Approved For Release 2001/03/07 : CIA-RDP96-00789R003200160001-9 ' a 3 \ The Gppication analyst, as opposed to an RV analyst, should construct such a list for each mission. While there may be considerable similarities between element lists for different missions, undoubtedly the lists will require specialization. In Section II-C below, we show ithe construction of one element list and how it can be applied to a set of 65; _. L. targets. 2. (U) Analysis of Complete Responses | Once an appropriate universal set of elements has been created, and fuzzy sets that define the target and the response have been specified, the comparison between them is straightforward. We have defined accuracy as the percent of the target material that is described correctly by a response. Likewise, we have defined reliability (of the viewer) as the percent of the response that is correct.6 AJthough in the laboratory it is required to provide a posterior probability estimates of the target~response match, in an agjicat'ms setting, this may be less important. All that is usually necessary is to describe the accuracy and reliability for complete responses, and for individual target elements of interest. These quantities for the jth sessions are R >, Wali C1 Tie > WiRjk and Kzt >, WR; O) Te _ £=1 aj= 4 > WT }.2 k=l ' (2) where the sum over k is called the sigma count in fuzzy-set terminology, and is defined as the sum of the membership values (1) for the elements of the response, the target, or their intersection, and n is the number of possible elements as defined by the element list. A fuzzy intersection is defined as the minimum of the intersecting fuzzy set membership values. In this version of the definitions, we have allowed for the possibility of weighting the membership values, Wt, to provide mission~defined relevances. (U) For the above calculation to be meaningful, the membership values for the targets ‘must be similar in kind to those for the responses. For most mission-dependent specifications, ‘this is generally not the case. The target membership values represent the degree to which a particular element is characteristic of the target, and the response membership values represent the degree to which the analyst is convinced that the given element is represented in the response. 3 Approved For Release 2001103/07(. C1A-RDP9S-00789R003200160001-9
Approved For Release poo1dero7 : CiackibP96-00789R003200160001-9 (U) Then, Ro(airport) = 0.20 is the chance probability (i.e., guessing airport during every session would only by 20 percent reliable). Assume that the, viewer mentioned airport Nr = 6 times and was correct Ne = 4 times. Then this viewer's reliability for airports is computed as R(airport) = 0.67 > Ro(airport) = 0.20. The viewer's accuracy for airports is computed as A(airport) = Nc/Ng = 0.80. Thus in this example, we can conclude that this viewer is reasonably accomplished at remote viewing an airport. B. (U) Prototype Analysis System (We assume that an‘ _ andlyst has constructed a mission-dependent universal ser of elements. We further assume ‘that there are a number of competing interpretations of the target site in question. 1 I 1. ; (U) Target Templates The first step in our prototype ‘analysis system is to define templates (i.e., general descriptions of classes of target types) of all competing target interpretations from the universal set of elements. { , ~\ Exactly what the templates should represent is entirely dependent upon what kind of information is sought. Both the underlying universal set of elements and the templates must be constructed to be rich enough to allow for the encoding of all the information 7 ~ That is, if neither the set of elements nor the templates can meaningfully represent information aboutd “the target 4 -then it will be unreasonable to consider asking, ‘ (@ievant queshmns abrt the site. Furthermore, a certain amount of atomization is necessary because such division into small units provides the potential for interactions within the universal set of elements. If the profile of a i “Wlacility consists of a single element, the template would be useless unless the response directly stated that particular element; rather, the profile should be constructed from groups of elemental features L { There are two different ways to generate target templates. The most straightforward 1 technique is also likely to be the most unreliable, because it relies on the analyst’s judgment of a single target type. With this method, the analyst, who is familiar with the intelligence problem at hand, simply generates membership values for elements from the universal set of elements based upon his or her general knowledge. Given the time and resources, the best way to generate template membership values is to encode known targets that are closely related. - Each template p is the average value across targets, and thus is more reliable. If it is known that some targets are more 5 Approved For Release 2001/03/07 : CIA-RDP96-00789R003200160001-9
Approved For Release 2001/03/0F CIA-RDP9p-00789R003200160001-9 “characteristic” of the target type than others, then a weighted average should be computed. In symbols ' ¥ > Duy k kez} uf A, «) Wk ket where the sums are over the available targets that constitute the template, wx are the: target weights, and the yj,« are the assigned membership values for target k. 2. (U) Archival Database —-~ | : A critical feature of an analysis system fog sRV data is that along with the current RV data to be evaluated, the individual viewer's past performance on an element-by-element basis must also be included. For example, if a viewer has been relatively unsuccessful at recognizing ) facilities, then a4 }reference in the current data should not contribute much in the overall analysis. \ As ground truth becomes available for each session, a performance database should be updated for each viewer to reflect the new information This database should be a fuzzy set whose membership values for each element are the reliabilities computed from Equation 3. /3. (U) Optimized Probability List L__} \ The goal of any {-~ prioritized and weighted list of target possibilities that results from a single remote viewing that is YRV analysis system | is to provide an a priori sensitive to the performance history of the viewer. Assuming that a template exists for each of the possibla 7 ~ interpretations, an analyst should adhere to the following protocol: (1) Analyze the RV data by assigning a membership value (1) for each element irffthe universal set of elements. Each ym represents the degree to which the analyst is is convinced that the particular element is included in the response. . EE ——e ———— (2) Construct a crisp set, Re, as an a-cut of the original response set. By adopting a threshold of 0.5, for example, then the resulting crisp set contains only those elements that the analyst deems most likely as being present in the response. (3) Construct an effective response set, Re, as Re = Re U Ra, where Ra is the reliability set drawn from the archival database. (00 nS” A 6 Approved For Release ee en eee
—_ Point Internation Approved For Release 2001/03/07-CIA“RDP96-00789R003200160001-9 U G € Final Report- -Objective D, Task 1 December 1986 Covering the Period 7 October 1985 to 30 September 1986 A SUGGESTED REMOTE VIEWING TRAINING PROCEDURE (U) € _ Prepared for: L J SRI Project 1291 — Approved by: This document consists of 86 pages. ~SPHGF=0287 copy On SD FRB )oved For Release, 2a01 M107 « cuaenhesennare9 g200490001-9 0 } nternationa TRY 290R_AONN + Cable: SRI INTL MPK + TWX: 910-373-2046 rh)
Approved — ‘ t (4) (5) For Release 2001/03107/: CIA-RDP9d.00789R0032001 60001-9 ~~ Using this effective response set, compute an accuracy and reliability in accordance with Equations 1 and 2. Then compute a figure-of-merit, Mj, for the jth competing interpretations as Mj=ajX rj. Of course, the accuracy and reliability use the effective response set from step 3 above. Order the Ms from largest to smallest value. Since the figures-of-merit range in value from 0 to 1, they can be interpreted as relative probability values for each of the alternative target possibilities. an By following such a protocol, an analyst can produce a list of target alternatives that is sensitive to the current remote viewing yet takes into consideration to the individual viewer’s archival record. (U) . Partial Application of Analysis System to Existing Target Pool We have used an existing target pool (developed under a separate program) as a test bed for the analysis system described above. 1. t (U) Criteria for Inclusion in the Target Pool Targets in this pool have the following characteristics: @ Each target is within an hour and a half automobile drive of SRI International. e Each target simulates A ~~ site { >» interest. e@ Each target fits generally within one of five functional categories: Production, Recreation, Scientific, Storage, and Transportation. .@ Each target meets a consensus agreement of experienced RV monitors and analysts about inclusion in the pool. (U) The pool consists of 65 targets. Initially, they were divided into 13 groups of five targets each, where each group contained one target from each of five functional categories. By carefully organizing the targets in this way, the maximum possible functional difference of the targets within each group was ensured. Table 1 shows a numerical listing of these targets. Approved 7 For Release 2001/03/07 : aaa pene
UNCL--T*IED Approved For Release 2001103107 : C1A-RDP96-00789R003200160001-2 Table 1 (U) Numerical Listing of Targets “-=msformer Station . Space Capsule . Pump Station 2 LJpark 24. Coastal Battery 46. Ice Plant : u:3iite Dish 25. Bay Area Rapid Transit 47. Caves/Cliffs - Tsupons Storage 26. Salt Refinery 48, Bevatron = wal Fleet 27. Candlestick Park 49. Barn t xxvel Quarry 28. Solar Observatory 50. Golden Gate Bridge 51. Modern Windmills 52. Baylands Nature Preserve -“mming Pool { “nervatory 29. Food Terminal 30. Pedestrian Overpass € ==on 31. Electrical Plant 53. Gas Plant 1( ——<ving and Receivir.z 32. White Plaza 54. Auto Wreckers 1. =zsnhouse 33. Space Shuttle 55. Fishing Fleet 56. Radio Towers 57. Vineyard 58. Pharmaceutical Laboratory 59. Toxic Waste Storage 60. Airport 61. Car Wash 12 ==nc Area 12 L-:lite Dishes 1¢ -==t Warehouse 15 zai Air Station 1€ i:2r Refinery 17 =2-round 34. Coastal Battery 35. Train Terminal 36. Sawmill 37. Pond 38. Wind Tunnel 39. Grain Terminal -~arium 40. Submarine 62. Old Windmill 19 lm Yard 41. Cogeneration Plant 63. Nuclear Accelerator 20 -raft 42. Park 64. Reservoir 21 s=.12e Treatment Plant 43. Linear Accelerator 65. Train Station 22. =.-er Tower . Dump - (U) Fuzzy Set Element. List } In FY 1089, we developed a prototype analysis system for analyzing targets an: “s=cnses int ~~ yemote viewings. A list of elements, based on target function (i.e., the =n specification), is arranged in levels from relatively abstract (information poor) to the relz: = ° complex (information rich). Having levels of elements is advantageous in that each can be ve cred separately in the analysis. (U) This universal set of elements (included as Appendix A) represents primary elexz-= in the existing target pool of 65 targets. The set was derived exclusively from this knov= -i:get pool. In an actual RV session, however, a viewer does not have access to the eleris= —st, and thus is not cunstrained to respond within its confines. An accurate RV analysis mus: ==::de any additional Gata that may be provided in the response; therefore, additional Spact 2: been provided on the analysis sheets (see Appendix A) to include elements that are part :- =e response but not :nitially included as part of the universal set. 8 Approved For Release ea : ee CSS
Approved For Release 2001/03107(; Cla-RDPSP-00789R003200160001-9 Cluster 1 Recreation Cluster 2 15 Transportation -~ wr r ee wee eee mew em ewer emer wre www wees Cluster 3 40 Weapons 4 Cluster 4 i} Technology 2 58 43 = 43 = Cluster 5 14 Storage 39 r Cluster 6 1g Production/Distribution - UNCLASSIFIED | Figure 1. . —_' Cluster Diagram fort . \, Targets (U) We used the technology cluster (i.e., number 4 in Figure 1) to apply Equation 4 to construct a technology target template. Table 2 shows the targets in this cluster, where the horizontal lines indicate the subclustering within the technology group shown in Figure 1. Approved For Release 2001/03/07 : CliA-RDP96-00789R003200160001-9 {,- |
Approved For Release 2001/03(07 : cia-rop96-00789R003200160001-9 (U) As a self-consistency check, we included the technology template in the total target pool and recalculated the clusters. As expected, the technology template was included within the subgroup of targets 3 and 13, and well within the technology cluster as a whole. D. (U) General Conclusions _ The goal of this effort was to develop an analysis system that would prove effective in providing a priori assessments of »remote viewing tasks. If the proper mission-dependent universal set of elements can be identified, then, using a viewer—-dependent reliability archive, data from a single remote viewing can be used to prioritize a set of alternative target templates so as to chose the most likely one for the mission. 12 Approved For Release aan te : CIA-RDPP6-00789R003200160001-9 |
Approved For Release 2001/03/07 : CIA-RDP96-00789R003200160001-9 REFERENCES (U) 1.. Puthoff, H.E., and Targ, R., “A Perceptual Channel for Information Transfer Over _ Kilometer Distances: Historical Perspective and Recent Research,” Proceedings of the «IEEE, Vol. 64, No. 3, March 1976, UNCLASSIFIED. 2. Targ, R., Puthoff, H.E., and May, E.C., 1977 Proceedings of the International - Conference of Cybernetics and Society, pp. 519-529, 1977, UNCLASSIFIED. 3. May, E.C., “A Remote Viewing Evaluation Protocol (U),” Final Report (revised), SRI - Project 4028, SRI International, Menlo Park, California, July 1983,' 4. May, E.C., Humphrey, B.S., and Mathews, C., “A Figure of Merit Analysis for Free-Response Material,” Proceedings of the 28th Annual Convention of the Parapsychological Association, pp. 343-354, Tufts University, Medford, Massachusetts, August 1985, UNCLASSIFIED. cae 5. Humphrey, B.S., May, E.C., Trask, V.V., and Thomson, M. J., “Remote Viewing Evaluation Techniques (U),” Final Report, SRI Project 1291, SRI International, Menlo Park, California, December 1986,4 6. Humphrey, B.S., May, E.C., Utts, J.M., Frivold, T.J., Luke, W.L., and Trask, V.V., “Fuzzy Set Applications in Remote Viewing Analysis,” Final Report—Objective A, Task 3, SRI Project 1291, SRI International, Menlo Park, California, December 1987, : UNCLASSIFIED. 7, May, E.C., Humphrey, B.S., Frivold, T J., and Utts, J M., “Applications of Fuzzy Sets to Remote Viewing Analysis (U),” Final Report—Objective F, Task 1, SRI Project 1291, SRI International, Menlo Park, California, December 1988, | 13 Approved For Release 2001/03/07 : CIA-RDP96-00789R003200160001-9
Approved For Release 2001/03/07 : C1A-RDP96-00789R003200160001-9 I INTRODUCTION (UV) 4 Through work at SRI International and other laboratories, a number of individuals have demonstrated an apparent ability to accurately perceive information, which is inaccessible through the "conventional" senses and to convey their impressions in words and symbols. At times these individuals can apparently describe events, places, people, objects, and feelings with very high quality. At SRI, the particular ability to | provide detailed descriptive information has been termed remote viewing (RV). Although latent ability and motivation undoubtedly play a | significant role, some accomplished remote viewers have claimed that this ability can be taught and learned to varying degrees. In FY 1986, SRI | awarded a subcontract to Consultants International (CI) to assemble a detailed report of subjective experience that might lead to a testable RV training methodology. CI was selected because of the long and successful | remote viewing experience of its founder, Mr. Gary Langford. CI’s reports detailing the suggested training methodology and the concepts upon which | the procedure is based are given in Appendices A and B. (U) SRI’s overview* contains, in condensed form, the basic concepts and techniques | that CI proposed and a critique of them. Selected RV examples will be shown to clarify and demonstrate the ideas involved. Certain figures appearing in this overview have been | abstracted from the CI report. Because the contents of this document are subjective and exploratory in nature, we will not examine RV from an experimental protocol or evaluation | perspective. (U) We emphasize strongly that these concepts and hypotheses have been arrived at ; almost entirely through personal observation, introspection and informal experimentation. Almost none of these concepts have been rigorously tested with sufficient data collection to *(U) This report constitutes Objective D, Task 1: Design, develop, and improve training protocols and methodologies for all RV subjects. 1 Approved For Release 2901/03/07 : eee areeR oo szoorsoors
Approved For Release 2001/93/07 : CHAR Fporno7eorposzooisooors III RESULTS AND DISCUSSION (U) (U) Whether the percipient is a novice, advanced or expert viewer the foregoing procedure applies. With rare notable exceptions, CI asserts that correct descriptions of targets are always built out of much smaller data bits that are gradually assembled into a whole.* As the viewer progresses from novice to expert, the amount of time spent on the various steps of the procedure changes. For example, an expert should find access routine and focus the most attention on details of form and function. A detailed discussion of the division of effort as a function of expertise may be found in Appendix A. A. (U) Anatomy of a Viewing (U) An example of how the foregoing process is applied by an expert may be found in Figures 4(a) through 4(f). These six figures comprise the entire response of the viewer for a given session. Other than the labeling, the transcripts have not been edited in any way. Where the viewer’s handwriting was illegible or where an abbreviation was used, we have provided a “translation.” 1. (U) Figure 4(a)--Initial Access Period. Note that the uniqueness requirement of the target has been satisfied by writing down name, date, time and session number. This is the access phase. Need and motivation for a description were provided by informing the viewer this RV was one of a series intended to calibrate the viewer’s proficiency. The objectify phase is indicated by the primary and multiple bits. The initial primary bits are of a steep angle drop-off and a flat area. Multiple bits (a series of connected impressions) serve to fill in the gap between the two primary bits. Access is brought to an end by writing "break." This amount of data is much greater than that which a novice would perceive during an initial access period. *(U) Experienced viewers do report very occasional sessions where detailed descritions of the target are possible during the initial access period. 11 Approved For Release 200yfasio7 : CURD Epona TaeRonszenisenes
Approved For Release 2001/03/07 CIA-RDP96-007}9R009200160001.9 es 2.  (U) Figure 4(b)--Second Access Period At this point, the viewer was overwhelmed by a vivid impression of cliffs with water and other features. The viewer correctly recognized this as IO and labeled it as such. IO is not considered valid data in subsequent analysis. 3. (U) Figure 4(c)--Third Access Period More primary bits are presented, and the viewer enters the Qualification phase for the first time (e.g., hard surface). For purposes of visual clarity, we will not routinely label the objectify and qualify phases in subsequent figures. However, the distinction can be easily made by the reader because primary and multiple bits always represent objectification, while any further description of form or function is qualification. 4. (U) Figure 4(d)--Fourth Access Period ) As the viewing proceeds, more time is spent on describing form and functional aspects. 5. (U) Figure 4(e)--Fifth Access Period f At this point in the session, the viewer has made use of a technique in which he retraces a bit to acquire more information. These advanced procedures are discussed more thoroughly in Appendix A. Note that the viewer has begun to arrange bits perceived during previous access periods into a more nearly pictorial representation. 6. (U) Figure 4(f)--Sixth and Final Access Period { | Note the detailed description of the elements of target. The bits have now been arranged into a more coherent whole (sometimes called a composite), and the viewer has provided a summary word that characterizes the entire target "ruins." ! whe actual target is shown in Figure 5. Aside from the obviously correct assessment of the target as ruins, it is very important to note that all of the other data bits are also correct. Furthermore, the session required only approximately 15 minutes to complete. Such a result is particularly compelling when compared with other free—-response techniques. 2 Approved For Release 2001/03/07 : G#R-ROP-00749R005200160001-9
Approved For Release ~ : CIA-RDPSF-00789R008200160001-8 —_— For example, telepathy experiments using the so-called Ganzfeld technique of sensory isolation typically require one-and-one-half hours, during which time the percipient produces extensive stream—-of-conscious descriptions.The sheer mass of data and dreamlike quality of the responses prevent any effective transcript analysis that might separate signal from noise. (U) In early RV experiments at SRI (c. 1975), unstructured free-response descriptions were used, but were limited to 15 minutes. Even with that restriction, discrimination between the product of imagination, memory, and RV was a burdensome analysis task. (U) The twin insights that mental noise can be briefly suppressed and that correct data appear in fleeting, indistinct, and sometimes symbolic form has resulted in an enormous increase in viewing efficiency. B. (U) Applications to RV Training (U) As the preceding example demonstrates, the procedure described earlier works well when used by the expert who invented it. The task that CI addressed in FY 1986 was to supply sufficient detailed instruction so that individuals with no prior exposure to RV could be trained. A test of this training methodology is presently underway. 13 Approved For Release 200 9607 : CIA-RDP96-00789R003200160001-9 Bee ee ee
-RDP96-00789R0032001 60001-9 id Approved For Release 2001/03/07 : “fe GOivad SSADOV TVILINI (nN) (&)b JNO \ SS300¥ ONI——wr7 Aq » hd 7 (7829) ss , ts SLIP AIWdILINN AdILO3F90 SLIG. AYVIIYd (1a6se4) el f199 | SDV 9g 475 9| 14 Approved For Release 2001/03/07 : CIA ene eemeiian
Approved For Release cain an ClA-RDP9p-00789R003200160001-2 INTERPRETIVE OVERLAY (U) SECOND ACCESS PERIOD FIGURE 4(b) 15 Approved For Release oon" : CIA-RDP9§-00789R003200160001-9
Li Approved For Release 2001/03/07 : i cueaianeas Caecum 4 | 8 = Hes sug hawt sone’ OBJECTIFY —= PRIMARY BITS T w+ -—ACCESS Approved For Release 2001/03/07 : Cu ROPssooTEERFasz00ren001 ~ bw An—END ACCESS (U) THIRD ACCESS PERIOD FIGURE 4(c)
Approved For Release 2001/03/07 : see" ( = S fep?-—runeron fan) (@) ic ut a | 2 Lu Le} Oo | 3 2 , s a | 6 E)} | : vt n te | gE 4 8 ; m ©O ri > © a <r. a = = n & om Lil © © ° > 3 - 17 Approved For Release eet : cen Petes none2oeneowrs®