Monday, June 23, 2008


Ira suggested that I try to summarize the field of BIOSEMIOTICS ― the study of how symbol systems control living organisms and societies. I’ll try to do this in a series of short posts of less than 750 words. Then you can ask questions if I am unclear, make comments or disagree with what I have said. Hopefully, we can clear up the problems, and go on to the next post.


A symbol system, like the genetic code, a natural language, mathematics, or an artificial computer language, requires a set of symbols and rules that reside in a memory. Memory-stored symbols are the fundamental and essential requirement for life. It is required for self-replication and all open-ended evolution. Memory is also necessary for any learning and thinking process in nervous systems. Memory is also a requirement for universal computation.

Memory and symbols can be physically implemented in endless ways, in molecules like DNA, in texts like this page, in photographs, in digital magnetic, electric and optical patterns in computers, and in neural patterns in the brain. But these particular types of memory are not what make memory of fundamental importance. So, what are the properties of memory that are essential in evolution, learning, and computation?

Two essential properties of memory are PERMANENCE and CHANGEABILITY. These properties sound incompatible, but they are complementary. In a previous post on the C- and L-minds, I compared permanence to the CONSERVATIVE aspect of memory, and I compared changeability to the LIBERAL aspect of memory. Clearly, success in adaptive evolution, learning, and social systems requires the proper balance of conservative permanence and liberal change. That is why I disagree with any liberal or conservative who claims an ideological superiority.

A good memory must also be quickly accessible, and its symbols must have the ability to effect or control a specific change. A gene must be capable of controlling protein synthesis. A brain must be capable of controlling muscles. A computer memory must be capable of changing the state of the hardware. In all of these symbol systems, genes, brains and computers, the memories have also evolved the property of self-reference. That is, genes can control their own expression, brains can think about their own thoughts (e.g., consciousness), and computer programs can address themselves. This turns out to be a mixed blessing. On the one hand, it allows organisms, brains and computers to inspect internal predictive models of the world. On the other hand, self-reference can lead to contradiction, infinite regress, and undecidable questions like whether we have free will.

While it is clear that evolution, learning, and computation could not occur unless memory has some degree of permanence and some degree of change, the nature and results of the changes are different in all three cases. In evolution, memory change is called mutation or variation, and changes are largely random. Natural selection determines the ultimate results. In nervous systems memory change is called learning. Learning is more complex and includes instruction, experience, reorganizing existing memory (thought or reasoning) and random or directed search, and often cultural selection. In computation, memory change is often called recursion or rewriting. A memory-stored program usually determines change, but programs can simulate random change and, model evolution, learning, and thinking.

Here is the classical problem of symbolic memory. The peculiar fact is that the physics of memory ― that is, the laws governing the material structures of memory symbols ― has no necessary relation to the function or meaning of the symbols. Symbol vehicles obey physical laws, but analysis of these diverse physical structures does not tell us what is important, namely the function or meaning of the symbols. Neither does analysis of these physical embodiments of memory tell us how the behaviors of memories differ in evolving organisms, brains and computers. Physical laws alone cannot predict or usefully describe the course of evolution, learning, thinking, or computation. Briefly, the problem is that symbols are arbitrarily related to their meaning or referent. The meaning or function of symbols is determined by a code or an interpreter. Symbols do not exist alone, but are a part of a language.

This fact has been a problem since the beginning of philosophy. It is
the root of the classical body-mind problem. Today in physics it is the basis of the measurement problem ― how the irreversible process interpreted as a measurement can arise from state-determined reversible laws. Some physicists also see this as an energy-information dichotomy. In biology this is the crux of the origin of life problem, how did this symbolic control of matter begin? How did molecules become messages? I call this the symbol-matter problem.

Monday, June 16, 2008

Happy Birthday TVPClub Blog!

Well, its been a year since the Welcome posting and the first Topic were posted on this Blog on June 16th, 2007!

Since that time, we've had some 80 new Topics and 535 Comments posted. We have a dozen authorized Authors who have either initiated new Topics or posted Comments, or both. They represent a diversity of philosophical, religious, and political viewpoints.

Most important, EVERYONE, WITHOUT EXCEPTION, HAS BEEN COURTEOUS! Differences have been expressed in clear and strong terms, but always with a sence of professional dignity and mutual respect.

Special thanks to the following Authors of new Topics: Joel Fox, Stu Dennenberg, JohnS, and Howard Pattee.

Anyone who has not Authored a new Topic is welcome to join this august group.


Ira Glickstein

Sunday, June 8, 2008

The TED Talks: "Memes" and "Temes"

[From Howard Pattee] Why is natural selection inevitable, in spite of brains, language, and technology? Before discussing this topic, listen to Susan Blackmore’s TED talk:

She believes human language and technology is a Pandora’s box.

I have called them a Promethean trade-off.

She points out that we should not look at memes from our human cultural perspective, but from evolution’s perspective. From our human perspective, human language looks like the greatest evolutionary discovery since the origin of life itself.

On the other hand, from an evolutionary perspective, genes that allow human language are only a very late evolutionary discovery with an untested survival value. Some language memes are adaptive; others are not. Language allows groups to communicate crucial survival information as well as telling lies. Persuasive memes can intensify competitive genetic traits beyond their natural adaptive value. Patriotic and religious memes are a prime example.

Natural language memes have been largely responsible for our cultures for well over 5000 years. However, artificial languages like mathematics and computer languages required for science and technology have given humans Promethean power over natural forces that can unselectively increase lifespan, counter local effects of genetic deficiencies, and unleash weapons of mass destruction. Biotechnology will eventually give us power to edit genetic instructions that have been tested by billions of years of natural selection and replace them with artificial instructions based on current human desires. None of these powers has any clear advantage from an evolutionary perspective.

Blackmore calls this new artificial level “temes” (technological memes). Imagine what greater powers another 5000 years of technology will give human culture (if it lasts that long). We should not forget that 5000 years is only a moment in evolutionary time, and that natural selection operates over indefinitely longer time scales. Natural selection will ultimately decide survival or extinction. We are still entirely dependent on the “selfish genes” to construct the neural architecture that allows natural and artificial memes and temes.

Howard Pattee