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One variation on this theme was developed by Budzinski. He found that “simple” right-brain messages
were better “understood” if the left-brain was occupied. Can this technique be adapted to AC experi-
ments.?
1.1.2.11 Redundancy /Multiple Pass/Multiple Detectors)
little formal investigation of redundancy appears in the literature. Sophisticated fuzzy-set analysis pro-
vides a legitimate method of applying redundancy.
1.1.2.12 Communication
AC techniques have been used in the past to “send” messages with 100% accuracy (e.g., a single 50-bit
binary message was flawlessly sent using a multiple pass procedure). Can modem and efficient tech-
niques (e.g., sequential sampling) improve the efficiency of such systems?
i
1.1.2.13 Creativity
. Do independent measures of various creative abilities correlate with AC functioning.
1.1.2.14 Analysis
The development of analysis techniques for AC data has traditionallybeen a major part of the Cognitive
Sciences Program. Simple techniques such as rank-order have the advantage of being understood by
everyone, but those same techniques grossly underestimate the information transfer bit-rate. Yet com-
plicated techniques such as fuzzy set analysis are difficult to understand and thus are prone to criticism.
Nonetheless, adaptive fuzzy set analysis has been shown to be very promising in developing a person-de-
pendent lexicon that can significantly improve the AC measure.
Can neuronetworks and/or genetic algorithms be applied to the fuzzy set approach to AC analysis?
1.1.3 Mixed
1.13.1
Internal Noise Source
The general problem of “internal” imagery is a current and major topic in traditional cognitive science.
With somd limited success AC practitioners have been able to separate internally generated “noise”
from target-related data. Canwe improve on this? What training from traditional cognitive sciences can
be adopted?
1.133
Training (Macroscopic/Operant)
Training has been particularly difficult. The good news is that we have not observed a so-called decline
effect (i.e., subjects start well and converge to the mean), but we also have not generally been able to
demonstrate learning. By macroscopic I mean specific protocols—lesson plan. By operant I mean can
response-neurons (hopefully identified in item 1.1.13 above) be trained by biofeedback techniques to
improve the quality of AC data?
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1.133
Session Protocols
Session protocols is a general area that includes feedback parameters, session structure (i.e., how long,
how often, technique), and data knowledge (Le., who knows what when). Another important aspect of
session protocol is when does the session begin and/or end?
One test of this is the “hooded” feedback protocol. In the falcon experiment, the AC participant is blind-
folded jest after the session. With possible variations on a theme, the blindfold is removed at specific
times during a physical visit to the target site. Some the sites revealed are decoy targets and occurbefore
and after the actual feedback target.
1.2 Transmission
,
,
Understanding hot, data propagate, from the source to the detector is a basic research problem. One
driving issue is that precognition (ii, data is received befon-m 3-space-the source emits it) is real.
X \
1.2.1
Basic
1.2.1.1
Dedsion Au gmentation Theory.
Decision Augmentation Theory (DAT) portents that the usual decision processes are augmented by sta-
tistical amounts of information-leakage from the future. One application of this model to lfnyears of
random number generator data has been particularly successful. The model should be expanded and
tested in a number of different experimental domains.
1 .2.1.2 Worm Holes /4-Dimensions)
Recent research in general relativity has suggested the possibility of realizable time travel through worm
holes in the 4-D manifold. Aboundary condition that emerged is that causality must be stochastic-not
unlike the conclusion forced by the precognition data. What is needed is some smart GR theorist to
calculate things for us.
1.2.13
Vector/Scalar Potential Propagation
The E&M: vector/scalar (A, O') potentials are under intense investigation.
After the Aharonof (sp?)
/Bohm effect demonstrated that A,<h are fundamental aspects of E&M theory, it is hoped that A,4>
waves could propagate. To date, theory and experiments have not confirmed such propagatron. Howev-
v
er, i/there were a possible A,<h propagation, it is likely that its properties would emulate some aspects of
AC. Thus we should look into it.
13.1.4
Ftnehastic Causality
Besides worm holes in 4-D, are there other implications of stochastic causality?
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1.3 Source
"Hie source of AC data has implications for basic and applied research. Tb date* no quantitative defini-
tion of the source exists.
Characterizing the source is the topic under discussion.
1.3.1
Basic
13.1.1
Informational
Informational entropy is a macroscopic aspect ofthe source (ie., target), whereasmeamng is a
al aspect of the target. (Itoccurs tome at this writing, meaningcannotbeatargetproperty.)
y
mean^hannon type at tins earl, stage of the thinking. Is these a form of “collected” entropy or rnforma-
tion compression that can be adapted to AC target descriptions?
13.12
Other Thermodynamk ^
can classical thermodynamics leach ns about the limitations of the infonnation
^m the
source? We can not assume at this smge that infonnato
Wh
are the thermodynamic consequences of this?
13.13
Vector/Sccd ftr Potential
Specifically, what are the source requirements that might emit A,4> waves?
1.3.2 Applied
13.2.1
Boundaries
V/hat are the sourceboundaries? Are they hard-edged (probabiy not)? From a pragmatic point of view,
the judge must determine where to stop the analysis. For example, if the Golden Gate Budge ts
target, does the City of San Francisco count since it is visible from the budge?
13.2.2
Definitions
Traditional AC research has focused on visual and or functional aspects of the target. There are other
dimensions that may be important. What are optimal huge, definitions for ail these domams
.
13.23
T!pman Sender
m the soiled telepathy mode, the scarce information is "transmitted” b, the mind of an observer. A
'
major amount of data (i-e., the Ganzfeid) hasbeen collected under this assumption. It
nccLy condition in that most of the SRI AC dam was collected without a human sender, bn, to
degree is a “sender” involved?
133.4
Demarcation CCoordinatfiS/BeaCQD)
The universe is a near-infinite collection ofpotential targets. How is the intended target to be specify?
Virtually eve^hing tha, has been tried (e,„ beacon, coordinates, encrypted —-abstract
words, etc.) have been successful. What does this mean? Are some better than others.
y.
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133.5
External Noise-Source.
Once the source ofAC data has been identified, targets can be selected on the bases of the lack of noise
located at the source. Can this type of noise be used as masking?
13.2.6
Inverse (Search!
The forward direction for AC experiments involve the demarcation of a target in an attempt to obtain
information about it. The inverse problem (i.e., as in physics, a very difficult one) involves giving all
information about the target except is demarcation. Where is that target?
1.3.3 Mixed
133.1
Physical Characteristics /Size/Composition)
Are atoms “visible” via AC techniques? How can mixed-sized targets be optimally detected?
1333
Type (Static/Dvnamic)
Are there specific target types that constitute “easier” AC sources?
If so, why?
1333
Qne-in~“n” fForced Choice/Binary Search^
In early experiments {circa 1930s), most of the effort involved guessing a target symbol from a limited set
(usually 5). There is a massive database that shows consistent statistical evidence for an anomaly, but the
effect size is quite small (e.g., 0.02). There are a number of possibilities for this* Maybe there is a basic
research reason such as entropy. There are internal reasons as well. For example, when an individual
knows the set of targets, strong internal imagery results from memory and imagination that masks a puta-
tive AC signal.
t
Notes: Research Plan
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