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ables include scores from the Q-sort personality test, and the Ap geomagnetic index, respectively. SAIC
routinely enters many experiment variables into an existing database, but we propose to update the da-
tabase with variables that are more useful in intelligence applications.
In addition, the protocol and
outcome of each trial will be coded into the database.
(S/NF) Once this database contains sufficient numbers of laboratory and intelligence experiments,
then relatively simple queries may reveal ranges or limits to specific variables. We propose to perform
such queries each time sufficient new data are added to the database.
1 .4.2
Quantitative Assessment (SOW 6.2.2)
(U)
(U) It is now clear that free response AC experiments can generate much larger effects than forced choice
protocols.
However, the problem of determining the quantity and accuracy of information in free response
experiments has not been satisfactorily resolved.
Such experiments typically generate both textual and visu-
al information.
This information has previously been assessed by ranking and descriptor set methods. Both
methods have
disadvantages:
ranking can
greatly underestimate
statistical
significance, while
descriptor-
based methods suffer from uncertainty as to how to define the conceptual categories used to distinguish
target and response elements. The research described below aims to improve these assessment methods.
1.4.2.1 Neural Networks (U)
(U) Neural networks have been widely applied to image and pattern recognition problems. However,
they have not been applied to the problem of assessing free response AC data.
SAIC will explore the
application of neural networks to the existing fuzzy set assessment method.
Neural networks will be
trained on fuzzy set encodings of stimulus-response pairs from AC trials by individual subjects.
If con-
sistent patterns between receivers’ responses and their intended targets exist, then neural networks can
be trained to recognize them. The trained networks can be used to assess additional AC data sets and
the results can be cross validated against existing fuzzy set scoring and ranking methods. Because neu-
ral network methods can discriminate complex mappings in the presence of noise, the method may yield
more precise estimates of target-response correlation than the current fuzzy set descriptor system.
1.4.2.2 Image Decomposition (U)
(U) Numerous techniques have been developed for image compression.
While most of these algo-
rithms compress images by exploiting redundancy in the pixel array, some recent techniques take a dif-
ferent approach based upon image decomposition.33 This “fractal image compression” method relies
on partitioning images into subsets that can be used to reconstruct the original by recursively applying
affine transformations to the subsets. When applied to conventional image compression, the technique
relies on a judicious choice of the original partitioning.
It may hold particular promise for assessing AC
responses because such responses seem to be characterized by a limited set of formal elements, which
give a natural set of basis elements or partitions. We propose to explore the fractal image analysis ofAC
responses to verify that such responses can be characterized by a relatively small set of underlying forms.
These will be used as the basis set for the partitioning of the target material used in the ACexperiments.
Judging schemes based upon these forms can then be investigated. A further refinement will involve
"learcKirigTorase'toToptimal basis elements for the partitioning of targets and responses using an effi-
cient search method such as a genetic algorithm. The goal will be to develop an analysis method that
avoids the arbitrariness associated with descriptor-based methods, while capturing much of the formal
richness of information seen in superior AC performance.
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1.4.2.3 Fuzzy Sets (U)
(U) We will continue to improve our standard fuzzy set approach to qualitative analysis of AC. In par-
ticular, we will determine if the sensitivity of the method can be improved by redefining the visual ele-
ments that are in current use.34
1.4.2.4 Intelligence Application Ifest-bed (S/NF)
(S/NF) One primary difficulty in assessing the quality of AC in intelligence applications is that fre-
quently there is little or no ground truth. Thus, we have had to rely upon other collection methods to
provide corroborating evidence. Even in those cases, the kind of information that is obtained is fre-
quently not helpful in learning how to improve AC for the collection of intelligence data.
(S/NF) Under an earlier program, we carried on two intelligence-like AC trials.35’36 These trials were
conducted just as if they were real-world problems except that the targets were chosen by the sponsor
specifically because complete ground-truth could be obtained. Thus, it was possible to provide quanti-
tative assessment in near-operational conditions.
(S/NF) We propose to conduct up to five such AC trials. We will provide up to four receivers for this
activity. The sponsor will provide a variety of different targets, most ofwhich will contain elements that
would normally be of interest to the intelligence community. As a calibration, we suggest that some of
the targets be AC sites that are used during laboratory investigations (e.g., bridges, buildings, etc.), and
that SAIC personnel should remain blind to the entire target pool.
(U) At the end of each trial, the sponsor and SAIC will construct an evaluation matrix, which may include
fuzzy sets, to compute the accuracy and reliability of the AC session. The results of that evaluation will be
entered into the tracking database so that receiver-dependent historical records will be preserved. SAIC
will provide summaries and raw data in a report at the end of each trial.
1.4.3
Intelligence Applications (SOW 6.2.3)
(S/NF)
(S/NF) At the sponsor’s request, SAIC will provide personnel to participate in intelligence applica-
tions of AC. This will include access to up to four receivers for a total of five separate target systems.
SAIC will provide summaries and raw data in a report at the end of each task.
SG1B
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1.5 Research Methodology and Support (SOW 6.3)
(U)
1.5.1
Committees (SOW 6.3.1)
(U)
(U) We propose to use the existing committees as support and quality control for methodological and
policy issues. These committees are the Scientific Oversight Committee (SOC), the Institutional Re-
view Board (IRB), and the Policy Oversight Committee (POC).
1.5.1.1 The Scientific Oversight Committee
(U)
(U) The five voting members of the SOC are respected scientists from the following disciplines: physics,
astronomy, statistics, neuroscience, and psychology. The membership is as follows:
• Steven A Hillyard, Ph.D.
•
S. James Press, Ph.D.
• Garrison Rapmund, M.D.
• Melvin Schwartz, Ph.D.
• Yervant Terzian, Ph.D.
• Philip G. Zimbardo, Ph.D.
Professor of Neuroscience, University of California, San Diego
Professor of Statistics, University of California, Riverside
Liaison with the Institutional Review Board (see below)
Director, High Energy and Nuclear Physics, Brookhaven NL
Chairman, Department of Astronomy, Cornell University
Professor of Psychology, Stanford University
(U) The SOC is tasked with three major responsibilities:
• Review and approve all experimental protocols prior to the collection of data.
• Critically review all experimental final reports as if they were submissions to technical scientific jour-
nals. All remarks are in writing and are included in the final technical report to the sponsor.
• Suggest directions for further research.
(U) In addition to these three responsibilities, the SOC members are encouraged to exercise un-announced
drop-in privileges to view experiments in progress.
1.5.1.2 Institutional Review Board
(U)
(U) The IRB’s responsibility is to assure the safety of human subjects in experiments and to assure the
sponsor that all research involving the use ofhuman subjects is in compliance with all appropriate feder-
al regulations. The IRB members represent the health, legal, and spiritual professions in accordance
with government guidelines. The membership is as follows:
• Byron Wm. Brown, Jr., Ph.D.
• Gary R. Fujimoto, M.D.
• John Hanley, M.D.
• Robert B. Livingston, M.D.
• Robin P. Michelson, M.D.
• Ronald Y. Nakasone, Ph.D.
• Garrison Rapmund, M.D. (Chair)
• Louis J. West, M.D.
Biostatistics, Stanford University
Occupational Medicine, Palo Alto Medical Foundation
Neuropsychiatry, University of California, Los Angeles
Neuroscience, University of California, San Diego
Otolaryngology, University of California, San Francisco
Buddhist Studies, Institute of Buddhist Studies, Berkeley, CA
Air Force Science Advisory Board
Neuropsychiatry, University of California, Los Angeles
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1.5.1.3 Policy Oversight Committee (U)
(S/NF) The POC’s responsibility is to advise SAIC and assure the Defence Intelligence Agency that
the activity under this contract fulfills the requirements of the intelligence community and the Depart-
ment of Defense.
In addition, the POC recommends policy for the establishment of a long-term pro-
gram for the application ofAMP to problems of interest to these communities.
1 .5.2 Management and Research Support (SOW 6.3.2) (U)
(U) We will provide technical, management, and administrative support for all research activity, which
will include the production of financial and interim technical reports.
1.5.3
National/International Conferences (SOW 6.3.3) (U)
(U) We will provide SAIC personnel to attend selected national/international conferences that relate
to biophysics, AMP, and neuroscience.
2. Quick Reaction Capability (SOW 7.0)
(U)
(U) We propose to reserve approximately five percent of the program effort in order to respond rapidly
to the sponsor’s request for briefings, technical papers, conference attendance, or unanticipated ex-
periments or applications.
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IV. GLOSSARY
(U)
(U) Not all the terms defined below are germane to this report, but they are included here for complete-
ness. In a typical anomalous mental phenomena (AMP) task, we define:
• Ml—A form of information transfer in which all known sensorial stimuli are absent. That is, some
individuals are able to gain access, by as yet an unknown process, to information that is not available
to the known sensorial channels.
• Agent—An individual who attempts to influence a target system.
• Analyst—An individual who provides a quantitative measure of AC.
• Feedback—After a response has been secured, information about the intended target is displayed to
the receiver.
• Monitor—An individual who monitors an AC session to facilitate data collection.
• Protocol
A template for conducting a structured data collection session.
• Receiver—An individual who attempts to perceive and report information about a target.
• Response—Material that is produced during an AC session in response to the intended target.
• Sender/Beacon
An individual who, while receiving direct sensorial stimuli from an intended target,
acts as a putative transmitter to the receiver.
• Session—A time period during which AC data are collected.
• Specialty—A given receiver’s ability to be particularly successful with a given class of targets (e.g.,
people as opposed to buildings).
• Target—An item that is the focus of an AMP task (e.g., person, place, thing, event).
• Thrget Designation—A method by which a specific target, against the backdrop of all other possible
targets, is identified to the receiver (e.g., geographical coordinates).
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V. REFERENCES
(U)
(U) All titles are unclassified.
1.
The technical final report for SAIC project 01-187-07-406 is nearing completion as ofAugust 1992.
2.
C. S. Rebert and A. Timer, “EEG Spectrum Analysis Techniques Applied to the Problem of PSI
Phenomena,”
Physician’s Drug
Manual,
Vol.
5,
No.
9-12,
pp.
82-88
(December
1974)
UNCLASSIFIED.
3.
R. Thrg, E. C. May, H. E. Puthoff, D. Galin, and R. Omstein, “Sensing of Remote EM Sources
(Physiological Correlates),”
Final Report, Project 4540, SRI International, Menlo Park, CA
(1977) UNCLASSIFIED.
4.
E. C. May, R. Thrg, and H. E. Puthoff, “Possible EEG Correlates to Remote Stimuli Under
Conditions of Sensoiy Shielding,” Electro/77 Professional Program, Meeting of the IEEE, New
York (April 1977) UNCLASSIFIED.
5.
E. C. May, W. L. W. Luke, V. V. Task, and T. J. Frivold, “Observation of Neuromagnetic Fields in
Response to Remote Stimuli,” Proceedings ofPresentedPapers, The Parapsychological Association
33rd Annual Convention, Chevy Chase, MD, pp 168-185, (August 1990) UNCLASSIFIED.
6.
B.
Boashash,
“Estimating
and
Interpreting
the
Instantaneous
Frequence
of
a
Signal—Part
1:
Fundamentals,” Proceedings ofthe IEEE, Vol. 80, No. 4, pp. 519-538 (April 1992) UNCLASSIFIED.
7.
H. Berger, “Uber das Elektrenkephalogramm des Menschen,
J. Psychol. Neuro., Vol 40. pp.
160-179 (1930). UNCLASSIFIED.
8.
G. Pfurtscheller and A. Aranibar, “Event-related Cortical Desynchronization Detected by Power
Measurements of Scalp EEG,” Electroencephalography and Clinical Neurophysiology, Vol. 42, pp.
817-826 (!977) UNCLASSIFIED.
9.
G.
Pfurtscheller and A. Aranibar, “Evaluation of Event-related Desynchronization (ERD)
Preceding
and
Following
Self-paced
Movement,”
Electroencephalography
and
Clinical
Neurophysiology, Vol. 46, pp. 138-146 (!979) UNCLASSIFIED.
10.
G. Pfuitscheller, G. Lindinger, und W. Klimesch, “Dynamisches EEG-Mapping—Bildgebendes
Verfahren fuer die Unterschung Perzeptiver, Motorischer und Kognitiver Hirnleistunger,” Z.
EEG-EMG, Vol 17. pp. 113-116 (1986) UNCLASSIFIED.
11.
W. Klimesch, G. Pfurtscheller, und G. Lindinger, “Das Corticale Aktivierungsmuster bei Verbalen
Gedaechtnisaufgaben,” Sprache Kognition, pp. 140-154 (1987) UNCLASSIFIED.
12.
J. Sergeant, R. Geuze, and W. Van Winsum, “Event-related Desynchronization and P300,”
Psychophysiology, Vol. 24, pp. 272-277 (1987) UNCLASSIFIED.
13.
L. Kaufman, B. Schwartz, C. Salustri, and S. J. Williamson, “Modulation of Spontaneous Brain
Activity during Mental Imagery,” Journal of Cognitive Neuroscience, Vol. 2, No.
2, pp. 124-132
(1990) UNCLASSIFIED.
14.
W.
Braud,
D.
Shafer,
and
S.
Andrews,
“Electrodermal
Correlates
of Remote
Attention:
Autonomic Reactions to and Unseen Gaze,” Proceedings ofthe ParapsychologicalAssociation 33rd
Annual Convention, Chevy Chase, MD (August 1990) UNCLASSIFIED.
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15.
W. Braud, D. Shafer, and
S. Andrews, “Further Studies of Autonomic Detection of Remote
Staring: Replications, New Control Procedures, and Personality Correlates,” Proceedings of the
Parapsychological
Association
35rd Annual
Convention,
Las
Vegas, NV
(August
1992)
UNCLASSIFIED.
SG1A
I
17.
C. Honorton, “Impact of the Sender in Ganzfeld Communication: Meta-Analysis and Power
Estimates,” Final Report, Psychophysical Research Laboratories (1992) UNCLASSIFIED.
18.
C. Honorton, “Effects of the Sender on Anomalous Communication in the Ganzfeld: Research
Protocol,” Final Report, Psychophysical Research Laboratories (1992) UNCLASSIFIED.
19.
C. Honorton, R. E. Berger, M. P. Varvoglis, M. Quant, E. I. Schechter, and D. C. Ferrari, “Psi
Communication in the Ganzfeld,” Journal of Parapsychology,
Vol. 54, pp. 99-137 (June 1990)
UNCLASSIFIED.
20.
J.
Block, The Q-Sort Method
in Personality Assessment and Psychiatric Research,
Consulting
Psychologists Press, Inc., Palo Alto, CA (1978) UNCLASSIFIED.
21.
E. H. Walker, "Quantum Mechanics/PSI Phenomena: The Theory and Suggestions for New
Experiments,” The Journal of Research
in PSI Phenomena,
Vol.
1. No.
1, pp. 38-52 (1976)
UNCLASSIFIED.
22.
E. H. Walker, “A Comparison of the Intuitive Data Sorting and Quantum Mechanical Observer
Theories,” The Journal ofParapsychology, Vol. 51. No. 3, pp. 217-228 (1987) UNCLASSIFIED.
23.
R. G. Jahn and B. J. Dunne, Margins ofReality: The Role of Consciousness in the Physical World,
Harcourt Brace Jovanovich, Orlando, FL (1987) UNCLASSIFIED.
24.
R. G. Stanford, “An Experimentally Testable Model for Spontaneous PSI Events,” Journal of the
American Society for Psychical Research, Vol, 68, pp. 34-57 (1974) UNCLASSIFIED.
25.
E. C. May, “Intuitive Data Sorting: An Informational Model of Psychoenergetic Functioning,”
Final Report—Objective E, Thsks 3 and 4, Project 1291, SRI International, Menlo Park, CA
(December 1986) UNCLASSIFIED.
26.
S. W. Hawking, A Brief History of Time: From the Big Bang to Black Holes, Bantam Books, New
York, NY (1988) UNCLASSIFIED.
SG1A
28.
C. Honorton and D. C. Ferrari, ‘“Future Ifelling:’ A Meta-analysis of Forced-choice Precognition
Experiments, 1935-1987,” Journal ofParapsychology, Vol. 53, pp. 282-308 (December 1989).
29.
M. Visser, “Traversable Wormholes: Some Simple Examples,” Physical Review D, Vol. 39, No. 10,
pp. 3182-3184 (May 1989) UNCLASSIFIED.
30.
D. L. Delanoy, “Characteristics of Successful Free-Response Thrgets: Experimental Findings and
Observations,” Proceedings of Presented Papers of the
Parapsychological Association
31st
Annual Convention, pp. 230-246, Montreal, Canada (August 1988) UNCLASSIFIED.
31.
C. Watt,
“Characteristics of Successful Free-Response Thrgets: Theoretical Considerations,”
Proceedings of Presented Papers of the Parapsychological Association 31st Annual Convention,
pp. 247-263, Montreal, Canada (August 1988) UNCLASSIFIED.
32.
E. C. May and N. D. Lantz, “Target and Sender Dependencies in Anomalous Cognition,”
Technical
Protocol,
Project
1-187-07-406-10,
SAIC,
Menlo
Park, CA
(December
1991)
UNCLASSIFIED.
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33.
R.D. Boss and E.W. Jacobs, “Fractal-Based Image Compression,” NOSC Technical Report 1315,
Naval Ocean Systems Center, San Diego CA 92152-5000, (September 1989) UNCLASSIFIED
34.
E. C. May,
J. M. Utts, B. S. Humphrey, W. L. W. Luke, T. J. Frivold and V. V Tlask, “Advances in
Remote-Viewing Analysis,” Journal of Parapsychology, Vol. 54, pp. 194-228 (September, 1990)
UNCLASSIFIED.
SG1A
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VI. RESUMES
(U)
(U) All the following resumes are unclassified.
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Edwin C. May, Ph.D.
Director, Cognitive Sciences Laboratory
EDUCATION
Ph.D., Physics, University of Pittsburgh, 1968
B. S., Physics, University of Rochester, 1962
EMPLOYMENT HISTORY
1991 - Date
1985 - 1990
1979 - 1985
1976 - 1979
1973 - 1979
1972 - 1979
1972 - 1976
1972 - 1976
1968 - 1971
1960 - 1964
Director, Cognitive Sciences Laboratory
Science Applications International Corporation, Menlo Park,
California
Program Manager, Cognitive Sciences Program
SRI International, Menlo Park, California
Senior Research Physicist for the Psychoenergetics Program
SRI International, Menlo Park, California
Consultant to the Psychoenergetics Program
SRI International, Menlo Park, California
Research Conslultant and Hardware Engineer
The Biofeedback Institute of San Francisco, San Francisco, California
Technical Consultant and Software Engineer
Digital Pathways, Inc., Mountain View, California
Physics Instructor
City College of San Francisco, San Francisco, California
Technical Consultant
Psychophysical Research Laboratories, Princeton, New Jersey
Postdoctoral Fellow
University of California, Davis,California
Summer Position, Earh and Planetary Sciences Department
The RAND Corporation, Santa Monica, California
SPECIALIZED EXPERIENCE
Currently, Dr. May is the Director of the Cognitive Sciences Laboratory (CSL) which currently employs
over twenty full or part-time researchers from a variety of disciplines. He refined his management skills
while being the Program Manager for a similar, five-year program at SRI International.
Dr. May has
been involved in various forms of anomalous cognition research for over 19 years. Prior to that, he accu-
mulated over 12 years experience in experimental physics research, nuclear reaction mechanisms, and
nuclear structure. His accelerator experience includes a three-stage tandem Van de Graaff (18 Mev); a
76-inch, variable energy cyclotron (50 Mev); an FM cyclotron (450 Mevprotons); fixed frequency cyclo-
tron (8 Mev); FN tandem Van de Graaff (18 Mev); and an EP tandem Van de Graaff (30 Mev). Other
specialize experience includes four years of y-ray spectroscopy (on and offline), one year of trace-ele-
ment analysis (x-ray, and a particle techniques), numerical analysis, Monte Carlo techniques, digital
signal processing, and cardiac blood flow research.
For over thirty years, Dr. May has participated in the design and construction of fast (< 0.1 ns) digital
electronics, and in the programming and implementation of sophisticated computer systems. Platforms
include UNIX workstations and various main frames.
Besides C, Dr. May is fluent in Fortran and a
variety of assembly and 4GL languages.
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