Approved For Release 2000/08/08 : CIA ROR PoR002200150001-1 Final Technical Report December 1987 ENHANCED HUMAN PERFORMANCE INVESTIGATION (U) By: EDWIN C. MAY Prepared for: PETER J. McNELIS, DSW CONTRACTING OFFICER'S TECHNICAL REPRESENTATIVE WARNING NOTICE RESTRICTED DISSEMINATION TO THOSE WITH VERIFIED ACCESS TO THE I PROJECT SG1A 333 Ravenswood Avenue Menlo Park, California 94025 U.S.A. (415) 326-6200 Cable: SR! INTL MPK al TWX: 910-373-2046 International SL eeppRroved For Release 2000/08/08 : CIA-ROP ESD BApIROOZ2ON Ed by ee
| | Internation ee LSS SF TTS sproyed For Release 2000/08/08 : CIA- -ROES-ROFPR002200150001- -1 Final Technical Report December 1987 Covering the Period 1 October 1986 to 30 September 1987 ENHANCED HUMAN PERFORMANCE INVESTIGATION (U) By: EDWIN C. MAY Prepared for: PETER J. McNELIS, DSW CONTRACTING OFFICER’S TECHNICAL REPRESENTATIVE SRI Project 1291 SG1A WARNING NOTICE RESTRICTED DISSEMINATION TO THOSE WITH VERIFIED ACCESS To THE RP RovecT Approved by: , Copy LA. of wh Copies. MURRAY J. BARON, Director This document consists of 53 pages. Geoscience and Engineering Center . SRI/GF-0308 CLASSIFIED BY: HQ, USAMRDC (SGRD-ZA) DECLASSIFY ON: OADR NOT RELEASABLE TO SECRET FOREIGN NATIONALS Sis ed For Retizase:B000/08/0@ --Ot intern fi (415) 326-6200 * Cable: YAURD MAGE onee RYB2 2001390 001-1 Nast 7 ®
Approved For Release 200PQROB AOUGH EPFA-p0789R002200150001-1 LIST OF TABLES LIST OF FIGURES I INTRODUCTION 2... ccc ccc eee eee teen ees A. OVETVIOW Cece eee eee eee ee tence eens B. Definitions ©... cc ee cee ee ee tte terete ene Cc. Program SCOpe oo... ccc cece eee eee ee en eee D. Program Objectives 0... 0. E. Program ReSOUrceS 0.6... cece eee eee ee eee eens II PROGRESS TO DATE 0... cece cece eee eet e ete eens A. Status of Subcontracts ............. eee B. Status of Consultants... 0.0... cece cece teens C. Progress to Date for Each Objective/Task ..............2.005 Ill PROBLEM AREAS ... cc ccc tent n eee tenes IV ADMINISTRATIVE COMMENTS ...... cee eect ee eee Vv PROJECT MILESTONE CHART ....... ccc cece eee ee eens VI COST SUMMARY 9... ccc ccc cece tent tenet netnee REFERENCES ocean eee eee eee nbn e ee nnn e eens TABLE OF CONTENTS (U) APPENDIX A — A POSTERIORI ASSESSMENTS OF THE SCIENTIFIC OVERSIGHT COMMITTEE iii OS 2 SS SS Ce PY Oo NR RR pa wm A ® Approved For Release oob bib Ach.S4i6t1&Q0789R002200150001-1
Approved For Release 200PNR0B JOYCE PPA-p0789R002200150001-1 F YY BD LIST OF TABLES (UV) Status of Subcontracts for FY 1987 0.0... cece ce cee n eee eee nene 4 Status of Consultants for FY 1987 0.0... cece cee cc eee ee ee neuen enas 5 Priority/Deliverable Assignments for FY 1987 ..... 0... ccc cece cue cece eee eeaes 46 Authorized Task Changes to FY 1987 Statement Of Work .......... 0.0.00 000. 47 Authorized Interpretations of FY 1987 Statement Of Work..............0.000, 47 Enhanced Human Performance Investigation--FY 1987 .......... 0. cee eeeees 48 Cost Analysis By Objective/Task--FY 1987 0.0... 0. ccc ccc eee c cece eee eenes $1 LIST OF FIGURES (U) The Attribute Set for the DACOS System ......... 0.00. ccc cece cece eueneeves 8 DACOS Hierarchy for Water-Present Targets .......... 0.0 c eee ee ce ecuceueues 9 DACOS Hierarchy for Water-Absent Targets ......... 000. cece cee cect eneuues 10 Hypnosis-RV Protocol 2.0... cc eect ee enn tence eeeeeentnaes 22 iv Approved For Release odebhbifeb Achnod Fd 6:20789R002200150001-1
Approved For Release 2000/QipRR EAL BaD P96 -00789R002200150001-1 I INTRODUCTION (UV) A, (U) Overview (S/NF) In accordance with the requirements set forth under the program, "Enhanced Human Performance Investigations" (Contract No. DAMD17-85-C-5130), this document provides a progress update for work performed by SRI International and its subcontractors during Fiscal Year 1987. The aim of the five-year program (FY 1986-1990) is to provide research and development in the area of psychoenergetics as a means to enhancing human performance for military applications. B. (U) Definitions (U) Psychoenergetic phenomena are defined here as direct interactions between human consciousness and the environment, which, although the mechanism is unexplained, can be observed and recorded. These human capabilities fall into two main categories: (1) the acquisition of information, and (2) the production of physical effects. These can be further defined as: e Remote Viewing (RV)/Extrasensory Perception (ESP) - The ability to gain access, by mental means alone, to concealed data or remote sites. e Remote Action (RA)/Psychokinesis (PK) - The ability to influence, by mental means alone, physical or biological systems. (On (U) Program Scope (S/NF) This program is designed to provide the necessary foundation to assess various aspects of psychoenergetics with the DoD’s needs in mind. The program is highly diverse and interdisciplinary; it spans many fields and involves academic and research facilities, subcontractors, and consultants. Furthermore, it initiates an in-depth investigation into the life sciences aspects of psychoenergetic phenomena. 1 Approved For Release 2000/SSS Ee <eaMeREP 96-00789R002200150001-1
Approved For Release 2000/08 edubemes -00789R002200150001-1 D. (U) Program Objectives (U) There are three basic program objectives: (1) to document that psychoenergetic phenomena are real and reproducible; (2) to determine the mechanism(s) underlying these phenomena; and (3) to bring the field of psychoenergetics into the mainstream of human performance research, by providing a scientific foundation equivalent to that of the rest of the performance research field. In the minds of some, there is no doubt that psychoenergetic phenomena are real and reproducible. In the minds of many others, both scientific professionals and informed lay persons, this is not the case. (S/NF) The categories of research interest under consideration form a hierarchy ranging from basic research on fundamental mechanisms to methodologies for applications including: ® Identifying explanatory mechanisms (e.g., electromagnetic effects, neurophysiological mechanisms). ® Specifying phenomenological properties (e.g., the effects of distance and shielding). @ Determining physical, physiological, and psychological correlates (e.g., geophysical environment, EEG and GSR measures, and personality profiling). ® Developing optimal strategies for use in applications (e.g., statistical averaging). E. (U) Program Resources (U) To meet the above objectives, the SRI program is using both in-house and external expertise. For over a decade, a core group of researchers at SRI has been studying a wide variety of subjects in psychoenergetics--augmented by access to specialty centers such as our neurosciences and our microbial genetics laboratories. (U) Some of the work is being subcontracted to institutions, groups, and consultants who have a demonstrated track record in this research area. Other subcontractors may have had no association with this field but, because of their specific area of expertise, can make valuable contributions to our program goals. Thus, the widest possible interdisciplinary viewpoints are available to the program, and the mixture of resources will ensure that peer group review and scientific interactions are maximized. Subcontractors and consultants currently include personnel from Princeton University, Syracuse University, John F. Kennedy University, the Palo Alto Medical Clinic, MARS Measurements Associates, Psychophysical Research Laboratories, the Parapsychology Sources of Information Center, Mind Science Foundation, and the Approved For Release 2000/08/¢6= -U0U789R002200150001-1
Approved For Release 2Q9 QQ 748A G4 AEPPB)00789R002200150001-1 (U) University of Delaware, plus the consultants having expertise in specific areas of interest to the program. 3 Approved For Release oobbbGL ASH BLER 0789R002200150001-1
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Approved For Release 200PIRCOR ACOSTP TE EP 0789R002200150001 -1 3. (U) Objective A, Task 3--Improve RV Evaluation a. (U) Fuzzy Set Applications in Remote Viewing Analysis (U) In FY 1987, fuzzy set mathematical techniques were applied to the problem of remote viewing analysis. Two analytical methods were developed: the first was designed to be sensitive to the verbal content of the RV response; the second was designed to account for the visual/spatial arrangements of response elements. A definition of “ground truth,” against which these new analytical techniques could be tested, was also devised. (U) The verbal method is predicated on the application of fuzzy set mathematics to the figure of merit (FM) technology.* The method also features a new descriptor list, which was introduced to provide a richer vocabulary for analysis. The list’s hierarchical structuring in levels, ranging from very abstract to very concrete, affords considerable flexibility for analytical manipulation of descriptor elements. A pilot application of the verbal analysis was shown to correlate highly with ground truth. (U) The combination of fuzzy set technology and the new descriptor list also proved effective for the visual/spatial approach. The implementation of these techniques——-in conjunction with a third technique known as “cluster analysis”-—has resulted in an algorithm for the production of orthogonal target sets. This has resulted in a significantly more effective rank-order analysis procedure. (U) The visual and verbal analyses were each determined to have certain strengths and weaknesses. The verbal analysis is statistically more powerful and provides a more comprehensive breakdown of the verbal information in an RV _ response. It is quite labor-intensive to apply, however, and it appears to be relatively insensitive to noisy RV data. “Noisy,” in this context, can be defined as a preponderance of incorrectly identified response elements. The visual analysis system is statistically much less powerful and is less capable of providing systematic objectification of the true RV signal content. It can be rapidly applied, however, and is sensitive to the primary manifestation of true RV signal in noisy data--namely, the visual arrangement of RV response elements, regardless of their verbal labels. Potential applications of these techniques in their current states have been suggested; areas of future research for their refinement have also been identified. * (U) The FM analysis has continued to undergo refinement since its inception in FY 1984. 6 Approved For Release 20bdREGL ASWEHE20789R002200150001-1
Approved For Release 20QOPIECOR ACY SRP PEEP 9789R002200150001 -1 b. (U) An Expert System Approach to Remote Viewing Analysis (1) (U) Motivations to Explore Expert Systems (U) The judging of RV transcripts has proven to be a difficult task for the experienced as well as the uninitiated analyst. Judging can be both analyst and viewer dependent, in that the combination of a viewer’s response style and an analysts’s interpretive style may enhance or hinder the analytic task. The process is, to a large extent, a subjective task that does not lend itself to a literal or procedural quantification. In an effort to render the judging more transferable, if not more uniform, we decided that, if development were feasible, an expert system to assist the analyst would prove invaluable. (U) The ultimate task would be to develop an expert system that could ask an analyst a series of questions about a given RV transcript, arrive at a composite description of the response, and map the response to a group (possibly with only a single member) of targets within a known target pool. The system would have standard data about a number of common abstract and concrete objects. Furthermore, the system would maintain a data base of the ideograms and idioms commonly used by a particular viewer and their possible/probable correspondences. By prompting the analyst for information about the concrete or unambiguous elements of a response (i.e., are there parallel lines, or are there elements labeled as structures), the system would combine the user~supplied data with data collected in previous experiments with the same viewer, to piece together composite hypotheses about the transcript. In an interactive exchange, the system would attempt to present the analyst with possible transcript interpretations of increasing complexity and/or concreteness until some kind of composite picture could be drawn. (U) Clearly, such an undertaking is very ambitious and well beyond our current expertise. Acting as a consultant, Dr. Jacques Vallee was to undertake the initial steps towards the development of such a system. As requested, we supplied him with the NExpert®* development system, an expert system shell which exploits non-monotonic reasoning (i.e., simultaneous forward and backward chaining or, analogously, simultaneous inductive and deductive reasoning). As the analyst’s task is by no means a clearly hierarchical or linear process, this feature of the NExpert® system is a clear necessity. (2) (U) Initial Goals (U) The delivery of an expert system matching our specifications, if indeed such a system can be devised, would obviously require numerous development cycles, and —e (U) NExpert® is a product of Neuron Data, Inc., 444 High Street, Palo Alto, California. 7 Approved For Release 20bd Rb ASS ED 0789R002200150001-1
Approved For Release 200PNQRQOB JOY SEP PEE) 0789R002200150001-1 (U) was therefore not an immediate expectation. After discussions with Vallee, it was decided that the thrust of the FY 1987 effort should be focused upon the mapping of response to target. Rather than assuming that the target possibilities are effectively infinite, the system should be designed and programmed to have prior knowledge of the structure and contents of the 200 targets in our current target pool. This design decision renders the problem much more tractable, and certainly does not prevent further efforts from focusing upon response analysis with little or no knowledge of the target universe. Once a system was in place, we would supply Vallee with transcripts from 1987 experiments to serve as test data for the system. (U) Even with a limited universe of targets, mapping a response to a single target is not a practicable goal. Many targets are visually similar, and the information contained in a typical response transcript is not sufficient to distinguish, for example, the Gobi desert from the Sahara. Rather, a more reasonable task is to break up the target pool into similar groups and map responses to a target type. As we had not yet determined these groupings, Vallee undertook the description and classification of the 200 targets and used the resulting target types as the basis for his work. (3) (U) Description and Classification of Sites (DACOS) (U) The system Vallee developed for categorizing the target pool, DACOS (Description and Classification of Sites), contains 40 distinct target categories made up of specific combinations of 27 possible target attributes. The set of attributes used is broken up into six types: Water, Structures, Interfaces, Contours, Land, and Features (Figure 1). Water Structures Interfaces Contours Land © Features Present Absent Waterfall Hilly i Rocky Absent Non-Urban Island Flat i Volcanic Urban Peninsula Mountainous Dense City Snowy (Glacial) Coastline Rugged Open City Green (Verdant) Lake Plowed River Monuments Ruins tT Tt Dunes Applicable Applicable if water if water is present is absent UNCLASSIFIED FIGURE 1 (U) THE ATTRIBUTE SET FOR THE DACOS SYSTEM ; ; Approved For Release cobb libfed Acad ft620789R002200150001-1
Approved For Release 2QQ)K ROB AOYSRPFR §30789R002200150001-1 (U) DACOS is a hierarchical system which is best described by a tree whose interior nodes each represent a decision point and whose levels each represent an attribute type. By selecting one of the possible paths at each node, one traverses the tree until a leaf node is reached, at which point a target category has been selected. As is clear by inspecting the hierarchy, it is possible to determine a target category by answering a maximum of five questions (Figures 2 and 3). Water Structures Structures Absent Water Structures Present Non-Urban Structures Urban UNCLASSIFIED Interfaces Contours Features Volcanic Island | Non-Volcanic Waterfall lacial Coastline ———_—_ Glacial (Snowy) Non-Glacial (S Lake acial (Snowy) Fiat River —_ Volcanic Hilly —-_ Non-Volcanic Island Coastline Lake Flat Hilly Island Peninsula Coastline Monuments Non-Monuments Hilly FIGURE 2 (U) DACOS HIERARCHY FOR WATER-PRESENT TARGETS 9 Approved For Release cobb bbfel A S.ot 6G 0789R002200150001-1
Approved For Release 209 CE ASS PETE fy 789R002200150001 -1 Water Structures Contours Land Features Volcanic Mountainous Non-Volcanic Structures Absent Rocky Rugged Dunes Snowy Plowed Fertile Arid Plowed Water Structures Absent Non-Urban Verdant (Green) Fertile Monuments Hilly———] Arid - Flat Verdant (Green) Snowy Monuments Dense city] Non-—Monuments Ruins Open City |" Non-Ruins Monuments Dense city] Hilly ———] Non-Monuments Open City Flat Structures Urban UNCLASSIFIED FIGURE 3 (U) DACOS HIERARCHY FOR WATER-ABSENT TARGETS (U) The computer system Dr. Vallee delivered was developed using the NExpert® system, but was later transferred and coded in BASIC for efficiency reasons. The DACOS program initially prompts the analyst for an answer to the question “Is there water present?” and, depending upon the response, continues to traverse the appropriate DACOS decision sub-tree asking further questions. The final output is a list of pairs, the category type with its corresponding confidence factor. These confidence factors directly reflect the number of attributes that correspond to any given category. For example, if the final attributes were Water, Urban, River, and Hilly, the categories under the Water-Absent node would have a factor of zero, the categories under the Water-Present, Non-Urban node and the Water-Present, No-Structures node would have a factor of one, etc. With these confidence factors, one can then determine a hierarchy of possible correspondences for the given transcript and propose a best match category. 10 Approved For Release 20bdPROLAS SE 0789R002200150001-1
Approved For Release 2E9FG AIS AGS FEE D°0789R002200150001-1 (4) (U) Results of Testing DACOS with Actual Remote Viewing Data (U) The data given to Vallee for testing consisted of 30 RV transcripts from one 1987 experiment. He then used the DACOS program to assign confidence factors to the different target categories. Overall, the DACOS performed only marginally better than chance. Out of 30 transcripts, the water attribute was correctly identified 17 times, the structures attribute 13 times, and the full target classification twice. Unfortunately, this series of remote viewings, when judged, did not show a significant RV effect, and thus proved a poor test case. Nonetheless, this exercise allowed us to evaluate the progress and direction of this work. (5) (U) Shortcomings of DACOS (U) The categories within the DACOS system were constructed to produce visually distinct or “orthogonal” target types. The first two attribute levels of the DACOS hierarchy, Water and Structures, are by far the most clear, simple, and symmetric; most important, they correspond to common elements of RV transcripts. Nonetheless, some of the target classifications are inappropriate for the kind of RV response data typically seen. Experience has shown that the visual content is the most important aspect of a target; the minute details of a target are often missed and thus should not overpower the overall description of the target. For example, although several of the water targets do picture water, the water is confined to such a visually insignificant region as to be either unnoticed or ambiguous. . (U) The deeper levels of the DACOS hierarchy do not maintain the symmetric nature of the first two levels because they inherit properties from the preceding levels. Furthermore, the attributes chosen for the deeper levels do not necessarily reflect the actual visual nature of the targets. The attribute Monument discriminates targets on a very high social and cognitive level; identifying a monument requires a significant amount of conceptual or functional knowledge about a particular site, and is not necessarily evident from the visual contents of a target. The attribute Non~Monuments, representing the explicit absence of monuments, is even more abstract and visually ambiguous. Although Vallee’s hierarchy yields a target category with a maximum of five questions, the choice of attributes is not ideal for the task. (U) Clearly, the most significant problem with the DACOS system is its strict hierarchical structure. The system, by its tree traversal method, makes each decision strictly binding; once water has been determined to be absent, the system does not have any means of reconsidering water as an attribute. Effectively, by completely isolating sub—sections of the decision tree, the DACOS system renders the attribute Hilly for Water-Absent targets to be distinct from the attribute Hilly for Water—Present targets because the categories on one side of the tree will receive credit and those on the other side will receive none. This occurs because the 11 Approved For Release 2000 Rb0L AS Hit G0789R002200150001-1
Approved For Release 2Q0pRQR{OB AOSSEPFE® 0789R002200150001-1 (U) system does not allow for any network or global attribute semantics. Ideally, the system would not force a decision about the presence or absence of an attribute if that information is not available; rather, it would consider the data already acquired, and prompt for alternative data. Further efforts in the development of an expert system must allow for a broad and dynamic evaluation of all of the data the analyst presents. (U) The DACOS system implicitly assumes that all the information it receives is certain. Clearly, a tool for the novice or uninitiated analyst must not expect that the analyst will be correct in 100% of his decisions. Furthermore, the system must not expect that an analyst will be able to render a meaningful decision about every possible attribute; in the absence of data about any given attribute, the analyst cannot necessarily assume that the attribute in question is in fact not present. For example, the particular attributes chosen for the lower levels, optimized for the minimality constraint Vallee imposed upon the system, do not best represent the elements typically contained in an RV response. The attributes Fertile, Arid, and Plowed are rarely seen; assuming that data pertaining to these three attributes cannot be discerned, eight categories are reduced to two, and the system has no way of resolving the analyst’s uncertainty. As all RV response data are, by their very nature uncertain, the expert system we envision must deal with uncertainty from the very start. (6) (U) Future Directions (U) The NExpert® development system offers many capabilities tailored to dealing with uncertain reasoning. Unfortunately, the power of NExpert® was by no means fully tapped by Vallee’s initial effort. For this reason, we cannot make a meaningful assessment of the potential utility of an expert system approach to RV analysis. Further development should continue in this area, but the development will clearly need to focus upon the rectification of two specific shortcomings of this year’s effort: (1) the integration of uncertain reasoning into the data analysis, and (2) the redefinition of an attribute set and network that allows for a more flexible and comprehensive evaluation of the response data. 12 Approved For Release 20hd Gd ASSAF Q0789R002200150001-1