Approved For Release LIM GLA Sal ELE 00789R002200390001-5 second viewer in the example above, for instance, would receive a higher score because his/her response is more unique to the target pool.) The target pool dependent scoring algorithm is less applicable, however, as an independent absolute measure of target contact--a necessary condition for correlation studies. If we remove the within-group control to eliminate a source of variance for a correlation measurement that is potentially unrelated to psi ability, we are obligated to provide some other form of control to demonstrate a deviation from mean chance expectation. FIGURE OF MERIT ANALYSIS The Figure of Merit analysis (FMA) was developed to address the problems associated with correlation studies and to provide a novel form of control. Target Information As in the PEP, the Figure of Merit analysis quantifies the target materia] into binary numbers corresponding to yes/no answers to a set of descriptors. Our descriptor list was developed on the basis of the target material (National Geographic magazine photographs), and on the basis of responses that might be expected a priori for our novice remote viewers. Table 1 shows the 20 descriptors that were used for the photon production experiment (Hubbard, May, and Puthoff, 1985). The questions are strongly oriented toward outdoor gestalts, typical of National Geographic magazine material. The horizontal lines separating the descriptors into groups of three are provided as an aid for translating binary numbers (derived from the yes/no answers to the questions) into an octal shorthand notation. A self-consistency check is performed on each coded target, and a set of logically consistent rules must be developed for a given descriptor list. One such example for the list ‘(shown in Table 1) involves bits 13 and 14. While it is possible to have a land/water interface that is not a river, canal, or channel, the reverse (i.e. to have a river, canal, or channel without having a land/water interface) is not possible by definition. Thus, if a target analyst asserted bit 14 without asserting bit 13, we could consider this an error in coding and assert bit 13. It is beyond the scope of this paper to provide all the logical consistency rules, but most of them are obvious from Table 1. Naturally, these rules must be defined in advance of any experimentation. A-4 UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200390001-5
Approved For Release 20)PDE/P3A\ SS FIB)00789R002200390001-5 Table 1 DESCRIPTOR-BIT DEFINITION Descriptor Is any significant part of the scene hectic, chaotic, congested, or cluttered? Does a single major object or structure dominate the scene? Is the central focus or predominant ambience of the scene primarily natural rather than artificial or manmade? Do the effects of the weather appear to be a significant part of the scene? (e.g., as in the presence of snow or ice, evidence of erosion, etc.) Is the scene predominantly colorful, characterized by a@ profusion of color, by a strikingly contrasting combination of colors, or by outstanding, brightly- colored objects (e.g., flowers, stained-glass windows, etc.—-not normally blue sky, green grass, or usual building color)? Is a mountain, hill, or cliff, or a range of mountains, hills, or cliffs a significant feature of the scene? Is a volcano a significant part of the scene? Are buildings or other manmade structures a significant part of the scene? Is a city a significant part of the scene? Is a town, village, or isolated settlement or outpost a significant feature of the scene Are ruins a significant part of the scene? Is a large expanse of water——s pecifically an ocean, sea, gulf, lake, or bay—a significant aspect of the scene Is @ land/water interface a significant part of the scene? Is @ river, canal, or channel a significant part of the scene? Is a waterfall a significant part of the scene? ls @ port or harbor a significant part of the scene? Is an island a significant part of the scene? ls a swamp, jungle, marsh, or verdant or heavy foliage a significant part of the scene Is a flat aspect to the landscape a significant part of the scene? is a desert a significant part of the scene, or is the scene predominately dry to the point of being arid? “ASS UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200390001-5
Approved For Release obddN Sos AS94515 2b0789R002200390001-5 Response Definition The descriptor list shown in Table 1 is applied in exactly the same way in order to define each remote viewing response. In the SRI program, remote viewers do not fil) in the descriptor list; rather, this task is performed by an analyst who is blind to the target. However, a set of a priori defined guidelines must be established in order to aid the analyst in consistently interpreting the responses. Analysis The target-pool independent scoring algorithm makes an assessment of the accuracy and reliability of a single response when matched only against the target material used in the session. As described above, the target and response materials are defined as the yes/no answers to a descriptor list (Table 1). Once the session material is coded into binary, we define session reliability and accuracy as follows: number of correct response bits ee a eee Accuracy = number of target bits = 1 number of correct response bits Reliability. =_ ————_—_— number of response bits = 1 In other words, the accuracy is the fraction of the target material that is correctly perceived, and the reliability is the fraction of the response that is correct. Neither of these measures, by themselves, is sufficient for a meaningful assessment. For example, in the hypothetical situation in which the viewer simply reads the Encyclopedia Britannica as his/her response, it is certain that the accuracy would be 1.0 simply because all possible target descriptors would have been mentioned. This would not be compelling evidence of psi. Similarly, in a response consisting of one correct word, the reliability would be 1.0, with little evidence of psi as well. We define the figure of merit (FM) as: Figure of Merit = Accuracy x Reliability The figure of merit, which ranges between zero and one, provides an accurate assessment of the response. In the example above where the Encyclopedia Britannica is the response, the FM will be low. Although the accuracy is one, the fraction of the response that is correct (i.e. the reliability) will be very small. Likewise, in the example of a single correct word as a response, the reliability is one, but the accuracy is low. A-6 UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200390001-5
Approved For Release 2E9PG CE ACY SEP PETS 0789R002200390001 -5 A figure of merit can be calculated for each session. For a series of sessions, the FM may be used to assess a viewer's progress on either a session—by-session or descriptor-by—descriptor basis or both. ABSOLUTE FIGURE OF MERIT - A METHOD OF CONTROL We have obtained an estimate of the meaning of FM on an absolute basis. Given the hypothetical situation in which ten viewers contribute 50 sessions each to a remote viewing series, a figure of merit can be calculated by the above technique for each session. If we add the number of responses for all viewers for each of the descriptor bits, we can obtain an estimate as to “response/analysis” bias that may have occurred during the series. For example, if bit number 1 were asserted 40 times in 500 sessions, we can assume on the average for this series (accounting for all known and unknown conditions) that the probability that bit 1 will be asserted in a given response is 40/500 or 0.08. By repeating this calculation for each of the descriptor bits, we can determine the probability of occurrence for all bits under exactly the same conditions that were used in the series. Since this procedure displays all response/analysis biases that may have developed during the series, we are able to use this information to construct computer-—generated “random” responses, with a total absence of psi functioning, that are subject to exactly the same biases that were observed in the series. Therefore, we are able to simulate the ideal control condition, which addresses an important question that is frequently asked by our critics: namely, how would an average viewer respond to a no-target session (i.e. the “monkey on a typewriter” scenario)? A simple bit-by-bit random generation of a response is completely inadequate because it does not account for the response biases observed during the series. The method for producing “random” sessions that do account for the biases is described below. A random number generator is used to create pseudo-responses that are assumed to be devoid of psi functioning. Each bit in a given pseudo-response is generated from the empirical “bias” described above. Once the complete response is generated, the same logical consistency rules (described above) are applied to finalize the pseudo-response. By this technique, a large set of pseudo-responses containing no psi information can be generated. To use these pseudo-responses, we must select, on a random basis, targets from the same set that were used during the series from which the biases were observed. A complete pseudo-session consists of a single pseudo-response and a single randomly selected target. The standard figure of merit analysis is applied to all of the pseudo-sessions in order to calculate figures of merit that have, by definition, no psi content. The resulting FMs are fit with a gaussian distribution to provide an estimate of the mean and standard deviation FM for random data. Figure 1 shows the results of one such fit for a total of 300 pseudo-sessions, using the remote viewings from a photon-production experiment (Hubbard, May, and Puthoff, 1985) as the bias data. From the chi-square, we note that a gaussian is a correct function to use for the fit. Since the gaussian is truncated at zero figure of merit, we must modify the usual z-score techniques to provide p-values for the individual session figure of merits. By definition, the A-7