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IV EXPERIMENTAL EFFORT (U)
A.
(U)
General
(U)
In pursuing the search task, SRI engaged several remote viewers (RVers) ranging
from
(1) volunteer subjects,
(2) experienced SRI RVers, through
(3) well-known professional
“dowsers” (who were contacted through the American Society of Dowsers).
In somewhat
extensive work with the
latter, every effort was made to determine whether whatever skills
could be demonstrated might be of a transferable nature.
B.
(S/NF/CL-3)
Simulation of “Bug" Search (Continuum)
(S/NF/CL/3)
Described here
is a test of whether a search procedure involving attempts
to locate small objects in a room
(e.g., a “bug”)
would be successful.
The target location
was a large conference room in which a
>1400 sq.
ft. area (37.5 X
37.5
ft) was designated
as the potential target area.
For each
trial,
a small hand—size object was chosen
(e.g., a
calculator) then placed somewhere in the conference room—the location was determined by
entry into a random number generator for x-y coordinates on a 20 X 20 unit grid.
(U) A total of 50
trials, 25 in each of two conditions
(labeled
I and
II), was carried
out with an experienced SRI RVer (#688)
as search percipient.
The RVer was in the RV
chamber on the third floor of the Radio Physics Laboratory (RPL)
; the target area was a
locked and guarded, nonoccupied conference room on the ground floor of the RPL.
1.
(U)
Condition
I
(U)
In Condition
I,
for each
trial, an experimenter (El) places an object
at a
location in the target room (determined by random number generator), then remains outside
the target room as a guard. A few minutes later,
at a previously—agreed—upon time,
Experimenter E2, who
is kept blind as to the object’s location, has the RVer indicate his
assessment of the object’s location.
The RVer places a mark on a piece of paper containing
a single blank square to represent the target room.
At the end of the
trial, the RVer turns
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his response over to E2, the two of them proceed to the target room to meet El, and they
all enter the room to obtain feedback.
Following feedback, the response coordinates,
obtained by use of a 20 X 20 grid square overlay on the response sheet, are determined,
and
(along with the target coordinates)
are entered into
a computer to compare the
probability of the observed result against chance.
(U)
In the 37.5-ft-square target area, the mean chance expectation (MCE)
target/response pair distance
is
19.5
ft.
In the 25
trials taken under Condition
I, the
target/response pair distances ranged from
a maximum of 15.6
ft to a minimum of 2.0
ft,
with a mean of 13.5
ft. This result
(a 31 percent reduction from MCE)
is
statistically
-3
significant at p =
1.7 X
.
As
a second measure of performance, the target/response pair
distances were ordered such that there was
a trend from larger to smaller distances as the
series progressed, providing some evidence of improvement in task performance over time,
but the trend was not
statistically significant.
2.
(U)
Condition II
(U)
The Condition
I and Condition
II series were carried out in an identical fashion,
except that in Condition
II, only one experimenter
(El) was involved.
Thus, El “hid” the
object for each
trial.
In the
25-trial sequence, the target/response pair distances ranged from
a maximum of 37.5
ft to a minimum of 3.75
ft, with a mean of 18.8
ft.
This result
(a 4
percent reduction MCE)
is
statistically nonsignificant.
In the second measure of perform-
ance, however, the target/response pair distances were again ordered from larger to smaller
as the series progressed, but,
in this case, the trend was
statistically significant
(p < 0.05).
Thus, evidence of improvement in task performance over time was established.
(U)
With regard to the difference in RVer performance between Conditions
I and
II
(which,
a one-way analysis of variance shows to be significant*), one can simply note the
difference in the two conditions.
Technically speaking, the RVer’s task was identical in the
two cases—namely,
to determine the location of a randomly-placed object in a target room.
The psychology differed somewhat, however,
in that during the less successful series
(Condition
II), one experimenter hiding an object could be said to have more of a “game”
*(U)
dfl =
1, df2 = 48, F = 4.94; p= 0.031.
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CENTER LANE-3/NOFORN
aspect to
it than in Condition
I, where the task
is structured more along application lines of
an experimenter and RVer searching for an object placed by a third party.
Another
difference
is that the second series followed the
first; the trend may simply reflect the
“decline”
effect that
is typical in the psychoenergetics field when experimentation becomes a
repetitive routine.
Further work along these lines would be required to clarify this particular
facet.
Nonetheless, with both sets of data combined, the overall result, a mean
target/response pair distance of 16.2
ft
(17 percent reduction of MCE),
is statistically
significant at p = 0.01.
Thus, the experimental series as a whole indicates that the
application of psychoenergetic search techniques, although not yet developed to high
accuracy, can augment other techniques in locating small target objects
in a remote,
otherwise inaccessible, space.
C.
(S/NF/CL-3)
Simulation of “Agent” Search, Facility Level (Continuum)
(U)
The RVer who participated in the above experiment was asked to take part in a
second experiment of a similar nature.
In the second case, the target was to be a person,
located somewhere on the grounds of the SRI 70-acre complex.
The RVer entered the RV
chamber on the third floor of the RPL, then an experimenter was sent to a random location,
determined by entry into a random number generator for x-y coordinates on a 20 x 20 unit
grid.
(U)
Forty trials were carried out—20 in each of two conditions; the second condition
differed from the
first only in that the RVer had in his possession a sample of hair from the
individual to be targeted
(to
test the so-called “witness” concept, part of the lore in dowsing
studies)
.
(U)
The outcome of this experiment was that neither series yielded results differing
from chance expectation, nor was there any significant difference between the two conditions.
Although the RVer expressed subjective differences in attempting to locate a person
(as
opposed to an object), no conclusions as to the difference between the two Experiments B
and C* could be drawn.
*(U)
Defined in Subsections B and C of this chapter and hereafter referred to as
Experiments B and C.
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D.
(S/NF/CL-3)
Simulation of
“ Agent”/Facility Search (Continuum)
(S/NF/CL-3)
A series of trials was undertaken with a special RVer, recommended by
the American Society of Dowsers, who responded to an invitation to participate in the SRI
search program
(#198).
An
initial exploratory series of eight
trials was carried out as in
Experiment B, Condition
I,
(simulation of “bug”
search, with a calculator as target object).
The results were not beyond chance expectation.
Debriefing of the RVer revealed a
preference for tasks that involved people,
as in locating people,
or places inhabited by
people.
Therefore,
a second exploratory series of fifteen
trials was performed, the
first ten
of which repeated the same experiment, but with an individual replacing the object.
For the
remaining five
trials, the target area in which the individual was located was expanded to the
SRI complex as in Experiment C above.
The overall result in this series also was not beyond
chance expectation, but the target/response pair distances were ordered such that there was a
trend from larger to smaller distances as the series progressed, which began to approach
statistical significance
(p = 0.07).
Therefore,
a more extensive series was planned in which
“peopled” locations were to be the targets.
(U)
For this series, the target area was a
> 20 sq. km area
(4.8 km x
4.8 km),
The
viewer was provided a
satellite photograph of the area with a 20 X 20 unit grid overlay.
In
order to investigate potential differences in targeting strategies,
a targeting protocol was
prepared for the series in which, on an intermixed basis, the RVer was targeted on being
(1)
given a photograph of the
site,
e.g.,
of a house,
(2) told that an experimenter known to the
RVer was at a
site,
(3) told the name of the
site
(e.g., Stacey’s Bookstore).
Twenty-one
trials were carried out under this protocol:
six under targeting method
(1),
six under
targeting method
(2), and nine under targeting method
(3).
(U)
In the 4.8-km-square area,
the mean chance expectation distance
is 2.5 km.
In
the 21-trial sequence, the target/response pair distances ranged from a maximum of 3.36 km
to a minimum of 0.34 km, with a mean of 1.55 km.
This result
(a 38 percent reduction
from MCE)
is
statistically significant
at p =
1.1 X
.
(U)
With regard to the difference in RVer performance between targeting conditions,
the mean target/response pair distance was least for the photo-targeting condition
(1),
median for the person-targeting condition
(2),
and maximum for the name-targeting
condition
(3)— 1.15 km,
1.58 km, and
1.80 km respectively.
One-way analysis of variance
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(U)
shows, however,
that the differences between the conditions were not statistically significant.*
Furthermore, no statistically-significant ordering of the target/response distances was observed
as the series progressed,
indicating a level performance throughout the
series.
E.
(U)
Binary Search (Discrete/Continuum)
(U) A “zeroing-in” approach that suggests
itself
is a sequential method whereby the
RVer makes a series of binary decisions to close
in, step-by-step, on the target.
If one
begins with a square map,
for example, the sequence of questions would be of the form
“left
or right half,” followed by “upper or lower half”
of the remaining half, followed again by
“left or right half”
of the remaining square, and so forth,
as indicated in Figure
2.
After n
binary decisions, the designated target area would be narrowed down to
l/2
nth of the original
area
(1/2,
1/4,
1/16,
1/32,
...).
Assuming a flawless sequence of binary decisions, one
could pinpoint a target location relatively quickly by this means.
(U)
The
statistics of binary
(p =
1/2)
sorting by psychoenergetic means are relatively
well known, and on the order of a few percent above chance—this
is
statistically significant,
but
is of
little use in applications.
One
is led naturally to consideration of the use of
redundancy in one form or another
(e.g.,
repetitive “guessing”),
in order to amplify the
small
statistical advantage available into an overall higher-accuracy result for the basic binary
process.
As part of the binary-approach study, we investigated implementation of the
redundancy concept, reduced to practice in the form of a hand-held calculator programmed
for statistical averaging of
(psi)
inputs by an RVer,
(U)
The targets for the series described here are the outcomes
(black/redf in roulette
wheel spins) that are being generated in a double-blind fashion by an experimenter.
The
task
is thus one of determining whether a roulette ball
is located in a red or a black bin.
As
the RVer attempts to identify the ball’s location,
several responses are entered into a
calculator for statistical averaging as described below.
*(U)
dfl =
2, df2 =
18, F = 1.43; p < 0.26.
t(U)
Green 0 and 00 are,
for the purposes of this study, taken to correspond to red and
black,
respectively.
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FIGURE
(U)
BINARY APPROACH TO TARGET LOCATION
(U)
The underlying analytical basis for the experimentation consists of a simple
majority-vote-of-five procedure, which we refer to as
a 5-bit majority-vote code.
Each
trial
consists of five individual subtrials, with a majority vote
(3 or more selections out of 5)
constituting the overall
trial selection.
(In practice, the five-subtrial-sequence can be
shortened, because once three selections of a particular alternative have been accumulated,
no more are required.)
A
critical feature of the procedure
is that the subtrials are arranged
to be independent by means of a calculator programming technique to be described shortly.
(U)
In the terminology of coding theory, the majority-vote-of-five procedure
constitutes an application of block coding theory in which a
(5,
1) block code
(block length
5,
1 information bit)
is used to correct
all single and double errors in the detection of a
5-unit block.
It can be shown
that, with regard to error-correcting coding, no better 2-letter
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(U)
(binary) 5-place alphabet than the majority-vote code exists,* and the argument holds for
all
other odd-length
(n,
1) block codes.
(U)
With the probability of being correct in a single subtrial given by
P, the probability
of a correct majority vote in five independent subtrials
is found by application of the
binomial distribution to be
P(corr) = lOp
(1 - p)
+ 5p
(1 - p) + p
.
Assuming the RVer's selections to be in the correct direction, with an average magnitude of
P > 0.5, the overall probability of error,
1 - P(corr),
for the 5-bit majority-vote code
is
shown in Figure
3.
As can be seen, the error rate decreases dramatically as higher P values
are reached.
(U)
As indicated
earlier,
a
critical element of the procedure
is to arrange for the five
subtrials to constitute independent attempts
at target specification.
The procedure chosen
is
one that
is especially amenable to implementation by the use of a programmable calculator
here one of the HP-4 1C series.
The calculator program used
is provided in Table
3.
(U)
Taking 0 and
to represent the two possible outcomes of interest,
a ball located
in the red bin or black bin,
respectively, were one to simply enter a series of 0’s and
l’s into
a calculator memory to represent selections,
the individual entries would not likely constitute
independent events.
To overcome this shortcoming, the calculator
is programmed such that
the
0 and
1 buttons used to enter selections do not,
in fact, provide direct access to the 0
and
1 memory registers, respectively.
Instead, an internal random number generator relabels
the 0 and
1 buttons,
subtrial to subtrial, on a random basis.
The RVer’s task, therefore,
reduces to one of simply pressing the “right” button,
subtrial to subtrial,
to accumulate
entries in the “right” memory location.
As a
result, the five subtrials are effectively
independent by virtue of the random renumbering of the buttons.
Although such a procedure
may seem somewhat abstract as
a means to implement the binary search procedure, the
*(U)
Slepian,
D., “A Class of Binary Signaling Alphabets,” The Bell System Technical
Journal , Vol.
35, pp. 203-234
(January 1956).
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0.5
0.6
0.7
0.8
0.9
1.0
A
PRIORI
PROBABILITY,
P, OF CORRECTLY
PERCEIVING A SINGLE
BIT
FIGURE
(U)
PROBABILITY OF
ERROR,
P E , WITH AND WITHOUT USE
OF A SIMPLE
5-BIT MAJORITY-VOTE CODE
(U)
potential for increasing accuracy on the basis of multiple choices per decision,
statistically
averaged,
is considered well worth the effort.
(U)
As a subtrial series
is carried out, the calculator registers the inputs until three
entries have been accumulated in either the 0 or
1 memory register (which takes as few as
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Table
HP 41-C CALCULATOR PROGRAM FOR
5 -BIT MAJORITY VOTE CODE
8ULBL
-BLOCK
N-
-BLK L?-
03 PROMPT
04 STO 06
06 STO 29
07 TINE
08 LN
09 ABS
10 STO 36
12 STO
13 STO 01
14 STO 82
15 STO 03
16 STO 04
18 STO 05
19*LBL
•BLK BIT"
20 XEQ
-RNG SUB-
-READY-
TONE: 7
23 TONE: 6
24 PROMPT
+
26 RCL 29
27 MOD
28 STO
38 ST+
IND
ST+ 02
32 RCL 06
+
35 2
/
3? RCL
IND 31
38 X=Y?
39 GTO -BLKDOHE-
40 GTO BLK BIT-
41*LBL
-BLKDONE-
42 FIX
'
43 BEEP
"CHOICE5 "
45 ARCL
46 AVIEU
4? STOP
-ftNSHER?-
49 PROMPT
50 STO 07
51 RCL
52 RCL 07
X=Y'J
54 GTO
’BLK HIT-
55 RCL
+
58 RCL 29
59 HOD
60 STO 32
RCL
INS 32
62 CTO
-END"
63*LBL
’BLK HIT-
65 ST+ 04
66 RCL
INS 31
674LBL
-END-
68 ST+ 83
69 TONE 6
70 -TRIAL NR=*
ARCL 85
72 AVI EH
73 STOP
74 FIX
75 RCL 03
76 RCL 02
/
106
*
80 TONE 6
"< BIT HITS5 *
82 ARCL
X
83 AVIEU
84 STOP
85 RCL 84
86 RCL 05
87 /
100
*
90 TONE
BLK HITS5
92 ARCL X
93 AVIEU
94 STOP
95 0
96 STO 00
97 STO 01
99 ST+ 05
108 GTO
-BLK BIT
101
.END.
01N.BL
"RNG SUB
02 9821
03 RCL 30
*
05 .211327
06 +
07 FRC
88 STO 30
09 RCL 29
*
INT
12 END
UNCLASSIFIED
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(U)
three, and up to five entries),
at which point the calculator program announces the overall
trial result.
(U)
To date, two RVers have completed a
100-trial series under the protocols
described;
their results are summarized in Table 4 and in Figures 4 and
5.
In the figures,
cumulative percentage hits are plotted for both the subtrial data and the majority-vote trial
data.
As
is evident by inspection, the 5-bit majority-vote procedure amplifies the
statistical
advantage of the subtrial entries,
as could be expected in the case of extra-chance RV
performance.
In particular, whereas only one of the two RVer’s subtrial data displayed in
Table 4 were significant [evaluated by Eq.
(1)], both RVer’s 5-bit majority-vote data
independently reached significance.
Table
(U)
BINARY DATA SUMMARY
Percipient
Summary Statistics
( subtrials
)
Summary Statistics
(5-bit majority-vote
trials)
#642
R = 52.6%
(219/416)
p
= 0.14
R
= 60.0%
(60/100)
p - 0.028
#730
R = 55.0% (220/400)
p
= 0.023
R
= 60.0% (60/100)
p
= 0.028
Combined
R = 53.8%
(439/816)
R = 60.0%
(120/200)
Data
p
= 0.015
p
= 2.8 x
10~ 3
UNCLASSIFIED
(U)
The use of a calculator as a convenient on-line psi—amplification tool appears to
hold promise as a
first step in the development of a binary search procedure.
The study
shows that the accuracy of the individual bit choices can be amplified by the use of the
statistical averaging procedure described.
The level of accuracy reached (60 percent)
although statistically significant, needs to be strengthened by further research before binary
search meets minimal requirements for application efforts.
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