PHENOMENOLOGICAL RESEARCH AND ANALYSIS

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• Collection: STARGATE
• Document Number (FOIA) /ESDN (CREST): CIA-RDP96-00787R000300230001-5
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• Document Page Count: 150
• Document Creation Date: November 4, 2016
• Document Release Date: June 11, 1998
• Sequence Number: 1
• Publication Date: February 3, 1993
• Content Type: RP

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Phenomenological Research
and Analysis
Edwin C. May, Ph.D., Wanda L. W. Luke, and Nevin D. Lantz, Ph.D.
AEIWWAF~_ ME 0
Science Applications International Corporation
An Employee-Owned Company
Contract MDA908-91-C-0037
(Client Private)
Submitted by:
Science Applications International Corporation
Cognitive Sciences Laboratory
1010 El Camino Real, Suite 33330P..O.. Box 1412, Menlo oPPark, CA94025 ? [phone]
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TABLE OF CONTENTS
LIST OF FIGURES ..................................................................iii
LIST OF TABLES ....................................................................iv
I OBJECTIVE ................................................................ 1
II BACKGROUND ............................................................ 3
1. Historical Perspective ..................................................... 3
2.
Current Program ........................................................ 4
EXECUTIVE SUMMARY .................................................... 5
1.
Target Dependencies ..................................................... 5
2.
Enhancing AC with Binary lkrgets .......................................... 6
3.
AC in Lucid Dreams ..................................................... 7
4.
Magnetoencephalograph .................................................. 8
5.
Enhancing AC of Binary Targets ........................................... 9
IV TARGET DEPENDENCIES ................................................. 11
1. Objective .............................................................. 11
2. Introduction ........................................................... 11
3. Approach ............................................................. 13
4. Hypotheses ............................................................ 25
5. Results and Discussion .................................................. 25
V ENHANCING DETECTION OF AC OF BINARY TARGETS .................... 31
1. Objective .............................................................. 31
2. Background ............................................................ 31
3. Approach ............................................................. 32
4. Results and Discussion .................................................. 36
VI MA GNETOENCEPHALOGRAPH ........................................... 39
1. Introduction ........................................................... 39
2.
Approach ..................:..........................................
40
3.
Results ................................................................
46
4.
Discussion .............................................................
46
5.
Suggested Research .....................................................
47
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VII,
ENHANCING DETECTION OF AC WITH BINARY ENCODING ...............
49
1.
Objective ..............................................................
49
2.
Background ............................................................
49
3.
Approach .............................................................
49
4.
Results ................................................................
52
5.
Discussions and Conclusions ..............................................
54
VIII
SUBCONTRACTS ..........................................................
57
1.
Edinburgh University ....................................................
57
2.
Psychophysical Research Laboratories (PRL) ...............................
57
3.
The Lucidity Institute ...................................................
58
IX
OTHER ACTIVITY ........................................................
61
1.
Correlations between AC and Geomagnetic Activity ..........................
61
2.
Assessment of Theoretical Constructs ......................................
62
3.
Anomalous Perturbation .................................................
63
4.
Fuzzy Set Analysis ......................................................
63
5.
Empirical Tlaining Overview .............................................
65
6.
A Potential New Training Method .........................................
68
X GLOSSARY ............................................................... 71
REFERENCES ..................................................................... 73
APPENDIX A: Target Elements for the Fuzzy Set Representation of AC Targets .............. 77
APPENDIX B: The Ganzfeld Novice: Four Predictors of Initial ESP Performance ............ 79
APPENDIX C: Impact of the Sender in Ganzfeld Communication:
Meta-Analysis and Power Estimates ...................................... 81
APPENDIX D: Effects of the Sender on Anomalous Communication in the Ganzfeld .......... 83
APPENDIX E: A Preliminary Study of Anomalous Perception During Lucid Dreaming ........ 85
APPENDIX F: Possible Effects of Geomagnetic Fluctuations on the
Timing of Epileptic Seizures ............................................ 87
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LIST OF FIGURES
1. City with a Mosque ............................................................. 14
2. Green Intensity Distribution for the City Target (Macrol-pixel 3,3) .................... 15
3. City with Mosque (I AS I = 1.88 bits) .............................................. 15
4. Pacific Islands (I AS I = 1.45 bits) ................................................. 16
5. Zener Target Cards (Average I AS I = 0.15 bits) ..................................... 16
6. Cluster Diagram for Dynamic Targets ............................................. 17
7. Cluster Diagram for Static Targets ................................................ 18
8. Target and Response with a post hoc Rating of 7 .................................... 22
9. Target and Response with a post hoc Rating of 4 .................................... 23
10. Target and Response with a post hoc Rating of 1 .................................... 24
11. Correlation of Post Hoc Score with Static Target AS ................................. 27
12. Correlation of Post Hoc Score with Dynamic Target AS ............................... 28
13. Two-tailed SA Decision Graph ................................................... 33
14. Operating Characteristic Function-1 Tail ......................................... 34
15. Operating Characteristic Function-2 Tail ......................................... 35
16. Sequence of Events for Stimuli Generation ......................................... 41
17. Phase Calculation for a Single Stimulus ............................................ 43
18. A Two-by-Five, Error Correcting Block Code ....................................... 52
19. Correlation: Levels of Visual Complexi with Post Hoc Ratin and Blind Ranking
for Post Hoc Scores Greater than Three (i.e., evidence for AC) ........................ 64
20. Correlation: Levels of Visual Complexity with Post Hoc Ratings and Blind Rankings
for all Post Hoc Scores .......................................................... 65
21. Experimental Paradigm for Raining .............................................. 70
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LIST OF TABLES
1. Effect Size as a Function of Target Type ........................................... 12
2. Experiment Conditions ......................................................... 19
3. 0-7 Point post hoc Assessment Scale ............................................... 21
4. Effect Sizes ................................................................... 25
5. ANOVA Results ............................................................... 26
6. Receiver 7 .................................................................... 36
7. Receiver 83 ................................................................... 37
8. Receiver 531 .................................................................. 37
9. Hypothesis Testing for Each Receiver ............................................. 46
10. Attributes for Thn Target Packs ................................................... 50
11. Statistics for the Sum-of-Ranks ................................................... 53
12. Statistics for First Place Ranks ................................................... 53
13. Receiver First Place Ranks ...................................................... 54
14. Analyst First Place Ranks ....................................................... 54
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1. OBJECTIVE
The objective of this document is to provide a technical final report on tasks 6.2, "Basic Research," 6.3,
"Applied Research," and 6.4, as listed in the 1991 Statement of Work. This report covers the time peri-
od from 4 February 1991 to 30 June 1992, and includes all subtasks.*
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II. BACKGROUND
With regard to this final report, anomalous mental phenomena (AMP) can be divided into two
broad categories:*
? Anomalous Cognition (AC): A form of information transfer in which all known sensorial stimuli are
absent.
? Anomalous Perturbation (AP): A form of interaction with matter in which all known physical mecha-
nisms are absent.
For the purpose of this document, we define research that is primarily directed at understanding the
nature of AMP (e.g., signal transmission, neurophysiology, etc.) as basic. Research that is primarily
directed at improving the quality of output (e.g., analysis techniques, choice of target material, etc.) as
applied. Basic and applied research domains are broad and are highly interactive and mutually support-
ive. Understanding the technical details of AC phenomena, for example, will improve its application
potential, and likewise, being sensitive to the restrictions of a real-world problem may provide insight
into underlying mechanisms.
1. Historical Perspective
Serious government research of AMP began in 1973 when a modest effort began at SRI International in
Menlo Park, California, to determine if AMP could be verified and to assess the degree to which AMP
could be applied in practical situations.
In fiscal year 1986, SRI International conducted the first coordinated, long-term examination of AC
and AP phenomena. This program had three major objectives:
? Provide incontrovertible evidence for the existence of AC and AP.
? Determine the physiological and physical basis for AC and AP
? Determine the degree to which AC data could be applied in practical situations.
The results and conclusions from this program were as follows:
? The first objective was partially met. An information transfer anomaly (i.e., AC) exists that could not
be explained by inappropriate protocols, incorrect analyses, or fraud; however, there was insufficient
evidence to conclude if AP existed.
? Significant progress was made in meeting the second objective. For example,
(1) The central nervous system (i.e., the brain) of individuals with known AC ability appeared to re-
spond to isolated AC stimuli.
? A definition of terms may be found in the Glossary in Section X on page 71.
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(2) Tvo physical models were constructed. One, called Decision Augmentation Theory, suggests a
possible physical transfer mechanism for AC data. The other is a speculative physical model for
the type of information that is sensed by AC.
Under Ithe same research program, different physical systems were examined for their susceptibility to
putative AP effects. They included single-cell algae, single alpha particles, and electronic devices such
as random number generators and piezoelectric strain gauges. However, in these carefully controlled
experiments, some with experienced AP subjects, no evidence of AP was observed.
2. Current Program
Beginning in February 1991, the sponsor initiated a comprehensive, 18-month investigation of AMP at
Science Applications International Corporation. The primary thrusts of this effort were to:
? Prepare a comprehensive, integrated, 5-year research plan.
? Conduct basic and applied research of AMP.
This finial report provides a comprehensive technical review of this program and suggests possible paths
of inquiry for the future. Major sections within this report are as follows:
? Primary experiments carried out under this program.
? Theoretical and analytical problems.
? Results from three subcontractors and their final reports.
In the following section, we provide an executive summary of this 18-month effort. The executive sum-
mary is designed for the non-technical reader; however, the technical and statistical details can be found
in the body of the report, which begins with Section IV on page 11. References to the statement of work
are given at the beginning of each section and elsewhere, where appropriate.
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III. EXECUTIVE SUMMARY
During the course of this 18-month contract, we conducted five experiments that were designed to ad-
dress specific issues of applied and basic research of AMP Additionally, we conducted a variety of other
investigations that did not require further experimentation. As an example of the latter, we applied
fuzzy set theory to the data from one of the experiments. In this section, we provide a non-technical
summary of the five experiments. Details on all tasks may be found in the body of the report.
A well-designed experiment provides valuable information regardless of the particular outcome. In our
experimental effort during this contract, three studies produced positive outcomes and two did not. All,
however, provided useful guidelines for a follow-on effort.
1. Target Dependencies
1.1 Abstract
The purpose of this experiment was to determine if the quality of AC depends upon an intrinsic target
property, which is called the change of entropy (i.e., the amount of information contained in visual tar-
get material). This was examined for two different target types, photographs and short video clips. A
second objective was to determine if the quality of AC depends upon a sender (i.e., a person who is
isolated from the receiver but who is focusing upon the target material).
The experimental results indicate that the quality of AC does not require a sender to know about, or to
focus his or her attention on, the target. Most importantly, we found a strong correlation between the
quality of the AC and the change of entropy in a target: That is, the more information determined by
information theory contained in the target, the better the AC. Should this result replicate in other ex-
periments, it may be the first indication of an independent physical variable that is fundamental to AC.
If so, this information can be used to vastly improve many other types of AC experiments.
1.2 Approach
Each of five receivers, who had previously demonstrated an AC ability, contributed 40 trials each. All
receivers worked alone from their homes and, at a prearranged time, conducted an AC trial for a target
that was located no less than 500 km away. The target was either a photograph from the National
Geographic magazine or a short clip from a video movie. For half of the trials, the experimenter acted as
a sender, and for all trials, the receivers were unaware of the target type or if there was a sender. After
receiving the responses by facsimile machine, the experimenter mailed each receiver the target as feed-
back. Standard statistical procedures were use to determine whether there were differences in AC
quality among these various conditions.
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1.3 Results
Three of the five receivers independently demonstrated significant evidence for AC. AC of photographs
was statically significant while AC of videos was not, but the difference between them was not large enough
to demonstrate a meaningful preference for the photographs. Using the combined data for all receivers, we
found a significant correlation, as measured by a subjective past hoc technique, between the change of target
entropy (i.e., its information content) and the quality of the response.
We did not observe statistical evidence of AC when the targets were video clips, unlike previous re-
search'by Honorton. We speculate that the receivers, who had rarely been exposed to this type of target,
were unable to discriminate AC from their internal, non-AC experiences. We expand this point in the
body of the report.
1.4 Conclusions
A sender is not an intrinsic requirement of AC; however, a sender might contribute to a conducive envi-
ronment for AC. In addition, it is unknown whether a sender facilitates the reception of specific target
elements. We are currently examining this last point with one of our subcontractors.
The total change of target entropy may be fundamental to the functioning of AC. This may shed impor-
tant light on the question, "What is being sensed by AC?" A number of replications are required, how-
ever, before we can be certain.
2. Enhancing Detection of AC of Binary Targets
2.1 Abstract
It is often thought that targets in AC experiments are much too complicated. Frequently they consist of
photographs of complex scenes such as a city near a mountain. This complexity makes it difficult to
quantitatively analyze the information contained in the target and the response. To eliminate this prob-
lem, researchers can use binary targets (e.g., red/black cards, 0/1), which are completely defined and can
be analyzed by simple statistics. Earlier experiments have used sophisticated mathematical procedures
to enhance the detection of AC of binary targets. The purpose of this experiment was to replicate these
earlier experiments.
In this experiment, one individual who had demonstrated an ability to use AC successfully when the
targets are single binary bits, continued to show his ability. In this case, however, we applied statistical
enhancement techniques from information theory to improve the scoring rate. We need to identify a
more robust statistical technique to improve the overall efficiency because the receiver was required to
"guess" over 21,000 times to reach 100 definite decisions about the binary targets. Of the 100 decisions,
he or she was correct 76 times.
2.2 Approach
Each of three receivers contributed 100 AC trials in a computer-driven, binary AC experiment. One
receiver had demonstrated consistent AC ability in similar experiments, whereas the other two receiv-
ers were inexperienced in binary target AC. It is beyond the scope of this summary to describe the math-
ematical technique since it may be found in detail in the body of the report. Simply stated, a sophisti-
cated procedure called sequential analysis was used to provide many redundant responses to a single
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binary number target (i.e., one or zero). Sequential analysis is particularly sensitive to whether there is
a "burst" of AC and can also determine to within statistical limits if no AC is present.
2.3 Results
The experienced receiver again produced significant evidence of AC of binary targets. That receiver's
hit rate of 51.6% before the application of sequential analysis was improved to 76% as a result of the
analysis. The other two receivers scored at chance expectation.
2.4 Conclusions
We confirmed earlier results that it is possible to enhance detection of AC with binary targets using
sequential analysis. A major difficulty, however, is that the receivers had to register a guess (i.e. by
pressing a computer mouse button) approximately 200 times for each sequential analysis trial. Thus the
technique, while capable of enhancing the detection of AC of binary targets, is particularly inefficient
due to excessive time expenditures.
3. AC in Lucid Dreams
3.1 Abstract
Throughout human experience, people have reported various types of AC in dreams, and laboratory
experiments in the 1970s confirmed that AC may occur in dreams. A lucid dream is defined as one in
which a dreamer becomes aware that she or he is dreaming. Extensive research has confirmed the exis-
tence of lucid dreaming, and that it is possible for the dreamer to signal the waking world about his or
her knowledge about the dream.
The purpose of this pilot study was to determine if AC could occur during lucid dreaming. We found
that AC can occur in lucid dreams. Because the dream-trials did not take place in the laboratory, there
was some difficulty in interpreting the results; however, it was clear that lucid dreams do not inhibit AC
functioning. Because of the success of this experiment, we will be repeating it in an appropriate sleep
laboratory.
3.2 Approach
This experiment was designed as a pilot effort Seven receivers, three experienced in lucid dreaming and
four experienced as AC receivers, participated in the study. The four AC receivers were first trained in lucid
dreaming before the AC trials began. During each trial, a target was selected randomly from the established
pool of National Geographic magazine photographs and doubly sealed in two opaque envelopes. The
dreamer/receiver placed the envelope next to the bed and was instructed, when a dream became lucid, to
"open" the dream envelope (i.e., not the real envelope) while still dreaming, study its content, and report
the experience upon waking. The target was provided as feedback once the data had been presented to the
experimenter. Our standard rank-order analysis was performed to determine if AC occurred in the study.
Since the trials were conducted in each receiver's own bedroom rather than under laboratory conditions, it
was difficult to "induce" a lucid dream on demand. Thus, the total number of trials was small (i.e., 21).
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3.3 Results
Our analysis confirmed that robust AC occurred during the study; however, the number trials was insuf-
ficientjto ascertain whether the lucid dream state improved AC functioning.
3.4 Conclusions
Because the size of the AC effect seen in this study is commensurate with that seen in other AC experi-
ments;! we conclude that the lucid dream state, at least, does not hinder AC functioning. In the body of
the report we suggest a refined experiment to increase the number of lucid dream trials in order to de-
termir}e if lucid dreaming might enhance AC.
4. M,agnetoencephalograph
4.1 Abstract
In this: experiment, we attempted to replicate a study in which we found that slow brain-wave patterns
appeared to be affected by an isolated flashing light. We were unable to confirm that result; however,
important insights resulted from our effort. We did not attempt to show behavioral evidence of AC
while measuring the brain waves. That oversight prevented us from determining if the target system was
valid for AC. An analogy might be that it would be a mistake to use only a light stimulus in a study of the
brain's response to audio information. In addition, we found post hoc that in general the statistical na-
ture of brain waves might have fundamentally prevented us from correctly measuring the instantaneous
slow rhythms. We suggest that an appropriate follow-on experiment, which remedies these two over-
sights, be conducted, because the statistical evidence for AC in other experiments strongly suggests that
the central nervous system must be involved at some level.
4.2 Approach
Eight individuals were exposed to approximately 1,000 isolated light flashes while their brain activity
was being monitored by a magnetoencephalograph, an instrument that measures the magnetic fields
produced by active neurons in the brain. The receivers were chosen to participate in the study based on
their successful participation in other AC investigations. We searched for subtle changes in their brain
activity by measuring various parameters of their alpha rhythms immediately before and immediately
after each light flash. A large alpha rhythm usually indicates that the brain is not particularly attentive
to external events, moving the body, or thinking. More traditional central nervous system research has
shown that any of these activities can cause a change in the alpha rhythm; therefore, it seems reasonable
to expect that if AC is a genuine phenomenon, then it too should induce changes in the alpha rhythm.
'Ib assure that any observed effects were not due to an artifact, an equivalent amount of data (i.e., con-
trol data) was collected without any receiver under the magneteoencephalograph.
4.3 Results
The earlier results could not be verified in this experiment. The mathematical analysis, which had been
used originally, did not reveal any unexpected changes in the subtle properties of the alpha rhythm. The
control data also showed no unexpected results.
We did, however, notice a difficulty in this analysis. Because brain waves are always present, there is
substantial activity that is considered to be noise (i.e., activity that is not directly related to reactions to
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stimuli). Our data contained substantial noise and, unfortunately, our analysis technique was so sensi-
tive to it that any brain response to the isolated flashing lights would not have been observed. Fortu-
nately, we have saved all the raw data from this experiment, so all that is required is to reanalyze the data
with improved techniques. We are currently engaged in that task.
4.4 Conclusion
Until this new analysis is complete, we are unable to determine whether the brain responds to isolated
stimuli. In the body of the report, we suggest that an improved protocol be implemented as part of the
continuing research effort.
5. Enhancing the Detection of AC with Binary Coding
5.1 Abstract
The literature reports many attempts at using various statistical approaches to enhance the detection of
AC. In this experiment, we used a standard technique from information theory (i.e., error correction
through redundancy coding). We were unable to demonstrate that this particular procedure was suc-
cessful. As a result of this experiment, we identified a number of improvements that might be applied in
new studies. For example, in our study, the statistical technique required special targets, which have not
been part of our usual collection. A replication will use a pool of targets that have been successfully used
in other experiments. We also learned that our statistical procedure was not sensitive to correct AC
responses that happened not to be part of the statistical procedure. We have identified a number of new
approaches that correct this problem.
5.2 Approach
Five receivers, who had previously demonstrated AC ability, contributed eight trials each. For each
trial, all receivers worked alone from their homes and, at a convenient time, conducted an AC trial for a
target that was located no less than 500 km away. The targets, which were photographs from the Nation-
al Geographic magazine, were chosen in accordance with specific design criteria and were available for
one week for each trial. lb use error correcting coding, we identified a series of questions that per-
tained to the presence or absence of specified target elements. In this way, a target element, for exam-
ple water, could correspond to a single binary bit in the error correcting code. That is, if water were
present in the target, the value of one would be assigned to it, otherwise it would be assigned a value of
zero. We created ten different sets of five target elements and chose photographs that matched the
presence/absence criteria. The presence or absence of particular target elements was dictated by the
requirements of the 5-bit binary error correcting code that we used in this study. The principle behind
error correcting coding in an AC application is that a receiver could "miss" one of the target elements
but still arrive at the correct target. Error correction is a common technique found in the computer
industry and in deep space communications. We were adapting its use for AC experiments.
After a receiver had completed an AC trial, the response was sent by facsimile to an experimenter in our
laboratory in Menlo Park, CA. By return facsimile, the receiver was sent five questions that required yes/no
answers for the presence or absence of the target elements. Upon the receipt of the completed questionn-
aire, the experimenter sent the photograph back as feedback.
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Three separate analyses were performed on these data. We used blind rank-ordering of the target and
three decoys for each trial to determine if AC occurred during the experiment. In addition, an indepen-
dent amalyst and the receiver separately answered the appropriate questions for each trial. The ana-
lyst's answers were compared with the receiver's answers after applying the error correcting code.
5.3 Results
We were unable to confirm the existence of AC in this experiment using the blind rank-order analysis.
While the receivers' answers to the questionnaires tended to be much more accurate than those provided by
the independent analyst, no answers were good enough to indicate AC using the error correcting coding.
5.4 Conclusions
As was the case in the target-dependencies experiment, we speculate that the receivers, who had rarely
been exposed to the type of targets that were used, were unable to discriminate the AC from their inter-
nal, non-AC experiences. We expand this point in the body of the report. In addition, we noticed that
the answer to a single question depended upon the correct perception of a single target element (i.e.,
water). This element might not be sensed, but others-not included in the five questions-might be
sensed. The method could not take into account these other responses. In the body of the report, we
suggest a new experiment that improves the target pool and connects the individual coding bits to
classed of elements, rather than single elements.
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IV. TARGET DEPENDENCIES
This section comprises the final report for SOW items 6.2.2.1 and 6.2.2.2.
I. Objective
There are two objectives of this pilot study:
(1) Explore the effects of target properties on AC quality.
(2) Determine the degree to which AC quality depends upon a sender.
2. Introduction
The field of parapsychology has been interested in improving the quality of responses to target material
since the 1930's, when J. B. Rhine first began systematic laboratory studies of extra sensory perception.
Since that time, much of the field's effort has been oriented toward psychological factors that may influ-
ence AC. In this section, we review the pertinent literature that categorizes targets that have been used
successfully in AC experiments.
At a recent conference, Delanoy reported on a survey of the literature for successful AC experiments.1
She categorized the target material according to perceptual, psychological, and physical characteristics.
Except for trends related to dynamic, multi-sensory targets, she was unable to observe systematic cor-
relations of AC quality with her target categories.
Watt examined the AC-target question from a psychological perspective.2 She concluded that the best
AC targets should be those that are psychologically meaningful, have emotional impact, and contain
human interest; those targets that have physical features that stand out from their backgrounds or con-
tain movement, novelty, and incongruity also should be good targets.
The difficulty with both the survey of the experimental literature and the psychologically oriented
theoretical approach is that understanding the sources of the variation in AC quality is problematical.
Using a vision analogy, energy sources of visual material are easily understood (i.e., photons); yet, the
percept of vision is not well understood. Psychological and possibly physiological factors influence what
we "see." In AC research, the same difficulty arises. Until we understand what factors influence the AC
percept, results of systematic studies of AC are difficult to interpret.
Yet, in a few cases, some progress has been realized. In 1990, Honorton et al. conducted a careful meta-
analysis of the experimental Ganzfeld literature.3 In Ganzfeld experiments, receivers are placed in a
state of mild sensory isolation and asked to describe their mental imagery. After each trial, the analysis
is performed by the receiver, who is asked to rank order four pre-defined targets, which include the
actual target and three decoys; the chance first-place rank hit rate is 0.25. In 355 trials collected from
241 different receivers, Honorton et W. found a hit rate of 031 (z = 3.89, p G 5 X 10'5) for an effect
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size of 0.141. In addition, he found that AC quality was significantly enhanced when the targets were
video clips from popular movies (i.e., dynamic) as opposed to static photographs (i.e., effect sizes of
0.32 and 0.05, respectively). All trials were conducted with a sender.
In a carefully conducted meta-analysis, Honorton and Ferrari report significant hitting in forced-
choice, precognition experiments.4 They analyzed 53 years of experiments conducted by 62 different
investigators using a limited set of symbols (Zener cards) as target material. Fifty thousand receivers
contributed a total of approximately 2 x 106 individual trials. The overall effect size was 0.020 corre-
sponding to a p -value of 6.3 x 10-25. Similarly, in an earlier review article, Honorton analyzed 7.5 x 105
forced-choice Zener card trials that were collected from 1934 to 1939 and found a significant overall
effect 'size of 0.016?0.0015
Puthoff and Targ published the results of 39 AC real-time trials where the targets were natural scenes in
the San Francisco Bay area .6 The effect size for the 39 trials was 1.15.
Table I summarizes these results for each target type:
Table 1.
Effect Size as a Function of Target Type
Target Type
Thals
Effect Size*
Symbols (Real-Time)
7.5 x 105
0.016 ? 0.001
Symbols (Precognitive)
2.0 x 106
0.020 ? 0.001
Static Photographs
165
0.05 ? 0.08
Dynamic Photographs
190
0.32 ? 0.07
Static Natural Scenes
39
1.15 ? 0.16
"Significance maybe computed as z = Effect Size /Error Shown.
The effect sizes shown in Table 1 are qualitatively monotonically related to target "complexity;" an ap-
propriate quantitative description for target type is currently unknown. Target "complexity," however,
was one of the experimentally observed and theoretically conceived attributes examined by Delanoy
and Watt, respectively.
A number of confounds exist in this database for the effect-size measures. For example, in all but the
Puthoi'f and Thrg study (where targets were natural scenes), the receivers were unselected. That is, they
did not participate in the various experiments on the basis of their known ability as receivers. So, is the
large effect size for the Puthoff and Thrg study because of the accomplished receivers, the natural-scene
targets, or some combination of both? While there are other exceptions, the preponderance of the data
was from unselected individuals. In many of the trials, a sender was concentrating on the target materi-
al, and as in most perception experiments, `psychological factors and boredom contributed to the vari-
ance in the effect sizes. .
In this', pilot experiment, we applied one physical measure to static and dynamic photographs to quantify the
relationship between target type and AC quality. By careful selection of target content, we minimized the
psychologiral factors in perception. In addition, we minimized individual differences by conducting many
trials with each receiver and by only choosing receivers who had previously demonstrated excellent AC skill.
M
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Because the historical database included trials with and without senders, we explored the effects of a
sender on AC quality, as well.
3. Approach
3.1 Target-pool Selection
The static target material for this pilot study was a set of 50 National Geographic magazine photographs.
This set was divided into 10 sets of five photographs that were determined to be visually dissimilar by a
fuzzy set analysis.? The dynamic target material was four sets of five 60 to 90 second clips from popular
video movies. These clips were selected because they had the following characteristics:
? Were thematically coherent.
? Contained obvious geometric elements (e.g., wings of aircraft).
? Were emotionally neutral in that they did not contain obvious arousing material.
The intent of these selection criteria was to control for cognitive surprise, to provide target elements
that are easily sketched, and to control for psychological factors such as perceptual defensiveness.
3.2 Target Preparation
The target variable that was considered in this experiment was the total change of Shannon entropy per
unit area, per unit time. We chose this quantity because it was qualitatively related to the "information"
contained in the target types shown in Table 1, and because it represented a potential physical variable
that is important in the detection of traditional sensory stimuli. In the case of image data, the entropy is
defined as:
Nk -1
Sk = - I pjk logz(pjk ), = 0 if pjk = 0,
j-o
wherepjk is the probability of finding image intensityj of color k. In a standard, digitized, true color
image, each pixel (i.e., picture element) contains eight binary bits of red, green, and blue intensity, re-
spectively. That is, Nk is 256 (i.e., 28) for each k, k = r, & b. The total change of the entropy in differential
form is given by:
dSk = IVSk 1 ? d + Has tit. (1)
That is, the total change of Shannon entropy is the change because of spatial variations in the static
targets added to the change resulting from frame-to-frame variations in the video targets.
We must specify the spatial and temporal resolution before we can compute the total change of entropy
for a real image. Henceforth, we drop the color index, k, and assume that all quantities are computed
for each color and summed.
3.2.1 Static Targets
Tb select the 50 static targets, 100 National Geographic magazine photographs were scanned at 100 dots per
inch (dpi) for eight bits of information of red, green, and blue intensity. At one centimeter spatial resolu-
tion, this scanning density provides 1,550 pixels for each 1-cm2 macro-pixel to compute the j .
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For a specified resolution, the target photograph was divided into an integral number of 1-cm2 macro-
pixels excluding a thin border, if necessary. The entropy for the (ii) macro-pixel was computed as:
N-1
Si j = - I pj logZ(pj
where pf was computed empirically from the pixels in the (i, j) macro-pixel only. For example, consider
the target photograph shown in Figure 1.
CPYRGHT
Figure 2 shows the probability density for the green intensity for macro-pixel (3,3), which is shown as a
white ''square in the upper left-hand corner of Figure 1.` The probability density indicate that most of
the intensity in this patch is near zero value (i.e., no intensity of green in this case). In a similar fashion,
Sy is calculated for the entire scene.
? The priginal photograph was 8.5 X 11 inches, and we have standardized on one centimeter resolution.
. - -
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Figure 2. Green Intensity Distribution for the City Target (Macro-pixel 3,3)
We used a standard algorithm to compute the 2-dimensional spatial gradient of the entropy. Figure 3
shows contours of constant change of entropy (calculated from Equation 1) for the city target. The total
change per unit area is 1.88 bits/cm."
CPYRGHT
3. MY with Mosque ( 133 1 00=9511M
The city target was chosen as an example because it was known (qualitatively) to be a "good" static
photograph for AC trials in earlier research. Figure 4 shows contours of constant change of entropy for
a photograph that was known not to be a "good" AC target.
* In this formalism, entropy is in units of bits and the maximum entropy is 24 bits.
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CPYRGHT
Figure 4. Pacific Islands (I AS 1.45 bits)
For comparison, we show in Figure 5 the traditional Zener cards, which were used as targets in most of
the forced-choice experiments shown in Table 1.
Figure 5. Zener Target Cards (Average. SAS],=;0.15 bits)
3.2.2 Dynamic Targets
The total change of entropy for the dynamic targets was calculated in much the same way. The video
targets were digitized at approximately one frame per second. The spatial term of Equation 1 was com-
puted' exactly as it was for the static targets. The second term was computed from differences between
adjacent 1-second frames. Or,
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) - S11(t) (2)
aat1 M d dt(t) - Islio + AtAt 11
where At is one over the digitizing frame rate (i.e., one second). We can see immediately that the dy-
namic targets have a larger 45 than do the static ones because Equation 2 is zero for all static targets.
3.2.3 Cluster Analysis
Using Equations 1 and 2, we computed AS for all the static and dynamic targets. These targets were
grouped, using standard cluster analysis, into relatively orthoginal clusters of relatively constant AS. Fuzzy
set analysis and inspection were used to construct packets of five visually dissimilar targets from within each
cluster. Our interim report, which is dated 15 February 1992, details the cluster analysis.8 Figures 6 and 7
show the dusters from that report for the dynamic and static targets, respectively.
-- ----
, ~ qqN ,
- - - - - - - - - - - - - I -
- - - - - - - - - - - -
Figure 6. Cluster Diagram for Dynamic Targets
For ease of reading, Figure 7 shows only those 50 static targets that were used to form the constant entropy
clusters, rather than the whole set of 100. We show the computed AS at the end of each duster leaf.
3.3 Target Selection
For a specified target type (e.g., static photographs), a target pack was selected randomly and one target
of the five within that pack was also chosen randomly.
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, Nrl 1
1
girl ;
,
earl
,
,
,
arl
arl ,
awl
ooto
ooro ,
,
--I
1
EIJT
Figure 7. Cluster Diagram for Static Targets
ft
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Aap
3.4 Receiver Selection
Each of five experienced receivers, who have produced significant AC effect sizes in previous investigations,
contributed 40 AC trials (ie., ten trials under each of the conditions shown in Table 2). 'Iivo of the receivers
resided in California while the other three resided in Kansas, New York, and Virginia.
Experiment Conditions
Condition
Target Type
Sender
1
Static
Yes
2
Static
No
3
Dynamic
Yes
4
Dynamic
No
3.5 Sender Selection
The sender for all trials was the principal investigator (PI), who was in Lititz, Pennsylvania.
3.6 Session Protocol
3.6.1 Target Preparation
Prior to beginning the experiment, an experiment coordinator randomly generated a unique set of 20
static and 20 dynamic targets for each of the five receivers. After a target was selected, it was immedi-
ately returned to the pool of possible targets and so could be used again. Within each target type, a
counter balanced set of sender/no sender conditions was also generated. A copy of each target was
placed in an envelope and a trial number, 1 through 40, was written on the outside. Those envelopes
containing targets from the no-sender condition were sealed while those for the sender condition re-
mained unsealed. Each set of 40 targets was packaged separately and shipped to the PI in Pennsylvania.
3.6.2 Trial Schedule
The experiment was conducted over a five month period. Individual schedules were developed with each
receiver so as to cause as little inconvenience to their daily routine as possible.
3.6.3 Session Sequence
For each trial and for each receiver, the PI proceeded as follows:
? Selected the appropriately numbered envelope from the box for the appropriate receiver.
? In the sender condition, looked at the selected target for 15 minutes and attempted to "transmit" it to the
intended receiver during that time period.
? In the no-sender condition for the static targets, placed the unopened envelope on an uncluttered
desk in the PI's office for the 15 minute trial period. In the no-sender condition for the dynamic tar-
gets, played the video repeatedly for 15 minutes with the sound turned off and the TV monitor in
another room.
? At the conclusion of the 15 minute trial period and after the receipt of the receiver's response by fac-
simile, sent a copy of the target material (i.e., either a photograph or video tape) to the receiver by
mail.
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During each trial, the receiver took these actions:
? At the prearranged time, withdrew to a quiet lighted room in his or her home and sat at a desk.
? Fora period lasting up to 30 minutes, wrote and drew his or her impressions of the intended target
material, which was located in Lititz, PA.
? At the end of the AC trial, sent a copy of the response to the PI by facsimile machine.
? By mail, obtained a copy of the target as feedback for the trial. The target copy and original response
were subsequently sent to the experiment coordinator in Menlo Park, CA.
We did not provide specific instructions beyond logistical information to the receivers, because the re-
ceivers were all experienced at this type of task.
When; the experiment coordinator received the receiver's response, all identifying information (i.e.
name;, date, and time of trial) was removed from the response. Periodically during the course of the
experiment the experiment coordinator provided an analyst, who was blind to the target choice, with a
set of responses and associated target packs for analysis. Each target pack consisted of the real target
and four decoy targets of the same target type and similar OS.
3.7 Analysis
3.7.1 Rank-Order
For each trial, there was a single response and its associated target pack (i.e., either static or dynamic).
During the first part of the analysis, a judge, who was blind to the condition and target for the trial, was
asked to rank-order the targets within the given pack. This was a forced rank, so regardless of the quali-
ty of match between the response and targets within the pack, the judge had to assign a first place match
to a response, a second place match to a response, and so on for each of the five targets. The output
from this part of the analysis is a rank-order number (i.e., one to five, one corresponding to a first place
match) for the correct target. As was described above, the targets within each pack were chosen to be
visually different from one another, but they all possessed similar AS. Thus, the rank number was not
biased because of entropy considerations.
For each receiver, target type, and condition there are 10 such rank-order numbers that constitute a
block of data. A rank-order effect size was computed for a block as:
Eij =
(3)
where hj is the average rank for target type i and sender condition j, and io is the expected average
rank,'which for this study is equal to three for all cases. In Equation 3, Nis the number of possible ranks
and is equal to five throughout this study. Reversing the sign in the numerator, Equation 3 reduces to:
3.7.21 Analysis of Variance
A two-way analysis of variance (ANOVA) was computed for each receiver. The two primary variables
weretarget type and sender condition (i.e., ANOVA main effects). Each of these variables possessed
two states as shown in Table 2.
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Possible Effect of Geomagnetic Fluctuations
on the Timing of Epileptic Seizures
JAMES SPOTTISWOODE ERIK TAUBOLL
MICHAEL DUCHOWNY VERNON NEPPE
ADDRESSES: Science Applications International Corporation, Cognitive Science Laboratory, 1010 El Camino
Real Suite 330, Menlo Park, California 94025, USA (S.J.P.Spottiswoode, BSc); Department of Neurology, Rik-
shospitalet 0027, Oslo 1, Norway (E. Tauboll, MD); Department of Neurology, Miami Children's Hospital, Salo-
man Klein Pavilion, Miami, Florida 33155 USA (M.S.Duchowny, MD); Division of Neuropsychiatry, Universi-
ty of Washington, Seattle, Washington 98195 USA (V.M.Neppe, MD). Correspondence to S.J.P.Spottiswoode.
Running head: Geomagnetism and epileptic seizure.
1
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Abstract
Some reports have suggested that epileptic seizures might occur more frequently at times of
enhanced disturbance of the geomagnetic field. This study examines this putative association
using 4161 seizures from 22 epileptic patients where the seizure times were known to within a
day or be'tter. A measure of the geomagnetic fluctuation level for the seizure day, and the
days preceding the seizures, was derived from the geomagnetic index ap. This daily index
was significantly higher on the seizure days than on the day prior to the seizures (p = 0.007)
and slightly higher than for the preceding 10 days (p = 0.1). The effect size for the increase
for the increase of geomagnetic activity on seizure days from the previous days was inhomo-
geneous across this group of patients (p = 0.04), suggesting an uncontrolled factor. However,
a regression of age, sex, seizure type and frequency onto effect size failed to reveal any signif-
icant loadings.
Key words: humans; geomagnetism; epilepsy; seizure; magnetic field
MR~
a
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Introduction
The reasons for the precise timing of epileptic seizures in most patients remain largely
unknown. Statistical studies of seizure timing have failed to identify clearly non-random
patterns such as clustering or periodicity in many patients.1,2 Several explanations for this
have been suggested, including the postulation of an inherently random endogenous
mechanism3 and the possibility that seizure occurrence might be more or less tightly coupled
to an exogenous variable which itself had nearly random statistics. In considering the second
of these hypotheses several workers have looked for a suitable environmental stimulus in the
very low frequency region of the electromagnetic (EM) spectrum. EM waves with
frequencies of 104 Hz or less have several natural sources, including lightning discharges and
ionospheric phenomena, and exhibit a complex distribution in time.4 These long wavelength
EM emissions are detectable everywhere on the globe and penetrate buildings and conducting
structures with little attenuation. Additionally there is some evidence that such low frequency
EM fields can interact with the functioning of biological systems, though the question is far
from settled.5'6 A connection between the triggering of epileptic seizure and low frequency
EM radiation therefore has a certain prima facie plausibility.
Some reports have suggested that epileptic seizure frequency may be correlated with
disturbances of the geomagnetic field (GMF).7'5'9 Fluctuations in the GMF are primarily
driven by changes in the sun's activity and major solar storms give rise to magnetic field
changes of up to 1000 nT at the earth's surface and cover a range of frequencies from
approximately 20 1Hz to 10 Hz .4 The literature on the effects of magnetic field exposure
upon epileptic seizure, while not extensive, contains some suggestive avenues of research.
Venkatraman5 originally suggested that there might be an association between magnetic
storms and epileptic attacks but did not provide any statistics to support this conclusion.
Rajaram & Mitra6 reported that monthly averages of admissions of epileptic cases rose during
periods of increased GMF variation. However, no attempt was made to control for other
factors which influence hospital admissions. According to Keshavan et all a decrease in
convulsive threshold in rats was observed during the GMF variation associated with a solar
eclipse. Persinger'? has suggested that increases in the GMF noise level suppress nocturnal
melatonin levels, precipitating seizures and consequent cardiovascular instability. Significant
correlations have also been reported between epileptic seizure onset and 10 kHz and 28 kHz
atmospherics.11 However a laboratory study of audiogenic seizure susceptible rats failed to
find an association between EM at these frequencies and seizure timing.12 There is also
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evidence, that exposure to relatively intense (105 nT) 60 Hz magnetic fields may actually
inhibit electrically kindled seizures in rats.13
Numerous biological effects from exposure to weak VLF and ULF magnetic fields
have been reported, as is demonstrated in reviews of this literature such as those by Adey5 and
Marino and Becker.6 Interest in the area has recently been stimulated by concern with the
possible carcinogenicity of the 50 and 60 Hz magnetic fields associated with power
generation and distribution. However, the physical mechanisms which might account for
biological sensitivity to weak, low frequency, magnetic fields such as the GMF remain
obscure. Adair14 has calculated the electric fields and other effects in cells and cell
membranes consequent upon 60 Hz magnetic fields of larger amplitude (and frequency) than
GMF fields. He finds that the induced electric fields are considerably smaller than the fields
due to thermal noise. However his arguments do not entirely rule out interactions involving
larger multicellular receptors. It is also possible that the putative association between
enhanced GMF disturbance and epileptic seizure may not be caused by the magnetic field
itself, but rather by some other environmental parameter15 which co-varies with the GMF
changes. The physics of possible mechanisms for electromagnetic triggering of epileptic
seizure is not well enough understood to suggest what frequency or amplitude of EM
radiation ',might be responsible for such an effect. While the epidemiological literature
suggests that a weak connection between seizure timing and enhanced GMF activity may
exist, the; evidence is not statistically assessable.
This study examines one of the hypotheses raised by the earlier literature, specifically
whether epileptic seizure timing in humans is associated with increased GMF fluctuations at
the time of the seizures. Seizure diaries from 22 epileptic patients, containing timings of 4101
seizures, were analyzed to see whether these events occurred at times of enhanced GMF
activity. By using seizure diaries, rather than hospital admission records, many potentially
confounding factors can be avoided. In the light of the earlier studies it was hypothesized that
the days on which epileptic seizures occurred would show higher levels of GMF activity than
that of the preceding days.
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3.7.3 Post-Hoc Assessment
Rank-order analysis does not usually indicate the absolute quality of the AC. For example, a response
which is a near-perfect description of the target receives a rank of one. Yet a response which barely
matches the target, may also receive a rank of one. Table 3 shows the rating scale that we used to per-
form a post hoc assessment of the quality of the AC responses regardless of their rank. The quality of an
AC response is defined as its visual correspondence with the intended target.
Score
Description
7
Excellent correspondence, including good analytical detail, with essentially no
incorrect information.
6
Good correspondence with good analytical information and relatively little
incorrect information.
5
Good correspondence with unambiguous unique matchable elements, but
some incorrect information.
4
Good correspondence with several matchable elements intermixed with
incorrect information.
3
Mixture of correct and incorrect elements, but enough of the former to indicate
receiver has made contact with the target.
2
Some correct elements, but not sufficient to suggest results beyond chance
expectation.
1
Very little correspondence.
0
No correspondence.
lb apply this subjective scale to a target-response trial, an analyst begins with a score of seven and deter-
mines if the description for that score is correct. If not, then the analyst tries a score of six and so on. In this
way the scale is traversed from seven toward zero until the score-description is correct for the trial.
Figures 8 through 10 illustrate the application of this scale and show that the quality of an AC response
is not necessarily indicated by its first-place rank. All three examples were given a rank of one in a blind
analysis. These examples were chosen from the experiment which is being described in this section (i.e.,
Section IV). The response to the waterfall target in Figure 8 included a number of pages of material
about a city and other man-made activity. In all of our analyses, we strictly adhere to the concept that
any material a receiver deletes from the response prior to feedback is not counted in the analysis. Thus,
the response in Figure 8 is considered as complete. The other examples are shown in their entirety.
The scale shown in Table 3 can be divided into two sections, 0-3 and 4-7. The upper portion of the scale
indicates clear contact, presumably by AC means, with the intended target material, while the remain-
der of the scale indicates little or no contact.
We used this scale to provide assessment scores to examine the correlation with the target entropy.
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 CPYRGHT
arr
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1) City, buildings seems to be a big leap
from what I am feeling about the target. I'll
restart
2) Troubled by city feeling. Could be that the
uprights are natural rather than man-
made. In which case the city interpretation
is incorrect and I am feeling MESA. I'll
check verticals.
3) DELETE Lights, structure, structures,
building, and city. We gots a waterfall,
dude.
Figure 8. Target and Response with a Post Hoc Rating of 7
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CPYRGHT
long
white rectangular box like an upside-down
sheet cake
same box
two circular shapes in front, like stepping
stones in a garden
Iona hollow tube, like crashing surf on a
beach - "Hawaii Pipeline"
Figure 9. Target and Response with a Post Hoc Rating of 4
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CPYRGHT
BEGIN-10:30 AM
puffy balls - almost cotton-like. Cottony
puffy, splotches. Movement - whizzing
through these cottony puffs fast. Damp-
ness. A long walkway & metal girders.
I keep wanting to say - specifically - air-
field landing strip. Flat land. Big airplanes
would land here like naval carriers. Has a
broken white line down the center of strip &
you see it straight on - like you would be
coming in for a landing.
Figure 10. Target and Response with a Post Hoc Rating of 1
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4. Hypotheses
4.1 Null Hypothesis
The overall null hypothesis was that i = 0.
4.2 Sender and Target Condition
Using an F-test we tested the hypothesis that the quality of AC does not depend upon a sender regard-
less of target type. Similarly, we used an F-test to test the hypothesis that the quality of AC does not
depend upon target type regardless of the sender condition.
The ANOVA also tests for potential interactions between the target and sender conditions. For exam-
ple, it might be that a sender is required for dynamic targets and not for static ones.
4.3 Target Entropy
The AC quality (i.e., scores greater than three from the post hoc scale in Table 3) of each trial was corre-
lated with targetd S. A significant correlation would indicate that target entropy and AC quality may be
linearly related.
5. Results and Discussion
5.1 Effect Size Analysis
Five receivers completed 40 trials each. Table 4 shows the effect sizes (i.e., z /jn) computed for the 10
trials in each cell. The shaded cells indicate 1-tailed significant results. Receiver 009 showed significant
evidence for AC in the static target, no-sender condition (p < 0.02); receiver 372 in the static target,
sender condition (p< 0.01); and receiver 518 in the static target, no-sender condition (p < 0.05). See
the underscored values in Table 4.
Receiver
Sender
Static
No Sender
Dynamic
No Sender
Static
Sender
Dynamic
009
-0.071
0.141
LL
-0.141
131
-0.071
0.495
-0.071
0.212
372
0.707
-0.283
0.141
-0.354
389
0.141
0.000
0.212
0.000
518
-0.088
0.283
0.530
-0.495
5.2 Analysis of Variance
Table 5 shows the results of an ANOVA on these data. Since there were 10 trials within each cell, the
degrees of freedom are the same for all receivers and, therefore, are only shown in the column headings.
Two receivers show significant main effects. Receiver 372 showed a tendency to favor static over dy-
namic targets (i.e., p !!:t:: 0.03), and receiver 518 showed a tendency to favor no sender (i.e., p < 0.04).
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See the underscored values in Table 5. That is, for these receivers the ANOVA hypothesis that the data
are d rawn from the same distribution is rejected. There were no significant interactions between target
type and sender condition.
ANOVA Results
Receiver
Sender Condition
Target Type
Interaction
F(1,36)
P -Value
F(1,36)
P -Value
F(1,36)
P -Value
009
038
0.5 4
0.68
0.42
2.08
0.16
131
0.18
0.67
1.66
0.21
0.18
0.67
372
1.01
0.32
5.47
Q,Q
0.61
0.44
389
0.01
0.91
0.33
0.57
0.01
0.91
518
4.43
Q.114
0.97
033
0.06
0.81
When we combined the data for static targets regardless of the sender condition (i.e., 100 trials), the
sum-o -ranks was 265 (i.e., exact sum-of-rank probability ofp < 0.0073, effect size = 0.248). The total
sum-o ranks for the dynamic targets was 300 0.5 effect = (i.e., p < - 00; size - 0.000).
5.3 Post Hoc Assessment
Tivo analysts independently rated all 100 trials (i.e., 20 each from five receivers) from the static-target
sessions using the post hoc rating scale shown in Table 3. All differences of assignments were resolved in
discussion, thus the resulting scores represented a reasonable consensus of the visual quality of the AC
for each trial.
We has specified in advance that for the correlation with the change of target entropy, we would only use
the section of the post hoc rating scale that represented definitive, albeit subjective, AC contact with the
target (i.e., scores four through seven). Figure 11 shows a scatter diagram for the past hoc rating and the
associated AS for the 28 trials with static targets that met this requirement. Shown also is a linear least-
squares 11 fit to the data and the linear correlation coefficient correlation (i.e., r = 0.461, df = 26).
This strong correlation suggests that AS is an intrinsic property of a static target and that the quality of
an AC response will be enhanced for targets with large AS. This correlation, however, might be a result
of AS and the past hoc rating independently correlating with the targets' visual complexity. For exam-
ple, an analyst is able to find more matching elements (i.e., a higherpost hoc rating) in a visually complex
target than in a visually simple one. Similarly, AS may be larger for more complex targets. If these hy-
potheses were true, the correlation shown in Figure 11 would not necessarily support the hypothesis
that AS is an important intrinsic target property for successful AC.
Tb check the validity of the correlation, we used a definition of visual complexity that was derived from a
fuzzy set' representation of the target pool? We had previously coded by consensus 131 different poten-
tial target elements for their visual impact on each of the targets in the pool. It is beyond the scope of
this report to provide the details of this technique since the details may be found in reference 7. It suf-
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fices to say, however, that the sigma-count (i.e., the sum of the membership values over all 131 visual
elements) for each target is proportional to its visual complexity. A list of these target elements may be
found in Appendix A.
Figure 11. Correlation of Post Hoc Score with Static Target AS
We computed the liner correlation coefficient for target complexity with the assigned post hoc rating.
For all 100 s tic targets used in this study we found r = 0.049, df = 98, and for target complexity with the
measured AS, we found r = -0.031, df = 98.*
On closer inspection neither of these small correlations is surprising. While it is true that an analyst will
find more matchable elements in a complex target, so also are there many elements that do not match.
Since the rating scale (i.e., Table 3) is sensitive to correct and incorrect elements, the analyst is not
biased by visual complexity.
The change of Shannon entropy is derived from the intensities of the three primary colors (i.e., Equa-
tion 1 on page 13) and is unrelated to large-scale objects or meaning, which is inherent in the definition
of visual complexity. Thus, we would not expect a correlation between AS and visual complexity.
Visual complexity, therefore, cannot account for the correlation shown in Figure 11; thus, we are able to
conclude that the quality of an AC response depends upon the spatial information (i.e., change of Shan-
non entropy) in a static target.
A single analyst scored the 100 responses from the dynamic tar ets using the post hoc scale in Table 3.
Figure 12 shows the scatter diagram for the post hoc scores and the associated AS for the 24 trials with a
score greater than three for the dynamic targets. We found a linear correlation of r = 0.043) df = 22.
? Usingjustthe28datapointsinFgurell,wefmdr--0.216,df-26andr-0.003,df=26fortheoorrelationwiththeposthoc
score and AS, respectively. Since these correlations are negative or very small, they do not alter the conclusion.
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0 0 0
o 0
0
8 8 0
2 ~
ir
r = 0.043
df =22
5 6 7 $
Post Hoc Score
Figure 12. Correlation of Post Hoc Score with Dynamic Target AS.
This small correlation is not consistent with the result derived from the static targets; therefore, we will
examine this case carefully. The total sum of ranks for the dynamic-target case was exactly mean chance
expectation, which may indicate that little AC was observed (see Section 5.2, above). Tiwenty-four
trials, however, received a post hoc score of four or more. We see in Figure 12 that only two trials re-
ceived a score of seven, and those trials were among the lowest AS. It may be that the small correlation is
strongly influenced by these two data points, and a more accurate determination of a putative effect
with dynamic targets requires more trials in a future experiment. To determine if there is a trend with-
out the' two data points with a score of seven, we computed the correlation for the remainder of the data
(i.e., r 0.293, df = 20). Given the past hoc nature of this calculation, all we can conclude is that we
should'conduct a similar experiment with dynamic targets to determine if a fundamental correlation
between AS and AC quality exists, as it does with the static targets.
There is a potentially more important reason that robust AC was not observed in the dynamic target set.
Most ptevious research has considered AC from a "systems" perspective in that the target and receiver
are thought of as a single AC unit.12 This is not particularly productive if we are searching for intrinsic
properties of target systems to guide target selection. An intrinsic target property is one that is in-
herently tied to the target (e.g., size, distance from the receiver, activity, entropy) and devoid of any
externerpretation. Interpretations, such as emotional impact, can be considered as extrinsic prop-
erties of thetaz' et or, moire precisely, intrm cis properties of the receiver. Extrinsic target properties
are critical when AC is viewed from a systems point of view; however, if these properties can be con-
trolled in experiments, then it is possible to examine intrinsic target properties with little confounding
interference from the extrinsic ones.
As an aid in understanding extrinsic noise properties of targets, we define target pool bandwidth as a
qualitative indicator of the number of disparate target elements in the pool. The dynamic targets, which
were clips from video movies, represent a large-bandwidth pool; such disparate scenarios as Superman
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in space, a nature segment on the Grand Canyon, and a James Bond thriller can be included in the same
target pool. Conversely, the well-known Zener cards represent a vary narrow target bandwidth. The
static targets, which are constructed from a collection of National Geographic magazine photographs,
represent an intermediate bandwidth; the size and general content of the material is roughly the same
throw out the pool.
We hypothesize that the bandwidth of the tar ge t pool is a source of intrinsic noise in the receiver. We
assume that the information that is gained by AC is small compared to other sensory mechanisms, and
the primary mental task for a receiver is to discriminate the AC data from internally generated, target-
unrelated information. For large bandwidth target pools that may contain almost anything, a receiver is
unable to censor his/her internal experience. Thus, target-related and target-unrelated material are
equally reported; therefore, large bandwidth pools are extrinsically noisy. Small bandwidth pools are
also extrinsically noisy but for a different reason. If a receiver is cognizant of all of a limited set of target
elements (e.g., Zener cards), then he/she has an internal discrimination problem. All target possibili-
ties are experienced with equal intensity because of knowledge about the pool and vivid short-term
memory. Assuming there is weak AC information about the specific target, then target-extrinsic noise
is generated because of the very low signal-to-noise ratio.
Most of our receivers have participated in many earlier experiments which used the static target pool,
and were unfamiliar with target pools with large bandwidths such as the dynamic pool. Historically, we
have observed AC effect sizes for static targets 50% to 100% larger than we found in this experiment.
The current protocol did not include monitoring the AC trials, and the receivers were blind to the target
tVe. It is impossible to determine from this experiment which factor was predominant, but if the band-
width argument is correct, we would expect a decrease in functioning for even the static targets because
receivers would not be able to self-censor their responses.*
We recommend that a new target pool be developed that limits the bandwidth of the dynamic targets
and that the static targets be specific frames from within the dynamic target pool. In this way, we can
control for target bandwidth effects between the target types. We recommend that a new experiment be
conducted with these new target pools.
5.4 Overall Conclusions
Based upon the results of this pilot experiment, we provide the following tentative conclusions:
? The ANOVA results suggest that a sender is not fundaments required for AC.
? Subject to the caveat suggested in the previous section, the ANOVA results suggest that AC quality
does not depend upon target type.
? AC quality for static targets is proportional to a target's spatial information (i.e., AS).
Because of the importance of determining if AS is an intrinsic target property for all AC targets, we urge
that this study be repeated with the improvements discussed above.
* It is important torecognize that limited, or evencomplete, knowledge of the targetpool cannotbias theblind rank-order statis-
ticbecause itisa differential measurewithin the pool. It may, however, change the mean of theposthoc scores, butcorrelations
are insensitive to means. Thus, correlations based upon the post hoc assessment remain valid.
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ABcT
V. ENHANCING DETECTION OF AC OF
BINARY TARGETS
This section constitutes the final report for SOW item 6.23.3.
1. Objective
The objective of this investigation was to replicate and extend an earlier study that enhanced the detec-
tion of AC of binary targets.
2. Background
In 1984, Puthoff used a majority vote procedure to statistically enhance the detection AC of binary tar-
gets.9 The chance probability of guessing a binary target correctly is 0.50. In Puthoff's experiment, his
best receiver, using AC methods, increased the probability to 60%. Using a majorityvote of five guesses
per bit, the probability of guessing the target correctly was increased by 18.3% from 60 to 71 percent.
In fact, if the probability of guessing a binary target is given by
P =Po+6,
(4)
where 6 is a non-negative constant much, much less than unity and PO = 0.5, then it can be shown that a
majority vote procedure is the most efficient method for obtaining an arbitrarily accurate guess. Let n be
the number of bits in a majorityvote procedure (i.e., n is assumed to be odd). Then the majority vote proba-
bility is given by a binomial sum as:
n "-1
P(n) = p" + (n n l) p"-' (1 - p) + ... + n 2 1 pi' (1 - p) Z ,
where p is the single bit probability given by Equation 4. By choosing n large, P(n) can approach unity.
The problem is that a majorityvote procedure is predicated on the assumption that a is not a function of
time, an assumption that is known not to be true in AC experiments. Ryzl attempted to solve this problem
by modifying a majority vote scheme to include on-line checks.10 He was able to demonstrate a 100% accu-
rate guess of 15 dedmal digits encoded as 50 binary digits (p = 10-15).
In 1985, Puthoff, May, and Thomson used a well-known technique called sequential analysis (SA) and,
for one receiver, realized a 3.7% enhancement 53.6 to 55.6 percent in a binary AC experiment.11 Dif-
fering from the usual statistical analysis, SA does not require that the sample size be specified in ad-
vance; however, by adjusting certain SA parameters, it is possible to set the expected number of trials in
the processes.
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In this',pilot experiment, we set the expected number of trials in an SA algorithm to match the temporal
variation of 6(t). Thus, we expected to realize a significant enhancement of binary AC.
3. Approach
3.1 Theoretical
Let p be the probability that a binary random variable has the value one. With SA, we test the hypothesis Hp:
p = pr, against the hypothesis HI: p = pj. After accepting Hp as true, define a to tie the probability that Hp
was false and that HI was true (i.e., Type I error). Likewise, after accepting HI as true, define ig to be the
probability that HI was false and that Hp was true (i.e., Type II error). Let n be the current sample number.
With parameterspp, P1, a, and/, SA defines two lines as follows:
y1 = a n + bl and yo =an - bo, where
ln(-?O)
1-P1
ln(~)
bo = A
In a
and b 1 = G , where
(5)
d = ln(~ - + In/' - Po
P1
Let Nbe the accumulated number of ones in a Bernoulli sampling situation. Then the general SA deci-
sion algorithm is as follows:
? Collect one binary sample and add its value (i.e., zero or one) to the accumulated number of ones, N.
? Compare the accumulated value to y1 and yo in Equation 5.
? If N y1 (n), then stop the sampling and conclude that hypothesis HI is true wiith a risk of being wrong
of/i.
? IfNT yo (n), then stop the sampling and conclude that hypothesisH0 is true with a risk of beingwrong
of a.
? If yp(n) < N < yj (n), then continuing sampling.
In the general theory of SA, this decision process always converges, and the expected number of samples
for a decision in favor of each hypothesis is given by.
EHO (n) - (1 - a) ln(1) + a In(1j) and
Po In(o) - (1 - Po) ln(r.)
(6)
ln(1p"-a) + (1 - )In('a?)
EH, (n) _
I-PO
P1 in( ) - (1 - 01-71
For an arbitrary value of p, we compute the probability that the SA algorithm will decide in favor of
hypothesis H1 (i.e., the operating characteristic function-OC) as:
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k
OC(p) = 1 -
(41)h
1-()
1-p0
-1
where p is given by
()
p(h) _ , where -- s h 5 +00.
(pp)~ - (ti-Pol
1-pl) h
(7)
3.2 A Two-tailed Example of Sequential Analysis
In this section we modify the formalism of SA to include a measure of the difference between the accu-
mulated number of ones and the expected number of ones. This will allow a two-tailed application of
SA. The only modification that is necessary to Equation 5 is that the slope, a, is now given by.
ln(1-p1) (8)
In this example we assume that a = P, so that the curves (see Figure 13) that define the decision algo-
rithm are symmetric. Let 8 be the accumulated excess number of ones (i.e., the number of ones minus
the expected number of ones). In the two-tailed case, the two hypotheses that are tested by SA become
Ho:p=po, andH1:p=P1 orp =1 -P1.
Figure 13. Tivo-tailed SA Decision Graph
When 8 enters either Region 1 or 2, stop the sampling and assume HI is true with a Type II error of
Likewise, if 8 enters Region 3, stop the sampling and assume Ho is true with a Type I error of a.
3.3 Hypotheses
The two hypotheses that were tested in this experiment are:
(1) Ho:p=po=0.5,and
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(2) H1:P=P1 =0.6or,p=1 -pl =0.4.
We chose Pp = 0.5 because that is the expected hit rate for binary guessing, and we chose p! = 0.6 because
that was the apprmtimate individual hit rate reported in Ryzl's and in Puthoff's i periment. We adjusted
the values of a and A such that the expected number of samples per SA decision was approximately 40, a
value that is consistent with anecdotal reports of how many trials can be collectu1 during a single session
before A a receiver becomes bored with the task. Figure 14 shows the OC curve for these parameters.
0.4
a
0
a
p0 = 0.50
pl = 0.60
a = 0.20
F-1 0 =0.20
Majority Vote of 5 --.
0.0 0.2 0.4 0.6 0.8 1.0
Event Probability
-+- Sequential Analysis
Figure 14. Operating Characteristic Function -1 Tail
For co'1inparison, we also show in Figure 14 the majority-vote-of-five curve that was successfully
employed in Puthoff's experiment. Using the SA method, we expect an enhancement of approximately
42% at, 0.6 hitting rate compared to the 18% seen by Puthoff. In addition, our two-tailed formalism
allows receiver to use AC to detect either a binary one or a binary zero. Another advantage of SA over
majority vote can be see from Figure 14. Forp less than 0.5, the Type I error is sharply reduced. Thus,
the false-positive decisions are reduced accordingly.
Figure 15 shows the OC for the 2-tailed SA scheme displayed in Figure 13. For this calculation, we as-
sume that the accumulated deviations result in an extreme decision (i.e., lines =L yl). That is, under the
null hypothesis, 80% of the decisions will be on the inner decision lines in Figure 13. Of the 20% re-
maining decisions, 50% will strike the upper decision line. This curve, therefore, demonstrates that
under the null hypothesis there will be no enhancement.
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3.4 Protocol
SA Parameters
p0 =0.40
p1 =0.60
a =0.20
I; =0.20
0.0 0.2 0.4 0.6 0.8 1.0
Event Probability
Figure 15. Operating Characteristic Function - 2 -Tail
3.4.1 Receiver Selection
Three receivers participated in this study. One (receiver 531) was selected because that individual had pro-
duced statistically significant results in earlier similar experiments.12,13 TWo receivers (7 and 83) were se-
lected because of their interest and because of successes in free-response AC experiments.
3.4.2 Target Selection
A Sun Microsystem's SPARC workstation used a feedback shift register algorithm to generate a single
binary target for each SA decision trial.14
3.4.3 Trial Definition
A trial was defined as an assertive SA decision. That is, eitherp = pi or p =1- pi. Decisions resulting
in p = pj were tabulated, but otherwise ignored. Each receiver contributed 100 trials.
3.4.4 Sample Definition
An experimental control program oscillated a single binary bit between one and zero as rapidly as pos-
sible. When a mouse button was pressed, the state of that oscillating bit represented the value of the
single sample.
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3.4.5 Trial Protocol
Each trial proceeded as follows:
(1) The receiver started the experimental control program, which asked for the receiver's name.
(2) The program determined if the current trial was a continuation of an earlier trial, or the beginning
of a new one.
(3) If the trial was being continued from an earlier session, the program read the previously saved data
and indicated that the receiver may begin.
(4) $f the trial was a new one, the computer randomly displayed a new target and indicated that the
receiver may begin.
(5) The receiver pressed a mouse button to indicate when the oscillating brit matched the predeter-
mined trial target bit. We emphasized that the task for the receiver is a precognition one; press the
r1ouse button at a time when the oscillating bit matches the displayed target bit.
(6) The value of this sample bit was used as input to the SA algorithm.
(7) The receiver ended the session at any time (i.e., either within a trial or at the end of one).
3.4.6 Analysis
The analysis was defined by the SA algorithm. The control program recorded the number of matches
between SA decisions (i.e., either one or zero) and the trial target bit. It also recorded the total number
of button presses and the number ofpn decisions that occurred during the 100 SA trials.
The binomial distribution was used to calculate a p-value for each receiver, but the normal approxima-
tion to the binomial distribution was used to compute the effect sizes.
4. Rgsults and Discussion
Tables 6 through 8 show the results for receivers 7, 83, and 531, respectively. The z-scores for the binomial
methods of analysis are shown for comparison only, since SA does not specify the number of samples, the
results tend to be inflated from their correct value. The binomial (decision) method included only those
samples that led to a definite SA decision, whereas the binomial (all) method included all samples.
Analysis Method
Hits
Thals
Rate
Z-Score
e
Sequential Analysis
49
101
0.485
-0.299
-0.030
B
L inomial (decision)
2,256
4,569
0.494
-0.&43
-0.125
Binomial (all)
s
7,856
15,747
0.499
-0.2.-9
-0.002
Receiver 7 inadvertently produced one extra trial; however, it did not affect the: overall score of 'mean
chance expectation (i.e., rate = 0.50). As shown in Section V.3.3, SA did not inflate the chance results
beyond what was expected..
Mq
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Analysis Method
Hits
Trials
Rate
Z-Score
8
Sequential Analysis
44
100
0.440
-1.20
-0.120
Binomial (decision)
1,916
3,966
0.483
-2.13
-0.034
Binomial (all)
9,422
18,937
0.498
-0.68
-0.005
Receiver 83 produced an overall score of mean chance expectation.
Analysis Method
Hits
Trials
Rate
Z-Score
e
Sequential Analysis
76
100
0.760
5.20
0.520
Binomial (decision)
2,842
5,059
0.562
8.79
0.124
Binomial (all)
11,008
21,337
0.516
4.65
0.032
Receiver 531 produced an overall significant score (i.e. Z = 5.2 p G 1 X 10-? e = 052). This receiver is
experienced at computer tasks and the result is consistent with his historical performance. A raw hit
rate of 0.516 is what is usually seen,12 and the effect size of 0.032 is consistent with other forced choice
AC experiments.
Although only one receiver of three produced significant evidence of AC, the result is illustrative of the
technique, and because of 531's previous performance, we consider that this result is not likely to be
spurious. While a 16-fold enhancement of effect size was realized by the SA method, it is particularly
inefficient; to obtain 100 decisions, 531 pressed the mouse button 21,333 times for an efficiency of
0.47%. It is possible that the efficiency could be improved if the basic SA method could include some
adaptive method. That is, the parameters of the analysis could be modified on the basis of the recent
scoring rate. If sufficient improvement could be realized, this method might be incorporated as an aid
in decision making in practical applications.
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VI. MAGNETOENCEPHALOGRAPH
This section comprises the final report for SOW item 6.2.1.
1. Introduction
In a series of electroencephalograph (EEG) experiments conducted at SRI International beginning in
1974, the central nervous system (CNS) of individuals was found to respond to remote and isolated visu-
al stimuli (i.e., a flashing light).15,16.17 In the first experiment, during randomly interleaved 10-second
epochs (i.e., trials), either a flashing light (16 Hz) or no light was present in a sensorially and physically
isolated room. Significant decreases of occipital alpha power of isolated receivers were observed by
Rebert and Tiirner.15 Tivo replications were conducted in collaboration with Galin and Ornstein at the
Langley Porter Neuropsychiatric Institute. As reported by May et al., the results were inconclusive; the
first replication confirmed the Rebert and Tbrner finding, a decrease of alpha power concomitant with
the flashing light, but the second replication attempt found an increase in alpha power.17
Under another program in FY 1988, SRI International and a biophysics group at a national laboratory
conducted an experiment using the magnetoencephalograph (MEG) technique. This experiment was
designed as a conceptual extension of the May et al. EEG experiment, although there were significant
differences in the protocol. Tivo types of stimuli were randomly presented to an isolated sender while
MEG data were collected from a receiver. The experimental stimulus (i.e., remote stimulus) was a 5-cm
square, linear, vertical sinusoidal grating lasting 100 milliseconds. The second stimulus, a control stimu-
lus (i.e., pseudostimulus), was simply a time marker corresponding to a blank screen in the data stream,
and was also presented to the sender. There was no change in the alpha power, as reported by May et
al., but a post hoc analysis revealed a root-mean-square average phase shift of the dominant alpha fre-
quency-18 A key result of that experiment was that similar "anomalous" phase shifts were obtained for
the remote stimuli and the pseudostimuli. Three candidate explanations for these results were sug-
gested. The observed phase shifts might have been:
? Spurious (i.e., statistical deviations within chance expectations)
? Electromagnetic artifacts
? Evidence of anomalous cognition
In order to determine which of these three candidate explanations was correct, we replicated the study at
the national laboratory as part of this current effort.
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2. Approach
In this section, we provide details of the replication of the 1988 MEG experiment.
2.1 Replication Protocol
2.1.1 Number of Trials
We assumed that the observed trial effect sizes that were reported for the previous MEG study resulted
from a putative AC effect.18 Under the remote stimulus condition (i.e., approximately 1,100 trials) we
found that the trial-level effect size was 0.060 ? 0.030. In statistical terms, this did not exceed mean
chance'' expectation.
Tb determine the number of trials necessary to provide a confident replication of the previous experi-
ment, we conservatively used the observed effect size minus one standard deviaidon (i.e., 0.030). Using
traditional statistical power analysis, we found that the probability of observing a significant AC effect
in 1,100 trials was 0.258. Conversely, if we require 95% confidence, a significant AC effect could be
observed in 12,026 trials, or approximately 120 blocks of 100 trials each.
Twelve individuals were initially identified as receivers for the formal replication; however, because of
scheduling difficulties only eight participated in the study. Seven receivers contributed ten blocks each,
and one receiver contributed five blocks. The statistical power for 7,500 trials was 0.83, which is the
probability of a significant :replication over the total of 75 blocks. In this case, a given receiver had a 60%
chance of demonstrating an independently significant result if the AC hypothesis is true.
2.1.2 Receiver Selection
Eight experienced receivers, who either participated in the earlier MEG study or were known to be
"good" receivers from other investigations, participated in the study.
2.1.3 Sender Selection
An SAIL experimenter acted as sender throughout the study. While it is assumed that a sender is not
necessary for AC, it may have a vital psychological function.
2.1.4 Stimuli
The following two types of stimuli were generated by a PC, and consisted of an internal image that could
be sent to a standard TV monitor for display:
? Remote stimuli (RS). A low spatial-frequency sinusoidal grating lasting 100 milliseconds was used as
a remote stimulus.
? Pseudo Stimuli (PS). All data bytes corresponding to the pseudo stimuli were zero. Thus, the entire
video image was a blank screen corresponding to a "time marker" in the data.
An HP workstation controlled the collection of data and the presentation of the stimuli. Using a multi-
ple congruent pseudo random algorithm (i.e., Rn+1 = ao x Rn + b0, where ao and bo are constants, and
0 s R < 1.0), the nth + 1 stimulus was generated 3.0 + 4.5 x R,a+1 seconds after the nth stimulus. The
algorithm was seeded from the system clock. The HP notified the PC of the type and time for a stimulus.
The PC waited until the next vertical retrace signal from its hardware-video-output board; switched
pointers within the retrace cycle from the blank inter-stimulus (IS) frame buffer to one which contains
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either the RS or PS; and reset the buffer pointers after 100 ms (i.e., the stimulus duration = 100 ms).
Figure 16 shows this sequence in graphical form.
Stimulus Buffer Pointers Standard 30 Hz
Type RS/PS RS Buffer Interleaved
Stimulus Initiation
IS Buffer Output Buffer
PS Buffer V
Figure 16. Sequence of Events for Stimuli Generation
2.1.5 Placement of the Seven-Sensor MEG Array
The placement of the seven-sensor MEG array was determined by an individual receiver's response to a
direct light stimulus. While being stimulated by randomly interleaved low and high spatial-frequency
gratings, sufficient stimuli (e.g., 30 to 50 of each type) were collected to produce good signal-to-noise
responses. The position of the sensor array, relative to head-based coordinates, was recorded manually
on a skull cap, so that the array could be repositioned accurately during subsequent experimental
blocks. The array positions that were used during the RS blocks were determined by the maximum re-
sponse to these direct stimuli. For this portion of the experiment, the stimuli were generated three to
four times faster (i.e., - 1 per second) than in the AC portion of the experiment.
2.1.6 Session Protocol
The session protocol was a follows:
(1) Using the marking on the skull cap, the MEG array was repositioned as close as possible to the
original calibration location.
(2) Its position was confirmed with direct stimuli, and adjustments were made, if they were necessary.
(3) The designated sender was positioned in front of the remote monitor, which was located approxi-
mately 40 in from the receiver.
(4) The video monitor, which presented the direct stimuli, was turned off.
(5) The receiver was instructed to relax with eyes closed. In addition, the receiver was given a few
possible strategies that included focusing attention on the display that the sender was observing,
on the sender, or on both.
(6) The receiver was notified, by intercom, that the run was about to begin.
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(7) The run began and seven channels of MEG data and one channel of stimulus data were collected for
two minutes. The raw data were saved to disk, and the appropriate parameters for the next run
were entered into the log book and the control program.
(8) After five runs, an experimenter quietly entered the MEG room, checked the MEG position, and
readjusted it, if necessary. No communication about the status of the e3 periment was provided.
(9) Five additional runs were collected.
(10) At the end of the block, the receiver entered the control room and was shown a computer display of
the results of the last run. The experimenter pointed out interesting portions of the display, but
cautioned that the final results required careful analysis of all the runs, not just the last one.
2.1.7 Controls
Tivo types of controls were used in this experiment to assure the validity of the experimental results:
? Within-block. The data in the inter-stimulus times (IS) were used as a within-block control.
? Between-block. Using a counterbalanced random protocol, either immediately before or immediate-
ly after each 20-minute experiment block, an additional block of ten runs was taken under the same
conditions as the experiment block, but without the receiver under the MEG. The sender, however,
was "sending" as before.
2.1.811 Data Recording
Along with the experimental parameters, eight channels of 200 per second data were digitally recorded
for later analysis (i.e., seven channels of MEG data and one channel of stimulus data).
2.1.9 Analysis
2.1.9.1 Overview
A block of data was ten, 2-minute runs. Each block contained approximately 100 RS and 100 PS
stimuli, respectively, from each of the seven sensors. The following was computed for each stimulus
type and for each sensor:
? Time averages for 0.5 second prestimulus to 0.5 second poststimulus.
? Separate average power spectra for the prestimulus and poststimulus periods.
? Averages of the phase shift observed at the dominant a-frequency, which was determined from the
centroid of the peak with the largest area above "background" for experiment blocks; 10.0 Hz was
used. for the between-block controls. The relative phase shift for a single stimulus is defined in Figure
17. The RMS average was computed over the total number of stimuli in the block.
The RIMS average phase was the dependent variable for the block. A Monte Carlo calculation was used to
determine whether the observed phase shifts deviated from those observed at random times throughout the
rest of the block-data record. Each Monte Carlo pass computed the RMS phase omcr random entry points,
which were determined by the same timing algorithm described above, into the sune 20-minute data set.
The timing algorithm was the same one used during the data collection; however, a new seed started the
process on each pass.
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x(t) linear Process Y(t)
Let: X(v) - FFT [x(t)l Then:
Y(v) - FFT [y(r)l
Phase - y'(v) - tan -t Int H(v)
e v
Poststimulus: y(t)
*4Y
H(v) = Y
Gain - IH(v)I
Figure 17. Phase Calculation for a Single Stimulus
Statistics (e.g., p-values, z-scores) were computed from the distribution of RMS phases derived from
the Monte-Carlo-pass distribution.
Conceptually, a 2-tailed z-score was calculated from a Monte Carlo distribution of phase shifts in the
following way: Let pp and o y be the mean and standard deviation of the Monte Carlo phase shift dis-
tribution, and To be the observed RMS phase shift. Since the distribution of averages is approximately
normal, compute:
Z = VI0 ,U and P = J e 2dg.
z
Since we did not specify a direction for a change in phase, the p-value for the block was given by.
p=2xP,
and the two-tailed z-score was computed from the inverse normal distribution for P In the experiment, the
empirical value of P was used. That is, the number of Monte Carlo-derived RMS phases that were greater
than or equal to the observed RMS phase was divided by the total number of Monte Carlo passes. There-
fore, the 1-a error estimate in P were computed from the binomial distribution for proportions. Or
P(1 -P)
1-a error in P = M
where M is the number of Monte Carlo passes.
For this replication, the analyst was "blind" to the identity of the receiver, the date, the experiment
condition (i.e., experimental or control run), and the stimulus type.
2.1.9.2 Details of the Analysis
Consider N blocks of experimental data. Let ?j, be the number of remote stimuli r for block j, and njp be
the number of pseudo stimuli p in block j. Similarly, define rjr and rjp as the corresponding effect sizes
for block j. We define the weighted effect size for each stimulus type, k, as
N
rk = >WJkE/k,
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nJk
Wjk= N
E nJk
k=r,p.
Tests Against the Null Hypothesis: The Average Effect Size e = 0. Since the experimental effect sizes,
ejk, are derived from normally distributed data (i.e., Monte Carlo calculations of the RMS phase shift),
then we know the standard error for each elk is
nJk
Thus, the variance of the weighted average effect size is
The z-sccore associated with a is
N ,/
Var((k) = I w Var( k) = N 1
J-1
n1k
-R Var(1k)
(1)
(2)
Equation 2 is used to test the average effect sizes of the RS and PS for the experimental and control
conditions against the null hypothesis of i = 0 for the experimental and control conditions.
Tests Against the the Null Hypothesis: s(RS) - e(PS) = 0. Within a given condition we cannot assume
that the phase shifts from an RS are independent from those associated with a PS. Thus, hypotheses
tests th at do not account for potential correlations between the RS and PS are i iappropriate. Because
of the simplicity of the individual ejk, we can compute the exact variance for the differences as follows.
Let thedifference between the effect sizes for RS and PS be
d1 = e1, - e1P.
Since there usually are a different number of stimuli for RS and PS, we define a weighting factor for the dj as
nj
S1J= N
Z nJ
J-1
n= nJ. X nJP
1 nJ, + nJP
Then the weighted mean difference is given by
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The variance ofd is given by:
N
Var(3) Q; Var(d),
-,
Var(d,) = Var(e,,) + Var(E, p) - 2 Cov(E,,,e,,),
Cov(EJ?E/P) = Q,,, Var(E,,) - Var(e1,).
Combining these equations with the definition for the variance of the effect size, gives the Yar(d) as
N r
Var(f) _ QJ L 1L + j - 2 Q,,, Var(E,,) ? Var(ej )],
J-i
a
17
Yar(a)
(3)
rests Against the Null Hypothesis: e(Experiment) -e(Control) = 0. Tb compare each stimulus type in
the experimental and control conditions, we assume that the data are independent. Thus, the z-score
for the difference is given by
17 a
Var(tk(e)) + Var(-r (c))
L "Jk() E ~,k~c)
J-1 J-1
(4)
where e and c represent the experiment and after-block control conditions, respectively, d is the
weighted difference for the stimulus type in the experiment and control conditions.
Equation 4 is used to test the difference between experimental blocks and their corresponding control
blocks. , , " - " '
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Table ',9 summarizes these results.
Hypothesis Testing for Each Receiver
Hypothesis
Thst Quantity
1. RS(e) have no effect.
n J, (e) 1sX P00` eIROp ft re ' s TV
monitors. The four elements in the judging pool are presented in one of four
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random sequences. The receiver is prompted to identify whatever
correspondences they perceive between their ganzfeld mentation and each of the
four potential targets. The receiver is given the option to view any or all of the
elements in the judging pool as many times as desired, then procedes to perform
the blind judging task. The program displays a judging scale (Figure 8) on the
receiver's monitor for each of the four possible targets in the judging pool. The
judging scale shows a brief descriptive name for each target, a thermometer-style
rating scale, and three buttons. Using a mini joystick, the receiver rates the degree
of perceived similarity between each potential target and their mentation. The
scale ranges from 0% to 100% and the current value of the scale is displayed both
numerically and graphically as the receiver clicks either the left or right arrow
buttons.
Figure 8. Video Ganzfeld Judging Scale
Judging Scale
-~'ii111Y111 W Yli?~`:IiY W ki:.i~::i11Y V:`~'riYit[-2]YYIY~:iYYYk'L'vi
?::O {.hii:W..{x:?:S'{{#?ti:?.v'Y{i:ti??}.; {,{.. vif?',.. ..?.~4.: i.:?~.
::~'{i:::{v\v;{4:{?::{{.ii.{.i: n{?ii`.{vv:ii'.{v:.v'+T::..tii`8, y:i:?ki^:iif}{{?:'.
When the receiver is satisfied with the rating assigned, she or he presses the
"OK" button. The judging procedure is repeated for each of the four potential
targets in the judging pool. The program checks for tied ratings and prompts the
receiver to re-rate in the event of a tie. Once the receiver has rated all of the
elements in the judging pool, the program converts the ratings to ranks and stores
the ratings and ranks as fields in the session database record. The program
calculates a standardized rating (z-score) based on the difference between the
rating assigned to the correct target and the mean of the "three decoy ratings
divided by the standard deviation of of all four ratings (Stanford & Sargent,
1983).
The program times the duration of the judging procedure from initial
presentation of the four judging pool elements to completion and adds it to the
session database record.
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current session," "Session log," and "Check System." The abort session option is
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used to terminate an ongoing session prior to completion. Premature termination
of a session may only occur in the event of a protocol violation (e.g., sender or
receiver leaving their respective rooms after the beginning of the session),
equipment failure, or an emergency situation. When "Abort session" is selected,
the system displays a dialog box prompting the experimenter to enter his or her
security password and indicate the specific reason for terminating the session. This
information, along with the the participant's ID, and the date and time are written
to a series abort file. Abort session is not available after the blind judging
procedure has been completed.
Session log enables the experimenter to register comments concerning the
current session and "Check System" performs diagnostics on the audiovisual and
randomization functions of the system.
TargIt Stimuli
Target Pool
Following Honorton, et al., (1990), target stimuli consist of brief (35-80 sec.) video
excerpts from a variety of films and documentaries. Two target pools, each containing 40
targets' (10 judging pools of four targets each), have been prepared. Each target pool is stored
on one, 90-min..5 in. VHS videocassette tape. Digital addresses on each videocassette enable
frame-accurate access of targets via the video ganzfeld/PC-VCR computer link. A unique
digital header is recorded on each videocassette and is read by the computer at the onset of
each experimental session. Accidental insertion of a videocassette other than that containing
the designated target pool is automatically detected and results in termination of the session.
Based on an analysis of target success-rates in the PRL experiments, approximately half of
the targets were taken from among the most successful PRL dynamic targets. The remainder
of the targets are new. Pool A will be exclusively used for the Novice Screening Series and
Pool B'B will be used for the Sender Comparison Series. Since the latter series will include
sessions in which the sender will be exposed only to the audio soundtrack portion of the
target, the elements in Pool B include a high proportion of targets with descriptive narration.
Measurement of Target Attributes
The quantification of complex target material has long eluded investigators of anomalous
communication. The quantitative characterization of target attributes is important for a
number of reasons, for example:
? Development of more statistically powerful methods, for assessing target/description
correspondences,
? Detection and elimination of targets associated with strong response bias (i.e., targets
that tend to be selected or rejected because of their intrinsic characteristics),
? Detection and elimination of targets that activate perceptual defense,
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? Identification of elements of target environments that may be especially amenable to
retrieval via anomalous communication.
Recently, major advances have been made with regard to certain aspects of this problem as
it specifically applies to remote viewing studies (May, et al., 1985; 1990). While aspects of
May's conceptual schema can also be applied to ganzfeld research, there are two aspects of
the latter that call for a somewhat different approach: (1) The standard ganzfeld mentation
protocol focuses upon the elicitation of unconstrained spontaneous imagery rather than an
explicit focus upon describing the target. (2) The video targets are themselves quite different
from those typically used in remote viewing research: They include auditory components (e.g.,
music, dialogue, narration, sound effects), occasionally major transitions in perspective, highly
evocative dramatic and comedic scenes, etc.
For these reasons, we have adopted a somewhat different approach, consisting of two
distinct aspects: (1) Specific descriptors tailored to the content of the target pools, and (2)
generic characteristics derived from environmental psychology.
Content-based Descriptors
Each target has been coded with respect to Theme, Tone, and Content. Each item is coded
Table 1. Content-based Descriptors
Nature/wildlife
Fantasy/religion/mythology
Aggression/battles/warfare/conflict
Social interactions
Sports/athletics/acrobatics
Art/dance/music
Places/travel/exploration
Cartoons/animation
People
Animals
Fantasy/mythical characters
Water
Rocks/hills/mountains
Trees/flowers/foliage
Land vehicles/scenes
Terrestrial flight scenes
Underwater vehicles/scenes
Architecture/urban scenes
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on a four-point scale, where 0 = absent, 1= present, 2= prominent, and 3=dominant. (See
Table 2). The content-based descriptors are used (a) in construction of orthoginal judging
pools and (b) in exploratory analysis of target attribute correlates of anomalous
communication.
Generic Characterization of Targets based on
Environmental Psychology Approaches
The above approach represents
what Mehrablan and Russell (1974) describe as "the most
common, but least parsimonious, approach... the use of the everyday language of specific
events and entities" (p. 6). They point out that this approach does not permit comparison
across environments, ".... and it is impossible to analyze behavioral changes as functions of
changes in environments so described" (p. 6). Mehrabian and Russell survey a wide array of
evidence pointing to the advantage of generic characterization of environments in terms of the
primary emotional responses they elicit and a (psychologically-based) measure of information
rate. Their general framework is illustrated below in Figure 9.
Figure 9. Mehrabian & Russell Framework for Characterizing Environments
THE ENVIRONMENT
Sense modality variables
(e.g., color and temperature)
Information rate
(characterizing the spatial and
temporal relationships among
the stimulus components of
an environment)
Ch4racteristic emotions
associated with
PERSONALITY
After Figure 1.1 of Mehrabian & Russell (1974).
PRIMARY
EMOTIONAL
RESPONSES
Pleasure
Arousal
Dominance
BEHAVIORAL
RESPONSES
Approach-avoidance
(which includes physical
approach, exploration,
affiliation, performance,
or other verbal and non-
verbal communications
of preference)
Within this framework, environments are coded using semantic differential scales measuring
the three primary emotional responses (pleasure or evaluation, arousal or activity, dominance
or potency) and information rate. The scales are reproduced in the appendix. Each of the
targets has been coded on these four scales. We believe that this approach may provide a basis
for broader comparison across laboratories and target sets than more traditional methods. It of
course remains to be seen how useful it will be as a predictor of success in anomalous
communication.
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Predictor Measures
Extraversion and Openness to Experience
Performance in anomalous communication tasks has been found to correlate with the
psychological trait of extraversion in a recent meta-analysis of 15 studies by five independent
investigators (Honorton, Ferrari, & Bern, 1990). The mean correlation is small (r = .20) but
consistent across investigators, studies, and personality measures.
While the meta-analysis provides strong evidence that a relationship exists between
anomalous communication and extraversion, it is silent as to the nature of the relationship.
Extraversion is commonly associated with sociability (gregariousness), but it is now known
that there are at least five other components of extraversion. For this reason, we have chosen
the NEO Personality Inventory (Costa & McRae, 1985), an instrument that measues six facets
of extraversion. Recent research implicates sensation seeking as an instrumental factor in the
ganzfeld experience (Glicksohn, 1991) and we are especially interested in the possibility that it
also correlates with performance in anomalous communication tasks. We also will use the
NEO PI Openness scale, and its six facets, because a number of studies have indicated a
relationship between anomalous communication and various measures of openness to
experience. Table 24ists the six facets of extraversion and openness.
Table 2. Facets of Extraversion and Openness
1. Warmth
2. Gregariousness
3. Assertiveness
4. Activity
5. Excitement Seeking
6. Positive Emotions
1. Fantasy
2. Aesthetics
3. Feelings
4. Actions
5. Ideas
6. Values
A computer program scores the questionnaire and presents graphic profiles for each of the
six facets of extraversion and openness. Statistical power analysis (Cohen, 1977) indicates that
a sample size of 200 subjects will achieve a 90% likelihood of detecting a correlation of .2 at
p