Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 Ud October 1987 INVESTIGATING THE SEMANTICS OF REMOTE. PERCEPTION WITH SIMILARITY ESTIMATES AND MULTIDIMENSIONAL SCALING. By: S. James P. Spottiswoode. Prepared for: Dr, Edwin C. May Geosciences and Engineering Center. SRI International, ; 333 Ravenswood Avenue, Menlo Park, California 94025. 9421 Charleville Boulevard, _ Beverly Hills, California 90212 U.S.A. (213) 276-5443 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 . October 1987 INVESTIGATING THE SEMANTICS OF REMOTE PERCEPTION WITH SIMILARITY ESTIMATES AND MULTIDIMENSIONAL SCALING. By: ; S. James P. Spottiswoode. Prepared for: Dr. Edwin C. May Geosciences and Engineering Center. SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025. 9421 Charleville Boulevard, Beverly Hills, California 90212 U.S.A. (213) 276-5443 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 TABLE OF CONTENTS ABSTRACT — wacscsssrscssseccceesesseoessessceensoneansescssescesseencseueecsnsenssnsecosauscueeasesspearateessses i LIST OF ILLUSTRATIONS — wscccscsccssssssesccvsssesssseessssnsensvorsesssesessecssantesneseesenssoserses iii LIST OF TABLES  wiesccccscsscsssscsssccsssosseecusssesecensecensecssnseseneesevessapsetsersanseegsaneesaseeeees iv 1 INTRODUCTION  wisssescsesssecesscensscccecssensevsneescvsneesseeensctsonsceessnaneecnsuensenssenesaees 1 Ll Similarity methods — ssssssssssssesssssssssssecsesecssssssssecessrsssssescessssssseusssenes 2 1.2 Multidimensional Scaling  w.csscscssccsssecesesessnsscessssssesesssessevssessnsesoosseveens 2 | 1.3 An Example of Multidimensional Scaling of Geographical Data .........000 3 1.4 Criticisms of Similarity Studies in Environmental Psychology — .....ssssseoees 5 1.5 The Assessment of Mean Chance Expectation — .....ssscssscsscesssscssssceereeeaseees 6 1.51 Mean Chance Expectation Calculation by Noise Addition ............. 7 1.52 Mean Chance Expectation Calculation by Permutation  .......ssscee 8 2 PILOT STUDY cicssccsscsecccssscssesscsssseesosnscseeesesocescensecsesoasssseesnersssssensesaeesaseesoese 11 2.1 Method oo. csecsceseecssccscecsssecescreesncacsesseseesceresstsesecsseeeseassesseseusensoreesonees 11 2.2 SUDJOCIS ......ceccsnsssececssensceenaccuunecsuraeeneeeeesanasssesdansaedenssnesecsscesseageseannn es 12 2.3 Targets —sssssccccessnscevscsonsnecosccenscesenesccescosanacnassscesscesecssassceseeaesaneesessnenees 12 2.4 Target Selection Method  ....ccssccccscsccssercesessscsscssnscscsosssssacssssenscsnseesosenss 14 2.5 RESULTS —sacssessseccsvessscssoncenseccessnssceneveoessssessseesecsessensaccesscesenseseneeeeeeessenss 15 3 COMPARISON STUDY  .uccsssscccsssssssrerescseccesssseesessecesseeeseseraesrees seeenctsensensseees 19 3.1 Method oe eesseeececcccccssececessesceenaceencseeesesssesvsssnseseueeaeedenenceaseacaceueeeeseenees 19 3.2 RESUIES —ssserecsssrsessssnssersssetsesssnentsncsscseseassscasasaceseeeeesenssceosceaecaeuneseaeasens 19 4 CONCLUSION a. cseseseccccssnteceessonecceccsonsccuesonsscreecoesnsarnnecseasesorseeseserspeceseousesons 20 3 APPENDICES 5.1 Appendix 1: Multidimensional Scaling Algorithm is.cecsccsssecsecsennrens 21 5.2 Similarity Data from the Pilot Study  .....csscsssessssetsessssscueeceeessetsseeee 22 6 REFERENCES 9 cisscssssssssccssscssseesesseucccsnssenenseescnssssssesseassessneeescnssanseneeesnaeeees teens 23 i i . Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 LIST OF ILLUSTRATIONS 1. Plot of 2 Dimensional MDS Solution from Comparison Study  ...cccsccsssseessenseseseseees 4 2. Plot of 3 Dimensional MDS Solution from Comparison Study  .....sssssssmeseeessetsers 5 3. Plot of Stress Against Treatment of Data for 10 POInts ....sscsssssssessersecnseersreserasens 8 4. Plot of Stress Against Treatment of Data for 13 POIMts .....cessecsressssseserseenenensreneess 9 5. Scale for Subjects to Indicate Similarity Estimate  .....ssscseessssesecssersssereereneneans 14 6. 2 Dimensional MDS Structure from the Pilot Study for Both Subjects Combined ....... 17 7, 2 Dimensional MDS Structure from the Pilot Study for Subject AV ....ssssssseseereeees 17 8. 2 Dimensional MDS Structure from the Pilot Study for Subject DK ....essssserseteees 18 9, 3 Dimensional MDS Structure from the Pilot Study for Both Subjects Combined _ ...... 18 Approved For Release 2000/08/08 ! CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 LIST OF TABLES 1. Dissimilarity Estimates from Comparison Study — ..ssscessscsesseenssecssrenensscesseneenes 4 2. Targets for Pilot Study  .....csssscseesssscnereneeerenssessesessesseeseeesens sv sseneencnsessoees 13 3. Stress Values for MDS Models of the Pilot Study .....cscssseseenees Leseaussseeasaseseaess . 16 4, Stress Values for MDS Models of the Comparison Study ......ccccscesensenresrreresossens 20 _ Approved For Release 2000/08/08 * CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 1 INTRODUCTION An extensive literature now exists on a form of communication in which persons acquire significant information about some visual scene with which they have no sensory connection.23,11 Typically such studies will employ target material which is relatively complex and unconstrained in comparison to the smalls sets of symbols like Zener cards used in many earlier parapsychological experiments. Target material in these studies may consist of pictures of geographical locations while the information produced by the subjects comprises more or less fleeting visual, tactile, kinesthetic and other impressions. This phenomenon has suffered from some terminological confusion, being denoted clairvoyance, remote viewing and remote perception by various workers; we shall use the latter term. A key problem in the study of remote perception revolves around the comparison of the target material with the subject's response. The methods used for the assessment of remote perception data have progressed from matching and ranking by judges to methods based on descriptor sets. The descriptor method has several advantages but has raised the question of whether judging methods could be further improved by designing a descriptor set which was optimized for the semantic structures preferentially available for apperception in remote perception experiments. It may be possible to base the quantification of the information content of transcripts on a small set of underlying semantic dimensions which might serve as "basis vectors" for the subject's internal representation of the target. If such basis vectors Could be found, complex constructs in the viewing data might be assembled by combining sets of data so expressed. Considerable work has been done on solving the analogous problems for visual perception,2»410.16 and a variety of techniques have been utilized for assessing the semantic structures created by a stimulus, with semantic differential scales, adjective lists and similarity estimates being frequently employed. It is possible to extract considerable information from these data, which may be broadly classified as objective, that is related primarily to the target material and subjective, that is related to the internal state of the subject. In particular, work has focussed on assessing the semantics of response to geographic and architectural environments; the field has become known as environmental psychology. In an important paper, Ward and Russell 16 compare several measurement techniques across a set of common stimuli which were pictures of geographical locations. They showed that using semantic differential scales and similarity estimates, it is possible to extract objective information about targets in terms of a small number of semantic dimensions such as predominately natural scenes versus predominately man-made scenes, open scenes versus enclosed scenes and the presence versus absence of land-water interfaces in the scenes. Subjective information is also determinable and examples of this are Approved For Release 2000/08/08 : CIA-RDP96-00789R0022001 30001 -3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001 -3 the affective dimensions of pleasure versus displeasure and arousal versus boredom. The relative subjective information rates of target scenes can also be estimated !9 as can information about preferred behaviors in the presented environment, 16 1.1 Similarity Methods q Among the various methods employed in these studies, that based on estimates of similarity given by subjects enjoys two advantages. Firstly, there is no linguistic component in the subject's task and therefore no assumptions have to be made about whether the words used in the semantic differential method, for instance, are relevant to the descriptive task or have a constant definition. In the case of remote perception data this might be a substantial advantage since little is known of what representational structures are employed. The second benefit of similarity information is that it exhibits less variance than semantic differential or adjectival methods 16 when the data is reduced to a small number of semantic dimensions. In a typical study employing the similarity method subjects are presented with all possible pairs of targets drawn from a set of targets. They are required to make a global similarity estimate of each pair of targets on a scale from 1 (very, very similar) to 7 (very, very different), The ordering of presentation is counterbalanced. The resulting symmetric matrices of similarity estimates can be analyzed for intersubject reliability and for a main effect due to the target pairs. Further analysis has used multidimensional scaling (MDS), usually with a Euclidean metric, to represent the similarity data in a space of chosen dimensionality. As atypical example, slides of 20 locations were used as stimuli. 16 The resulting 190 pairs of targets were assessed by 41 subjects. Intersubject reliability was approximately 0.9, a main effect in the data due to the stimulus pairs was found at a level of p < 0.0001. MDS analysis in a 5 dimensioned Euclidean space, followed by rotation of the structure, yielded the following dimensions: natural, water scenes versus man-made, land scenes; natural scenes versus man- made scenes; open versus enclosed scenes; natural, open scenes versus man-made enclosed scenes; and natural water scenes versus natural land scenes. 1.2 Multidimensional Scaling MDS is a technique in which similarity, or dissimilarity data, can be represented in a metric space of chosen dimensionality. The method finds a configuration of points in the given space, one for each of the stimuli, such that stimuli which are adjudged similar are close and ones found different are far apart. Formally, the technique finds a set of points in the given space such that the distances between them are maximally monotonic with the dissimilarity data. The resulting structure can be rotated so as to find dimensions with easily interpretable semantic content. The method takes as input a matrix of Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 dissimilarities, djj, and attempts to find n points such that the distances between the points, Djj, in the 2 z ) (dy 7 Py) 3 = iz j 2 > 2 izj chosen space are such that, is minimized. S is the stress index defined by Kruskal.&:7 Thus the Djj are as much alike the dij as possible in a least squares sense. This procedure can be run in a space of any dimensionality for the Dij. Many algorithms have been developed for this purpose; the one used in this study follows that due to Kruskal. This nonmetric MDS algorithm assumes that measurements are nominal or ordinal in character. It is therefore appropriate for the dissimilarity estimates and it has been widely used for this purpose in psychology and sociology. It may be mentioned that the nonmetric algorithm employed in this study produces solutions which are invariant under any monotonic transformation of the input dissimilarity data. | MDS bears some resemblance to factor analysis and principal components analysis. However, MDS is capable of extracting fewer salient dimensions from a set of dissimilarity data than either of these techniques. A further difference is that the two above-mentioned techniques assume that there is a linear relationship between the variables and stimuli and, because of their inherent linearity, these methods tend to overestimate the number of dimensions needed to satisfactorily represent some data. For further details of the algorithm used in the pilot study see Appendix 1. 1.3. An Example of the MDS Analysis of Geographical Data As an example we take the comparison study described below wherein the 10 targets used in the remote perception pilot study were assessed for similarity by normal perception. Three subjects were presented with all 45 possible pairings of the 10 targets in a randomized order of presentation. Each subject marked the global similarity of the pair of targets on a scale going from very, very similar to very, very different. The estimates from the 3 subjects were averaged to give the following dissimilarity matrix: Approved For Release 2000/08/08 3 CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 Table 1 DISSIMILARITY ESTIMATES FOR COMPARISON STUDY de o a=) E = = — o cm he q - Target Number Plots of the 2 and 3 dimensional MDS solutions for this data are given below. Water fal | e *Lake Valley Mountain ® Dune coastline Grand Canyon @ eFrialds Port ecity with canal eo City -1 fe) 1 a FIGURE 1. PLOT OF 2 DIMENSIONAL MDS SOLUTION FROM COMPARISON STUDY. Approved For Release 2000/08/08. CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 In this 2 dimensional solution the Y axis appears to be interpretable as predominately natural versus man-made scenes and the X axis as predominately horizontal versus vertical elements in the scenes. Waterfall Dune, coast! ine Fields Lake Grand Canyon Mountain Port City with canal FIGURE 2, PLOT OF 3 DIMENSIONAL MDS SOLUTION FROM COMPARISON STUDY. This 3 dimensional solution further separates and clarifies the semantic components. Reading from negative to positive, the X axis gives man-made versus natural scenes, the Y axis gives no land- water interface versus land-water interface while the Z axis. goes from primarily horizontal images to scenes with strong vertical components. These interpretations are speculative as the data set is very small, but they may serve to demonstrate the method. 1.4 Criticisms of MDS Studies in Environmental Psychology Tversky has pointed out that it may not be possible to map similarity data into a metric space by MDS or similar methods because psychological similarity data may be incompatible with the assumptions of such a representation. 14.15 He cites the example of similarity estimates of countries: 5 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 for instance Cuba might be ranked as globally similar to Russia and globally similar to Jamaica. However Jamaica and Russia would not be estimated as globally similar, Thus the. triangle inequality would be violated and therefore no model based on metric spaces can represent this kind of data adequately. Such concept crossing semantic structures may contribute stress to the models presented here. A further problem with similarity data is that examples are easily found which show that similarity is not a symmetric relation. Writing aSb for a is similar to b, thatis, aSb #> bSa. Thus distance metrics, for which D(a,b) = D(b,a), will again not adequately represent such data. Another criticism of environmental psychological models of the kind considered here was raised by Daniel and Ittelson.! The structures resulting from MDS of similarity estimates obtained from subjects viewing photographs of locations may be approximately reproduced by replacing the photographic stimuli with terse verbal descriptions of those locations. Little difference to the MDS structure results if subjects are presented with the two statements "Kansas wheat field” and "sunset over a mountain lake" and asked to rate their “global dissimilarity" or whether they are shown pictures of these scenes and asked the same question. It is argued that whenever subjects are required to respond to very diverse environmental settings with unfamiliar, poorly specified, or inappropriate response categories, little or nothing will be learned about the effects of features of the environment on perception or behavior. Instead, there will be a tendency for a priori semantic relationships to emerge, relationships that are only very abstractly related to the physical features of the environment. However, the similarity method does reliably discern the differing targets. For instance target 4 might be far from target 9 in the MDS structure and this might be true whether targets 4 and 9 are presented as short phrases, pictures or visits to the actual locations. The modality of the information obtained by a respondent in remote perception protocols is very variable, a fact that plagues attempts to construct descriptor sets which cover specific and abstract components at the same time. The insensitivity of the similarity approach to this difference might therefore be advantageous in the remote perception context. 1.5 The Assessment of Mean Chance Expectation by Noise Addition and Permutation Similarity estimates are unusual as parapsychological data in that there is no "correct" answer for the similarity estimate obtained in each trial. How then are such data to be assessed for extra chance effects? An obvious, but inapplicable, method is to examine successive measurements of each dij for any tendency to clump around one value. This method could not be used in the pilot study since many of the djj would only have one experimental measurement. An alternative method of finding a chance expectation requires an additional hypothesis. If it is assumed that the dissimilarity estimates obtained from a remote perception protocol do in fact have a structure compatible with a low dimension Euclidean space, then it is possible to compare the stress value of the MDS configuration for the similarity data 6 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 with stress values from random pseudo-similarity data. This is the method employed in the pilot study. The additional assumption required for this method certainly holds for the type of targets used in the pilot study when assessed for similarity by normal perception, as in the comparison study given here. Whether it remains valid under remote perception conditions is unknown. To implement this method it is necessary to examine the relationship between stress and the standard deviation of the injected noise. This relationship is also a function of the number of points and the dimension of the space of the calculation. These four parameters, stress, noise, dimensionality of representation and number of points are connected by functions for which closed form solutions are not known. There is also very little numerical work on them in the literature. 12,17 Two methods for evaluating the mean chance expectation (MCE) for stress values from MDS's of dissimilarity data were explored in this study. The first, examined by Young,!7 involves the generation of a dissimilarity matrix by the following method: 1.51 MCE Calculation by Noise Addition (a) Choose coordinates of m points in a space of n dimensions. Let the coordinates of point i be (xi}} jj = 1,...M). (b) To calculate the distance between points i and j perturb the coordinates of points i and j by adding a random normal deviate, R of mean = 0 and chosen standard deviation, to all of the coordinates of each of the points. Then calculate the distance djj from n dij = C) Ux» + R)- + RD)? °° k=l (c) Repeat (b) till all the dij are found. ; (d) Compute the stress for an MDS solution of the djj so obtained in an n dimensional space. (e) Repeat steps (b) through (d) till enough values of the stress have been obtained for the distribution of stress values to be known. Whilst the above method of injecting noise into a set of coordinates is useful for exploring relationships between stress, noise level, number of points and number of dimensions, an alternative method which gives an exact measure of the mean chance expectation in an experimental context is provided by permutation. 7 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 1.52 MCE Calculation by Permutation. (a) Measure a set of experimental dissimilarity measurements dj, (b) Compute a MDS configuration for this data in a space of chosen dimension. Let the resulting stress be Se. (c) Generate a random permutation of the dij, That is djj -> dx) where k,l are random integers in [1,...,m], where m = n(n - 1)/2, . (a) Compute an MDS structure for the permuted matrix. Call the stress for this configuration Sp. ) Repeat c and d till enough values for Sp are obtained that the distribution of “Sp is well defined. ( Compare the experimental stress value Se with the distribution of Sp's and compute the chance probability by direct integration or other methods. These methods can be exemplified by taking the data from anormal perception control study of the 10 locational targets used in the pilot study. The following plot shows the effect of adding normally distributed noise to an exact array of 10 points, following the first of the above methods, and also the Stress values from permuted matrices from the comparison study in this report. Kruskal Stress (a) , 0.05 0.1 0.2 0.3 Permuted Standard Deviation of Noise FIGURE 3. -PLOT OF STRESS AGAINST TREATMENT OF DATA FOR 10 POINTS. Approved For Release 2000/08/08 : CIA-RDP96-00789R0022001 30001 -3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 As can be seen, the two methods give close agreement for the chance level stress value for this number of points in a 2 dimensional representation. The same exercise can be repeated for other numbers of points. For instance with 13 points in 2 dimensions: Kruskal Stress 8) 05 0.1 0.2 0.3 Permuted Standard Deviation of Noise FIGURE 4. PLOT OF STRESS AGAINST TREATMENT OF DATA FOR 13 POINTS. As can be seen from the above, it is possible to use the permutation method to distinguish extra chance similarity values in the pilot study, or similar experiments, always providing that the additional assumption of low stress structure in the noise free data is assumed. It can also be seen that as the number of points in the configuration is increased the magnitude of the chance expectation stress increases whilst its variance decreases. Thus for a given experimental stress value, the Z-score of the stress value will be larger the greater the number of points. However, in designing a remote perception study one would like to use the minimum number of points so as to reduce the necessary number of trials. The number of trials is, of course, proportional to the square of the number of points. The question therefore arises what minimum number of points is required to establish a structure. Young!?, continuing investigations by Shepard, shows that the accuracy of the metric data recovered from an MDS of noisy data increases as the number of points increases, and varies inversely with the increase in error and with the number of dimensions scaled. Young's data show that with 10 points a 2 dimensional structure can be recovered with 72% accuracy of the original distances, or dissimilarities, in the presence of a normally distributed error of standard deviation one half of the standard deviation of the true distances in the configuration. Metric recovery of 3 dimensional structure is 68% under these conditions which require 45 similarity values to be estimated. A 10 target set was therefore chosen for the pilot study as a fs) Approved For Release 2000/08/08 :“CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 compromise between the desire to increase the number of points allowing for good metric recovery and tightly distributed MCE stress values and the requirement to keep the number of similarity values to be t measured small. This analysis can be extended to the case where part of the similarity matrix is unmeasured and where those elements lacking an experimental value are filled by the mean value of the matrix elements. In this circumstance, investigation has shown that the variance of the stress values of the permuted matrices is increased and thus a given low value of stress from experimental data will have a reduced Z- score with respect to the broadened chance distribution. However experiments have shown that the method is quite tolerant to missing data and provided that less than 30% of the matrix elements are lost, the recovered 2 dimensional structure (with 10 data points) is usually close to the structure derived from the full similarity matrix. 10 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 2 PILOT STUDY The aim is to collect dissimilarity estimates for pairs of targets from a set of 10 photographs of geographical locations in a remote perception protocol and analyze these by multidimensional scaling. As this study is very much conceived as exploratory in nature there are no formal null hypotheses, Similarity data has never before been obtained in this experimental setting and therefore it is unclear what results may be produced. However, by analogy with the MDS studies of similarity data of geographical stimuli discussed above, it may be expected that the MDS structure resulting from the similarity estimates obtained through remote perception will show a stress below that expected by chance. The chance expectation value of the stress will be calculated by the permutation method described above; the experimental values for dissimilarities will be randomly permuted and a chance expectation distribution of stress values thereby found. The experimental stress will be compared to this distribution. Two and three dimensional MDS analyses will be so performed. 2.1 Method The set of 10 targets used in the pilot study require the measurement of 45 quantities for the matrix of similarities. Owing to the necessity of keeping the subjects blind to the target pair used in each trial it was necessary to choose the target pair for each trial by random selection from the whole set of 45 pairs. Using the pool without replacement would otherwise have been preferable, since then 45 trials would have guaranteed that each target pair had a trial assigned to it. For the utilized method of selection with replacement, the planned 45 trials for each subject would give a fair chance of 80% coverage of the similarity matrix for the combined subject data. Since we are trying to ascertain 45 continuous numerical quantities with the very noisy remote perception channel, many more trials than this would be preferable, To the author's knowledge, this experiment breaks new ground in remote perception protocols in several ways and some aspects of it appear problematic. Firstly subjects are required to produce descriptions of two targets in each trial before feedback is presented. The success of the method depends on their ability to produce some accurate information to both targets in each trial; successful description of only one will not suffice as there will be no information on which to base the similarity estimate. Secondly, the subjects become familiar with all the 10 targets after a few trials and this complicates their task because of their propensity to fixate on one of the targets from memory rather than the fleeting, and often incomprehensible, imagery thought to constitute ideal remote perception data. Both these effects were hopefully ameliorated by encouraging the subjects to limit the time of their efforts to an absolute minimum and to produce extremely sparse transcripts of the two targets. Subjects were requested to cease their remote perception effort as soon as they felt they had some visual material and an overall gestalt of 11 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 the scene. Another concern was whether the subjects would be able to correctly assign the remote perception information to each target in a trial. If some of the perceptual elements were to be mixed between the targets, the pair might be judged over-similar. If there was a consistent bias of this kind across the experiment, all the similariy estimates would be biased towards excessive similarity and the resulting MDS structure would be unchanged, since the structure is invariant under monotonic transformations of the experimental data. However, the effect might not be so consistent. Also it is possible that the order of the transcripts might not correspond to the target order; in other words the first transcript produced might refer primarily to the second target and vice versa. This should not effect the similarity estimate and hence the MDS analysis. 2.2 Subjects. Two subjects were chosen for this pilot study; each being male and in their thirties. One, AV, had considerable experience of remote perception experiments. The other, DK, had not previously taken part in any formal parapsychological studies, but was of the belief that he could successfully remotely perceive targets. The purpose of the experiment in elucidating the semantics of remote perception was explained to both participants and both appeared highly motivated to have the experiment succeed. 2.3. Targets. In selecting ten scenes of geographic locations for the pilot study, several criteria were used: @ To use targets which were similar to those in use at SRI International so that compatibility of the database would allow conclusions to be drawn from the pilot study which would find application at SRI. It was thought that the data from the pilot study might be analyzed by a descriptor set method and a comparison drawn between the estimated similarities from the pilot study and the encoded transcripts. (b) To include in the set several pairs which would have been judged fairly similar, and several very different, on the basis of Ward and Russell's 16 results. On the assumption that similarity estimates derived by remote perception approximate those derived from perceptual studies, this would ensure that the target set for the pilot study would have target pairs at the extremes of the similarity - dissimilarity axis. In the low signal to noise ratio regime of remote perception studies this maximizes the possibility of an extra-chance result. (c) To include targets at the extremes of the semantic dimensions found in normal perception similarity studies of geographic locations. 12 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 Table 2. _ TARGET POOL FOR PILOT STUDY. ee ee A eR a OS RE eS rt ey eine Se a SI NS PR ee eit ae nami pe Pilot Study Target No. SRI Target Number Target Short Name Target Description a cre ee a me ee ee ee ce es te ae ee eh ene vee a gs ce re metas ee a, Se a Se em Se SSS ss SE are cae ene mes end 1 Waterfall Waterfall, dark vertical rock, trees. 2 | 393 | Mountain | Venicalockand snow fields Dak sey, 3 323 | Mountain Lake | Blue crater lake, snow clouds small cliffs. os) 3a | Valey | 1 Deep U shape, mountains, woods snow iver, 6 | ~s«*d bon | Htaton city buituings boars 7 447 | Dune coastline | Wind sculpted dune field slopes wo beach, wea. og 401 | Fields | Flat Kansas fields, eilinear panera, church, re 334 | City withcanal | Canal, city withskyscrpers big | 382. | City | OldEwopeanciy chuchspice,hils ee me cee oe em cee mee ce ey sn a wes wen wes en Ay re ween sy ses int sm ey cp sn ee OS LS SU el EE A NO SD A SS GPSS ED eas va rum The pilot study targets include three city scenes, and six scenes where there is little evidence of man-made activity. Also present are six scenes with water (either rivers or shoreline) clearly visible and four with no water present. Thus the set includes several targets which would probably be adjudged similar and several which are very dissimilar as well as targets which are at the extremes of the man-made versus natural and land-water interface versus no land-water interface semantic dimensions. The ten targets chosen for the study were identified from the large collection of pictures of geographical 13 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 locations at SRI. The pictures give a wide view over the scene and some of them appear to be side looking aerial shots. All were reproduced onto color photographic prints of 20cm. x 25cm. size. 2.4 Target Selection Method A program was written in FORTRAN to control the target selection in the experiment. This program incorporated a pseudo random number generator algorithm due to Lewis & Payne.8 The output of the implementation of their algorithm used in this experiment was checked against values given in the cited reference to ensure correct functioning. The algorithm uses the feedback shift register method and has been extensively checked for randomness. The random number generator was seeded once at the beginning of the study prior to the first trial. Thereafter all targets pairs were determined by this choice; the targets for a given trial being unknown to the experimenter and subjects until that trial's completion. This method of target selection was adopted in order to reduce any effects of intuitive data selection due to the experimenter? With the method used here only one decision point occurred in the entire experiment (rather than one decision point for each trial) and consequently the opportunity for fortuitous target selection by paranormal selection on the part of the experimenter was largely eliminated. In each trial the subjects were given the following instructions: "You are required to do two precognitive remote viewings on two target pictures. These pictures are of geographical locations somewhere around the world. The viewings will be rather quick and the aim is to perceive a few of the most salient features of the locations. You are encouraged to make drawings and verbal comments. It is suggested that you might describe the first target for approximately five minutes, take a short break and then describe the second target for a similar period. You will then be asked to mark your estimate of the similarity of the two targets on a scale as below: Estimate similarity... Very, very different Very, very similar FIGURE 5. SCALE FOR SUBJECTS TO INDICATE SIMILARITY ESTIMATE. 44 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 Immediately after this you will be shown the first of the two targets and allowed to examine it. A minute later the second target will be shown. This completes the trial. It is hoped that we can complete two, or perhaps three, trials per session." Each trial took approximately 30 minutes and followed the above sequence. Subjects made drawings with written comments for each target description and took a break of approximately 5 minutes between remote perception efforts on the two targets. There was no delay between the end of the second remote perception and the entering of the similarity estimate which was then followed by immediate feedback to both targets. The position of the mark made on the scale by the subject was read off on a separate scale in which "very, very different" corresponded to 10 and "very, very similar* to 0. The scale was read to one decimal place. 25 Results— Owing to limitations of experimental time only 65 of the planned 90 trials were completed. One subject, AV completed 35 trials giving values for 24 of the 45 matrix elements, while the other subject DK completed 30 trials which also gave 24 measured matrix elements. In order to examine the data from both subjects together, each subject's dissimilarity estimates were normalized to unity standard deviation and mean of 5 and then these readings were averaged to-give a dissimilarity matrix for both subjects combined. In this matrix, 36 of the 45 elements had experimentally determined values. In all these matrices unmeasured elements were filled with the mean of the remaining matrix elements. Owing to the random target selection method, some matrix elements in the combined matrix had 4 separate trials, whilst for 9 elements there was no experimental reading obtained. Given the reduced number of trials, the MDS analysis is only really valid for the combined matrix of both subject's data; the dissimilarity matrices for the separate subjects have 46% of the values missing and under these conditions, given the noisy data, there is no likelihood of recovering structural information. Nevertheless, for completeness, the 2 dimensional scaled solutions are given for the separate subject data. The three matrices of dissimilarity values, from each subject separately and from the two combined, were permuted 25 times and the new matrices scaled. The stress values obtained from these permutations were used to provide an estimate of the mean chance expectation for stress and a T-test was applied to determine if the stress values for the experimental dissimilarity matrices were significantly different from chance expectation. 15 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
al Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 Table 3 STRESS VALUES FOR MDS MODELS OF THE PILOT STUDY. Subject Model Experiment NCE Probability Dimension Stress Stress {1 Tailed These stress values do not differ significantly from chance. The values for the separate subjects are higher than mean chance expectation, in the opposite direction to that hypothesized. This may be due to the very high proportion of values in these matrices being mean values inserted to cover cases where no experimental data was obtained. Given the small number of trials in relation to the 45 data points in the dissimilarity matrix it is unsurprising that these chance level stress values occurred. The stress value, 0.14, of the comparison study described below is plausibly a minimum for the stress which might be observed in the remote perception case, Noise added to the measured values of the dissimilarities would increase the observed stress from this value towards the mean chance expectation value derived from permuted dissimilarity matrices. In spite of the high observed stress values in the pilot study, it is possible that the resulting MDS structures will show organization which can be semantically interpreted. The 2 dimensional structures for the combined subject data and for each subject separately are given below. 6 ; Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3 Dune coastline C City e e Grand Canyon Port *City with canal Y gountain *Fiealds eWater fal | eValley *Lake FIGURE 6. 2 DIMENSIONAL MDS STRUCTURE FROM BOTH SUBJECTS COMBINED. 2 eDune coast! ine City ePort Mountain Valley » ecity with canal wields etake  *Grand Canyon Cd Waterfal | FIGURE 7. 2 DIMENSIONAL MDS STRUCTURE FROM SUBJECT AV. 17 Approved For Release 2000/08/08 : CIA-RDP96-00789R002200130001-3