Objective Bayesian Reality 8

There is a near consensus amongst scientists working in this field that life evolved from chemistry on our planet through natural processes. Fundamental to any definition of life are (a) reproduction, and (b) controlled local entropy reduction. These characteristics might be taken as a rough demarcation between chemistry and life. There has been much research aimed at identifying plausible chemical systems as candidates for the precursor of life. One such area of research is 'RNA World' which has marshalled extensive evidence in support of various theories of how RNA chemistry may have evolved the ability to catalyze its own reproduction.[i]

It is perhaps the central organizing principal of Biology that once life did emerge from chemistry it evolved to its present state through the operation of Natural Selection. As Dobzhansky, a leading researcher of the twentieth century, stated "Nothing in Biology makes sense except in the light of evolution".[ii]

The emergence of life and its subsequent evolution created the scientific subject matter of biology. The laws of biology, from genetics to population dynamics, may be said to be emergent in the sense that they are not directly derivable from the laws of physics. Natural Selection might be understood as a Darwinian process that, once instantiated in the realm of chemistry/biology, was able to discover survivable biological designs. The design specifications of this emergent biological world form the scientific laws of biology.

Since Darwin published his theory almost one hundred and fifty years ago the evidence in support of it has poured in from numerous disciplines, most notably perhaps genetics. DNA was identified during the 1950s as the molecular unit of storage for much of life's heritable designs. Strong arguments have been made for the biological unit of selection operating at the genetic, organism and population levels.[iii]^,[iv] Most likely biological entities are selected at each of these levels. We will examine this process at the organism and population level below and also its operation in the emergent biological field of behavioural science and neuroscience.

Organism genetics

The design specifications of most organisms are coded in their DNA. A subset of the DNA codes for the assembly of specific proteins which, in concert, compose and orchestrate much of life's chemistry. The successful functioning of this DNA is entirely dependent on the environment in which it finds itself. The organism 'expects' to find itself in an environment similar to that experienced by its ancestors. Crucial components of the genome's expectations include:

1) A specific internal chemistry of the cell including cytoplasm, enzymes and other proteins.

2) Specific sub cellular bodies and organelles including ribosomes and mitochondria.

4) Specific features in the external environment including presence or absence of oxygen, pH range, and available sources of energy which the organism is adapted to consume.

At the time of its conception an individual organism contains inherited DNA that has been under continuous design since the beginnings of biological time. This DNA contains knowledge of the environments experienced by the organism's ancestors.[v] It may be said to represent a model of the environment in which the organism expects to find itself and that this model is constructed from prior information accumulated by its ancestors. The organism's subsequent interactions with its environment test this model. Large surprises or 'unanticipated' features of an organism's environment for which it is ill suited may result in the organism's death, leaving the field open for those models with variations more closely in tune with the actual environment. In this manner the genetics of individual organisms come to roughly track and model their environments.

Population genetics

Population genetics is the study of the change and frequency distribution of alleles under the influence of evolutionary forces.[vi] Alleles are gene sequences that may code for a specific protein and that vary between organisms within populations of the same species. For instance a population may contain variable alleles coding for differing colorations of the organisms. Typically each member of a population will contain a full complement of that species’ genetics, but the population as a whole will contain a frequency distribution of the specific alleles at each gene location on the chromosome.

In a manner analogous to the genetics of organisms the frequency distribution of alleles may be said to represent a model of the environment in which the population 'expects' to find itself. This model has been designed from prior data due to selection pressures operating on the population's ancestors. This prior data, or experiences of ancestors, has been subjected to an inference process (evolution by natural selection), the result being a model which provides expectations concerning the current environment. As the population interacts with its environment this model is tested. Some changed features of the environment may be expected by some alleles but not by others, in which case the alleles that more accurately model the environment may be more heavily represented in subsequent generations. Those alleles that are 'surprised' by a feature of the environment may become less represented or may disappear from the population.

Neuroscience

Obviously much of the neurology evolved by an organism serves the purpose of providing it with information concerning the world around it. This information is taken in by the organism in the form of sensory data which is further processed by the organism's neurology to form models of the external world and to influence its behaviour in that world.

Prior data in the form of experiences of ancestral organisms has shaped the neurological mechanisms producing these models but the brain also possesses a powerful emergent ability to update its models from sensory data in near real time. In this respect the brain gathers prior data over the course of its lifetime and continuously uses new data to update its models. These models are tested against the actual experience of the organism in the real world and large discrepancies between the model and reality may motivate either learning or negative selection pressure.

A new theory that details this process (called the “Bayesian Brain”) has recently been developed and is hailed by many researchers in the field as the most promising integrated theory of neurology yet developed.[vii]^,[viii]An emergent feature of this theory is that the selection pressure on mental models to conform with external reality may take the form of a biological instantiated drive to reduce the system's free energy rather than through direct reproductive success.[ix] This feature allows the organism the ability to adapt its behaviour to changes in the environment in near real time as opposed to generational change.

Free energy in this context refers not to thermodynamics but rather to information exchange. It is a general property of adaptive systems that they contain internal models of external reality which guide their interactions with their environment.[x] Free energy is a measure of the discrepancy between the model and the actual environment or of the degree of 'surprise' that the system experiences when it interacts with the environment.

Again this theory can be fitted nicely to the general conceptual model we have been developing for the operation of knowledge mechanisms.

[i] Gilbert, Walter (February 1986). "The RNA World". Nature 319: 618. doi:10.1038/319618a0

[ii] Dobzhansky Theodosius (1973), Nothing In Biology Makes Sense Except In The Light of Evolution, American Biology Teacher, vol. 35, pp. 125-129.

[iii] Dawkins R. (1976).The Selfish Gene. Oxford University Press

[iv] Holldobler Bert, Wilson E.O., (2008), Superorganism, WW Norton; 1 edition (Oct 28 2008)

[v] Plotkin, Henry C. (1993). Darwin Machines. Harvard University Press, Cambridge Massachusetts.

[vi] Wikipedia article, Population Genetics, http://en.wikipedia.org/wiki/Population_genetics, as viewed January 12, 2008

[vii] Kenji Doya (Editor), Shin Ishii (Editor), Alexandre Pouget (Editor), Rajesh P. N. Rao (Editor) (2007), Bayesian Brain: Probabilistic Approaches to Neural Coding, The MIT Press; 1 edition (Jan 1 2007)

[viii] Huang Gregory (2008), Is This a Unified Theory of the Brain?, New Scientist May 23, 2008.

[ix] Friston K, Stephan KE. ,Free energy and the brain, Synthese. 2007. 159:417–458

[x] Friston K, Stephan KE. ,Free energy and the brain, Synthese. 2007. 159:417–458