REMOTE VIEWING EVALUATION TECHNIQUES

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• Collection: STARGATE
• Document Number (FOIA) /ESDN (CREST): CIA-RDP96-00789R003800450001-1
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• Document Creation Date: November 4, 2016
• Document Release Date: May 18, 2000
• Sequence Number: 1
• Publication Date: December 1, 1986
• Content Type: REPORT

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Final Report- -Objective A, Task 4 December 1986
REMOTE VIEWING EVALUATION TECHNIQUES (U)
By: BEVERLY S. HUMPHREY VIRGINIA V. TRASK
EDWIN C. MAY MARTHA J. THOMSON
PETER J. McNELIS, DSW
CONTRACTING OFFICER'S TECHNICAL REPRESENTATIVE
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Final Report- -Objective A, Task 4 December 1986
Covering the Period 1 October 1985 to 30 September 1986
9
By: BEVERLY S. HUMPHREY VIRGINIA V. TRASK
EDWIN C. MAY MARTHA J. THOMSON
PETER J. McNELIS, DSW
CONTRACTING OFFICER'S TECHNICAL REPRESENTATIVE
REMOTE VIEWING EVALUATION TECHNIQUES (U)
SG1A
WARNING NOTICE
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Geoscience and Engineering Center
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ABSTRACT (U)
(U) A simplified automated procedure is suggested for the analysis of free-response
material. As in earlier similar procedures, the target and response materials are coded as
yes/no answers to a set of questions (descriptors). By definition, this coding defines the
complete target and response information. The accuracy of the response is defined as the
percent of the target material that is correctly described (i.e., the number of correct response
bits divided by the number of target bits = 1). The reliability of the response is defined as
the percent of the response that is correct (i.e., the number of correct response bits divided
by the total number of response bits = 1). The figure of merit is the product of the accuracy
and reliability. The advantages and weaknesses of the figure of merit are discussed with
examples. Mean chance expectations (MCE) are calculated for the figure of merit, and
recommendations are made to extend current techniques and to explore new technologies.
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TABLE OF CONTENTS (U)
ABSTRACT .................................................................. ii
LIST OF ILLUSTRATIONS .................................................... iv
LIST OF TABLES ............................................................ v
I
II
INTRODUCTION .................................................... 1
METHOD OF APPROACH ............................................ 3
A.
Figure of Merit Analysis .......................................... 3
1.
2.
Overview .................................................. 3
Mathematical Formalism ..................................... 3
a.
Definitions ..... .............................. 3
b.
Linear Least-Squares Analysis ............................ 6
c.
Mean Chance Expectation ...
........................ 7
d.
Probability Assessment and Analysis
....................... 8
B. Fuzzy Set Theory--An Enhancement Technique for Descriptor List
Technology .................................................... 11
1.
2.
3.
4.
Overview ................................................. 11
A Tutorial ................................................ 12
Initial Application to RV Evaluation ........................... 14
Potential Future Applications ................................. 14
III
RESULTS ......................................................... 16
A.
Inter-Analyst Reliability Factors ................................... 16
B.
Response Definition: Descriptor List Formulation .................... 19
1.
Novice Response Descriptor List .............................. 19
2.
Advanced Response Descriptor List ........................... 20
C.
Target Definition: Implications for Target Pool Composition ........... 25
IV RECOMMENDATIONS .............................................. 27
A. Similarity Experiment ........................................... 27
B. Al Techniques ................................................. 28
C. In-House Effort ................................................ 29
V CONCLUSIONS .................................................... 31
REFERENCES ............................................................... 32
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LIST OF ILLUSTRATIONS (U)
1.
An MCE Figure of Merit Distribution ....................................... 9
2.
The Fuzzy Set "Very Young" .............................................. 13
3.
Comparison Between Types of Analysts ...................................... 19
DO
4.
Baylands Nature Interpretive Center, With RV Response ........................ 24
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LIST OF TABLES (U)
1. Descriptor-Bit Definition .................................................. 4
2. Viewer No. 454 Results Coded by Four Analysts .............................. 18
3. Candidate Abstract Descriptors for Novice Responses ........................... 21
4. Comparison of Target vs. Response Coding for "Baylands" Target ................ 22
5. Potential Problem Areas for Novice Targets .................................. 26
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I INTRODUCTION (U)
'(S) Since the publication of the initial remote viewing (RV) effort
at SRI International*t, 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 an intelligence-oriented 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.
(S) 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 to the intelligence
community, 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 as
intelligence data. What is needed is a specific technique to allow
assessments that are mission-oriented.
(S) 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
operational RV intelligence 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) 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.
(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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(U) DESCRIPTOR-BIT DEFINITION
Bit
No.
Descriptor
1
Is any significant part of the scene hectic, chaotic, congested, or cluttered?
2
Does a single major object or structure dominate the scene?
3
Is the central focus or predominant ambience of the scene primarily natural
rather than artificial or manmade?
4
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.)
5
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)?
6
Is a mountain, hill, or cliff, or a range of mountains, hills, or cliffs a significant
feature of the scene?
7
Is a volcano a significant part of the scene?
8
Are buildings or other manmade structures a significant part of the scene?
9
Is a city a significant part of the scene?
10
Is a town, village, or isolated settlement or outpost a significant feature of the
scene?
11
Are ruins a significant part of the scene?
12
Is a large expanse of water--specifically an ocean, sea, gulf, lake, or bay--a
i
ifi
f
gn
s
cant aspect o
the scene?
13
Is a land/water interface a significant part of the scene?
14
Is a river, canal, or channel a significant part of the scene?
15
Is a waterfall a significant part of the scene?
16
Is a port or harbor a significant part of the scene?
17
Is an island a significant part of the scene?
18
Is a swamp, jungle, marsh, or verdant or heavy foliage a significant part of
the scene?
19
Is a flat aspect to the landscape a significant part of the scene?
20
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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(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
m
Rk= Z R.k
j=1
where R. k = 1 if bit [j] were answered affirmatively and equal to 0 otherwise. Likewise
the total number of bits in a specific target, [k], as
m
T k = Tk
j=1
where T. k = 1 if bit [j] were answered affirmatively and equal to 0 otherwise.
(U) The accuracy of response, [k], (the percent of target [i] that is
described correctly) is given by
MR k T, i
a ki T.
j=1 T.
The reliability of response [k] (the percent of response [k] that was correct) is given by
m R,k T,i j
j r ki = . R
j=1 k
(U) Finally, the figure of merit for response [k] matched against target [i] is
s given by
M ki = aki X r
(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 0 i).
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b. (U) Linear Least-Squares Analysis
(U) After [n] remote viewing sessions have been completed and the analysis
described above performed using k = i, there are [n] figures of merit, one for each RV
session, in order of session number. To examine if there are any systematic variations within
this data, a best-fit straight line is fitted through the figures of merit using standard
techniques. If [x] is the session number (x=1, ..., n), consider a straight line defined as
M (x) = a + b (x-x)
where a = M(x) and x = constant, and [a] and [b] are the intercept and slope, respectively.
Suppose there are [n] pairs of points, (x,M x) . Then the slope, which is calculated by a
standard least-squares technique, is given by
n n nn~+
n z x M x- Ex L.r M x
b x=1 x=1 x=1 , where A is given by
n n 2
=n~x2-I
x=1 x=1 ff
The intercept is given by
a = a + b x , where a~ is given by
n n n n
E x22;Mx - 2: x TxMx
x=1 x=1 x=1 x=1
If we set x = to the average value of the session number, then
1 n
x = n Fx
x=1
and [a] becomes the average value of the figure of merit. Thus
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(U) This analysis produces the average figure of merit for a series and an
indicator, the slope, of learning.
(U) The calculation for the mean chance expectation (MCE) must be
sensitive to a number of possible artifacts or confounding factors:
? Viewer variations (e.g., viewers' different response biases).
? General knowledge of the target pool (e.g., targets are known
to be National Geographic magazine photographs).
? Specific knowledge of the target pool resulting from
trail-by-trial feedback.
? Methodological considerations (e.g., viewers are asked to
respond with more data at the end of the series compared to
what was asked of them at the beginning of the series).
All of these factors will affect the expected average figure of merit and any session-to-session
systematic variation that may be present.
(U) A method for determining the figure of merit MCE, which requires the
fewest number of assumptions about the structure of the data or the response biases, involves
the cross-matching of all the responses to the same target set used in the series in question.
A cross-match is defined as a comparison between a response and a target other than the one
used in the session. If a figure of merit distribution is calculated for a large number of
cross-matches, a number of the confounding factors listed above will be addressed. To
determine the session-to-session dependencies of the MCE, however, the session order must
be preserved. By preserving the order of the responses and by calculating [n] sets of
cross-matches at a time, MCE figure of merit, slope, and intercept distributions can be
calculated.
(U) As before, let [n] be the number of sessions in a series for a single
viewer. Also, let the order of the responses, Rk be preserved. Define [N] as the number of
cross-match cycles through the ordered set of [n] responses. The MCE calculation proceeds
as follows:
1. Randomly choose a target order, i = 1,n , such that k ; i
where k = 1, n is the preserved response order.
2. Calculate the figure of merit for the kth response/target
cross-matches as
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M ki = a ki X r ki , k 34 i,
where a ki and r ki are the accuracy and reliability for response [k]
cross-matched against target [i], respectively.
3. Do step 2 above for all [n] sessions.
4. Calculate a slope and intercept for the resulting figures of
merit by the linear least-squares analysis described above.
5. Repeat steps 1 through 4 above for [N] cycles to produce
MCE figure of merit, slope, and intercept distributions.
(U) It is important to note that MCE distributions are generated for each
viewer and are not summed across viewers. Therefore, individual viewer differences in
response "biases" are accounted for by definition.
(U) This procedure also accounts for general knowledge of the target pool
by the viewer, because information learned by this method in a given session will not
necessarily be associated with the intended target for that session. The net effect of this type
of artifact will be to "bias" the MCE figure of merit distribution toward larger values.
(U) Because the order of viewings is preserved, any knowledge of the target
pool that is learned by the viewer as a result of trial-by-trial feedback is accounted for in two
ways:
1. Information resulting from increasing knowledge of the target
pool will "bias" the MCE figure of merit distribution toward
larger values.
2. Information resulting from increasing knowledge of the target
pool as a function of session number will "bias" the MCE
slope distribution toward larger values.
(U) Similarly, any artifact caused by methodological considerations as a
function of session, will also "bias" the MCE figure of merit and slope distributions.
d. (U) Probability Assessment and Analysis
(U) There are a number hypotheses that can be tested using the various
MCE distributions described above:
s An individual remote viewing is statistically beyond MCE.
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r The series generated by a single viewer shows statistical
evidence for remote viewing.
? There is evidence above MCE for remote viewing "learning."
? The mean of the observed figure of merit distribution is
significantly larger than the mean of the MCE distribution.
r The observed figure of merit distribution is significantly
different than the MCE distribution.
(U) Using the MCE figure of merit distribution, a straightforward calculation
of areas will determine whether a particular figure of merit from a single session is significant.
Figure 1 shows an example of an MCE figure of merit distribution described above. Mkk is
the figure of merit resulting from session [k]. The probability of obtaining a figure of merit of
Mkk or larger caused by artifact is the patterned area divided by the total area under the curve
shown in Figure 1. This technique can be used to assess the chance likelihood for all sessions
in a series by a single viewer.
FIGURE 1 (U) AN MCE FIGURE OF MERIT DISTRIBUTION
(U) To determine whether there is statistical evidence of remote viewing
within a given series for a single viewer, the p-values for the individual sessions must be
combined. The primary method used for combining p-values was developed by Fisher.5 A
X2 with two degrees of freedom is computed for each p-value and summed. The resulting X2
is evaluated with 2n degrees of freedom where [n] is the number of p-values that were
combined. If [k] is the session number, the appropriate total X2 is given by
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2
}{ = T -2.0 In p k, df = 2n
k=1
where the p are the p-values for each of the [k] sessions. A second technique involves
k
testing the significance of the average p-value across all sessions. A standard z-score is
calculated by
Z = V-1 -2n (0.5 - p) ,
where p is the average p-value and [n] is number of sessions s
(U) These two measures are sensitive to different aspects of the remote
viewing series. For approximately 20 or more sessions, the two techniques will yield similar
probability estimates if there is slight, but consistent evidence of remote viewing. On the other
hand, if there are a few very good results (i.e., individual p-values < 0.001), then the X2
technique more accurately reflects the series as a whole.
(U) As an example of consistency, suppose 20 sessions having individual
p-values of 0.35 each are analyzed. Then the z-score for the average p-value is 2.32,
corresponding to a combined p-value of 0.01. The X2 technique yields a total X2 of 42.0
with df = 40, corresponding to combined p-value of 0.40. To illustrate the X2 technique's
sensitivity to "good" remote viewings, consider the following p-values for 5 individual sessions:
0.45, 0.72, 0.55, 0.001, and 0.00005. The average p-value technique yields a combined
p-value of 0.11, while the X2 technique yields a combined p-value of approximately 0.0005.
(U) To determine whether there is evidence of "learning" and whether the
means of the actual and MCE figure of merit distributions are significantly different, an
ANOVA technique is used.? By transforming the data about the average value of the session
number, the slope and intercept hypothesis testing may be done separately. The F-ratios
(from the ANOVA) for the two tests are given below.
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F (slope) =
n 2
n (b - b') 2 X (nn 1) ( k
k=1 n
F (intercept) =
n (a-a')2
df1 = 1; df2 = (n-2)
where [a] and [b] are the intercept and slope from the remote viewing figure of merit data,
and [a'] and [b'j are the intercept slope from the MCE figure of merit data. A is given by
z,Mk+na2+b2Z k2+2abZk-2a ZMk- 2b~kMk
k=1 k=1 k=1 k=1 k=1
(U) Because the F-ratio for the slope for the figure of merit data is a
statistical test between the observed slope and that computed from the MCE, it constitutes an
estimate of the probability that remote viewing "learning" occurred over and above any
contribution that might have occurred because of some artifact. The F-ratio for the intercept
constitutes an estimate of the probability that the mean of the figure of merit distribution is
different from the mean of the MCE. We use a standard X2 measure, a more sensitive test,
to determine if the observed figure of merit distribution is statistically identical to that of the
MCE distribution.
B. (U) Fuzzy Set Theory--An Enhancement Technique for Descriptor List
Technology
(U) The figure of merit analysis is predicated on descriptor list technology, which
represents a significant improvement over earlier "conceptual analysis" techniques, both in
terms of "objectifying" the analysis of free response data and in increasing the speed and
efficiency with which evaluation can be accomplished. It has become increasingly evident,
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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."8
(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.
(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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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 1
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.
FIGURE 2 (U) THE FUZZY SET "VERY YOUNG"
(U) Clearly, a different set of numerical values would be assigned to the ages 1
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
"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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(U)
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 would 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(OM) is the probability (derived from ANOVA) of observing OM 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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(U) VIEWER NO. 454 RESULTS CODED BY FOUR ANALYSTS
Analyst (j)
AM
p(OM)
r
(pj - p374) vs. pa
df
p(r)
Slope(r)
Overall
Series p
374
0.019
0.441
-
-
-
-
0.316
432
0.004
0.881
0.421
43
0.0040
0.326
0.702
579
0.007
0.768
0.555
43
0.0001
0.509
0.867
642
0.012
0.573
0.552
43
0.0001
0.558
0.909
(U) All three analyst candidates produced highly significant positive correlations
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-value estimates of the same data for a
literal and an interpretive analyst from a AM perspective. An analyst with a large and
positive OM 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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Interpretive Analyst
Literal Analyst
0 M
Figure of Merit
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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(U)
(and less interpretive) descriptions, such as rounded, vertical, and wavy, however, are
typically encouraged and are commonly found in transcripts. The addition of abstract
descriptors, therefore, would enable analysts to quantify the contents of a transcript literally,
without having to make the considerable interpretive leap from nebulous responses to highly
specific, concrete descriptors. Table 3 summarizes some of the more common abstract
descriptions gleaned from novice responses, and provides suggestions for candidate abstract
descriptors for incorporation into future lists. *
(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
(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, would 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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(U) CANDIDATE ABSTRACT DESCRIPTORS FOR NOVICE RESPONSES
Typical Novice Responses
i
ptor
Suggested Abstract Descr
Category of Response
Actual Responses
Patterns--curved,
Curved, circular, circle, oval,
Are patterns of round, curved,
circular,
ellipse, round, wavy, rolling,
or circular lines significant at
rounded
contours, contoured, sloping
the site?
Patterns--straight,
Straight, angled, parallel,
Are patterns of straight, angled,
angled
horizontal lines, verticality,
or parallel lines significant at
vertical lines, vertical objects,
the site?
diagonal
Patterns--combined
Cone
Are a mixture of curved and
curved and
straight patterns significant
straight
at the site?
No discernible patterns
Irregular, shapeless, uneven,
There are no significant
rough, bumpiness, rugged
patterns at the site.
terrain, clusters, irregular blobs,
irregular shapes
Distinct boundaries
Areas of light and dark,
Distinct boundaries between
light and dark contrast
light and dark are significant
at the site.
or
Contrasting areas of light and
dark are significant at
the site.
Unspecified (generic)
Water, wavy, waves, rippling,
Is water a significant part
water
water movement, water blue
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).
UNCLASSIFIED
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(U) COMPARISON OF TARGET VS. RESPONSE CODING FOR "BAYLANDS" TARGET
13
14
15
16
17
18
22
1
0
Is ambient noise significant at the site?
23
0
0
Is noise generated by the target?
24
0
0
Is noise generated by people adjacent to the target?
19
20
21
*1 =Yes, 0=no
UNCLASSIFIED
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Target
Coding*
Reponse
Coding*
There are no significant patterns.
Are patterns of straight, parallel, or angled lines significant at the site?
Are patterns of round, curved, or circular lines significant at the site?
Are a mixture of round and straight patterns significant at the site?
Is a significant part of the scene hectic, chaotic, congested, or cluttered?
Is a significant part of the scene clean, empty, or open?
Is a significant part of the scene inside?
Is a significant part of the scene outside?
Is water a significant part of the scene?
Is sculptured water a significant part of the scene (fountains, etc.)?
Is natural water a significant part of the scene (lakes, ponds, streams, etc.)?
Are buildings or other manmade structures a significant part of the scene?
Is a single structure a significant part of the scene?
Is/are functional (useful, moving parts, etc.) structure(s) at the site?
Is/are artistic (there to look at) structure(s) at the site?
Is a single color predominant at the scene?
Is foliage a significant part of the scene?
Is foliage natural in appearance at the scene?
Is foliage significantly sculpted, manicured, or pruned at the scene?
Is the scene predominantly void of foliage?
Is motion significantly important at the site?
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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
(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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O K;7 LwK-" G
Ar So, ei 'iZ
'Il}gfNA"JS *S
W r1Edfr Yo J S1v~r~ D
(Puvec+n'JE l
LO24, O aT ElXs6 of J4yr,Q.
,ier aE
0o So~'I 5"DE
sl~
FIGURE 4 (U) BAYLANDS NATURE INTERPRETIVE CENTER, WITH RV RESPONSE
~244 s FEED
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(U) The final factor that led to forfeiture of information entails the degree of
specificity of the descriptor list. A highly detailed response of advanced RV quality will suffer
if the descriptor list quantifying it cannot register detail. In the Figure 4 example, pertinent
(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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(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
appropriateness for this task. Table 5 provides specific examples of how the current novice
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
difficulties for novice viewers.
Table 5
(U) POTENTIAL PROBLEM AREAS FOR NOVICE TARGETS
Condition Violated
Target Type
Specific Target Problem
Viewer
Emotional
Intended
expectation
impact
use of list
Close-up photo of a small
Dimensionality
feature (e.g., a flower,
V
V
tree trunk, etc.)
Reflections of rock form-
Unusual perspective
ations in a still pool
Moon--off-earth photos
V
V
Sunken ship ruins
Underwater photos
V
V
Oil derricks
Technical site
V
V
Whale slaughter
V
V
Black & white photos
V
Standing dead trees
Significant target element
(without foliage)
for which there was no
V
V
descriptor
Photos with people
Significant target element
and/or animals
for which there was no
V
descriptor
Ornate interior of the
Too complex for
Vatican during a
novices
V
V
ceremony
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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:
? 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
(U) 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 would hold
important implications for descriptor list development and possibly for identification of
fundamental commonalities of perception across viewers.
B. (U) Al Techniques
(U) According to the following excerpt from the letter proposal submitted by
consultant J. Vallee, the proposed Al approach
"...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."t?
(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.
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(U) SRI in-house approaches will focus on improvement and refinement of existing
technologies. One potential line of inquiry would focus on identifying an appropriate set of
abstract descriptors to add to the current novice list. This effort would be aimed at mitigating
some of the inter-analyst reliability problems that have been encountered with novice data
(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
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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(S) Finally, work will be initiated to develop mission-specific
descriptor lists for technical site applications. It is anticipated that
efforts along these lines will enable us to better address intelligence
community requirements.
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V CONCLUSIONS (U)
(U) The FY 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.
(S) 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, operational 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 the
Parapsychological Association, pp. 343-354, Tufts University, Medford, Massachusetts
(August 1985) UNCLASSIFIED.
5. Fisher, R. A., "Statistical Methods for Research Workers," Oliver & Boyd (7th ed.),
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.
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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