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CONTENTS
LIST OF ILLUSTRATIONS
I
OBJECTIVE
II
INTRODUCTION, AND BACKGROUND
A.
Location of Unknounl^HBBH^Targets
B.
Remote Viewing
(RV)
as
a Location Technology.
C.
Conclusion
*
V
'
III
METHOD OF APPROACH
*
‘
*
A.
Step
1—Microcomputer-Based Screening Tiaining.
.
.
1.
Sequential
Sampling
Statistical Averaging
Procedure
*
#
System Error
.
Test Data
4.
Summary.
.
B.
Step
2— Simulation Testing
C #
Step
Demo ns tration-of -Feasibility
lielcl Study.
.
IV
PROPOSED PROGRAM
.
.
REFERENCES
APPENDIX
iii
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ILLUSTRATIONS
Computer Modeling Task
Decision Graph for Site Selection
Average Number of Trials
n^
to Screen Positive
Decision Graphs
for Site Selections
Based on the
Data
of
Subject
(Table
1) Screening Study,
Resulting
in Twelve
Consecutive Correct
Selections
ixx
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I
OBJECTIVE
The purpose
of
this document
is
to provide an outline of
a
program
to
assess
the feasibility of usinp RV detection techniques
to determine
the
location ofAflHW^^H^aargets
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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II
INTRODUCTION AND BACKGROUND
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
lias
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
sucli
as
natural formations,
roads,
bui ldings
,
i nterior
laboratory
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-
tor example,
only
a
finite number of
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 sot
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 tho*ggg^tarRet
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/
From an unselected population of 2000 university
pai ticipating
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
=
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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Tabic
ELECTRON I CALLY-AUTOMATED SCREENING STUD^
Sub ject
Hit
Rate
( 1 05 Expected)
Probability of Obtaining
Such
a
Result
by Chance
(one-tai led
)
24 .81
to
X
o
1 to 00
20.67c
y
10-14
16.27
X
10" 6
16.07
X
t—
• o1 CD
15.67
y
10-5
11 .87
nonsi pnlf i cant
11.47
nonsignificant
o • 00
,-s
nonsignificant
9.47
nonsignificant
7.85
nonsignif i cant
capable
of amplifying even small statistical advantapes
to arbitiaiil\
hiph-accuracy results.
To cite an example,
Czech researcher
Dr.
Milan
Ryzl,
a
chemist with
the
Institute
of
Biolopv of
the Czechoslovakian
Academy of Science,
carried
out an experiment with
a
subject
whose
base
performance
level was
that
he was
pencrally capable
of penciatinp
bettci
than
„
6 O';;
hit
rate
tarpetinp
on sequences
of
random binary dipits,
or
bits
(0,
1), where
chance expectation was
507
.
For
the purpose
of showing
the power of
psi
enhancement
by statistical
averapinp techniques,
Ryzl
chose
as
a
task
the acquisition, without error,
of
a
50-dipit
random binary sequence.
The effort
took
19,350 calls,
averapinp
sec per
call.
The
hit
rate
for individual
calls
was
61. 97,
11,978 hits and
7372 misses.
By means
of repeated
passes
through
the
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III
METHOD OF APPROACH
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—Mi crocomputer- 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;;anco,
(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
bo available).
.
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.
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
( ixed-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
compr>'ed with other methods
is
that,
on
the average,
fewer
trials
per
decision arc 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-hi t-rate )
a
data stream belongs.
They are:
p
,
the
o
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
o
of
selections
expected in
the presence
of
a
functioning RV capability
(e.g.,
p^
= 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);
O',
an a priori assigned acceptable error rate
(c.g.
f
O' = 0.05)
for
concluding
that
accumulated selections
of
a
particular choice derive from
the
p
(RV)
distribution when in fact
they derive from the
p
(chance)
o
distx ibution
(Type
I error);
8,
an
a^ priori assigned acceptable error rate
(e.g.,
s 0.05)
for concluding that
accumulated selections
of
a particular
choice
derive
from
the
p^
(chance) distribution when
in
fact
they derive
from the
p
(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
^ Figuie
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
uppcx* and
lower
decision
lines
are given
in the Appendix.
With
the
appropx'iate equations
pi'ogi'ammed
into
the
microcomputcx*
,
the
computer automatically records all
data
(tx*ial
numbei’,
tax’get
response
pair),
and displays on the video graphics
system
progx*ess
on
a
target
decision graph.
A cumulative record of
remote viewer selections
is
compiled
bj
the
computer* until either the upper
ox*
lower* decision
line
is
reached,
at which
point
a decision
is
made.
Also given
in
the Appendix arc
the
equations
for
the average
number of trials
to make decisions,
positive or
negative.
A plot
of
the
average
number*
of
trials
to
x*each
a positive decision
fox*
typical
cases
of
interest
is
shown
in Figure
3, where
5 r
;
(g,
8) error
rates
have been
assumed.
As
an example,
we
see
that
for
a 2.5
x expectation
rate
(k
= 2.5)
hitter,
n^ ~ 62
trials
ax*e 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>c
)
in this
case
~
ot
,
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
P
e
, c
Of
(b)
If
the
RV detection task
is approached
as
a
blind one-in-N
task
(c.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
c
)
~
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:
Pe,c
For example, with
N = 20,
a
17i individual-target
error
rate
(a
= 0.01)
leads
to
P
= 0.17,
or
a
confidence
factor
-
P
=
83*
e,c
c,c
this
provides
~ a
17-fold increase
in odds
over
the one-in- twenty confi-
dence
factoi’ 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. 8S
hit
rate on a one-in-ten
task).
With the
parameters
set
to correspond
to
a
twice-chance-expectation requirement and 5%
(or,
B) 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
In the screening, training program,
participants would
be screened
trained
by carrying out
the
task described
in this section,
first with
trial-bv-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
.
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
B&t^ets
by RV detection techniques
,
it
is
recommended that suffi-
ciently large-scale screening
to meet
these requirements
be considered.
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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 jUHJ^situat ion 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
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 generated
by
the participant (s )
in the site selection
procedure would then
be
tabulated and discussed
Should
the
results appear encouraging,
then Step
3 would
be engaged.
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 propram 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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IV
PROPOSED PROGRAM
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/ train
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
(Step 2).
L
L
l
U
u
•
Carry out
a
demonstration-of-feasibi 1 ity
field study
on
a sponsor-designated
test
site of
interest
(Step 3).
•
Evaluate data
sets
to provide estimates
of:
(a)
Percentage of population trainable.
(b)
(c)
It
is
proposed
that
the above program
be pursued on
a
throe-man-year
level-of-ef fort
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.
Level
of proficiency
to which
task
performance
can
be developed.
Usefulness
of
1 oca ting-
detection techniques.
targets
by
RY