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I INTRODUCTION (U)
Since the publication of
the
initial remote viewing
(RV)
effort
at SRI
International*
,
two basic questions have remained
in evaluating
remote viewing data:
•
What
is
the definition of
the
target
site?
•
What
is
the definition of
the RV response?
In
the development of meaningful evaluation procedures,
we must address
these two questions,
whether the RV task
is a research-oriented one
(in
which
the target pool
is known)
,
or
mission
(in
which
the
target may not be known)
.
(U)
In the older, IEEE-style, outbound experiment,
definitions of target and response
were particularly difficult to achieve.
The protocol for such an experiment dictated that an
experimenter travel to some randomly chosen location at a prearranged time; a viewer’s task
was to describe that location.
In trying to assess the quality of the RV descriptions
(in a
series of trials,
for example), an analyst visited each of the
sites and attempted to match
responses to them.
While standing at a
site, the analyst had to determine not only the
bounds of the
site, but also the
site details that were to be included in the analysis.
To cite a
specific example using this protocol:
if the analyst were to stand
in the middle of the Golden
Gate Bridge, he/she would have to determine whether the buildings of downtown San
Francisco, which are clearly and prominently visible, were to be considered part of the
Golden Gate Bridge target.
The RV response to the Golden Gate Bridge target could be
equally troublesome, because responses of this sort were typically
15 pages of dream-like free
associations.
A reasonable description of the bridge might be contained in the response— it
might be obfuscated, however, by a large amount of unrelated material.
How was an analyst
to approach this problem of response definition?
(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
(U)
References are
listed in order of appearance at the end of this report.
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(U)
determined that a coherent concept should not be reduced to
its component parts.
For
example,
a small red VW car would be considered a single concept rather than four separate
concepts, small,
red, VW, and car.
Once a transcript had been “conceptualized,” the
list of
concepts constituted, by definition, the RV response.
The analyst 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.
During an FY 1982 program,
a procedure was developed
to define
both the
target and response material. 3
It became evident that before a
site can be quantified,
the overall remote viewing goal 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 that
is useful
then specific
items at
the site are
important while others
remain
insignificant.
For example,
let us assume
that an office
is
a
hypothetical
target and that
a single computer
in
that office
is of
specific
interest.
Let us also assume,
hypothetically,
that
a viewer 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.
Although this kind of
a response might provide excellent
evidence for remote viewing,
the
target of
interest
(the computer)
is
completely missed— this response,
therefore,
is of no
interest*
- What
is needed
is
a specific technique
to allow
assessments
that are mission-oriented.
<
(This report describes
a computerized RV evaluation procedure
that
was
initially developed
in FY 19844 and has been expanded and refined
in FY
1986.*
In
its current evolution,
it
is an analysis that has been aimed
primarily at
simpler,
research-oriented tasks using a known target pool.
It
is anticipated,
however,
that
future refinements
to existing procedures
in addition
to
the advances
of proposed new technologies,
will allow
evaluation techniques
to begin
to address
the more complex
issue of
k RV
^collection.
*(U) This report constitutes Objective A, Task 4, "Remote Viewing Evaluation Techniques.
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II METHOD OF APPROACH (U)
A.
(U)
Figure of Merit Analysis
(U)
Overview
(U)
Current approaches in evaluation technology have focused on the refinement
and extension of the figure of merit analysis.4
Defined in general terms, this procedure
generates a figure of merit (M) between 0 and
1, which provides an accurate assessment of
an RV response.
The M
is the product of the accuracy and reliability with which an RV
response describes
its correct target, as determined by an analyst’s coding of RV targets and
responses according to a “descriptor list.”
Table
1 provides a representative example of such
a
list, which was used in an FY 1986 novice RV training program.
Each of the items in a
descriptor list requires a binary decision from the analyst as to the item’s presence or absence
in each of the targets and responses.
The mathematical formalism for converting the analyst’s
binary codes into Ms and their controls
is detailed in Section A.2. below.
2.
(U)
Mathematical Formalism
\
a.
(U)
Definitions
(U)
For a single viewer, the overall method of analysis consists of calculating
a figure of merit, M, for each viewing session, and then comparing these Ms to a control set
of figures of merit.
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*
.
ufflll'jjil? ^ v*\
(U)
DESCRIPTOR-BIT DEFINITION
Table
.
-
. ;
• <
. is
Bit
No.
Descriptor
Is any significant part of the scene hectic, chaotic, congested, or cluttered?
Does a single major object or structure dominate the scene?
Is the central focus or predominant ambience of the scene primarily natural
rather than artificial or manmade?
Do the effects of the weather appear to be a significant part of the scene?
(e.g., as in the presence of snow or ice, evidence of erosion,
etc.)
Is the scene predominantly colorful, characterized by a profusion of color,
by a strikingly contrasting combination of colors, or by outstanding, brightly-
colored objects
(e.g., flowers, stained-glass windows,
etc.—not normally
blue sky, green grass, or usual building color)?
Is a mountain,
hill, or
cliff, or a range of mountains,
hills, or cliffs a significant
feature of the scene?
Is a volcano a significant part of the scene?
Are buildings or other manmade structures a significant part of the scene?
Is a city a significant part of the scene?
Is a town,
village, or isolated settlement or outpost a significant feature of the
scene?
Are ruins a significant part of the scene?
Is a large expanse of water—specifically an ocean, sea,
gulf, lake, or bay—
a
significant aspect of the scene?
Is a land/water ' interface a significant part of the scene?
Is a river, canal, or channel a significant part of the scene?
Is a waterfall a significant part of the scene?
Is a port or harbor a significant part of the scene?
Is an island a significant part of the scene?
Is a swamp, jungle, marsh, or verdant or heavy foliage a significant part of
the scene?
Is a
flat aspect to the landscape a significant part of the scene?
Is a desert a significant part of the scene, or is the scene predominately dry
to the point of being arid?
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aagafeg?
(U)
Let
[n] be the number of sessions to be analyzed.
Suppose also that"
the descriptor
list contains [m]
bits. We then define the total number of bits in a specific
response,
[k], as
where R
=
if bit
[j] were answered affirmatively and equal to 0 otherwise.
Likewise
j K
the total number of bits in a specific target,
[k] , as
T * = STj k
„
j=l
where T
=
if bit
[j] were answered affirmatively and equal to 0 otherwise.
J K
(U)
The accuracy of response,
[k] ,
(the percent of target
[i] that is
described correctly)
is given by
a
_ y
n
a
ki
"
T
j=l
*i
The reliability of response
[k]
(the percent of response
[k] that was correct)
is given by
m
R
T
= y
jk
jl
ki
"
p
j=i
Rk
(U)
Finally, the figure of merit for response
[k] matched against target
[i]
is
given by
M ki =
a « X
r w
ki
ki
(U)
The analysis can be considered from two perspectives: matches—i.e.,
the figure of merit is calculated by matching a response against
its intended target
(i.e., k =
i),
and cross-matches—i.e., the figure of merit
is calculated by matching a response against some
target other than
its intended one
(i.e., k
i).
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