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4.2 Attempts at Message Sending Using AC
The objective of this experiment was to adapt a standard AC experiment to a forced-choice situation.
An additional objective was to incorporate fuzzy set technology into a “crisp,” two-by-five, error cor-
recting block code to improve AC detection in a message sending/receiving metaphor. We reported this
experiment in an interim technical report; however, we summarize it here for completeness.
4.2.1
Background
In the Spring of 1992, SAIC conducted a pilot experiment that was designed to explore the potential for
maximizing the reliability of AC responses through objective and rapid analysis.
In this study, we re-
verted to using a dichotomous binary procedure as opposed to a fuzzy set technique. By carefully select-
ing the dichotomous elements, we could use standard block coding techniques to incorporate complete
single error correction, also including a few two-fold corrections as well. We used a message sending
motif as a test-bed for this kind of analysis.
Unfortunately, in that experiment, only one receiver demonstrated an effect size larger than 0.20 (i.e.,
0.22) for evidence of an AC phenomena, and no evidence of enhanced detection of AC was seen.
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Phenomenological Research and Analysis: Final Report
A number of difficulties were discovered in this experiment that may have rendered the results incon-
clusive:
• In an attempt to make the targets dichotomous within target packs and at the same ^e interesting to
view, targets within the pool ranged in scale from a panoramic scene of a cityscape to aP^Wh.o1
three chairs or an image of three geometric shapes, and thus possessed a
1large arge *P
tareets
(i.e., a large set of differing target elements).
Since receivers were told in advance that theta g
could contain absolutely any material, they were unable to censQr the,^
may have resulted in enhanced intrinsic receiver noise, and thus added
noise
to the response.
• Each encoding bit was linked to only one precept (e.g., the single target element of wafer). This exag-
gerated the importance of the chosen dichotomous elements.
For example, if a receiiver faded to
sense water in the target but managed to sense most other aspects of the target, regardless of wheth ^
they were part of the bit structure, then the BCH coding was not particularly applicable.
• In an AC application, a fundamental imbalance existed in the bit structure. The BCH coding assumes
tLTbina^ro is “akertive.” That is, in AC when water is not indicated in the response, Us equiva-
lent to indicating the water is definitely not in the target. Unfortunately, in AC experiments it is pos-
sible or even likely that unless a receiver specifies explicitly that wafer is not present, then the p
ence or absence
is indeterminate.
Maybe water exists in the target but was not noticed or was
unreported by the receiver. Similarly, wafer may not exist in the target and a non-response is equiva-
lent to an assertive no. These two cases are, of course, indistinguishable. The net effect is to render
the BCH coding invalid.
In the current experimental protocol, we attempted to correct the problems discussed above so that
potential enhancement of the detection of AC might be optimized. The following modifications were
made:
• The target-pool bandwidth was reduced by using the National Geographic static target pool, which
has been successful for many previous AC experiments.
• Sensitivity to single BCH encoding bits was reduced by using a number of fuzzy-set elements to define
each BCH bit. Thus, each BCH bit did not rely upon a single precept, but rather represented classes
of different precepts.
We had anticipated that these improvements would allow for much stronger AC and provide a more
sensitive test of whether BCH error-correcting could be successfully applied to AC detection. We use
long-distance associative AC tasks as a test bed for this procedure.
This experiment was similar to a traditional AC experiment. A target was selected randomly; a receiver
was asked to describe that target; and a quantitative assessment of the match was made. It differs, how-
ever, only in the construction of the targets and in the quantitative analysis.
4.2.2 Conclusions
No receiver produced significant deviations in the sum-of-ranks statistic, and binary numbers were not
determined beyond chance expectation. Even though our best receivers participated m this particular
study, their results were not up to the standard which we have traditionally seen from them. We could
speculate that possibly the experimental conditions were significantly different from their usual ex-
pectations (i.e., at home or in our laboratory) and that travel and performance anxiety may have con-
tributed to the lack of AC functioning; however, very occasionally do they not perform as expected and
excuses are not necessary. We strongly urge that a replication be tried under laboratory conditions to
test the new approach of the fuzzy set encoding. At this time, even though there are a few excellent ex-
uA***
4*
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Phenomenological Research and Analysis: Final Report
amples in the literature of using AC for message sending, we suggest that this might not be an optimum
use of the phenomenon.
4.3 Improved Analysis
We have delivered an approach to the analysis of application-oriented AC using fuzzy set technology as
part of a separate document. We summarize the pertinent aspects of the approach here for complete-
ness.
We have been conducting application-like experiments for a number of years. These test-bed experi-
ments have an advantage in that total ground truth is known in advance. A list of items, therefore, can
be constructed that-would generally be of interest. We illustrate this approach to fuzzy set analysis with
one of our test-bed experiments. We constructed three categories of items: (1) Functions of the Site, (2)
Physical Relationships, and (3) Objects. Thble 4 shows a partial list of these three types of items for our
test-bed experiment. The complete list spans many pages.
Thble 4.
Partial Element List for a Test-bed Experiment
Thrget/Response Element
w
T(H)
R(f)
Functions (1.0)
Directed Energy
1.0
0.9
Test Experiment
1.0
1.0
Noise Generation
0.4
0.6
Operation in Space
0.0
1.0
Relationships (0.75)
Power Source Above Beam Line
1.0
0.0
Electrons Flow Through Beam Line
1.0
0.7
Pipes in and out of Sphere
1.0
Objects (0.5)
External Electron Beam
2.5
0.0
High Security Area
1.0
Bundled Metal Rods
1.0
Two types of data must be incorporated into such a list to provide an accurate measure; an apriori list of
items that are definitely part of the target and items that are mentioned by the receiver that were not
recognized as being part of the target.
In Thble 4, we have indicated overall weighting factors of 1.0,
0.75, and 0.5 for functions, relationships, and objects, respectively. That is, in this experiment, we were
primarily interested in functions.
Depending upon the task, the formalism accepts any appropriate
weighting factors. The column w is a within-group weighting factor. That is, the item Directed Energy is
five time more important than is Noise Generation. T(p) represents the degree to which the item is pres-
ent in the target. For example, although Noise Generation is present in the target, it is roughly only 40%
apparent; whereas Pipes in and out ofSphere is not present at all. R(p) is the degree to which the analyst
is convinced that the element is indicated in the response. For example, the analyst was 90% convinced
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