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D.
(U) Program Objectives
(U) There
are
three
basic program
objectives:
(1)
to document
that psychoenergetic
phenomena
are
real and reproducible;
(2)
to
determine
the mechanism(s)
underlying these
phenomena;
and
(3)
to
bring
the
field
of psychoenergetics
into
the
mainstream
of human
performance research, by providing a
scientific foundation equivalent to that of the rest of the
performance
research
field.
In
the minds
of some,
there
is no doubt
that psychoenergetic
phenomena are real and reproducible.
In the minds of many others, both scientific professionals
and informed lay persons,
this
is not the case.
(S/NF)
The categories
of research
interest under consideration form
a
hierarchy ranging from basic research on fundamental mechanisms
to
methodologies
for applications
including:
•
Identifying explanatory mechanisms
(e.g.,
electromagnet ic
effects,
neurophysiological mechanisms)
.
•
Specifying phenomenological properties
(e.g.,
the effects
of
distance and
shielding)
.
•
Determining physical,
physiological
,
and psychological
correlates
(e.g.,
geophysical environment,
EEG and GSR
measures,
and personality profiling).
•
Developing optimal
strategies
for use
in applications
(e.g.,
statistical averaging)
.
E.
(U) Program Resources
(U) To meet the above objectives, the SRI program
is using both in-house and external
expertise.
For over a decade, a core group of researchers at SRI has been studying a wide variety
of
subjects
in
psychoenergetics—augmented
by
access
to
specialty
centers
such
as
our
neurosciences and our microbial genetics laboratories.
(U) Some of the work
is being subcontracted to institutions, groups, and consultants who
have a demonstrated track record in this research area.
Other subcontractors may have had no
association with
this
field but, because
of their specific area
of
expertise, can make valuable
contributions to our program
goals.
Thus,
the widest possible
interdisciplinary viewpoints are
available to the program, and the mixture of resources
will ensure that peer group review and
scientific
interactions
are
maximized.
Subcontractors
and
consultants
currently
include
personnel from Princeton University, Syracuse University, John F. Kennedy University, the Palo
Alto Medical
Clinic, MARS Measurements Associates,
Psychophysical Research Laboratories,
the
Parapsychology
Sources
of
Information
Center,
Mind
Science
Foundation,
and
the
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University of Delaware, plus the consultants having expertise in specific areas of interest to the
program.
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3.
(U)
Objective A, Task 3—Improve RV Evaluation
a.
(U)
Fuzzy Set Applications in Remote Viewing Analysis
(U)
In FY
1987,
fuzzy
set
mathematical
techniques
were
applied
to
the
problem of remote viewing
analysis.
Two
analytical methods were developed:
the
first was
designed to be sensitive to the verbal content of the RV response: the second was designed to
account
for the
visual/spatial arrangements
of response
elements.
A
definition
of “ground
truth,”
against which these new analytical techniques could be tested, was also devised.
(U) The
verbal
method
is
predicated
on
the
application
of
fuzzy
set
mathematics
to
the
figure
of merit (FM)
technology.*
The method
also
features
a new
descriptor
list,
which was introduced
to provide
a
richer vocabulary
for
analysis.
The
list’s
hierarchical
structuring
in
levels,
ranging
from
very
abstract
to
very
concrete,
affords
considerable flexibility for analytical manipulation of descriptor elements. A pilot application of
the verbal analysis was shown to correlate highly with ground truth.
(U) The combination of fuzzy set technology and the new descriptor
list also
proved effective
for the visual/spatial approach.
The implementation of these techniques— in
conjunction with a third technique known as “cluster analysis”—has resulted in an algorithm for
the production of orthogonal target
sets.
This has
resulted
in a
significantly more
effective
rank-order analysis procedure.
(U) The
visual and
verbal
analyses were each determined
to have
certain
strengths and weaknesses.
The verbal analysis
is statistically more powerful and provides a more
comprehensive
breakdown
of
the
verbal
information
in
an
RV
response.
It
is
quite
labor-intensive to apply, however, and
it appears to be relatively insensitive to noisy RV data.
“Noisy,”
in this context, can be defined as a preponderance of incorrectly identified response
elements.
The
visual analysis system
is
statistically much
less powerful and
is
less capable of
providing systematic
objectification
of the
true RV signal content.
It can be rapidly applied,
however, and
is sensitive to the primary manifestation of true RV signal in noisy data—namely,
the
visual arrangement of RV response elements,
regardless
of their verbal
labels.
Potential
applications
of these techniques
in
their current
states have been suggested;
areas
of future
research for their refinement have also been identified.
*
(U)
The FM analysis has continued to undergo refinement since
its inception in FY 1984.
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b.
(U) An Expert System Approach to Remote Viewing Analysis
(1)
(U)
Motivations to Explore Expert Systems
(U) The judging of RV transcripts has proven to be a difficult task for
the experienced
as
well
as the
uninitiated
analyst.
Judging can be both analyst and viewer
dependent,
in that the combination of a viewer’s response style and an
analysts’s interpretive
style may enhance or hinder the analytic task. The process
is, to a large extent, a subjective task
that does not lend
itself to a
literal or procedural quantification.
In an
effort to render the
judging more transferable,
if not more uniform, we decided that,
if development were
feasible,
an expert system to
assist the analyst would prove invaluable.
(U) The ultimate task would be to develop an expert system that could
ask an analyst a series of questions about a given RV transcript, arrive at a composite description
of the response, and map the response to a group (possibly with only a single member) of targets
within a known target pool.
The system would have standard data about a number of common
abstract and concrete
objects.
Furthermore,
the system would maintain
a
data base
of the
ideograms
and
idioms commonly
used
by
a
particular
viewer
and
their
possible/probable
correspondences.
By prompting the analyst for information about the concrete or unambiguous
elements of a response
(i.e., are there parallel lines, or are there elements labeled as structures),
the system would combine the user-supplied data with data collected in previous experiments
with the same
viewer,
to piece
together composite hypotheses
about the
transcript.
In an
interactive exchange, the system would attempt to present the analyst with possible transcript
interpretations of increasing complexity and/or concreteness until some kind of composite picture
could be drawn.
(U)
Clearly, such an undertaking is very ambitious and well beyond our
current expertise.
Acting as a consultant, Dr. Jacques Vallee was to undertake the
initial steps
towards the development of such a system.
As requested, we supplied him with the NExpert®*
development
system,
an
expert system
shell which
exploits non-monotonic
reasoning
(i.e.,
simultaneous
forward
and
backward
chaining
or,
analogously,
simultaneous
inductive
and
deductive
reasoning).
As the
analyst’s
task
is by no means
a
clearly
hierarchical
or
linear
process,
this feature of the NExpert® system
is a clear necessity.
(2)
(U)
Initial Goals
(U) The
delivery
of an
expert system matching our
specifications,
if
indeed such a system can be devised, would obviously require numerous development cycles, and
—
(U)
NExpert®
is a product of Neuron Data,
Inc., 444 High Street, Palo Alto,
California.
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was therefore not an immediate expectation.
After discussions with Vallee,
it was decided that
the
thrust of the FY 1987
effort should be focused upon the mapping
of response
to
target.
Rather than assuming that the
target possibilities are
effectively
infinite,
the system should be
designed and programmed
to have prior knowledge
of the
structure and contents of the 200
targets
in
our
current
target
pool.
This
design
decision
renders
the problem much more
tractable, and certainly does not prevent further efforts from focusing upon response analysis
with little or no knowledge of the target universe.
Once a system was in place, we would supply
Vallee with transcripts from 1987 experiments to serve as test data for the system.
(U) Even with a limited universe
of targets, mapping a response to
a
single target
is not
a practicable
goal.
Many targets
are
visually
similar, and the information
contained in a typical response transcript
is not sufficient to distinguish,
for example, the Gobi
desert from the Sahara.
Rather, a more reasonable task is to break up the target pool into similar
groups and map responses
to a
target
type.
As we had not yet determined these groupings,
Vallee undertook the
description and
classification
of the 200
targets and used the
resulting
target types as the basis for his work.
(3)
(U)
Description and Classification of Sites (DACOS)
(U) The
system
Vallee
developed
for
categorizing
the
target
pool,
DACOS (Description and Classification of Sites), contains 40 distinct target categories made up
of specific combinations of 27 possible target attributes.
The set of attributes used
is broken up
into she types: Water,
Structures, Interfaces, Contours, Land, and Features (Figure
1).
Water
Structures
Interfaces
Contours
Land
Features
Present
Absent
Absent
Non-Urban
Urban
Waterfall
Island
Peninsula
Coastline
Lake
River
T
Hilly
Flat
Mountainous
Rugged
Fertile
Arid
Dense City
Open City
T
Rocky
Volcanic
Snowy
(Glacial)
Green (Verdant)
Plowed
Monuments
Ruins
Dunes
Applicable
if water
is present
Applicable
if water
is absent
UNCLASSIFIED
FIGURE
(U)
THE ATTRIBUTE SET FOR THE DACOS SYSTEM
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(U) DACOS
is a hierarchical system which
is best described by a tree
whose interior nodes each represent a decision point and whose levels each represent an attribute
type.
By selecting one of the possible paths at each node, one traverses the tree until a leaf node
is reached,
at which point a target category has been selected.
As
is
clear by inspecting the
hierarchy,
it
is possible to determine a target category by answering a maximum of five questions
(Figures 2 and
3).
Water
Water
.
Present
Structures
Structures
Absent
Structures
Non-Urban
Structures
Urban
Interfaces
Island —
Waterfall
Coastline
Contours
Island
Coastline
~ River
Island
Peninsula
Coastline
Features
' Volcanic
Non-Volcanic
Glacial (Snowy)
Non-Glacial (Snowy)
Volcanic
Non-Volcanic
Monuments
Non-Monuments
UNCLASSIFIED
FIGURE 2
(U)
DACOS HIERARCHY FOR WATER-PRESENT TARGETS
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Water
Structures
Structures
Absent
Water
_
Absent
Contours
I— Mountainous
Rugged
I— Flat
Structures
Non-Urban
Hilly-
I— Flat
I
Structures
Urban
Hilly-
Land
' Fertile
1— Arid
|— Fertile
*— Arid
I— Dense City—
Q
^— Open City —
|
Features
I— Volcanic
Non-Volcanic
|— Rocky
Dunes
Snowy
j— Plowed
— Verdant (Green)
P
Plowed
Verdant (Green)
Monuments
Snowy
Monuments
£
Dense City
Open City
ity—
Q
Non-Monuments
Ruins
Non-Ruins
Monuments
Non-Monuments
UNCLASSIFIED
FIGURE
(U)
DACOS HIERARCHY FOR WATER-ABSENT TARGETS
(U) The computer system Dr. Vallee delivered was developed using the
NExpert® system, but was
later transferred and coded
in BASIC
for efficiency reasons.
The
DACOS program
initially prompts the
analyst
for an answer
to the question
“Is there water
present?”
and, depending upon the
response,
continues to
traverse the
appropriate DACOS
decision sub-tree asking further questions.
The
final output
is a
list of pairs, the category type
with its corresponding confidence factor.
These confidence factors directly reflect the number of
attributes that correspond to any given category.
For example,
if the final attributes were Water,
Urban,
River, and
Hilly, the categories under the Water-Absent node would have
a factor of
zero,
the
categories
under
the
Water-Present,
Non-Urban
node
and
the
Water-Present,
No-Structures node would have
a factor of one,
etc.
With these confidence
factors, one can
then determine a hierarchy of possible correspondences for the given transcript and propose
a
best match category.
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(4)
(U)
Results of Testing DACOS with Actual Remote Viewing Data
(U) The data given to Vallee for testing consisted of 30 RV transcripts
from one 1987 experiment,
He then used the DACOS program to assign confidence factors to
the
different
target
categories.
Overall,
the DACOS performed
only marginally
better than
chance.
Out of 30 transcripts, the water attribute was correctly identified 17 times, the structures
attribute
13 times, and the
full target classification twice.
Unfortunately,
this series of remote
viewings, when judged, did not show a significant RV effect, and thus proved a poor test case.
Nonetheless,
this exercise allowed us to evaluate the progress and direction of this work.
(5)
(U)
Shortcomings of DACOS
(U) The
categories
within
the DACOS
system
were
constructed
to
produce
visually
distinct
or
“orthogonal”
target
types.
The
first two
attribute
levels
of the
DACOS hierarchy, Water and Structures, are by far the most clear, simple, and symmetric; most
important, they correspond to common elements of RV transcripts.
Nonetheless, some of the
target
classifications
are
inappropriate
for
the
kind
of RV
response
data
typically
seen.
Experience has shown that the visual content is the most important aspect of a target; the minute
details of a target are often missed and thus should not overpower the overall description of the
target.
For example, although several of the water targets do picture water, the water is confined
to such a visually insignificant region as to be either unnoticed or ambiguous.
(U) The deeper levels of the DACOS hierarchy do not maintain the
symmetric nature of the
first two levels because they inherit properties from the preceding levels.
Furthermore,
the
attributes chosen
for the deeper levels do not necessarily
reflect the
actual
visual nature of the targets.
The
attribute Monument discriminates targets on a very high social
and
cognitive
level;
identifying
a monument
requires
a
significant amount
of conceptual
or
functional knowledge
about
a
particular
site,
and
is
not
necessarily evident from
the
visual
contents
of
a
target.
The
attribute Non-Monuments,
representing
the
explicit
absence
of
monuments,
is even more abstract and visually ambiguous.
Although Vallee’ s hierarchy yields a
target category with a maximum of five questions, the choice of attributes is not ideal for the task.
(U)
Clearly, the most significant problem with the DACOS system
is
its
strict
hierarchical
structure,
The system, by
its
tree
traversal method, makes each
decision
strictly binding; once water has been determined to be absent, the system does not have any
means of reconsidering water as an attribute.
Effectively, by completely isolating sub-sections of
the decision tree, the DACOS system renders the attribute Hilly for Water-Absent targets to be
distinct from the attribute Hilly for Water-Present targets because the categories on one side of
the tree will receive credit and those on the other side will receive none.
This occurs because the
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(U)
system does not allow for any network or global attribute semantics.
Ideally, the system would
not force
a decision about the presence or absence
of an
attribute
if that information
is not
available; rather,
it would consider the data already acquired, and prompt for alternative data.
Further
efforts
in the development of an expert system must
allow
for
a broad and dynamic
evaluation of all of the data the analyst presents.
(U) The DACOS system implicitly assumes that
all the information
it
receives
is certain.
Clearly, a tool for the novice or uninitiated analyst must not expect that the
analyst will be correct in 100% of his decisions.
Furthermore, the system must not expect that an
analyst will be able to render a meaningful decision about every possible attribute; in the absence
of data about any given
attribute,
the
analyst cannot necessarily assume
that the
attribute
in
question
is in fact not present.
For example, the particular attributes chosen for the lower levels,
optimized for the minimality constraint Vallee imposed upon the system, do not best represent
the elements typically contained in an RV response. The attributes Fertile, Arid, and Plowed are
rarely seen; assuming that data pertaining to these three attributes cannot be discerned,
eight
categories are reduced to two, and the system has no way of resolving the analyst’s uncertainty.
As all RV response data are, by their very nature uncertain, the expert system we envision must
deal with uncertainty from the very start.
(6)
(U)
Future Directions
(U) The NExpert® development system offers many capabilities tailored
to dealing with uncertain reasoning.
Unfortunately, the power of NExpert® was by no means
fully tapped by Vallee’s initial effort.
For this reason, we cannot make a meaningful assessment
of the potential utility of an expert system approach to RV analysis.
Further development should
continue in this area, but the development will clearly need to focus upon the rectification of two
specific shortcomings of this year’s effort:
(1) the integration of uncertain reasoning into the data
analysis, and (2) the redefinition of an attribute set and network that allows for a more flexible
and comprehensive evaluation of the response data.
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