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Final
Report
December
1984
TARGET SEARCH TECHNIQUES
(U)
By:
HAROLD
E. PUTHOFF
EDWIN
C. MAY
Prepared
for:
DEPARTMENT OF THE ARMY
USAINSCOM
FORT GEORGE
G. MEADE, MARYLAND
20755
Attention:
LT. COL. BRIAN BUZBY
WARNING NOTICE
CENTER LANE SPECIAL ACCESS PROGRAM.
RESTRICT DISSEMINATION TO THOSE WITH VERIFIED ACCESS.
CATEGORY 3
SR
vlnternation
333 Ravenswood Avenue
Menlo
Park,
California
94025
U.S.A.
(415)
326-6200
Cable:
SRI
INTL MPK
'proved F51FftelWM^TO/08/1 5
: CPSfe6ftfi3f«2ROaO6COB5Oe02^LE TO
CENTER LANE-3
FOREIGN NATIONALS
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CONTENTS (U)
(U)
LIST OF ILLUSTRATIONS
(U)
LIST OF TABLES
I
(U)
OBJECTIVE
II
(U)
INTRODUCTION
A.
(U)
General
B.
(U)
Search Categories
HI
(U) METHOD OF APPROACH
A.
(U)
Continuum Search—Statistical Approach
B.
(U)
Discrete Search—Statistical Approach
V
(U)
EXPERIMENTAL
A.
(U)
General
B.
(S/NF/CL-3)
Simulation of “Bug” Search (Continuum).
1.
(U)
Condition
I
2.
(U)
Condition
II
C.
(S/NF/CL-3)
Simulation of “Agent”
Search,
Facility Level
(Continuum)
D.
(S/NF/CL-3)
Simulation of “Agent” /Facility Search (Continuum).
E.
(U)
Binary Search (Discrete/Continuum)
F.
(U)
Computer Assisted Search (CAS)
1.
(U)
Basic Investigation (Simulation)
2.
(U)
Location of Real-World Targets (Known) .......
iv
iv
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3.
(U)
CAS Against Real Targets (Application)
G.
(S/NF/CL-3)
Client-Controlled Long-Distance Test of “Agent/
Building Search,
Facility Level (Continuum)
1.
(U)
Long-Distance “Agent” Search
2.
(U)
Long-Distance “Building” Search
V
(U)
Summary
A.
(U)
Overview
B.
(U)
Focus of Investigation
C.
(U)
Recommendations for Follow-On Actions.
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UNCLASSIFIED
ILLUSTRATIONS
Search Matrix
.
Binary Approach to Target Location
Probability of Error, PE
, with and without Use of a Simple 5-Bit
Majority-Vote Code
.
Random Binary Task Performance,
Percipient #642.
Random Binary Task Performance,
Percipient #730.
Choice Histogram for Percipients 531 and 859
TABLES
Table of Mean Distances
.
HP 41-C Program:
Cumulative Binomial Distribution
HP 41-C Calculator Program for 5-Bit Majority Vote Code.
Binary Data Summary
Results of Basic Investigation
Long-Distance “Agent” Search:
Client-Determined Target Sites
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I
OBJECTIVE (U)
(S/NF/CL-3)
The objective of this effort
at SRI International
is to investigate a
particular aspect of psychoenergetic phenomena called Target Search.
This search technique
is designed to determine the location of objects,
individuals, and facilities where the potential
target area can range from room- to global-sized dimensions.
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II INTRODUCTION (U)
A.
(U)
General
(S/NF/CL-3)
A continuing requirement in military operations
is determining the
location of tactical and strategic military targets of interest, whose positions are not known or
are known only approximately.
Examples range from the location of
(1) a “bug”
in a secure
facility,
(2) a command post in a tactical situation or
(3)
a submarine in a strategic problem.
(S/NF/CL-3) A potential match to the above requirement
is a claimed ability in the
broad field of psychoenergetic functioning; namely, the ability to search for and locate water,
oil, minerals,
objects,
individuals,
sites of archaeological significance, and so forth.
This
ability can be contrasted to the related psychoenergetic ability “remote viewing,”
in the
following manner.
In remote viewing, the RVer
is given location information
(coordinates,
beacon”
agent, picture), and (RV) asked to provide data on target content
(e.g., BW R&D
facility); in “search,” the RVer
is given information on target content, then asked to provide
location data
(e.g., position on a map).
The two functions are thus complimentary to each
other,
(U)
The
ability to locate targets
is most often referred to as “dowsing”
in the Western
literature, and “biophysical effect (BPE)”
in the Soviet/East Bloc literature.
In this report,
we shall refer to such techniques simply as "search.”
Although much of the literature
is
anecdotal, * attempts to quantify the ability and to determine
its mechanisms have been
pursued, t
*2?
• F°r
,
lhe m0St comPrehensive and authoritative survey of the claims for dowsing, see
Christopher Bird, The Divining Hand ,
E,
P. Dutton, New York, NY (1979).
t(u)
See,
for example, papers published by Z. V. Harvalik, beginning 1970,
in The
American Dowser, the journal of the American Society of Dowsers,
(Harvalik
is the
ex-director of the basic research group of the U.S. Army Engineering Laboratories, Fort
Belvoir, VA.)
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UNCLASSIFIED
III METHOD OF APPROACH (U)
A.
(U)
Continuum Search—Statistical Approach
(U)
The first-order requirement in carrying out a continuum-search effort
is to
determine an appropriate method of evaluation.
The approach chosen here
is a modification
of a procedure developed by Dean Radin at Bell Laboratories for evaluating
geometric—distance scores in a perceptual task.*
In this approach, one begins by assuming
that the target area of interest
is in the form of a square
(a useful, but not necessary,
requirement). A grid system
is then laid down over the square in the form of an n x n
matrix (20 X
20,
say),
to yield n
separate grid elements (400 for a 20 X 20 grid,
for
example).
Using this grid as an approximation to a continuum, one can then calculate
exactly the a priori chance distribution that any given search response would
lie
at any given
distance from a particular target location.
From this, an evaluation can be made on the
quality of response
(see Figure
1).
(U)
In the 20 X 20 case,
for a target square not on the edge, there
is one chance in
400 of the searcher landing directly on the target square by chance, four chances in 400 of
being one unit away (above, below, and to the sides),
four chances of being \fl units away,
and so forth.
Counting out from any particular target square, the inhibition of responses
beyond the boundary of the overall square
(at certain distances in certain directions)
is easily
taken into account in the counting process.
Thus, an exact calculation can be made on the
basis of straightforward counting (taking into account edge effects)
for the probabilities of
all
possible target/response pair distances.
One result of these calculations for the 20 x 20 case
(taken as standard)
is Table
1,
in which the mean distance from each given target square to
all possible response squares in the grid
is displayed.
As a reference, the grand mean
chance expectation (MCE)
target response pair distance in the 20 X 20 grid
is 10.41 units.
*(U)
Radin,
D.,
“Evaluating Geometric Distance Scores in a Perceptual Task,” Research
in
Parapsychology 1981, pp.
163-164, Scarecrow Press,
Inc., Metuchen, NJ (1982).
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UNCLASSIFIED
FIGURE
(U)
SEARCH MATRIX
(U)
Thus,
if the search area of interest
is 20 km x 20 km, the MCE distance for random
attempts to locate a random target
is 10.41 km.
B.
(U)
Discrete Search—Statistical Approach
(U)
In the discrete search case (one choice from among K possibilities), the
appropriate evaluation
statistic
is the cumulative binomial distribution.
The parameters are as
follows.
(U)
vVe define a
trial as a single attempt to pinpoint a target as being at one of K
possible target locations.
Let p = 1/K be the probability of a chance hit in the location
attempt in a single
trial, and q =
1 - p be the probability of a chance failure in a single
trial.
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Table
(U)
TABLE OF MEAN DISTANCES
(Beginning Row
1,
left
to right,
square
(1,1),
(1,2)...,
through upper left-hand quadrant of
x 20 matrix)
14.65585,
12.20876,
14.04415,
11.50216,
13.48960,
10.86094,
12.99671,
10.29114,
12.56888,
9.79695,
. 20876,
9.38139,
11.91840,
9.04665,
11.69934,
8.79433,
11.55270,
8.62554,
11.47920,
8.54097,
14.04415,
11.91840,
13.40802,
11.20110,
12.83167,
10.54992,
. 31976,
9.97108,
11.87571,
9.46895,
11.50216,
9.04665,
11.20110,
8.70647,
10.97406,
8.45003,
10.82211,
8.27849,
10.74596,
8.19254,
13.48960,
11.69934,
12.83167,
10.97406,
. 23547,
10.31541,
11.70606,
9.72981,
11.24699,
9,22171,
10.86094,
8.79433,
10.54992,
8.45003,
10.31541,
8.19049,
10.15851,
8.01685,
10.07989,
7.92986,
12.99671,
11.55270,
12.31976,
10.82211,
11.70606,
10.15851,
11.16104,
9.56839,
10.68848,
9.05630,
10.29114,
8.62554,
9.97108,
8.27849,
9.72981,
8.01685,
9.56839,
7.84182,
9.48751,
7.75412,
12.56888,
11.47920,
11.87571,
10.74596,
. 24699,
10.07989,
10.68848,
9.48751,
10.20417,
8.97343,
9.79695,
8.54097,
9.46895,
8.19254,
9.22171,
7.92986,
9.05630,
7.75412,
8.97343,
7.66607
Grand Mean = 10.41513
UNCLASSIFIED
(U)
In a senes of location attempts, the important statistic
is the probability of obtaining at least
R hits in N trials, by chance, because this distribution establishes the basis against which the
efficiency of the search method must be compared.
This
statistic
is given exactly by the
cumulative binomial
(Bernoulli)
distribution.
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UNCLASSIFIED
(U)
Probability of at least
^
N(
N_ (
R hits in N trials
" ^ R
~\
(N-i)!
P
q
'
0)
Although probabilities are listed for several representative values in published tables,*
it
is
perhaps most convenient to calculate them directly with the aid of a standard programmable
calculator.
A program for the Hewlett-Packard HP 41-C series calculator
is provided for
convenience as Table
2.
(U)
To complete discussion of the binomial distribution, we note that the mean
number of hits expected by chance in N trials
is p = Np, while the standard deviation
(measure of expected spread about the mean)
is given by o = VNpq
(U)
Throughout the
statistical evaluations, whether for continuum or discrete search,
we shall adhere to the standard convention that a result obtained in testing can be interpreted
as evidence for psychoenergetic access
if the probability of that result occurring by chance
is
less than p = 0.05.
* (U)
Tables of Cumulative Binomial Probability Distribution
Cambridge, MA (1955).
Harvard University Press,
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Table
(U)
HP 41-C PROGRAM:
CUMULATIVE BINOMIAL DISTRIBUTION
01+LBL
"CUM BIN"
44+LBL
SCI
45 RUN
X=0?
04 STO
GTO
-fi PRIORI
P^“
48 STO
06 PROMPT
49+LBL
0? STO
58 RCL
LN
RCL
09 STO 03
/
RCL 02
53 RCL
II
CHS
*
STO
+
14 STO
57 ST-
LN
16 STO 05
59 ST-
1?
"NR HITS?"
RCL
18 PROMPT
X=0?
STO
62 GTO
“NR TRIALS?"
63 GTO
PROMPT
64+LBL
22 STO 87
65 RCL
23+LBL 08
66 LN
24 RCL
RCL
25 RCL
+
-
EtX
STO 89
78 ST+
RCL 05
RCL
29 *
TONE
RCL
73 PSE
RCL
*
75 ST+
+
76 RCL
STO
-
36 STO
RC* r
37 RCL
80 X>Y?
STO
GTO 08
39 RCL 86
82 RCL
RCL
"Pj
R
OR H0RE="
X>Y?
TIROL
X
42 GTO
85 RVIEH
43 XOY
86 BEEP
87 END
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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
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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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
-3
.
(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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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
lor 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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i
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)
V/ith 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) = 10p
(l - p)
+ 5p
(l - 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,”
Journal , Vol.
35, pp. 203-234
(January 1956).
The Bell System Technical
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UNCLASSIFIED
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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HP 41 C CALCULATOR PROGRAM FOR
5 -BIT MAJORITY VOTE CODE
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 major ity- vote
trials)
#642
R
=
52.67,
(219/416)
p = 0.14
R =
60.07,
(60/100)
p = 0.028
#730
R
=
55.07,
(220/400)
p
= 0.023
R
=
60.07,
(60/100)
p
= 0.028
Combined
R =
53.87,
(439/816)
R
=
60.07,
(120/200)
Data
p
= 0.015
p
= 2.8 x
lO" 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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F.
(U)
Computer Assisted Search (CAS)
(S/NF/CL-3)
To determine the applicability of computers as aids in the general search
problem, we have developed a graduated series of studies—beginning at a basic level of
investigation and evolving toward real-time DoD applications.
As a
first step in determining
the degree to which a computer can be used as an aid in search problems, an experiment
was conducted that demonstrated that an abstract computer-generated target could be located
by psychoenergetic means.
The next stage involved testing whether an “association” between
an abstract computer “target” and an actual target could be established.
1.
(U)
Basic Investigation (Simulation)
(U)
We have conducted an experiment to determine
if an abstract computer-
generated target can be located by psychoenergetic means.
To accomplish this, we designed
an experiment that would also provide information with regard to two possible mechanisms:
•
To search in space for a target that remains fixed for the duration of a
trial.
•
To search for a target that
is rapidly moving in space.
(U)
The
first case
is the more familiar “dowsing”
situation;
a target, whose
location
is unknown, must be found by psychoenergetic means.
The second case was
established on the basis of earllier research at SRI.
Namely, that
it
is possible for an
individual to initiate an action at the proper time to optimize the result of a psychoenergetic
experiment.
(U)
During the
CAS experiment, seven individuals were asked to contribute 50
trials each.
To test the “dowsing” hypothesis, the target location was fixed throughout a
trial.
To test the timing hypothesis, the target location was changed once each millisecond.
Dynamic versus static target trials were determined by a balanced random protocol that was
the same for each of the participants.
Furthermore, they were unaware that there were two
test conditions. A bounded area representing the perimeter of a 20 X 20
cell matrix was
shown to the participant, who could register his/her response by moving a graphics pointing
device (mouse), and could press a button to indicate the choice.
Each participant was told
that the target could be any place within the display boundary, and when the moment
“seemed right,” could register his/her choice by pressing the button on the mouse.
Each
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participant was constrained to provide a few
trials (< 5)
at each
sitting.
The analysis that
was used
is described in Section
III, A.
(U)
From a purely mechanistic point of view, we might expect that locating a
dynamically-moving target would be “easier" than a static one.
Because the target
is moving
once a millisecond, the target
is, on the average,
actually “under” the mouse button twice
each second.
Thus, two times a second, the participant has the opportunity to locate the
target correctly.
He/she must, however, press the mouse button
at the proper time
(finding
a dynamically-moving target translates to finding a moment in time to initiate a response).
In the static case,
it
is possible
(worst case)
that the location
will never be under the mouse
button, because the RVer,
in moving the button display around the screen, misses the actual
location.
Thus, we would expect a bias in favor of finding the dynamic target simply because
the number of opportunities of registering the correct response
is greater.
(U)
The results of the basic investigation are shown in Table
5.
Five of the seven
participants produced significant evidence of psychoenergetic functioning
(p < 0.0004).
Of
these
five, three produced results favoring the timing hypothesis, and two favored the
“dowsing” hypothesis.
Two participants produced significant differences between temporal
and spatial “dowsing;” one (#531) favoring temporal, the other (#807) favoring spatial.
Participant 531 showed a 27 percent reduction from the mean chance expectation (MCE)
distance of 10.4 units, while Participant 807 showed a
16.1 percent reduction.
Overall, there
is strong evidence that individuals can locate abstract computer generated targets,
yet there
is
no evidence that there
is a preference for the timing technique.
2.
(U)
Location of Real-World Targets (Known)
(S/NF/CL-3)
The
first step in determining
if the CAS technique
is capable of
locating unknown DoD targets of interest,
is to demonstrate that the technique can be applied
to real-world targets whose locations are known.
This step has been taken under
client-controlled test conditions, as described in Section G.
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Table
(U)
RESULTS OF BASIC INVESTIGATION
(p-values)
RVer
Space
Time
Difference*
531
.560
.009'*’
• 038 t
240
.365
.042’*’
. 164
859
.528
. 042 ^
.100
452
.752
.911
.691
310
. 184
.363
. 652
807
.047^
*
.994
*
.998
164
. 031 ^
.295
. 826
Tested against
the hypothesis that
time > space.
'Significant
^Target avoidance.
UNCLASSIFIED
3.
(U)
CAS Against Real Targets (Application)
(S/NF/CL-3)
As a final test, can CAS techniques be used to locate unknown
targets that are of interest to the DoD?
The use of both static and dynamic techniques will
be considered.
(U)
In the case of the
latter, a useful technique
is to divide the search space into
N regions. A counter
is incremented once each millisecond as before, but
it begins
at one
and recycles after N +
1.
The extra
cell
is used to cover the case of “none of the above”.
To illustrate how this technique might work,
let us consider the case of a kidnap victim who
is suspected of being held somewhere in the west Beruit area.
This area
is divided into 20
regions of interest.
(The CAS counter then cycles through 21.) A large number of trials are
collected for a given participant, and a large number of participants could be used.
Statistics
developed during the known target portion of the study would be used as an input to the
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evaluation program to determine the most likely location.
Should
this technique prove
successful,
it has wide application beyond the spatial location of targets of interest.
Because
the CAS algorithm only involves selecting the proper moment to register a response, the
meaning of the numbers used
in the cycle counter
is completely arbitrary, and thus can
correspond to the elements of any discrete search problem.
G.
(S/NF/CL-3)
Client-Controlled Long-Distance Test of
“AgenC’/Building Search,
Facility Level (Continuum)
(S/NF/CL-3)
As a measure of progress made during the contract effort,
a test was
proposed
in which the Search team put together by SRI would attempt to locate targets
chosen by, and under the control
of, the client (Army INSCOM).
For this purpose two
experimental series were designed and carried
out.
1.
(U)
Long-Distance “Agent” Search
(S/NF/CL-3)
For the
first test series, Army INSCOM personnel chose a series of
ten locations on the
Ft. Meade, Maryland, base.
Each of the ten locations were to be
visited, in turn,
for one hour,
at predetermined
times, by an INSCOM “agent.”
Members
of the Search team where given a photograph of the “agent”
to be located,
a listing of the
target times (1100 and
1600, EST, on 28-30 November, and on 3-4 Dec.), and a map of
the
Ft. Meade base marked with a
1.5 km X
1.5 km square indicating the overall target area
of iterest.
(U)
Four Search RVers were chosen for this task (Nos.
164,
688,
198, and 232),
three of whom demonstrated success
in other search tasks covered in this report, the fourth
(#232) being reported as having been successful in experiments conducted in a training class
taught by one of the others
(#198).
During the time-frame of the experiment, two of the
RVers were located in California (Nos. 164 and 688), while the remaining two were
in
Florida.
Thus, the experiment was carried out over baselines of several thousand km.
(S/NF/CL-3)
The experiment was carried out with the SRI researchers blind as to
the target locations
(that
is, the experiment was double blind.)
At the end of the targeting
period, the search-determined locations were transmitted to INSCOM,
after which a
list of
the actual target locations used were made available to the SRI team.
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UNCLASSIFIED
(U)
In the 2.25 km square area, the mean chance expectation (MCE)
distance
is
780 m.
Of the four Search RVers,
all generated lesser mean distances
(see Table
6), and
the results of three of the RVers are each independently
statistically significant.
In the forty
trials generated
(4 RVers X
trials each),
20 percent
(8) yielded target-response distances
within
3 squares
(225 m) on the 400-square matrix used for analysis;
this includes a direct
hit which
is expected only once in 400
trials by chance.
The overall combined probability
for this 40-trial series can be determined from the raw data
Z
i
Z =
——
‘
P <
2.5
x 10
VN
Thus,
this test under client—controlled double—blind conditions yields convincing evidence
that psychoenergetic search processes can to
a
statistically significant degree be used as
a
viable search tool.
Table
(U)
LONG-DISTANCE “AGENT” SEARCH:
CLIENT-DETERMINED
TARGET SITES
(MCE Distance = 780 m)
RVer
Mean Target
Response
Distance
Area
Reduction
P
164
391 m
75%
2. 62 X 10~ 3
688
616 m
3 8%
*
0.39
198
447 m
67%
. 45 X 10“ 2
232
492 m
60%
4.65 X 10
-2
*Nons ignif icant
.
UNCLASSIFIED
(U)
An additional facet that surfaced in the above application was the fact that,
due to scheduling conflicts, some percentage of the search attempts were carried out
at some
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time following the target period, ranging from as
little as a few minutes to as much as two
days.
In these cases, the RVer nonetheless concentrated on the scheduled past time period,
rather than on the
(present) time of the actual search attempt.
Detailed examination of the
data generated by the three significant RVers shows that although 40 percent each of their
data was produced on this retrospective basis, there was no
statistically significant difference
between the real-time and retrospective conditions (one-way analysis of variance, dfl =
1,
df2 = 8, F = 0.54,
2.40, and
1.51,
respectively,
for RVers
164,
198 and 232;
F = 5.32 required for significant differences
at the p = 0.05
level).
2.
(U)
Long-Distance “Building” Search
(U)
The dynamic CAS technique was used to attempt to locate a building at the
sponsor’s
facility,
that building being known only to the sponsor. A map of the sponsor’s
facility, unlabeled as to buildings, was divided into 20 numbered equal-area rectangles in a
4X5 matrix,
( 4 columns,
5 rows).
Thus,
starting in the upper left corner, the upper row
contained rectangles
1 - 4; the second row, 5-8; third row 9 - 12; fourth row,
13 -
16;
fifth (bottom) row,
17 - 20.
(U)
An incremental counter was used to cycle through the numbers
1 - 20
at a
rate of
1 millisecond each. A search RVer’s task was to interrupt the counter
(by pressing
the ’’mouse” button)
at the number corresponding to the map area that contains the building
designated as
’’target” by the sponsor, given only the name of the building (Whitesell
building)
.
For each RVer,
a histogram
is constructed corresponding to the number of times
he/she selects each of the numbers.
(U)
The two individuals
( 531 and 859
) who had done well in the abstract
experiment in timing were asked to provide 50
trials each to
start;
in the event an
individual’s selections resulted in a
tie or ties for a maximally-selected rectangle,
trials were
added in 5-tnal blocks until a single choice was outstanding.
In the case of 531, 55
trials
were required to meet this criterion;
for 859, 75 were required.
(U)
In Figure
6 we display the histograms for each participant.
For RVer 531,
the expected number of choices for each rectangle
is 55/20 = 2.75; the maximally-selected
rectangle
( #17
) was chosen 6 times, the second choice
( #14
) was chosen 5 times.
For
RVer
859, the expected number of choices for each rectangle
is 75/20 = 3.75; the
maximally-selected
( #6
) was chosen 7 times, the second choice
( nos.
4 and
18, a
tie
)
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(U)
was chosen
6 times.
Following RVer selections, the answer
( rectangle #15
) was provided
to the experimenters, who,
in turn, provided
it to the search RVers as feedback.
(U)
With regard fo evaluation of the results,
as in the previous section one can
use the 400-square matrix to calculate the probability of observed separations between target
and response rectangle centers as compared with the MCE distance.
For RVer 531, the
first-choice selection yields a target/response rectangle separation distance somewhat greater
than MCE
( 3.4%
), while the second choice yields a better result, a 52.0% reduction from
MCE.
For RVer 859, the first-choice selection yields a 9.5% reduction from MCE, while
the second
( a tie
)
yields one result at 38.5% reduction from MCE, a second
at 24.8%
greater than MCE distance.
Given the small-sample nature of this particular pilot test of the
CAS technique,
it
is not possible to draw meaningful
statistical conclusions.
We are
nonetheless encouraged by observation of relatively large reductions from MCE among the
top two selections for each of the search RVers, and therefore consider this procedure a
promising candidate for further development.
Number
e
of
Choices
unclassified
Map Position
FIGURE
CHOICE HISTOGRAM
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V SUMMARY (U)
A.
(U)
Overview
(S/NF/CL
3)
The research effort described in this report addresses the continuing
requirement in military options
develop techniques
locate tacUcal and strategic
terest whose positions are no, Known,
o, are known only approximately.
Specifically
we
ave investigated the possih.e use of psychoenergetic search techniques as an adjunct to oiher
m I dT r
0" 8 '
EXPerime" tS haVe bee" Ca"ied
examining several promising
lethods that have emerged
in the field over the pas, decade or so.
Care has been taken to
assess the vartous procedures unde,
strict double-blind conditions, and have included
application to the
,oca,ion of targets under Cent contro, over baselines of several
B,
(U)
Focus of Investigation
(U>
In
this study the search techniques under consideration have been investigated in
rr:/;r:r
,he ,ocauons °f^ ““ * -— ***
stare
or an individual or building over transcontinental baselines.
The
methods examined range from simple one-step map marking
("map dowsing"),
to
techniques".
““
toTOhta« -Phis.iced statistical averaging
(U)
Strong evidence emerged in this stud, that certain individuals using certain
techniques were capable o, narrowing down the area of targe, location to a statistically
Z
‘
ndiCatin8 ,he baS ‘C Viabm 'y ° f ^ ™<*™«*«* approach as a search
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C.
(U)
Recommendations for Follow-On Actions
(U)
Given the quality of response to the search task, and the observations emerging
from detailed examination of various facets of that response, the following recommendations
for follow-on actions are offered:
1*
Since various individuals showed “preferences”
for various tasks
(as far as
their capabilities were concerned), examine personality profiles obtained for
these individuals, and determine by testing with other individuals
if
personality structure
is an important factor.
2.
Given the apparent relative insensitivity to RVer-target baseline distance
emerging in this pilot work, examine this variable in depth in further
studies.
3.
Given the apparent relative insensitivity to temporal distance between RVer
and target event
(at least for retrospective conditions) observed in this pilot
work, examine this variable in further depth.
4.
Investigate further the power of statistical averaging techniques to determine
whether the modest amplification obtained with such techniques to date can
be improved significantly,
5.
Follow up on statements by Search RVers that success
is often associated
with certain physiological sensations
(e.g.,
“stickiness”
in the fingers during
“map dowsing”), by investigating the possibility of using
physiological-correlate measures to obtain a more sensitive indicator of
“signal” detection.
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