Regarding consciousness causing wavefunction collapse

[Trollfaz]

What are the experiments that disprove the idea that consciousness causes wavefunction collapse?

 

[Demystifier]

What are the experiments that disprove the idea that consciousness causes wavefunction collapse?

There are no such experiments (despite the fact that a paper coauthored by my brother (who is a psychologist by education) claims the opposite).

 

[Trollfaz]

Is there any proof for the consciousness causes collapses idea?

 

[Demystifier]

Nope!

 

[cosmik debris]

I believe this idea was entertained by a few in the very early days of QM and only for a short time, but the mythology persists.

Cheers

 

[atyy]

There are no such experiments (despite the fact that a paper coauthored by my brother (who is a psychologist by education) claims the opposite).

Does consciousness cause wave function collapse in Bohmian Mechanics?

 

[bhobba]

Is there any proof for the consciousness causes collapses idea?

Of course not. Its very much like solipsism - inherently unprovable. Even the reason for its introduction, which leads to all sorts of weird effects - is no longer is relevant. Its very backwater these days - like Lorentz Ether Theory is to relativity. You can't disprove it, but modern presentations of SR based on symmetry make it totally irrelevant.

Thanks
Bill

 

[Trollfaz]

But didn't the scientists conducted the double slit experiment without anyone recording the results, but with the detector on?

 

[Demystifier]

Does consciousness cause wave function collapse in Bohmian Mechanics?

No, why do you ask?

 

[Demystifier]

But didn't the scientists conducted the double slit experiment without anyone recording the results, but with the detector on?

Yes, but scientists didn't check whether detector detected anything when nobody was looking at it.

 

[vanhees71]

Hm, but you can look later on the photoplate or (nowadays) the digitallly stored measurement data and check what the detector has registered. The investigated system only "cares" about what it's really interacting with, i.e., the detector and not with some "consciousness" (whatever that might be) looking at the result (maybe 100 years later)!

 

[Demystifier]

Hm, but you can look later on the photoplate or (nowadays) the digitallly stored measurement data and check what the detector has registered. The investigated system only "cares" about what it's really interacting with, i.e., the detector and not with some "consciousness" (whatever that might be) looking at the result (maybe 100 years later)!

Yes, but if you look later, you only know what is there later. You cannot know what was there before. You can only assume that it was there before, but you cannot prove that assumption by scientific method. You can "prove" it by using some philosophy, but philosophy is not science, right?

 

[vanhees71]

Now you got me ;-).

 

[atyy]

No, why do you ask?

In Bohmian Mechanics, the wave function of the universe does not collapse. Yet Bohmian Mechanics says that predictions obtained with collapse are correct. Since objectively the wave function of the universe does not collapse, I thought wave function collapse in Bohmian Mechanics is subjective (ie. requires consciousness).

 

[Demystifier]

In Bohmian Mechanics, the wave function of the universe does not collapse. Yet Bohmian Mechanics says that predictions obtained with collapse are correct. Since objectively the wave function of the universe does not collapse, I thought wave function collapse in Bohmian Mechanics is subjective (ie. requires consciousness).

This is very much like saying that validity of Bayes formula for conditional probability requires consciousness. Would you say that Bayes formula requires consciousness?

 

[vanhees71]

With an argument involving Bayes and his (purely mathematical) theorem nowadays you can argue for anything you like, including a huge pile of bovine excrements. SCNR

 

[Demystifier]

With an argument involving Bayes and his (purely mathematical) theorem nowadays you can argue for anything you like, including a huge pile of bovine excrements. SCNR

How that works? I would also like to know that general powerful technique of argumentation based on Bayes.

 

[vanhees71]

Well, you can, e.g., create a whole new philosophy "of it all" called "quantum Bayesianism".

 

[atyy]

This is very much like saying that validity of Bayes formula for conditional probability requires consciousness. Would you say that Bayes formula requires consciousness?

I'm not sure. My instinct is to say it depends.

If interpreted in a frequentist sense, then Bayes's theorem does not require consciousness.

If interpreted in a subjective Bayesian sense, then Bayes's theorem does require consciousness.

I don't believe the objective Bayesian approach makes any sense.

 

[UsableThought]

Well, you can, e.g., create a whole new philosophy "of it all" called "quantum Bayesianism".

Surely no need to "create" since the name at least is already in use? E.g.

https://plato.stanford.edu/entries/quantum-bayesian/

https://arxiv.org/pdf/quant-ph/0608190.pdf

http://www.physics.usyd.edu.au/~ericc/SQF2014/slides/Ruediger%20Schack.pdf

etc.

I know about this only because it is one of many interpretations discussed in Michael Raymer's July 2017 book from Oxford U. Press, Quantum Physics: What Everyone Needs to Know.

And certainly @atyy is correct when he says "If interpreted in a subjective Bayesian sense, then Bayes's theorem does require consciousness"; here's a syllogism from the last link above, a slide show put together by Schack:

A quantum state determines probabilities through the Born rule.
Probabilities are personal judgements of the agent who assigns them.
HENCE: A quantum state is a personal judgement of the agent who assigns it.​

 

[Mentz114]

Surely no need to "create" since the name at least is already in use? E.g.

https://plato.stanford.edu/entries/quantum-bayesian/

https://arxiv.org/pdf/quant-ph/0608190.pdf

http://www.physics.usyd.edu.au/~ericc/SQF2014/slides/Ruediger%20Schack.pdf

etc.

I know about this only because it is one of many interpretations discussed in Michael Raymer's July 2017 book from Oxford U. Press, Quantum Physics: What Everyone Needs to Know.

And certainly @atyy is correct when he says "If interpreted in a subjective Bayesian sense, then Bayes's theorem does require consciousness"; here's a syllogism from the last link above, a slide show put together by Schack:

A quantum state determines probabilities through the Born rule.
Probabilities are personal judgements of the agent who assigns them.
HENCE: A quantum state is a personal judgement of the agent who assigns it.​

Sounds wise. How does the personal judgement of the agent affect a future interaction or measurement of the state. Is there still a state if there is no agent ?

 

[Trollfaz]

I heard from Sean Carroll that if our consciousness does indeed affect the experiment, then it is through the four fundamental forces or an unknown fifth force. He argued that the "fifth force" would have already been detected if it exists, but the fact that nothing is found shows that psychokinesis is wrong, we cannot change the wavefunction with our consciousness.

 

[vanhees71]

Ok, it's a matter of opinion, but I consider this subjective interpretation of probabilities as gibberish. Nobody following this new idea (why it is attributed to poor Bayes is not clear to me either by the way) has ever been able to explain to me what this means for real-world measurements, which use of course the frequentist interpretation of probabilities, and the frequentist interpretation just works. So why do I need a new unsharp subjective redefinition about the statistical meaning of probability theory?

 

[Trollfaz]

Thats why i would say that the global consciousness project, dean radins double slit experiment are pseudoscience. The conclusion are all derived from cherry picking of data.

 

[stevendaryl]

Ok, it's a matter of opinion, but I consider this subjective interpretation of probabilities as gibberish. Nobody following this new idea (why it is attributed to poor Bayes is not clear to me either by the way) has ever been able to explain to me what this means for real-world measurements, which use of course the frequentist interpretation of probabilities, and the frequentist interpretation just works. So why do I need a new unsharp subjective redefinition about the statistical meaning of probability theory?

I would say that Bayesian probability is probability done right, but luckily for frequentists, the difference between a correct Bayesian analysis and in incorrect frequentist analysis disappears in the limit of many trials.

Suppose I flip a coin once and I get heads. So the relative frequency for heads is 1. Does that mean that the probability is 1? Of course not! I don't have enough data to say that. So I flip the coin 10 times, and I get 4 heads and 6 tails. Does that mean that the probability of heads is 40%? No, those 10 coin flips could have been a fluke. So I flip the coin 100 times or 1000 times. How many flips does it take before I know that the pattern isn't a fluke? The answer is: there is never a time that I know for certain that it isn't a fluke.

Bayesian reasoning is reasoning in the presence of uncertainty, when there is a limited amount of data. But we're ALWAYS in that situation.

 

[stevendaryl]

I would say that Bayesian probability is probability done right, but luckily for frequentists, the difference between a correct Bayesian analysis and in incorrect frequentist analysis disappears in the limit of many trials.

Suppose I flip a coin once and I get heads. So the relative frequency for heads is 1. Does that mean that the probability is 1? Of course not! I don't have enough data to say that. So I flip the coin 10 times, and I get 4 heads and 6 tails. Does that mean that the probability of heads is 40%? No, those 10 coin flips could have been a fluke. So I flip the coin 100 times or 1000 times. How many flips does it take before I know that the pattern isn't a fluke? The answer is: there is never a time that I know for certain that it isn't a fluke.

Bayesian reasoning is reasoning in the presence of uncertainty, when there is a limited amount of data. But we're ALWAYS in that situation.

In practice, frequentist probability is more mathematically tractable than Bayesian probability. Using Bayesian probability, there is always a potentially infinite number of hypotheses about what is going on, and the only effect of data gathering is to shift the relative likelihood of the various possibilities. In contrast, frequentist probability has a criterion for rejecting hypotheses. The hypothesis that a coin is a fair coin can be rejected if repeated coin flips show a departure from 50/50 that is larger than the level of significance. So a frequentist approach is a lot less cluttered, since you are constantly clearing away falsified hypotheses.

 

[vanhees71]

I would say that Bayesian probability is probability done right, but luckily for frequentists, the difference between a correct Bayesian analysis and in incorrect frequentist analysis disappears in the limit of many trials.

Suppose I flip a coin once and I get heads. So the relative frequency for heads is 1. Does that mean that the probability is 1? Of course not! I don't have enough data to say that. So I flip the coin 10 times, and I get 4 heads and 6 tails. Does that mean that the probability of heads is 40%? No, those 10 coin flips could have been a fluke. So I flip the coin 100 times or 1000 times. How many flips does it take before I know that the pattern isn't a fluke? The answer is: there is never a time that I know for certain that it isn't a fluke.

Bayesian reasoning is reasoning in the presence of uncertainty, when there is a limited amount of data. But we're ALWAYS in that situation.

Well, you should do the analysis in a complete way and give the uncertainties (e.g., by giving the standard deviations of your result). The point is that, as you admit, to get the probabilities from experiment you have to repeat the experiment often enough to "collect enough statistics". That's the frequentist approach to statistics, which is well founded in probability theory in terms of the law of large numbers.

 

[stevendaryl]

Well, you should do the analysis in a complete way and give the uncertainties (e.g., by giving the standard deviations of your result). The point is that, as you admit, to get the probabilities from experiment

I'm not admitting that. I'm saying that it's actually impossible to get objective probabilities from experiment.

you have to repeat the experiment often enough to "collect enough statistics".

No, that's what frequentists say--that you have to collect enough data. I'm saying the opposite, that there is no such thing as collecting enough statistics. No matter how much data you collect, your estimate of probability will always be subjective.

That's the frequentist approach to statistics, which is well founded in probability theory in terms of the law of large numbers.

I'm saying that opposite of that. The law of large numbers doesn't support the frequentist approach. What the law of large numbers says is that the difference between the (incorrect) frequentist approach and the (correct) Bayesian approach goes to zero as the number of trials goes to infinity.

 

[vanhees71]

Hm, how do you then explain the amazing accuracy with which many of the probabilistic prediction of QT are confirmed by experiments, using the frequentist interpretation of probability?

Or, put in another way. How do you, as a "Bayesian", interpret probabilities and how can you, if there's no objective way to empirically measure probabilities with higher and higher precision by "collecting statistics, verify or falsify the probabilistic predictions of QT?

 

[stevendaryl]

Well, you should do the analysis in a complete way and give the uncertainties (e.g., by giving the standard deviations of your result).

The frequentist approach to giving uncertainties is just wrong. It's backwards.

Let me illustrate with coin flipping. Suppose you want to know whether you have a fair coin. (There's actually evidence that there is no such thing as a biased coin: weighting one side doesn't actually make it more likely to land on that side. But that's sort of beside the point...) What you'd like to be able to do is to flip the coin a bunch of times, and note how many heads and tails you get, and use that data to decide whether your coin is fair or not. In other words, what you want to know is:

• What is the probability that my coin is unfair, given the data?

But the uncertainty that frequentists compute is:

• What is the probability of getting that data, if I assume that the coin is unfair?

By itself, that doesn't tell us anything about the likelihood of having a fair or unfair coin.

(Note: technically, you would compute something like the probability of getting that data under the assumption that the coin's true probability for head, $P_H$, is more than $\epsilon$ away from $\frac{1}{2}$)

 

[stevendaryl]

Hm, how do you then explain the amazing accuracy with which many of the probabilistic prediction of QT are confirmed by experiments, using the frequentist interpretation of probability?

I already said how: The difference between the (incorrect) frequentist analysis and the (correct) Bayesian analysis goes to zero in the limit as the number of trials becomes large.

Or, put in another way. How do you, as a "Bayesian", interpret probabilities and how can you, if there's no objective way to empirically measure probabilities with higher and higher precision by "collecting statistics, verify or falsify the probabilistic predictions of QT?

For a Bayesian, at any given time, there are many alternative hypotheses that could all explain the given data. Gathering more data will tend to make some hypotheses more likely, and other hypotheses less likely. The point of gathering more data is to decrease your uncertainty about the various hypotheses. But unlike frequentists, nothing is ever verified, and nothing is every falsified. That isn't a problem, in principle. In practice, it's cumbersome to keep around hypotheses that have negligible likelihood. So I think there is a sense in which Popperian falsification is a heuristic tool to make science more tractable.

 

[vanhees71]

I'm again too stupid to follow this argument. I'd describe the coin-throughing probability experiment as follows. I assume that the coin is stable and there's a probability $p$ for showing head (then necessarily the probability for showing tail is $q=1-p$).

As a frequentist, to figure out the probability $p$ I have to through the coin very often and check the relative frequencies with which I get head or tail, and standard probability theory tells me that this is not as stupid an idea as you tell since we can easily verify the Law of Large Numbers for this simple case. The probability for getting $0 \leq H \leq N$ head obviously is
$$P_N(H)=\binom{N}{H} p^H(1-p)^{N-H}.$$
To go on I define the generating function
$$f(x)=\sum_{H=1}^N \binom{N}{H} \exp(x H) p^H(1-p)^{N-H}=(1+p \exp x-p)^N$$
to evaluate the expectation value for $H$ and its standard deviation
$$\overline{H}=\langle H \rangle =f'(0)= N p, \quad \sigma_{H}^2=\langle H^2 \rangle-\langle H \rangle^2=Np(1-p).$$
The expectation value of the relative frequency for head is thus
$$p_N = \frac{\overline{H}}{N}=p$$
and its standard deviation
$$\sigma_{p_N}=\frac{\sigma{H}}{N}=\frac{p(1-p)}{\sqrt{N}}.$$
For large $N$ the probability distribution for $p_N$ is Gaussian around the mean value $p$ with a width of $\mathcal{O}(1/\sqrt{N})$, i.e., for $N \rightarrow \infty$ the relative frequencies for head converge in some weak (or "probabilistic") sense to $p$.

That's more a plausibility argument than a real strict proof, but it can be made rigorous, and it shows that the frequentist interpretation is valid. I don't thus see any need to introduce another interpretation of probabilities than the frequentist one for any practical purpose.

Of course, if you cannot make $N$ very large for some reason, you have to live with large uncertainties. Then you might start with philosophical speculations about the "meaning of probabilities for a small number of events", since physics claims to be an objective science there are some demands for a discovery (e.g., the famous $5\sigma$-significance rule in HEP physics).

 

[vanhees71]

I already said how: The difference between the (incorrect) frequentist analysis and the (correct) Bayesian analysis goes to zero in the limit as the number of trials becomes large.

How then can the "frequentist analysis" be wrong? It cannot be wrong, because in the hard empirical sciences we consider only sufficiently often repeatable observations as clear evidence for the correctness of a probabilistic description. "Unrepeatable one-time experiments" are useless for science.

For a Bayesian, at any given time, there are many alternative hypotheses that could all explain the given data. Gathering more data will tend to make some hypotheses more likely, and other hypotheses less likely. The point of gathering more data is to decrease your uncertainty about the various hypotheses. But unlike frequentists, nothing is ever verified, and nothing is every falsified. That isn't a problem, in principle. In practice, it's cumbersome to keep around hypotheses that have negligible likelihood. So I think there is a sense in which Popperian falsification is a heuristic tool to make science more tractable.

Then Bayesianism is simply irrelevant for the natural sciences.

 

[stevendaryl]

Let me bring up a hoary example illustrating the problem with the frequentist notion of uncertainty.

Suppose you're a doctor, and you have some fairly accurate test for some disease. You've confirmed that:

• If you have the disease, there is a 99% probability that you will test positive, and only a 1% chance that you will test negative.
• If you don't have the disease, there is a 99% probability that you will test negative, and only a 1% chance that you will test positive.

So you test a patient, and he tests positive for the disease. You tell him: "You probably have the disease; but there is a 1% uncertainty in the diagnosis." Should the patient be worried, or not?

Well, 99% certainty sounds pretty certain, so the patient ought to be worried. But the Bayesian analysis would tell us this:

• Let $p(D)$ be the a priori probability that the patient has the disease (before any tests are performed).
• Let $p(\neg D) = 1 - p(D)$ be the a priori probability that he doesn't have the disease.
  
• Let $p(P|D)$ be the probability of testing positive, given that the patient has the disease (99% in our example).
• Let $p(P|\neg D)$ be the probability of testing positive, given that the patient does not have the disease (1% in our example).
• Then the probability of the patient having the disease, given that he tests positive, is $p(D|P) = p(P|D) \frac{p(D)}{p(P|D) p(D) + p(P|\neg D) P(\neg D)}$

If $p(D) = 0.0001$ (1 in 10,000) then this gives us: $P(D|P) \approx$ 0.98%. In other words, the probability that he doesn't have the disease is 99%.

So the 1% uncertainty in the test accuracy is completely inaccurate as a way to estimate the uncertainty in whether the patient has the disease.

 

[vanhees71]

What has this example to do with what we are discussing?