“In philosophy, the computational theory of mind is the view that the human mind is best conceived as an information processing system and that thought is a form of computation.” –wikipedia
Computation doesn’t necessarily involve mathematics, but is defined as, “the process of taking input and following a step by step algorithm to get a specific output.” The process that takes input from the senses and produces a reasonable facsimile of an external reality, certainly involves a precise process that repeats steps. However, the computer does its job by repeating small computations in a rapid string of steps, the biocomputer has billions of cpu’s available to parallel process information. Step by step is a very linear description of something that is obviously very nonlinear and extremely dynamic. So, there has to be a better term for the process than computation, but it will do for now. Recent research into neuron function has given us some clues as to how this works.
Modern brain imaging applied to basic conditioning experiments, seen through the lens of computer science theory, has produced an amazing new understanding of how the subconscious mind functions. The behavior of these dopamine neurons shows some pretty sophisticated “thinking” going on at the cellular level. These neurons act as if they are predicting the future.
An article on SeedMagazine.com titled: A New State of Mind, by Jonah Lehrer, relates the discovery of very interesting attributes of “dopamine neurons”.
The conditioning experiment, much like the flatworms in the aquarium, requires the subject to learn a simple sequence of events. In this case, the stimulus was followed by a reward instead of a shock.
“His experiments observed a simple protocol: He played a loud tone, waited for a few seconds, and then squirted a few drops of apple juice into the mouth of a monkey. While the experiment was unfolding, Schultz was probing the dopamine-rich areas of the monkey brain with a needle that monitored the electrical activity inside individual cells. At first the dopamine neurons didn’t fire until the juice was delivered; they were responding to the actual reward. However, once the animal learned that the tone preceded the arrival of juice — this requires only a few trials — the same neurons began firing at the sound of the tone instead of the sweet reward. And then eventually, if the tone kept on predicting the juice, the cells went silent. They stopped firing altogether.”
This behavior had everybody stumped. It seemed like the dopamine was carrying information about the reward, but why would it stop firing? The answer came when the data from these experiments cross pollinated with a theoretical computer model called, temporal difference reinforcement learning (TDRL). From the field of artificial intelligence, this model was an attempt to program “neuron like” performance, using simple protocols for goal oriented action.
“The basic premise is straightforward: The software makes predictions about what will happen — about how a checkers game will unfold for example — and then compares these predictions with what actually happens. If the prediction is right, that series of predictions gets reinforced. However, if the prediction is wrong, the software reevaluates its representation of the game.”
These neurons were acting just like theoretical neurons would act. These cells were making predictions!
“Once the cells memorize the simple pattern — a loud tone predicts the arrival of juice — they become exquisitely sensitive to variations on the pattern. If the cellular predictions proved correct and the primates experienced a surge of dopamine, the prediction was reinforced. However, if the pattern was violated — if the tone sounded but the juice never arrived — then the monkey’s dopamine neurons abruptly decreased their firing rate.
“What’s interesting about this system is that it’s all about expectation. Dopamine neurons constantly generate patterns based upon experience: If this, then that. The cacophony of reality is distilled into models of correlation. And if these predictions ever prove incorrect, then the neurons immediately readjust their expectations. The discrepancy is internalized; the anomaly is remembered.”
This demonstrates the very core concept of LifeOS, that information processing is fundamental to cellular activity and that such activity constitutes intelligent action. To memorize, make predictions, evaluate results and readjust expectations are all intelligent actions that we would normally expect from individuals, but these same kinds of “computations” are being accomplished at the cellular level.
Representation of Meaning
“The computational theory of mind requires representation because ‘input’ into a computation comes in the form of symbols or representations of other objects. A computer cannot compute an actual object, it must interpret and represent the object in some form and then compute the representation.” –http://en.wikipedia.org/wiki/Computationalism
I think this comes from the view that these computations are done by an abstract information processing machine, like our computers. With computers, input has to be converted to numbers that can be computed. In other words, the meaning is being provided from the outside; the machine simply processes numbers. The computer was designed as a general purpose machine, not at all concerned with meaning. The biological information processing system evolved with meaning as an integrated function.
Then if you see these neurons as firing in synch, they produce a dynamic hologram that represents the “state” of the organism. In the holographic theory of mind, the hologram generated by matter is the representation, already in the proper format. This dynamic hologram is the representation, memory and computational process all rolled into one. It exists at the atomic level.
This produces a fundamental “mind” that permeates all matter. DNA amplifies this fundamental process to produce Life. The brain further refines the process to produce mobile agents capable of micro management. The frontal cortex allows complex abstract thought, and enhanced imagination, making for nearly unlimited capability for manipulation of matter and energy.
In this model, the computation is the comparison of present interference patterns with the past and the generation of expectations for future patterns.
The Difference that Makes a Difference
Remember back in Information Theory we talked about it. How do biological systems recognize this crucial information? In computers it is done mathematically by assigning values to events and the range of deviation they register. Those values will repeat over time producing a pattern that can be evaluated for unusual behavior. In our biological holographic system, those patterns are represented by the interference patterns created by the dynamic coherent electromagnetic field. As the present patterns are laid down in this holographic memory, any deviation from preceding patterns are immediately evident to the field. Errant interference patterns cause a disturbance in the field that the entire field is aware of at once.
What Bateson and his cohorts created mathematically for computers exists as a fundamental process in biological information processing systems.