Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 Final Report- -Objective E, Tasks 3 and 4 December 1986 19bu : INTUITIVE DATA SORTING: AN INFORMATIONAL MODEL OF PSYCHOENERGETIC FUNCTIONING By: EDWIN C. MAY Prepared for: PETER J. McNELIS, DSW CONTRACTING OFFICER'S TECHNICAL REPRESENTATIVE 333 Ravenswood Avenue Menlo Park, California 94025 U.S.A. (415) 326-6200 = core SRI 30 Mo 910-373- internatig iAyah oved For! Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Spree ved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 Final Report--Objective E, Tasks 3 and 4 December 1986 Covering the Period 1 October 1985 to 30 September 1986 INTUITIVE DATA SORTING: AN INFORMATIONAL MODEL OF PSYCHOENERGETIC FUNCTIONING By: — EDWIN C. MAY Prepared for: PETER J. McNELIS, DSW CONTRACTING OFFICER’S TECHNICAL REPRESENTATIVE SRI Project 1294 Approved by: ROBERT S. LEONARD, Executive Director Geoscience and Engineering Center an aoe ooo Avenue ° Menlo Park, California 94025 - U.S.A. 326-6200 - Cable: SRI INTL MPK * TWX: 910-373-2046. approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 ABSTRACT We develop a comprehensive model of psychoenergetic functioning called Intuitive Data Sorting (IDS). Extending purely philosophical arguments, we derive specific mathematical predictions for the interpretation of random number generator experiments. Two experiments are analyzed: (1) a pseudorandom number generator (PRNG) experiment conducted at SRI International, and (2) a random number generator experiment conducted at the Princeton Engineering Anomalies Research laboratory at Princeton University. Preliminary results from the PRNG experiment are in statistical agreement with the IDS model. We show, however, that the Princeton University RNG data were collected under unfavorable conditions to serve as a test of the IDS model; we recommend an RNG experiment protocol that will allow a more favorable test. Li Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 TABLE OF CONTENTS ABSTRACT oo cc cece ce ee eee ene e eee ene eee een eet eer eee Treas i LIST OF ILLUSTRATIONS ..... cece eee reer ete teeter eee nee ence eee iv LIST OF TABLES 1... ccc cece ee ee eee teen eee rn ener een ree EES Vv I INTRODUCTION occ ee en eee en en eer Een ene 1 A. A Conceptual Thought Experiment ..... 66sec eer reer trent e neers 2 B. A Practical Thought Experiment ...-. 5.6 eee ee ce tee ene eens 3 II BACKGROUND ....- eee eee eee eect ence eee nner teen nets 6 Ill METHOD OF APPROACH .... 0. ccc cere eee ee een ee ee een e nee 8 A. Theoretical Considerations 6.6... cece eee cen eee eee een eee 8 1. RA Data Reduction 2.0... ccc cee eee eee ene tren n renee 8 2. Mean Chance Expectation 0.0.0... cece eee erence ee teen een nns 9 a. Theoretical Considerations for Overall Mean Chance Expectation .. 9 b. Theoretical Considerations for an RA Interaction ........-.--++5 11 c. Theoretical Considerations for the IDS Model ....-....-+--e05: 14 d. IDS vs. RA in Perspective 0... 0c eee eee eee eee eens 17 3. Analysis and Hypothesis Testing .....+ 6. essere reese renee ee ncee 18 B. The SRI Pseudorandom Data 1.0... cece cette teen n ees 20 1. Justification for a Pseudorandom Number Generator Experiment ..... 20 2. PRNG Experiment Description «0.6.1... 66 eee e eee etree ees 20 C. The Princeton Engineering Anomalies Research Data ..-..+++++sereess 21 IV RESULTS AND DISCUSSION .....: cece eee eee eee etn ee ete ens 24 A. The PRNG Experiment Results 0.6... 6. ee reer eect tere eee e eee eens 24 B. The PEAR Results ..... 0. cee rece ee eee tte eee er ee eS 25 1. Data Reduction and Analysis 2.0.0... e eee eee eee eee nes 25 9. Discussion of the PEAR Results .. 0... cece eee eee tee eee eee 27 Vv CONCLUSIONS AND RECOMMENDATIONS ... 2-6 0st eee ee tee ete ees 29 REFERENCES oo cece cee cee nee eee rere nee eee reer e er tae e nas 30 iil Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 LIST OF ILLUSTRATIONS 1. Generalized Decision Process 1.1... ce cece eee tenet eee eet e eens 1 2. A Conceptual Thought Experiment «1.6... 06. See eee nee een tee eens 3 3. A Practical Thought Experiment ...... 0... c cece eee eee tee teen eens 4 4. One RA Model Compared with MCE ... cc ee eee eee tee eee eee eens 14 5, One IDS Model Compared to MCE and One RA Hypothesis ....--. +e eee e eres 16 6. MCE and PK— Data... ccc ccc eee ee eee eet ee tener te eee etna 28 iv Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
en Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 LIST OF TABLES {. Exact and Approximate Values of In(|Ap|) «+6. - sree tere e errr t etree es 11 2. Raw Data for Operator 10--Princeton University Data... 6. cece eee eee eee 23 3. MCE for Each Data Set ..... cee reece etree erent ene erence renee eens ees 25 4. Results of the Analysis of the PEAR RNG Data .....--ees seer eee reer rerr cere 26 v Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
rn ee Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 I INTRODUCTION Since 1979, SRI International has been constructing a model of psychoenergetic functioning that may provide an explanation for a broad range of experimental data.* The idea first occurred to us as part of an interpretation of an experiment we conducted that year.'t In that experiment, it appeared that individuals were able to make decisions (psychoenergetically) based upon information propagating backward in time. This unorthodox concept, in its generalized form, is shown schematically in Figure 1 as one of the inputs to a decision process. Analysis (real-time) Experience FIGURE 1 GENERALIZED DECISION PROCESS For example, suppose that you had to decide if it were safe to cross a highway. Clearly, one input to that decision is real-time analysis: There is a continuous stream of traffic; don’t cross the road. Suppose that there was only one car on the road, but it was at some distance away. Experience might suggest that you are unable to run fast enough to avoid being hit; don’t cross. Without defining intuition, it might tell you not to cross the highway even though ” This report constitutes deliverable “a”--final report on RA perturbation of device—driven random sequences—-for Objective E, Task 4, Conduct a retrospective test and analysis of the Intuitive Data Sorting (IDS) model, and the deliverable—--final report on RA activity upon pseudorandom number generators~—Objective E, Task 3, Investigate RA activity by examining statistical changes of state of pseudorandom number generators. t References are listed at the end of this report. Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R0005001 40001 -3 all other indicators suggest that it is safe. We recognize that all these processes are intermixed, and that there may be others that we have not yet considered. Our model proposes that there is one other source of input to the decision process that has not yet been considered—-information propagating backward in time from the future. You have an impression (maybe a visual experience) that indicates that you are dead on the highway; don’t cross the road. While there are specific examples of information propagating backward in time in physics (e.g., the Dirac equation--a positron traveling forward in time is mathematically identical to an electron traveling backward in time), the idea that it is possible at the macroscopic level is not generally accepted. It is beyond the scope of this report to discuss the profound implications for physics and philosophy if it were true. Since the late 1930s, however, the parapsychological research journals have been reporting evidence that information from the future is available in the present——at least statistically. We are not able, at this time, to provide a complete analysis of this literature, which claims evidence for precognition--the parapsychological term for accessing information from the future. Rather, we provide a brief discussion of two of the pertinent reports. In 1983, C. T. Tart reviewed the precognition “forced choice” literature as part of an investigation to determine information rates for both real-time and precognition experiments? In this context, “forced choice” implies that the subject was aware of the limited number of possible targets, but was blind to the actual target chosen for any single trial. Tart found 32 studies, of this type, that claimed statistically significant evidence for precognition. In 1986, Nelson e¢ al, reported the results of over 400 “free~response” precognition trials demonstrating strong statistical evidence for information flow from the future.> 4 “Free-response” experiments differ from “forced-choice” in that the target material is relatively unbounded (i.e., while the general nature of the target material might be known by the subject, the range of target possibilities is very large and is unknown to the subject). Because these reports provide strongly suggestive evidence for macroscopic information flow from the future, we proceed with the development of the model. A. A Conceptual Thought Experiment Consider an arbitrarily complex experiment as shown in Figure 2. Further, suppose that this experiment has one result and that there is a single decision required before the experiment is conducted. (We recognize that complex experiments produce more than Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
catamaran Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 one result and require many decisions for their design; therefore, we are considering only the primary result and decision, and are including the lesser results and decisions as part of the complexity of the experiment.) There are no limitations placed upon the complex experiment. In principle, it might be conducted by many researchers at different laboratories for many years. To be specific, let us assume that the single decision involves turning a knob to one of five settings, 1 through 5. Also consider that the single result is a meter reading in which we assume a high reading is “good.” Our model proposes that the following internal dialogue by the decision maker is possible: Information Flow (Backward in Time) Single Very : Human Complex Single Decision Experiment Result FIGURE 2. A CONCEPTUAL THOUGHT EXPERIMENT If I were to conduct this experiment with the knob set at 2, let me “peek” into the future and examine the result. I perceive the meter showing a low reading and thus I reject that option. Suppose I were to conduct this experiment with the knob set at 4. I perceive the meter showing a very high reading. Because I like that result, I will set the knob at 4, and conduct the experiment! For the above dialogue to be sensible, we must be able to “sample” the future—-reject the unwanted ones, and “select” a preferred one. When the model is formulated mathematically and applied to a specific set of experiments, it will provide compelling evidence (see below) that this unorthodox idea is possible. B. A Practical Thought Experiment By describing a practical thought experiment, we will illustrate an important consequence of the model. If we are allowed to “sample” the future, then what was previously thought of as a cause~and-effect relationship might be confused with an informational relationship involving no causality at all. Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
ee Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 Suppose that we wanted to demonstrate that cyclamates cause cancer in laboratory rats. Further, assume that normal double blind protocols are in effect. A laboratory assistant is asked to administer injections to two separate groups of rats-—-one a control group, the other a target group. By double blind, we mean that the assistant does not know which is the control group, nor which injections will be cyclamates rather than biologically neutral cyclamate placebos. Let us assume that the experiment produces a statistically significant (e.g., p < 0.01) separation between the control and target groups (i.e., the probability of a target rat contracting cancer is significantly larger than for a control rat). This result is shown schematically in the lower half of Figure 3. The conclusion that is drawn from this experiment is that whatever was in the syringe caused cancer in the rats. Psychoenergetic Sorting of Rats ~« Normal Population Control Group = <« Drug Group FIGURE 3. A PRACTICAL THOUGHT EXPERIMENT To illustrate an informational explanation of this same result, we describe a totally fraudulent way in which this experiment might be conducted. Suppose that the researcher is motivated to cheat, and in doing so is able to select from a normal population of rats those that are predisposed toward cancer. (Because this is a thought experiment, we can invent a mechanism by which he/she could do this.) We assume that this researcher is sophisticated in statistical matters and sorts the selected rats into target and control groups in such a way (by cross mixing them) to produce the identical results described above. Furthermore, to preclude any possible confounding of the fraudulent outcome, the researcher replaces the contents of the syringes with distilled water. Then he/she lets the experiment proceed as above. Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
een AA A I TT Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 The statistically significant result (p < 0.01) would still be interpreted as before. Whatever was in the syringe caused cancer in the rats—~in this case distilled water! Thus, a purely informational process (knowledge of the individual rats’ predisposition toward cancer) is mistaken as a causal one (distilled water causes cancer in rats). The researcher, in the above example, need not be fraudulent to produce the identical result. We propose that by statistically “peeking” into the future he/she is able select the predisposed rats. For example, having picked a rat, he could sample the future to determine if that rat is likely to contract cancer. If so, he would add it to the target group; if not, it would be included in the control group. Thus, this researcher has “simply” psychoenergetically sorted rats (see Figure 1). We call this putative ability Intuitive Data Sorting (DS). Because large bodies of research have concluded some form of cause-and-effect relationship based upon statistical hypothesis testing, it is important to determine if our model has any scientific basis. What follows is the application of our model to a large body of psychoenergetic experimental data. 5 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 II BACKGROUND In 1969, SchmidtS introduced a type of psychoenergetic experiment in which individuals were asked to “modify” the statistics of “true” random number generators (RNG), ie., devices that generate sequences of numbers based on some fundamental random process such as radioactive decay or thermal noise. Since publication of that important initial paper, we have been able to locate (through 1984) 56 pertinent references in English language journals and reports describing a total of 332 individual binary RNG experiments. We simulated an additional 95 nonsignificant experiments to account for a “filedrawer” problem (i.e., experiments that were conducted and not published because they were not significant).6 By including these simulated studies in the data base, we refrain from using an artificially inflated data base because of selected reporting of “successful” studies. In the preliminary analysis of these data, we calculated that the probability that the observed deviations occurred by normal statistical fluctuations alone was p < 3.9 x 10-18 during experimental conditions, and pS 0.78 under control conditions. Clearly, there is a statistical anomaly within these data. Since 1969, there has been considerable discussion about mechanisms that can explain these RNG results.5;% 8 (For the purpose of this report, we assume that artifact and incorrect statistics have been accounted for. Radin has shown that this assumption is true to first order;® a detailed meta-analysis is now in progress to determine the overall validity of the RNG data base.) The most frequently proposed explanations are remote action (RA) and precognition. Under an RA hypothesis, by definition, a participant “forces” a physical modification in a source of random signals so as to produce a change in the output statistics. Alternatively, under the IDS hypothesis, we propose that humans can make decisions (by psychoenergetic means) to take advantage of the natural and unperturbed fluctuations of a system. In the context of an RNG experiment, it appears that individuals can anticipate locally deviant subsequences from within larger and unperturbed sequences and make decisions based upon that knowledge. Suppose that an individual is asked to “make” the RNG produce more binary ones (1s) than zeros (0s). Rather than “causing” the device to . produce binary ones, we suggest that the participant has simply initiated the run by anticipating when the RNG was going to produce a series of ones as part of its natural Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
me eemrree cree eremermmmremareencrensrencrermers cesses acacia Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 binomial fluctuation. Thus, the participant has capitalized upon natural events, rather than “causing” anything to occur. In our final report to a client in FY 1984, we applied the IDS formalism to the data base of RNG experiments described above.? We found that the data were described by the IDS model rather than the RA hypothesis. There were problems, however, with the comparison of the IDS model to the previously published RNG data base. The IDS formalism is derived from the assumption that the sequence length, [n], results from a single press of a button. None of the experiments in the data base were reported in that way. All of the data were aggregates over many button presses. While we were able to draw conclusions based upon averaged data, (e.g., on the average, IDS appears to account for the results in the historical data base), the ideal test of IDS must be conducted using data resulting from single button presses. There are two questions that should be addressed when conducting single button press experiments: e Is IDS possible under conditions that preclude any RA? If so, are there any sequence length dependencies in the ability? ® Can IDS provide an interpretation of RNG data? We have addressed both issues as separate tasks for FY 1986. The first task was to examine the IDS model, at SRI under a condition that precludes RA--using pseudorandom number generators. To know if IDS is possible, in principle, we must conduct experiments that have as few confounding factors as possible. The second was to examine the IDS model using “true” RNGs. The only data of this type that was available was collected by the Princeton Engineering Anomalies Research (PEAR) Laboratory, in the school of Engineering at Princeton University.1° We therefore let a subcontract to this laboratory to provide us with their single button press RNG data. Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
em area mr rere eer aermrncerrmrreesemnecsremm cas caaacaacaaaaaaaa aaa Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 III METHOD OF APPROACH Our method of approaching the data generated at SRI and Princeton proceeds in three phases: © Theoretical considerations--a derivation of mean chance expectation for the various models under study. e The SRI data—-details of the pseudorandom data collection and display. ® The PEAR data—~details of the Princeton “true” RNG data collection. A. Theoretical Considerations The data analysis proceeds in three steps, data reduction, definition of mean chance expectation, and analysis and hypothesis testing. 1. RA Data Reduction For each button press, the raw data consist of a sequence length [n}] and the number of “hits” [h] (e.g., binary 1s). We transform these data into logarithmic form for analysis by computing the following quantities for each data point: h PE? inApl=in](h-o5)| In(n) ss, and p - 0.5 2 = Al 2 n If the linear correlation coefficient for all pairs of data points, In(n) and In|Ap], is significant, then a straight line may be fit to the data, and thus, the raw data are reduced to two coefficients--the slope and the intercept. Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
rn ee Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 2. Mean Chance Expectation In the absence of all forms of psychoenergetic functioning, we must determine, theoretically, the mean chance expectation (MCE) for the type of data described above. a. Theoretical Considerations for Overall Mean Chance Expectation We define the sequence length, [n], as the number of samples collected from an RNG as a result of a single button press. If we consider all possible values of sequence lengths, it is convenient to use the binomial statistic in its exact form for n < 200, and use the normal approximation for larger sequence lengths. We use the observed fractional hit rate, p (hits/trials), minus the expected fractional hit rate, po (equal to 0.5 for binary RNGs), as the dependent variable, Ap = p - po, and [n] as the independent variable. For the “continuous” region where n > 200, Ap is normally distributed about a mean of zero with a standard deviation of oo given by 0 = ’ where qo = (1 - po). For convenience, we shall examine the statistical properties of [n] and Ap in logarithmic form--In(n) and In(Ap), respectively, and, without loss of generality, consider only the absolute value of Ap, |Ap|. The expected value of In|Ap| is given by [ore] 2 f -o,5 (AB. In |Ap| e %* d (p) ~0 In | Ap| (1) foe) 2 f -0.5 (Ae. e % ~0 d @p) Following the usual definition of a z-score, let Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
em nn A Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 Substituting into Equation (1), we find that In |Ap| = In o, +In|z| And because Py Io 05 = n in |Ap| = 0.5Inp\d, +In|[z| - 0.5Inn . (2) Equation (2) is a simple way to express the null hypothesis of no psychoenergetic functioning for the RNG data. If many RNG runs of varying sequence lengths are conducted in the absence of all psychoenergetic phenomena, the natural logarithm of the sequence lengths and their associated logs of |Ap|’s are linearly related--having a slope of 0.5, and an intercept determined by po, go and the average value of In|z]--a known constant. Given the “unboundedness” of |z| (i.e., 0 < |z| < co), it may surprise some readers that there is a linear relationship between the expected value of In|Ap| and In(n). To demonstrate this linear relationship from a different perspective, we calculate the expected linear correlation coefficient, r, under the null hypothesis:°* -0.5774 x (Sx- Su) EET (3) 19.7392 + (Sx- Sy) 3 Sx and Sm are the logarithms of the maximum and minimum sequence lengths, respectively. Equations (2) and (3) form the basic set of relationships that describe, in detail, the expected results under the null hypothesis. A significant note must be added at this point. Equation (2) represents the MCE under a normal approximation. As we will show, for any actual experimental case, the * The authors wish to acknowledge and thank Dr. J. Utts for deriving this relationship. Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
a Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 difference between normality and the exact binomial calculation becomes important in determining the expected slope of the MCE line. Table 1 shows a comparison between the normal approximation and the exact binomial calculation for the expected value of In|Ap| as a function of selected sequence lengths. Table 1 EXACT AND APPROXIMATE VALUES OF In|Ap| Sequence | Exact Normal Percent Length Binomial Approx. Error 2000 10000 100000 For any given experiment, the MCE line should be computed in the following way: e Compute the exact value for In|Ap| for each sequence used in the experiment. e Fit the above result with a weighted straight line. The weighting factor for each sequence length is the number of trials, for that sequence length, that were conducted in the experiment under study. e Use the slope and intercept from the above for the MCE values. b. Theoretical Considerations for an RA Interaction Because in the most general case of RA a subject could “perturb” the RNG device in any way, the Gaussian in Equation (1) must be replaced by an arbitrary function, f(Ap,n). Or, Jin |Ap| f(Ap,n) dp) ~0 In |Ap| = J tcap.n) d@p) ~0 11 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
ae Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 To evaluate this general relationship, we must assume some specific form for f(Ap,n). From this point on in the development of the model, we will assume that RA induces a minor perturbation in the physical system. Thus, we assume that f(Ap,n) remains Gaussian, but RA shifts its mean——slightly. We consider a class of RA models in which RA perturbs (to a small degree) a binomial distribution by shifting its mean. The theoretical task is to evaluate the expected value for the In|Ap| using Equation (1). Define Ap =P -P, AP, = P, - Po AP, = P -P, = AP- AP, As before, po is the mean of an unperturbed distribution, and now pa is the mean of a shifted distribution. Apa is the difference between the observed fractional hitting rate, p, and the shifted mean, pa, and Aps is the shift in the means of the two distributions. We have derived the expected value of In|Ap| elsewhere, but we present a simplified version of it here. For this calculation, we assume that the RNG is binary (i.e., Po = 0.5). Under these conditions, the expected value of In|Ap| is given by In |Ap| = 0.5 In(0.25) + In [zf(Ap,) +2,] - 0.5 Infn) , (4) where Zs = o, and f(Ap,) = V i - 4Ap? This latter term arises because the variance of a binomial distribution depends upon the mean of that distribution. If the shift of the distribution is small (i.e., Aps * 0), then f(Aps) * 1. For any RA model under consideration, we must define how the mean of 12 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 the shifted distribution behaves as a function of the sequence length, [n], and knowing that [z] is distributed normally, the evaluation of Equation (4) reduces to the evaluation of the expectation of In(zf(Aps) + zs). Or, 2 -0.5 Z In [z f(AP,) + In [zf(AP,) + 2s] = } 7 _Jintears zs] e dz . (5) Let us now consider a general class of binary RNG models where the mean of a binomial distribution is shifted by an amount: Pp, = OS (1+a) » and where [a] is an RA strength parameter. That is to say, the properties of the RNG device are modified by RA such that the mean probability of producing a hit has been shifted from 0.5 to pa. There are many other models that we might consider, but this particular one has been proposed by the PEAR group. They report that if n = 200, [a] is approximately 0.001.101 In other words, this model implies that individuals cause the mean probability of producing a hit to be constant, regardless of the sequence length. (The logarithm term in Equation 4 does retain an n-dependency through the zs term even though Apa does not.) RA perturbs the device on a bit-by-bit basis that is independent of the number of bits in the sequence. Figure 4 shows the result of evaluating Equation (4) for various values of [a] compared to the MCE line. 13 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
snc TA Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 3x10° 1x10 lA P| 3x10 1 x10” 3x10 2.5 x 10° 2.0 x 10° 8.0x 10° Sequence Length (n) FIGURE 4 ONE RA MODEL COMPARED WITH MCE c. Theoretical Considerations for the IDS Model Equation (2) represents the MCE condition in which no psychoenergetic interaction is present. Because one premise of the IDS model is that no causal interactions occur, there is only one term in Equation (2) that can possibly account for a non-causal, but psychoenergetic effect. The 0.5 In(poqo) and the -0.5 In(n) terms are consequences of the standard deviation for the unperturbed distribution of Ap; therefore, the only remaining term, the expected value for In|z|, must contain the IDS considerations. 14 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
seman ence Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 In general terms, the expected value for In|z] is given by f In(z) g(z.n) dz ~0 (6) In |zl = Ff ean dz ~0 The function g(z,n) is the distribution that reflects a subject’s ability to select subsequences, leading to z~scores. For example, if a subject were always able to select subsequences (regardless of sequence length) leading to z = 2.15, then g(z,n) contains no n-—dependence and is a Dirac delta function at z = 2.15. If g(z,n) is simply a Gaussian with a mean of zero and a standard deviation of one, then Equation (6) reduces back to the null hypothesis. To determine predictions under the IDS model, Equation (6) must be evaluated for different assumptions for the function g(z,n). Consider the case in which g(z,n) is not a function of the sequence length. Then, g(z,n) = y(z). Equation (6) becomes a sequence length independent constant, which can only affect the intercept of a straight line, regardless of any details of y(z). Figure 5 shows the RA hypothesis from Figure 4 and an example of the IDS case described above. Suppose, however, g(z,n) does contain some n-dependencies. Even under the IDS hypothesis, we might expect this to be the case. Recalling that IDS is a psychoenergetic decision algorithm, we can imagine that better “decisions” result when a lot of “information” is available, or the reverse: “decisions” are inaccurate if there is not enough “information.” It is possible that inaccurate “decisions” are made in the presence of too much “information” as well. Elementary information theory tells us that the amount of information in a sequence of length [n] is proportional to [n] if the bits in the sequence are independent. Therefore, we can equate “information” in the above paragraph with the sequence length. 15 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 3x10” 1x10” |Ap| 3x10- 1x10- IDS (n Independent) 3x10- 2.5x 10° 2.0 x 10° 8.0 x 10° Sequence Length (n) FIGURE 5 ONE IDS MODEL COMPARED TO MCE AND ONE RA HYPOTHESIS We now examine the case in which g{z,n) retains a sequence length dependency. To define g(z,n), we transform the coordinates similar to what was done in the RA derivation. Let Az = Z - Zo Az, = Zar-~ Zo Az, = Z-Z, = Az- Az, , 16 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
ee Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 where za is the mean of a “shifted” Gaussian z-score distribution having a standard deviation of o. Zo is the mean of the “unshifted” z-score distribution (i.e., zo = 0.0). Using this transformation, g(z,n) becomes 2 ~0.5 (Aza) ) g@n) = e o(n) . Let zeta be given by _ AZa g = o Then Equation (6) becomes oO 2 f -0.5 C2 In |z| = J — In(of + 6)e dg, (7) ~0 where } AZs _ cs = Oo ; (09 7 1) We notice that Equation (7) is of the same form as Equation (5)--including their relative n-dependencies. Thus, depending upon the parameters involved in both equations, we would be generally unable to differentiate between RA and n-dependent IDS. d. IDS vs. RA in Perspective The IDS model appears to have the sensitivity to differentiate between some causal processes and some informational ones. In particular, the most obvious causal model (i.e., the “interaction” is, on the average, the same for each bit in the sequence) contains vastly different predictions than the most obvious IDS model (i.e., sequence length independent of the z-scores). There is another class of RA models that have attracted attention: p= 0.5 (1+ a Tn) 17 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
a TT a Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 This particular form of [pa] leads directly to an n-independent zs= a. F(Aps) in Equation (5) becomes (Ap,) = [i-2]" mf This function is approximately equal to one for most actual cases (i.e., a = 0.1, n > 200). We note here, that it may be possible to separate even this RA model from IDS at small sequence lengths. Thus, Equation (5) reduces to a sequence length independent constant and is equivalent to the IDS case of g(z,n) = y(z) in Equation (6). Therefore, RA (of this form) and IDS are indistinguishable. All other forms of n-dependent RA contain a strong n-dependence in the zs term. To separate RA from IDS in this case, requires a different approach altogether. Suppose that RNG experiments continue to produce significant results that appear to be independent of all known physical parameters (e.g., source type--B decay, noise, distance, shielding, etc.). Further, suppose that PRNG experiments continue to produce significant results. We would then argue that IDS is the preferred choice for a mechanism because: e Precognition can be shown to be true by separate and fundamentally different experiments. e IDS can be shown to be possible (PRNG results). Results of one of our RNG experiments’ suggest that human-mediated RA must be able to switch on and off within 1 ms. e There is no known force in nature that can interact equally with the weak nuclear and electromagnetic forces. To us, it would seem more parsimonious to assume that humans are more able to anticipate the unperturbed natural fluctuations of an RNG, rather than generate “forces” that must conform to such a set of attributes. 3. Analysis and Hypothesis Testing The first step, after data reduction, is to determine if we are justified in continuing with a linear analysis. To accomplish this, we calculate the linear correlation coefficient for all data points [In(n), In|Ap|], and determine if there is a significant correlation (i.e., [r] significantly different than 0.0) and, as a side interest, determine if the correlation is significantly different than expected [i.e., Equation (3)]. If the correlation is significantly different than 0.0, we fit the data with a straight line, where the intercept is determined at the average value of In(n) for the data set. By 18 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 transforming the data about the average value of In(n) slope and intercept hypotheses testing may be done separately. To determine if the observed line is significantly different from the MCE line, we use an ANOVA technique.!2 The F-ratios (from the ANOVA) for the two tests are given below. Let n, = In(s, ), where s, is the sequence length for the kth data point. Let nn be the average value for the n, over [m] data points, or 1 2 nm = = DD Mk k=1 The F-ratios are given by 2 = n2 —2 m(b-b) x ( OS De _ n-) F(slope) = Kel , dfl = 1; df2=(m-2) , A and 2 m (a-a’) F(intercept) = ———————— » dfl = 1; df2 = (n-2) , A where [a] and [b] are the intercept and slope for [m] data points, (In(n), In| A pl). [a’] and [b’] are the intercept (at n, =n) and slope for the MCE line. A is given by m 2 > 2m 2 m m m > M, +ma +b Sng + 2ab DNy- 2a DM, - 2b Dyn,My k=1 k=1 k=1 k=1 k=1 A = m —- 2 M,, is the observed value of In|Ap| for the kth data point. If the F-ratio for the slope is not significant, then we can conclude that the z-scores do not have a sequence length dependency. Furthermore, if the F-ratio for the intercept is significant, then there is evidence in support of the IDS model. Deviations from this scenario will be discussed below. 19 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
aa Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 B. The SRI Pseudorandom Data In 1985, Radin and May described a protocol that could be used as the pseudorandom portion of a comprehensive test of the IDS model, and presented pilot data that appeared to support the IDS model.'* We have modified and extended that original proposal to form the basis of our pseudorandom investigation. 1, Justification for a Pseudorandom Number Generator Experiment We have proposed an elaborate model (IDS) that predicts significantly different results from those expected from an RA interaction. As part of a systematic investigation of the validity of the model, we must determine if an IDS “interaction” can be demonstrated, in principle, under conditions that preclude RA. Because there has been no evidence to date to support the idea that computer hardware is susceptible to a putative RA interaction, we assume that a purely pseudorandom number generator (PRNG), which is seeded by a computer clock, constitutes an environment that precludes RA. 2. PRNG Experiment Description The primary concept for this experiment is to study |Ap| as a function of sequence length. To accomplish this, we design a PRNG experiment in which a single trial contains the following steps: Select a sequence length, [n], from a limited menu of lengths. Collect [n] bits from a PRNG that has been seeded from a computer system clock at the moment when the participant presses a button. e Calculate |Ap| and a z-score, and display the z-score to the participant. ® Store raw data for later analysis. Since the data will be analyzed in logarithmic form, we chose the following 10 sequence lengths because they are relatively evenly spaced when they are expressed as logarithms, and they did not allow Ap = 0.0 {i.e., the In|Ap| will remain finite): 101, 201, 5014, 10014, 3501, 7001, 10001, 35001, 70001, and 100001. While we may have wanted to explore larger sequence lengths than 100001, we were limited by the speed of the Sun Microsystems Model 3/160-C computer, and by human factor considerations. The delay between a button press and the display of the result was approximately 1.5 seconds. This 20 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
ee ea meri eee eererienerrnrmamea nme aaaaaaaaa asia Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 delay was constant in spite of the actual sequence length chosen, in order for a given trial to maintain a double blind condition with respect to sequence length. At the first trial of a series, the above sequences were placed in a random order and stored as part of a data file. The sequence length, for a given trial, was taken in order from this randomized list. Thus, the sequence length ordering was repeated every 10 trials. The PRNG that was used (a Kendel shift register feedback algorithm) has been studied extensively theoretically by Lewis'4 and experimentally by May.’ This algorithm meets the accepted tests for “randomness” ‘8 and was checked further in this particular experiment using control trials. The low order 15 bits from the system clock were used as seeds for the PRNG. For each trial, the seed, sequence length, number of ones in the sequence, z-score, time (to the nearest second), and date of the trial were stored as part of a data file for later analysis. C. The Princeton Engineering Anomalies Research Data* In order not to “pre-select” data to support (or not) a given hypothesis, we obtained all of the data to date (September 1986) from one of PEAR’s best RNG participants. A subset of these data has been reported previously, but we have a complete set as of this writing.1° A single trial was defined as a continuous collection of binary bits from a “true” RNG (i.e., the sequence was derived from the noise associated with a back-biased PN junction). For the data under study there were two trial lengths, 200 bits and 2000 bits. To avoid problems of a possible single dimensional “bias” in the hardware, a target bit was toggled at a rate such that each new bit from the generator was compared to the one’s (1s) complement of the previous target bit. Data were collected in two fundamental modes, manual and automatic. In the manual mode, a single button press resulted in a single trial. During the automatic mode, a single button press resulted in 50 consecutive trials. In the IDS formalism, is the total number of bits (sequence length) resulting in a single button press is the independent variable. Thus, there are only 4 allowed values for this independent variable: 200, 2000,, 10000, and 100000. We wish to express our appreciation to R. Jahn, R. Nelson and B. Dunne for providing access to their data and for their assistance in transferring it to our computer system. 21 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
‘OOOO Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 For all sequence lengths, data were collected as a function of “aim” © PK+ —- The participant attempts to force matches between the target bit and “response” bit from the generator. e PK- — The participant attempts to force mismatches between target bit and “response” bit from the generator. e BL -- The participant makes no attempt to modify the bits from the generator. These data are referred to by PEAR as the baseline data. The order of “aim” was determined in two modes: the volitional modes, in which the participant chooses the order of the triad, and the random mode, in which an RNG determines the order of the triad. Table 2 shows the data files that were used for the IDS analysis. A few of the button presses produced results in which the number of matches between target and response bits was exactly equal to one-half of the sequence length. Those special cases are ignored in our analysis to avoid computing logarithms of zero. The analysis will focus upon the specific aim regardless of volitional/random control of the aim. 22 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 Table 2 RAW DATA FOR OPERATOR 10--PRINCETON UNIVERSITY DATA Sequence Button Number Data Set | Length | | Presses z= | 20 | 200 332 All PK + 2000 286 (A_PK) 10000 1000000 200 All PK + 2000 (PK+) 10000 1000000 200 All PK - (PK-) 2000 10000 Volitional PK + (V_PK+) 1000000 200 2000 10000 1000000 200 Volitional PK — 2000 (V_PK-) 10000 1000000 200 Random PKt 2000 (R_PK+) 10000 7 1000000 200 2000 10000 1000000 200 Random PK- (R_PK-) All Baseline 2000 (BL) 10000 1000000 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 IV RESULTS AND DISCUSSION The results of the SRI pseudorandom number generator experiment and the PEAR experiment will be discussed separately here and then compared in Chapter V, Conclusions and Recommendations. A. The PRNG Experiment Results In 1985, Radin and May reported pilot results for two participants (I.D. 105 and I.D. 531) who were selected on the basis of past successful performances in similar tasks.1 For example, Participant 531 was the most significant contributor in our 1979 RNG experiment. In the 1985 pilot experiment, Participants 531 and 105 contributed 500 and 298 trials, respectively. The analysis showed that neither of the participants produced sequence length dependencies different from MCE (i.e., a slope of -0.5). However, the analysis revealed that both individuals showed independently significant evidence for IDS (i.e., the intercepts were significantly above MCE at the p < 0.005 level for each participant). Thus, our tentative conclusion from these data is that IDS appears possible, at least with these two participants. During the FY 1986 program, we conducted the experiment in two phases: a screening and an experiment phase. For the pilot phases, we asked 20 individuals to contribute 100 trials each under the protocol described above. All but 4 of them completed this task. For availability reasons, the 4 participants contributed varying numbers of trials (less than 100). We had decided to select 7 individuals from within the pilot group to participate in a formal PRNG IDS experiment. The criterion for being included in the formal group was that the participant had to produce a significant increase above MCE of the variance of the z-score distribution over 100 trials (the MCE variance = 1.0). Of the 16 participants who finished the 100 trial series, only one, 531, met the above requirement (variance = 1.37, p < 0.008). The second best performer, however, produced a variance = 1.21 (p < 0.07). Judging from the 1984 study, we would not expect to see a significant intercept with only 100 trials, and none were observed. While it is particularly interesting that Participant 531 maintains his/her consistent performance, we felt that we should continue the pilot screening until we are able to select 7 24 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 significant participants. Thus, at this point, we do not have any results to report for the formal experiment. B. The PEAR Results 1, Data Reduction and Analysis Using the exact binomial procedure described in Chapter III, we have computed the MCE lines for the various data sets (see Table 3). These MCE lines were used as the basis for the IDS analysis that follows. Table 3 MCE FOR EACH DATA SET Data Set Pe [ome [ome [me [une [are [we [ior Intercept Intercept (X=0) | 0) Intercept (X-bar) Slope X-bar We have analyzed the PEAR data from a “top down” perspective (i.e., beginning with the most combined data and ending with the most condition specific data). The first requirement from Chapter III is that we must determine if a linear analysis is appropriate for these data. For all data sets, the linear coefficient was strongly significant (i.e., r * -0.6 for all data sets) when compared to r= 0. Therefore, we are justified in continuing with the IDS analysis. Table 4 shows the results of this analysis (the data set abbreviations are taken from Table 2). 25 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 Table 4 RESULTS OF THE ANALYSIS OF THE PEAR RNG DATA Data Set Variable ree ee [om oe oe a Pies | 804 | nies | ise | sri | ono | ane | aos | aoe | Mean-Z 0.018 Z-Variance 1.036 p-of-Variance 0.038 Stouffer's Z -1.031 1.946 | -3.367 -0.613 1.516 -0.821 1.219 |-4,124 p-of-Z 0.849 0.026 3.80(-4)} 0.730 0.065 0.794 0.038 1.86(-5) MCE X-bar Intercept Slope DATA Intercept Slope F-Intercept 0.06 0.32 0.03 0.21 0.42 0.01 0.01 0.024 p-value 0.784 0.570 0.859 0.649 0.626 0.920 0.913 0.878 F-Slope 2.01 2.09 12.20 0.26 0.24 4.16 2.20 8.34 p-value 0.155 0.148 4.79(-4)] 0.609 0.626 0.041 0.137 0.004 The meaning of MCE and data variables and their associated F-ratios have been discussed in Chapter III. The z-score variables are included here in order to make comparisons with the results published previously for part of these data.!2 The mean z-score is the average value of the z-scores for all the trials shown in each column; the MCE is zero. The variance for the z-score is calculated for the same data; the MCE is one. Note that both of these quantities were noé calculated as absolute values, The Stouffer’s z-score is the proper way of combining all the z-scores to test against the MCE hypothesis: no psychoenergetic functioning. The Stouffer’s z-score is given by'® where [n] is the number of trials. 26 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 2. Discussion of the PEAR Results We note that our analysis is consistent with PEAR’s in that the difference between the PK+ data (the participant was tasked to force more matches than MCE regardless of the origin of the “aim” request——volitional or random) and PK- data as measured by the Stouffer’s z-score (zaitf = 3.75, p < 8.6 x 10-%) is highly significant. While the magnitude of the effect is small, it is, nonetheless, persistent and statistically robust. We are able to discuss the IDS analysis for all of the PEAR data by examining the most deviant (from MCE) data set. Figure 6 shows the best fit line for the PK- data compared to its MCE. We plot this particular data set because it produced the most significant deviation (p < 4.79 x 10-4) from the MCE slope. While the slope of the data line is significantly more positive than the MCE slope, the intercept is not significantly different. In effect, the data line has rotated about its intercept point (i.e., sequence length = 1470). Yet, the Stouffer’s z-score indicates strong evidence for some form of psychoenergetic functioning (p < 3.8 x 10-4) for this data set. The resolution of this apparent inconsistency involves understanding a fundamental, and unfortunate, problem with these PEAR data in general. Their data were not collected to provide a specific test of our IDS model. Thus, the sequence lengths that were chosen and, more importantly, the number of trials collected at each sequence length, were not optimized for our test. In the extreme, if all the data were collected at a single sequence length, our IDS analysis is completely inappropriate (i.e., the IDS formalism requires testing as a function of sequence length). To first order, these PEAR data suffer from the same problem. Sixty-five percent of the total data shown in Figure 6 were collected at a single sequence length (i-e., 2000). When we examine these data at each sequence length, we find that the Stouffer’s z-scores are -2.98, -0.80, -2.87, and —2.69 for sequence lengths of 200, 2000, 10000, and 100000, respectively. Thus, most of the data for this set are not significant, even though when they are combined across sequence lengths, they are highly significant. The situation described above is similar for all data sets; none of the data sets produced significant intercepts. Because the data were not collected uniformly as a function of sequence length, it is difficult to interpret the results of analysis. We feel that it is premature to speculate upon forms of either RA or IDS models that can fit these data. a7 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3
Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3 3x10- 1x10° [Ap| 3x10" 1x10 PK- 4 4 p(slope) < 4.8 x 10 3 x10 2.5 x 10 2.0 x 10° 8.0 x 10° Sequence Length (n) FIGURE 6 MCE AND PK- DATA 28 Approved For Release 2000/08/07 : CIA-RDP96-00787R000500140001-3