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Neural correlates of free will 
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#19
Mar811, 04:15 PM

PF Gold
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I would just say pick up Luria's The Working Brain, published in 1973, and read chapter nine. The broad outlines were worked out 50 years ago, and the gaps have been filled in by electrophysiology and animal studies much more than neuroimaging. Read Graybiel on the striatum or Passingham on the frontal lobes for example. The neural correlates of freewill are one of the "easy problems" even if you are a Chalmerite by persuasion. But who really reads neuroscience textbooks? 


#20
Mar811, 04:18 PM

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Oh, and... nerd that I am, I read them... I read and read them, often for fun. So... that's me... that's a serious bias on my part I guess. 


#21
Mar811, 04:22 PM

PF Gold
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The question then is how do we model complexity. It could be that it is just determinism~randomness made more complicated. Or it could be that in creating the simple model, we left out the "something else"  a story about the global constraints  which is what models of complexity require. 


#22
Mar811, 04:25 PM

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#23
Mar811, 06:50 PM

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That said, I'm not seeing determinism and randomness as just usefull tricks to guide interpretation. To me this has a precise meaning in terms of theory of computability and theory of complexity. I equate determinism with computabilty, and randomness with BPP class of complexity. Let's begins with the latter: about everyone thinks that P=BPP, meaning that randomness is unlikely to provide any observable change from a more classical universe (that remains to be proven, however). That's exactly the situation with manyworlds versus Copenhagen interpretation: the first is purely deterministic without randomness, the second uses randomness to a large extent, and it does not make any difference in what we expect to see. The former is more subtile: yes one will never prove that the universe is computable/determinist. However, the reverse (the universe being uncomputable/non deterministic) is IMHO theorically provable (can you compress most arbitray binary strings? If yes congratulation: you have hypercomputing abilities). So the question, to me, is not whether we can prove that the universe is deterministic. The question is: should we think otherwise when otherwise is such an extraordinary claim? To me extraordinary claims are good to Occamise until we find reasons not to. 


#24
Mar811, 07:07 PM

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In the short run view, where global constraints by definition look "eternal", this is very valid and useful as a modelling approach. But it does not answer the larger case of the long run view where global contraints may be presumed to vary over time. Even the laws of physics could have evolved. Real complexity modelling involves allowing the global constraints to develop, to selforganise. It is this intrinsic holistic dynamism that a strictly localised view, based on the standard dichotomy of random vs determined, misses. 


#25
Mar811, 07:39 PM

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#26
Mar811, 08:35 PM

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#27
Mar811, 08:37 PM

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Science is a method, it's no guarantee that the universe is comprehensible.



#28
Mar811, 08:40 PM

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Hi Ken G,



#29
Mar811, 09:00 PM

PF Gold
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http://arxiv.org/abs/0906.3507 You will see that Franks makes the argument that it does not matter whether the microscale is ontically random or ontically deterministic because it is the global constraints (the information preserved at the global scale and which acts topdown) which explains the patterns of nature. We already knew this of course. You can generate fractals either by deterministic iterative equations or suitable stochastic processes. It looks the same in the end as what matters is the information represented as the global constraints. But Franks makes this explicit. There is a topdown view which is not reducible to the bottom up. The whole is more than the sum of its parts (whether they be random or determined). And this is true even for simple systems (like those with a gaussian, or even simpler(!) powerlaw, statistics). It is of course obviously true for complex systems like life and mind. 


#30
Mar811, 09:02 PM

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Hi apeiron,
There are some areas in his paper I’m not too sure about. Take for example: In another questionable section he states: I suspect that the punchline to all this is that the proposal these folks are after is that higher order levels influence the future higher order levels by influencing lower order levels. That of course is strong downward causation. I don't see any room for a 'medium' causation that somehow doesn't allow a higher level to influence a lower level but still allows higher levels to have some kind of influence. The higher level is made up of lower level constituents, so if there's no change in the lower level constituents caused by the higher level, there's no change. I think this is a good lead in to strong emergence and strong downward causation which, in one way or another, is necessary for mental causation and free will. The question really is, can the higher physical levels somehow subordinate the local interactions of neurons? And if so, how? 


#31
Mar811, 10:32 PM

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#32
Mar811, 10:42 PM

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#33
Mar811, 11:04 PM

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Q_Goest,
"Not quantum" doesn't mean classical. Nonlinear dynamics and complex systems are modern physics; in my undergrad curriculum they are taught in the twosemester modern physics course, after quantum and relativity. They do make use of classical physics (moreso than QM does, for instance) but they are not constrained by classical physics, especially because they allow for dissipative (and stochastic) processes. Dissipiative processes in thermodynamics are irreversible. Moving through a conservative force, like gravity, your can completely recover your ground... in the real world we have friction: a dissipative process from which heat and entropy flow. This all becomes very important in turbulence models, where heat dissipation and entropy are rampant among correlated deterministic behavior (and change the deterministic behavior that is chaotic, so it's hard to predict how small, random changes from heat dissipation can manifest large consequences) On stochastic nonholonomic systems N. K. Moshchuk and I. N. Sinitsyn Journal of Applied Mathematics and Mechanics Volume 54, Issue 2, 1990, Pages 174182 CUMULANTS OF STOCHASTIC RESPONSE FOR A CLASS OF SPECIAL NONHOLONOMIC SYSTEMS Shang Mei and Zhang Yi Chinese Physics Vol 10 No 1, January 2001 


#34
Mar811, 11:05 PM

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#35
Mar811, 11:26 PM

PF Gold
P: 3,080

My point about nonlinear dynamics in general is that it starts with a kind of fiction, which is that the system has "a state." Mathematically, if we have nonlinear dynamics, and start at a state, we have deterministic evolution that obeys sensitivity to initial conditions. However, if we don't actually have a state, but instead a collection of states, involving some uncertainty, then our initial uncertainty grows with time. Mathematically, we would still call that deterministic, because we have a bundle of deterministic trajectories that fan out and cover most or all of the accessible phase space. But physically, if we have an initial uncertainty that grows, we cannot call that deterministic evolution, because we cannot determine the outcome. Hence, if we cannot assert that the reality begins in "a state", we cannot say that its future is determined either. Rather, we see determinism for what it is a gray scale of varying degree of predictability, not an absolute state of how things evolve.
The Catch22 of chaotic systems is we cannot demonstrate that the system does begin in a state other than a state of uncertainty, nothing else is actually demonstrable. It is purely a kind of misplaced faith in a mathematical model that tells us a macroscopic system actually has a state. Even quantum mechanically, a macro system is treated as a mixed state, which is of course not distinguishable from an uncertain state (and here I do not refer to the Heisenberg uncertainty of pure states, but the garden variety uncertainty of mixed states). 


#36
Mar911, 12:57 AM

PF Gold
P: 2,432

What he is talking about here is the symbol grounding issue  how nonholonomic constraints can actually arise in the natural world. Genetic information has to make itself separate from what it controls to be able to stand as a level of topdown control. So sure, the models behave a certain way  unfold mechanistically from their initial conditions. And it certainly resembles the observables of real world systems like the weather. But we also know that the models depend on unrealistic assumptions (such as a real world ability to measure initial conditions with complete accuracy). From a philosophical view, you just can't jump from "looks like" to "is". Especially when you know there are ways that "it isn't". This is how brains work. A neuron has many degrees of freedom. A particular neuron (in a baby's brain, or other unconstrained state) will fire to just about anything. But when a global state of attention prevails, the firing of that neuron becomes highly constrained. It becomes vigorous only in response to much more specific inputs. This is a very basic fact of electrophysiology studies. So it is not just a theory, it is an observed fact. And yes, this is not the way machines work in general. But I think you are also reading your own beliefs into the words here. If you dig out Stephen Grossberg's neural net papers, or Friston's more recent Bayseian brain papers, you will get a much more elegant view. Yet one with the same essential logic. All he is saying is that there is an ambient emotional state in the classroom  a generally shared state averaged across a connected set of people. Any newcomer then will respond to this globally constraining atmosphere. Here is a pop account with some useful illustrations. http://www.sciencedaily.com/releases...0325132326.htm Here is a rather general review. http://pbs.jhu.edu/bin/q/f/YantisCDPS2008.pdf 


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