ABPIONES,FPb.BelsARs,2OONO8/ OR, GIA-RDP96-00789R00929021000158, ugmentation ugus May et al. (1985) were explicit about the character of the sequences that fit the IDS model. Specifically, Walker quotes May et al. “Using psi-acquired information, individuals are able to select locally deviant subsequences from a large random sequence.” (Italics are used in the original May paper.) The very next sentence on page 249 of the reference says, “Such an ability, if mediated by precognition, would allow individuals (subjects or experimenters) to initiate a collection unit of continuous samples (this has been reported as a trial, a block, a run, etc.) in such a way as to optimize the final resuit. (Italics added here for emphasis.) Walker continued, “Indeed, the only way the subject can produce results that agree with the data is to wait for an extra-chance run that matches the experimental run length.” In the final analysis, Walker actually supported our contention that individuals select deviant subsequences. Both from our text and the formalism in our 1985 paper, it is clear that what we meant by a “large random sequence,” was large compared to the trial length, n. In his second criticism of IDS in the same paper, Walker proposed that individuals would have to exhibit a physiologically impossible control over timing (e.g., when to press a button). As evidence apparently in favor of such an exquisite timing ability, he referred to the data presented by Radin and May (1986) that we have discussed above. (Please see Figure 5.) Walker suggested that Radin and May’s result, therefore, supported his quantum mechanical observer theory. It is beyond the scope of this paper to critique Walker’s quantum mechanical models, but we would hope they do not depend upon his analysis of Radin and May’s results. The enhanced hitting at zero seed and the suppressed values + one 20 ms clock tick that we show in Figure 5 is the expected result based upon the well-understood properties of PRNG?’s and does not represent the precision of the operator’s reaction time. We must consider how it is possible with normal human reactions to obtain significant scores, which can only happen in 20 ms windows. In typical visual reaction time measurements, Woodworth and Schlos- berg (1960) found a standard deviation of 30 ms. If we assume these human reactions are typical of those for AC performance and are normally distributed, we compute the maximum probability of being within a 20 ms window, which is centered about the mean, of 23.5%. For the worst case, the operators must “hit” significant seeds less often than 23.5% of the time. Radin and May do not report the number of significant runs, so we provide a worst-case estimate. Given that they quote a p-value of 0.005 for 500 trials, we find that 39 trials must be independently significant. That is, the accumulated binomial proba- bility is 0.005 for 39 hits in 500 trials with an event probability of 0.05. This corresponds to a hitting rate (i.e., 39/500) of only 7.8%, a value well within the capability of human reaction times. We recognize that it is not a requirement to hit only on significant seeds; however, all other seeds leading to positive Z- scores are less restrictive then the case we have presented. The zero-center “spike” in Figure 5 misled Walker and others into thinking that exceptional timing was required to produce the observed deviations. As we have shown this is not the case, and, therefore, Walker’s second criticism of the JDS theory is not valid. Bierman — 1988 Bierman (1988) attempted to test the 7DS model with a gifted subject. His experimental design ap- peared to meet most of the criteria for a valid test of the model; however, Bierman found no evidence for AMP and stated that no conclusions could be drawn from his work. We encourage Bierman to con- tinue with this design and to be specific with what he would expect to see if DAT were the correct mecha- nism compared to if it were not. Approved For Release 2000/08/08 : CIA-RDP96-00789R003200210001-3
Approved For Release 2000/08/08 : CIA-RDP96-00789R003200210001-3 pplications of Decision Augmentation Theory V5 25 August 1994 Braud and Schlitz — 1989 Braud and Schlitz (1989) conducted an electrodermal! PK experiment specifically to test the DS model. They argued that if the mechanism of the effect were informational, then allowing participants more opportunities to select locally deviant values of the dependent variable should yield stronger effects. In their experiment, 12 electrodermal sampling epochs were either initiated individually by a press of a button, or all 12 were determined as a result of the first button press. Braud and Schlitz hypothesized that under JDS, they would expect to see a larger overall effect in the former condition. They found that the single button press data yielded a significant result; whereas the multiple press data scored at chance (tsinglel31] = 2.14, truyi[31] = —0.53). They concluded, therefore, that their data were more consistent with an influence mechanism than with an informational one. In both button-press conditions, the dependent variable was averaged over all 12 epochs; therefore, the formalism discussed in this paper cannot be applied because the data should have been averaged over at least two different values. The idea of multiple decision points, however, is still valid. As stated in their paper, the timing of the epochs was such that 20 seconds of the 30 second epoch was independent of the button-press condition and could not, therefore be subjected to a DAT-like selection. To examine the consequence of this overlap, we computed the effect size for the single button case as 0.359 (Rosenthal, 1991, Page 19, Equation 2.16). Since data for the 20 seconds is the same in each condition, the operator can only make AC-mediated decisions for the first 10 seconds of the data. If we assume that on the average the remaining 20 seconds meets mean chance expectation and the effect size is constant then we would expect an effect size of (0.359 + 0+0)/3 =0.119.* The measured effect size was 0.095, which is consistent with this prediction. Braud and Schlitz’s idea was good and provides a possible way to use DAT effectively. Because of the epoch timing and the consistency of the effect sizes, however, we believe they have interpreted their results in favor of causal mechanism prematurely. Aside from the timing issues, their protocol compli- cates the application of DAT further. To observe an enhanced effect because of multiple decision points, Z-scores should be computed for each decision and combined as a Stouffer’s Z where the de- nominator is the square root of the number of decision points. In their protocol, they only combine the dependent variable. Vassy — 1990 Vassy (1990) used a similar timing argument to refute the /DS model as did Walker (1987). Vassy gener- ated PRNG single bits at a rate of one each 8.7 ms. He argued that if DS were operating, that a subject would be more likely to identify bursts of ones rather than single ones given the time between consecu- tive bits. While he found significant evidence for the primary task of “selecting” individual bits, he found no evidence that these hits were imbedded in excess clusters of ones. We compute that the maximum probability of a hit within an & 7 ms window centered on the mean of the normal reaction curve with a standard deviation of 30 ms (Woodworth and Schlosberg, 1960) is 11.5%. Vassy quotes an overall Z-score for /00 runs of 2.39. From this, we compute a mean Z of 0.239 for each run of 36 bits. To obtain this result requires an excess hitting of 0.717 bits, which corresponds to an ex- * Asa function ofa, DAT predictsa //\/n dependency in the effect size; however, at a fixed, asin this case, the effect size should be constant. Approved For Release 2000/08/08 : CIA-RDP96-00789R003200210001-312
ABPKANGAS SPE RRIGAR FA Q02108/98.4,G A-RDP96-00789R005200210004 58, cess hitting rate of 2%. Given that 77.5% is the maximum one can expect with normal human reaction times, Vassy’s results easily allow for individual bit selection, and, thus, cannot be used to reject the DAT model on the basis of timing. Braud — 1990 In a cooperative effort with SRI International, Braud (1990) conducted a biological AP study with hu- man red blood cells as the target system. The study was designed, in part, as a prospective test of DAT, so all conditions for a valid test were satisfied. Braud found that a significant number of individuals were independently able to “slow” the rate of hemolysis (i.e., the destruction of red blood cells in saline solu- tion) in what he called the “protect” mode. Using data from the nine significant participants, Braud found support in favor of PAP over DAT. Figure 6 shows the results of our re-analysis of all of Braud’s raw data using our more modern formalism of DAT. Effort Data Control Data Predicted AP Predicted DAT nN < N Gc Fe o = mtb 2 4 6 Number of Test Tubes Figure 6. DAT Analysis of Hemolysis Data. The solid line indicates the theoretical expected value for MCE. The squares are the mean values of Z2 for the control data, and the error bars indicate the 1-standard error for the 32 trials in the study. We notice that the contro] data with eight test tubes is significantly below MCE (t = —2.79, df = 62, p< 0.996). Compared to the MCE line, the effort data is significant (t = 4.04, df = 31, p <7.6x 107°) for eight test tubes and nearly so for n = 2 (t = 2.06, df = 31, p < 0.051). The x atn = 8 indicates the calculated value of the mean of Z? assuming that the effect size at n = 2 was entirely because of AP; similarly, the x atn = 2 indicates the calculated value assuming that the effect size, which was observed atm = 8, was totally due to AP These AP predictions are not significantly different from the observed data (t = 0.156, p < 0.431, df = 62 and t = 0.906, p < 0.184, df = 62, atn = 2 and 8, respectively). Whereas DAT predicts no differences between the data at the end points for n, we find a significant difference (t = 2.033, p < 0.023, df = 62). That is, to a statistical degree the data atn = 8, cannot be explained by selection alone. Thus, we concur with Braud’s original conclusion; these results indicate a possible causal relationship between mental intent and biological consequences. It is difficult to conclude from our analysis of a single study with only 32 trials that AP is part of nature; nonetheless, this result is very important. It is the first data we have encountered that supports the PAP Approved For Release 2000/08/08 : CIA-RDP96-00789R003200210001-8
o ABRESNES EA RSISARS ROBE AB 5 fA-RDP96-00789ROOA IORI NOP isch hypothesis, which is in direct opposition to the substantial support against PAP resulting from the analy- sis of the RNG data sets. In addition, May and Vilenskaya (1993) and Vilenskaya and May (1994) re- port that the preponderance of AMP research in the Former Soviet Union is the study of AP on biologi- cal systems. Their operators, as do ours, report their internal experiences as being a causal relationship between them and their biological targets. Dobyns — 1993 Dobyns (1993) presents a method for comparing what he calls the “influence” and “selection” models, corresponding to what we have been calling DAT and PAP. He uses data from 490 “tripolar sets” of experimental runs at PEAR. For each set, there was a high aim, a baseline and a low aim condition. The three values produced were then sorted into which one was actually highest, in the middle, and lowest for each set. The data were then summarized into a3 x 3 matrix, where the rows represented the three intentions, and the columns represented the actual ordering. If every attempt had been success- ful, the diagonal of the matrix would consist of the number of tripolar sets, namely 490. We present the data portion of Dobyns’ Table from page 264 of the reference as our Table 2: Table 2. Scoring Data From Dobyns (1993) Dobyns computes an aggregate likelihood ratio of his predictions for the DAT and PAP models and con- cludes in favor the the influence model with a ratio of 28.9 to one. However, there are serious problems with the methods used in Dobyns’ paper. In this paper we simply outline only two of the difficulties. To fully explain them would require a level of technical discussion not suitable for a short summary such as this. One problem is in the calculation of the likelihood ratio function using Equation 6, which we reproduce from page 265 of the reference: Blpiq) = PLPEPs = [Be] Ba)” [a], qq Ail (42) 143 where p and q are the predicted rank frequencies for each aim under the influence and selection models, respectively, and the » are the observed frequencies for each aim. We agree that this relationship cor- rectly gives the likelihood ratio for comparing the two models for one row of Table 2. However, immedi- ately following that equation, Dobyns writes, “The aggregate likelihood of the hypothesis over all three Approved For Release 2000/08/08 : CIA-RDP96-00789R003200210001-34