Approved For Release 2001/03/07: CIA-RDP96-00749R003200160001-9 { overall remote viewing must be clearly defined. If the goal is simply to demonstrate the existence of the RV phenomena, then anything that is perceived at the site is important. But if the goal is to gain informationt ; “~——"\ then specific items at the site are important, while others remain insignificant. For example, consider an office as a hypothetical target. Let us assume that a single computer in the office is of specific interest. Suppose an RVer gives an accurate description of the shape of the office, provides the serial number of the typewriter, and gives a complete description of the owner of the office. While this kind of a response might provide excellent evidence for remote viewing, the target of interest (the computer) is completely missed . —_—_-— . What is needed is a specific technique to allow assessments that are mission oriented. { The procedure developed during FY’82 was a first attempt at solving the mission orientation problem. In this technique, the transcript is conceptualized as described above, and a similar process is applied to the sites. A target site is conceptualized as a set of target elements, which are to be considered “mission independent.” In the office example above, target elements might be: desk, safe, window, telephone, computer, and chair. A second layer of conceptualization is then applied, which is “mission specific.” Each target element is assigned a number between 1 and 5 corresponding to the mission’s relevance. Again, in the office example, the computer would be assigned a relevance factor of 5 (most relevant), while all other target elements would be assigned a factor of 1 (least relevant). The target elements and their relevance factors constitute the site definition and mission orientation. The final report for the FY'82 task? described in detail how a mission specific assessment was made. Although. the procedure proved to be quite sensitive, it was nonetheless cumbersome and difficult to apply. ' This report describes a major advance over the FY’82 technique. The original idea, which involves computer-automated scoring of RV data, was developed at the Anomalies Laboratory of Princeton University.4 We have significantly extended and modified the Princeton technique and have developed procedures that can be used in actual applications. 4 Approved For Release 2001/03/07}: CIA-RDP96-€0789R003200160001-9
Approved For Release 2001/03/97 : GIA-RDP96{00789R003200160001-9 a IV METHOD OF APPROACH (U) ~———-} The overall method of approach was to begin with the Princeton group’s known evaluation procedure, then determine what would be appropriate for our environment. The next step was to expand the analysis concept to be more responsive tod requirements, and to integrate the entire procedure with our on-line data bases. A. (U) The Princeton Evaluation Procedure (PEP) (U) In general, the Princeton Evaluation Procedure (PEP) is based on comparing a priori, quantitatively-defined target information with similarly quantitatively-defined response information. (A complete description of this procedure can be found in Reference 4.) The procedure was developed for use as a research tool in the university environment, where complete knowledge of the target sites could be obtained. Once the target and response information was defined, the PEP applied various methods of mathematical comparisons to arrive at a meaningful assessment score. 1. (U) Target Information (U) The definition of a particular target site (usually outdoor sites in and around Princeton, New Jersey) was contained in the yes/no answers to a set of questions called descriptors. These descriptors were designed in such a way as to characterize the typical Princeton target. By definition, the only target information to be considered in the analysis was completely contained in the yes/no answers of the descriptor questions for that site. For example, one descriptor from their list, “Are any animals, birds, fish, major insects, or figures of these significant in the scene?” defines the animal content of the site. The question would be answered “yes” for a zoo and a pet store target, but answered “no” for a typical campus building target. Similarly a set (30 for the PEP) of yes/no responses constitutes the target information. \ 4 Approved For Release are : CIm-ROP9F.00789R0082001500019
Approved For Release 2001/03/0f : daa e ereeRooezoonsooens 2. (U) Response Definition (U) The descriptor list for the target sites is used as a definition of the response as well. For a given RV session, an analyst (blind to the target site) attempts to answer the 30 questions based entirely on the single RV response. Using the same example above, an analyst would have to decide if a particular verbal passage or a quick sketch could be interpreted as animals or not. For some responses this might be an easy task, “I get a picture of a purple cow.” Most responses, however, require a judgement, “I hear a funny sound and there may be an odd smell in the air.” Nonetheless, the yes/no answers to the 30 ) questions constitute the only response information that will be used in the analysis. 3. (U) Analysis (U) For a given response/target combination, the information is strictly contained in the yes/no answers to the descriptors. A binary number (30 bits long for PEP) is constructed for the target and the response descriptor questions respectively. A yes answer is considered a binary “1” while a no answer is considered a binary “0.” The resulting two 30-bit binary numbers can then be compared by a variety of mathematical techniques to form a score for that specific RV session. For a series of RV sessions, a quantitative assessment is made by comparing a given response (matched to its corresponding target site) against the scores computed by matching the response to all other targets used in the series. This procedure has the added advantage of a built-in, within-group control. In other words, this assessment determines the uniqueness of the target/response match compared with all other possible matches for the series. B. (U) Problems with the PEP There are a number of problems with the PEP when the conditions under which the PEP was developed are no longer valid. Because we are trying to develop an RV analysis procedure that is useful both in the RV training environment as well as i applications, we have identified four basic problems with using the PEP for our purposes: @ The bit descriptors were not appropriate for our training environment. e The PEP was not interfaced to a standard data base management system (DBMS). Approved For Release 2001/03/07 ; CIA-RDP96-00789R003200160001-8
. J Approved For Release 2001103107: CIA-RDP96-00789R003200160001-9 @ The cross-target scoring procedure was not sensitive toe \ requirements. ® Any cross-target scoring procedure is inappropriate for a training environment. (U) As stated above, the PEP descriptors were optimized for natural outdoor sites in the Princeton area. Because we planned to use different target material, the PEP descriptor list was completely inadequate. Having obtained the computer codes used at Princeton, we noticed that the PEP required a special on-line, within-code data base. We felt this was an inefficient way to procede because we already had most of our data in a commercial DBMS, Ingres.5 ~S_ One of the principal problems of RV used as an adjunct to conventional TT" collection techniques is that RVers tend to add information, sometimes called analytical overlay (AOL), to the response. If training techniques are to be developed that are sensitive to — requirements, they must attempt to inhibit AOL. Specifically, any training analysis procedure must be particularly sensitive to the addition of extraneous information. The PEP was completely insensitive to this requirement. (U) We also observed that for the purposes of training, any scoring procedure that cross compares a training response against all targets in the target pool, might penalize excellent RV simply because of the lack of target pool orthogonality (i.e., how different one target is to the next). For example, consider a typical National Geographic Magazine photograph of a flat desert showing few features. A very good description of this site will also match many other similar sites in the target pool. Thus, a comparison of the actual match with others in the pool will tend to reduce the score for reasons other than the quality of the particular RV response. (U) We, therefore, felt obligated to modify the PEP in such a way to address the above criticisms. C. (U) The SRI Evaluation Procedure (SEP) , r The SRI Evaluation Procedure (SEP) was developed to address not only various RV training programs, but also the potential application of the SEP tot -problems. Thus, it was recognized that the SEP must contain cross comparison analytical procedures that 7 Approved For Release aed : asl aaaeaeieeacaaiaaaal
Approved For Release “et CIA-RDP96}00789R003200160001-9 a —, were sensitive to AOL, and at the same time, provide a meaningful assessment of RV responses that were independent of other targets in the pool. 1. (U) Target Information ) As in the PEP, the SRI Evaluation Procedure quantifies the target material into binary numbers corresponding to yes/no answers to a set of descriptors. Before any of the training programs had begun, a descriptor list was developed on the basis of the target material (National Geographic Magazine photographs), and on the responses that might be expected for novice RVers. Table 1 shows the 20 questions (descriptors) that were used for the Alternate Training Task.6 This descriptor list, while applicable to a novice RV training environment, is not appropriate for either advanced training orf \ applications. The questions are strongly oriented toward outdoor gestalts typical of National Geographic Magazine material. Each descriptor list must be tailored to the application requirements. The hotizontal lines separating the descriptors in groups of three are an aid in translating binary numbers (derived from the yes/no answers to the questions) into an octal shorthand notation. (U) To illustrate exactly how a target might be coded into an octal number, let’s consider a photograph of San Francisco on a clear day showing the bay, the central city sky- scrapers, and the centrally—located hill (Twin Peaks). Referring to Table 1 Bit Numbers 1, 6, 8, 9, 12, 13, 16 and 17 would all be answered “yes” and thus would be assigned a binary “1.” The remaining questions would all be answered “no” and thus be assigned a binary “0.” Staring with Bit Number 1 on the left, the binary number that defines the information for this target is 10000101100110011000. This representation, while convenient for computers, is difficult for humans; therefore, we convert it to the octal representation as a shorthand. Using the horizontal lines shown in Table 1 as divisions, we consider each triad of bits as a binary number ranging from 000 to 111. Table 2 shows the binary-number triad to octal conversion factors. (U) Rewriting the above binary number with triad separations for clarity, we have 10 000 101 100 110 011 000. Using Table 2, we find that this binary number converts to 2054630g. This octal number is the shorthand notation for all the information contained, by definition, in the San Francisco target example. All targets in the data base are coded by the same technique. Approved For Release et : CIA-RDP96-00789R003200160001-9
pproyed For Release 2004/03/07 : CIA-BDP96-00789R003200160001-9 2 ~ Final Report-—Task 6.0.3 October 1989 Covering the Period 1 October 1988 to 30 September 1989 A PROTOTYPE ANALYSIS SYSTEM FOR SPECIAL REMOTE VIEWING TASKS (U) _ for: SRI Project 1291 . _ y 50 oples internation Approved by: is document consists of 27 pages COPY 1 ORR OT RELEASABLE TO FOREIGN NATIONALS 333 Ravenswood Ave. « Menlo Park, CA 94025 tama 415 326-6200 « TWX: 910-373-2046 © Telex: 334-486 L~AQproved For Release 2001/03/07 : CIA-RDP96-00789R003200160001-9
™ \ - " Approved For Release 2001/0307 : CIA-RDP 6-00789R003200160001-9 soT Ti ABSTRACT (U) iis fez RY RI Tong We ‘Have e Uevéioped a prototype analysis system for remote viewings | conducted _ 7 2s “di ; “adaitit Biget of ~wtnférest. The system uses individual viewers’ performance e histories boa 4 : | > BO i i éonfunetion with current “data to prioritize a set of possibled ) interpretations of the it Approved For Release moore: cians anreeRooszoosen00 -9
Approved For Release 2001198107 : CIA-ROP96-00789R003200160001-9 ~ (U) LIST OF TABLES (U) Numerical Listing of Targets ..... 0 sc cece cece cree cee ee cece enc eettenenns (U) Technology Cluster... .... ccc cece cence cence cece erence recent teeee 11 (U) Principal Elements Contained in the Technology Template .....00eeceeeeeees 1 (U) LIST OF TABLES 1 _ { Cluster Diagram for | N Targets .......- cee eee eee eee 10 #3 iv Approved For Release 2001193107 : cia-Rippee-007eeR005200160004-
Approved For Release 2001/03/0ff : ClA-RDPSB-00789R003200160001-9 I INTRODUCTION (U) (U) Since 1973, when the investigations of the human information-accessing capability called remote viewing (RV) first began at SRI International, '* evaluating the quality of the information obtained has been a continuing challenge. In order to develop valid evaluation procedures, two basic questions must be addressed: (1) What constitutes the target? (2) What constitutes the response? __* If the RV task is research-oriented, the targets are known, ‘and therefore can be precisely defined. In applicachon —oriented tasks, however, the targets are generally unknown and their descriptions are problematical. In both task domains, RV responses tend to consist of sketches and written phrases. A method to encode unambiguously this type of “natural language” is one of the unsolved problems in computer science, and there has been little progress to date. Thus, a complete definition of an RV response is also problematical. ¥ An opplicet ton-oriented RV task poses further problems. High-quality RV does hot always provide useful opplicach't For example, the RV may provide a additional support for information that has been verified from other sources, but provide no new information. In some cases, however, an overall low-quality RV may provide key elements that positively influence an analyst’ S interpretation. Another characteristic of current laboratory analysis techniques is that they do not \provide an a priori assessment of the RV quality. While this is not a problem in the A So . applications require such evaluation. An RV analyst cannot provide . laboratory, ‘ ; ratings from the RV alone; rather, the analyst must provide a priori probabilities that individual RV-response elements (og concepts) are present at the target site. It remains the responsibility of an analyst to ‘determine whether such data are ultimately useful. Analysis of laboratory RV has been a major part of the ongoing Cognitive Sciences Program.?-7 For FY 1989, we focused on the development of a prototype analysis system that would provide the needed a priori assessments for appli cach'on tasking| 1 (U) References are at the end of this report. t aad weabhtn Fee OO. - —— | 1 Approved For Release 2001/03/07 a ne I.
Approved For Release 200110467 : CIA-RBP96-00789R003200160001-9 II METHOD OF APPROACH (UV) The analysis of remote viewing (RV) data in an apaiicaton environment differs considerably from laboratory analysis. Most often, analysts have incomplete or no information about the target site and are required to provide a priori assessments of data gathered from RV sessions. In this section we outline a prototype analysis system for ' ““s RV that uses concepts from fuzzy set theory, historical archival data, and “templates” of typical —_ targets. In addition, we apply this prototype system to an existing target pool as an illustration of the power of the technique. A. (U) Fuzzy Set Formalism ) A more complete description of the full fuzzy set formalism can be found in our literature.®:7 For the purpose of this report, we have summarized that formalism in general terms that are not specific to either laboratory experiments or Applia-ha, tasking. 1. (U) Construction of Target and Response Fuzzy Sets (U) A formal definition of a target and its associated RV response (i.e., the data obtained from an RV session) is necessary to any analysis system. To use the fuzzy set method, a universal set of elements is constructed on which target and response descriptions are based. These elements should contain descriptive aspects of the target material and incorporate items that typify responses from the intended viewers. This universal set should also be extendible (i.e., allow for additional items that may arise in the responses). (U) In general, the task of an RV analyst is to assign a membership value (p) between 0 and 1 to each element in the universal set. The numerical value for each element in a response is assigned by the degree to which the analyst is convinced that the given element is present in that response. Membership values for target elements are assigned on the basis of the degree to which the elements contribute to the target description. i | In the laboratory, the targets are known, so that defining a universal set of elements is comparatively straightforward.®-? In application tasks, however, defining a single universal set of elements that is appropriate for all operations is difficult. Because the usual ‘ 'task is so highly mission-dependent, defining a single universal set of elements that is customized to that mission becomes easier. 2 Approved For Release 200110307 : CIA-RD#96-00789R003200160001-9