CLASSIF 8: D Approved For Release 2000/08/08 : CIA-R ED P96-00789R002200400001-3 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 fill 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 scorittg algorithm makes an assessment of the accuracy and reliability of a single response when matched only against the target materia 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 SO *.. 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 g 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. . UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200400001-3
Approved For Release ZoDb@/baA SiS Hel&s.00789R002200400001-3 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 ofa 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 UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200400001-3
Approved For Release JIN Gh A Sa BER 0780R002200400001-3 probability of observing a figure of merit, fo, or greater is the area under the FM-gaussian for f => fy divided by the total area under the FM-gaussian. An exact p-value is calculated as follows: Define the minimum value of a Z-like statistic as p =-—, min C where » and o are the mean and standard deviation of the best-fit gaussian respectively > - (p = 0.132 and o = 0.163 in the example). Define a second Z-like statistic as, where f, is the observed figure of merit. Let P.., and P, be the p-values calculated in the usual way assuming Z inert Z, were valid z-scores. Then, the correct p-value is given by ’ Po p-value S P min Utts and May (1985) have provided an exact method for combining p-values to enable an overall series evaluation. For mean p-values calculated for a series greater than .1, and the number of sessions greater than 6, a close approximation for the combined Z-score is given by (Edgington, 1972) Zz = (0.50-P)xV12N , combined where p is the average p-value for N sessions. A-8 UNCLASSIFIED Approved For Release 2000/08/08 : CIA-RDP96-00789R002200400001-3