Shannon Entropy: A Possible Intrinsic Target Property

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annon Entropy: A Possible Intrinsic Target Property V4 30 January 1995

Shannon Entropy:
A Possible Intrinsic Target Property”

by

Edwin C. May, Ph.D.
S. James P. Spottiswoode
and
Christine L. James
Science Applications International Corporation
Cognitive Sciences Laboratory
Menlo Park, CA

Abstract

We propose that the average total change of Shannon’s entropy is a candidate for an intrinsic target
property. An intrinsic target property is one that is completely independent of psychological factors
and can be associated solely with a physical property of the target. We analyzed the results of two
lengthy experiments that were conducted from 1992 through 1993 and found a significant correlation
(Spearman’s @ = 0.337, df = 31, t = 1.99, p < 0.028) with an absolute measure of the quality of the
anomalous cognition (AC). In addition, we found that the quality of the AC was significantly better for
dynamic targets than for static targets (t=1.71, df=36, p< 0.048). The 1993 correlation with the change
of entropy replicated a similar finding from our 1992 study. Using monte carlo techniques, we demon-
strate that the observed correlations were not due to some unforeseen artifact with the entropy calcula-
tion, but perhaps the correlation can be accounted for because of the difference in some other measure
between static and dynamic targets. The monte carlo results and the significant correlations with static
targets in the 1992 study, however, suggest otherwise. We describe the methodology, the calculations,
and correlations in detail and provide guidelines for those who may wish to conduct similar studies.

* This paper has been accepted for publication in the Journal of Parapsychology.

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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 sigmoidal, that is, there is a threshold for physical excita-
tion, a linear region, and a saturation level above which more input produces the 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 at the pre-perceptual cellular front end. For example, a putative AC 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 AC 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 the AC 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 AC 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 in specific domains. Without controlling

* The Cognitive Sciences Laboratory has adopted the term anomatous mental phenomena instead of the more widely known psi.
Likewise, we use the terms anomalous cognition and anomalous perturbation for ESP and PK, respectively. We have done so
because we 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.

Transmission

Detector

Source

AC Response

Figure 1. Information-transfer Metaphor

For example, suppose that the quality of an AC 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. Ifthe research objective is to understand the properties of AC rather than
understanding how an AC 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

* A person’s psychological reaction to a target (ie., “detector” efficiency) is an important contributing factor to the total re-
sponse as indicated in the references sited above; however, it is possible to reduce this contribution by careful selection of the
target pool material.

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AC. We can easily reduce the variance by asking given receivers to participate in a large number of trials
and not combining their results.

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/psychological response is likely to be
more uniform across targets (i.., 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 AC research would be significantly improved, because we
would be able to control a source of variance that is target specific. The remainder of this paper de-
scribes the analysis of two lengthy studies that provide the experimental evidence to suggest that the
average of the total change of Shannon’s entropy may be one such intrinsic target property.

Approach

The AC 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 weighted average of the proba-
bility of occurrence of all possible events in the system. Entropy, used in this sense, is defined as a mea-
sure of our uncertainty, or lack of information, about a system. Suppose, for example, we had an octag-
onal fair die (i.c., 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. Con-
versely, 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 of Shannon’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. Thus, it is not possible to give a prescription on how to
chose a high change-in-entropy photograph based on it pictorial content. Perhaps, after much experi-

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ence, it may be possible to recognize good targets from their intensity patterns; at the moment we do not
know how to accomplish this.

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:

Ny
St = — > Pmrloge(Pme)s (1)

mad
where pmx is the probability of finding image intensity m 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, Ny is 255 (i.e., 23-1) for each k, k = 7, g, b. For color, k, the total change of the
entropy in differential form is given by:

35,

dS, = |¥S, + dr| + |

dt. (2)

The first term corresponds to the change of the entropy spatially across a single photograph of video
frame. Imagine a hilly plane in entropy space; this term represents the steepness of the slope of the hills
(i.e., the change between adjacent macro-pixels, as defined below). The second term adds time changes
to the total change. Not only does the entropy change across a scene, but a given patch of the
photograph changes from one scene to the next. Of course this term is zero for all static photographs.

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.

To compute the entropy from Equation 1, we must specify empirically the intensity probabilities, p,,. 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., villages, towns,
cities, etc.), water (e.g., coasts, rivers and streams, waterfalls, etc.), and topography (e.g., mountains,
hills, desserts, etc.).

© Size homogeneity. Target 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 a video segment was digitized (i.e., 640 x 480 pixels)
for eight bits of information of red, green, and blue intensity. The video image conformed to the NTSC
standard aspect ratio of 43, so we arbitrarily assumed an area (i.e., macro-pixel) of 16x12 = 192 pix-
els from which we calculated the p,,. 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 20 x 15 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 the pn.

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.

For this macro-pixel size, the target frame was divided into a 40x40 array. The entropy for the (ij)th
macro-pixel was computed as:

N-1
Si = > Pm log2(Pm )s

m=0
where p,, is computed empirically only from the pixels in the (i, ;) macro-pixel and m is the pixel intensi-

ty. For example, consider the white square in the upper left portion of the target photograph shown in
Figure 2.

3 Ks

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 (ie., no
intensity of green in this case). In a similar fashion, the Sj are calculated for the entire scene. Since and

j range from zero to 40, each frame contains a total of 1,600 macro-pixels.

We used a standard image processing algorithm to compute the 2-dimensional 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 and was computed by the relations shown as Equations 3.

2 2
~ dS,; dS;;
dS,;
Sj = (Sisajea 7 Sis1j-1) + 208; j41 7” Sij-1) + (Si-1,j41 7” Si-1,j-1) (3)
dx
iy

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0.4 — .

Probability Density
2
NO
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0.05 20 40 60 80 100

Intensity (m)

Figure 3. Green Intensity Distribution for the City Target (Macro-pixel 3,3).

The total change of entropy for the dynamic targets was calculated in much the same way. The video
segment was digitized at one frame per second. The spatial term of Equation 2 was computed exactly as
it was for the static frames. The second term, however, was computed from differences between adja-
cent, 1-second frames for each macro-pixel. Or,

at At | At \’

where At is one over the digitizing frame rate. We can see immediately that the dynamic targets will
have a larger 4S than do the static ones because Equation 4 is identically zero for all static targets.

(4)

In Lantz, 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 Lantz, Luke, and May’s (1994) experiment demonstrated significant evidence
for AC; however, this procedure does not usually indicate the absolute quality of the AC. For example, a
response that is a near-perfect description of the target receives a rank of one. But a response which is
barely matchable to the target may also receive a rank of one. Table 1 shows the rating scale that we used
to assess the quality of the AC responses, regardless of their rank.

To apply this subjective scale to an AC trial, an analyst begins with a score of seven and determines if the
description for that score is correct. If not, then the analyst tries a score of six and so on. In this way the
scale is traversed from seven to zero until the score-description seems reasonable for the trial.

For all analyses in the 1992 and 1993 studies, we decided a priori to use only the upper half of the rating
scale. As the strength of the AC-functioning increases, by definition there is less incorrect information
(i.e., noise). In other words, the noise contribution to each score level decreases in some unknown way
as the the AC increases. Thus, we limited the noise contribution by using only the upper half of the scale
in the analysis.

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Table 1.
0-7 Point Assessment Scale
Score Description

7 Excellent correspondence, including good analytical detail, with essentially no
incorrect information

6 Good correspondence with good analytical information and relatively little
incorrect information.

5 Good correspondence with unambiguous unique matchable elements, but
some incorrect information.

4 Good correspondence with several matchable elements intermixed with
incorrect information.

3 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.

1 Little correspondence.

0 No correspondence.

Anomalous Cognition Experiment — 1992

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 (i.e., sender vs no sender); therefore, they combined
the data for static targets regardless of the sender condition (i.e., 100 trials). The sum-of-ranks was 265
(i.e., exact sum-of-rank probability of p < 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

To examine the relationship of entropy to AC, two analysts independently rated all 100 trials (i.e., 20
each from five receivers) from the Static-target sessions using the rating scale shown in Table 1, post
hoc.” All differences of assignments were verbally resolved, thus the resulting scores represented a rea-
sonable estimate of the visual quality of the AC for each trial.

We had specified, in advance, for the correlation with the change of target entropy, we would only use
the section of the post hoc rating scale that represented definitive, albeit subjective, AC contact with the
target (i.e., scores four through seven). Figure 4 shows a scatter diagram for the post hoc rating and the
associated AS for the 28 trials with static targets that met this requirement. Shown also is a linear least-
squares fit to the data and a Spearman rank-order correlation coefficient (9 = 0.452, df = 26, t =2.58,
P<7.0 x 10-4),

This strong correlation suggests that AS is an intrinsic property of a static target and that the quality of
an AC response will be enhanced for targets with large AS. It is possible, however, that this correlation
might be a result of AS and the post hoc rating independently correlating with the targets’ visual com-

* This was conduced post hac because we did not realize until after the judges completed their blind analysis and had been given
feedback on the study outcome that a rating scale is more sensitive than ranking. We used this result toforma hypothesis that
we tested in the second study.

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plexity. For example, an analyst is able to find more matching elements (i.e., a higher post hoc rating) in
a visually complex target than in a visually simple one. Similarly, AS may be larger for more complex
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.

To check the validity of the correlation, we used a definition of visual complexity, which was derived
from a fuzzy set representation of the target pool. We had previously coded by consensus, 131 different
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 ele-
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 (9 = 0.041,

t =0.407, df = 98, p < 0.342). On closer inspection this small correlation was not surprising. While it is
true that an analyst will find more matchable elements in a complex target, so also are there many ele-
ments that do not match. Since the rating scale (i.e., Table 1) is sensitive to correct and incorrect ele-
ments, the analyst is not biased by visual complexity.

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Figure 4. Correlation of Post Hoc Score with Static Target AS.

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 (@ = —0.028, t =—0.277, df = 98, 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 an AC response depends upon the spatial information (i.e., change of Shan-
non entropy) in a static target.

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A single analyst scored, post hoc, the 100 responses from the dynamic targets using the scale in Table 1.
Figure 5 shows the scatter diagram for the post hoc scores and the associated AS for the 24 trials with a
Score greater than three for the dynamic targets. We found a Spearman correlation of 9 = 0.055

(¢ =0.258, df = 22 p < 0.399).

This small correlation 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 dynamic-target case was exactly mean chance
expectation, which indicates that no AC was observed (Lantz, Luke, and May, 1994). May, Spotti-
swoode, and James (1994) propose that the lack of AC might be because an imbalance of, what they call,
the target pool bandwidth. That is, the number of different cognitive elements in the dynamic pool 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 of AC.

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Figure 5. Correlation of Post Hoc Score with Dynamic Target AS.

Anomalous Cognition Experiment — 1993

The details of the 1993 study may also be found in Lantz, Luke, and May (1994). In that study, they
included a static ys dynamic target condition, and all trials were conducted without a sender. They
changed the target pools so that their bandwidths were similar. They also included a variety of other
methodological improvements, which are not apropos to this discussion.

Lantz, 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 targets, therefore, were
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
decoys—in the usual way. Lantz, Luke, and May computed effect sizes in the same way as in the 1992
study.

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Three receivers individually participated in 10 trials for each target type and a fourth participated in 15
trials per target type. Lantz, Luke, and May reported a total average rank for the static targets of 2.22
for 90 trials for an effect size of 0.566 (px75x1 0-); the exact same effect size was reported for the
dynamic targets.

Entropy Analysis

Differing from the 1992 experiment, an analyst, who was blind to the correct target choice used the
scale, which is shown in Table 1, to assess each response to the same target pack that was used in the
rank-order analysis. The average total change of Shannon’s entropy (i.e., Equation 2) was calculated
for each target as described above. Figure 6 shows the correlation of the blind rating score with this
gradient. The squares and diamonds indicate the data for static and dynamic targets, respectively.

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Figure 6. Correlations for Significant Receivers

The key indicates the Spearman correlation for the static and dynamic targets combined. In addition,
since the hypothesis was that anomalous cognition would correlate with the total change of the Shannon
entropy, Figure 6 only shows the scores in the upper half of the scale in Table 1 for the 70 trials of the
three independently significant receivers. The static target correlation was negative (@ = —0.284,

t =—1.07, df = 13, p < 0.847) and the correlation from the dynamic targets was positive (@ = 0.320,

t =1.35, df = 16 p < 0.098). The strong correlation for the combined data arises primarily from the
entropic difference between the static and dynamic targets. The rating scores were signficantly stronger
for the dynamic targets than for the static ones (¢=1.71, df=36, p<0. 048).

As a control check for possible unforeseen artifacts, we conducted a monte carlo analysis as follows.
The actual blind rating scores greater than three were correlated with the gradient of Shannon’s entro-
py from a target chosen randomly from the pool of the appropriate target type (i.e., only from the static
pool or dynamic pool if ratings were originally from a static or dynamic target, respectively). After 100
such monte carlo trials, we found the Spearman rank correlation was 9 = —0.0501+0.311. Ifwe assume
that the standard deviation for the actual data is the same as that in the monte carlo calculation (i.e.9 =
0.337£0.311) we find a significant difference between these cross-match controls and the actual data

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(tage = 4.98, df = 62, p < 2.68 x 10~°). This analysis assumes that the visual correspondence between a
response and its intended target remains the same, but the gradient of the entropy is random. Thus, it
appears that the data correlation does not arise from an artifact.

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 in the 1993
study. With this new entropy data, the correlation dropped from a significant 0.452 to 0.298 which is not
significant (t = 1.58, df = 26, p < 0.063). Combining this data with the static results from the 1993
experiment (i.c., significant receivers) the static correlation was 9 = 0.161 (t = 1.04, df = 41, p<0.152).
The correlation for the static targets from the 1992 experiment combined with the significant static and
dynamic data from the 1993 experiment was significant (9 = 0.320, df = 59, t = 2.60, p <0.006). These
post hoc results are shown in Figure 7. The combined data from the two experiments, including all receiv-
ers and all scores greater than four, give a significant correlation (o = 0.258, df = 64, t = 2.13, p < 0.018).

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tory

ii

°

Rating Score

Figure 7. Correlations for Combined Experiments

We conclude that the quality of AC appears 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 that AC functions similarly to the other sensorial systems, is correct, we would predict
that the AC 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 pj which we computed from all of the
640 x 480 pixels. The resulting correlation for the significant receivers in the 1993 experiment was 9 =
0.234 (t = 1.34, df = 31, p < 0.095). This correlation is considerably smaller than that from the gradient
approach, however, not significantly so. We computed the average of the 5; for the 1,600 macro-pixels
as a second way of measuring the spatial entropic variations. We found a significant Spearman’s cor-
relation of 9 = 0.423 (t = 2.60, df = 31 p < 0.007) for the significant receivers in the 1993 experiment.
The difference between the correlation of the quality of the AC with the frame entropy and with either

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measure of the spatial gradient is not significant; however, these large differences are suggestive of the
behavior of other sensorial 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.

Since we observed a significant AC-score difference in the 1993 study, perhaps our correlations with AS
arise because of some other non-entropy related property such as motion. Yet, the monte carlo results
demonstrated that the correlations vanished when the AC-scores were kept constant and random cross-
target values were used for AS. Because the randomizations were exclusively within target type, the
correlation between the AC-scores and AS should have remained significant had some non-entropy fac-
tor distinguishing static and dynamic targets been operative. In addition, the within-static target AS in
the 1992 study significantly correlated with the the AC quality.

We conclude 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 that is maximized
for the gradient of Shannon’s entropy yet meets reasonable criteria for the target pool bandwidth. If the
Shannon information is important, than we should see exceptionally strong AC. We also must improve
the absolute measure of AC. 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 correlations as are abso-
lute measures (Lantz, Luke, and May, 1994); but, perhaps, if the AC effect size is significantly increased
with a proper target pool, the rank-order correlations will be strong enough. A more sensitive and well-
defined rating scale should also improve the analysis. It may be time consuming, however it is also im-
portant to understand the dependency of the correlation on the digitizing resolution. In the first experi-
ment, we digitized the hard copy photographs using a flatbed scanner with an internal resolution of 100
dots/inch and used 640 x 480 pixels for the static and dynamic targets in the second experiment. Why
did the correlation drop for the static targets by nearly 35 percent when the digitizing resolution de-
creased by 20 percent?

We noticed, post hoc, that the correlations exhibit large oscillations around zero below the cutoff score
of four. If we assume there is a linear relationship between AC scores and the total change of Shannon
entropy, we would expect unpredictable behavior for the correlation at low scores because they imply
chance matches with the target and do not correlate with the entropy.

To determine if we are observing behavioral evidence for receptor-like functioning for the detection of
AC, we must identify threshold and saturation limits. This can be accomplished if future experiments.

It is absolutely critical to confirm our overall results and to provide answers to some of the enigmas 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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