APPLICATION OF FUZZY SETS TO REMOTE VIEWING ANALYSIS

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Final Report--Objective F, Task 1. December 1988
Covering the PC'riod 1 October 1967 to 30 September 1988
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APPLICATIONS OF FUZZY SETS TO REMOTE
VIEWING ANALYSIS (U)
Prepared for:
SRI Project 1291
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I INTRODUCTION (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
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 formali sm underlying this
procedure is known as the "figure of merit" (FM) analysis. This method is predicated on
descriptorilist technology, which represented a significant improvement over earlier "conceptual
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.
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(U) It became increasingly evident, however, that this particular application of descriptor
listswas inadequate in providing discriminators that were "fine" enough to describe a complex
arget accurately; it was also unable to exploit fully the more subtle or abstract information
ontent of the RV response. To decrease the granularity of the RV evaluation system, therefore,
technology would have to evolve in the direction of allowing the analyst a gradation of
gment about target and response features, rather than the hard-edged (and rather imprecise),
-or-nothing binary determinations. A preliminary survey of various disciplines and their
Valuation methods (spanning such diverse fields as artificial intelligence, linguistics, and
onmental psychology) revealed a branch of mathematics, known as "fuzzy set theory,"
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
rdeveloped.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
udging
(U) During FY 1988, the analysis task was to determine appropriate parameters for fuzz
y
demote viewing analysis. To accomplish this task, SRI reanalyzed the RV data collected
during'FY1987, trimmed the National Geographic magazine target pool, and explored various
'
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o'encode RV data in an entropy formalism. *
(U) This report
constitutes the deliverable for Objective F, Task 1.
UNCLASSIFIED
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II TECHNICAL DISCUSSION (U)
A. (U) Retrospective Analysis
i 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.
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 S 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.
j 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.
Figure 1 shows these two distributions. The black histogram clearly demonstrates
a bias (Xz = 11.30, df = 6; p < 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.
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evidence of RV during FY 1987.
such bias for the intended target (X2 = 9.29, df = 6; p < 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
16
FIGURE 1 (U) COMPLEXITY DISTRIBUTIONS FOR FIRST-RANKED
AND INTENDED TARGETS
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).
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,One of the most pressing problems in remote viewing, one which must be solved
y basic models can be developed, is determining the quantitative amount of information
nsferred during the procedure. There have been a number of attempts to quantify the
A
a ion content in natural scenes in the past, but none of them appeared to work as a
19S'le description of either the target or the response.
One approach that has been tried in the past is to define an entropy-like measure for
ments of a fuzzy set-8 Unfortunately, these approaches assume that some estimate of a
Qm fuzzy set can either be assumed or calculated. In remote viewing terms, this amounts
ring how a viewer might respond in a session in which there was no defined target. In
}onse experiments, this is referred to as a response bias. Response biases are difficult to
5p
:To:obtain an estimate of the average response bias of a given viewer during an RV
we modified an earlier attempt. Assuming all response errors are due to bias, we define,
given viewer, a bias fuzzy set, B, whose elements and membership values are defined by
rN N
/Lk(B) = N[/zk.j(R) - luk.t(T n R)1 J. 1:1 i J
141 general case, the ?(X) notation indicates that the ?-values are from the set X, and T and
the target and response sets, respectively. In words, the above relationship is the total
Mponse in a series of N trials (for a given element) minus that part of the response that was
possessed some overlap with the intended target). Each element, k, in B,
'esents the average value for the response being incorrect, or, ostensibly, a result of bias. (See
ppendix 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
uce the assigned (by the analyst) ?-values by a percent equal to their associated value in B to
4i
fCount_ fof the bias contribution,
/k (R') =,uk(R) (1-/2k(B))
example, if the bias membership value for the roads-bit was 0.15, the transformed value
d.;be (1.0 - 0.15) times the assigned value, or a 15 percent reduction of the assigned value.
epresents the adjusted value (by the average bias) for a response, R.
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(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) I/Sk(X) log2(Uk(X)) -1(1-ilk (X)) log2(1-fzk(X))
k k
(U) If the usual probabilistic interpretation of entropy is to be adopted, then we must
scale the ?-values to the interval [0.5,1]. The maximum uncertainty about a given bit is a
?-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 t-values would be zero; the transformed values would be 0.5. If we consider the target
set to be an a-cut of the target fuzzy set T, we define the maximum entropy possible for a
response, Ho, as follows:
Ho = - 1 0.510$2(0.5) - Z 0.5log2(0.5) = # of target bits.
k(target bits) k(target bits)
For any non-null response, the entropy is defined as
H(R') = - E/Uk(T n R') 1og2(uk(T n R')) - 1: (1-?k(T n R'))1o92(1 -uk(T n R')).
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
(judged) 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
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(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
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
with the complexity (defined by the sigma count) of the associated targets.
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).
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.
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III CONCLUSIONS AND RECOMMENDATIONS (U)
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___,_,1 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 ?-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
applying information theory to natural scenes and natural language.
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REFERENCES (U)
1
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 28th 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)
UNCLASSIFIED.
UNCLASSIFIED
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APPENDIX (U)
UNIVERSE OF ELEMENTS FOR TARGET AND RESPONSE FUZZY SETS (U)
(This Appendix Is Unclassified)
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