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(U>
however, that current lists are inadequate in providing discriminators that are “fine” enough
both to describe a complex target accurately and to exploit fully the more subtle or abstract
information content of the RV response.
To decrease the granularity of the RV evaluation
system, therefore,
it was determined that the technology would have to evolve in the direction
of allowing the analyst a gradation of judgment about target and response features, rather than
the current “all-or-nothing” binary determinations. A preliminary survey of various
disciplines and their evaluation methods (spanning such diverse fields as artificial intelligence,
linguistics, and environmental psychology) has revealed a branch of mathematics, known as
“fuzzy set theory,” which provides a mathematical framework for modeling situations that are
inherently imprecise.
The principal architect of fuzzy sets, L. A. Zadeh, has stated:
" One of the aims of the theory of fuzzy sets
is the development of a methodology
for the formulation and solution of problems which are too complex or ill-defined
to be susceptible to analysis by conventional techniques.” 6
(U)
Because the task of RV analysis requires human judgments about^imprecise
situations—namely, the categorization of natural sites and the interpretation of abstract
representations of those sites—it would appear, according to the above definition, that fuzzy
set theory
is a promising line of inquiry.
In the next section, some of the basic concepts of
fuzzy set theory will be examined, with the aim of understanding how this technology might be
applied to the specific problem of RV evaluation.
2.
(U) A Tutorial
(U)
In traditional set theory, an element
is either a member of a set or
it
isn’t—e.g., the number 2
is a member of the set of even numbers; the number 3
is not.
Fuzzy set theory
is a variant of traditional set theory, in that
it introduces the concept of
degree of membership: herein lies the essence of its applicability to the modeling of imprecise
systems.
For example,
if we take the concept of age (known as a linguistic variable in fuzzy
set parlance), we might ascribe to
it certain subcategories
(i.e., fuzzy sets) such as very young,
young, middle-aged,
old,
etc.
Looking at very young,
only, as a fuzzy set example, we must
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(U)
define what we mean by this concept vis-a-vis the linguistic variable age.*
If we examine the
chronological ages from
1 to 30, we might subjectively assert that we consider the ages
through 4 to represent rather robustly a spectrum of the concept very young, whereas the age
of 30 probably does not accurately represent very young at
all.
As depicted in Figure 2, fuzzy
set theory allows us to assign a numerical value between 0 and
1 that represents our best
subjective estimate as to how much each of the ages
1 though 30 embodies the concept very
young.
at
i
FIGURE 2
(U) THE FUZZY SET “VERY YOUNG”
(U)
Clearly,
a, different set of numerical values would be assigned to the ages
through 30 for the fuzzy sets young, middle-aged, and old—e.g., the age of 6 might receive a
value of 0.5 for very young, but a value of 1.0 for young, depending on context, consensus,
and the particular application of the system.
In this way we are able to provide manipulatable
numerical values for imprecise natural language expressions; in addition, we are no longer
forced into making inaccurate binary decisions such as, “Is the age of 7 very young—yes or
no?”
(U)
It is important to note that the design of the fuzzy application occurs in accordance with the
subjectivity of the system designer.
Fortunately, the fuzzy set technology is rich enough that it allows for a
virtually unrestricted range of expression.
Technically speaking, young is the fuzzy set and very
is a
modifier, but it is beyond the scope of this paper to present terminology in depth.
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3.
(U)
Initial Application to RV Evaluation
(U)
In FY 1986, work began on an initial application of fuzzy set technology to
RV evaluation, which simply entails an extension of the current descriptor
list capabilities.
In
coding the targets, an analyst employs numerical values between 0 and
1, inclusively, to rate
each of the 20 descriptors according to the importance of its visual representation in the
target.
For example, in rating a National Geographic magazine picture of the Khyber Pass,
an analyst might ascribe a value of 0.80 for a “mountain” descriptor, a value of 0.20 for a
“desert” descriptor, and values for other appropriate descriptors in accordance with their
perceived importance to the target as a whole.*
(U)
The rating of responses
is considerably more subjective than the rating of
targets.
The analyst is required to apply a “confidence rating”—i.e., again, a value between 0
and
1, inclusively—as to what degree an abstract ideogram
is representative of a given
descriptor.
For example,
if a novice subject draws a conical-shaped object and labels
it,
“fuzzy cone. ..wider at the bottom...” the analyst may decide that there
is some justification
for interpreting this ideogram as a volcano covered with vegetation.
Clearly, however, the
confidence factor for making this highly subjective determination
is quite low; the net result
might be, therefore, that the “volcano" descriptor might receive a rating of 0.15, while
r
“foliage" might receive 0.05.
(U) We anticipate that the primary effect of implementing this rudimentary
application of fuzzy set technology will be to “fine tune” the figure of merit scores, such that
they are more representative of the “true” information content of an RV response.
The
current figure of merit application penalizes certain responses and inflates others (especially
given the “noisy” aspect of novice data)
, based on the correctness or incorrectness of the
analyst's “all-or-nothing” determination with regard to any given descriptor.
To summarize,
fuzzy set technology is attractive in two important respects:
(1)
it affords the analyst a wider
range of expression, thereby enabling him/her to provide a more realistic portrayal of the
information contained in both targets and responses, and (2)
it
is compatible with the figure
of merit mathematical formalism.
4.
(U)
Potential Future Applications
(U)
It
is anticipated that the initial application of fuzzy set technology to RV
responses and targets will greatly enhance the accuracy with which their information content is
*
(U)
Coding of both targets and responses might be more “objectively" arrived at via the consensus of a
group of experienced analysts.
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depicted. A problem remains, however, with the inherently large granularity of the current
descriptor
list, which is independent of the potential “fineness" of its application allowed by
fuzzy set theory—although the analyst will be allowed a gradation of response in interpreting
an abstract ideogram
(i.e., the confidence factors ranging from 0 to
1), he/she will
still be
constrained to interpreting that ideogram according to 20 concrete descriptors.
These
descriptors are significantly limited in their ability both to portray rich environments and to
distill the most usable information from abstract RV mentations.
(U)
It is projected that future descriptor lists will afford the analyst greater latitude
in interpreting the more abstract aspects of RV responses, by providing basis vector
descriptors.
Such descriptors would represent, in essence, the lowest practicable common
denominator of abstraction from which more concrete descriptors might be generated using
fuzzy set operations (such as intersection and union). An example of a basis vector descriptor
might be the concept of vertical, which
is an abstraction that is represented to varying degrees
in such concrete descriptors as building,
cliff, mountain,
waterfall,
etc.
(U)
Ultimately, we envision that evaluation would proceed along the lines of
analyzing both the RV responses and targets in terms of fuzzy-weighted basis vector
descriptors. A comparison of basis vector descriptors between responses and targets could
then be effected, which would culminate in a figure of merit analysis reflecting the subject’s
ability to debrief the more abstract components of the psi signal.
By using fuzzy set
operations, concrete target and response descriptors could subsequently be generated on a
“best fit” basis from the basis vector descriptors, and a figure of merit evaluation could be
performed at this higher-order level also.
The primary benefits of this type of procedure
would be in providing objectification of abstract response data, and in affording more
automated interpretation of these data in concrete terms.
Furthermore,
it would also be
possible to track, in a systematic and quantifiable manner (on both a “subject-by-subject”
and
across subject” basis), the kinds of abstract signals that subjects are receiving reliably;
presumably,
this capability might then be used to illuminate important lines of future
investigation within RV fundamentals.
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III RESULTS (U)
(U)
The results of the FY 1986 evaluation effort have been obtained primarily from
two sources:
(1) identification of inter-analyst reliability factors, based on analysis of figure of
merit statistics, and (2) insights into descriptor
list formulations and target pool composition,
based on post hoc analysis and observations.
Each of these areas
is explored in turn below.
A.
(U)
Inter-Analyst Reliability Factors
(U) A method was developed in FY 1986 for rating the abilities of potential remote
viewing analysts.
The most direct method of accomplishing this was simply to ask a candidate
to analyze a known series of remote viewings; the results could then be compared to those
produced by a proven analyst, 374.
(U)
Three individuals, 432, 579, and 642, were asked separately to score
first the
targets and then the responses used in a remote viewing series from a novice viewer.
They
used a twenty-bit descriptor list (see Table
1) under a “blind” protocol.
The procedure
described in Section II.A. was used to calculate figures of merit, session p-values, and overall
p-values for each analyst.
\
(U)
Novice remote viewing data, which have been collected under our stimulus/response
protocol, contains two distinguishing characteristics:
•
The data tend to be sparse and abstract.
•
The data tend to be noisy
(i.e., large amounts of incorrect information).
(U)
If the descriptor
list contains mostly concrete items rather than abstract concepts
(e.g.,
“Is there a waterfall?” versus “Are there vertical features?”), then an analyst who
is
unwilling or unable to interpret abstract and/or sparse data will miss whatever remote viewing
information may be present.
In the extreme case, a literal analyst may not answer any
questions on the descriptor
list affirmatively.
If
it
is assumed that there was some remote
viewing information present in the abstract response, then
it
is clear that the literal analyst will
miss
it.
As the responses become less abstract and possibly more accurate, the difference
between an interpretive and literal analyst becomes less important.
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(U)
Based upon these concepts, three hypotheses were formulated that could be tested
with the three candidate analysts listed above:
•
For an analyst to be sensitive to novice data, he/she must be willing to
interpret abstract data.
•
An interpretive analyst cannot demonstrate a remote viewing effect where
there
is none.
•
For literal analysts, the difference between their p-values and those of
an interpretive analyst will correlate significantly, on a session-by-session
basis, with their own session p-values.
(U)
The
first hypothesis
is true by inspection.
Because the only remote viewing output
that
is analyzed
is the one coded into the descriptor
list,
it follows that an analyst must
interpret abstract data, or there are no data for analysis.
(U)
Given that the analyst must be interpretive, we must consider whether an artifact
could be induced by being interpretive.
This
is not the case.
Because the analyst
is “blind”
to the correct target for a given session, there
is no reason to expect that the interpretation of
the abstract response would be selective in such a way as to match the intended target better
than any other target in the series.
Because the probability assessment of a single session
involves the MCE cross-matched figure of merit distribution, any “enhanced”
effects are-
canceled by the differential comparison.
(U)
As a result,
it was predicted that the difference between the means of the actual
figure of merit and the MCE (AM) would reflect the remote viewing information content, and
that the difference wotjld decrease as the analyst tends to be more literal.
Table 2 shows the
results from four analysts, in assessing the same 45 novice remote viewing sessions.
It should
be noted that p(AM)
is the probability (derived from ANOVA) of observing AM under the
MCE hypothesis.
Table 2 also shows the probability correlation,
[r], described above,
its
degrees of freedom,
[df],
its associated probability,
[p(r)], the slope,
[Slope (r)], of the
regression line, and the overall p-value achieved by each analyst for the series of 45 remote
viewings.
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Table *2
(U)
VIEWER NO. 454 RESULTS CODElSfB’^^'O^^Nffl^T
df
P(r)
Slope (r)
-
-
-
0.316
0.0040
0.326
0.702
0.0001
0.509
0.867
0.0001
0.558
0.909
374
0.019
0.441
432
0.004
0.881
0.421
579
0.007
0.768
0.555
642
-0.012
0.573
0.552
UNCLASSIFIED
(U)
All three analyst candidates produced highly significant positive correlations
V
between their p-values and the difference between their p-values and 374’s p-value.
This
indicates that the literal and interpretive analysts will tend to provide more similar p-value
estimates as the quality of the data improves.
(U)
Another indication of the difference between a literal and interpretive analyst is
AM.
Even the most interpretive analyst did not find significance in this data set—i.e.,
p(AM) = 0.44.
Figure 3 demonstrates the effect on p-vaiue estimates of the same data for a
literal and an interpretive analyst from a AM perspective.
An analyst with a large and
positive AM will observe a larger number of significant sessions
(i.e., that portion of the curve
labeled “Matches” above Mkk—the critical value of the figure of merit) than an analyst with a
small, or negative, value of AM.
Thus, an optimal way of selecting analysts
is to choose those
with larger values for AM and smaller values for Slope (r).
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FIGURE 3
(U)
COMPARISON BETWEEN TYPES OF ANALYSTS
%
B.
(U)
Response Definition: Descriptor List Formulation
1.
(U)
Novice Response Descriptor List
(U) A post hoc examination of the FY 1986 novice RV transcripts has resulted in
a summary
list of responses that were considered by the analysts to be the most troublesome
to interpret within the highly specific framework of a twenty-bit descriptor list (see Table
1)
In the RV training paradigm currently used by novices, interpretation by the viewer
is largely
discouraged.
As a result, concrete words, such as city,
lake,
tree, or boat, are often labeled
as analytical overlay (AOL) and must be discarded from the analysis by definition.
Abstract
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(and less interpretive) descriptions, such as rounded,
typically encouraged and are commonly found in transcripteT^TOgg^lu^^^^g
descriptors, therefore, would enable analysts to quantify the contents^r5^ran^>Dj
without having to make the considerable interpretive leap from nebulous xeiponse^
specific, concrete descriptors.
Table 3 summarizes some of the more common"abs(
descriptions gleaned from novice responses, and provides suggestions for candidate
descriptors for incorporation into future lists.*
ab
|^c
^
(U)
It has yet to be determined how abstract and concrete descriptors will be
structured within a given
list—e.g., their interdependence could be either hierarchical in
nature or configured along the lines of semantic networks.
Whatever the mathematical
formalism,
it is anticipated that the addition of abstract descriptors will alleviate much of the
burden of novice response interpretation for analysts.
2.
(U)
Advanced Response Descriptor List
v
(U)
An FY 1986 experiment consisting of 12 outbound sessions was performed in
which an advanced remote viewer (No. 342) was permitted, in an unsupervised fashion,, to
create his own descriptor
list in advance of the experiment.
The viewer was told only that the
experiment was to be of the outbound beacon variety using San Francisco Bay Area targets
and that his
list should consist of approximately 20 to 30 descriptors.
He was given the
novice RV descriptor
list as a template (see Table
1).
The hypothesis under test was that the
viewer, himself, woulA be most knowledgeable about his internal perceptions and would
therefore be most qualified to objectify these perceptions in the form of his own
“personalized” descriptors.
*
*
(U) Table 3 is not meant to be an exhaustive
list of potential abstract descriptors.
It is merely meant to be
illustrative by highlighting some of the more commonly encountered novice RV responses.
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Table 3
(U) CANDIDATE ABSTRACT DESCRIPTORS FOR NOVICE RESPONSES
Typical Novice Responses
Category of Response
Patterns— curved,
circular,
rounded
Patterns— straight,
angled
Patterns— combined
curved and
straight
No discernible patterns
Distinct boundaries
Actual Responses
Suggested Abstract Descriptor
Curved, circular,
circle, oval,
ellipse, round, wavy, rolling,
contours, contoured, sloping
Straight, angled, parallel,
horizontal lines, verticality,
vertical lines, vertical objects,
diagonal
Irregular, shapeless, uneven,
rough, bumpiness, rugged
terrain,
clusters, irregular blobs,
irregular shapes
Areas of light and dark,
light and dark contrast
Unspecified (generic?)
Water, wavy, waves, rippling,
water
water movement, water blue
UNCLASSIFIED
Are patterns of round, curved,
or circular lines significant at
the site?
Are patterns of straight, angled,
or parallel lines significant at
the site?
Are a mixture of curved and
straight patterns significant
at the site?
There are no significant
patterns at the
site.
Distinct boundaries between
light and dark are significant
at the site.
or
Contrasting areas of light and
dark are significant
at
the site.
Is water a significant part
of the scene?
(U)
Table 4 provides a comparison of target and response codings (assigned by an
analyst on a blind basis)
for the target matched with
its correct response (see Figure 4).*
This particular example was chosen because our subjective appraisal tells us that the quality o
fthe response does not seem to be reflected in its overall p-value (0.3289).
The analyst’s
coding of the response and target were evaluated on a post hoc basis and were found to be
(U) The abstract descriptors proposed by the viewer appear to hold promise for codifying some of the
information contained in novice responses (see Table 3)
.
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-
(U)
reasonable—i.e., only one or two bit assignments were arguable, and their reassignment would
not have resulted in a significant p-value, which this session seemed to merit.
(U)
With analyst error seemingly eliminated, attention was focused on the efficacy
of the descriptor list itself.
It was concluded that the
list was deficient in
its ability to capture
certain kinds of information, which largely accounted for the perceived accuracy of the
response, including:
•
The juxtaposition of elements
(i.e., spatial relationships)
•
The “novelty factor” of certain elements in the response
•
The high specificity of named (or alluded to) target elements.
(U)
No mechanism exists, as yet, within descriptor list technology for capturing
the information contained in the spatial relationships between elements.
Clearly, as in the
example cited in Figure 4, this type of information can be very significant—i.e., the viewer
drew his response from the beacon’s actual perspective on the target.
Spatial relationships
appear to be particularly significant in advanced RV responses, in which complex,^composite
drawings are much more prevalent than in novice responses.
This type of information may
eventually be accounted for by employing new technologies such as rule-based expert systems
t
(see Section IV.B.), which lend themselves well to recognition of juxtaposed elements.
(U)
Another factor that is often thought to be important both in novice and
advanced RV responses
is—for want of a better term—the novelty or strangeness of an
element in a response.
An example of this in Figure 4 might include the odd shape of the
structure’s roof—i.e., a curved roof
is a slight departure from normal expectation.
The
operative information here
is embodied in the idea of “architectural oddity,” a concept that
is
quite central to the target and is higher in information content than what
is expressed by the
various combinations of pattern and structure descriptors alone.
Another example
is the
viewer’s statement,
“...like a fence present but not a fence...,” which is a somewhat odd and
uncertain phrase, but actually describes the catwalk guardrails in the target quite well.
An
experienced analyst might consider this latter type of information to be qualitatively better,
because
it represents a viewer’s attempt to objectify his perception without succumbing to the
pitfalls of analytical overlay (AOL)
.
Analyst observations about response novelty can be
systematically tested by devising an element-by-element analysis that can be applied across a
wide qualitative range of responses.
If the analyst “lore”
is correct,
it might be captured
either by a “novelty” fuzzy weighting factor applied to descriptor lists or by expert systems
capabilities.
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No.
342
UNCLASSIFIED
t,
[eS-lZr-r
h'rf/.
—
/
/.ice
t-ooK-ixc
f
Ar So* eTrt,Aas
/
THAfAAAiwJS *S
’
ir co«
?*°*
Otrtfi* 'toJ SWV*1 -
Uave*n<>0
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.
iocurfo *r e&z*
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*/*>«
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,MuW0 06T*erS
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•
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FIGURE 4
(U)
BAYLANDS NATURE INTERPRETIVE CENTER, WITH RV RESPONSE
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(U)
The final factor that led to forfeiture of information entails
specificity of the descriptor
list. A highly detailed response of advanced RV
if the descriptor list quantifying
it cannot register detail.
In the Figure 4 example,
(and possibly unique) pieces of data—like poles being used in the structure’s
construction—are relegated to relatively nonunique bit categories such as “patterns of straight,
parallel, or angled lines..."
Additional highly specific, concrete descriptors
(e.g., poles,
pathways,
etc.), therefore, are essential for descriptor lists quantifying high-quality RV data.
(U)
The question of whether a viewer is better able to devise his own descriptor
list remains unanswered.
Subjectively,
it
is felt that analyst-derived lists have tended to be
more concrete in .nature and that the advanced series described here would have benefited
from that kind of emphasis.
There
is also the additional fact that the viewer was not aware of
the logical consistency rules governing descriptor formulation and application, and that an
analyst’s awareness of these procedural mechanics would have been beneficial to the
construction of the
list.
The viewer’s insights into abstract descriptor composition, however,
were quite invaluable and hold important implications for novice
list construction in particular.
C.
(U)
Target Definition: Implications for Target Pool Composition
(U)
A few preliminary guidelines governing target pool composition have been distilled
from two sources:
(1) the opinions of RV monitors about the appropriateness of various kinds
of RV targets for novice viewers, and (2) RV analysts’ assessments about the difficulties
encountered in using the twenty-bit descriptor list to score the 412 targets currently in the
novice target pool.
(U)
As a general rule, the current subjective consensus
is that targets are inappropriate
for training purposes
if they exhibit any of the following qualities:
•
They are contrary to the viewer’s expectations.
•
They are imbued with negative emotional impact.*
•
They violate the “spirit"
of the descriptor list’s intended use.
*(U)
Laboratory anecdotal evidence suggests that targets having negative emotional impact often result in
psi-missing responses.
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i
(U)
A wide range of target types was used in the FY 1986 novice RV training series
and have been subjectively determined on a post hoc basis to be of varying degrees of
pf
appropriateness for this task.
Table 5 provides specific examples of how the current novice
t
target pool may be problematic, given the following assumptions:
(1) novice viewers had
anticipated that targets for this series would consist of pictures taken from National.
Geographic Magazine featuring large, outdoor,
gestalt scenes
(e.g.
cities, mountains, lakes) of
roughly the same dimensionality; and
(2) the twenty-bit descriptor
list was appropriate for
coding targets of this type only and nothing else—e.g., use of the
list for technical sites or for
targets featuring “unnatural” expressions of the bits was inappropriate.
(U)
While
it has yet to be determined empirically
(i.e., by systematically examining
figures of merit) whether these target types are actually problematic,
it is currently the
subjective opinion of the evaluation team that these kinds of targets would pose the greatest
ri
difficulties for novice viewers.
t
Table 5
(U)
POTENTIAL PROBLEM AREAS FOR NOVICE TARGETS
Target Type
Specific Target Problem
Condition Violated
Viewer
expectation
Emotional
impact
Intended
use df
list
Close-up photo of a small
feature
(e.g., a flower,
tree trunk,
etc.)
Dimensionality
ys
Reflections of rock form-
ations in a
still pool,
Unusual perspective
*s
ys
Moon—off-earth photos
ys
ys
Sunken ship ruins
Underwater photos
ys
S'
Oil derricks
Technical site
ys
is
Whale slaughter
ys
Black & white photos
ys
Standing dead trees
(without foliage)
Significant target element
for which there was no
descriptor
ys
is
Photos with people
and/or animals
Significant target element
for which there was no
descriptor
ys
Ornate interior of the
Vatican during a
ceremony
Too complex for
novices
ys
ys
UNCLASSIFIED
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IV RECOMMENDATIONS (U)
(U)
The results of the FY 1986 evaluation effort have illuminated several areas Of
investigation that may hold promise for improving RV evaluation procedures.
These areas
include
(1) identification of new descriptor lists that more accurately reflect target and
response information,
(2) implementation of enhancement techniques
(e.g., fuzzy set theory)
for attaining greater accuracy from descriptor lists,
(3) systematic examination of inter-analyst
reliability factors, and (4) development of new technologies
(e.g., expert systems)
for
capturing analysts’ insights with greater efficiency.
Several parallel approaches, which address
various aspects of these areas, have been targeted for preliminary research in FY 1987.
These include:
V
•
A “similarity" experiment (proposed by S.
J. P. Spottiswoode) in which
an attempt will be made to identify underlying semantic structures in
remote viewing descriptions of target materials.
•
An approach using artificial intelligence (AI) techniques (proposed by J.
Vallee)
for recognizing, analyzing, and describing target materials.
•
In-house approaches directed at
-
Improvement of existing descriptor
lists by incorporating more
-
abstract descriptors into novice
lists and more concrete descriptors
into! advanced
lists.
Implementation of fuzzy set mathematical weighting factors into
existing descriptor lists in an attempt to decrease their granularity
-
Assessment of the relative merits of analyst-derived versus
percipient-derived descriptor
lists.
Identification of possible percipient-specific “ideogramic
dictionaries,” which might serve as prescriptive, guides for the RV
analyst.
Development of mission-specific descriptor
lists
(e.g., for technical
sites)
.
Each of these approaches
is outlined briefly below.
A.
(U)
Similarity Experiment
(U)
According to the proposal submitted by consultant S.
J. P. Spottiswoode, the
proposed similarity experiment
is aimed at improving existing evaluation techniques through
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...quantification of the informational content of transcripts on a small
set of underlying semantic dimensions which might serve as basis vectors
for the viewer’s internal representation of the target.
If such basis
vectors can be found, complex constructs in the viewing data might be
assembled by combining sets of data so expressed .”9
Using well-established techniques from the area of environmental psychology
(which have been used to solve analogous problems in “normal” perception), the proposed
experiment will attempt to isolate underlying semantic structures in RV perceptions.
In
essence, percipients will be asked to remote view—in sessions of two targets each—all possible
pairs of targets and to estimate the similarity between targets in each pair.
The data will be
analyzed for important factors such as intersubject reliability, presentation order effect, and
target pair effects.
Multidimensional scaling (MDS) analysis*
will then be applied to identify
the underlying semantic dimensions.
Identification of semantic structures wouUl hold
important implications for descriptor
list development and possibly for identification of
fundamental commonalities of perception across viewers.
V
B.
(U)
AI Techniques
(U)
According to the following excerpt from the letter proposal submitted by
consultant J. Vallee, the proposed AI approach
(U)
(U)
...will seek to build an expert system for target recognition by analyzing
the process that enables a human expert monitor to provide an
interpretation of a remote viewing session, or a judge to match a given
description with an actual target.
It
is expected that a rule-based expert system can be developed in a
series of iterations starting with the simple “twenty questions” framework
already used in the project.
Later this will lead to a fully-developed
interactive model. We envision this “smart monitor” taking the analyst
from simple scene and “gestalt” recognition to the detection of breaks,
contradictions and, possibly, analytical overlays as well .” 10
(U)
Assuming promising results with the initial task of target definition,
it is anticipated
that the expert system will ultimately be expanded to play a more active role in operational
remote viewing sessions through on-line capture of respondents’ ideograms and interactive
analysis of their features.
*
(U)
It is beyond the scope of this discussion to describe the details of the MDS analysis.
I’J
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C.
(U)
In-House Effort
(U)
SRI in-house approaches will focus on improvement and, refinement pgjexisting
technologies.
One potential line of inquiry would focus on
abstract descriptors to add to the current novice
list.
This eifort wmild be aimed at
some of the inter-analyst reliability problems that have been encountered with novice^ti
.
••
• •
(see Section III.A.).
An attempt would also be made to incorporate additional concrete
descriptors into advanced
lists.
(U)
A second approach would endeavor to complete work begun in FY 1986
namely, a reanalysis of novice data using fuzzy mathematical weighting factors with the current
list of twenty descriptors.
The hypothesis
is that the greater latitude afforded by fuzzy set
membership values
(as opposed to the
“all or nothing” capability of the descriptors in their
current configuration)
will significantly decrease the granularity of the current
list—i.e., will
allow capture of more information.
If the post hoc reanalysis yields promising results, the
fuzzy set approach would be benchmarked on a blind basis against the current binary
¥
approach.
(U)
A third effort would systematically evaluate the efficacy of viewer-derived versus
(r
analyst-derived descriptor lists.
While viewers are sensitive to their internal perceptions, they
are not cognizant of the requirements of the analytical procedures; conversely, the analyst is
privy to the linguistic/analytical aspects of descriptor
lists, but may be unaware of how to
optimize a viewer’s perceptions using descriptors.
The hypothesis
is that the combined insights
of analyst and percipient will synergistically result in the optimal formulation of descriptor
lists.
One way to test this hypothesis would be to compare statistics across analyst-derived versus
percipient-derived versus combined analyst/percipient-derived descriptor
lists on the same set
of RV data.
(U)
A fourth approach would endeavor to take a retrospective look at viewers’
ideograms and their possible range of meaning.
If, for example, a viewer typically draws a
“tic-tac-toe,” cross-hatch-style ideogram, and post hoc analysis reveals that the drawing
correctly corresponds to the presence of a city in targets 80 percent of the time, this
information might be used to assign an a priori fuzzy weighting factor of 0.8 when the
ideogram is encountered on a blind basis.
An in-depth examination of a viewer’s ideograms,
therefore, might result in the development of viewer-specific, prescriptive guides for the
assignment of fuzzy weighting factors in assessing RV responses.
This type of information
could also conceivably be automated and updated through iterative expert system capabilities.
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* Finally , work will be
init.iat.ed
to develop mission—specif ic
descriptor lists for technical site applications,
i
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V CONCLUSIONS (U)
(U)
The FT 1986 evaluation effort has resulted in
(1) refinement and extension of
current techniques, and
(2)
identification of candidate new technologies for preliminary
research.
(U)
The mathematical formalism for the current evaluation procedure—the figure of
merit analysis—is well understood and stable.
In addition to the system’s
ability to provide a
reasonable assessment of remote viewing data,
it has also provided a mechanism for systematic
examination of inter-analyst reliability factors.
(U)
The descriptor
lists that currently form the basis for the
figure of merit analysis
have been evaluated on a post hoc basis.
Preliminary observations indicate that
lists designed
for novice responses require greater abstract descriptor capability, whereas
lists designed for
advanced responses
(i.e., higher-quality data) require greater concrete descriptor capability.
It
is anticipated that fuzzy set technology will assist in formalizing the interdependence
between abstract and concrete descriptors, by providing a mathematical framework through
which basis vector descriptors can be combined to form concrete descriptors.
(U)
Research into new technologies for RV evaluation
will begin in FY 1987.
One of
these approaches, the proposed ‘‘similarity" experiment, shows promise for identifying basis
vector descriptors. A second approach, using rule-based expert systems,
will explore a
different dimension by endeavoring to capture RV analysts’ expertise
in codifying targets.
Should this
initial effort in
artificial intelligence prove successful,
it
will be expanded to
address the more difficult problem of response interpretation.
At.
It
is hoped
that
this multifaceted approach
to
the refinement
of
RV evaluation procedures will result
in increased capabilities
for
addressing the more complex problems
of mission-oriented^
RV.
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REFERENCES (U)
1.
Puthoff, H. E.
and Targ, R., "A Perceptual Channel for Information Transfer Over
Kilometer Distances: Historical Perspective and Recent Research," Proceedings of the
IEEE, Vol. 64, No. 3 (March 1976) UNCLASSIFIED.
2.
Targ, R., Puthoff, H. E., and May, E. C., 1977 Proceedings of the International
Conference of Cybernetics and Society, pp. 519-529 UNCLASSIFIED.
3.
May, E. C., “A Remote Viewing Evaluation Protocol (U),” Final Report (revised), SRI
Project 4028,
SRI International, Menlo Park, California
(July 1983) SECRET.
4.
May, E. C., Humphrey, B.
S., and Mathews, C., “A Figure of Merit Analysis for
Free-Response Material,” Proceedings of the 28th Annual Convention of tfie
Parapsychological Association, pp. 343-354, Tufts University, Medford, Massachusetts
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5.
Fisher, R. A.,
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London, England (1938) UNCLASSIFIED.
6.
Edgington, E.
S., “A Normal Curve Method for Combining Probability Values from
Independent Experiments,” Journal of Psychology, Vol. 82, pp. 85-89 (1972)
UNCLASSIFIED.
t
7.
Cooper, B. E.,
“Statistics for Experimentalists,” pp. 219-223, Pergamon Press, Oxford,
England (1969) UNCLASSIFIED.
8.
Zadeh, L. A., “Fuzzy Sets versus Probability,” Proceedings of the IEEE, Vol. 68, No.
3, p. 421 (March 1980) UNCLASSIFIED.
9.
Spottiswoode,
S.
J.
P., “Proposals for Experimental Studies on Semantic Structure and
Target Probability in Remote Viewing,” Private Communication
(July 1986)
UNCLASSIFIED.
10.
Vallee,
J., “Applications of Artificial Intelligence Techniques to Remote Viewing:
Building an Expert System for Target Description,” Private Communication (August
1986) UNCLASSIFIED.
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