"Approved For Release 2000/08/10 : CIA-RDP96-00789R003800440001-2. UNCLASSIFIED _ (U) “,..quantification of the informational content of transcripts on a small set of underlying semantic dimensions which might serve as basis vectors for the viewer's internal representation of the target. If such basis vectors can be found, complex constructs in the viewing data might be assembled by combining sets of data so expressed.”® (U) . Using well-established techniques from the area of environmental psychology (which have been used to solve analogous problems in “normal” perception), the proposed experiment will attempt to isolate underlying semantic structures in RV perceptions. In essence, percipients will be asked to remote view--in sessions of two targets each-~all possible pairs of targets and to estimate the similarity between targets in each pair. The data will be analyzed for important factors such as intersubject reliability, presentation order effect, and target pair effects. Multidimensional scaling (MDS) analysis* will then be applied to identify the underlying semantic dimensions. Identification of semantic structures would-hold important implications for descriptor list. development and possibly for identification of fundamental commonalities of perception across viewers. B. (U) AI Techniques (U) According to the following excerpt from the letter proposal submitted by consultant J. Vallee, the proposed AI approach “,..will seek to build an expert system for target recognition by analyzing the process that enables a human expert monitor to provide an interpretation of a remote viewing session, or a judge to match a given description with an actual target. : It is expectéd that a rule-based expert system can be developed in a series of iterations starting with the simple “twenty questions” framework already used in the project. Later this will lead to a fully-developed interactive model. We envision this “smart monitor” taking the analyst from simple scene and “gestalt” recognition to the detection of breaks, contradictions and, possibly, analytical overlays as well.” 10 (U) Assuming promising results with the initial task of target definition, it is anticipated that the expert system will ultimately be expanded to play a more active role in operational remote viewing sessions through on-line capture of respondents’ ideograms and interactive analysis of their features. ee . (U) It is beyond the scope of this discussion to describe the details of the MDS analysis. 28 Approved For Release 2000 NEC EAR SSI PIB B 03800440001 :
Approved For Release 2000/08/10-: Cc. (U0) In-House Effort (U) SRI in-house approaches will focus on imp ove technologies. One potential line of inquiry would. focus ¢ 0 ’ descriptors into advanced lists. (U) A second approach would endeavor to complete work begun in FY 1986-- namely, a reanalysis of novice data using fuzzy mathematical weighting factors with the curren list of twenty descriptors. The hypothesis is that the greater latitude afforded by fuzzy set membership values (as opposed to the “all or nothing” capability of the descriptors in ‘their 7 current configuration) will significantly decrease the granularity of the current list--i.e., will allow capture of more information. If the post hoc reanalysis yields promising results; the fuzzy set approach would be benchmarked on a blind basis against the current binary ~ approach. * (U) A third effort would systematically evaluate the efficacy of viewer—derived versus analyst-derived descriptor lists. While. viewers are sensitive to their internal perceptions, they are not cognizant of the requirements of the analytical procedures; conversely, the analyst is privy to the linguistic/analytical aspects of descriptor lists, but may be unaware of how to optimize a viewer’s perceptions using descriptors. The hypothesis is that the combined insights of analyst and percipient will synergistically result in the optimal formulation of descriptor lists. One way to test this liypothesis would be to compare statistics across analyst-derived versus — percipient-derived versus combined analyst/percipient-derived descriptor lists on the same set of RV data. ‘(U) A fourth approach would endeavor to take a retrospective look at viewers’ ideograms. and their possible range of meaning. If, for example, a viewer typically draws a “tic-tac-toe,” cross~hatch-style ideogram, and post hoc analysis reveals that the drawing correctly corresponds to the presence of a city in targets 80 percent of the time, this information might be used to assign an a priori fuzzy weighting factor of 0.8 when the ideogram is encountered on a blind basis. An in-depth examination of a viewer’s ideograms, therefore, might result in the development of viewer-specific, prescriptive guides for the assignment of fuzzy weighting factors in assessing RV responses. This type of information could also conceivably be automated and updated through iterative expert system capabilities. 29 Approved For Release 2oo0daN GLASSIF $&-2)3200440001-2
Approved For Release 2000/08/10 : CIA-RDP96-00789R003800440001-2 . Finally, work will be initiated to develop mission-specific descriptor lists for technical Site applications. | Approved For Release 2000/08/10 : CIA-RIgP96-00789R003800440001-2
Approved For Release 200 ' $-00789R003800440001-2 V CONCLUSIONS (UV) (U) The FY 1986 evaluation effort has resulted in (1) refinement and extension of current techniques, and (2) identification of candidate new technologies for preliminary research. (U) The mathematical formalism for the current evaluation procedure——the figure of merit analysis--is well understood and stable. In addition to the system’s ability to provide a reasonable assessment of remote viewing data, it has also provided a mechanism for systematic examination of inter—analyst reliability factors. (U) The descriptor lists that currently form the basis for the figure of merit analysis have been evaluated on a post hoc basis. Preliminary observations indicate that lists designed for novice responses require greater abstract descriptor capability, whereas lists designed for advanced responses (i.e., higher-quality data) require greater concrete descriptor capability. It is anticipated that fuzzy set technology will assist in formalizing the interdependence between abstract and concrete descriptors, by providing a mathematical framework through which basis vector descriptors can be combined to form concrete descriptors. (U) Research into new technologies for RV evaluation will begin in FY 1987. One of these approaches, the proposed “similarity” experiment, shows promise for identifying basis vector descriptors. A second approach, using rule-based expert systems, will explore a different dimension by endeavoring to capture RV analysts’ expertise in codifying targets. Should this initial effort in artificial intelligence prove successful, it will be expanded to address the more difficult problem of response interpretation. aN It is hoped that this multifaceted approach to the refinement of RV evaluation procedures will result in increased capabilities for addressing the more complex problems of mission-orientedy RV. Approved For Release 2000/08/10 : CIA-RDP9§,00789R003800440001-2 f-
Approved For Release 2000/08/10 : CIA-RDP96- 00789R003800440001 -2 UNCLASSIFIED 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 UNCLASSIFIED. 3. May, E. C., “A Remote Viewing Evaluation Protocol (U),” Final Report (revised), SRI Project 4028, SRI International, Menlo Park, California (July 1983) SECRET. 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. 5. Fisher, R. A., “Statistical Methods for Research Workers,” Oliver & Boyd (7th ed.), London, England (1938) UNCLASSIFIED. 6. Edgington, E. S., “A Normal Curve Method for Combining Probability Values from Independent Experiments,” Journal of Psychology, Vol. 82, pp. 85-89 (1972) UNCLASSIFIED. ‘ 7. Cooper, B. E., “Statistics for Experimentalists,” pp. 219-223, Pergamon Press, Oxford, England (1969) UNCLASSIFIED. 8. Zadeh, L. A., “Fuzzy Sets versus Probability,” Proceedings of the IEEE, Vol. 68, No. 3, p. 421 (March 1980) UNCLASSIFIED. 9. 'Spottiswoode, S. J. P., “Proposals for Experimental Studies on Semantic Structure and Target Probability in Remote Viewing,” Private Communication (July 1986) UNCLASSIFIED. 10. Vallee, J., “Applications of Artificial Intelligence Techniques to Remote Viewing: Building an Expert System for Target Description,” Private Communication (August 1986) UNCLASSIFIED. 32 ~ Approved For Release 2000/08 INGEARS SHPHE Ds00440001-2 |