FEASIBILITY STUDY ON THE USE OF RV DETECTION TECHNIQUES TO DETERMINE LOCATION OF TARGETS

• Document Type: CREST
• Collection: STARGATE
• Document Number (FOIA) /ESDN (CREST): CIA-RDP96-00788R001300200001-6
• Release Decision: RIFPUB
• Original Classification: U
• Document Page Count: 17
• Document Creation Date: November 4, 2016
• Document Release Date: April 5, 2000
• Sequence Number: 1
• Publication Date: April 2, 1981
• Content Type: REPORT

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Revised 2 April 1981
FEASIBILITY STUDY ON THE USE
OF RV DETECTION TECHNIQUES
TO DETERMINE LOCATION Ofjjfj~,TARGETS:m111w-6I(U)
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LIST OF ILLUSTRATIONS . . . . . . . . . . . . . . . . . . .
I OBJECTIVE . . . . . . . . . . . . . . . . . . . . . .
II INTRODUCTION, AND BACKGROUND . . . . . . . . . . . . . .
A. Location of Unknown Targets. . . . . . .
B. Remote Viewing (RV) as a Location Technology. . .
C. Conclusion . . . . . . . . . . . . . . . . . .
III METHOD OF APPROACH . . . . . . . . . . . . . . . . . .
A. Step 1--Microcomputer-Based Screening Training. .
1. Sequential Sampling Statistical Averaging
Procedure . . . . . ... . . . . . . . . . . .
2. System Error . . . . . . . . . . . . . . . .
3. Test Data . . . . ... ? . . . ? ? ? . ' ? ? .
4. Summary . . . . . . . . . . . . . . . . . .
B. Step 2--Simulation Testing. . . . . ? ? . . . . .
C. Step 3--Demonstration-of-Feasibility Field Study.
IN' PROPOSED PROGRAM . . . . . . . . . ? . . . . . . . . .
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . .
APPENDIX . . . . . . . . . . . . . . . . . . . . . . . . . .
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1 Computer Modeling Task . . . . . . . . . . . . . . . . . .
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2 Decision Graph for Site Selection . . . . . . . . . . . . .
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3 Average Number of Trials n1 to Screen Positive . . . . . .
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4 Decision Graphs for Site Selections Based on the Data of
Subject 1 (Table 1) Screening Study, Resulting in Twelve
Consecutive Correct Selections . . . . . . . . . . . . . .
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OBJECTIVE
The purpose of this document is to provide an outline of a program to
assess the feasibility of using RV detection techniques to determine the
location of argets of interest.
Throughout this document the abbreviation RV refers to the term "remote
viewing," not to its other use as "re-entry vehicle."
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Location of Unknown Targets
B. Remote Viewing (RV) as a Location Technology
Of particular interest along the psychoenergetic lines is a human
information-accessing capability that we call "remote viewing" (RV). The
RV phenomenon, under study at SRI International for the past nine years,
pertains to the ability of certain individuals to access and describe, by
means of mental processes, information blocked from ordinary perception
by distance or shielding, and generally believed to be secure against such
access. This has included the ability of subjects to view remote geographical
locations given only geographical coordinates or a designated person on whom
to target.
The RV abilities of several subjects have been developed to the point
where they can describe--often in great detail--geographical and technical
material such as natural formations, roads, buildings, interior laboratory
apparatus, and real-time activities.
In problems of the location type (which have not been addressed in
any detail in former programs) the general prospect of a continuum of
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possible locations can often be reduced to that of a set of discrete possi-
bilities. This is because, for example, only a finite number of
sites are available, or because specifying one of a
number of grid squares is sufficient to define location. If a location
task can be so defined (to be one of a discrete set of possibilities),
then a detection method can be designed around one of the standard formats
for RV testing, a statistical form of shell game which is a direct analog
of the discrete location problem.
One of the standard formats for RV testing is a computerized form of
"shell" game which is a direct analog of the target location situa-
tion. The testing procedure addresses the basic problem.of choosing, by
RV techniques, a "correct" answer from among a number of possible alterna-
tives. An example is provided by an electronically-automated screening
study carried out by SRI consultant Charles Tart. Subjects were asked to
determine which one of ten possible positions on a circular display had
been designated as an active target by the electronic test device's random
number generator. .3 From an unselected population of 2000 university
students participating in a mass card screening program, seventy of the
better subjects accepted an invitation to be further screened using the
automated electronic testing system. Of these, ten were finally chosen
to participate in a formal study involving 500 trials each. The results
obtained with these ten subjects are shown in Table 1. It is seen that
five of the ten subjects scored significantly above chance, all in the
range of 1.5-2.5 times chance expectation. The best subject averaged a
24.8`: hit rate (-2.5 x chance) over the 500-trial sequence; the probability
of such a result or better occurring by chance is only p = 2 x 10-28.
Furthermore, as good as these results are, the potential utility of
such results can be further enhanced by the use of error-correcting
statistical averaging techniques. Such techniques have proven themselves
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ELECTRONICALLY-AUTOMATED SCREENING STUD\
Subject
Hit Rate
(10: Expected)
Probability of Obtaining
Such a Result by Chance
(one-tailed)
1
24.8(
2 y 10-28
2
20.61;(
1 x 10-14
3
16.21;
2 x 10-6
4
16.0`1
4 x 10 6
5
15.61;
2 Y 10 5
6
11.8;
nonsignificant
7
11.4`;
nonsignificant
8
10.8r:
nonsignificant
9
9.4(
nonsignificant
10
7.V
nonsignificant
capable of amplifying even small statistical advantages to arbitrarily-
high-accuracy results. To cite an example, Czech researcher Dr. Milan
Ryzl, a chemist with the Institute of Biology of the Czechoslovakian
Academy of Science, carried out an experiment with a subject. whose base
performance level was that he was generally capable of generating better
than _. W; hit rate targeting on sequences of random binary digits, or
bits (0, 1), where chance expectation was 50,.
For the purpose of showing the power of psi enhancement by statistical
averaging techniques, Ryzl chose as a task the acquisition, without error,
of a 50-digit random binary sequence. The effort took 19,350 calls,
averaging 9 sec per call. The hit rate for individual calls was 61.9`,,
11,978 hits and 7372 misses.7 By means of repeated passes through the
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sequence and an elaborate (though inefficient ) majority-vote p
with 100% accuracy all 50 bits. The
the subject was able to identify . ,
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chance is only one in 10.
probability that he did so by
C. Conclusion
Thus, data already extant from RV detection experiments indicate that
(a) one target from among a number can, with some statistical advantage,
be determined by RV detection techniques, and (b) the accuracy of doing so
can be amplified by statistical averaging techniques. These observations
thus provide a sound basis upon which to estimate the feasibility of RV
distributed targets, and the protocols in
detection of randomly
use are essentially directly applicable in their present form.
* b a factor of about 20 could be expected on
An increase in efficiency by
the basis of a statistical averaging procedure more optimum than that
used in the experiment.I
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With regard to determining the vulnerability of targets to
RV detection, an approach that recommends itself is a gradient-scale three-
step program involving (1) microcomputer-based screening/training, (2)
simulation testing, and (3) demonstration-of-feasibility field study.
Each of these are discussed below.
A. Step 1--Microcomputer-Based Screening/Training
The first step of the program would involve screening/training a
population of volunteers using microcomputer-based modeling of the
location problem. Basically, the individuals participating as remote
viewers are asked, in repetitive trials, to determine which one of twenty
possible locations (schematically represented as circles on a computer-
driven graphics display) has been designated as the simulated
target by the computer's random number generator. The computer display
is driven by an LSI-11 microcomputer which, on a trial-by-trial basis,
generates a new random display of the circles (to circumvent bias on the
part of the remote viewer due to previous choices). The individual enters
his selections by button press on a hand device positioned over an X-Y
grid (see Figure 1, where a one-in-ten case is shown), and the computer
responds by giving immediate feedback as to the correct answer (to encourage
learning). As the trials progress, the selections are computer analyzed
on line by a statistical averaging program, the output of which indicates
whether one of the possibilities has been chosen statistically significantly
more often than expected by c.ance. (In the later application phase
essentially the same procedure is followed, with the circles internally
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keyed to actual target site possibilities. The procedure differs only in
that trial-by-trial feedback would, of course, not be available).
1., Sequential Sampling Statistical Averaging Procedure
An efficient statistical method for the screening/training
process is provided by a sequential-sampling technique used in production-
line quality control.a The sequential method gives a rule of procedure
for making one of three decisions (with regard to each of the possible
choices) following each trial, which consists of a remote viewer entering
a selection: the accumulated selections have met a pre-established hit-
rate criterion (decision positive); the accumulated selection do not
exceed chance expectation (decision negative); continue trials (insufficient
data to make a decision). The sequential sampling procedure differs from
`fixed-trial-length procedures in that the number of trials required to
reach a decision is not fixed, but depends on the results accumulated with
each trial. The principal advantage of the sequential sampling procedure
as comps"ed with other methods is that, on the average, fewer trials per
decision are required for an equivalent degree of reliability.
To apply the sequential analysis procedure to screening training,
we must a priori define the hit rate we require to conclude that useful
RV detection is taking place, and what statistical risks we are willing to
accept for making an incorrect decision.
To meet these criteria, sequential analysis requires the speci-
fication of four parameters to determine from which of two distributions
(chance or required-hit-rate) a data stream belongs. They are: p , the
0
fraction of selections of a particular target expected in the chance
condition (e.g., p = 1/20 for the case under discussion); p , the fraction
0 1
of selections expected in the presence of a functioning RV capability (e.g.,
pl = 0.125 for a 2.5 x chance-expectation requirement, a value that might
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be chosen because of previous performance in a successful one-in-twenty
task); 0', an a priori assigned acceptable error rate (e.g., of = 0.05) for
concluding that accumulated selections of a particular choice derive from
the pl (RV) distribution when in fact they derive from the p (chance)
distribution (Type I error); 8, an a priori assigned acceptable error rate
(e.g., 8 = 0.05) for concluding that accumulated selections of a particular
choice derive from the p (chance) distribution when in fact they derive
0
from the p 1 (RV) distribution (Type II error).
With the parameters thus specified, the sequential sampling
procedure provides for construction of a decision graph of the type shown
in Figure 2. The decision graph illustrates the rules of procedure for
making one of the three possible decisions following each trial: continue
test before making a decision (unshaded middle region in Figure 2);
decision positive (upper shaded region in Figure 2); decision negative
(lower shaded area in Figure 2). The equations for the upper and lower
decision lines are given in the Appendix.
With the appropriate equations programmed into the microcomputer,
the computer automatically records all data (trial number, target response
pair), and displays on the video graphics system progress on a target
decision graph. A cumulative record of remote viewer selections is
compiled by the computer until either the upper or lower decision line is
reached, at which point a decision is made.
Also given in the Appendix are the equations for the average
number of trials to make decisions, positive or negative. A plot of the
average number of trials to reach a positive decision for typical cases
of interest is shown in Figure 3, where 5'( (u, 9) error rates have been
assumed. As an example, we see that for a 2.5 x expectation rate (k = 2.5)
hitter, n1 62 trials are required on the average to reach a positive
decision on a one-in-twenty target.
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2. System Error
The overall system error is dependent on the type of mode
employed in site penetration attempts.
(a) If the RV detection task is approached with a tentative
choice having already been made (presumably by more conventional means),
then the task of the remote viewer is to verify or reject the tentative
decision as a backup test. In this mode, only a single decision graph
is plotted in the target choice of interest. The probability of error
due to chance (Pe e) in this case - a, being given by the product of the
probability of making a selection even though operating at chance, and
the percentage of such selections that correspond to an incorrect decision:
N
Per e= 1 ~, 1 I a
(b) If the RV detection task is approached as a blind one-in-N
task (e.g., one-in-20 task), the N decision graphs are plotted in parallel,
one for each of the N target choices, as each selection is being made. In
this case, to a good approximation the graphs can be treated in the chance
condition as independent, and the probability of error due to chance
(Pe CMi. Specifically, it is given by the product of the probability
of making at least one selection in the N graphs by chance (which is one
minus the probability of making no selections), and the percentage of such
selections that correspond to an incorrect decision:
PC , c = (N N l I C1 - (1 - a)N]
For example, with N = 20, a 1'(* individual-target error rate
(a = 0.01) leads to P = 0.17, or a confidence factor 1 - P = 0.83;
e,c e,c
this provides - a 17-fold increase in odds over the one-in-twenty confi-
dence factor expected by chance.
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3. Test Data
As a test of the above procedure applied to real data, the data
generated by Subject #1, Table 1, were processed by passing it through
the sequential analysis statistical averaging program (500 trials, 24.8,
hit rate on a one-in-ten task). With the parameters set to correspond to
a twice-chance-expectation requirement and 5cl'(. ( , S) error rates, the
results are as shown graphically in Figure 4: twelve correct selections,
in a row, of one-in-ten targets were made in 452 trials. Although the
data was gathered under the condition that the correct answers were stored
in the computer during the runs, and therefore trial-by-trial feedback
could be given as the random number generator stepped through its program,
the conditions are nonetheless sufficiently similar to the projected task
that the results can be taken as evidence that the proposed approach is
sound.
4. Summary
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In the screening.'training program, participants would be screened
trained by carryin- out the task described in this section, first with
trial-by-trial feedback to encourage learning, and then without feedback
to model properly an application study. In this initial phase the target
for each run would be designated internally by the computer's random number
generator.
ciently large-scale screening; to meet these requirements be considered.
Carried out on a large-enough scale, the screening training
program described in this section would provide realistic estimates of
the percentage of population trainable in this task, and the levels of
proficiency to which performance in this task could be developed. In a
program designed to assess to its fullest the feasibility of locating
targets by RV detection techniques, it is recommended that suffi-
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B. Step 2--Simulation Testing
The participants who emerge from Step 1 with successful performance
profiles would then be asked to participate in Step 2. For this step, a
model of an actual situation with a random one-in-twenty designated
target would be constructed. The subject's access to the mockup during
experimental runs would be by way of video monitor, although secondary
means such as maps or photographs might be utilized in later stages of
the study if appropriate.
To carry out the test, a participant (or participants) would be
briefed as to the task and then be asked to proceed as in Step 1. The
sequential sampling parameters in the microcomputer analysis program would
be set in accordance with the performance profile established by the par-.
ticipant(s) in the Step 1 screening-'training study.
In Step 2 the mechanics of microcomputer recording and analysis of
subject selections would be the same as in Step 1. Step 2 differs from
Step 1, however, in that a participant's selection from the random circle
display, internally keyed to numbered sites, cannot be internally compared
to a recorded correct answer.
the results appear encouraging, then Step 3 would''be engaged.
The results generated by the participant(s) in. the site selection
C. Step 3--Demonstration-o:-Feasibility Field Study
The final step in the three-step vulnerability assessment program
would consist of a field-demonstration test involving,
Data would be
taken using the successful remote viewers of Step 2, both to determine
the degree of correlation between performance on the tasks of Steps 2 and
3, and also to evaluate actual performance in the field study,
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The possibility of success in such a field study is buttressed by
the fact. that the procedures described here have been used by us success-
fully in an exploratory program to determine the locations of hidden
material.
Following a series of such tests, performance profiles for the
individual remote viewers would. be computed and the overall data set
would be evaluated to provide an.estimate as to the usefulness of RV
techniques
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To accomplish the proposed program, SRI proposes to provide the
necessary personnel, facilities, and materials to perform the outlined
work, summarized below, and to report on the results thereof.
? Provide to the sponsor the details of the statistical
package and hardware setup tailored to sponsor-designated
task requirements.
? Screen.itrain a population of volunteers on an LSI-11
microcomputer-modelled location problem, first with
real-time feedback, then without (Step 1).
? Carry out simulation tests on mockups of an actual
target situation using participants with successful
performance profiles from Step 1 (Step 2).
? Carry out a demonstration-of-feasibility field study
on a sponsor-designated test site of interest (Step 3).
? Evaluate data sets to provide estimates of:
L
(b) Level of proficiency to which task performance
can be developed.
(c) Usefulness of locating; targets by RV
detection techniques.
It is proposed that the above program be pursued on a three-man-year
level-of-effort basis. If programmed as a two-year effort, an expenditure.
of somewhat less than $200K for the first year is envisioned. An itemized
cost breakdown can be provided on request.
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