Super Bowl XLIII Field RNG Demonstration (Part Four)

Super Bowl XLIII Field RNG Exploration (Part Four)

by, Bryan Williams

Results: Super Bowl XLIII – Feb. 1, 2009

Super Bowl XLIII was marked by several notable moments, including the longest yard run in Super Bowl history, a valiant comeback effort by the Arizona Cardinals, and the Pittsburgh Steelers becoming the most winning Super Bowl team. It might be reasonable to think that the attention and emotional response to these and other moments might be conducive to a mass “group mind” effect, and to explore that idea, we tested two basic predictions for this field RNG demonstration (Part Two): one for the football game itself, and one for halftime.

First Prediction: Football Game

Figure 1 shows the graphical representation of the resulting field RNG output throughout the duration of the football game.


Figure 1. Graphical representation of the field RNG data collected during Super Bowl XLIII, 4:31 – 8:39 PM Mountain Time (+2 Eastern), February 1, 2009. The level of statistical significance at p = .05 (i.e., odds of 20 to 1 against chance) as time passes is indicated by the smoothly curved red arc.

Similar to the previous four Super Bowls (Part 3), the data from this year’s Super Bowl are mostly random, with no clear signs of a directional trend. The statistical outcome further confirms its random nature overall (Chi-Square = 14888.75, 14880 df, p = .478). It is perhaps of interest, however, that the data show a deep “valley” around halftime, marked by a rather steady decreasing trend, followed by a counterbalancing steady positive trend. Falling within the range of halftime, this was explored further in the second prediction test.

Second Prediction: Halftime Show

The “valley”-shaped trend can be seen in more detail in Figure 2, which displays the RNG data output during halftime.


Figure 2. Graphical representation of the RNG data from the Super Bowl XLIII halftime show, 5:55 – 6:28 PM Mountain Time (+2 Eastern), February 1, 2009.

The data begin to rather steadily decrease around 6:00 PM Mountain Time (+2 Eastern), lasting until about three minutes before the end of the concert given by Bruce Springsteen and the E Street Band. This trend extends so far below expectation that it begins to approach statistical significance, indicated by the bottom red curved arc. A counterbalancing positive trend then brings the data level again with chance expectation overall (Chi-Square = 1979.18, 1980 df, p = .501). While it looks interesting on the surface, it must be kept in mind that such trends are expected to occur in random data from time to time, so there is no clear indication that it is “group mind” related.

Combined Results: Five Consecutive Super Bowls

With Super Bowl XLIII, I (B.W.) have collected field RNG data from five consecutive Super Bowls from 2005 to 2009. Given the weak and subtle nature of the effects involved in RNG-based PK and field RNG studies, it might be instructive to examine a combined result across all five games to see what it might have to say about the mass “group mind” effect. Figure 3 shows such a combined result across all five Super Bowls, with the data combined by way of a Stouffer’s Z-score taken across the five RNG outputs for each second (Part Two), covering the period from the moment of kickoff to the end of the game and the trophy presentation. On average, halftime began about 88.4 minutes into the game and lasted 29.4 minutes. The game lasted about 215.4 minutes on average. The approximate moments of these events are indicated in the graph.


Figure 3. Graphical representation of the field RNG data combined across five consecutive Super Bowls (XXXIX – XLIII) by way of a Stouffer’s Z-score. The approximate times for halftime and the game end, averaged across all five Super Bowls, are indicated by black tickmarks along the pink line of expectation.

The data across all five games are clearly within the range of chance (Chi-Square = 15011.38, 14880 df, p = .223), although they are in the predicted direction overall and some degree of structure seems to be visible. Keeping in mind that these visible trends may be random artifacts (and thus we should be cautious about attributing too much meaning to them), it is rather interesting that a strong positive trend occurs about 45 minutes into the game, along with a sharp “valley” following halftime (120 minutes). Visually, the strong positive trend occurring toward the end of the game seems consistent with the kind of trend posited in the first prediction, and seems subjectively consistent with the idea that as the game comes down to its exciting conclusion and a winning team is determined, peoples’ attention becomes focused and their emotions strong. But again, we cannot say with certainty that this trend reflects a mass focused “group mind” effect, since such a trend occurs by chance every once in a while in the fluctuations of random data.


Figure 4. Graphical representation of the field RNG data combined across five consecutive Super Bowl halftimes by way of a Stouffer’s Z-score.

Figure 4 shows the combined result across all five Super Bowl halftimes. Rather than showing a steady positive trend as predicted, these data show a somewhat steady decreasing trend, opposite to the second prediction, that approaches statistical significance. Overall, these data begin to level out, bringing them well within chance (Chi-Square = 2122.8, 2170 df, p = .762).

What Can We Say About the Super Bowl So Far?

In a manner very similar to other sporting events (Part One), it seems so far that the Super Bowl does not clearly produce persuasive field RNG results for a mass “group mind” effect as predicted. Yet, keeping in mind the possibility of statistical artifacts, it also seems that some degree of structure may occasionally be seen in the field RNG data that is at least in line with the proposed effect.

So why doesn’t the Super Bowl clearly show a mass “group mind” effect, if it regularly draws the mass attention and emotion of millions of Americans each year? It’s a good question, but not an easy one to answer. Let’s take a brief look at some of the possible answers.

The answer that most skeptics would probably rush to is that the mass “group mind” effect simply does not exist. However, considering the significant field RNG results obtained by other researchers for various kinds of events aside from sports (e.g., Bancel & Nelson, 2008; Hirukawa & Ishikawa, 2004; Nelson, 2001; Nelson et al., 1996, 1998; Radin, 1997, Ch. 10; Rowe, 1998), this answer does not seem to be the most plausible one.

Considering the weak and subtle nature of the effects seen in RNG-PK and field RNG studies, another possible answer could be that the effect is there somewhere in the random noise, but it is so weak that it is simply “drowned out” by the noise. To get an idea of this, we might take a football analogy: Finding such a weak and subtle effect would be analogous to trying to hear what the person sitting next to you in the stadium is whispering while you’re sitting in the midst of a roaring crowd of football fans. And to explore this, the combined result across all five Super Bowls was examined. Assuming for the moment that it is not due to noise or statistical artifacts, some structure began to become visible in the data (Figures 3 & 4), at least hinting at the plausibility of this answer. Additional explorations could perhaps shed better light on this issue.

Yet another possible answer may be found in consideration of the venue and how it might relate subjectively to the generation of a mass “group mind.” While the Super Bowl draws mass attention and emotion, the possibility that these are shared is not always clear-cut. While one part of the crowd gets excited and focused as their team is winning, the other part of the crowd may be discouraged and lose interest as their team unfortunately loses. In such a case, we might think of the subjective situation as being rather “unfocused” or even counterbalanced. The venues where group mind effects tend to be found have been ones where there appears to be great attentional focus, strong rapport, and shared emotions across all involved. Coming from the above perspective, the Super Bowl and other sporting events may be somewhat counter to that situation, and thus may not be fully conducive to a mass group mind.

Lastly, there might be an “experimenter effect” at work in the data, wherein the experimenter (unconsciously) affects his own data by way of method, perspective, or even psi ability (Kennedy & Taddonio, 1976; White, 1976). In my case, such an effect would appear to be a suppressive one, such that the group mind effect is somehow prevented from showing up in the data. The likelihood of this answer may be somewhat lessened by the observation that the overall RNG outcomes over the five Super Bowls have not conformed to my predictions, and are thus not in line with my intentions. However, the experimenter effect remains to be a complicated and persistent issue within parapsychology, so this answer must still be considered.

Thus, we are faced with a range of possible answers, all equally applicable in this case and worthy of further study. Perhaps the best thing to come out of this Super Bowl exploration is that it allowed us to look at what kinds of events might be more conducive to the group mind effect, and what kinds might not be. Though it is counter to what we might intuitively expect, the current field RNG evidence from the Super Bowl and other sporting events suggests that these events mostly fall within the latter category. Examining events in this manner could have the advantage of allowing us to better determine which events we might focus on for further replication and closer study of the mass group mind effect. It was with this aim that the previous Super Bowl explorations, as well as the current demonstration, were carried out. It is hoped that the other, more primary aim for the current demonstration – that it would be interesting and instructive for readers of Public Parapsychology – was also met over the course of the Super Bowl weekend.

The rest of the series can be found in Parts One, Two, and Three.

Bryan Williams

Bryan Williams is a Native American student at the University of New Mexico, where his undergraduate studies have focused on physiological psychology and physics. He is a student affiliate of the Parapsychological Association, a student member of the Society for Scientific Exploration, and a co-moderator of the Psi Society, a Yahoo electronic discussion group for the general public that is devoted to parapsychology. He has been an active contributor to the Global Consciousness Project since 2001.

Acknowledgments

This long-term field RNG exploration of the Super Bowl by B.W. was made possible in part by support from the Parapsychology Foundation in New York. Appreciation must be extended to Dean Radin of the Institute of Noetic Sciences for making available software for data collection, and to Roger Nelson of the Global Consciousness Project and PEAR for helpful suggestions and advice on field RNG methodology.

References

Bancel, P., & Nelson, R. (2008). The GCP event experiment: Design, analytical methods, results. Journal of Scientific Exploration, 22, 309 – 333.

Hirukawa, T., & Ishikawa, M. (2004). Anomalous fluctuation of RNG data in Nebuta: Summer festival in Northeast Japan. Proceedings of Presented Papers: The Parapsychological Association 47th Annual Convention (pp. 389 – 397). Cary, NC: Parapsychological Association, Inc.

Kennedy, J. E., & Taddonio, J. L. (1976). Experimenter effects in parapsychological research. Journal of Parapsychology, 40, 1 – 33.

Nelson, R. D. (2001). Correlation of global events with REG data: An Internet-based, nonlocal anomalies experiment. Journal of Parapsychology, 65, 247 – 271.

Nelson, R. D., Bradish, G. J., Dobyns, Y. H., Dunne, B. J., & Jahn, R. G. (1996). FieldREG anomalies in group situations. Journal of Scientific Exploration, 10, 111 – 141.

Nelson, R. D., Jahn, R. G., Dunne, B. J., Dobyns, Y. H., & Bradish, G. J. (1998). FieldREG II: Consciousness field effects: Replications and explorations. Journal of Scientific Exploration, 12, 425 – 454.

Radin, D. I. (1997). The Conscious Universe: The Scientific Truth of Psychic Phenomena. San Francisco: HarperEdge.

Rowe, W. D. (1998). Physical measurement of episodes of focused group energy. Journal of Scientific Exploration, 12, 569 – 581.

White, R. A. (1976). The limits of experimenter influence on psi test results: Can any be set? Journal of the American Society for Psychical Research, 70, 333 – 369.

Super Bowl XLIII Field RNG Demonstration (Part Three)

Super Bowl XLIII Field RNG Demonstration (Part Three)

By, Bryan Williams

Super Bowl Field RNG Explorations: 2005 – 2008

To further explore the plausibility of a mass “group mind” effect occurring in conjunction with the widespread attention and emotional response to the Super Bowl, I collected field RNG data during the past four consecutive Super Bowls. Here, we provide a brief summary of the results, which add further basis for the planned field RNG demonstration here on Public Parapsychology. As described in Part Two, two individual predictions were made each year for the Super Bowl: one for the football game, and one for the halftime show [1].

First Prediction: Football Game

Based on all of the hype I had heard about it around my local university, I first decided to collect field RNG data during Super Bowl XXXIX in February 2005 (this is the only year in which the two test predictions were not specified in advance of the event, so examination was made after the fact). Figure 1 shows the graphical representation of the RNG output during the game.


Figure 1. Graphical representation of the field RNG data collected during NFL Super Bowl XXXIX, 4:37 – 8:14 PM Mountain Time (+2 Eastern), February 6, 2005. The level of statistical significance at p = .05 (i.e., odds of 20 to 1 against chance) as time passes is indicated by the smoothly curved red arc.

Ordinarily, one would expect to see RNG data produce a nominally random sequence over time that hovers around mean chance expectation (MCE; indicated in the graph by the pink horizontal line at zero) with no steady directional pattern. The data in Figure 1 show such a sequence throughout the first 90 minutes of the game, but then seem to take on a steadily increasing trend during the halftime period. The data even out following halftime, then steadily decrease around 7:00 PM Mountain time, and gradually return to a random sequence towards the end of the game. Overall, the result is consistent with chance (Chi-Square = 13066.37, 13055 df, p = .470) [2].

With the two predictions pre-specified for the first time in 2006, a follow-up exploration was done during Super Bowl XL, and the result is shown in Figure 2.

Figure 2. Graphical representation of the field RNG data collected during NFL Super Bowl XL, 4:27 – 8:03 PM Mountain Time (+2 Eastern), February 5, 2006.

The data are mostly random as expected, with the exception of a sharply increasing trend soon after the first score that lasts until halftime, after which it sharply decreases back to MCE. In all, the result is almost exactly at chance and statistically non-significant (Chi-Square = 12955.93, 12955 df, p = .496).

Super Bowl XLI on February 4, 2007, once again drew a lot of hype in my local university community, mainly because one of the Chicago Bears players was a New Mexico native. Interested to see if this might help facilitate a mass group mind, I again collected data, which are shown in Figure 3.

Figure 3. Graphical representation of the field RNG data collected during NFL Super Bowl XLI, 4:27 – 7:58 PM Mountain Time (+2 Eastern), February 4, 2007.

The data in Figure 3 show a modest increasing trend throughout most of the game, reversing into steady decreasing trend during the last hour of play. Although in the predicted direction, the overall result is nonsignificant (Chi-Square = 12693.98, 12685 df, p = .476).

Figure 4. Graphical representation of the field RNG data collected during NFL Super Bowl XLII, 4:28 – 8:03 PM Mountain Time (+2 Eastern), February 3, 2008.

The data from the most recent Super Bowl XLII on February 3, 2008, shown in Figure 4, are mostly random throughout, with little sign of a clear trend, and very close to chance overall (Chi-Square = 12890.83, 12901 df, p = .524).

Second Prediction: Halftime Show

Given that the halftime concerts tend to draw great attention (and in some cases, participation) by the crowd, the second prediction focused on the RNG data during the halftime period. Initially, when analyzed with theoretical mean and SD, these data seemed to produce some promising results for Super Bowls XXXIX and XL. However, after being reanalyzed using the empirical mean and SD of their datasets, the results fell to chance, suggesting that they are statistical artifacts due to the difference between the theoretical values, and the empirical values obtained from the RNG output [3, 4]. Since these and the other halftime results are mostly consistent with chance expectation, we will not present them here.

Tentative Conclusion

In general, the field RNG explorations conducted over the past four consecutive Super Bowls have not shown clear statistical evidence for a mass “group mind” effect. At times, some graphical results appear to show some brief trends in line with the predictions (e.g., the game results for Super Bowl XLI), although these are not clearly distinguishable from pure chance fluctuations that are expected to occasionally occur in random data.

It is also important to keep in mind that the magnitude of the effect observed in both RNG-based PK studies and field RNG studies is appreciably small, so obtaining clear results on the level of individual events can often prove difficult. Given this, we will examine a combined result using the data from all of the Super Bowl explorations (including those for the upcoming Super Bowl XLIII) in Part 4.

Will the results for Super Bowl XLIII and its halftime be similar to those described here? The answer will be revealed in Part 4, to appear in the days following Super Bowl Sunday...

The rest of the series can be read in Parts One, Two, and Four.

Bryan Williams

Bryan Williams is a Native American student at the University of New Mexico, where his undergraduate studies have focused on physiological psychology and physics. He is a student affiliate of the Parapsychological Association, a student member of the Society for Scientific Exploration, and a co-moderator of the Psi Society, a Yahoo electronic discussion group for the general public that is devoted to parapsychology. He has been an active contributor to the Global Consciousness Project since 2001.

Notes

[1] A description of the procedure, statistical analysis, and predictions used in each exploration is provided in the second post.

[2] An important technical note: When the analyses for Super Bowls XXXIX – XLI were first carried out, the theoretical mean and standard deviation (SD) were used in calculating the statistical outcomes (see Post 2). Following the decision to use the empirical mean and SD of the device output in May 2007, each result for the football game and the halftime show was recalculated using the empirical mean and SD of its respective dataset. As a result, the results for the above three Super Bowls have changed from their original results as first calculated with theoretical mean and SD (See Notes 3 & 4). Having been originally calculated with empirical mean and SD, the Super Bowl XLII results remain unchanged.

Super Bowl XLIII Field RNG Demonstration (Part Two)

Super Bowl XLIII Field RNG Demonstration (Part Two)

By, Bryan Williams

In this post, I provide a basic summary of the procedures, statistical analysis, and predictions to be used for the planned Super Bowl XLIII field random number generator (RNG) demonstration at Public Parapsychology. The methods follow those used in my previous Super Bowl field RNG explorations (coming in Part Three), and are closely modeled after those developed by the PEAR Laboratory for use in their field RNG studies (Nelson et al., 1996, 1998), and by the Global Consciousness Project for individual event analysis (Bancel & Nelson, 2008; Nelson, 2001). For more complete details, interested readers are referred to these publications, as well as to other field RNG studies that have used these same methods (e.g., Bierman, 1996; Crawford et al., 2003; Hirukawa & Ishikawa, 2004; Nelson & Radin, 2003; Rowe, 1998). We invite any questions, comments, or concerns from readers regarding these methods.

Procedure

For each Super Bowl exploration, an Orion RNG [1] is set up to run continuously on a personal computer (PC) one hour before the football game. This PC is located in a room about twelve feet from where B.W. usually watches the televised Super Bowl broadcast in the living room of his central New Mexico (USA) home. In order to mark the occurrence of notable events (such as kickoff, the scoring of the first two goals, and the halftime period), a paper time log is kept by B.W. as he watches the game, and the time for each event is noted in Mountain Standard Time using a wristwatch that is roughly synchronized to the PC’s internal clock beforehand. The PC’s clock is itself synchronized in advance with an Internet-based timeserver to ensure accurate time. Following the game, the RNG is allowed to run for up to 15 minutes, then it is shut off and the data stored in the PC’s memory is saved to hard disk for analysis.

Analysis

The PC uses a custom software package [2] developed by researchers at the Institute of Noetic Sciences to collect 200 random bits per second (= 1 test “trial”) from the RNG. Each bit consists of a binary number (either a “1” or a “0”) that is randomly determined by sampling the electronic noise source. For simplicity, this process can be thought of as being analogous to flipping a coin, with “heads” representing the “1”-bit, and “tails” representing the “0”-bit. When we flip a coin, each side has a 50/50 chance of turning up, and the same goes for each kind of bit (i.e., the theoretical probability of occurrence for each kind of bit is 1/2, or p = .5). Thus, the RNG can be seen as flipping 200 electronic “coins” per second. The software then counts the number of “heads” (i.e., “1”-bits) that came up in the 200 flips, and stores the number as the trial outcome value. Given the 50/50 probability of occurrence in theory, roughly 100 “heads” and 100 “tails” should be generated on average by the RNG over a long sequence of trials. In a traditional test of psychokinesis (PK), the goal is to attempt to upset this balance of heads and tails through mental intention on the RNG, such that more of one outcome is produced over the other. If the mass “group mind” effect is related to PK, then presumably the same should be observed in the field RNG data during moments of focused group attention and emotional response.

Statistical analysis of the RNG data proceeds using techniques that follow from classical statistical methods (Aron & Aron, 1997; Snedecor & Cochran, 1980). For those readers with a technical mind who are curious about the details, the following steps are taken in the analysis (those of you unfamiliar with statistics may want to skip ahead to the predictions):

1.) The trial output of the RNG follows a binomial distribution that has a theoretical mean of 100 and a theoretical standard deviation (SD) of 7.071. [3] To represent a basic measure of the deviation from the mean, each trial outcome value is converted into a z-score using the equation:

z = (x – M) / SD

where x is the outcome value for each trial, M is the mean, and SD is the standard deviation. Initially, the theoretical mean (100) was used for M, and the theoretical SD (7.071) for SD in the analysis of the Super Bowl data. However, it should be pointed out that, although the Orion RNGs tend to closely match the theoretical values for the binomial distribution overall, it is possible for an individual RNG to produce a small bias of the mean due to the nature of its random source. In other words, the mean and SD of each RNG should not be expected to exactly equal the theoretical values each and every time [4]. For that reason, in May of 2007, I made the decision to begin using the mean and SD empirically calculated from all of the RNG trial outcome values for M and SD, respectively, as a way to account for any potential mean bias in the RNG. This issue becomes relevant for the results of my previous Super Bowl explorations (discussed in Part Three).

2.) Each resulting z-score is squared to form a positive value that is Chi-Square distributed, and that has one degree of freedom (df).

3.) Given that Chi-Square values can be summed together as they are in the standard calculation of the Chi-Square statistic (e.g., Aron & Aron, 1997, p. 235), all of the individual values are added together across time to represent the overall measure of the deviation from the mean in the RNG data. Their associated degrees of freedom are similarly added together. A probability value can then be obtained from the total Chi-Square and degrees of freedom.

4.) The values can be cumulatively plotted over time in a graph as Chi-Square – 1 (i.e., the 1 df is subtracted from each of the associated Chi-Square values) to visualize the trends in the RNG data as time passes.

With the accumulation of RNG data that I collected from previous Super Bowls, it is also possible to examine a combined result across all Super Bowls using a Stouffer’s Z-score, calculated by adding together the z-scores for each individual second (Step 1) from each year, then dividing by the square root of the number of scores added (the analysis then proceeds as in Steps 2 – 4). This will be done with the previous field RNG data, along with the data collected during the planned demonstration, in order to assess the combined result across five consecutive Super Bowls.

Predictions

To explore a mass group mind effect, two test predictions are annually made for the Super Bowl. The first test prediction is for the football game itself, covering the time spanning from the moment of kickoff to the end of the televised broadcast (the latter was included to allow for any residual effects that may occur in conjunction with the trophy presentation and crowd response). Throughout this time period (averaging around 3.5 hours total), it is predicted that a steadily increasing non-random pattern (i.e., a positive deviation from the expected mean) will be observed in the field RNG data, which overall will be significantly different from chance (based on the resulting probability value for the total Chi-Square and df values).

Considering the excitement and focused crowd attention that is often generated by the halftime concerts, the second test prediction specifically concerns the halftime show, covering the time from the start of the halftime highlights to the beginning of the 3rd Quarter. During this halftime period (averaging around 30 minutes total), another steadily increasing non-random pattern is predicted to occur in the RNG data.

To be consistent with my previous Super Bowl explorations, both of these predictions will be further tested for the planned demonstration. In the next post, we will examine the results of my previous explorations.

The rest of the series can be found in Parts One, Three, and Four.

Bryan Williams

Bryan Williams is a Native American student at the University of New Mexico, where his undergraduate studies have focused on physiological psychology and physics. He is a student affiliate of the Parapsychological Association, a student member of the Society for Scientific Exploration, and a co-moderator of the Psi Society, a Yahoo electronic discussion group for the general public that is devoted to parapsychology. He has been an active contributor to the Global Consciousness Project since 2001.

Notes

[1] In brief, the Orion RNG is a small external hardware circuit that uses electronic noise as its source of randomness. It is manufactured by Orion/ICATT Interactive Media in Amsterdam, the Netherlands, and detailed specifications of the device can be found on the company’s website.

[2] This is the Microsoft Windows-based “FRED” software package, developed by researchers associated with the Institute of Noetic Sciences in Petaluma, CA.

[3] This value can be obtained by the statistical equation for the standard deviation of a binomial random variable: SD = Sqrt [Npq], where N is the total number of bits per trial (200), p is the theoretical probability for a bit (.5), and q = 1 – p (Utts & Heckard, 2006, Section 8.4)

[4] Put another way, whenever the mean and standard deviation of all the trial outcome values generated by the RNG are calculated, they should not be expected in every case to be exactly equal 100 and 7.071, respectively. Instead, they tend to fluctuate somewhere around these two values.

References

Aron, A., & Aron, E. N. (1997). Statistics for the Behavioral and Social Sciences. Upper Saddle River, NJ : Prentice-Hall.

Bancel, P., & Nelson, R. (2008). The GCP event experiment: Design analytical methods, results. Journal of Scientific Exploration, 22, 309 – 333.

Bierman, D. J. (1996). Exploring correlations between local emotional and global emotional events and the behavior of a random number generator. Journal of Scientific Exploration, 10, 363 – 373.

Crawford, C. C., Jonas, W. B., Nelson, R., Wirkus, M., & Wirkus, M. (2003). Alterations in random event measures associated with a healing practice. Journal of Alternative and Complementary Medicine, 9, 345 – 353.

Hirukawa, T., & Ishikawa, M. (2004). Anomalous fluctuation of RNG data in Nebuta: Summer festival in Northeast Japan. Proceedings of Presented Papers: The Parapsychological Association 47th Annual Convention (pp. 389 – 397). Cary, NC: Parapsychological Association, Inc.

Nelson, R. D. (2001). Correlation of global events with REG data: An Internet-based, nonlocal anomalies experiment. Journal of Parapsychology, 65, 247 – 271.

Nelson, R. D., Bradish, G. J., Dobyns, Y. H., Dunne, B. J., & Jahn, R. G. (1996). FieldREG anomalies in group situations. Journal of Scientific Exploration, 10, 111 – 141.

Nelson, R. D., Jahn, R. G., Dunne, B. J., Dobyns, Y. H., & Bradish, G. J. (1998). FieldREG II: Consciousness field effects: Replications and explorations. Journal of Scientific Exploration, 12, 425 – 454.

Nelson, R. D., & Radin, D. I. (2003). FieldREG experiments and group consciousness: Extending REG/RNG research to real-world situations. In W. B. Jonas & C. C. Crawford (Eds.) Healing, Intention, and Energy Medicine: Science, Research Methods and Clinical Implications (pp. 49 – 57). Edinburgh, UK: Churchill Livingstone.

Rowe, W. D. (1998). Physical measurement of episodes of focused group energy. Journal of Scientific Exploration, 12, 569 – 581.

Snedecor, G. W., & Cochran, W. G. (1980). Statistical Methods (7th Ed.). Ames, IA: Iowa State University Press.

Utts, J. M., & Heckard, R. F. (2006). Mind on Statistics (3rd Ed.). Belmont, CA: Duxbury Press.

Super Bowl XLIII Field RNG Demonstration (Part One)

Super Bowl XLIII Field RNG Demonstration (Part One)

by, Bryan Williams

Based on the widespread interest and attention annually given to the NFL Super Bowl, I wish to take the opportunity to present on Public Parapsychology a demonstration of a field Random Number Generator (RNG) analysis of the upcoming Super Bowl XLIII on February 1, 2009, which I hope will be both interesting and informative for our readers. In this first post of three, I offer a brief background on field RNG studies of sporting events that provides the foundation for this planned demonstration.

Introduction

Many Americans would probably agree that Super Bowl Sunday is an event they look forward to every year with anxious anticipation. The big football parties with family and friends, the amusing TV commercials and halftime concerts, and the general excitement of the football game itself are all things that tend to make this particular Sunday stand out from all the rest in terms of enjoyment. Given that the Super Bowl is such a social sporting event in the United States, with the excitement stirring the attention and emotions of millions of football fans across the country, it might seem reasonable to think that it could be conducive to short-lived psi effects, particularly psychokinesis (PK, or “mind over matter”). If millions of fans are cheering in unison – not only those in the crowd at the stadium, but also those sitting at home watching the live TV broadcast – then one might be able to metaphorically envision a unified cheer, a mass chorus of raised voices that at times may be as rhythmic as an orchestra. Another metaphor may be that as a large group of fans watch the game together and share the same emotional reactions, they can be seen as sharing the same frame of mind. Focusing their collective attention on the game, cheering along with family, friends, and other spectators – they are not acting like individual minds. Rather, they are acting, in a sense, like a single mass “group mind” that is being moved by the excitement. And if such a mass group mind is moved during the game, then perhaps it might subtly move the matter in the surrounding physical environment along with it.

During the 1990s, as part of an effort to extend and apply their extensive laboratory findings on PK to more natural settings [1], researchers at the Princeton Engineering Anomalies Research (PEAR) Laboratory began deploying portable random number generators (RNGs)[2] at various group events to explore the plausibility of such a mass “group mind” effect on matter. These events included ceremonial rituals, stage performances, parties, and healing workshops. Rather than being purely random as expected, the combined streams of data from the RNGs during these events tended to show a steady non-random pattern that was significantly different from chance by statistical standards (Nelson et al., 1996, 1998), hinting that there could be something to the notion of a mass “group mind.” From these experiments, one may wonder: Could sporting events like the Super Bowl be conducive to a mass “group mind” effect on matter?

Field RNG Studies of Sporting Events

When they began these “field RNG” studies [3], the PEAR researchers did examine a small number of sporting events, including several Princeton University football games. The RNG data showed little indication of a group mind effect, although the researchers noticed that most of the games were rather lacking in crowd enthusiasm (Nelson et al., 1998, pp. 442 – 443).

Despite the null results of the PEAR group, other researchers tried looking at other sports in their own field RNG studies. Dick Bierman (1996) of the University of Amsterdam had set up a field RNG in the home of a Dutch family for a study that coincided with the 1995 European soccer final. While the family (and presumably many other people throughout the Netherlands) watched the soccer match on TV and cheered the Dutch team to victory, the RNG ran silently in the background. The RNG data during the 90-minute game showed a steadily increasing non-random pattern that was significantly beyond chance, while the control data collected 90 minutes before the start of the game for comparison were purely random as expected.

In a similar study, German researchers Johannes Hagel and Margot Tschapke (2004) of the Institut für Psycho-Physik in Köhn had collected streams of data from three field RNGs during a highly charged home soccer game won by the local Köhn team. Analysis revealed increasing non-random patterns in the data from two of the RNGs that persisted for several hours following the game, when the people of Köhn had walked through the streets in celebration.

At least two field RNG studies have previously looked at the Super Bowl directly. As part of their examination of sporting events, the PEAR researchers had run two separate field RNGs during Super Bowl XXX in January of 1996. Although the data from each of the RNGs showed modest increases away from expected randomness, their overall results were insignificant (Nelson et al., 1998, pp. 440, 443). While at the University of Nevada at Las Vegas, Dean Radin (1997, pp. 167 – 168) had made his own independent examination of Super Bowl XXX using six field RNGs. To see how the RNG data might correlate with audience attention, Radin split the six data streams into periods of “high” and “low” interest, based on ratings given to each period by one or more experimenters watching the broadcast. Periods of “high” interest might include the game itself and the halftime concert, while periods of “low” interest might include pre-game broadcast commentary and the commercial periods (of course the latter is debatable now, since the commercials tend to be quite amusing, and the interest they draw often competes with the game itself). While not notable by statistical standards, there were slight indications that the RNG data during the “high” interest periods were gradually moving away from expected randomness, while the data from “low” interest periods remained random.

Inspired in part by the field RNG studies, the Global Consciousness Project (GCP) was founded in 1998 to further explore “group mind” effects on global scale when major world events occur. To do this, the GCP set up and monitors an Internet-based global network of RNGs that continually run 24/7, sending their data to a server in Princeton, NJ, for archiving and analysis (Bancel & Nelson, 2008; Nelson, 2001). In addition to examining formally defined global events, the GCP informally explores local events of interest on occasion. One such event was Super Bowl XXXVII in January of 2003 [4]. Although not statistically significant overall, the data from the 50 active RNGs in the GCP network at that time seemed to show a strong non-random trend during the start of the game that was consistent with the predicted effect. Despite interesting internal trends in some cases, GCP examinations of other sporting events, including the 2002 World Cup (Event #112) and two World Series games (2001 & 2008; Event #89 & #279, respectively), have produced insignificant outcomes for reasons that remain unclear.

In all, field RNG studies of the Super Bowl and other sporting events have produced a “mixed bag” of results, making it unclear as to whether such events are conducive to a mass “group mind.” In the next post, I will provide a summary of additional field RNG explorations of the Super Bowl carried out by myself, which have further motivated Public Parapsychology’s planned field RNG demonstration, and I will provide an overview of the planned procedures, analysis, and predictions for the demonstration.

The rest of the series can be read in Parts Two, Three, and Four.

Bryan Williams

Bryan Williams is a Native American student at the University of New Mexico, where his undergraduate studies have focused on physiological psychology and physics. He is a student affiliate of the Parapsychological Association, a student member of the Society for Scientific Exploration, and a co-moderator of the Psi Society, a Yahoo electronic discussion group for the general public that is devoted to parapsychology. He has been an active contributor to the Global Consciousness Project since 2001.

Notes

[1] The details of these findings can be found in a journal article describing the PEAR Lab’s 12-year research database on PK (Jahn et al., 1997). Electronic copies of this and other PEAR publications cited here have been made available for download at the PEAR Lab’s archival website.

[2] The PEAR Lab regularly uses the term “random event generator” (REG) as another name for RNG. For the most part, the two terms – RNG and REG – are synonymous, and we will use only one term (RNG) here for convenience.

[3] As first explained by Nelson et al. (1996, p. 112), the name “field RNG” can have a double meaning. Besides reflecting the fact that the RNGs have been taken out of the laboratory and into the field, the name can also provide a symbolic reference to a concept derived by Nelson et al. to think about the “group mind” effect. To affect the field RNG, the group mind effect might be thought of as an invisible PK-related “field” that extends out into the surrounding environment to affect matter, analogous to the way a magnetic field seems to extend out from the magnet to affect iron. It should be kept in mind that while this concept provides a useful way to think about how a group mind effect may work, it is purely metaphorical in nature and not currently supported by any clear evidence.

[4] This informal GCP exploration of Super Bowl XXXVII can be found at the GCP website. Links to GCP examinations of the other sporting events mentioned elsewhere in the text can be found on the GCP’s formal results page.

References

Bancel, P., & Nelson, R. (2008). The GCP event experiment: Design, analytical methods, results. Journal of Scientific Exploration, 22, 309 – 333.

Bierman, D. J. (1996). Exploring correlations between local emotional and global emotional events and the behavior of a random number generator. Journal of Scientific Exploration, 10, 363 – 373.

Hagel, J., & Tschapke, M. (2004). The local event detector (LED) – an experimental setup for an exploratory study of correlations between collective emotional events and random number sequences. Proceedings of Presented Papers: The Parapsychological Association 47th Annual Convention (pp. 379 – 388). Cary, NC: Parapsychological Association, Inc.

Jahn, R. G., Dunne, B. J., Nelson, R. D., Dobyns, Y. H., & Bradish, G. J. (1997). Correlations of random binary sequences with pre-stated operator intention: A review of a 12-year program. Journal of Scientific Exploration, 11, 345 – 367.

Nelson, R. D. (2001). Correlation of global events with REG data: An Internet-based, nonlocal anomalies experiment. Journal of Parapsychology, 65, 247 – 271.

Nelson, R. D., Bradish, G. J., Dobyns, Y. H., Dunne, B. J., & Jahn, R. G. (1996). FieldREG anomalies in group situations. Journal of Scientific Exploration, 10, 111 – 141.

Nelson, R. D., Jahn, R. G., Dunne, B. J., Dobyns, Y. H., & Bradish, G. J. (1998). FieldREG II: Consciousness field effects: Replications and explorations. Journal of Scientific Exploration, 12, 425 – 454.

Radin, D. I. (1997). The Conscious Universe: The Scientific Truth of Psychic Phenomena. San Francisco: HarperEdge.

Guest Blog: Using Brain Imaging as a Direct Test for Psi

Despite impressive statistical evidence, there are still a number of skeptics and critics of parapsychology who say that they still do not find the case for psi phenomena convincing largely because there is still no developed theory that relates psi to human brain functioning. As a case in point, professional skeptic Michael Shermer (2003) once wrote in his monthly op-ed column in Scientific American that, with respect to telepathy, “Until psi proponents can elucidate how thoughts generated by neurons in the sender’s brain can pass through the skull and into the brain of the receiver, skepticism [that psi exists] is the appropriate response…” (p. 32). While it seems that the mechanism may be a bit more complex than the simple picture Shermer paints of it, he does at least have a fair point in that the search for the neuropsychological correlates of psi should be an important focus for parapsychology if it looks to ever achieve wide mainstream acceptance.

Over the years, the search has largely been limited to using scalp electrodes connected to an electroencephalograph (EEG) in order to look for any brain wave patterns that might be associated with psi functioning (Ehrenwald, 1977). However, with the advent of brain imaging technology, there is the promise of peering through the skull to get a possible glimpse of the brain areas that might be involved. This promise is apparently what spurred the design of a new study just published in the latest issue of the Journal of Cognitive Neuroscience (Moulton & Kosslyn, 2008), which focused on the attempt to test for ESP using functional magnetic resonance imaging (fMRI). The study actually represents an attempt by mainstream researchers to experimentally reproduce psi effects, and was conducted by Samuel Moulton, a graduate student in the psychology department of prestigious Harvard University, along with Stephen Kosslyn, the prominent psychologist best known for his brain studies on mental imagery and visual perception (e.g., Ganis et al., 2004).

The premise for the study was based in part on a series of studies by Norman Don, Bruce McDonough, and Charles Warren of the University of Illinois at Chicago, in which they recorded the event-related brain wave potentials (ERPs) [1] of participants engaged in a precognition test disguised as a gambling task (Warren et al., 1992; Don et al., 1998; McDonough et al., 2002). They found that, in cases where they had correctly selected the precognition target, the participants’ ERPs were significantly different in wave structure from the ERPs associated with incorrect selections, suggesting that, on a brain wave level, the participants were sub-consciously “responding” more distinctly to the correct ESP target. Moulton and Kosslyn predicted that the brain as a whole might act similarly, the result of which might be detectable using MRI.

To test this, they gathered 19 pairs of people who were emotionally or biologically related [2] for a telepathy-type test, with one being the sender and the other being the receiver. The receiver’s head was placed into the MRI scanner and they were shown (by way of a mirror) two pictures during each test trial, one of which had been randomly selected as the ESP target. They selected which of the two they thought was the target by a button press and were given feedback (with a 50/50 chance of being right) a few seconds after, all while being scanned by the MRI. In another room, the sender viewed the actual target pictures for each trial, attempting to “send” their contents to the receiver.

The overall results indicated that the receivers had correctly chosen the ESP target about 50% of the time, exactly at the level expected by chance alone, thus indicating no evidence of ESP. As a group, the receivers’ MRI scans also did not reveal any difference in brain activity between correct and incorrect trials, although at least one participant had shown less activity in several brain areas (with most reduction being in the temporal lobe) during correct trials as compared to incorrect trials. Given that this participant was the only one to show this reduction, as well as the scanning artifacts that can potentially occur in MRI, it is difficult to tell whether this result was meaningful or not. In all, Moulton and Kosslyn conclude that their study constitutes strong evidence against ESP.

Although the study was innovative and thus seemed promising, there were a few issues about psi that may account for the reason no clear brain correlates were found. Moulton and Kosslyn seemed to assume from the outset that ESP is fundamentally different from normal sensory perception, in that it should evoke neural patterns distinct from those for sensory perception (p. 183). This does not seem to fit well with what ESP may be trying to tell us when looked at up close. Unlike normal sensory perception, ESP has no characteristic experience to call its own; there is nothing in the ESP experience that clearly tells us that any (sensory) part of the experience is a feature of ESP only. Instead, ESP is multi-sensory, and seems to incorporate the same sensory modes that we use in normal perception, only in the absence of stimuli (e.g., people say that they see or hear things during an ESP experience, just as they would in normal perception). In other words, ESP appears to be sensory perception in borrowed garb. If ESP really does “borrow” the sensory modes of ordinary perception, then we might expect the same brain areas active in ordinary perception to be active in ESP. This possibility may be indicated by the results of two MRI studies focusing on the telepathy-related phenomenon of sensory stimulation at a distance (Richards et al., 2005; Standish et al., 2003). In the studies, a sender was presented with an intense stimulus (a bright flash) in one room, which was expected to activate the main visual regions in the occipital cortex in the back of the brain. In the MRI room, the receiver in the scanner had shown activation of that same visual region at the same time that the sender saw the flash, despite the fact that the sender’s main visual pathway was blocked (their eyes were covered by opaque goggles). Since the stimuli in Moulton and Kosslyn’s study were pictures, we might also expect the visual regions to be active. A look at the MRI images published in their report indicates that they were, both in the psi and non-psi conditions. If the above view has any merit, then it may have been the case that the psi-related activity was simply “masked” by its shared functional regions with visual perception (this also assuming that some degree of ESP was present in their data despite being statistically undetectable; recall that the results on the ESP test were at chance). Also, it is possible that the psi signal is so weak that it is barely indistinguishable from the wide degree of noise that may be present in MRI scanning, again assuming that there was any ESP at all. Given the chance results, we can hardly expect a brain correlate to be visually apparent if there was no evidence for ESP in the study. For these reasons, using brain imaging itself as a direct test for psi may not be a good choice use of the technology.

Furthermore, to really seek out the possible brain correlates of psi, we may have to instead turn to those who have them more often than ordinary people (psychics), and see how their brains may differ (if they do at all) from ordinary people. Some preliminary results seem to suggest very slight structural differences (Persinger et al., 2002; Roll et al., 2002), but this work needs to be followed up on in order to give clearer answers. As much as I admire Kosslyn, it seems that any studies he does in this area will need to take a bit more careful consideration of their underlying assumptions.

- Bryan Williams

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Bryan Williams is a Native American student at the University of New Mexico, where his undergraduate studies have focused on physiological psychology and physics. He is a student affiliate of the Parapsychological Association, a student member of the Society for Scientific Exploration, and a co-moderator of the Psi Society, a Yahoo electronic discussion group for the general public that is devoted to parapsychology. He has been an active contributor to the Global Consciousness Project since 2001, and was the recipient of the Charles T. and Judith A. Tart Student Incentive Award for Parapsychological Research from the Parapsychology Foundation in 2003. As of August 2007, he is the author of seven articles (two co-authored with William G. Roll) that have appeared in the Proceedings of the Parapsychological Association Convention. His native ancestry lies with the Laguna Pueblo in western New Mexico, and the tribe’s long-held beliefs in survival and the concept of spirits is one of the things that spurred his interest in parapsychology.

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Notes

[1] Event-related potentials are tiny changes in electrical voltage detectable along the surface of the scalp, which are usually the result of sensory stimulation.

[2] This is based on findings suggesting that psi experiences tend to be more common among people who are emotionally close or are members of the same family (e.g., Broughton & Alexander, 1997).

References:

Broughton, R. S., & Alexander, C. H. (1997). Autoganzfeld II: An attempted replication of the PRL ganzfeld research. Journal of Parapsychology, 61, 209 – 226.

Don, N. S., McDonough, B. E., & Warren, C. A. (1998). Event-related brain potential (ERP) indicators of unconscious psi: A replication using subjects unselected for psi. Journal of Parapsychology, 62, 127 – 145.

Ehrenwald, J. (1977). Psi phenomena and brain research. In B. B. Wolman (Ed.) Handbook of Parapsychology (pp. 716 – 729). New York: Van Nostrand Reinhold.

Ganis, G., Thompson, W. L., & Kosslyn, S. M. (2004). Brain areas underlying visual mental imagery and visual perception: An fMRI study. Cognitive Brain Research, 20, 226 – 241.

McDonough, B. E., Don, N. S., & Warren, C. A. (2002). Differential event-related potentials to targets and decoys in a guessing task. Journal of Scientific Exploration, 16, 187 – 206.

Moulton, S. T., & Kosslyn, S. M. (2008). Using neuroimaging to resolve the psi debate. Journal of Cognitive Neuroscience, 20, 182 – 192.

Persinger, M. A., Roll, W. G., Tiller, S. G., Koren, S. A., & Cook, C. M. (2002). Remote viewing with the artist Ingo Swann: Neuropsychological profile, electroencephalographic correlates, magnetic resonance imaging (MRI), and possible mechanisms. Perceptual and Motor Skills, 94, 927 – 949.

Richards, T. L., Kozak, L., Johnson, L. C., & Standish, L. J. (2005). Replicable functional magnetic resonance imaging evidence of correlated brain signals between physically and sensory isolated subjects. Journal of Alternative and Complementary Medicine, 11, 955 – 763.

Roll, W. G., Persinger, M. A., Webster, D. L., Tiller, S. G., & Cook, C. M. (2002). Neurobehavioral and neurometabolic (SPECT) correlates of paranormal information: Involvement of the right hemisphere and its sensitivity to weak complex magnetic fields. International Journal of Neuroscience, 112, 197 – 224.

Shermer, M. (2003). Psychic drift. Scientific American, 288, 32.

Standish, L. J., Johnson, L. C., Kozak, L., & Richards, T. (2003). Evidence of correlated functional magnetic resonance imaging signals between distant human brains. Alternative Therapies in Health and Medicine, 9, 128, 122 – 125.

Warren, C. A., McDonough, B. E., & Don, N. S. (1992). Event-related brain potential changes in a psi task. Journal of Parapsychology, 56, 1 – 30.

A Possible Brain Area for OBE's?

Recently there has been a notable increase in the number of research articles relating to the study of out-of-body experiences (OBEs) that have been published in the mainstream literature. Most of these articles have focused on the search for the areas of the brain that may be associated with one common feature of the OBE – seeing one’s own body from a distance. The latest contribution to this search comes from a team of neurosurgeons led by Dr. Dirk De Ridder from the University Hospital of Antwerp, Belgium, and their findings are reported in a case study that was just published in the November 1 issue of the prestigious New England Journal of Medicine (De Ridder et al., 2007). Their report appears to build upon earlier brain studies related to the artificial induction of features often associated with OBEs.

The history of such studies dates back to the early 1940s, when Canadian neurosurgeon Wilder Penfield was able to induce OBE-like sensations in a female epileptic patient by electrically stimulating the right side of her brain in the area around the superior temporal gyrus, a fold along the upper surface of the temporal lobe across the way from the parietal lobe (Penfield & Erickson, 1941). The patient had the feeling of floating away and stated, “I have a queer sensation as if I am not here…As though I were half here and half not here.” Penfield’s work was rediscovered in late 2002 when Dr. Olaf Blanke and his associates at the University Hospital of Geneva, Switzerland, were able to induce similar floating sensations in a female patient being treated for complex partial epilepsy. Electrodes had been implanted into the right side of the patient’s brain around the angular gyrus, an area located at the boundary between the temporal and parietal lobes, to measure her seizures. When she was given mild electrical stimulation in this area via the electrodes, the patient reported instant feelings of “lightness” and “floating” close to the ceiling, and stated, “I see myself lying in bed, from above, but I only see my legs and lower trunk” (Blanke et al., 2002). Blanke and his associates were able to further explore OBEs, as well as the similar experience of autoscopy [1], in this patient and four other neurologic patients in a later study, finding that the patients’ experiences may be associated with damage or impairment in the area surrounding the temporal-parietal lobe junction (Blanke et al., 2004). The area around the temporal-parietal junction appears to be involved in the culling together and processing of sensory information relating to the perception and spatial orientation of one’s own body [2], and Blanke and his associates theorize that OBE-like perceptual illusions may arise from functional disruption as a result of the brain damage or impairment in this area (Blanke et al., 2004, 2005; Blanke & Mohr, 2005).

The latest case study by De Ridder et al. (2007) conceptually reproduces the work of Blanke and his associates. The study involves a 63-year-old male patient being treated for tinnitus [3] by way of electrodes implanted in the area over the temporal-parietal junction. When the right side of his brain was electrically stimulated through the electrodes, the patient experienced a sensation that gave him the impression that his self had separated from his body, moving to a location just behind and to the left of his body. He did not, however, report taking the perspective of his separated “out-of-body” self (i.e., he was still seeing his surroundings from within his own body), nor did he report seeing an image of his own body. On average, the patient’s sensation of leaving the body lasted about 17 seconds, and no changes in his state of consciousness occurred during them. Brain scans using positron-emission tomography (PET) revealed widespread activity in the area around the temporal-parietal junction, near the angular gyrus.

Although the study by De Ridder et al. does provide useful supplementary data on the function of the temporal-parietal junction, I personally think that labeling the patient’s induced sensation as an OBE is something of a misnomer. As noted, the patient did not perceive his surroundings from outside his body, nor did he report seeing his own body, suggesting that his sensations did not take on the classic structure of an OBE. The patient’s separated self was always stationary, and could not be moved voluntarily, whereas people often report being able to move about freely in their out-of-body form in natural OBEs.

Similar arguments can be made about the induced OBE-like sensation in Blanke et al.’s epileptic patients. A close look at their experiences reveals illusory features (e.g., perceiving distortions of the body and shadowy figures) that are not commonly found in natural OBEs and are more suggestive of hallucinations. Thus, the features of naturally occurring OBEs in healthy people and the OBE-like experiences in these epileptic patients can be considered different, and may not be easily comparable. Some attempt has recently been made to artificially induce similar OBE-like perceptions of the body in healthy people, although this has been through the use of virtual reality (Ehrsson, 2007; Lenggenhager et al., 2007), which again does not allow for direct comparisons. Perhaps most central of all, the findings of Blanke et al. and De Ridder et al. still cannot adequately account for ESP-related OBEs in which individuals describe people and events at a distance that are later verified as accurate (Alvarado, 2000, pp. 199 – 200; Tart, 1998), nor can they account for the successful results of studies where some aspect of the OBE was “detected” using physical and animal detectors (Morris et al., 1978; Osis & McCormick, 1980). In the case of Blanke et al., the EEG findings of Tart (1998) and others (Alvarado, 2000, pp. 189 – 190) have yet to be incorporated into their theoretical considerations as to their possible role [4]. In short, while these recent mainstream studies go a way in advancing our knowledge about brain areas involved in body perception, they still have miles to go in adequately explaining complex OBEs.

- Bryan Williams

Notes

[1] Autoscopy is a neurological phenomenon in which an individual reports seeing an illusory duplicate image of their own body in physical space. It has also been traditionally known as the doppelgänger, or “double,” experience. It is distinguished from the OBE in that the individual still perceives their surroundings from a perspective within their own body (whereas the individual perceives things from a perspective somewhere outside their body in the OBE).

[2] This would be consistent with the area’s proximity to the somatosensory cortex, located in the parietal lobe, which processes incoming sensory information from the body’s sensory organs.

[3] Tinnitus is a chronic hearing condition in which noises such as buzzing or ringing are frequently heard in the ear, which in this patient’s case may be caused by an abnormality in the auditory nerve and/or its brain connections.

[4] These EEG studies, conducted with relaxed or sleeping individuals, have found that OBEs tend to be associated with brain wave patterns in the alpha wave range with no rapid eye movement (REM) sleep, suggesting that they occur during a relaxed, sensory-reduced state; and that they are not dream-related.

References

Alvarado, C. S. (2000). Out-of-body experiences. In E. Cardeña, S. J. Lynn, & S. Krippner (Eds.) Varieties of Anomalous Experience: Examining the Scientific Evidence (pp. 183 – 218). Washington, D.C.: American Psychological Association, Inc.

Blanke, O., Landis, T., Spinelli, L., & Seeck, M. (2004). Out-of-body experience and autoscopy of neurological origin. Brain 127(2), March. pp. 243 – 258.

Blanke, O., & Mohr, C. (2005). Out-of-body experience, heautoscopy, and autoscopic hallucination of neurological origin: Implications for neurocognitive mechanisms of corporeal awareness and self consciousness. Brain Research Reviews 50(1), December 1. pp. 184 – 199.

Blanke, O., Mohr, C., Michel, C. M., Pascual-Leone, A., Brugger, P., Seeck, M., Landis, T., & Thut, G. (2005). Linking out-of-body experience and self processing to mental own-body imagery at the temporoparietal junction. Journal of Neuroscience 25(3), January 19. pp. 550 – 557.

Blanke, O., Ortigue, S., Landis, T., & Seeck, M. (2002). Stimulating illusory own-body perceptions. Nature 419(6904), September 19. pp. 269 – 270.

De Ridder, D., Van Laere, K., Dupont, P., Menovsky, T., & Van de Heyning, P. (2007). Visualizing out-of-body experience in the brain. New England Journal of Medicine 357(18), November 1. pp. 1829 – 1833.

Ehrsson, H. H. (2007). The experimental induction of out-of-body experiences. Science 317(5841), August 24. p. 1048.

Lenggenhager, B., Tadi, T., Metzinger, T., & Blanke, O. (2007). Video ergo sum: Manipulating bodily self-consciousness. Science 317(5841), August 24. pp. 1096 – 1099.

Morris, R. L., Harary, S. B., Janis, J., Hartwell, J., & Roll, W. G. (1978). Studies of communication during out-of-body experiences. Journal of the American Society for Psychical Research 72(1), January. pp. 1 – 21.

Osis, K., & McCormick, D. (1980). Kinetic effects at the ostensible location of an out-of-body projection during perceptual testing. Journal of the American Society for Psychical Research 74(3), July. pp. 319 – 329.

Penfield, W., & Erickson, T. C. (1941). Epilepsy and Cerebral Localization. Springfield, IL: Charles C. Thomas.

Tart, C. T. (1998). Six studies of out-of-body experiences. Journal of Near-Death Studies 17(2), Winter. pp. 73 – 99.