mechanics would have been impossible).

An alternative ontology might instead make use of collections of spatial and topological relationships between objects, and object parts, where the relationships could be binary, ternary, etc., with partial orderings of size, area, volume, angle, distance, direction, straightness, curvature, regularity, where some of the relationships are detected and represented in far more detail than others, e.g. relationships between objects or surfaces (including surfaces of manipulators) in the immediate environment, or relationships between objects on which actions are being, or are intended to be performed. A network of partial orderings of size, distance, could be enhanced by semi-metrical relationships, e.g. A is longer than B, and the difference is more than three times and less than four times the length of B. If B is a pace for a walking animal that could be relevant to choosing routes for walking. Different kinds of information about partial orderings might be relevant to grasping and manipulating objects in the immediate environment.

There's lots more to be said about the alternatives, their biological uses, their evolution, their development in individuals, the forms of representation used, the forms of reasoning, their roles in perception of different sorts (visual, haptic, auditory, or multi-modal perception, or a-modal reasoning), and about how organisms differ. E.g most of this would be impossible for microbes.

I suspect this ability to perceive and reason about semi-metrical partial orderings is part of what accounts for the early discoveries leading to Euclidean geometry, including the examples summarised here, and that in humans many transitions in representation of spatial structures, relationships, processes and interactions occur in the first few years of life that have not been noticed or studied by developmental psychologists. (Though Piaget seems to have thought about some of them.) They also have not been noticed by roboticists, especially 'enactivist' roboticists who focus mainly on online intelligence ignoring offline intelligence, briefly mentioned below.

Too often researchers think that what they do effortlessly needs no explanation -- so they look for explanations of failures (e.g. change blindness, lack of "conservation", etc.) instead of first looking for explanations of successes, without which it is impossible to construct explanations of what goes wrong.

Some examples of matter-manipulation competences:

• Sample grasping scenarios
• Orthogonal Recombinable Competences Acquired by Altricial Species

I suspect that biological evolution changed the information processing architectures of some organisms so as to allow more intelligent 'look ahead' to guide choices, or to allow different exploration strategies to be selected explicitly on the basis of information available instead of being 'hard-wired' in search strategies.

Such transitions have happened many times in the history of programming language development. An example was the transition from the Planner AI language to the Conniver language at MIT in the early 1970s.
[Ref Sussman and McDermott, 1972

There are other transitions where failures discovered during a search process can be found to be detectable at an earlier stage, an example being the process of "compiling critics" modelled in Sussman's Hacker program [[REF]].
Compare recording 'ill-formed' substrings during parsing to constrain future search, and the development of 'caching' mechanisms, mentioned below.
[[Other examples of transitions from cognition to meta-cognition needed.]]

Finding out when such transitions occurred in evolution will be much harder. Some cases of occurrence in individuals may turn out to be examples of what Karmiloff-Smith refers to as "Representational Re-description" in Beyond Modularity (1992)

Conjecture: There are MANY more transitions made explicitly by programming language designers that are analogous to transitions made implicitly in changes of biological information processing.

• Collective behaviours: the individuals merely react to what is happening in their environment, including what conspecifics are doing.
• Collaborative behaviours: the individuals share goals and deliberatively make use of what others are and are not doing in controlling their own behaviour so as to achieve the goals.

http://www.cs.bham.ac.uk/research/projects/cogaff/talks/wonac
    1. Evolution of two ways of understanding causation:
       Humean and Kantian. (PDF),
    2. Understanding causation: the practicalities
    3. Causal competences of many kinds

These mechanisms, and related mechanisms involving architecture-based motivation discussed above, are probably deeply involved in the later development of processes involving aesthetic enjoyment -- creating, observing, taking part in processes and structures that do not necessarily directly serve any obviously biological need, e.g. some of the play in young mammals. [Much more needs to be said about this.]

For example, servo control can make use of physical/mechanical compliance (as in use of padded skin, or physically compliant manipulators) or virtual compliance e.g. allowing perceived/sensed changes in relationships and in forces to affect changes in applied forces, where the perceptual component could be haptic/tactile, kinaesthetic or visual. See http://www.cs.bham.ac.uk/research/projects/cogaff/misc/information-based-control.html

Such advances in online intelligence do not necessarily provided advances in offline intelligence, e.g. the ability to think about the past, or future, or what might have happened under different conditions.

Varieties of deliberation are discussed here.

Many products of robotic research show very impressive online intelligence without any offline intelligence, e.g. the amazing BigDog robot built by Boston Dynamics.

See also this discussion of some of Karen Adolph's work on young children: http://tinyurl.com/CogMisc/online-and-offline-creativity.html

Note (modified 25 Nov 2012):
  Serious muddles about ventral and dorsal "streams" of visual processing arise
  from failure to understand the different information processing requirements for
  (a) servo-control based on transient, constantly changing (mostly scalar?)
      information, and
  (b) acquiring and storing information for possible multiple uses at different
      times, including describing, planning, answering questions, etc.

  Referring to these as "where" and "what" functions betrays a deep but common
  failure to understand requirements for working systems of different sorts. The
  more recent replacement of these labels with the labels "action" and "perception"
  betray a failure to appreciate the variety of functions of perception, including
  its role in online intelligence.
  See On designing a visual system

Examples: a robot, like Boston Dynamics' BigDog produces very impressive behaviours. But it does not know what it has done, what it will do, what it hasn't done but could have done, why it did the one and not the other, what would have happened if it had selected a different option, what options might be available in a few seconds time, what the consequences of those various options are, and many more.

Many questions need to be answered: What sorts of evolutionary transitions led to such counterfactual-metacognitive capabilities in humans? Which other animals have them? At what stage do they develop in children, and how?

How does all that relate to the "proto-mathematical" ability to look at a triangle and ask what would happen to the area if one of the vertices moved relative to the opposite side, as illustrated here?

Note: there is much more to be said about "offline intelligence" and how almost all the research inspired by a concern with embodiment, dynamical systems, enactivism (etc.) fails to address some of the deepest aspects of biological intelligence (a recurring theme here).

So the ordinary concept of "language" like the ordinary concept of "tool-use" does not pick out a well defined scientifically useful class of phenomena, and diverts attention away from deep similarities between things for which we do not use the same label in ordinary speech. (Compare the unobvious similarity between graphite and diamond.)

Infinite competence behind finite performances. (Inserted: 22 Jun 2014) A feature common to both (a) human languages (spoken, signed, or written) and (b) internal forms of representation required for percepts, goals, preferences, plans, questions, puzzles, explanations, ... including some of the contents of non-human animal minds, is that a type of form of representation is required that allows
     (i) structural variation in what is represented,
    (ii) variation in complexity of what is represented,
    (iii) no fixed limits on the complexity that can be accommodated,
    (iv) some sort of compositional semantics insofar as the content represented
        in a complex structure is systematically related to the contents of the
        parts of the structure and the current context.

Condition (iv) is essential for coping with structural novelty, e.g. perceiving, or thinking about, or intending to construct, something never previously encountered. If everything perceived, intended, hypothesised, explained, or predicted could be adequately represented by a collection of N scalar measures then all variability would be accommodated by the possible set of values those measures. But many animals evolved abilities to cope with environments in which objects, processes, and actions exist with much more complex forms of variation, including structural variation (illustrated by the variety of plant and animal forms and the variety of ways in which different collections of plants, animals, non-living matter can be assembled and interact -- for instance the variety of processes involved in assembling a nest from twigs, or weaving a nest from leaves).

An observation due to Chomsky half a century ago (although he did not express it like this) is that any economical solution to the problem of being able to cope with the kinds of variation that can occur in human experiences will have the potential to cope with infinite variation. We now know that infinite potential (or competence) can exist in a computer, for example insofar as it includes and can execute a recursive definition of the factorial of a number) despite it having performance limits due to memory limits, or memory addressing limits. Those performance limits can co-exist with the infinite competence in the factorial procedure, which works in principle for any integer input, no matter how large. How biological information processing systems actually implement that infinite competence is an empirical question. How many forms of solution to that problem and by what evolutionary pathways is a problem for the M-M project.

Eventually a deep theory of evolution of forms of information processing will need to account for the various intermediate stages that can occur, both in evolution and in individual development, both in humans and in other intelligent species. A particular requirement that follows from (iii) is that no matter what the size limits of individual brains, the organisms need forms of representation and mechanisms of operation, that in principle have infinite generative competence. As Chomsky noticed in connection with human languages, actual performance may be limited by various aspects of the implementation.

Some of the issues are discussed in connection with human language in
http://ling.auf.net/lingbuzz/001611
    Ian Roberts, 2009,
    The Mystery of the Overlooked Discipline: Modern Syntactic Theory
    and Cognitive Science,

Unfortunately, most linguists who understand this point don't seem to realise that it is a special case of requirements for animal intelligence encountered long before the evolution of human intelligence and human language. See also Chappell and Sloman (2007)

Immanuel Kant had similar ideas in relation to human mathematical competences.

Sometimes educational policies that try to emphasise 'understanding' at the expense of 'memorising' miss an extremely important function of memorising as an aid to altering the level of complexity of what the learner can understand.

Presumably there was some sort of evolutionary transition between being able to work out plans or solutions to problems and having mechanisms for storing results of such computations for future use.

A closely related, but more subtle development is the ability to remember discoveries about what does not work, so as to reduce the risk of following false trails in planning, reasoning, designing, doing mathematical reasoning, etc. Compare the work of Sussman on 'compiling' critics mentioned above.

Transitions of this sort occurred several times during the development of programming languages in the 20th century. A tutorial introduction to use of patterns in programs manipulating list structures is here.

For more complex species, evolution seems to have "discovered" the advantages, especially as life-spans increase, of more powerful ways of enhancing the genetically specified design, to cope better with threats, opportunities and constraints in each individual's environment, i.e. replacing pre-configured with meta-configured competences.

In some cases, e.g. in humans and some other altricial species, evolution also seems to have discovered the advantages of not only slowing down physical development while information processing mechanisms adapt to each individual's circumstances, but also staggering the onset of various kinds of later learning that build on the products of earlier learning: delayed activation of a meta-cognitive learning mechanism allows it to start looking for patterns in what "lower order" mechanisms have discovered when the patterns are richer and more stable, instead of wasting effort analysing patterns that are spurious, because based on too few instances and tests. This may be specially important in cases where learning cannot easily be undone. These ideas are developed in a little more detail in Chappell and Sloman (2007).

These are crude analyses: far more details, based on far more examples, are needed.

All living things have semantic competences insofar as they can use any information at all, whether with external or purely internal referents. A subset seem to have meta-semantic competences regarding themselves or others. These competences may be genetically fixed for some species and in others may develop under multiple influences (meta-configured competences, mentioned above). In humans many kinds of social/cultural education, and in some cases therapy can enhance meta-semantic competences, whether self-directed or other-directed (e.g. getting better at telling whether your actions are upsetting someone).

Human infants (and perhaps the young of some other species) need to develop a variety of meta-semantic competences, some self-directed some other-directed, some combined with counterfactual reasoning (e.g. "what could I have done differently?", "How would A have responded if I had not done X?", "Can A see this part of X?", "Can A tell what I can see?", "What does A think B did?"). Psychologists have used the label "mind-reading" for this sort of capability, but mostly restricted it to a small set of competences involved in working out what another believes or thinks, especially in situations where they don't have up to date evidence. This is just one of many cases where a fashion for a particular kind of research has spread because it is easy to vary experimental details, without ever thinking about the kinds of mechanisms required to make any of the competences involved possible at all.

For example, meta-semantic competence requires an architecture that supports referential opacity as well as referential transparency -- the usual default. Referential transparency refers to properties of representations where replacing item A in a larger representation referring to object O with item B also referring to O makes no difference to what is represented by the whole structure, and whether it is true or false. For example, if Fred is chairman of the club, then if it's true that the chairman of the club is a cricketer, then it is also true that Fred is a cricketer. But in a referentially opaque context, e.g. "Joe believes that the chairman of the club is a cricketer" replacing "the chairman of the club" with "Fred" can turn a true statement into a false one, or vice versa.

Some researchers favour trying to model such effects by extending the language used with a new operator (e.g. "believes that") and modifying normal inference rules. I suspect that what is really needed is a change in the architecture, to support a separation between information structures accepted as true (beliefs) and the information structures that represent possibilities that are not accepted. This is essential for planning, and for perception of affordances.

    "John is in the kitchen and Mary is in the pantry"

    P or Q
    Not-P

    Therefore Q

A. Sloman, 1968/9, Explaining Logical Necessity,
Proceedings of the Aristotelian Society, 69, pp. 33--50,
http://www.cs.bham.ac.uk/research/projects/cogaff/07.html#712

A reverse process seems to me to be far more common and far more important: some feature or competence is produced in members of a species by natural selection. Then later, because the genetically-specified competence is too specific to be useful in enough different situations, the competence may be split into some general framework, provided by the genome, and context-specific details acquired by some sort of adaptation to the details of the environment. (E.g. locomotion that evolved for relatively flat terrain might be replaced by a general competence to acquire locomotion suited to the individual's environment, which may be rocky, or on a mountain slope, etc.

In some cases this could lead to an inherited group of partly similar competences being split into a number of inherited sub-competences that can be combined in different ways, using learning mechanisms to find the combinations that are useful for an individual's environment. (EXAMPLES NEEDED. REF Deacon?).

In more sophisticated cases, instead of learning (e.g. by experimentally finding out what works), a process of creative problem-solving or planning may enable individuals to work out new ways of combining fragments of old (learnt or inherited) competences. This could have the effect that parts of the genome specifying a particular combination of competences might become redundant because individuals who need that combination can synthesise it through planning or learning, when needed, and perhaps synthesise a combination better tailored to the particular environment than the previously evolved version.

This covers a vast mixture of types of process, mechanism, form of representation, information content, and uses of information, on many scales, for many purposes.

The vast majority of successful organisms on this planet, whether measured by individual numbers, variety of species, or biomass, lack brains. Brainless organisms provide both the base of food pyramids for others, and in some cases essential forms of symbiosis (e.g. bacteria in the human gut.) Lacking brains does not stop them processing information, e.g. in controlling their reactions to their immediate environment and internal processes, including reproduction and growth.

Even in organisms that have brains there is a vast amount of sub-organism control (including homeostasis) and learning (adaptation) that does not use brain mechanisms, e.g. in metabolism, reproduction, growth, brain development, immune reactions, and many more -- but I suspect that only a tiny subset has so far been identified.

The majority of such cases, and certainly all the earliest cases, historically and developmentally, seem to rest on molecular information-processing, for example processes required for building brains, which, at least initially cannot use brains, though later on in life that can change in various ways as discussed briefly in [*].

It is commonplace to ask how the physical changes (e.g. construction of new complex molecules, or changes in the availability of oxygen) occurred.

But it is also important to ask "Where does all the information come from?" -- e.g. the information specifying complex organisms used in their reproduction.

    Compare Paul Davies,
    The Fifth Miracle: The Search for the Origin and
        Meaning of Life, 1999

Exactly what information emerges may not be totally determined in the initial state, if some of the interactions leading to new physical structures and new types of information processing are physically unpredictable.

Another possibility is that external perturbations (e.g. asteroid impacts, changes in radiation reaching the planet, could significantly alter the environment in which already evolved organisms continue evolving -- including changing the physical properties of the environment or eliminating or reducing other relevant species, e.g. prey or predators.

The latter would be a special case of a process that happens continually, namely the environment for any species can be changed as a result of evolutionary changes in other species in that environment, including prey (food), predators, parasites, symbiants, etc.

• Early forms of reproduction involves self-assembly of a new structure within an old one until it becomes detached and self-sustaining (possibly continuing to grow). The information about the design of the copy is implicit in the mechanisms that allow (possibly random) chemical processes within the original organism to have the side effect of growing a copy.
• Another form, presumably later, involved assembly within the "parent" of specifications for offspring, which can be assembled into a structure that is excreted, possibly with a small initial food supply, and which thereafter continues self assembly in an external medium from which it extracts nutrients. This mechanism would allow each individual to produce offspring that grow in parallel, enabling production of far more offspring in any time than the previous mechanism. This mechanisms requires the information given to the offspring to include information about how to pass on the information -- an early example of meta-morphogenesis.
• A variant for external assembly of a copy is to produce the reproductive information for growing a new individual, and also a protective shell, and a store of nutrients in the shell. This would allow new copies to become larger and more complex before requiring the ability to absorb, or seek and find, nutrients in the environment.
• Another variant is parasitic insertion of the instructions for building a copy into another living organism from which the offspring can gain nutrients, and perhaps also other benefits such as temperature control and in some cases different resources at different stages of development.
• A variant of a parasite using a host of a different type could be a parasite using a host of the same general type, which might have the advantage of providing a development environment better suited to the copy -- conspecific parasitism(?).
• This might have led to development of insertion appendages, and later still to defensive mechanisms that enabled hosts to use inserted embryos to carry some of their own reproductive information and later still sexual differentiation that replaced mechanisms for exploitation with mechanisms for cooperation in reproduction.
• In that case a subset of the transmitted information would have to be of two kinds, one for production of a new host for developing embryos and one for providing some of the reproductive information and possibly other functions, e.g. protection. (Of course, this is wild speculation, merely intended to illustrate very crudely some possible varieties of information processing in reproduction. There are many more detailed transitions)

Memes are self-reproducing information structures that move between information users with capabilities for communication, imitation, teaching, learning, and related competences.

A full discussion would need to include the transitions that led to production of information-processing machinery capable of supporting meme construction and reproduction -- very different from the mechanisms involved in encoding, copying, transmitting, using, interpreting information in genes.

• Memes (originally defined by Dawkins) were supposed to be information structures that were "replicators" that could be passed from individual to individual.

• Copying information content is a special case of something more general, and more subtle, since the possession, provision or use of information by one individual A can trigger use of a different, but related, kind of information by something else B, that observes, manipulates, or interact with A.

• That sort of influence can operate genetically on a species, or cognitively on an individual.

• The influence can take very different forms, some involving very specific directly triggered responses (eye blink in response to rapid motion towards the eye), others involving highly context-sensitive, goal related, intelligent responses (e.g. saccades during searching) possibly involving creative planning or problem-solving.

• Interactions over time between predator and prey, or collaborators, parents and siblings, and extended interactions can lead to new strategies for catching, avoiding being caught, collaborating, tending, etc.

• Such invoked new forms of information and information-processing, can then, in turn trigger further new forms in prey, predators, etc. This is an old idea studied, for example, in connection with evolutionary "arms races", but it is often used to investigate "evolutionarily stable strategies", whereas my point is that there need not be anything stable about the process: in some cases it may generate constant novelty, but not only through competition, as in arms races.
  • items in the environment can have potential affordances that influence the evolution of species so as to enable them to detect and use those affordances.

  • individuals detecting affordances in objects or situations may work out goals, or strategies for making use of those affordances, in a systematic way. E.g. pickability of berries can lead to strategies for picking the berries by hand in one species, and by mouth in another, because of morphological differences.

  • Typical behaviours of one species (or individual) can influence evolution or learning of information processing competences of other species (or other individuals), for example competences of predators, prey, collaborators, parents, or offspring. (So-called evolutionary 'arms races' are a special case of this.)

    Note: learning by imitation can be seen as a special case of this more general kind of learning by external provocation and detection of new affordances. (The information-processing requirements for learning by imitation are ignored by many who regard that as an explanatory category.)

• At various stages organisms must have developed abilities to control speeds of processes, e.g. speed of motion, if self-propelled, and in articulated parts, controlling speed of motion involves controlling temporal coordination of moving parts, e.g. legs, wings, fins, etc.
• Various rhythmical internal processes can also be speeded up or slowed down, such as heart beats and breathing.
• Organisms that depend on cooperation of various sorts need to be able to control and coordinate times of action, e.g. mating swarms of some insects, flashing of fireflies, migration in birds, coordinated hunting by dolphins and some sharks.
• A hunter trying to intercept a moving prey animal has to control speed so as to arrive at location where the two trajectories intersect at the same time as the prey.
• Some tasks require estimates of time for something unseen to happen, e.g. something coming back into view after going behind an obstacle.
• Speech requires complex coordination of timing of breathing, tongue movements and other mouth movements.
• Sign language also involves coordination of different parallel movements.
• Additional time competences are required for building histories: information about what happened when and where, and temporal relations (e.g. "I left before he arrived"), and abilities to reason about the future and make and compare plans. Some plans only have temporal ordering with no reference to how long each step takes. Some specify time intervals.
• to be continued ...