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being built up from the incoming data bits.
To
ovei'come
the effects
of
this
type of overlay,
training
to recognize and discriminate against
associational
images
is required.
(3)
Monitor Overlay
.
This
is
comprised of
noise
intruding
into the remote viewer's awareness
inadvertently as
a
result
of undisciplined talk
or actions
on the
part
of
the session monitor.
Examples
cover
a broad spectrum,
ranging
from,
»
stimulation of sailboat
images
by a
casual
pre-session discussion on sailing,
to the subtle
reinforcement
(e.g.,
by body
language)
of
certain
responses
that match
the monitor's
biases and
preconceptions
as
to
the nature
of
target;
in
short, any action on the
part
of
the monitor
that
degrades
the remote viewer's attentiveness
to
the task at hand.
To bring this under
control,
;
a standardized monitor behavior must
be introduced
in which,
for example,
the monitor
is restricted
to
the use
of certain standard phrases during his
monitoring of
a CRV session.
(4)
Environmental Overlay
.
This
type
of overlay has
its
source
in the physical surroundings
of
the CRV
session.
Specifically,
conditions
of
the session
chamber
(e.g.,
obtrusive shapes,
sounds,
visual
highlights)
are
found
to insinuate themselves
into
the CRV response.
A mundane example:
an after-
image produced
by
a strong vertical
line in the
session chamber
can lead
to
a predominant vertical
line
in
the
"target" image.
More esoteric examples
involve peripheral and subliminal perception of
environmental
features,
since,
as
is known from the
study
of subliminal perception,
information not
processed at
a conscious
level
can nonetheless
infiltrate perceptual and
thought
processes.
Environmental overlay can
be minimized by judicious
control of environmental factors,
such as
by providing
a
quiet
,
dimly
lit
,
relatively homogeneous monochrome
visual
field absent
of strong features
and peripheral
clutter
Although
the
latter overlays
can be dealt with by controlling
elements
in
the environment
of
the remote viewer,
the analytical and
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associational
"f ill-in-the-blanks” overlays stem directly from cognitive
processes within the remote viewer.
Our investigation of these overlay
patterns
leads
to
a model
of
RV functioning shown schematically in
Figure
1.
With
the application
of the "stimulus”
(e.g.,
the reading of
a coordinate)
there appears
to be
a momentary burst
of
"signal"
that
enters
into awareness
for
a few seconds,
and then fades
away.
The overlays
appear to
be triggered at
this point
to fill
in the
void.
Success
in
handling these
complex processes apparently requires that
a remote viewer
learn to "grab" incoming data bits while simultaneously attempting to
control
the overlays.
A strict and disciplined methodology to perform
thjjs delicate and difficult
task,
involving repeated coordinate presenta-
tion and quick-reaction response,
has
been developed and
is presently
being confirmed with
four remote
viewers;
#002 who was
primarily responsible
for developing
the basic
concept,
and Nos.
009,
131,
and 504 who are in
the role
of
trainees with regard to this particular methodology.
The
procedure designed to minimize
overlays
coupled with use
of
a specifically
designed acoustic-tiled featureless room with homogeneous
coloring to
minimize environmental overlay, and adoption of
a uniform,
limited monitor
behavior role
to minimize monitor overlay,
constitute the basic methodology
for noise reduction
in our newly- dove loped approach
to
CRV.
With regard to mapping the
signal
characteristics
of
the
CRV
channel,
a progressive multistage target acquisition process appears
to
be emerging.
The
stages outlined
in Table
1 appear to track an increasing
contact with
the target
site that
takes place during the CRV process.
An example of these
stages
of elaboration in a completely successful
remote viewing would
be
the
series:
(1)
Recognition and decoding of
major gestalts
Land surrounded by water
,
an island
(2)
Achieving sensory contact
with target
Humid sensation,
tropical
feeling
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(3)
Experiencing motion and
mobility within target
(4)
Recognition and decoding of
minor signals while sustaining
major gestalts
(5)
Decoding special characteristics
of target
(6)
Analytical recognition and
decoding of significant aspects
of the target
Rising up,
a panoramic
view
Mountains
on the
island,
a
small port
city on the
water's edge
Large areas
devoted to
agriculture
Some tourism,
agriculture
devoted primarily to sugar
cane,
main island
in Fiji
islands
Table
CRV STAGES OF TARGET ACQUISITION
.
Recognition and decoding of gestalts
2.
Achieving sensory contact with target
3.
Experiencing motion and mobility within target
4.
Recognition and decoding
of minor signals while sustaining
gestalts
ma jor
5.
Decoding special
characteristics
of
target
6.
Analytical recognition and decoding
of significant aspects
target
of
the
Knowledge of
the above multistage process
of target acquisition
appears
also to provide a filtering function,
in that apparent
data
that
does
not
emerge somewhat
in this order tends
to be overlay
(e.g.,
immediate
recognized image
of
the
St.
Louis Arch
[Stage
6]
as
first response to
coordinate presentation).
For the
training procedure,
in which feedback plays an essential
role,
a pool
of several hundred target
locations was prepared using
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material
from a
large library
of National Geographic magazines.
The
coordinates
for the
chosen sites were obtained from the Second Edition
(Revised)
of the
Ti mes Atlas
of
the World— Comprehensive Edition
,
Houghton
MiflinCo.,
Boston
(1971).
In
a
typical training
session,
a
half dozen targets
are
chosen
at
random for use.
In the early stages
of
the
process
the monitor makes
himself aware of the
target
material he
is using so that
he can provide
running
feedback during
the
session.
(We
call
this
a Class
C target
protocol.)
During
this
phase
the monitor
is allowed only
a
few stock
phrases
("correct,"
"near
the target," etc.)
so as
to minimize
cueing
and
leading.
Once
some degree
of apparent
competence has been reached,
the
monitor
is
given targets
to which
he
is
blind
(a
Class
B target
protocol)
so as
to eliminate
confounding
of
the results
by potential
cueing.
In
this
case
feedback information
is accessed
by both monitor and remote viewer
only at
termination of
the CRV session.
A training series with
a
given
remote viewer generally consists
of roughly
a
hundred
of these
trials
spread out
over
a two-month period.
The output
of
a
sample successful
trial
is
shown in Figure
2.
In detail,
the training procedure
is
as
follows:
(1)
The remote viewer and monitor seat
themselves
at opposite
ends
of
a table,
the former with
a supply of paper and
a
pen,
the
latter with target
folders
(contents
initially
unknown)
and reference atlases.
(2)
The remote viewer
is
instructed that
the monitor will
begin
the CRV process
by selecting
a
folder and reading aloud
target
coordinates
printed on the outside.
The remote
viewer
is
to note down on paper any immediate impressions
j|(
(which
he may also express aloud
)
and then, rather than
embellishing
on his
first
impressions,
to ask
for the
coordinates
to
be read aloud again so that
the original
process
may be repeated,
etc.,
until
a
coherent picture
of
the
site emerges.
*
The session
is
tape recorded.
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(3)
Following
these
instructions,
the monitor selects
a
folder and begins
the process described above.
(4)
After one or more repetitions
of
the coordinates
(each followed
by a CRV response)
leads
to
recognizable target
characteristics,
the
folder
is accessed by the monitor,
and the atlas
consulted
(if necessary)
in order
to give
feedback.
In the
Class
B protocol
this
is
the termination of
the
session.
In the Class
C protocol
a
line
is
drawn
on the remote viewer's data
sheet
to
separate
the
data thus generated from further data,
since up to
this
point
the data were generated in a double-blind
protocol
and
can
be objectively evaluated
later
as
a test
of
target acquisition.
(5)
In the Class
C protocol,
having terminated the target
acquisition "test"
phase,
feedback
can now be given
and/or further data solicited.
The feedback given at
this
point
is non-negative,
ranging from "correct,”
through
"near the target," to
"you are at another
target”
(giving the remote viewer the benefit
of
the
doubt).
The monitor then has
the option of terminating
the viewing, asking for more detail
("there's something
ten miles
to the
north that should
be visible”)
or
restarting
the process when the viewer's original
description did
not
correspond to the target
site.
(In the
latter
case the monitor
can,
of
course,
guide
or
cue
the remote viewer into a correct response.
This
is acceptable
in the non-test
part of the sequence,
however,
and provides an opportunity to investigate
whether such cueing procedures
ca.n
be useful
in
operationally oriented applications
in which one
guides
the remote viewer onto the
target site with
cues
"a,"
"b,"
...
"f," and then asks
for detail
in
a
nearby region,
or concerning an unknown,
"g.")
To give an example, we present
here
a summary of results obtained
with
a remote viewer who was
a relative neophyte with regard
to CRV.
He
was exposed
to this
protocol,
a few targets
per session,
over
a several-
day period, resulting in a data pool of 26 CRV target viewings.
They
were:
Salt Lake Desert,
Utah;
Lake Erie;
Chicago;
Mono Lake;
Aruba
Island;
Lake Okeechobee;
Yount's
Peak, Wyoming;
Pitcairn
Island;
Pike's
Peak;
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Los Angeles;
Atlantic Ocean;
Rio de
Janeiro;
Kansas
plains;
St.
Peter and
Paul
Islands;
Randall Dam,
South Dakota;
Lake Titicaca;
Cape May;
Niagara
Falls;
Munich;
Amazon River;
Midwestern plains;
Venezuelan Peninsula;
Sierra
Blanca Mountain;
Oregon Desert;
Panama
Canal;
Puerto Rico.
Following the first pilot session of
five,
in which essentially
immediate feedback was given
(Class
C protocol),
the remaining twenty-one
were carried out with delayed feedback
(Class
B protocol)
and
thus
provided
material
that
could
be assessed objectively.
As
a
first
cut
the targets
can be categorized roughly into five groups
(mountains,
flats, water,
cities,
islands/peninsulas).
The
target /response matrix obtained in the
series
is
as shown in Table
2.
The probability of such an alignment
Table
DISTRIBUTION OF CRV TARGET/RESPONSE MATCHINGS
Transcripts
Targets
Mountains
Flats
Wa ter
Cities
Islands/
Peninsulas
Mountains
Flats
Water
Cities
Is lands /Peni nsulas
,
o
occurring by chance alone can
be
calculated
by
a direct-count-of-
permu "fations method
(see Appendix),
and
leads
to p
= 1/51
= 0.0083.
The
distribution of responses
is therefore statistically significant.
Further-
more
,
beyond simple statistics,
certain individual responses were excep-
tionally accurate during the acquisition "test" phase.
In the
final
trial
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in this series,
for example, when the target
coordinates were for Guayama
m Puerto Rico,
the viewer described
a
"fishing village on the southeast
coast
of
a boat-shaped island," which
is an entirely correct description
of the
locale
at
the target
coordinates.
He then drew an island,
resembling
Puerto Rico in both shape and orientation.
A secondary application of the target pool/training mode procedure
is
as an auxiliary calibration tool\
—^v
T
,
.
.
,
'
i
.
Immediately
prior,
during,
or after
a4
~ ~
—
.
,
.
,
,
,
"
| task
(which we designate
Class
A protocols),
a National Geographic CRV can
be used
to determine
whether
a remote viewer
is
"on."
This
procedure was used immediately following the third and
final
scan of one
of
the operational
CRVs
described
in the next
section
of this report.
Under Class
B protocol
(monitor
blind
to target,
no
feedback during session)
coordinates
for the Sault
Ste.
Marie
Locks
in
the Soo Canal were given.
The CRV response,
shown in Figure
3,
centered
on
a
channel with
islands
in it,
leading
to
a
large
lake,
and traversed
by
a
large white bridge,
a result
of high quality.
Eventual
feedback on
the
^target
of interest
revealed matching quality.
CRV Applications
In this program SRI
is
charged with investigating
RV in order to provide data
Specifically,
SRI has
been taskedfwith examining
a series
of geographic coordinates using
RV
techniques with the goals
of:
(1)
Establishing
the authenticity and reliability
of
the RV phenomenon.
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(2)
Developing and refining experimental techniques and
understanding of the RV phenomenon.
(3)
Establishing the
best potential kinds
of
targets and
best potential use
of
the RV phenomenon.
In response
to these requirements SRI
has pursued application tasks
of
interest
'
t.
Hiese tasks
(Class
A protocols)
have
been pursued during
the time
frame
in which
the reliability-improvement
program of
the previous section has
been in effect.
Therefore,
the quality
v
,
of response to these
tasks
provides
an.,£ndirect measure
of
the efficiency
of
the reliability-improvement procedures.
The CRV tasks
described below were carried out
in response
to quick
reaction requirements'
"
nvolved
in monitoring the
progress
of
the work.
During these scans
all
SRI personnel were kept
billed to
the target.
The
tasks and associated response data are outlined
here;
in summary form.
Complete documentation
(transcripts,
messages,
evaluation,
etc.)
can
be made available
«
""
1.
k_Site
No.
("Mound"
Site)
•
RV Session Dates:
June
1979
(Session
1);
5 July
1979
(Session 2).
•
Remote Viewer:
#009
•
Interviewer:
<
(Session
1);
H.
Puthoff
(Session 2).
Two scans were carried out
on
ysite designated'
j
(during
a site visit)
to
be
a target
o^f interest.
For
Scan
1,
Viewer #009 was closeted alone with
~ —no other
personnel were present.
The target
site was designated by coordinates
only
(latitude and
longitude
to seconds).
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An evaluation was provided
SRI
A second scan was
then carried out with
the
same viewer
on
July 1979,
targeting not
by coordinate,
but
on the basis
of familiarity
with the
site established
in Scan
1.
Details
can be made available through
separate channels.
.
Site
No.
(Ship Construction Site
)
•
RV Session Dates:
12 July
1979
(Session
1);
17 July 1979
(Session 2).
•
Remote Viewer:
#009
•
.Interviewer:
H.
Puthoff
It
was arranged
in advance
that
an SRI
remote viewer would
attempt
to target
on
a site designated only on the basis
of the
following:
On the day of viewing
a
‘representative, known to the viewer, would be
carrying an envelope,
inside
of which were
coordinates
of
a
target
of
interest,
location and function unknown even to him.
This was
to
con-
stitute an exercise
in abstract
targeting
Two scans were
carried
out on this
basis on different
dates.
The viewer's response
centered on the description of
a building of specific
design,
together with information on internal
layout and activities,
certain quite unique elements
of which have been verified
as
being correct.
r
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3 .
Caribbean Site
•
RV Session Dates:
14 December
1979
(Session
1);
17 December
1979
(Session
2);
18 December
1979
(Session 3);
21 December
1979
(Session
4);
28 December
1979
(Session
5)
.
•
Remote Viewers:
#698
(Session
1);
#002
(Sessions
2-4);
#009
(Session
5),
•
Interviewer:
H.
Puthoff
A total
of five
remote viewing sessions,
involving three remote
viewers, was
carried
out
on a Caribbean site designated*
———
supplied
to SRI
H
—
Targeting was
on
the
basis
of coordinates
The analysis
of
this
site
has
been completed.
Each
of
the three
viewers individually supplied pertinent,
relevant
data with regard
to
the
target
site, and
their data
taken together resulted
in
a target/transcript
correspondence rating of
(given by user)
on
a
0-7
point evaluation scale
shown in Table
.
The results generated
in these ^
tasks
to date,
all
obtained with remote viewers
incorporating
the
procedures developed
in
the reliability-improvement
program,
appear
to provide our
first
(and
encouraging) evidence with regard
to
a possible upgraded
level
of per-
formance.
Further data
needs
to
be generated,
however,
before
a definitive
assessment
can
be
provided, and
this requirement will
be pursued during
the
remainder
of the program.
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Table
0-7 POINT EVALUATION SCALE FOR TARGET/TRANSCRIPT CORRESPONDENCE
=
Excellent
correspondence,
including good analytical detail
(e.g.,
naming the site by name),
and with essentially no incorrect
information
=
Good correspondence with good analytical information
(e.g.,
naming
the
function)
and relatively
little
incorrect information.
=
Good correspondence with unambiguous unique matchable elements
,
but
some incorrect
information.
=
Good correspondence with several matchable elements
intermixed
with incorrect
information.
=
Mixture of
correct
and incorrect elements
,
but enough
of the
former
to indicate viewer
has
made
contact with the site.
=
Some
correct elements,
but
not
sufficient
to suggest results
beyond
chance expectation.
=
Little correspondence.
=
No correspondence.
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Appendix
STATISTICAL PROCEDURE FOR FIRST-CUT ANALYSIS
OF CRV RESULTS
A precise measure
of
the statistical significance
of
a matrix
of
target/transcript
correspondences
is given
by
a direct-count-of-permutations
*
method
of great generality.
It
is
an exact
calculation method requiring
no approximations
such as normality assumptions.
Furthermore,
the
judging
process that went
into generating the matrix
is
not required
to
be
inde-
pendent
transcript-to-transcript
nor target-to-target
.
The only require-
ment
is
that no artifactual
information
is
provided
as
to the order
of
targets and transcripts.
In particular,
it
can be shown that
if targets
are used with replacement
or are nonorthogonal
(the case here),
then the
method applies even in the
case
in which there
is trial-by-trial feedback
and the target
pool
is known
a priori
to both remote viewer and interviewer.
Thu$
the
possibility
of interviewer
cueing or subject guessing
based on
a
priori knowledge of
the target
pool
is handled
at
a fundamental
level
by
a statistical
procedure
that assumes
the worst.
The argument
is
as
follows
.
In the absence
of knowledge
as
to which transcript was generated
in
response
to which target,
one observes
that
in setting up the
target-
trgnscript
matrix there are
nl
possible ways
to
label the columns
(tran-
scripts),
given any particular order
of
the rows
(targets),
and vice versa.
Thus,
there are
nl
possible matrices
that
could
be constructed from the
*
C.
Scott,
"On the Evaluation of
Verbal Material
in Parapsychology:
A
Discussion of Dr.
Pratt's monograph,"
Jour.
Soc.
Psych.
Res .,
Vol
.
46,
No.
752,
pp.
79-90
(June
1972).
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<Z=d
(98)e
@>
nC=9e
^-RDP96-007^
Final Report
Covering
the Period October
1983
to October
1984
May
1985
AN AUTOMATED RV EVALUATION PROCEDURE
(U)
SRI
Project 7408-12
c<py
icfl
This document consists of 28 pages.
-Qni/Or-0275
333 Ravenswood Avenue
'
Menlo
Park,
California
94025
•
U.S.A.
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II SUMMARY OF RESULTS (U)
^
We have modified a computer-automated remote viewing analysis procedure,
first developed at Princeton University, to be more responsive to the needs of tht*
community.
Our procedure
is based upon defining the information content in both a RV
response and
its associated target as the presence or absence of a series of items
(called
I
,
;
descriptors).
Various mathematical comparisons can be made between responses and targets.
By Refining RV accuracy as the fractional part of the target information that was correctly
perceived, and defining RV reliability as the fractional part of the response that was correct,
we ^re able to construct an RV "figure of merit
” as the product of the two.
The RV figure
of njerit
is a sensitive, target-pool-independent assessment of the quality of a single, remote-
viewing response.
(U)
We have developed a technique to assess an analysts’ RV judging ability by using a
standardized test case of a series of remote viewings.
Judging consistancy in a training
environment
is the most important factor in assessment
ability.
Thus,
it
is a requirement that
the same analyst assess the information content in both the response and the target.
In a
training environment, an analyst would
first determine the information content in
all of the
targets
in the target pool before assessing the information content in any RV response.
All of
the RV assessments are done without knowledge of the particular matching target.
I~
We have suggested ways in which a priori probabilities, on a descriptor-
by-d^scriptor basis, can be used as RV assessments in the absence of any knowledge of the
site.
This technique requires the building of track records for each item on a viewer-by-
viewer basis);
'
,
As the track records begin to stabilize, we
will be able to
integrate the analysis techniques described in
this report!
“*
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III BACKGROUND (U)
(U)
Since publication of the results from the
initial remote viewing effort at SRI
International, 1 * two basic questions remained about the evaluation of RV data:
•
What
is the definition of the target site?
•
What
is the definition of the RV response?
For example, consider a typical IEEE-style, outbound-experimenter remote-viewing trial.
After an experimenter travels to a randomly chosen location at a prearranged time, a remote
viewer’s (RVer)
task
is to describe that location.
In trying to assess the quality of the RV
descriptions
(e.g.,
in a series of trials), an analyst must go to each of the
sites and attempt to
match responses to them.
For example, while standing at a
site, the analyst must decide not
only the bounds of the
site, but must also determine the
site details that should be included in
his/her analysis.
While standing in the middle of the Golden Gate Bridge, should the analyst
consider the buildings of downtown San Francisco, which are clearly and prominantly
visible,
as part of the Golden Gate Bridge target?
Similarly, the RV response to the Golden Gate
Bridge target might be
15 pages of dream-like free associations. A reasonable description of
the bridge may be contained in the response; however,
it might be obfiscated by a large
amount of unrelated material.
How should an analyst approach this problem?
(U)
The
first attempt at quantitatively defining an RV response involved reducing the
raw transcript to a series of declarative statements called concepts.-2
Initially,
it was
determined that a coherent concept should not be reduced to
its component parts.
For
example, a small red VW car should be considered as a single concept rather than four
separate concepts:
small, red, VW, and car.
Once a transcript had been “conceptualized,”
that
list of concepts constituted, by definition, the RV response.
The analyst, then, rated the
concept
lists against the
sites.
Although this represented a major advance over previous
methods, no attempt was made to define the target site.
I
J
DurinS the FY ’82 program, we developed a procedure to define both the target
and the response material. 2 We learned that before a
site could be quantified, a goal for the
*(U)
References are
listed in the order of appearance
at the end of this report.
r
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overall remote viewing must be clearly defined.
If the goal
is simply to demonstrate the
existence of the RV phenomena, then anything that
is perceived at the
site
is important.
But
if the goal
is to gain information^
""“^then specific
items at the
site are important, while others remain insignificant.
For example, consider an
Office as a hypothetical target.
Let us assume that a single computer in the office
is of
specific interest.
Suppose an RVer gives an accurate description of the shape of the
office,
provides the serial number of the typewriter, and gives a complete description of the owner of
the
office.
While
this kind of a response might provide excellent evidence for remote viewing,
jhe target of interest
(the computer)
is completely missed!
.
,
“
What
is needed
is a specific technique to allow assessments that are
mission oriented.
|
~ The procedure developed during FY’82 was a
first attempt at solving the
jnission orientation problem.
In
this technique, the transcript
is conceptualized as described
above, and a similar process
is applied to the
sites. A target site
is conceptualized as a set of
target elements, which are to be considered “mission independent.”
In the office example
above, target elements might be: desk,
safe, window, telephone, computer, and chair. A
second layer of conceptualization
is then applied, which
is “mission specific.”
Each target
element
is assigned a number between
1 and
5 corresponding to the mission’s relevance.
Again,
in the office example, the computer would be assigned a relevance factor of 5 (most
Relevant), while
all other target elements would be assigned a factor of
(least relevant).
The
target elements and their relevance factors constitute the
site definition and mission
Orientation.
The
final report for the FY’82 task3 described in detail how a mission specific
assessment was made.
Although, the procedure proved to be quite sensitive,
it was nonetheless
cumbersome and difficult to apply.
This report describes a major advance over the FY’82 technique.
The original
idea, which involves computer-automated scoring of RV data, was developed at the Anomalies
Laboratory of Princeton University.4 We have significantly extended and modified the
Princeton technique and have developed procedures that can be used in actual*
applications.
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IV METHOD OF APPROACH (U)
*
J The overall method of approach was to begin with the Princeton group’s known
evaluation procedure, then determine what would be appropriate
for our environment.
The
next step was to expand the analysis concept to be more responsive to(
—
requirements, and to integrate the entire procedure with our on-line data bases.
A.
(U)
The Princeton Evaluation Procedure (PEP)
(U)
In general, the Princeton Evaluation Procedure (PEP)
is based on comparing
a priori,
quantitatively-defined target information with similarly quantitatively-defined response
information.
(A complete description of this procedure can be found
in Reference
4.)
The
procedure was developed
for use as a research tool in the university environment, where
complete knowledge of the target sites could be obtained.
Once the target and response
information was defined, the PEP applied various methods of mathematical comparisons to
arrive
at a meaningful assessment score.
1.
(U)
Target Information
(U)
The definition of a particular target site
(usually outdoor sites
in and around
Princeton, New Jersey) was contained in the yes/no answers to a set of questions called
descriptors.
These descriptors were designed in such a way as to characterize the typical
Princeton target.
By definition, the only target information to be considered in the analysis
was completely contained in the yes/no answers of the descriptor questions for that
site.
For
example, one descriptor from their
list, "Are any animals,
birds,
fish, major insects, or figures
of these significant
in the scene?” defines the animal content of the
site.
The question would
be answered “yes”
for a zoo and a pet store target, but answered "no”
for a typical campus
building target.
Similarly a set (30
for the PEP)
of yes/no responses constitutes the target
information.
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well.
2.
(U)
Response Definition
(U)
The descriptor
list for the target sites
is used as a definition of the response as
For a given RV session, an analyst (blind to the target site) attempts to answer the 30
questions based entirely on the single RV response.
Using the same example above, an
analyst would have to decide
if a particular verbal passage or a quick sketch could be
interpreted as animals or not.
For some responses this might be an easy task,
“I get a picture
of a purple cow."
Most responses, however, require a judgement.
“I hear a funny sound and
there may be an odd smell in the air.”
Nonetheless, the yes/no answers to the 30 questions
constitute the only response information that
will be used in the analysis.
3.
(U)
Analysis
(U)
For a given response/target combination, the information
is
strictly contained in
ihe yes/no answers to the descriptors.
A binary number (30 bits long for PEP)
is constructed
for the target and the response descriptor questions respectively.
A yes answer
is considered a
binary “1” while a no answer
is considered a binary "0.”
The resulting two 30-bit binary
numbers can then be compared by a variety of mathematical techniques to form a score
for
that specific RV session.
For a series of RV sessions,
a quantitative assessment
is made by
comparing a given response (matched to
its corresponding target site)
against the scores
computed by matching the response to
all other targets used in the series.
This procedure has
the added advantage of a built-in, within-group control.
In other words,
this assessment
determines the uniqueness of the target/response match compared with
all other possible
rpatches
for the
series.
R.
(U)
Problems with the PEP
There are a number of problems with the PEP when the conditions under
vyhich the PEP was developed are no longer valid.
Because we are trying to develop an RV
analysis procedure that
is useful both in the RV training environment as well as
inf
Applications, we have identified four basic problems with using the PEP for our purposes:
•
The bit descriptors were not appropriate
for our training environment.
•
The PEiP was not interfaced to a standard data base management system
(DBMS).
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•
The cross-target scoring procedure was not sensitive ten
^
requirements.
•
Any cross-target scoring procedure
is inappropriate
for a training
environment.
(U)
As stated above, the PEP descriptors were optimized for natural outdoor sites in
the Princeton area.
Because we planned to use different target material, the PEP descriptor
list was completely inadequate.
Having obtained the computer codes used at Princeton, we
noticed that the PEP required a special on-line, within-code data base.
We
felt this was an
inefficient way to procede because we already had most of our data in a commercial DBMS,
Ingres . 5
> One
the principal problems of RV used as an adjunct to conventional
collection techniques
is that RVers tend to add information, sometimes called
analytical overlay (AOL), to the response.
If training techniques are to be developed that are
sensitive to'
requirements, they must attempt to inhibit AOL.
Specifically, any
training analysis procedure must be particularly sensitive to the addition of extraneous
information.
The PEP was completely insensitive to this requirement.
(U)
We also observed that for the purposes of training, any scoring procedure that
cross compares a training response against
all targets in the target pool, might penalize
excellent RV simply because of the lack of target pool orthogonality
(i.e., how different one
target
is to the next).
For example, consider a typical National Geographic Magazine
photograph of a
flat desert showing few features.
A very good description of this
site
will also
match many other similar sites in the target pool.
Thus,
a comparison of the actual match
with others in the pool
will tend to reduce the score for reasons other than the quality of the
particular RV response.
(U)
We, therefore,
felt obligated to modify the PEP in such a way to address the above
criticisms.
C.
(U)
The SRI Evaluation Procedure (SEP)
\
; The SRI Evaluation Procedure (SEP) was developed to address not only various
RV training programs, but also the potential application of the SEP to»
~ .problems.
Thus,
it was recognized that the SEP must contain cross comparison analytical procedures that
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j
were sensitive to AOL, and at the same time, provide a meaningful assessment of RV
responses that were independent of other targets in the pool.
1.
(U)
Target Information
)
As in the PEP, the SRI Evaluation Procedure quantifies the target material
into binary numbers corresponding to yes/no answers to a set of descriptors.
Before any of
the training programs had begun, a descriptor
list was developed on the basis of the target
material (National Geographic Magazine photographs), and on the responses that might be
expected for novice RVers.
Table
1 shows the 20 questions
(descriptors) that were used for
thO Alternate Training Task. 8
This descriptor
list, while applicable to a novice RV training
environment,
is not appropriate for either advanced training or<
^applications.
The
questions are strongly oriented toward outdoor gestalts typical of National Geographic
Magazine material.
Each descriptor
list must be tailored to the application requirements.
The
horizontal
line's separating the descriptors in groups of three are an aid
in translating binary
numbers (derived from the yes/no answers to the questions)
into an octal shorthand notation.
(U)
To illustrate exactly how a target might be coded into an octal number,
let’s
consider a photograph of San Francisco on a clear day showing the bay, the central city sky-
scrapers, and the centrally-located
hill (Twin Peaks).
Referring to Table
Bit Numbers
1,
6,
8,
9,
12,
13,
16 and
17 would
all be answered “yes” and thus would be assigned a binary
“1.”
The remaining questions would
all be answered “no” and thus be assigned a binary “0.”
Starting with Bit Number
1 on the
left, the binary number that defines the information for this
target
is 10000101100110011000.
This representation, while convenient for computers,
is
difficult for humans; therefore, we convert
it to the octal representation as a shorthand.
Using the horizontal lines shown in Table
1 as divisions, we consider each triad of bits as a
binary number ranging from 000 to
111.
Table 2 shows the binary-number triad to octal
conversion factors.
(U)
Rewriting the above binary number with triad separations for clarity, we have
10 000
101
100 110 011 000.
Using Table
2, we find that this binary number converts to
2054630 a
.
This octal number
is the shorthand notation for
all the information contained, by
definition,
in the San Francisco target example.
All targets in the data base are coded by the
same technique.
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(U)
V
combination would provide good RV assessments.
The correlation coefficients averaged over
all analysts were 0.431 and 0.462 for the Princeton and the SRI procedures respectively.
W(iile
this difference
is not significant, there
is a bias in favor of the SRI procedure.
Within
the SRI procedure. No. 642 was the
least consistent analyst.
There were no significant
differences between the
full and the selective scoring.
I
In summary, we have developed a computer-based RV analysis tool that
is
The figure-of-merit analysis
applicable
for both the training*
allows target-pool-independent assessment of the relative progress of RV trainees.
Within a
given training program absolute probabilities
(against chance) can be assigned
for a single
training session.
\
\ By carefully creating an appropriate
t
*** descriptor
list, and by tracking
figures of merit on a bit-by-bit basis,
'
The figure-of-merit analysis requires that complete descriptor
information of the
site be known, t
~
^^^™^^as feedback information
is
available, descriptor track records (figure-of-merit analysis) can be kept over many sessions to
provide accuracy and
reliability data on a viewer-by-viewer basis.
Thus, viewers can be
selected on the basis of their a priori probabilites on the^
idescriptors of interest,
and a priori assessments of their responses can be made by using the same track record.
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Final Report-rT&sk
6.0.3
October 1969
Covering the Period
1 October 1988 to 30 September 198
A PROTOTYPE ANALYSIS SYSTEM FOR SPECIAL
REMOTE VIEWING TASKS (U)
Prepared for:
SRI Project 1291
I
J
Approved by:
L
Copy 5 of 5 Copies
ils document consists of 27 pages
CDfy lifyA
OT RELEASABLE TO
FOREIGN NATIONALS
333 Ravenswood Ave,
• Menlo Park, CA 94025
415
326-6200
• TWX: 910-373-2046
•
Telex: 334-486
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ABSTRACT (U)
err.
fc?
]T
•_ Wlteve^evefoped a prototype analysis system for remote viewdng^conducted
^’al'ainsf&rgets off^" ^“ih&rest. The system uses individual viewers’ performance histones
ih ^orjftinction with currehtdata to prioritize a set of possible*
> interpretations of the
site.
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•’*3
(U)
LIST OF TABLES
1.
(U) Numerical Listing of Targets
2.
(U) Technology Cluster
3.
(U)
Principal Elements Contained in the Technology Template
(U)
LIST OF TABLES
1.
l
Cluster Diagram for
I
^ Targets
iv
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I INTRODUCTION (U)
(U)
Since
1973, when the
investigations of the human information-accessing capability
called remote viewing (RV)
first began
at SRI
International, 1 *
evaluating the
quality of the
information obtained has been
a continuing challenge.
In order to develop
valid
evaluation
procedures, two basic questions must be addressed:
(1)
What constitutes the target?
(2)
What constitutes the response?
*
if the RV task
is research-oriented, the targets are known, and therefore can be
precisely defined.
In
-oriented tasks, however, the targets are
generally unknown
and their descriptions are problematical.
In both task domains, RV responses tend to consist of
sketches and
written
phrases.
A method
to
encode
unambiguously
this
type
of
“natural
language”
is one of the unsolved problems in computer science, and there has been little progress
to
cjate.
Thus, a complete definition of an RV response
is also problematical.
1 An Of puoctt oh-oriented RV task poses further problems.
High-quality RV does
not always provide useful
For example, the RV may provide additional support for
information that has been verified from other sources, but provide no new information.
In some
cases, however, an overall low-quality RV may provide key elements that positively influence an
analyst’s interpretation.
I
~
Another characteristic of current laboratory analysis techniques
is that they do
not provide an
a
priori assessment
of the RV
quality.
WTiile
this
is not
a problem
in the
laboratory ,
’
_
applications
require such
evaluation.
An RV analyst cannot provide
ratings from
the RV alone;
rather,
the
analyst must provide
a
priori
probabilities that individual RV-response elements (of concepts) are present at the target site.
It
remains the responsibility of an
analyst to determine whether such data are ultimately
useful.
•
Analysis
of
laboratory RV
has been
a
major
part
of
the
ongoing
Cognitive
Sciences Program. 2 '7
For FY
1989, we focused on the development of a prototype
analysis
system that would provide the needed a priori assessments for ctp^lic^'cvt tasking
*
(U) References are at the end of this report.
(T^
'~
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II METHOD OF APPROACH
(U)
|
The analysis of remote viewing (RV) data in an app
li' udnofs environment differs
considerably from laboratory analysis.
Most often, analysts have incomplete or no information
about the target site and are required to provide a priori assessments of data gathered from RV
sessions.
In
this section we outline a prototype
analysis system
for
* RV that uses
concepts from fuzzy set theory, historical archival data, and
templates
of typical*
targets.
In addition, we apply this prototype system to an existing target pool as an illustration of
the power of the technique.
A.
(U) Fuzzy Set Formalism
J|| A more complete description of the
full fuzzy set formalism can be found in our
literature
6 - 7
For the purpose of this report, we have summarized that formalism in general terms
that are not specific to either laboratory experiments or &ppKGo_-b'i^ tasking.
1.
(U)
Construction of Target and Response Fuzzy Sets
(U) A formal definition of a target and
its associated RV response
(i.e.,
the data
obtained from an RV session)
is necessary to any analysis system. To use the fuzzy set method, a
universal
set of elements
is constructed on which target and response descriptions are based.
These elements should contain descriptive aspects of the target material and incorporate items
that typify responses from the intended viewers.
This universal set should
also be extendible
(i.e., allow for additional items that may arise in the responses).
-
(U)
In
general,
the
task
of an RV
analyst
is
to
assign
a membership
value
(p)
between 0 and
1 to each element in the universal set. The numerical value for each element in a
response
is assigned by the degree to which the analyst
is convinced that the given element
is
present in that response.
Membership values for target elements are assigned on the basis of the
degree to which the elements contribute to the target description.
£
j
in the laboratory, the targets are known, so that defining a universal set of
elements
is comparatively straightforward. 6 - 7
In
tasks, however,
defining a
single
universal
set of elements that
is appropriate
for
all operations
is
difficult.
Because the usual
""
f task is so highly mission—dependent, defining a single universal set of elements that is
customized to that mission becomes easier.
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(
The OfpU'ctAitxl
analyst, as opposed to an RV analyst, should construct such
a
list for each mission.
While there may be considerable similarities between element
lists for
different missions, undoubtedly the
lists will require specialization.
In Section II-C below, we
show the construction of one element
list and how
it can be applied to a
set of 65
i
I
targets.
L
i
2.
(U)
Analysis of Complete Responses
\
Once an appropriate universal set of elements has been created, and fuzzy
sets fhat define the target and the response have been specified, the comparison between them is
straightforward. We have defined accuracy as the percent of the target material that is described
correctly by a response.
Likewise, we have defined reliability (of the viewer)
as the percent of
the response that
is correct. 6
Although
in the laboratory
it
is required to provide a posterior
probability estimates of the target-response match,
in an
«i^>| icA.-h'fcvj
setting,
this may be
less
important.
All that
is usually necessary
is to describe the accuracy and reliability for complete
responses, and for individual target elements of interest.
These quantities for the jth sessions are
X Wk (Rj f| Tjh
_
Jc *
rJ~
n
L
* *
i
and
I
£ WtCKj n Tjh
k -
!
aj =
S
X Wjjt
( 1 )
(2 )
\yhere the sum over k is called the sigma count in fuzzy set terminology, and is defined as the sum
qf the membership values
(p.)
for the elements of the response, the target, or their intersection,
i
and n
is the number of possible elements as defined by the element
list. A fuzzy intersection is
defined as the minimum of the intersecting fuzzy set membership values.
In this version of the
definitions, we have
allowed
for
the
possibility of weighting
the membership
values,
Wit,
to
provide mission-defined relevances.
(U)
For the above calculation to be meaningful,
the membership values for the targets
must be similar in kind to those for the responses.
For most mission-dependent specifications,
this
is generally not the case.
The target membership values represent the degree
to which a
particular element
is characteristic of the target, and the response membership values represent
the
degree
to which
the
analyst
is convinced
that
the
given
element
is represented
in the
response.
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Then, R0 (airport) = 0.20
is the chance probability
(i.e..
guessing airport during every session
would only by 20 percent reliable).
Assume that the? viewer mentioned airport Nr = 6 times and
was correct Nc = 4 times.
Then this viewer’s reliability for airports
is computed as R (airport) =
0.67 > Ro(airport) = 0.20. The viewer’s accuracy fox
: airports is computed as A (airport) = NdN0
= 0.80.
Thus in this example, we can conclude th* this viewer
is reasonably accomplished at
remote viewing an airport.
(
j
B.
(U)
Prototype Analysis System
r
We assume
that an
*
• analyst has constructed
a mission-dependent
universal of
elements.
We
further
assume’ that
there
are
a
number
of
competing
interpretations of the target site in question.
i
1.
(U) Target Templates
I
The
first step in our prototype “analysis system
is to define templates
(i.e.,
general descriptions of classes of target types)
of
all competing target interpretations from the
universal
set of elements.
i
Exactly what the templates should represent
is entirely dependent upon
what kind
of information
is sought.
Both the underlying universal
set of elements and the
templates must be constructed to be rich enough to allow for the encoding of all the information
—
That is,
if neither the set of elements nor the templates can meaningfully
represent information about!
' -the -MW.
"
* then
it will be unreasonable to consider
asking.
rfc**ui*
the
site.
Furthermore,
a
certain
amount of atomization
is necessary because such division into small unitsprovides the potential
for interactions within the universal set of elements.
If the profile of a
\
^facility consists of a
single element, the template would be useless unless the response directly stated that particular
element-
rather,
the
profile should be
constructed from groups
of elemental
features L
{
There
are
two
different
ways
to
generate
target
templates.
The
most
straightforward technique is also likely to be the most unreliable, because
it relies on the analyst’s
judgment
of
a
single
target
type.
With
this method,
the
analyst, who
is
familiar
with
the
intelligence problem
at
hand,
simply
generates membership
values
for
elements
from
the
universal
set
of elements based upon
his
or her
general knowledge.
Given
the
time and
resources, the best way to generate template membership values
is to encode known targets that
,
,
,
.
,
Each template
ji
is the average value
are closely related
__*>
across targets, and thus
is more reliable.
If
it
is known that some targets are more
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“characteristic” of the target type than others, then a weighted average should be computed.
In
symbols,
WkUj.k
T _ * -
»
=—
?
(4)
0>k
k = l
wheije the sums are over the available
targets that constitute the template,
cot are the target
weights, and the
pj.k are the assigned membership values for target k.
2.
(U) Archival Database
i A critical feature of an analysis system fo^
*RV data
is that along
with
the
current RV data
to be
evaluated,
the
individual
viewer's past performance on an
elempnt-by-element basis must also be included.
For example,
if a viewer has been relatively
unsuccessful at recognizing
) facilities, then a^
, reference in the current data should not
contribute much in the overall analysis.
^
As ground truth becomes available for each session, a performance database
should be updated
for each viewer to
reflect the new information
This database should be
a
fuzzy set whose membership values for each element are the
reliabilities computed from
^Equation
3.
3.
(U) Optimized Probability List
c_ ^ The goal
of any
~
>RV
analysis system
is to provide an a
priori
prioritized and weighted
list of target possibilities that results from a single remote viewing that
is
sensitive to the performance history of the viewer.
Assuming that a template exists for each of
the ppssiblet
interpretations, an analyst should adhere to the following protocol:
(1)
Analyze the RV data by assigning a membership value
(p)
for each element
irifthe
universal
set of elements.
Each
p. represents the degree to which the
analyst
is
convinced
that the particular element
is included
in the response.
| (2)
Construct a
crisp
set, Rc,
as an a-cut of the original response
set.
By adopting a
threshold
of
0.5,
for example,
then
the
resulting
crisp
set
contains
only
those
elements that the analyst deems most likely as being present in the response.
(3)
Construct an effective response set, Re, as Re = Rc u R«. where R«
is the reliability set
drawn from the archival database. f'"~“
“
, —
\
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!
|
I
I
I
I
Final
Report- -Objective
D,
Task
December
1986
Covering
the
Period
October
1985
to 30 September
1986
A SUGGESTED REMOTE VIEWING
TRAINING PROCEDURE
(U)
SRI
Project
1291
[
'^CIA-RDP9^-(
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(4)
Using this effective response
set, compute an accuracy and reliability in accordance
with Equations
1 and 2. Then compute a figure-of-merit, Afj, for the jlh competing
interpretations as
Mj = aj x
rj
.
Of course, the accuracy and
reliability use the
effective response
set from step
above.
(5)
Order the Ms from largest to smallest value.
Since the figures-of-merit range in value
from 0 to
1, they can be interpreted as relative probability values
for each of the
alternative target possibilities.
By fallowing such a protocol, an analyst can produce a
list of target alternatives that is sensitive to
the hurrent remote viewing yet takes into consideration to the individual viewer’s archival record.
C.
(U)
Partial Application of Analysis System to Existing Target Pool
(U) We have used an existing target pool (developed under a separate program) as a test
bed for the analysis system described above.
1.
(U)
Criteria for Inclusion
in the Target Pool
Targets in this pool have the following characteristics:
•
Each target
is within an hour and a half automobile drive of SRI International.
•
Each target simulates df"
feite
___ *' interest.
•
Each
target
fits
generally within one
of
five
functional
categories:
Production,
Recreation,
Scientific, Storage, and Transportation.
Each
target
meets
a
consensus
agreement
of
experienced RV
monitors
and
analysts about inclusion in the pool.
(U) The pool consists of 65 targets.
Initially, they were divided into 13 groups of five
targets each, where each group contained one target from each of five functional categories.
By
carefully organizing the targets in this way, the maximum possible functional difference of the
targets within each group was ensured.
Table
1 shows a numerical
listing of these targets.
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