Phenomenological Research and Analysis — Technical Final Report (1993)

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Phenomenological Research

'
and Analysis
‘

Authors:
Edwin C. May, Ph.D., Wanda L. W. Luke, and Nevin D. Lantz, Ph.D.

3 February 1993

Science Applications International Corporation
An Employee-Owned Company

Presented to:

U. S. Government

Contract MDA908—91 —C—0037
(Client Private)

Submitted by:

Science Applications International Corporation
Cognitive Sciences Laboratory

A . -8292
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Technical

nal Report

LIST OF FIGURES 20... cece cece cece ene n een eee n ene renee eee renee neon ne eeeeeeneeees iii
LIST OF TABLES ............005 coon eee ecereeacsseunsbesees eee e ee eeeeeeeseeees iV
) Ri ©) = 2) =) Od 0 4 apn 1
TE BACKGROUND .... cece ccc ccc ence c eee n nae re eres eee e seen net eeeeeneaes 3
1. Historical Perspective .......... 0. cece cece cece cree e enn n eee eeereeeeeeeeeeers 3

2. Current Program ....... cece cece cece etter eee sense e en eennenereseeeee 4

I EXECUTIVE SUMMARY ..............ceeeeeeeres cece cence ene e nee eeeeeenee 5
1. Target Dependencies .......... cece cece ener e eee e eee ne eeeeeseeeeens 5

2. Enhancing AC with Binary Targets ........ ccc cece ccnnncccccvecveccscceerees 6

3. ACin Lucid Dreams ......... ccc ccc cece eee e eee e rece eee e seer eeeeneees 7

4. Magnetoencephalograph ............ccceeecereencee scene n eee eeeeeseeneeees 8

5. Enhancing AC of Binary Targets ....... cc. cess eee e eee c eee c ence enseeeneeees 9

IV TARGET DEPENDENCIES ..........cccccceseeeee eee en rent eeeeeeeeeeeneees 11
1. Objective ...... ccc ccc ccc cee eee e eee eee enna eee e eee e eee eeteeetenees 11

2.  ImtrOduction ..... 0... ccc ccc cece cern nee renee eee reese ee eneeserenee 11

Re PR 0 0) 9) 50): | UD 13

4. Hypotheses ........ cece ccc cece eee cere e ee ee nen e eee e nese seen nee eenees 25

5. Results and Discussion ........:cccecceeeeeeee eee eeseee nee neseetensenees 25

V ENHANCING DETECTION OF AC OF BINARY TARGETS .........0sesseeees 31
1. Objective 2... ccc cece cece eee eee cree nner e cena eee eee eee nes 31

2. Background ......cccc ccc e cc ces acces eee ees e eee e nescence eee eeeenneenes 31

3. Approach .......ccccescceeeeceeeeee eee cece eee eee ease ete secneneenas 32

4. Results and Discussion ......... cc ccc esce cece cece ene se ee een seen neneeees 36

VI MAGNETOENCEPHALOGRAPH ........scccccec cece eee e sees eeneeeveseces 39
1. Introduction ......... 0 cc cece ec cee cece e recente een eee e cena esesseees 39

2. Approach .......sseeeeeveeee Cece e eee eee enone enna seer seeseeeeeeseeses 40

KR 51 | 46

4. Discussion 1.0... 0. ccccee eee c eee ece ence eee n nee ee nnn e ene c eee eeeeeenneees 46

S. Suggested Research ......... ccc cee cccc essa ev eeseeersesvees see eeeeeceees 47

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I. 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.”

* This report constitutes the deliverable DI-MISC-80508 under contract number MDA908-91-C-0037.

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ll. BACKGROUND

——— ree
a

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:

e 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.c., 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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LIST OF FIGURES
1. City with a Mosque 20... .... ccc ccc cece cern ence ence een tees sees cneneeeeneee 14
2. Green Intensity Distribution for the City Target (Macrol-pixel 3,3) ............eeeee eee 15
3. City with Mosque (|AS| = 1.88 bits) ..... 0... cece cee nee e cent eet e tence ena tenenees 15
4, Pacific Islands (JAS| = 1.45 bits) 0.0... 0... cece eect cece ene nee teste eee enenenes 16
5. Zener Target Cards (Average |AS| = 0.15 bits) ...... 0... cece cee e eee e ee ceceteecenes 16
6. Cluster Diagram for Dynamic Targets ..........ccccssccscccce rece resseeneeeeeseees 17
7. Cluster Diagram for Static Targets 0.0... 0... ccccee seen nee cee eec cee ee ens seeneeees 18
8. Target and Response with a post hoc Rating Of 7 ..........ccccceccce nsec cecceenccers 22
9. Target and Response with a post hoc Rating Of 4 10... .. ccc csc c cece cece ence eeecenes 23
10. ‘Target and Response with a post hoc Rating Of 1 .........cc ces c cence ce secenneeeecees 24
11. Correlation of Post Hoc Score with Static Target AS ........ cesses scccceceseeceeeecs 27
12. Correlation of Post Hoc Score with Dynamic Target AS ..........cc cece cece cree cease 28
13. Two-tailed SA Decision Graph .......... 0c. c cece ccc e eee c ene enceeeenasteenerernes 33
14. Operating Characteristic Function—1-Tal .......... 0 ccc ccc ccc e ee eet ee eneceeenens 34
15. Operating Characteristic Function—2Tail 10... ... ccc cece cece cece nce eneeceaeeeees 35
16. Sequence of Events for Stimuli Generation ........... ccc sce ccc ce cere csc cecteceees 41
17. Phase Calculation for a Single Stimulus ........ 0... cece cence secceec ce ereesseneeees 43
18. A Two-by-Five, Error Correcting Block Code .........0ccscccucceseceucecceucuseuces 52
19. Correlation: Levels of Visual Complexity with Post Hoc Ratings and Blind Ranking
for Post Hoc Scores Greater than Three (i.e., evidence for AC)............. cece e eee eees 64

20. Correlation: Levels of Visual Complexity with Post Hoc Ratings and Blind Rankings
for all Post Hoc ScOresS .... cece ccc c cence cece een eee ence eens eee t nena eesnee bees 65

21. Experimental Paradigm for Training ........... ccc cscescces cece eee eesteeeeseenee 70

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ll. 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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binary number target (i.c., 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¢., 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 (Le., 21).

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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.c., error correction
through redundancy coding). We were unable to demonstrate that this particular procedure was suc-
cessful. Asa 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. To 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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IV. TARGET DEPENDENCIES

This section comprises the final report for SOW items 6.2.2.1 and 6.2.2.2.

1. 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.!
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. 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 al. found a hit rate of 0.31 (z = 3.89 p<5 x 10-5) for an effect

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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:

N,-1

St a > Pjlog,[pi)s = 0 if Djs = 0,

j=
where pj is the probability of finding image intensity j 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, Nj is 256 (i.e., 28) for eachk, k =7, 8 b. The total change of the entropy in differential
form is given by:

dS, = \VS,t° dr + oe dt. (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 rea] 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

‘To 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-cm? macro-pixel to compute the p,.

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0.4 — r
. 1 z
Bb
z+ |
A 0.2, 4
=
0.0 all ll. . 4
CPYRGHT 0 20 40 60 80 100
Intensity (j)

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.*

Figure 3. City with Mosque (|AS| = 1.88 bits)

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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ot at At |’

where 4t is one over the digitizing frame rate (i.c., one second). We can see immediately that the dy-
namic targets have a larger 4S 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 clusters from that report for the dynamic and static targets, respectively,

B 3-

&

%

B

<

5 eee efe ena cy

So al» ‘
g@gi.,!
, Bad all
Leen eeee eee cee vids

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 cluster 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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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). ‘Two of the receivers
resided in California while the other three resided in Kansas, New York, and Virginia.

Table 2
Experiment Conditions
Condition | Target Type Sender
————SS
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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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.

Table 3.
0-7 Point Post Hoc Assessment Scale
Score Description
a |

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. _

To 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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4. Hypotheses

4.1 Null Hypothesis
The overall null hypothesis was that ¢ = 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 target 4S. 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/ in) 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.

Table 4.
Effect Sizes
Receiver Sender j No Sender | No Sender {| Sender
Static Dynamic Static Dynamic
a __ —= ———
009 ~0.071 0.141 0,636 —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 Varlance

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 < 0.03), and receiver 518 showed a tendency to favor no sender (i.e., p < 0.04).

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white rectangular box like an upside—down

a sheet cake

1 :
LO Ves
long Ss
4

same box fl

° >
two circular shapes in front, like stepping

stones in a garden

a

long hollow tube, like crashing surf on a
beach — “Hawaii Pipeline”

dark interior

Figure 9. Target and Response with a Post Hoc Rating of 4

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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.

Py 2.5[ T T T —T 1]
< r fr =0.461 7
5 | | df = 26 a
q ° —_
a 2.07 ° 1
& L ° .
5 ° 4

B st °
2 L ° ° > ‘
mH 6 8
| L ° 4
8 1.0+ ° 8 ° ° 7
an g 8 1

he
a A
o 4
2 0.5 +
0.0 l ! L I :
3 4 5 6 7 8
Post Hoc Score

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 static 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.c., 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 targets 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.

* Using just the 28 data points in Figure 11, we findr = —0.216, df = 26 andr = 0.003, df = 26 for the correlation with the post hoc
score and AS, respectively. Since these correlations are negative or very small, they do not alter the conclusion.

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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
throughout the poo.

We hypothesize that the bandwidth of the target 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
type. 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 fundamentally 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.

* Itisimportant to recognize that limited, or evencomplete, knowledge of the target pool cannot bias the blind rank-order statis-
ticbecause itis a differential measure within the pool. It may, however, change the mean of the posthoc scores, but correlations
are insensitive to means. Thus, correlations based upon the post hoc assessment remain valid.

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V. ENHANCING DETECTION OF AC OF
BINARY TARGETS

This section constitutes the final report for SOW item 6.2.3.3.

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. 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 majority vote 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

Pp =po+d, (4)
where 6 is a non-negative constant much, much less than unity and pp) = 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 majority vote procedure (Le., 1 is assumed to be odd). Then the majority vote proba-
bility is given by a binomial sum as:

n
PQ) = p" + (, - 1) pa —p)terrt+ + (aga ns 7 ps(1—p)?

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 majority vote procedure is predicated on the assumption that e 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.!° He was able to demonstrate a 100% accu-
rate guess of 15 decimal 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.!! 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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OC(p) = 1- = » where p is given by

(7)

P(A) = ; » where -o Shs +o.

3.2 A Two--talled 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 mo‘“ification that is necessary to Equation 5 is that the slope, a, is now given by:
inf) 8)

A — Po

In this example we assume that a = 8, so that the curves (see Figure 13) that define the decision algo-
rithm are symmetric. Let 5 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, and Hy: p = py orp = 1 — py.

+ Y YY Uy Line y,

a=

Line -yo

Accumulated Excess Number of Ones
So

Sample Number

Figure 13. Two-tailed SA Decision Graph

When 6 enters either Region 1 or 2, stop the sampling and assume H; is true with a Type I error of B.
Likewise, if 5 enters Region 3, stop the sampling and assume Hp 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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4.0

0.8

0.6

0.4

Probability of P1 Decision

0.2

1 | L i i l rT 1 4 l Ll 4 ai ] L L L ! l i 2 ] i

0.0

T T T T T T T qT T | T T qT | 7 qT T if T T T ] T

0.0 0.2 0.4 0.6 0.8 1.0
Event Probability

Figure 15. Operating Characteristic Function — 2-Tail

3.4 Protocol

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.!2:13 ‘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, either p = py or p = 1 — pj. Decisions resulting
in p = po 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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Table 7
Receiver 83
Analysis Method Hits Trials Rate | Z-Score e
Pe ean
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.

Table 8
Receiver 531
Analysis Method Hits Trials Rate | Z-Score £
SS SS
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 PS1X 10-7, c = 0.52). 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,!2 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).15-1617 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 Turner.!5 Two 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 Turner finding, a decrease of alpha power concomitant with
the flashing light, but the second replication attempt found an increase in alpha power.!7

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. Two 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.!8 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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either the RS or PS; and reset the buffer pointers after 100 ms (ie., the stimulus duration = 100 ms).
Figure 16 shows this sequence in graphical form.

dA... y
Stimulus Buffer Pointers Standard 30 Hz
Type RS/PS RS Buffer SJ Interleaved
Stimulus Initiation

IS Buffer e4 Output Butter |

PS Buffer

Ht |

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 m 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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F Prestimulus: x(t) ay y(t)
x(t) | Linear Process y(t)
Y@)
bee, XO) = FFT Ixt0 then: ™ X6)
" Y() = FFT LO) Gain = |H@)!
Phase = Wy) = uan—(FTGy)

Figure 17. Phase Calculation for a Single Stimulus

Statistics (¢.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 py and ow be the mean and standard deviation of the Monte Carlo phase shift dis-
tribution, and Wo be the observed RMS phase shift. Since the distribution of averages is approximately
normal, compute:

W- _a52
=f and paz |e 055 de.
z

Since we did not specify a direction for a change in phase, the p-value for the block was given by:
p=2*xP,

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-o error estimate in P were computed from the binomial distribution for proportions. Or

1-o error in P = Pan?

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 mr be the number of remote stimulir for blockj, and nj, be
the number of pseudo stimuli p in block/. Similarly, define g, and Gp as the corresponding effect sizes
for block j. We define the weighted effect size for each stimulus type, k, as

N
RE, = SD Wyebjas
jl

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