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Target Information
The definition of a particular target site (usually outdoor sites in and around Princeton,
New Jersey)
is contained in the yes/no answers to a set of questions called descriptors.
These
descriptors are designed
in such a way
as to characterize the
typical Princeton
target.
Each
descriptor bit
is weighted by
its a priori probability of occurrence in a large
target pool.
By
definition,
the
only
target information that
is
to be
considered
for
analysis,
is that which
is
contained completely in the yes/no answers to the descriptor questions (with their associated set of
descriptor weights) for the site in question.
For example, one descriptor from the Princeton list,
“Are any animals, birds,
fish, major insects, or figures of these significant in the scene?” defines
the animal content of the site.
The question would be answered “yes” for a zoo and a pet store
target, but “no" in
all probability for a typical campus building target.
Similarly, a set of yes/no
responses (30 for the PEP)
constitutes the target information.
Response Definition
The descriptor list for the target sites is used as a definition of the response as well.
For
a given remote viewing session, the remote viewer (or an analyst who
is blind to the target site)
attempts to answer the 30 questions on the basis of that single response only.
In the example
above,
it would be necessary for a viewer (or analyst) to decide whether or not a particular
verbal
passage or a quick sketch could be interpreted as depicting animals.
For some responses this
might be an easy task,
e.g.
“I get a picture of a cow.”
Most responses, however, are somewhat
ambiguous and require a judgment, e.g. “I see a farm.”
Nonetheless, the yes/no answers to the
30 questions constitute the only response information that are used in the analysis.
Analysis
For a given response/target combination, the information is contained exclusively in the
yes/no
answers
to
the
descriptors. Two
binary numbers
(30
bits
long
each
for PEP)
are
constructed, one for the target and one
for the response descriptor questions, respectively.
A
“yes” answer is considered a binary “1,” while a “no” answer is considered a binary
0.
The
resulting
two,
30-bit
binary numbers
can then be compared by
a
variety
of mathematical
techniques involving use of the weighting factors, to form a score for that specific remote viewing
session.
For a series of sessions, a quantitative assessment is made by comparing a given response
(matched to
its corresponding target site) against the scores that are computed by matching the
response to
all other targets used in the
series.
This procedure has the added advantage of a
built-in, within-group control.
In other words, this assessment determines the uniqueness of the
target/response match as compared with
all other possible matches for the series.
Advantages of the PEP
There are
a number of obvious and proven advantages (Dunne, Jahn, and Nelson
1983) of the Princeton Evaluation Procedure:
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•
Automation - Rapid and accurate analysis of a large number of
free-response sessions can be accomplished with ease.
•
Archives - With the aid of computer database management, large
numbers of free-response sessions can be organized and maintained in a
usable manner.
•
Control - The cross-target scoring procedure provides a powerful
built-in within-group control.
•
Use - PEP
is widely distributed and provides a commonalty of analysis
procedure across laboratories.
Disadvantages of the PEP
There are actually very few disadvantages to PEP. A common problem that has been
observed before (Dunne, Jahn, 1982) arises in the "granularity” of the descriptor list.
With any
finite list of binary-type descriptors,
it is always possible that a response will appear to be correct
with “analogue" analysis procedures but will be evaluated as incorrect with the “digital” approach.
Another disadvantage of PEP (also noted above, op cit)
is that any given descriptor list is likely to
be
applicable
only
to
a
given
target pool
type
(i.e.,
Princeton
area
natural
sites,
National
Geographic magazine
photographs,
etc.).
Lastly,
one
of PEP’s
strong
points—namely,
the
cross-match, built-in, within-group control—is also potentially one of its weaknesses.
Since nearly
all of the
various PEP scoring algorithms
involve
bit-by-bit weighting,
which is based upon relative probability of occurrences, a given response/target score depends not
only upon the correctness of the response, but also upon the nature of the remaining targets in the
pool.
Thus, a score for a given session depends upon the quality of response and the target pool.
The following hypothetical example
illustrates this dependency:
a given target has 10 of 30 bits
present; furthermore, a few bits (e.g. 3) are particularly rare when compared to the remaining bits
(i.e. they possess comparatively large weighting factors). Let us assume that two different viewers
provide responses to this target and that each asserts 8 descriptors in the response, 6 of which are
correct.
If the
first viewer’s response contains only one of the rare bits, while the other viewer’s
response contains all three, the second viewer’s score will be considerably larger as a consequence
of the weighting factors.
Such a scoring discrepancy forces us to define what the purpose of the remote viewing
session
is.
If the
goal
is to demonstrate the existence of psi phenomena, then the PEP
is a
perfectly adequate system of analysis, and it exhibits all of the advantages described above.
If the
goal, however,
is to demonstrate correlation effects
(e.g., correlation of free-response material
with
personality,
physiology, environment,
etc.), then the
scoring
difficulties described above
confound the correlation measurement.
To
summarize,
a
target pool dependent
scoring procedure
provides an
important
measure of a viewer’s
ability to discriminate from among a number of possible targets.
(The
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second viewer in the example above, for instance, would receive a higher score because his/her
response
is more unique to the target pool.)
The target pool dependent scoring algorithm is less
applicable,
however,
as
an
independent
absolute
measure
of
target
contact—a
necessary
condition for correlation studies.
If we remove the within-group control to eliminate a source of variance for a correlation
measurement that
is potentially unrelated to psi ability, we are obligated to provide some other
form of control to demonstrate a deviation from mean chance expectation.
FIGURE OF MERIT ANALYSIS
The Figure of Merit analysis (FMA) was developed to address the problems associated with
correlation studies and to provide a novel form of control.
Target Information
As in the PEP, the Figure of Merit analysis quantifies the target material into binary
numbers
corresponding
to yes/no answers
to
a
set
of
descriptors.
Our
descriptor
list was
developed on the basis of the target material
(National Geographic magazine photographs), and
on the basis of responses that might be expected a priori for our novice remote viewers.
Table
shows the 20 descriptors that were used for the photon production experiment (Hubbard, May,
and
Puthoff,
1985).
The
questions are
strongly oriented toward outdoor
gestalts,
typical of
National Geographic magazine
material.
The horizontal
lines separating the
descriptors into
groups of three are provided as an aid for translating binary numbers (derived from the yes/no
answers to the questions) into an octal shorthand notation.
A self-consistency check
is performed on each coded
target, and a
set of logically
consistent rules must be developed for a given descriptor
list.
One such example
for the
list
' (shown in Table 1) involves bits 13 and 14. While it is possible to have a land/water interface that
is not a river, canal, or channel,
the reverse (i.e. to have a river, canal, or channel without having
a land/water interface)
is not possible by definition.
Thus,
if a target analyst asserted
bit 14
without asserting bit 13, we could consider this an error in coding and assert bit 13.
It is beyond
the scope of this paper to provide all the logical consistency rules, but most of them are obvious
from Table
1. Naturally, these rules must be defined in advance of any experimentation.
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Is any significant part of the scene hectic, chaotic, congested, or cluttered?
Does a single major object or structure dominate the scene?
!s the central focus or predominant ambience of the scene primarily natural
rather than
artificial or manmade?
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,)
Is the scene predominantly colorful, characterized by a profusion of color,
by a strikingly contrasting combination of colors, or by outstanding, brichtlv-
colored objects
(e.g., flowers, stained-glass windows, etc.—not normally
blue sky, green grass, or usual building color)?
ieatur^ofYhe scene?
C, 'ff ’ °r S r£m9e °f moLjntains ’
hills - or cliffs 0 significant
Is a volcano a significant part of the scene?
Are buildings or other manmade structures a significant part of the scene?
Is a city a significant part of the scene?
Is a town, village, or isolated settlement or outpost a significant
feature of the
sc©n©
i
Are ruins a significant part of the scene?
Is a large expanse of water—specifically an ocean, sea,
gulf,
lake, or bav—
a
significant aspect of the scene?
y
Is a land/water interface a significant part of the scene?
Is a river, canal, or channel a significant part of the scene?
Is a waterfall a significant part of the scene?
Is a port or harbor a significant part of the scene?
Is an island a significant part of the scene?
Is a swamp, jungle, marsh, or verdant or heavy foliage a significant part of
the scene?
Is a
flat aspect to the landscape a significant part of the scene?
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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