|
Author
|
Topic: Do Biological Systems Follow Quantum Rules?
|
Michael M. Halassa
Member
Member # 625
|
posted 26. February 2003 11:04
I would like to gain insight into the question of how much quantum laws (like the uncertainity principle) are relevant to biological behaviour.
To be more specific, identical twins do not have the same behaviour although the have the same set of genes.
Now, according to the Gerald Edelman's Theory of Neuronal Group Selection (TNGS), the wiring of the brain during development is different even in identical twins.
Is this because the environment is different for identical biological systems, or is it an inherent property of systems to be governed by the uncertainity principle?
Am I quantum or classical?
![[Smile]](smile.gif)
IP: Logged
|
|
Erik
Member
Member # 160
|
posted 26. February 2003 15:09
It may or may not be the case that quantum mechanics is important for biological behaviour. What can be said is that there is currently no empirical evidence to support either position.
As for identical twins, the fact that they start out with the same genes is not even nearly strong enough to enforce the same microscopic state. The fact that identical twins are not identical in all respects is therefore not support for a role for QM.
IP: Logged
|
|
gedanken
Member
Member # 594
|
posted 26. February 2003 15:32
I think one has to be careful here as to what is meant.
Of course all the chemical/electrical/physical/structural properties of the body (which of course lead to all the physical motions or changes that are part of behavior) depend highly on the quantum mechanical nature of atomic bonds, etc.
I think that the question needs to be made more explicit, like what is the role of quantum indeterminacy in terms of degree of unpredictability.
Beyond that, a claim of not depending on quantum behavior is a claim that the human does not operate according to physical principles as has been observed in all other tests of the relationships of physics.
IP: Logged
|
|
Jacob A. Salamon
Member
Member # 503
|
posted 26. February 2003 15:45
Ultimately, this all seems to lead us back to the old Free Will and Determinism debate. Where by determinism we mean the strict mechanistic variety. If man is nothing more than a machine subject to all the laws of physics, then how can he be free or responsible for his actions?
On the other hand - if man is subject to random indeterministic quantum mechanical laws then he isn't free either and again not responsible for his actions.
So where is the loophole to get around both sides of the problem, and give man free will?
IP: Logged
|
|
warren_bergerson
Member
Member # 262
|
posted 27. February 2003 08:38
Michael,
The question, IMO, is whether the causal relationships associated with biological systems are best expressed in terms of classic causal determinism, in terms of quantum or statistical uncertainty, or whether some other model/explanation such as ‘dynamic and teleological causation’ is appropriate.
All three models or principles have the ability to explain the phenomena where the same input, cause or stimulus produces different output, effect, or responses at different places and times.
In the classic model, deviation from expected or predicted is interpreted as noise or inefficiency. The ‘Under ideal conditions qualifier in traditional theories recognizes the existence of noise, inefficiency or measurement error. The quantum model suggests that at certain levels of detail, the classical predictive model breaks down and the ‘predicted’ output is a stochastic distribution of output. As I will discuss later, the ‘dynamic and teleological’ model, like the classic model, explains response variance in terms of noise or inefficiency. Although all three models recognize a degree of indeterminacy in observed raw data, the specific nature of the indeterminacy is different for the three models.
The classic model asserts there is some mathematical function F such that under ideal conditions F(S)=R . The set of observed responses, denoted here are OR, include the predicted R and variations due to noise, inefficiency or measurement error. The classical model fails primarily because of the inability of scientists to identify functions F(S)=R which could predict R.
In a somewhat trivial sense, the quantum or stochastic model avoids the problem of finding F by defining F in terms of observed data. In other words, the quantum model starts with F(S)=OR. But as should be obvious, if stochastic or quantum theories are to have any predictive or scientific value, the stochastic uncertainty at one observed level must produce predictive results with a predictable level of uncertainty at a higher level.
Quantum or stochastic uncertainty does not appear to work for biological systems because there are identifiable examples 1)where high level uncertainty appears to be much less than would be predicted by observed low level uncertainty, and 2)where high level uncertainty appears to be much higher than would be predicted by observed low level uncertainty.
Mutations appear to provide an example of lower than expected high level uncertainty. Cell division, as I have heard it described results in a certain measurable level of low level random copying errors or mutations. However, if you look at mutation rates at higher levels (those passed to offspring) you find that ‘error correction’ mechanisms have operated to dramatically reduce both the number and the diversity of the mutations observed. Stochastic or quantum principles are not compatible with the self correcting phenomena observed in biological systems.
High level human decision making appears to provide an example of high level response variance being much greater than would be predicted by observable low level uncertainty. If you observe low level processing in neurons you find very specific accurate processing with low levels of uncertainty. This translates into very precise, complex high level behavior such as piano playing. But if you look at high level human decision making, such as ‘accepting or rejecting scientific theories’ or ‘making investment decisions’ you find a great deal of uncertainty or ‘free will’. You frequently find experts making very bad decisions despite the fact they had the information, the alternatives, and the incentive to make much better decisions. High level uncertainty greater than predicted by low level uncertainty is not compatible with stochastic or quantum models. It would be like finding that the laws of gravity produce reliable predictions for objects the size of baseballs, but the impact on large objects like planets is unpredictable.
While not compatible with either the classical or the quantum model, the behavior of biological systems does appear to be compatible with the dynamic and teleological causation model. This model suggests that the output, responses or effects generated by biological systems can be predicted under ideal conditions by algorithms of the type ‘F(S, G)=R where G is the goal or purpose of the behavior. This model asserts that a function Ft controlling an input-output or cause and effect relationship at time t can change at time t+1. By knowing the goal or purpose of the S-R relationship, we can predict which Ft would, under ideal conditions, be applicable at time t. Being able to predict Ft we can predict the expected R. There will still be observed deviations OR from R, but they are explainable in terms of the inefficiency in the biological process involved with finding Ft.
IP: Logged
|
|
Michael M. Halassa
Member
Member # 625
|
posted 27. February 2003 12:51
Hi Warren,
Thanks alot for the insight. But I have a few questions:
quote:
In a somewhat trivial sense, the quantum or stochastic model avoids the problem of finding F by defining F in terms of observed data. In other words, the quantum model starts with F(S)=OR. But as should be obvious, if stochastic or quantum theories are to have any predictive or scientific value, the stochastic uncertainty at one observed level must produce predictive results with a predictable level of uncertainty at a higher level.
I don't exactly understand why
quote:
High level human decision making appears to provide an example of high level response variance being much greater than would be predicted by observable low level uncertainty. If you observe low level processing in neurons you find very specific accurate processing with low levels of uncertainty. This translates into very precise, complex high level behavior such as piano playing. But if you look at high level human decision making, such as ‘accepting or rejecting scientific theories’ or ‘making investment decisions’ you find a great deal of uncertainty or ‘free will’. You frequently find experts making very bad decisions despite the fact they had the information, the alternatives, and the incentive to make much better decisions. High level uncertainty greater than predicted by low level uncertainty is not compatible with stochastic or quantum models. It would be like finding that the laws of gravity produce reliable predictions for objects the size of baseballs, but the impact on large objects like planets is unpredictable.
Synaptic contacts involved in decision making (things we often associate with free will), compared to those involved in automatic behaviour (piano playing) are themselves different. When playing the piano for the first time, it's much worse than making a decision. You can only translate the neuronal processes into precise behaviour after strengthening the synapses, which takes alot of practice.
But Maybe I'm just missing your point, I would appreciate further insight
IP: Logged
|
|
warren_bergerson
Member
Member # 262
|
posted 28. February 2003 09:20
Michael,
Quote: But Maybe I'm just missing your point, I would appreciate further insight
To back up a bit, the question of quantum versus classical refers, IMO, not to the nature of biological systems (or the physical universe) but to the logical mathematical form used to express predictive scientific models or theories. Newton and Einstein expressed their theories in the classical form. Quantum theories, as I understand them, are theories expressed in terms of stochastic distributions.
If we are attempting to develop a predictive model of behavior, then we are attempting to find a mathematical algorithm F such that F(S)=R. More accurately, we are looking for an algorithm F which will predict R ‘under ideal conditions’. The classical and quantum models or approaches represent different methods of treating the ‘under ideal conditions’ qualifier.
The classical type of predictive theory has the form F(S)+N=R where N is noise, measurement error, inefficiency, or ‘miscellaneous unimportant causal factors’.
The quantum or stochastic type of predictive theory has the form F(S)=SDR where SDR is a stochastic distribution of responses. A stochastic model or theory can include a noise factor or the noise can be included in the stochastic distribution.
As is widely recognized, the results of a single occurrence of a stochastic or quantum theory are indeterminant or unpredictable. It is not as widely recognized that quantum or stochastic models, to be valid and useful must have SDR’s which can produce reliable predictions over large sets of occurrences.
It is sometimes suggested that because the behavior of biological systems is, or appears to be, at least partially indeterminent, the relationships must be stochastic or quantum. This is an inaccurate and misleading interpretation. In order to generate a meaningful and useful predictive model or theory, stochastic approaches must be based on an SDR capable of producing reliable predictions. As the examples I sited were intended to show, there are a variety of reliable techniques for determining if ‘observed indetermanency’ translates into a workable predictive SDR. At the very least, the available evidence does not support the idea that useful predictive modes of behavior can be generated by stochastic or quantum theories. [Note: Statistics is a complex form of math and applying statistical concepts to the construction of models and theories is very complex. Even among groups of applied mathematicians (such as actuaries) who work regularly with such math and modeling applications, very few individuals would actually claim to be experts on the subject. The professionals who work regularly with this type of model generally recognize that misunderstanding, misuses, and abuses of statistical models are probably as common as legitimate uses. ]
If we conclude that neither classical deterministic models nor stochastic models are likely to produce reliable, predictive scientific models or theories, then what are the possible alternatives? My proposed answer is the dynamic and teleological type model or theory.
As I view the issue, the input-output or S-R relationships associated with behavior at any point in time are relatively simple. It is not difficult to find algorithms Ftl(S)=R which will predict the response R generated by input S at time t and location l. The problem in producing a predictive theory is that the algorithm Ftl is dynamic or changeable. In order to predict the R that will be produced by S at time t and location l, my approach suggests, you must first be able to predict what Ftl will be applicable at time t and location l.
My analysis suggests that Ftl and thus R can be reliably predicted ‘under ideal conditions’ with models of the type F(S, G)=R or under observed conditions with a model of the type F(S, G) +N =R where N is noise, measurement error or inefficiency and G is the goal or purpose of the processing. With respect to high level human decision making, N, in this approach, represents what we commonly call free will. The exact how and why of reliable predictions from dynamic and teleological models and theories is beyond the scope of this thread( but a subject I plan to discuss in the future).
SUMMARY
I am attempting to make three points here.
1) IMO, the classical versus quantum issue is about formulating reliable predictive scientific theories, not about the inherent nature of physical or biological systems
2) There are lots of effective techniques for determining if quantum or stochastic approaches are likely to produce reliable predictive results. The available evidence, IMO, conflicts with the suggestion that stochastic approaches will produce reliable predictive models.
3) If neither the classical nor the stochastic approaches seem likely to produce predictive models or theories, what are the possible alternatives. I am suggesting that the dynamic and teleological approach is a viable option.
IP: Logged
|
|
|
Printer-friendly view of this topic