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Unconscious Research of Global Consciousness

Donate Some say that the collective emotions of humans can influence electronic hardware.  

by Brian Dunning

Filed under Paranormal

Skeptoid Podcast #49
June 10, 2007
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Unconscious Research of Global Consciousness

In this episode we're going to take a look at a project that has captured imaginations for more than a decade, the Global Consciousness Project, which posits that events that emotionally affect large numbers of people cause measurable changes in the output of random number generators.

The principal public face of the Global Consciousness Project is Dr. Dean Radin, an electrical engineer and Ph.D. in psychology. Supporters like to say that the project is part of Princeton University, but this is not so. The project director, Roger Nelson, is in the Mechanical and Aerospace Engineering department there, but that's about the whole depth of the connection. Some of Nelson's resources, like the website, are hosted by Princeton. The project is funded by private donations through the Institute of Noetic Sciences in Petaluma, California.

It is worthy to note that I cannot, in good conscience, criticize Dean Radin. He is said to be an awesome fiddle and banjo player, and the world needs more fiddle and banjo music. So, Dr. Radin, when you hear this podcast, know that I am at heart a supporter; and when you put down your random number generator, and pick up your banjo, I'll be in the front row. If you want to do some good in the world, stick with what works. Now let's talk about this Global Consciousness Project of yours.

As of 2007, 65 people at various locations around the world have a small hardware random number generator, which they call an egg, connected to a computer. All day, every day, each one spits out random numbers, which are regularly transmitted through the Internet to Nelson's server in Princeton, New Jersey. When the researchers choose an important world event, they pull the data from that time and put it through a series of filters and analyses and find patterns they say are improbably less random. I'm not going to go into all the details of how they do this, it's really boring and confusing if you're not a statistician, but they do openly publish all their methodology on Nelson's website at noosphere.princeton.edu. Their theory is that somehow, the collective consciousness of all the emotional or psychological energy of people focused on the chosen event, somehow affects the random number generators. They do not presume to have any hypothesis for how or why this might be possible, or what the mechanism might be, or really any satisfactory answers to any questions that mainstream scientists have asked them. They simply put forth their findings for what they're worth, and they urge you and and I and everyone else to look at their results and hopefully conclude, as they have, that there's something to all of this, and that it's worthy of further research.

The problem is that people outside their lab either fail to reach the same conclusions or find their methodology so flawed that it's pointless to even review the findings. They do publish what they call criticism on their web site, but it's mainly comments and suggestions from their associates. There is not a lot of published criticism of Global Consciousness out there to cite, and one reason is that their theory lacks consistent claims that are specific enough to be tested. Here are two fundamental questions that they must answer and have not:

  1. What type of event qualifies as "significant"? They pick events themselves, without any defined criteria. When they choose an event, they fail to test if there are any other simultaneous events in other parts of the world that might override any effect. What happened in Ghana during the OJ Simpson trial? There are no controls over what types of event triggers an examination of the data, and no controls to eliminate prospective events due to conflicting events.

  2. What type of effect in the data constitutes a result? Again, no criteria. They maintain no standards for what constitutes a correlation: whether it's a trough or a spike or some other type of anomaly; whether it should happen before, during, or after the event; how long before or after the event it should be found, or what the duration should be. In fact, their "results" are all over the map.

So, as they look for undefined results from undefined events, they still manage to make additional errors in their methodology. Here are some of the most flagrant:

  1. The analysis is not blinded in any way. When something happens, they look at their data until they find patterns. Proper analysis would come from isolated statisticians with no reference indicating a timeline on the data, knowledge of what to look for, or knowledge of what world event is being matched.

  2. They do not look for alternate causes of their data anomalies. Sunspots? Cell phone calls?

  3. They make claims of specific numbers for how they beat chance. Clearly, it's impossible to have any meaningful metrics, given the lack of standards for scoring or choosing events.

  4. They make no attempts to falsify their theory. They should be looking for alternate causes of the anomalies they claim to find in the output from their eggs, such as sunspots or electromagnetic interference from other devices. They should be looking for alternate or additional effects caused by human emotions, like errors in calculators or digital watches. Why not cell phones or toasters? If this effect is real, their eggs would not be the only things affected. Whenever a Global Consciousness event happens, there should be well known and well established failures of, or anomalies in, electric and/or computerized devices worldwide. It's improbable that these supposed effects would seek out and affect only one specific application of common hardware components used in many other devices. They do not look at other species besides humans whose emotions might be responsible for the effects. Why not dolphins or whales, or for that matter ants? Most of the living matter on earth is ants, and ants certainly have collective behavior. If collective consciousness did have a measurable affect on hardware, ants are the first place I would look.

One of their biggest claims to fame is the finding of a massive data anomaly, stronger than any other found, at the time of the 9/11 terrorist attacks, and Radin calculated that it was 6000:1 that this spike in the data was due to chance. Such a finding would make sense if the theory were true (although 9/11 probably didn't bother very many ants). You'll hear this result time and time again if you listen to one of Radin's lectures or read their materials. But you will have to go out on your own to find a dissenting opinion, which can be heard from anyone else who has actually looked at their data. One such person is Jeffrey Scargle of the NASA Ames Research Center, who undertook an analysis on his own time. Scargle's finding on the 9/11 data was "I personally disagree with the conclusion that anomalous effects have been unequivocally established" and "I judge the degree of cogency of all of the results in both (Radin's and Nelson's) papers as low." Scargle attributes their positive findings to the questionable application of an XOR filter to the raw data, the use of a discredited p-value test, the lack of blinding, limited choice of likely effects, and a suspicious process that he describes as "data fiddling".

Dr. Edwin May and James Spottiswoode also performed an independent analysis of Radin's 9/11 results. Their conclusion states in part:

We show that the choice was fortuitous in that had the analysis window been a few minutes shorter or 30 minutes longer, the formal test would not have achieved significance... We differ markedly with regard to the posted conclusions. Using Radin's analysis, we do not find significant evidence that the GCP network's EGG's responded to the New York City attacks in real time. Radin's computation of 6000:1 odds against chance during the events are accounted for by a not-unexpected local deviation that occurred approximately 3 hours before the attacks. We conclude that the network random number generators produced data consistent with mean chance.

Now let's talk about the elephant in the room. To any reasonable person, the whole concept of global consciousness is ridiculous at face value. This is true of many pseudosciences. But all that should raise is a red flag; people used to think flight was ridiculous too. But when you find red flags everywhere, they start to add up. Let's look back at Skeptoid episode #37, How to Spot Pseudoscience, and see if there are any other warning signs. Here's one: They make their announcements through mass media, rather than through scientific journals. When respected journals won't touch research, it's a pretty good indicator that there's something wrong. But radio shows like Coast to Coast AM, that promote pseudoscience, are all over it. Another warning is that their claim is based on some unknown form of energy or force. Also, the claim fails the Occam's Razor test. Again, this doesn't prove anything, it's just another red flag. Which is more likely to be true: That there's nothing to the idea of global consciousness, which is what the consensus of mainstream science maintains; or that these few people using tremendously flawed methodology have uncovered something so profound it would change the way we view everything, and is based on some mystical force unknown to science? Another problem is that the claim comes only from one source that's dedicated to supporting that cause. Legitimate research is always successfully replicated by independent labs. When it's not, you have good reason to be skeptical. Global consciousness does pass a few of these tests, but legitimate research and facts always pass all of them.

Now, Dr. Radin, I know I said I wouldn't criticize you, but I do have to take issue with one of your quotes. You said:

There is no kind way to say this, but the most stubborn skeptics do not understand scientific methods or the use of statistical inference, nor do they appreciate the history, philosophy or sociology of science. Their emotional rejection of the evidence seems to be motivated by fundamentalist beliefs of the scientific or religious kind.

This is a classic straw man argument against the scientific method. You're dismissing the rejection of your questionable evidence by calling it emotional and suggesting that it's motivated by a quasi-religious fundamentalist belief in science. OK, whatever. But when you declare that the people who fail to use your methods to find your same results "do not understand scientific methods," you're really pushing credibility. You're not the only person in the world who understands the scientific process. In fact, you don't appear to understand it very well at all. Please, do us all a favor. Foggy Mountain Breakdown. Go.


By Brian Dunning

Please contact us with any corrections or feedback.

 

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Cite this article:
Dunning, B. "Unconscious Research of Global Consciousness." Skeptoid Podcast. Skeptoid Media, 10 Jun 2007. Web. 15 Dec 2024. <https://skeptoid.com/episodes/4049>

 

References & Further Reading

Haahr, M. "Introduction to Randomness and Random Numbers." Random.org. Random.org, 7 Apr. 2007. Web. 17 Oct. 2009. <http://www.random.org/randomness/>

May, E., Spottiswoode, J. "Global Consciousness Project: An Independent Analysis of The 11 September 2001 Events." The Laboratories for Fundamental Research Innovative Interdisciplinary Research. LFR, 15 Sep. 2006. Web. 1 May. 2007. <http://www.lfr.org/LFR/csl/library/Sep1101.pdf>

Park, Robert L. Superstition: Belief in the Age of Science. Princeton, New Jersey: Princeton University Press, 2008. 139-141.

Radin, D., Nelson, R. "Meaningful Correlations in Random Data." Global Consciousness Project. Global Consciousness Project, 1 Jun. 2009. Web. 29 Jan. 2010. <http://noosphere.princeton.edu/>

Scargle, Jeffrey. "Was There Evidence of Global Consciousness on September 11, 2001?" Journal of Scientific Exploration. 1 Oct. 2002, Volume 16, Number 4: 571-577.

Webb, G.I. "Discovering significant patterns." Machine Learning. 1 Jul. 2007, Volume 68 , Issue 1: 1-33.

 

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