Ann ed For Release 2000/08/RECREPP96-00789ROO2200350001-9
Final Report- Oblective F, Task I December 1988
~overlng the Period I October 1987 to 30 September 1986
0
0
APPLICATIONS OF FUZZY SETS TO REMOTE
VIEWING ANALYSIS (U)
By: EDWIN C. MAY THANE J. FRIVOLD
BEVERLY S. HUMPHREY JESSICA M. UTTS
Prepared for:
Peter J. McNelis, DSW
CONTRACTING OFFICER'S TECHNICAL REPRESENTATIVE
SRI Project 1291
WARNING NOTICE
n
RESTRICTED DISSEMINATION TO THOSE WITH VERIFIED ACCESS SG1A
TO THE PROJECT
Approved by:
Copy -~-~-of 5 Copies
MURRAY J. BARON, Director This document consists of 9 pages
Geosclence and Engineering Center SRI/GF-0318
CLASSIFIED BY: HQ, USAMRDC (SGRD-ZA)
DECLASSIFY ON: OADR
NOT RELEASABLE TO
FOREIGN NATIONALS
ow
SECRET
333 Ravenswood Ave. 9 Menlo Park, CA 94025
ted For R61"giP-266OYd-W68"t(A--*bFS~'6!b"O~VOI~bO2200350001-9
Approved For Release 210pfW?
..6 W ~%JgflqVD00789RO02200350001-9
MW
AW
I INTRODUMON (U)
(U) Since the publication of results of the initial remote viewing (RV) effort at SRI
International (SRI)*' two basic questions have remained in evaluating remote viewing data:
~ What is the definition of the target?
~ What is the definition of the RV response?
(U) The first attempt at quantitatively, defining an RV response involved reducing the raw
transcript to a series of declarative statements called concepts.2 It was found 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. It was also
extremely labor intensive and did not readily allow for rapid processing of RV data.
(U) In 1983, a procedure was developed to define both the target and response material.3
Now 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 specific information
about the RV process, then possibly specific items at the site are important while others remain
insignificant.
(U) In 1984, work began on a computerized evaluation procedure, which underwent
significant expansion and refinement during 1985.4 The mathematical formalism underlying this
NNW procedure is known as the "figure of merit" (FM) analysis. This method is predicated on
descriptor list technology, which represented a significant improvement over earlier "conceptual
am analysis" techniques, both in terms of "objectifying" the analysis of RV data and in increasing the
speed and efficiency with which evaluation can be accomplished. These techniques were based
upon the pioneering work of Honorton et al. to encode target and response material in
accordance with the presence or absence of specific elements.5
(U) References may be found at the end of this report.
UNCLASSIFIED
Approved For Release 2000/08/08 : CIA-RDP96-00789ROO2200350001-9
Approved For Release Oft SISIFRED00789ROO2200350001-9
(U) It became increasingly evident, however, that this particular application of descriptor
lists was inadequate in providing discriminators that were "fine" enough to describe a complex
target accurately; it was also unable to exploit fully the more subtle or abstract information
content of the RV response. To decrease the granularity of the RV evaluation system, therefore,
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 hard-edged (and rather imprecise),
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) revealed a branch of mathematics, known as "fuzzy set theory,"
which provides a mathematical framework for modeling situations that are inherently imprecise.
(U) During FY 1986 and FY 1987, a fuzzy set implementation of remote viewing analysis
was developed. 6,7 The primary application of this new technology, however, was to create an
objective measure for target orthogonality, The orthogonal targets were then used in rank-order
judging.
(U) During FY 1988, the analysis task was to determine appropriate parameters for fuzzy
set remote viewing analysis. To accomplish this task, SRI reanalyzed the RV data collected
during FY 1987, trimmed the National Geographic magazine target pool, and explored various
ways to encode RV data in an entropy formalism.*
*(U) This report constitutes the deliverable for Objective F, Task 1.
2
UNCLASSIFIED
Approved For Release 2000/08/08 : CIA-RDP96-00789ROO2200350001 -9
Approved For Release 2000/ _~.Mw&.Aft ~. M ~ - P96-00789ROO2200350001-9
11 TECHNICAL DISCUSSION (U)
A. (U) Retrospective Analysis
(S/NF) We have reanalyzed all of the remote viewing experiments conducted during FY
1987 that used National Geographic magazine targets. There were a total of 292 sessions from
the tachistoscope, real-time versus precognition, and hypnosis experiments. Using an overall
p-value < 0.05 as a definition of statistical evidence of RV, only the real-time versus
precognition experiment failed to meet that criterion.
(S/NF) During FY 1987, the analysis of these data used a subjective rank-order
technique. For each RV response, the intended target and 6 decoys were ranked in order from
most to least correspondence. The combined average sum-of-ranks was 3.781, where the
expected average was 4.00 (z = 1.87; p :.-5 0.031). Thus, even including the real-time versus
precognition experiment, the total RV effort for FY 1987 showed statistical evidence of an
information transfer anomaly.
(S/NF) It is possible that a mechanism other than psychoenergetics could account for this
overall result. Suppose that analysts tended to rank the target packs in order of complexity--the
most complex first, the least last. That is to say, a target with an abundance of elements would
have more correspondence with any response, psychoenergetically mediated or not. To examine
this hypothesis, complexity was defined as the total number of target elements such that their
membership (in the target fuzzy set) was non-zero.* Two distributions were then constructed:
(1) The distribution of complexities for the targets ranked first by the analyst
(2) The distribution of complexities for the correct target regardless of rank.
(S/NF) Figure I shows these two distributions. The black histogram clearly demonstrates
a bias (X2 = 11.30, df = 6; p :5 0.08) on the part of the analysts to favor the most complex target
as the best match to a given response. This is to be expected, in that the instructions to the
analysts are to find the best match between target and response. Thus, especially for noisy data,
it is not surprising to find such a bias. On the other hand, the complexity distribution shows no
*(U) The universe of elements for the target fuzzy sets was described during FY 1987,7 but
is repeated here in the Appendix.
3
Approved For Release 2000/08/08: CIA-RDP96-00789ROO2200350001-9
Approved For Release 2000. P96-00789ROO2200350001-9
(S/NF)
such bias for the intended target (X2 9.29, df = 6; p :!5; 0. 16). In other words, since the
intended target is chosen by a random number generator, the cross-hatched histogram is a
simple test of the randomization algorithm. To test the null hypothesis that the proportions are
the same in the two distributions, a chi-square was computed where the expected value in each
cell was the row-total times the column-total divided by the grand-total. The proportions are
significantly different for these distributions (X2 = 15.35, df = 6; p < 0.018). Thus it is unlikely
that judging bias in favor of the most complex target can account for the overall significant
evidence of RV during FY 1987.
B. (U) Target Pool Reduction
(U) To provide a more manageable target pool for rank-order judging, we reduced the
original National Geographic magazine target pool from 200 to 100 targets. The fuzzy set
approach, in conjunction with cluster analysis, was used to produce 20 sets of 5 orthogonal
targets. These sets were "fine tuned" by visual inspection to provide the best possible target sets.
Approximately 20 percent of the targets required changing. The set of 100 targets was
photographed and duplicated to form to identical target pools, one for analysis purposes only and
the other for target purposes only. Separating these functions into two separate pools ensured
that there could be no inadvertent handling cues (i.e., the experiment team "marking" the
intended target so the analyst could recognize it).
4
--je-e-Im n eir
Approved For Release 2000/08/08 : CIA-RDP96-00789ROO2200350001-9
SECRET/NOFORN
FIGURE I (U) COMPLEXITY DISTRIBUTIONS FOR FIRST-RANKED
AND INTENDED TARGETS
mw Approved For Release 2tyllet8A(S,&fFMD00789ROO2200350001-9
C. (U) Entropy Encoding
ow
(U) One of the most pressing problems in remote viewing, one which must be solved
before any basic models can be developed, is determining the quantitative amount of information
ON that is transferred during the procedure. There have been a number of attempts to quantify the
information content in natural scenes in the past, but none of them appeared to work as a
MW reasonable description of either the target or the response.
(U) One approach that has been tried in the past is to define an entropy-like measure for
00i the elements of a fuzzy set. 8 Unfortunately, these approaches assume that some estimate of a
"random" fuzzy set can either be assumed or calculated. In remote viewing terms, this amounts
no to knowing how a viewer might respond in a session in which there was no defined target. In
free-response experiments, this is referred to as a response bias. Response biases are difficult to
measure, and are very strong functions of time.
MW
(U) To obtain an estimate of the average response bias of a given viewer during an RV
series, we modified an earlier attempt. Assuming all response errors are due to bias, we define,
for a given viewer, a bias fuzzy set, B, whose elements and membership values are defined by
N N
pk(B) =1[Z,"J(R) - ZYkXTnR)
N J=1 1=1 I
In the general case, the g(X) notation indicates that the g-values are from the set X, and T and
R are the target and response sets, respectively. In words, the above relationship is the total
response in a series of N trials (for a given element) minus that part of the response that was
correct (i.e., possessed some overlap with the intended target). Each element, k, in B,
represents the average value for the response being incorrect, or, ostensibly, a result of bias. (See
the Appendix for the universe of elements, k).
(U) There are a number of ways in which this bias set can be used. One is to simply
reduce the assigned (by the analyst) g-values by a percent equal to their associated value in B to
MW account for the bias contribution,
Yk W) =,Uk (R) (I -,uk (B)).
For example, if the bias membership value for the roads-bit was 0.15, the transformed value
Moli would be (1.0 - 0. 15) times the assigned value, or a 15 percent reduction of the assigned value.
R' represents the adjusted value (by the average bias) for a response, R.
w1i
5
so UNCLASSIFIED
Approved For Release 2000/08/08 : CIA-RDP96-00789ROO2200350001-9
Approved For Release 2ty1qtl8A(SAS-fl"DO0789ROO2200350001-9
(U) For the first attempt at using R' to obtain an estimate of the accurate information
transferred during a remote viewing experiment, we used basic information theory. In the
theory, entropy is defined as a measure of uncertainty. The more uncertain, the larger the
entropy. Correspondingly, complete certainty implies zero entropy. In symbols, a formal
definition of entropy13 for a fuzzy set is given as
H(X) 2:,Uk(X)1092(Uk(X))-2:(I-Ak(X))1092(1-Ak(X))
k k
(U) If the usual probabilistic interpretation of entropy is to be adopted, then we must
scale the g-values to the interval [0.5,1]. The maximum uncertainty about a given bit is a
g-value of 0 (assigned by an analyst). If this value is shifted to 0.5, then H(X) is a maximum.
(U) The most uncertain response that a viewer can contribute is a blank page. All the
assigned g-values would be zero; the transformed values would be 0.5. If we consider the target
set to be an oe-cut of the target fuzzy set T, we define the maximum entropy possible for a
response, HO, as follows:
H~ = - 1 0.5 log,(0.5) 0.5 log2(0.5) of target bits.
k(targetbits) k(targetbits)
For any non-null response, the entropy is defined as
H(R') = - 2: Yk (TnR') 1092 (Uk (TnR')) - 10 -.Uk (TnR')) 10920 -,Uk(TnR')).
The sums in H(R') are over all bits in T or R'. It is important to realize that the primed values in
R are used, so H(R') accounts for a possible response bias. Finally, the information perceived
Oudged) in an RV session is defined as the difference between the maximum uncertainty of a
response and its observed uncertainty. In symbols:
AH(R') = HO (R') - H(R')
(U) In order to test this and other ideas, it was necessary to have a database of encoded
targets and responses. Thus, all of the responses for the tachistoscope experiment were coded in
the fuzzy set representation using the universe of elements shown in the Appendix.
(U) To determine whether such an information encoding made sense, it was important to
develop a criterion to define success. Since we could use the above formulation for the actual
response, the modified (by the bias) responses, or a set of randomly assigned cross-matches
6
UNCLASSIFIED
Approved For Release 2000/08/08 : CIA-RDP96-00789ROO2200350001-9
dft_~_'dft
Approved For Release 200...... -P96-00789ROO2200350001-9
maw
(U)
so (i.e., responses assigned to targets that were not the intended target for the session), we were
able to explore a number of options. If the information channel is not saturated, then it is
Ow reasonable to assume that the more information available in the target, the more information
could be received via remote viewing. The criterion that was adopted was that the information
calculated from a set of randomly selected cross-matches could not show significant correlations
go
with the complexity (defined by the sigma count) of the associated targets.
MW (SINF) Unfortunately, this method failed. A strong correlation was found between the
cross matches and target complexity. In retrospect, the problem is obvious. Even using the
modified responses, the probability of a match with a random target increases with target
complexity (i.e., the more that is said, the more likely that there is a match to a random target).
(S/NF) We explored a number of different variations on the above formalism. To date,
however, we have been unable to arrive at an appropriate formulation that meets the above or
other criteria for a measure of information transfer during remote viewing experiments.
Approved For Release 2000/08/08 CIA-RDP96-00789ROO2200350001-9
Zy"DE
Approved For Release 2000/0F1W,*&~ ~IM-E%Z;P96-00789ROO2200350001-9
III CONCLUSIONS AND RECOMMENDATIONS (U)
(SINF) It is extremely likely that there is an even more fundamental reason why the
various procedures failed as a measure of information transfer during remote viewing. The
elements from which the target and response sets are drawn are not of equal weight in
information space. For example there is considerably more information (in any sense of that
term) contained in an element such as church compared to an abstract element such as
horizontal lines. Yet in this first attempt, the g-values were all weighted equally.
(U) One direction that deserves exploration is limiting the target descriptions (by the
weighting factors for each element in the fuzzy set) to sets of targets that appear to have constant
"information" content. This might allow for a more systematic search for an appropriate
information representation.
(U) Another problem was that the most uncertainty in a response was assumed to be a
blank page. In the final days of FY 1988, Dr. L. Gatlin, a specialist in biological information
systems, suggested that we approach the problem from a different point of view. The most
uncertain situation is that in which a viewer is completely driven by his/her own response biases.
Thus, Ho should be calculated from the bias set, B, or something like it.
(U) It is very important to continue along these lines. Until a meaningful encoding of the
information transferred during remote viewing experiment is found, there is little hope of success
for quantitative modeling. We recommend that a consultant be found who is a specialist in
mew
applying information theory to natural scenes and natural language.
8
jftffW^r%0M- -
., a. %W 1% 16 1
Approved For Release 2000/08/08 : CIA-RDP96-00789ROO2200350001-9
MW
Approved For Release 2EPMEBAS13MM00789ROO2200350001-9
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 (1977)
UNCLASSIFIED.
3. May, E. C., "A Remote Viewing Evaluation Protocol (U),"
Final Report (revised), SRI
International 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 281h Annual
Convention of the
Parapsychological Association, pp. 343-354, Tufts University,
Medford, Massachusetts
(August 1985) UNCLASSIFIED.
5. Honorton, C., "Objective Determination of Information Rate
in Psi Tasks with Pictorial
Stimuli," Journal of the American Society for Psychical
Research, Vol. 69, pp. 353-359
(1975) UNCLASSIFIED.
6. Humphrey B. S., May, E. C., Trask, V. V., and Thomson,
M. J., "Remote Viewing
Evaluation Techniques," Final Report, SRI International
Project 1291, SRI International,
Menlo Park, California (December 1986) SECRET.
7. Humphrey B. S., May, E. C., Utts, J. M., Frivold, T. J.,
Luke, W. L., and Trask, V. V.,
"Fuzzy Set Applications in Remote Viewing Analysis," Final
Report, SRI International
Project 1291, SRI International, Menlo Park, California
(December 1987)
UNCLASSIFIED.
8. Luca A, De, and Termini, S., "A Definition of Nonprobabilistic
Entropy in the Setting of
Fuzzy Sets Theory," Information and Control, Vol. 20, pp.
301-312 (1972)
MW UNCLASSIFIED.
am
"Mi
.W10
ow
9
.WW UNCLASSIFIED
Approved For Release 2000/08/08 : CIA-RDP96-00789ROO2200350001-9
ow
Approved For Release 2UMEPASISif4egOO789ROO2200350001-9
APPENDIX (U)
UNIVERSE OF ELEMENTS FOR TARGET AND RESPONSE FUZZY SETS (U)
(This Appendix is Unclassified)
A-1
UNCLASSIFIED
Approved For Release 2000/08/08 : CIA-RDP96-00789ROO2200350001-9
t I I t I t
M
CL
-n
0
M
M
IQ
0
0 C:
0
0 z
00
0-
00
0 >>
>
U
tom
0)
6
0
4
00
CA)
4D
CONCRETE
DESCRIPTOR
LEVELS
I
Experiment:
Trial:
Response:
Coder:
Viewer:
LEVEL SINGLE STRUCTURES SUBSTRUCTURES
1 fort
2 castle
3 palace
4 church (other religious buildings, monastery)
5 mosque
6 pagoda
7 coliseum (stadium,
amphitheater, arena)
10 boats (barges)
bridge
pier Oetty)
[dam (lock, spillway))
i
ed vehicles
[moto
(cars, trucks, trains)]
13 column
14 spire (minaret, lower)
9
is fountain
16 fence
17 arch
18 wall (e.g., the Great Wall)
19 monument
20 roads
M
CL
-n
0
(D
CO)
M
I.J
0
0 C=
z
00
00
0
> CAW
CA
U
6
0
4
00
to
CA)
01
4D
t t
t
CONCRETE DESCRIPTOR 11 Experiment:
LEVELS
Trial:
Respon se:
Coder:
Viewer:
LEVEL SETTLEMENT ELEVATION LAND/WATER NO VEGETATION AMBIENCE/
WATER OR
INTERFACE FUNCTION
VEGETATION
agricultural
21 port (harbor) 23 24 industrial
C
[oasis] fields
22 (orchards) 25 recreational
26 religious
27 mechanical
7
28 technical
29 agricultural
0
3
commercial
ruins mesa
34 (incompletelateau) 36 waterfall 39 desert 40 forest 31 wilderness
(
buildings) p 37 glacier 41 jungle 32 urban
38 canal(channel, 42 swamg) 33 rural
d r r
(mars (pastoral)
manma
e 131 )iistorical
waterway) karchaeological)
54 unbounded large vegetation
43 isolated 46 single peak
settlement expanse of water F
so (trees)
hills (slopes, ~ocean, sea)
44 town (village) completely bounded
471 humps
55
bumps, ex
anse or water
mounds) p
45 city 48F__1 mountains(lake, pool, pond)
partially bounded
5 49 cliff (S) 56 expanse of water (bay)
so [plain, delta]57 island
valley (cleft, 68 river (stream, creek)
irrc
l
I
51
,
.4 59 coastline
Ee
ression
p
52 canyon
.S3 [crater,
bowl-
shape, regular
depression]
(D
CL
-n
0
(D
0 Z
00
0
00
0 >>
0
0
(D
M
4
00
co
;U
CA
71
(0
t t
t
ABSTRACT I
DESCRIPTOR
LEVELS
Experiment:
Trial:
Response:
Coder:
Viewer:
QUALITIES
LEVELCOLOR OTHER IMPLIED IMPLIED IMPLIED AMBIENCE
VISUAL TEXTURE TEMPERATURE MOVEMENT
61 yellow 71 shiny (reflective) so smooth 85 hot 89 flowing 91 congested
(cluttered,
dense, busy)
62 orange 72 [gold) 81 fuzzy 96 cold (snow, ice) 90 F other f
l
serene (peace
implied u
3 red 73 [silver] 82 graig (sandy, 87 humid movement 92 ,
unhurried,
unfrenefic)
64 blue 74 [chrome] Crum ly) 99 dry (arid) 93 closed in
rocky (ragged,
93 ru d d (claustrophobic)
,ag,e
W(,,
65 green 73 [copper]
le
4 " 94 open (spacious,
rough5 vast, expansive)
,
66F purple 76 obscured (fuzzy, 84 striated
(pink) dim, smoky)
67 brown 77 cloudy (foggy, 93 ordered (aligned)
(beige) misty)
96 disordered
68 black 78 old (jumbled,
unaligned)
69 white weathered
79 (eroded,
70 grey incomplete)
ARCHETYPES
STRUCTURE ELEVATION INTERFACE UNIQUENESS AMBIENCE
97 building(s) rise (vertical rise ligbildark areas single (or central)
(structureks)) 99 as well as slope) too swaths) 104 manmade
1- predominant feature
106 (or altered)
3 99 flat 101 boundaries odd (or surprising)
105 osition of 107
natural
juxta
102 !and[water p
elements
interface
103 !and/sky
I interface (horizon)
I t 1. 1 1 1 k I I t t I
>
0
(D
CL
-n
0
(D
111.)
Q C
Q
Q Z
Q
!~O. r)
Q
00
>>
0
6
Q
4
00
Q
Q
Q
Q
W
C"
Q
Q
7a,
ABSTRACT DESCRIPTOR Experiment:
LEVELS 11
Trial:
Response:
Code
Viewer:
---- - --- - -----
2-D & 3-D GEOMETRIES
RECTILINEAR CURVILINEAR MIXED IRREGULAR REPEAT
LEVEL I
FORMS FORMS FORMS FORMS MOTIF
rectangle 112 circle 114 cylinder 117 -3 irregular repeat
109 (square, (oval, motif
sphere) F forms
box) 115 cone (irregular
113 [torus] features)
triangle
semicircle
109 (trapezoid, 116 (hemisphere,
F
pyraitud) uume)
o1ber polygonal
110 (> 4 sides:
hexagon,
octagon, etc.)
cToss-hatch
i
d)
(gr
1-D GEOMETRY VISUAL CORRESPONDENCE
119 stepped 127 arc (curve)130 meandering Curve tank order fraction
120 parallel lines128 wave form
(ripples)
121 vertical lines129 spiral
122 horizontal
lines
1 123 diagonal lines
124 V-shape
125 inverted V-shape
J26F___j other
angles