Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 _ As can be seen from Figure 6, the data from the subjects combined has resulted in a MDS structure which appears to group the valley, waterfall and lake targets, all of which have prominent land- water interfaces visible and are predominantly natural. However the dune coastline target also meets these criteria and this is not in the same grouping. The urban scenes, the city, port and city with canal targets, are also fairly close in the combined Subject structure. We may therefore conclude that there is weak evidence visible in this similarity data of discrimination of scenes containing land-water interfaces from those without and between natural versus man-made scenes. The 3 dimensional structure for the combined subject data given in Figure 9 also appears to group the urban scenes, but there is little evidence of interpretable structure. These differentiations have not been tested Statistically, but it appears that more data of higher quality would be required to confirm them. 3 COMPARISON STUDY In order to be able to compare the similarity values and semantic structure seen in the pilot study with the corresponding data obtained by normal perception, a small study was performed using the same target material, but unfortunately not the same subjects, as in the pilot study. 3.1 Method Three subjects, all of whom had participated in other studies of remote perception but who otherwise had no connection with this Study participated. The subjects were requested to give a global rating of similarity or difference on the scale shown in Figure 5 for all 45 pairs of the 10 targets used in the pilot study. In a session of approximately one hour the subjects viewed the pairs of color pictures at one meter distance and made a mark on the scale giying the perceived Similarity. The target pairs were } presented in a random order, 3.2 Results The dissimilarity ratings are given in Table 1 and the resulting 2 and 3 dimensional MDS Structures in Figures 1 and 2 respectively. The stress values were compared with those obtained by permuting the experimental dissimilarity matrix randomly and.computing an MDS structure exactly as in the pilot study. Using 25 permutations to give chance stress estimates, the following statistics were derived, Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 Table 4 STRESS VALUES FOR MDS MODELS OF THE COMPARISON STUDY. Dimension | Stress Stress 1 Tailed ee eer [ee oe These stress results confirm that the similarity estimates produced in this experiment are significantly structured. The resulting 2 and 3 dimensional MDS structures are interpretable in terms of discrimination occurring due to man-made versus natural features and the presence or absence of land-water interfaces and possibly pronounced vertical topography (see Section 1.3). 4 CONCLUSION In this report we have described a novel method for investigating the semantics of remote perception. The method appears to have merit in terms of its potential ability to discriminate aspects of remote perception targets which may be preferentially perceived in this context. Therefore the method may find use in the development of judging methods both by providing information on which descriptor sets design can be optimized and also by judging remote perception efforts by similarity estimates directly. Itis clear that the pilot study presented here has provided only very weak evidence of the discrimination of targets by similarity estimates. However, given the large amount of quantitative data required for the reliable determination of semantic structures and the unreliability of remote perception, it might be expected that such a small study is unlikely to give clear results. Given a data set an order of magnitude larger, useful results might well emerge. Approved For Release 2000/08/08 - CIA-RDP96-00789R002200130001-3
| Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 5.1 Appendix 1 The MDS algorithm used in the pilot study follows that due to Kruskal. ©? The program begins by computing an initial configuration of points whose configuration is a linear function of the input data. This is achieved by by a metric MDS method in which missing values in the input dissimilarity matrix are replaced with the mean value of all elements of the matrix. Then the values are converted to distances by adding a constant. A scalar products matrix B is then calculated by following the procedures in Torgerson. !3 The initial configuration matrix for the non-metric MDS is then found from the eigenvectors of B using the Young-Householder procedure. Nonmetric optimization then proceeds by iterating the following sequence of steps: at the beginning of each iteration the configuration is normalized to have zero centroid and unit dispersion. Next, Kruskal's DHAT (fitted) distances are | computed by a monotonic regression of distances onto data. The stress, S, is calculated and checked for whether it has decreased sufficiently from the last iteration. If it has not, the negative gradient is computed for each point by taking the partial derivatives of stress with respect to each dimension. Points in the configuration are moved along their gradients by steps proportional to the derivatives and the next iteration starts. After the last iteration the configuration is shifted so that its centroid lies at the origin so that it has unit dispersion. For further details, see Wilkinson 18 and the cited references. 2 | Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 5.2 Appendix 2 DATA FROM PILOT STUDY. DISSIMILARITIES NORMALIZED TO UNITY STANDARD DEVIATION AND MEAN OF 5. UNS nSTOnnOnrn Onn IETS Trial | Subject} Targets | Dissimilarity Trial | Subject | Targets] Dissimilarity one 60 ces woe meres of es are ocr ym fl er ne ew wf wo a NN ot ES a mw ek ee es eee rcs yen nn he cane mem et ree af rece ae rene soe anne fo mn come eet eee ss aren mre mt ven SO 32 AV 21 5.438 26 AV 7 6 5.541 40 DK 2 1 4.221 43 AV 7 6 4.458 9 AV 3 1 3.219 29 DK 8 2 5.182 2 DK 3 1 4,322 15 DK 8 3 5.991 i 23 AV 3 2 5.541 38 AV 8 5 4.509 56 DK 41 5.789 — 22 DK 8 5 4.272 39 DK 4 3 5.182 57 DK 8 6 6.193 . 13 DK 4 3 5.233 10 AV 8 7 6.108 30 DK 4 3 5.485 65 DK 8 7 3.210 51 DK 5 1 3.058 54 AV 9 1 3.684 8 AV 5 3 4.819 63 DK 9 2 4.727 14 DK 5 3 3.210 37 AV 9 3 3.116 5 DK 5 4 6.648 33 AV 9 3 5.593 41 AV 5 4 4.458 61 DK 9 3 6.244 27 AV 6 1 5.386 59 AV 9 4 3.735 47 AV 6 2 3.942 24 AV 9 5 6.108 7 DK 6 2 3.614 58 AV 9 5 4.303 52 DK 6 3 5.233 28 AV 9 7 3.735 1 DK 6 3 5.688 34 AV 9 7 5.438 50 DK 6 4 5.384 3 AV | 10 1 5.696 21 DK 6 5 4.778 53 AV | 10 1 5.696 45 DK 6 5 5.283 11 DK | 10 1 3.159 19 DK 7 1 6.244 17 AV | 10 2 6.418 42 AV 72 4.716 64 AV | 10 2 6.057 62 AV 7 2 5.335 4 AV | 10 5 5.335 36 AV 7 2 5.696 55 AV | 10 6 3.632 12 AV 7 3 6.366 35 AV | 10 7 4.406 | 20 AV 7 4 6.728 16 AV | 10 7 6.366 18 DK 7 4 4.980 25 AV | 10 7 3.735 31 AV 7 5 4.354 67 DK | 10 7 4.778 | 46 DK 75 6.042 44 AV ; 10 8 5.335 66 DK 8 5 5.334 6 DK | 10 8 4.778 60 DK | 10 8 5.738 22 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 6 REFERENCES 1. Daniel, T.C, and Ittelson, W.H., "Conditions for Environmental Perception Research: Comment - on "The Psychological Representation of Molar Physical Environments" by Ward and Russell,” The Journal of Experimental Psychology: General, Vol. 110, No. 2, pp.. 153 - 157 (1981). 2. Dunne BJ., Jahn.R.G. and Nelson R.D., “Precognitive Remote Perception," Technical Note PEAR 83003, Princeton Engineering Anomalies Research Laboratory, Princeton University School of Engineering/Applied Science, Princeton, New J ersey (1983). 3, Garling,T. "A Multidimensional Scaling and Semantic Differential Technique Study of the Perception of Environmental Settings," Scandinavian Journal of Psychology, Vol. 17, pp. 323 - 332 (1976). 4, Garling, T. “The Structural Analysis of Environmental Perception and Cognition: A Multidimensional Scaling Approach,"Environment and Behaviour, Vol. 8, No.3, pp. 385 - 390 (1976). 5. Jahn R.G. , Dunne B.J. and Jahn E.G., "Analytical Judging Procedure for Remote Perception Experiments,"Journal of Parapsychology, Vol. 44, pp. 208 - 231 (1980). 6. Kruskal, J.B., “Multidimensional Scaling by Optimisation of Goodness of Fit to a Nonmetric Hypothesis," Psychometrika, Vol..33, pp. 469 - 506 (1964). 7, Kruskal, J.B., "Nonmetric Multidimensional Scaling: A Numerical Method," Psychometrika, Vol. 29, pp. 115 - 129 (1964). 8. Lewis, T.G. and Payne, W.H., "Generalized Feedback Shift Register Pseudorandom Number Generator," Journal of the Association for Computing Machinery, Vol. 20, No. 3, pp. 456 - 463 (1973). 9, May, E.C., “Intuitive Data Sorting: An Informational Model of Psychoenergetic Functioning," SRI International , Menlo Park, California (1986). 10. Mehrabian, A. and Russell, J.A., "A Verbal Measure of Information Rate for Studies in Environmental Psychology," Environment and Behaviour, Vol. 6, No.2, pp. 231 - 251 (1974). 11. Puthoff H.E. and Targ R., "A Perceptual Channel for Information Transfer over Kilometer Distances: Historical Perspective and Recent Research," Proceedings [EEE, Vol. 54, pp. 329 - 354 (1976), 12. Spence, I. & Ogilvie, J.C., "A Table of Expected Stress Values for Random Rankings in Nonmetric Multidimensional Scaling,” Multivariate Behavioural Research, Vol. 8, pp. 511 - 517 (1973), 13. Torgerson, W.S., Theory and Methods of Scaling, John Wiley & Sons, New York (1958). 14. Tversky, A., "Features of Similarity," Psychological Review, Vol. 84, pp. 327 - 352 (1977). 15. Tversky, A. and Gati, I, "Similarity, Separability and the Triangle Inequality," Cognition and Categorization, Vol. 1978 (1978). Approved For Release 2000/08/68’: CIA-RDP96-00789R002200130001-3