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The sources of noise in the forced-choice domain are reasonably understood (i.e., memory in conjunc-
tion with complete knowledge of the target pool elements). A new insight for us was another potential
source of noise in the free-response domain. To understand this noise source, we must first assume that
A
C
data are weak and difficult to recognize. Thrget pools which contain a large number of diverse cogni-
tive elements, in conjunction with receivers who believe that this is the case, are a source of noise. Re-
ceivers will tend to report any imagined impressions, since those impressions might be part of the target.
Sincere is assumed to be weak, most of the generated impressions are from the receiver's imagination
rather than from the target. Furthermore, it follows that the noise will increase when these impressions
are unable to be internally edited and must be reported. That is, noise is generated not so much from an
active imagination, but imagination coupled with an agreement not to edit the internal experience.
Editing our internal experience is something we all do in our daily communication: we rarely report to a
friend that our mind momentarily wandered during an interesting discussion. Humans appear to have
an ability for multi-processing, but we use situational filters to communicate coherently. So, why would
we deny this same ability to participants inAC experiments? In Figure 1, we represent schematically the
contributions to the noise produced by memory and the noise produced by not editing imagination.
Differentiable Cognitive Elements in the Thrget Pool
Figure 1.
Schematic Representation of Sources of Cognitive Noise
As the number of differentiable cognitive elements in a target pool increases from two (for a binary
choice) to nearly infinite (for the universe), we propose that there is a trade-off between noise arising
from memory and noise arising from unedited imagination.
For target pools containing fewer ele-
ments, the noise contribution from memory (i.e., the curve labeled “Memory” in Figure 1) exceeds im-
pressions arising from edited imagination. Regardless of one's internal fantasies, there is usually a com-
plete protocol restriction on allowable responses. The reverse
is true for target pools that contain a
large number of cognitive elements: the contribution to the noise because of unedited imagination ex-
ceeds that arising from memory.
In this case, protocols usually suggest that receivers report nearly all
internal impressions (e.g., in the ganzfeld protocol), and since there will likely be far more of these im-
pressions than there are target elements, the noise is increased. At the same time, since there are a large
number of elements, and because it is difficult to remember all possible elements and their factorial
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combinations, the contribution to the noise due to memory is reduced. We suggest that out National
Geographic magazine target pool represents a good compromise: there are enough differentiable ele-
ments to reduce the effects of memory, but few enough to allow reasonable editing of internal experi-
ences that arise from imagination.
The; receivers in our experiments have, over time, learned the natural limitations of the National Geo-
graphic target pool by experience and by instruction. They have become skilled at internal editing and
do not report impressions that they know are absent from the overall target pool—thus there is less
incorrect material in their responses.
In Lantz, Luke, and May’s 1992 experiment, where the dynamic targets could be virtually anything, the
receivers were unable to produce significant evidence oL4C. They also produced, what is for us, signifi-
cantly reduced functioning with static targets. We speculate that this drop of functioning in both target
conditions arose because the protocol would not allow the receivers to edit their internal experience.
Since the dynamic targets could consist of anything, and since the receivers were blind to the static-vs-
dynamic target condition, they were unable to edit their imaginations, even for the static targets. To
illustrate this point, suppose that half the target pool were ESP cards and the other half were the ganz-
feld dynamic targets, but the receivers were blind to the target condition. In any given trial, even though
the target is actually the star ESP card, the receiver is inclined to report all internal imagery, whether it
be cartoon figures, car races, and/or sex scenes from movies. This increased the incorrect information
over what it would be for a simpler target pool of ESP-cards alone.
A strongword of caution is in order. Editing of internal experience because of sensory knowledge of the
target pool cannot inflate a differential rank-order statistic.
It will, however, bias any rating scale to-
ward larger values. This is not a problem if ratings are used in correlational or comparative studies.
We define targetpool bandwidth as the number of differentiable cognitive elements in the target pool.
Forced-choice experiments usually represent small bandwidths, video clips usually represent a large
bandwidth, and the National Geographic magazine photographs represent an intermediate bandwidth.
At this time, the definition is qualitative, but we will indicate ways in which it can be made more quanti-
tative. Nonetheless, the target pool bandwidth concept is testable.
The following hypotheses formed the basis of Lantz, Luke, and May’s second study in 1993:
(1) A significant increase ofAC will be observed for dynamic targets if the dynamic pool is designed
with an intermediate target pool bandwidth that matches the static pool from the 1992 study.
(2) An increase ofAC will be observed for static targets because the receivers will be able to edit their
internal experience.
Summary of the second Anomalous Cognition Experiment - 1 993
The details of the 1993 study may also be found in Lantz, Luke, and May (1994).
In that study, they
included a static vs dynamic target condition to replicate the findings from the ganzfeld, but dropped
the sender condition: all trials were conducted without a sender.
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Target Pools - 1993
For this experiment, Lantz, Luke, and May redesigned both the static and dynamic targets with the
constraint that they all must conform to the topic, size, and affectivity homogeneity of the original static
targets. Surprisingly enough, they identified a large number of videos that could be edited to produce
50 National Geographic-Yike segments: an airplane ride through Bryce Canyon in Utah or a scanning
panoramic view of Yosemite Falls. Lantz, Luke, and May selected a single frame from within each dy-
namic target video clip, which was characteristic of the entire clip, to act as its static equivalent.
Thus, they were able to improve the target pools in two ways:
(1) The dynamic pool possessed an intermediate target pool bandwidth.
(2) The bandwidth of the dynamic and static pools were nearly identical, by design.
Data Analysis and Results - 1993
For each response, a single analyst conducted a blind ranking of five targets—the intended one and four
decoys—in the usual way.
Lantz, Luke, and May computed effect sizes in the same way as in the 1992
study.
Three receivers individually participated in 10 trials for each target type and a fourth, 372, participated
in 15 trials per target type. Thble 3 shows the average rank, the effect size, and its associated p-value for
the static target condition. We see that the combined data is significant and three of the four receivers
produced independently significant results.
Thble 3.
Results for Static Targets - 1993 Experiment
Receiver
<Rank>
ES
p-value
2.20
0.565
0.037
372
1.87
0.801
9.7 x 10~4
389
3.10
-0.071
0.589
518
1.90
0.778
7.2 xlO-3
Totals
2.22
0.550
1.1 x 10"5
Lantz, Luke, and May observed a nearly significant increase otAC for the static targets in the 1993 ex-
periment compared to that of the 1992 experiment (X2 = 3.158, df=\,p< 0.075), and three of the four
receivers improved from their 1992 results.
Thus, the second hypothesis (i.e., an increase in AC for
static targets) was strongly supported. Thble 4 shows the same data for the dynamic targets.
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Thble 4.
Results for Dynamic Targets - 1993 Experiment
Receiver
<Rank>
ES
p-value
1.70™
0.919
372
1.93
0.754
1.8 xlO” 3
389
3.00
0.000
0.500
518
2.40
0.424
0.091
..
Ibtals
2.22
0.550
1.1 xlO-5
Using the rank-order statistics above, Lantz, Luke, and May saw no difference between static and dy-
namic targets in their 1993 study. The first hypothesis was confirmed: they observed a significant in-
crease ofAC with dynamic targets in 1993 from that of 1992 (X2 = 9.942, df=
1, p < 1-6 x 10
•*).
A detailed analysis of the static vs dynamic target issue may be found in Lantz, Luke, and May (1994)
and in May, Spottiswoode, and James (1994).
General Discussion and Conclusions
One possible interpretation of the results from Lantz, Luke, and May’s two experiments
is that the
noise was sharply reduced by narrowing the target pool bandwidth. They observed a significant increase
ofAC with the dynamic targets and a large increase with the static ones. Caution is advised in that this
analysis ispost hoc, and there were a number of potential contributing factors. For example, in the first
experiment, receivers were not monitored and were at distances ranging from a few 100s to 1000s of km
from the targets.
In addition, feedback was delayed for a few days due to the delivery time of the U.S.
postal service. In the second experiment, the receivers were monitored, given immediate feedback, and
the targets were meters away.
Yet, we find the bandwidth analysis compelling because of its common
sense” appeal. Since the properties attributed to target pool bandwidth may be subjected to experimen-
tal scrutiny, we urge that such studies be carried out. For example, is there a parabolic-like functional
relationship between the target pool bandwidth and theAC effect size?
To conduct such experiments, we need to develop a quantitative definition of target pool bandwidth.
This implies a quantitative definition of cognitive content, and we have been applying our fuzzy set
analysis (May, Utts, Humphrey, Luke, Frivold, and Trask, 1990) toward this end. We are also looking at
other measures that might be used. Nonetheless, it seems clear that a quantitative definition of band-
width is within reach. Once realized, and if the target pool bandwidth idea can be verified, we all may
benefit from a specific protocol that will reduce the noise in free-response/lC experiments.
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References
Bern, D. J. and Honorton, C. (1994). Docs psi exist? Replicable evidence for an anomalous process of
information transfer. Psychological Bulletin. 115, No. 1, 4-18.
Honorton, C. and Harper, S. (1974). Psi-mediated imagery and ideation in an experimental procedure
for regulating perceptual input. The Journal of the American Society for Psychical Research. 68,
156-168.
Honorton, C. (1975). Error Some Place! Journal ofCommunication, 103-116.
Honorton,
C.
and
Ferrari, D.
C.
(1989).
“Future
Telling:” A
meta-analysis
of
forced-choice
precognition experiments, 1935-1987. Journal ofParapsychology, 53, 281-308.
Honorton, C., Berger, R. E., Varvoglis, M. P., Quant, M., Den, P., Schechter, E.
I., and Ferrari, D. C.
(1990) Psi Communication in the ganzfeld. Journal ofParapsychology, 54, 99-139.
Lantz, N. D. and Luke, W. L. W., and May, E. C. (1994). Thrget and sender dependencies in anomalous
cognition experiments. Submitted for publication in the Journal ofParapsychology.
May, E. C, Utts, J. M., Humphrey, B. S., Luke, W. L W., Frivold, T. J., and Trask, V. V. (1990). Advances
in remote-viewing analysis. Journal ofParapsychology, 54, 193-228.
May, E. C., Spottiswoode, S. J., and James, C. L. (1994b). Shannon entropy as an intrinsic target
property: Tbward a reductionist model of anomalous cognition.. Submitted for publication in the
Journal ofParapsychology.
Rosenthal, R. and Rubin, D. B. (1989). Effect size estimation for one-sample multiple-choice-type
data: Design, analysis, and meta-analysis. Psychological Bulletin, 106, 332-337.
Rosenthal, R. (1991). Meta-analytic proceduresfor social research (Rev. ed). Newbury Park, Ca: Sage.
Schlitz, M.
J. and Honorton, C.
(1992). Ganzfeld
psi performance within an
artistically
gifted
population. The Journal oftheAmerican Societyfor Psychical Research, 86, No. 2, 83-98.
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Introduction
The psychophysical properties of the five known senses are well known (Reichert, 1992). At the
front
end,” they share similar properties.
For example, each system possesses receptor cells that convert
some form of energy (e.g., photons for the visual system, sound waves for the audio system) into electro-
chemical signals. The transfer functions are sigmoid; that is, there is a threshold for physical excitation,
a linear region, and a saturation level above which more input produces that same output. How these
psychophysical reactions translate to sensational experience is not well understood, but all the systems
do possess an awareness threshold similar to the subliminal threshold for the visual system.
Since all the known senses appear to share these common properties, it is reasonable to expect that if
anomalous cognition {AC)* is mediated through some additional “sensory” system, then it, too, should
share similar properties. For example, a putative/lC sensory system should possess receptor cells that
have a sigmoidal transfer function and exhibit threshold and saturation phenomena. As far as we know,
there are no candidate neurons in the peripheral systems whose functions are currently not understood.
So, if receptor cells exist, it is likely that they will be found in the central nervous system. Since 1989, our
laboratory has been conducting a search for such receptor sites (May, Luke, Trask, and Frivold, 1990);
that activity continues.
There is a second way in which receptor-like behavior might be seen in lieu of a neurophysiology study.
If either an energy carrier for AC or something that correlated with
it were known, then it might be
possible to infer sigmoidal functioning at the behavioral level as opposed to the cellular level. Suppose
we could identify an intrinsic target property that correlated with /lC behavior. Then, by manipulating
this variable, we might expect to see a threshold at low magnitudes and saturation at high magnitudes.
To construct such an experiment, it is mandatory that we eliminate, as much as possible, all extraneous
sources of variance and adopt an absolute measure for theA
C
behavior (Lantz, Luke, and May, 1994).
We can reduce one source of variance by considering what constitutes a good target in an AC experi-
ment. Delanoy (1988) reported on a survey of the literature for successful^C experiments and catego-
rized the target material according to perceptual, psychological and physical characteristics. Except for
trends related to dynamic, multi-sensory targets, she was unable to observe systematic correlations of
AC quality with her target categories.
Watt (1988) examined the target question from a theoretical perspective. She concluded that the “best
AC targets are those that are meaningful, have emotional impact, and contain human interest. Those
targets that have physical features that stand out from their backgrounds or contain movement, novelty,
and incongruity are also good targets.
In trying to understand these findings and develop a methodology for target selection for process—ori-
ented research, we have constructed a metaphor. Figure 1 shows three conceptual domains that con-
tribute to the variability in AC experiments. The engineering metaphor of source, transmission, and
detector allows us to assign known contributors to the variance of specific domains. Without controlling
becausewe believe that these terms are more naturally descriptive of the observables and are neutral in that they do not imply
mechanisms. These new terms will be used throughout this paper.
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or understanding these sources, interpreting the results from process-oriented research is problemati-
cal, if not impossible.
Figure 1.
Information-transfer Metaphor
For example, suppose that the quality of an A
C
response actually depended upon the physical size of a
target, and that affectivity was also a contributing factor. That is, a large target that was emotionally
appealing was reported more often more correctly. Obviously, both factors are important in optimizing
the outcome; however, suppose we were studying the effect of target size alone. Then an “attractive”
small target might register as well as a less attractive large target and the size dependency would be con-
founded in unknown ways.
Our metaphor allows us to assign variables, such as these, to specific elements. Clearly, an individual’s
psychological response to a target is not an intrinsic property of a target; rather,
it is a property of the
receiver. Likewise, size is a physical property of the target and is unrelated to the receiver. Generally,
this metaphor allows us to lump together the psychology, personality, and physiology of the receiver and
consider these important factors as contributors to a detector “efficiency.” If it is true that an emotion-
ally appealing target is easier to sense by some individuals, we can think of them as more efficient at
those tasks.
In the same way, all physical properties of a target are intrinsic to the target and do not
depend on the detector efficiency. Perhaps, temporal and spatial distance between target and receiver
are intrinsic to neither the target nor the receiver, but rather to the transmission mechanism, whatever
that may be.
More than just nomenclature, our metaphor can guide us in designing experiments to decrease certain
variabilities in order to conduct meaningful process-oriented research. Some of the methodological
improvements seem obvious. If the research objective is to understand the properties ofAC rather than
understanding how anAC ability may be distributed in the population, then combining results across
receivers should be done with great caution. To understand how to increase high jumping ability, for
example, it makes no sense to use a random sample from the general population as high jumpers; rather,
find a good high jumper and conduct vertical studies (no pun intended). So, too, is it true in the study of
AC. We can easily reduce the variance by asking given receivers to participate in a large number of trials
and not combining their results.
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May, Spottiswoode, and James (1994) suggest that by limiting the number of cognitively differentiable
elements within a target, the variance can also be decreased. A further reduction of potential variance
can be realized if the target pool is such that a receiver's emotional/psychoiogical response is likely to be
more uniform across targets (i.e., reducing the detector variance as shown in Figure 1).
Having selected experienced receivers and attended to these methodological considerations, we could
then focus our attention on examining intrinsic target properties.
If we are successful at identifying one
such property, then all process-oriented^C research would be significantly improved because we would
be able to control a source of variance that is target specific. The remainder of the paper describes two
lengthy studies that provide the experimental evidence to suggest that the average of the total change of
Shannon’s entropy is one such intrinsic target property.
Approach
TheAC methodological details for the two experiments can be found in Lantz, Luke, and May (1994).
In this section, we focus on the target calculations and the analysis techniques.
Shannon Entropy: A Short Description
Building upon the pioneering work of Leo Szilard (1925, 1929), Shannon and Weaver (1949) developed
what is now called information theory.
This theory formalizes the intuitive idea of information that
there is more “information” in rare events, such as winning the lottery, than in common ones, such as
taking a breath. Shannon defined the entropy for a given system as the logarithmic average of the prob-
ability of occurrence of all possible events in the system.
Entropy, used in this sense,
is defined as a
measure of our uncertainty, or lack of information, about a system. Suppose, for example, we had an
octagonal fair die (i.e., each of the eight sides is equally likely to come up). Applying Equation 1, below,
to this system gives an entropy of three bits, which is in fact the maximum possible for this system.
If, on
the other hand, the die were completely biased so that the same side always came up, the entropy would
be zero.
In other words, if each outcome is equally likely then each event has the maximum surprise.
Conversely, there is no surprise if the same side always lands facing up.
In the case of images, a similar analysis can be used to calculate the entropy. For simplicity, consider a
black and white image in which the brightness, or luminance, of each picture element, or pixel, is mea-
sured on a scale from zero to 255, that is, with an eight bit binary number. Equation 1 can again be used
to arrive at a measure of the picture’s entropy. As with the other sensory systems were gradients are
more easily detected, we shall show that the gradient ofShannon's entropy is correlated with AC perfor-
mance far better than the entropy itself.
In other sensory systems, receptor cells are sensitive to incident energy regardless of “meaning”, which
is ascribed as a later cognitive function. Shannon entropy is also devoid of meaning. The pixel analysis
ignores anything to do with cognitive features. From this point of view, a photograph of a nuclear blast
is, perhaps, no more Shannon-entropic than a photograph of a kitten; it all depends on the intensities,
which were used to create the photographs.
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Target Calculations
Because of the analogy with other sensorial systems, we expected that the change of entropy would be
more sensitive than would be the entropy alone. The target variable that we considered, therefore, was
the average total change of entropy. In the case of image data, the entropy is defined as:
"k
m
Sk = “ ^PmklOgliPmk)’
^
mm 0
where pwt is the probability of finding image intensitym of color k. In a standard, digitized, true color
image, each pixel (i.e., picture element) contains eight binary bits of red, green, and blue intensity, re-
spectively. That is, Nk is 255
(i.e., 28-l) for each k,k = r,g,b.
For color, k, the total change of the
entropy in differential form is given by:
dSk =
\VSk
•
dr
| +
(
>
We must specify the spatial and temporal resolution before we can compute the total change of entropy
for a real image. Henceforth, we drop the color index, k, and assume that all quantities are computed
for each color and then summed.
Tb compute the entropy from Equation 1, we must specify empirically the intensity probabilities,/^.
In
Lantz, Luke, and May’s 1993 experiment, the targets were all video clips that met the following criteria:
• Topic homogeneity. The photographs contained outdoor scenes of settlements (e.g., viHageMowns,
cities, etc.), water (e.g., coasts, rivers and streams, waterfalls, etc.), and topography (e.g., mountains,
hills, desserts, etc.).
• Size homogeneity. Thrget elements are all roughly the same size. That is, there are no size surprises
such as an ant in one photograph and the moon in another.
• Affectivity homogeneity. As much as possible, the targets included materials which invoke neutral
affectivity.
For static targets, a single characteristic frame from avideo segment was digitized (i.e., 640x480 pixels)
for eight bits of information of red, green, and blue intensity. The video image conformed to the NTSC
standard aspect ratio of 4 x3, so we arbitrarily assumed an area (i.e., macro-pixel) of 16x12-192 pix-
els from which we calculated thepm .
Since during the feedback phase of a trial the images were dis-
played on a Sun Microsystems standard 19-inch color monitor, and since they occupied an area approxi-
mately 20x15 cm square, the physical size of the macro-pixels was approximately 0.5 cm square. Since
major cognitive elements were usually not smaller than this, this choice was reasonable-192 pixels
were sufficient to provide a smooth estimate of thepm .
For this macro-pixel size, the target frame was divided into a 40x40 array. The entropy for the (y)’th
macro-pixel was computed as:
N-\
S,j = - 2 PmlOg2(Pm ).
mmO
Where nu, is computed empirically only from the pixels in the ft ft macro-pixel andm is the pixel intensi-
ty. For example, consider the white square in the upper left portion of the target photograph shown in
Figure 2.
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Figure 2.
City with a Mosque
The green probability distribution for this macro-pixel (3,3) is shown in Figure 3. The probability densi
ty and the photograph itself indicate that most of the intensity in this macro-pixel is near zero (i.e., no
intensity of green in this case). In a similar fashion, the 5iy are calculated for the entire scene. Since* an
j range from zero to 40, each frame contains a total of 1,600 macro-pixels.
Figure 3.
Green Intensity Distribution for the City Thrget (Macro-pixel 3,3).
We used a standard image processing algorithm to compute the 2-dimcnsional spatial gradient for each
of the 1,600 macro-pixels. The first term in Equation 2 was approximated by its average value over the
image.
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The total change of entropy for the dynamic targets was calculated in much the
segment was digitized at one frame per second. The spatial term of Equation
wa
p
it was for the static frames. The second term, however, was computed from differences between a
cent, 1-second frames for each macro-pixel. Or,
(3 )
dSij
d(
AS^i0
At
[Si j(t
4- At) - St/CO
~At
where J. is one over Or. digitizing frame rate. We can see immediately that .he dynamic targets will
have a largerAS than do the static ones because Equation 3 is identically zero for all static targets.
In
Lentz,
Luke, and May’s
1992
experiment,
the
static
targets were
digitized from
scanned
photographs. This difference and its consequence will be discussed below.
AC>Data Analysis
Rank-order analysis in Lentz, Luke, and May’s (1994) experiment demonstrated sl^Bcant evidence
to,AC; however, this procedure does not usually indicate the absolute quality of the/IC. For example,
response that is a near-perfec. description of the target receives a rank of one. But a response
barely matchable to the target may also receive a rank ofone. Table 1 shows the rating scale that we used
to assess the quality of theAC responses, regardless of their rank.
lb apply this subjective scale to onAC trial, an analyst begins with a score ofseven and determines if the
description for that score is correct. If not, then the analyst tries ascore of six and soon
In this way
scale is traversed from seven tozera until the score-description seems reasonable for the Inal.
Table 1.
0-7 Point Assessment Scale^_
_
Score
Description
Excellent correspondence, including good analytical detail, with essentially no
incorrect information
—
Good correspondence with good analytical information and relatively little
incorrect information.
—
Good correspondence with unambiguous unique matchable elements,
ut
some incorrect information.
—
Good correspondence with several matchable elements intermixed with
incorrect information
Mixture of correct and incorrect elements, but enough of the former to indicate
receiver has made contact with the site.
Some correct elements, but not sufficient to suggest results beyond chance
expectation.
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Anomalous Cognition Experiment - 1 992
In Lantz, Luke and May’s 1992 experiment there were no significant interactions between target condi-
tion (i.e„ static vs dynamic) and sender condition (Le, sender vs no sender); therefore, they combi
the data for static targets regardless of the sender condition (Le., 100 trials). The sum-0 -ran
.
wa
(i.e„ exact sum-of-rank probability ofp < 0.007. effect size = 0.248). The total sum-of-ranks for the
dynamic targets was 300 (i.e.,p < 0.50, effect size = 0.000).
Entropy Analysis
lb examine the relationship of entropy toAC. two analysts independently rated all 100 trials 0-e
i10
each from five receivers) from the statictarget sessions using theporr Hoc rating scale shown, m ThMe 1.
All differences of assignments were verbally resolved, thus the resulting scores represented a reason-
able estimate of the visual quality of theAC for each trial.
We had specified, in advance, for the correlation with the change of target entropy, we would only use
the section of theporr hoc rating scale that represented definitive, albeit subjective,AC contact with he
target (i.e, scores four through seven). Figure 4 shows a scatter diagram for the port hoc ra ing and the
associated AS for the 28 trials with static targets that met this requirement. Shown also isa mear^
squares fit to the data and a Spearman rank-order correlation coefficient (p = 0.451 df - 26. ' -2.58.
p < 7.0 x 10-3).
This strong correlation suggests that AS is an intrinsic property of a static target and that the quality of
anAC response will be enhanced for targets with large AS.
It is possible, however, that this correlation
might be a result of AS and thepori hoc rating independently correlating with the targets visual com-
plexity. For example, an analyst is able to find more matching elements (i.e, a higherporr hoc rating) in
a visually complex target than in a visually simple one.
Similarly, AS may be larger
or more “mP
targets. If these hypotheses were true, the correlation shown in Figure 4 would not support the hypoth-
esis that AS is an important intrinsic target property for successful AC.
lb check the validity of the correlation, we used a definition of visual complexity, wh,ch
d®nvet
J
from a fuzzy set representation of the target pool. We had previously coded by consensus, 131 deferent
potential target elements for their visual impact on each of the targets in the pool. We assumed that the
sigma-count (i.e., the sum of the membership values over all 131 visual elements) for each target is pro-
portional to its visual complexity. A description of the fuzzy set technique and a list of the target
ments may be found in May, Utts, Humphrey, Luke, Frivold, and Trask (1990).
The Spearman rank correlation between target complexity and post hoc rating was small (p -M41,
df=98,t =0.407, p < 0.342). On closer inspection this small correlation was not surpnsi
g.
tree dial an analyst will find more matchablc elements in a complex target, so also are there many e e-
ments that do no, match. Since to rating scale (he., Thble 1) is sensitive ,o cotrec, and incorrect eie-
ments, the analyst is not biased by visual complexity.
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Since the change of Shannon entropy is derived from the intensities of the three primary colors (i.e.,
Equation 1 on page 5) and is unrelated to meaning, which is inherent in the definition of visual com-
plexity, we would not expect a correlation between AS and visual complexity. We confirmed this ex-
pectation when we found a small correlation (p = —0.028, df = 98, t -—0.277, p < 0.609).
Visual complexity, therefore, cannot account for the correlation shown in Figure 4; thus, we are able to
suggest that the quality of anAC response depends upon the spatial information (i.e., c ange o
an-
non entropy) in a static target.
A single analyst scored the 100 responses from the dynamic targets using thepoa hoc scale in Thble l.
Figure 5 shows the scatter diagram for theposr hoc scores and the associated AS for the 2>Imals with_a
score greater than three for the dynamic targets. We found a Spearman correlauon ofg - 0.055, df -21
(r =0.258, p < 0.399).
This small correladon is not consistent with the result derived from the static targets; therefore, we ex-
amined this case carefully. The total sum of ranks for the dynamictarget ease was «acdy mean chance
expectation, which indicates that no AC was observed (Lantz, Luke, and May, 1994).
May, Spottl-
swoode, and James(1994) propose that thelackofACmight be because an imbalanceof, what they call,
the target pool bandwidth. Utat is, the number of different cognitive elements in the dynamte poo
far
exceeded that in the static pool. This imbalance was corrected in the 1993 study and is analyzed below.
Regardless, we would not expect to see a correlation if there is no evidence ofAC.
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Figure 5.
Correlation of Port Hoc Score with Dynamic Target &S.
Anomalous Cognition Experiment - 1993
The detaiis of .he 1993 study may also to found m
“ de^C
methodological improvements, which are not apropos to this discussion.
Lanta Luke, and May selected a single frame from each dynamic target video clip, which was character,
istic of the entire clip, to act as its static equivalent. The static and dynamic
g
digitized with the same resolution and could be combined for the correlations.
For each response, a single analyst conducted a blind ranking of five targets
the intended one and four
d”"e usual way. UnL, Luke, and May computed effect sizes in the same way as in the ,992
ml receivers individually participated in 10 trials for each targe, type and a fourth participated in 15
ttialTpmTarget type. Lantz^Luke^and May reported a total average rank for the static mrgets
for 9oTrials for an effect size of 0566 <p < 7.5 x 1O'3
); the exact same effect sue was repo
e
dynamic targets.
Entropy Analysis
Differing from the
^
a’TaT^TueTlrag
total change of Shannon's entropy (i,„ Equation 2) was calculated
""described abL.
Ffcire 6 shows the correlation of the blind rating score with this
lr diem. The squares and diamonds indicate me data for static and dynamic targets, respectively.
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Figure 6.
Correlations for Significant Receivers
The key indicates the Spearman correlation (or the static and dynamic targets combined.
In addition,
since thehypothesis was that anomalous cognition would correlate with the total change of.hu^Shannon
entropy, Figure 6 only shows the scores in the upper half of the scale in Thble 1 for the 70 mate of
e
three independently significant receivers. The static target correlation was negattve fe --0.2S
,
)
73, t =-1.07, p < 0.847) and the correlation from the dynamic targets was positive (<? - 0.32U,
_ ft,
t -ust p < 0.098). The strong correlation for the combined data arises primarily from t e
entropic difference between the static and dynamic targets.
General Conclusions
To understand the differences between the results in the two experiments, we re-digitized the static set
of targets from the 1992 experiment with the same hardware and software that was used ini the 19
study. With this new entropy data, the correlation dropped from a significant 0.4
to
'
W “
significant (t = 1.58, df = 26, p < 0.063). Combining this data with the static results from the
experiment (i.e., significant receivers) the static correlation was q = 0. 161, df
41 (t
.
,p
)•
The correlation for the static targets from the 1992 experiment copied with
dynamic data from the 1993 experiment was significant (p — 0.320, df — 59, t
.
,p_
•
)•
post hoc results are shown in Figure 7. The combined data from the two experiments, including all re-
ceivers and all scores greater than four, give a significant correlation (q = 0.258, df - 64, t- 2.U,
p< 0. 018).
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Figure 7.
Correlations for Combined Experiments
We conclude that the quality olAC appear, to correlate linearly with the average total change of the
Shannon entropy, which is an intrinsic target property.
These two experiments may raise more questions than they answer.
If our conservative approach,
which assumes thatAC functions similarly to the other sensorial systems, is correct, we would predict
that the/lC correlation with the frame entropy should be smaller than that for the average total change
of the entropy. We computed the total frame entropy from the /5 all of the 640 X 480 pixels. The resu t-
ing correlation for the significant receivers in the 1993 experiment was £? = 0.234, df - 31
(f - 1.34,
p<0 095). This correlation is considerably smaller than that from the gradient approach, however, not
significantly so. We computed the average of the ,% for the 1,600 macro-pixels as a second way of mea-
suring the spatial entropic variations. We found a significant Spearman's correlation of p - 0.423, df
31 (, = 2 60, p< 0.007) for the significant receivers in the 1993 experiment The difference between the
correlation of the quality of theAC with the frame entropy and with either measure of the spatial gradi-
ent is not significant; however, these large differences are suggestive of the behavior of other sensonal
systems (i.e., an increased sensitivity with change of the input).
We have quoted a number of different correlations under varying circumstances and have labeled these
as post hoc. For example, hardware limitations in 1992 prevented us from combining those data with the
data from 1993. Thus, we recalculated the entropies with the upgraded hardware in 1993 and recom-
puted the correlations. Our primary conclusions, however, are drawn only from the static results from
the 1992 experiment and the confirmation from the combined static and dynamic 1993 results.
It is clear from our analysis that we may have identified an intrinsic target property that correlates with
the quality of anomalous cognition. Our results suggest a host of new experiments and analyses before
we can come to this conclusion with certainty. For example, suppose we construct a new target pool
a
is maximized for the gradient of Shannon’s entropy yet meets reasonable cntena for the target pool
bandwidth.
If the Shannon information is important, than we should see exceptionally strong
.
also must improve the absolute measure ofAC.
While dividing our zero-to-seven rating scale in two
makes qualitative sense, it was an arbitrary decision. Rank order statistics are not as sensitive to cor-
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relations as are absolute measures (Lams, Luke, and May, 1994); but, perhaps, if ttaMC effect stze »
significantly increased with a proper target pool, the rank-order correlations wdl be strong enough,
maybe time consuming; however, it is also important to understand the dependency of the correlabon
on the digitizing resolution. In the first experiment, we digitized the hard copy photographs using a
flatbed scanner with an internal resolution of 100 dots/inch and used 640 X 480 pntels for the static an
dynamic targets in the second experiment. Why did the correlation drop for the static targets by nearly
35 percent when the digitizing resolution decreased by 20 percent?
We noticed, post hoc, that the correlations exhibit large oscillations around zero below the cutoff score
of four.
Ifwe assume there is a linear relationship between/lC scores and the total change of Shannon
entropy, we would expect unpredictable behavior for the correlation at low scores because they unply
chance matches with the target and do not correlate with the entropy.
Since we are suggesting a reductionist perspective, we speculate that the linear correlation suggests be-
havioral, albeit circumstantial, evidence for receptor-like functioning for the detection otAC. lb deter-
mine if this is true, we must identify threshold and saturation limits.
Itis absolutely critical to confirm our overall resultsand to provide answers to some of theenigmas from
our experiment.
If we have identified an intrinsic target property, then all of our research can precede
more efficiently.
Consider the possibilities if we were able to construct a target pool and eliminate a
known source of variance. Psychological and physiological factors would be much easier to detect. Giv-
en the availability of inexpensive video digitizing boards for personal computers, replication attempts
are easily within the grasp of research groups with modest operating budgets.
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CPYRGHT
Rof6rences
Bern, D. J. and Honorton, C. (1994). Does psi must? Replicabla evidence for an anomalous process of
infonnation transfer. Psychological Bulletin. 115, No. 1, 4-18.
Convention, Montreal, Canada, 230-246.
Waft
r
HORR'I
Characteristics of
successful
free-response
targets:
Theoretical
considerations.
VES%Z The ParapsychologTcal Association 31st Annual Convention,
Montreal, Canada, 247-263.
Lantz, N. D. and Luke, W. L. W, and May, E. C. (1994). Thrget and sender dependencies in anomalous
cognition experiments. Submitted for publication m the Journal ofParapsycho
gy.
xa
c n
t „l^ W I W TVask. V V and Frivold, T. J. (1990). Observation of neuromagnetic fields in
^£^££22
of Presented Pape*,
33rd Annual Convention, National 4-H Center, Chevy Chase, MD, 168-185.
Mav E. C. Spottiswoode, S. J. P., and James, C. L. (1994). Managing the target pool bandwidth: Noise
^reduction
in anomalous cognition experiments.Submitted
for
publication
in
e
ou
Parapsychology.
May, E. C, Urn, J. M., Humphrey, B. S, Luke, W.LW
Frivold T J,and TVask, V. V. (1990). Advances
in remote-viewing analysis. Journal ofParapsychology, 54, 193-228.
Reichert, H. (1992). Introduction to Neurobiology, Oxford University Press, New York, NY.
Shannon, C. E. and Weaver, W. (1949). The Mathematical Theory of Communication. University of
Illinois Press, Urbana, IL.
Szilard L (1925) On the extension of phenomenological thermodynamics to fluctuation phenomeM.
z±c£,7i%A 32. 753.788. English fransla.ion
p
in The Code^dWod^Uo Ssdard.
Scientific Papers. B. T. Feld and G. W. Szilard, Ed. MIT Press Cambridge, 1972, 34-102.
Szilard, L. (1929). On the decrease of entropy in a thermodynamic system by the
i
intelligent beings. Zeitschrift fur Physik, 53, 840-856
IEnglish
Abridge
1972^
Leo Szilard: Scientific Papers. B. T. Feld and G. W. Szilard, Ed. MIT Press Cambridge,
103-129.
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Phenomenological Research and Analysis: Final Report
APPENDIX E
Security Measures in an Automated Ganzfeld System
and
SAIC FINAL REPORT: Ganzfeld Experiment
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Security Measures in an Automated Ganzfdd System
by
Kathy S. Dalton
Robert L. Morris
Department of Psychology
University of Edinburgh*
&
Dean 'Rfldin
Cotter for Advanced Cognitive Science
University of Nevada
&
Richard Wiseman
Psychology Division, Hatfield Campus
University of Hertfordshire
CPYRGHT
Abstract
The past success rate of the automated ganzfeld system has brought with it
both praise and criticisms from experimenters and critics alike. A new, unproved
approach to security measures within die ganzfeld setting is described, along with the
implications that the need for such precautions entails. The specific example of the
current automated ganzfeld system and its security precautions in use at the Kocsdcr
Chair nf Parapsychology in Edinburgh University is covered in some detail, with
recommendations for future improvements.
•The Koestler Chair of Parapsychology would like to gratefully acknowledge
the American Society for Psychical Research, the Parapsychology Foundation, ond
the Society for Psychical Research for financial support of personnel and equipment
germane rn this project.
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