Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2.22 April 1994 Shannon Entropy as an Intrinsic Target Property: Toward a Reductionist Model of Anomalous Cognition by Edwin C. May, Ph.D. S. James P. Spottiswoode (Consultant) and Christine L. James Science Applications International Corporation Cognitive Sciences Laboratory Menlo Park, CA Abstract We propose that the average total change of Shannon's entropy is a candidate for an intrinsic target property. We analyze the results of two lengthy experiments that were conducted from 1992 through 1993 and find a significant correlation (Spearman's Lo = 0.337, df = 31, t = 1.99, p :!~~ 0.028) with an absolute measure of the quality of the anomalous cognition. The 1993 result replicated the similar find- ing from the 1992 study. We describe the methodology, the calculations, and correlations in detail and provide guidelines for those who may wish to conduct similar studies. In addition, we provide circum- stantial evidence which leads us toward a reductionist view of anomalous cognition. Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 1 Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an intrinsic Target Property V2. 22 April 1994 IntrodUction The psychophysical properties of the five known senses are well known (Reichert, 1992). At the "front end," they share similar properties. For example, each system possesses receptor cells that convert some form of energy (e.g., photons for the visual system, sound waves for the audio system) into electro- chemical signals. The transfer functions are sigmoid; that is, there is a threshold for physical excitation, a Uear region, and a saturation level above which more input produces that same output. How these psychophysical reactions translate to sensational experience is not well understood, but all the systems do possess an awareness threshold similar to the subliminal threshold for the visual system. Since all the known senses appear to share these common properties, it is reasonable to expect that if anomalous cognition (AQ* is mediated through some additional "sensory" system, then it, too, should share similar properties. For example, a putative A C sensory system should possess receptor cells that have a sigmoidal transfer function and exhibit threshold and saturation phenomena. As far as we know, there are no candidate neurons in the peripheral systems whose functions are currently not understood. So, if receptor cells exist, it is likely that they will be found in the central nervous system. Since 1989, our laboratory has been conducting a search for such receptor sites (May, Luke, Trask, and Frivold, 1990); that activity continues, There is a second way in which receptor-like behavior might be seen in lieu of a neurophysiology study. If either an energy carrier for AC or something that correlated with it were known, then it might be possible to infer sigmoidal functioning at the behavioral level as opposed to the cellular level. Suppose we could identify an intrinsic target property that correlated with AC behavior. Then, by manipulating this variable, we might expect to see a threshold at low magnitudes and saturation at high magnitudes. Tb construct such an experiment, it is mandatory that we eliminate, as much as possible, all extraneous sources of variance and adopt an absolute measure for theA C behavior (Lantz, Luke, and May, 1994). We can redude onc source of variance by considering what constitutes a good target in an AC experi- ment. Delatioy (1988) reported on a survey of the literature for successful AC experiments and catego- rized the target material according to perceptual, psychological and physical characteristics. Except for trends related to dynamic, multi-sensory targets, she was unable to observe systematic correlations of A C quality with her target categories. Watt (1988) examined the target question from a theoretical perspective. She concluded that the "best" AC targets are those that are meaningful, have emotional impact, and contain human interest. Those targets that have physical features that stand out from their backgrounds or contain movement, novelty, and incongruity are also good targets. In trying to understand these findings and develop a methodology for target selection for process-ori- ented research, we have constructed a metaphor. Figure 1 shows three conceptual domains that con- tribute to the variability inAC experiments. The engineering metaphor of source, transmission, and detector allows us to assign known contributors to the variance of specific domains. Without controlling The Cognitive Sciences Laboratory has adopted the term anomalous mentalphenomena instead of the more widely knownpsi. Likewise, we use the terms anomalous cognifion and anomalousperturbation for ESP and PK, respectively. We have done so because we bel ieve that these te rms, are more naturally descriptive of the obse rvables and are neutral in that they do not imply mechanisms. These new terms will be used throughout this paper. Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 2 Approved For Release 2000/08/08: CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an intrinsic Target Property V2.22 April 1994 or understanding these sources, interpreting the results from process-oriented research is problemati- cal, if not impossible. Transmission Source Detector 0 U Figure 1. Information-transfer Metaphor For example, suppose that the quality of an A C response actually depended upon the physical size of a target, and that affectivity was also a contributing factor. That is, a large target that was emotionally appealing was reported more often more correctly. Obviously, both factors are important in optimizing the outcome; however, suppose we were studying the effect of target size alone. Then an "attractive" small target might r egister as well as a less attractive large target and the size dependency would be con- founded in unknown ways. Our metaphor allows us to assign variables, such as these, to specific elements. Clearly, an individual's psychological response to a target is not an intrinsic property of a target; rather, it is a property of the receiver. Likewise, size is a physical property of the target and is unrelated to the receiver. Generally, this metaphor allows us to lump together the psychology, personality, and physiology of the receiver and consider these important factors as contributors to a detector "efficiency." If it is true that an emotion- ally appealing target is easier to sense by some individuals, we can think of them as more efficient at those tasks. In the same way, all physical properties of a target are intrinsic to the target and do not depend on the detector efficiency. Perhaps, temporal and spatial distance between target and receiver are intrinsic to neither the target nor the receiver, but rather to the transmission mechanism, whatever that may be. More than just nomenclature, our metaphor can guide us in designing experiments to decrease certain variabilities in order to conduct meaningful process-oriented research. Some of the methodological improvements seem obvious. If the research objective is to understand the properties ofAC rather than understanding how an AC ability may be distributed in the population, then combining results across receivers should be done with great caution. To understand how to increase high jumping ability, for example, it makes no sense to use a random sample from the general population as highjumpers; rather, find a good high jumper and conduct vertical studies (no pun intended). So, too, is it true in the study of AC We can easily reduce the variance by asking given receivers to participate in a large number of trials and not combining their results. Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 3 Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an intrinsic Target Property V2. 22 April 1994 may, Spottiswoode, and James (1994) suggest that by limiting the number of cognitively differentiable elements within a target, the variance can also be decreased. A further reduction of potential variance can be realized if the target pool is such that a receiver's emotional/psychological response is likely to be more uniform across targets (i.e., reducing the detector variance as shown in Figure 1). Having selected experienced receivers and attended to these methodological considerations, we could then focus our attention on examining intrinsic target properties. If we are successful at identifying one such property, then all process-orientedAC research would be significantly improved because we would be able to-control a source of variance that is target specific. The remainder of the paper describes two lengthy studies that provide the experimental evidence to suggest that the average of the total change of Shannon's entropy is one such intrinsic target property. Approach The A C methodological details for the two experiments can be found in Lantz, Luke, and May (1994). In this section we focus on the target calculations and the anajysis techniques. Target Calculations Because of the analogy with other sensorial systems, we expected that the change of entropy would be more sensitive than would be the entropy alone. The target variable that we considered, therefore, was the average total change of entropy. In the case of image data, the entropy is defined as: Nk Sk Y_P.k1092(P.k)1 .-0 wherePmk is the probability of finding image intensity rn of color k. In a standard, digitized, true color image, each pixel (i.e., picture element) contains eight binary bits of red, green, and blue intensity, re- spectively. That is, Nk is 255 (i.e., 28 - 1) for each k, k = r, & b. For color, k, the total change of the entropy in differential form is given by: dSA = RS, I - 1r + as dt. (2) 1 Lal~tll We must specify the spatial and temporal resolution before we can compute the total change of entropy for a real image. Henceforth, we drop the color index, k, and assume that all quantities are computed for each color and then summed. To compute the entropy from Equation 1, we must specify empirically the intensity probabilities,p,,. In Lantz, Luke, and May's 1993 experiment, the targets were all video clips that met the following criteria: o Thpic; homogeneity. The photographs contained outdoor scenes of settlements (e.g., villages, towns, cities, etc.), water (e.g., coasts, rivers and streams, waterfalls, etc.), and topography (e.g., mountains, hills, desserts, etc.). ~ Size homogeneity. Target elements are all roughly the same size. That is, there are no size surprises such as an ant in one photograph and the moon in another. ~ Affectivity homogeneity. As much as possible, the targets included materials which invoke neutral affectivity. For static targets, a single characteristic frame from a video segmentwas digitized (i.e., 640 x480 pixels) for eight bits of information of red, green, and blue intensity. The video image conformed to the NTSC Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 4 Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2. 22 April 1994 standard aspect ratio of 4 x 3, so we arbitrarily assumed an area (i.e., macro-pixel) of 16 x 12 = 192 pix- els from which we calculated the p,,,. Since during the feedback phase of a trial the images were dis- played on a Sun Microsystems standard 19-inch color monitor, and since they occupied an area approxi- mately 20 x 15 cin square, the physical size of the macro-pixels was approximately 0.5 cm square. Since major cognitive elements were usually not smaller than this, this choice was reasonable-192 pixels were sufficient to provide a smooth estimate of the p,,,. For this macro-pixel size, the target frame was divided into a 40 x40 array. The entropy for the (4j)'th macro-pixel was computed as: N-1 Sij Z P. log2(p.)' M-0 wherep,,, is computed empirically only from the pixels in the (4 j) macro-pixel and m is the pixel intensi- ty. For example, consider the white square in the upper left portion of the target photograph shown in Figure 2. The green probability distribution for this macro-pixel (3,3) is shown in Figure 3. The probability densi- ty and the photograph itself indicate that most of the intensity in this macro-pixel is near zero (i.e., no intensityof green in this case). In a similar fashion, the Sij are calculated for the entire scene. Sincei and j range from zero to 40, each frame contains a total of 1,600 macro-pixels. Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 5 Figure 2. City with a Mosque Approved For Release 2000108/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2. 22 April 1994 We used a standard image processing algorithm to compute the 2-dimensional spatial gradient for each of the 1,600 macro-pixels. The first term in Equation 2 was approximated by its average value over the image. The total change of entropy for the dynamic targets was calculated in much the same way, The video segment was digitized at one frame per second. The spatial term of Equation 2 was computed exactly as it was for the static frames, The second term, however, was computed from differences between adja- cent, 1-second frames for each macro-pixel. Or, asij zsij(t) sjj(t + At) - Sjj(t) (3) at _1~ _t A t I I where At is one over the digitizing frame rate. We can see immediately that the dynamic targets will have a larger AS than do the static ones because Equation 3 is identically zero for all static targets. In Lantz, Luke, and May's 1992 experiment, the static targets were digitized from scanned photographs. This difference and its consequence will be discussed below. AC-Data Analysis Rank-order analysis in Lantz, Luke, and May's (1994) experiment demonstrated significant evidence forA C; however, this procedure does not usually indicate the absolute quality of theA C. For example, a response that is a near-perfect description of the target receives a rank of one. But a response which is barely matchable to the target may also receive a rank of one. Thble 1 shows the rating scale that we used to assess the quality of the A C responses, regardless of their rank. To apply this subjective scale to an A C trial, an analyst begins with a score of seven and determines if the description for that score is correct. If not, then the analyst tries a score of six and so on. In this way the scale is traversed from seven to zero until the score-description seems reasonable for the trial. Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-06 Figure3. Green Intensity Distribution for the City Urget (macro-pixel 3,3). Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2. 22 April 1994 Thble 1. 0-7 Point Assessment Scale ScoreDescription 7 Excellent correspondence, including good analytical detail, with essentially no incorrect information 6 Good correspondence with good analytical information and relatively little incorrect information. 5 Good correspondence with unambiguous unique matchable elements, but some incorrect information. 4 Good correspondence with several matchable elements intermixed with incorrect information. 3 Mixture of correct and incorrect elements, but enough of the former to indicate receiver has made contact with the site. 2 Some correct elements, but not sufficient to suggest results beyond chance expectation. Lit 1e correspondence. 0 No correspondence. Anomalous Cognition Experiment - 1992 In Lantz, Luke and May's 1992 experiment there were no significant interactions between target condi- tion (i.e., static vs dynamic) and sender condition (i.e., sender vs no sender); therefore, they combined the data for static targets regardless of the sender condition (i.e., 100 trials). The sum-of-ranks was 265 (i.e., exact sum-of-rank probability of p < 0.007, effect size = 0.248). The total sum-of-ranks for the dynamic targets was 300 (i.e., p < 0. 50, effect size = 0. 000). Entropy Analysis To examine the relationship of entropy to AC, two analysts independently rated all 100 trials (i.e., 20 each from five receivers) from the. static-target sessions using thepost hoc rating scale shown in Table 1. All differences of assignments were verbally resolved, thus the resulting scores represented a reason- able estimate of the visual quality of the A C for each trial. We had specified, in advance, for the correlation with the change of target entropy, we would only use the section of thepost hoc rating scale that represented definitive, albeit subjective,AC contact with the target (i.e., scores four through seven). Figure 4 shows a scatter diagram for thepost hoc rating and the associated AS for the 28 trials with static targets that met this requirement. Shown also is a linear least- squares fit to the data and a Spearman rank-order correlation coefficient Qo = 0.452, df = 26, t =2.58, p < 7.0 x 10-~. This strong correlation suggests that AS is an intrinsic property of a static target and that the quality of anAC response will be enhanced for targets with large AS. It is possible, however, that this correlation might be a result of AS and thepost hoc rating independently correlating with the targets' visual com- plexity. For example, an analyst is able to find more matching elements (i.e., a higherpost hoc rating) in a visually complex target than in a visually simple one. Similarly, AS may be larger for more complex Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 7 Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2. 22 April 1994 targets. If these hypotheses were true, the correlation shown in Figure 4 would not support the hypoth- esis that AS is an important intrinsic target property for successfulAC. 2.5 0.452 0 df = 26 2.0 t = 2 58 . p = 0.008 Cli 1.5 - 0 1.0 - U (1) 0.5 0. 0 4 5 6 7 a Rating Score Figure4. Correlation of Post Hoc Score with Static Target AS. Tb check the validity of the correlation, we used a definition of visual complexity, which was derived from a fuzzy set representation of the target pool. We had previously coded by consensus, 131 different potential target elements for their visual impact on each of the targets in the pool. We assumed that the sigma-count (i.e., the sum of the membership values over all 131 visual elements) for each target is pro- portional to its visual complexity. A description of the fuzzy set technique and a list of the target ele- ments may be found in May, Utts, Humphrey, Luke, Frivold, and Trask (1990). The Spearman rank correlation between target complexity andpost hoc rating was small (Lo = 0.041, df = 98, t =0.407, p < 0.342). On closer inspection this small correlation was not surprising. While it is true that an analyst will find more matchable elements in a complex target, so also are there many ele- ments that do not match. Since the rating scale (i.e., Table 1) is sensitive to correct and incorrect ele- ments, the analyst is not biased by visual complexity. Since the change of Shannon entropy is derived from the intensities of the three primary colors (i.e., Equation 1 on page 4) and is unrelated to meaning, which is inherent in the definition of visual com- plexity, we would not expect a correlation between AS and visual complexity. We confirmed this ex- pectation when we found a small correlation (Q = -0.028, df = 98, t =-0.277, p:!~~ 0.609). Visual complexity, therefore, cannot account for the correlation shown in Figure 4; thus, we are able to suggest that the quality of an AC response depends upon the spatial information (i.e., change of Shan- non entropy) in a static target. A single analyst scored the 100 responses from the dynamic targets using the post hoc scale in Table 1. Figure 5 shows the scatter diagram for the post hoc scores and the associated AS for the 24 trials with a Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 8 Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2.22 April 1994 score greater than three for the dynamic targets. We found a Spearman correlation of o 0. 055, df 22 (t 0. 258, p < 0. 3 99). 4 0 79 0 4) 2 - U Q) rA 3 4 5 6 7 8 Rating Score Figure5. Correlation of Post Hoc Score with Dynamic Target AS. This small correlation is not consistent with the result derived from the static targets; therefore, we ex- amined this case carefully. The total sum of ranks for the dynamic-target case was exactly mean chance expectation, which indicates that no AC was observed (Lantz, Luke, and May, 1994). May, Spotti- swoode, and James (1994) propose that the lackofACmight bebecause an imbalance of, what theycall, the target pool bandwidth. That is, the number of different cognitive elements in the dynamic pool far exceeded that in the static pool. This imbalance was corrected in the 1993 study and is analyzed below. Regardless, we would not expect to see a correlation if there is no evidence of AC. Anomalous Cognition Experiment - 1993 The details of the 1993 study may also be found in Lantz, Luke, and May (1994). In that study, they included a static vs dynamic target condition, and all trials were conducted without a sender. They changed the target pools so that their bandwidths were similar. They also included a variety of other methodological improvements, which are not apropos to this discussion. Lantz, Luke, and May selected a single frame from each dynamic target video clip, which was character- istic of the entire clip, to act as its static equivalent. The static and dynamic targets, therefore, were digitized with the same resolution and could be combined for the correlations. For each response, a single analyst conducted a blind ranking of five targets-the intended one and four decoys-in the usual way. Lantz, Luke, and May computed effect sizes in the same way as in the 1992 study. Three receivers individually participated in 10 trials for each target type and a fourth participated in 15 trials per target type. Lantz, Luke, and May reported a total average rank for the static targets of 2.22 Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 9 Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2.22 April 1994 for 90 trials for an effect size of 0.566 (p :!~~ Z5 x 10-5); the exact same effect size was reported for the dynamic targets. Entropy Analysis Differing from the 1992 experiment, an analyst, who was blind to the correct target choice used the scale, which is shown in Table 1, to assess each response to the same target pack that was used in the rank-order analysis. The average total change of Shannon's entropy (i.e., Equation 2) was calculated for each target as described above. Figure 6 shows the correlation ofthe blind rating score with this gradient. The squares and diamonds indicate the data for static and dynamic targets, respectively. 4 ......... . ..... 1 0.337 3 3 df = 31 1 0 3 = 1.9910 Dynamic t .991 J p 1 = 0.028 .028 0 ~ Combine :-:: C,mbined 0 0 _ U 0 Static ......... % . 5 6 7 a Rating L Score Fig ure 6. Correlations for Significant Receivers The key indicates the Spearman correlation for the static and dynamic targets combined. In addition, since the hypothesis was that anomalous cognition would correlate with the total change of the Shannon entropy, Figure 6 only shows the scores in the upper half of the scale in Table 1 for the 70 trials of the three independently significant receivers. The static target correlation was negative (,o -0.284, df 13, t = -1.07, p < 0.847) and the correlation from the dynamic targets was positive (,o 0.320, df = 16, t =1.35, p < 0.098). The strong correlation for the combined data arises primarily from the entropic difference between the static and dynamic targets. General Conclusions Tb understand the differences between the results in the two experiments, we re-digitized the static set of targets from the 1992 experiment with the same hardware and software that was used in the 1993 study. With this new entropy data, the correlation dropped from a significant 0.452 to 0.298 which is not significant (t = 1. 58, df = 26, p :!~~ 0. 063). Combining this data with the static results from the 1993 experiment (i.e., significant receivers) the static correlation waso = a 161, df = 41 (t = 1. 04, p:!E~ 0. 152). The correlation for the static targets from the 1992 experiment combined with the significant static and dynamic data from the 1993 experiment was significant (,o = 0.320, df = 59, t = 2.60, p:!~ 0.006). These post hoc results are shown in Figure 7. The combined data from the two experiments, including-all re- Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-qo Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2.22 April 1994 ceivers and all scores greater than four, give a significant correlation (Lo 0.258, df 64, 1 2.13, p < 0.018). 4 2 Dynamic 0 18 Combined CZ 2 0 0 0 4) 0 0 E3 0 Static U M 0 ..........L ..........L.......... L......... ......... 3 4 5 6 7 a Rating Score Figure 7. Correlations for Combined Experiments We conclude that the quality of A C appears to correlate linearly with the average total change of the Shannon entropy, which is an intrinsic target property. These two experiments may raise more questions than they answer. If our conservative approach, which assumes that AC functions similarly to the other sensorial systems, is correct, we would predict that theAC correlation with the frame entropy should be smaller than that for the average total change of the entropy. We computed the total frame entropy from thepj all of the 640 x 480 pixels. The result- ing correlation for the significant receivers in the 1993 experiment was Lo = 0.234, df = 31 (t = 1.34, p < 0.095). This correlation is considerably smaller than that from the gradient approach, however, not significantly so. We computed the average of the Sij for the 1,600 macro-pixels as a second way of mea- suring the spatial entropic variations. We found a significant Spearman's correlation of Lo = 0.423, df = 31 (t = 2 60, p:!E~ 0.007) for the significant receivers in the 1993 experiment. The difference between the correlation of the quality of theAC with the frame entropy and with either measure of the spatial gradi- ent is not significant; however, these large differences are suggestive of the behavior of other sensorial systems (i.e., an increased sensitivity with change of the input). We have quoted a number of different correlations under varying circumstances and have labeled these asposthoc. For example, hardware limitations in 1992 prevented us from combining those data with the data from 1993. Thus, we recalculated the entropies with the upgraded hardware in 1993 and recom- puted the correlations. Our primary conclusions, however, are drawn only from the static results from the 1992 experiment and the confirmation from the combined static and dynamic 1993 results. It is clear from our analysis that we may have identified an intrinsic target property that correlates with the quality of anomalous cognition. Our results suggest a host of new experiments and analyses before we can come to this conclusion with certainty. For example, suppose we construct a new target pool that is maximized for the gradient of Shannon's entropy yet meets reasonable criteria for the target pool Approved For Release 2000/08/08 : CIA-RDP96-00789ROO300049000619 Approved For Release 2000/08/08 : CIA-,RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2.22 April 1994 bandwidth. If the Shannon information is important, than we should see exceptionally strongAC. We also must improve the absolute measure ofAC. While dividing our zero-to-seven rating scale in two makes qualitative sense, it was an arbitrary decision. Rank order statistics are not as sensitive to cor- relations as are absolute measures (Lantz, Luke, and May, 1994); but, perhaps, if the AC effect size is significantly increased with a proper target pool, the rank-order correlations will be strong enough. It may be time consuming; however, it is also important to understand the dependency of the correlation on the digitizing resolution. In the first experiment, we digitized the hard copy photographs using a flatbed scanner with an internal resolution of 100 dots/inch and used 640 X 480 pixels for the static and dynamic targets in the second experiment. Why did the correlation drop for the static targets by nearly 35 percent when the digitizing resolution decreased by 20 percent? We noticed, post hoc, that the correlations exhibit large oscillations around zero below the cutoff score of four. If we assume there is a linear relationship betweenAC scores and the total change of Shannon entropy, we would expect unpredictable behavior for the correlation at low scores because they imply chance matches with the target and do not correlate with the entropy. Since we are suggesting a reductionist perspective, we speculate that the linear correlation suggests be- havioral, albeit circumstantial, evidence for receptor-like functioning for the detection ofAC. To deter- mine if this is true, we must identify threshold and saturation limits. It is absolutely critical to confirm our overall results and to provide answers to some of the enigmas from our experiment. If we have identified an intfinsic target property, then all of our research can precede more efficiently. Consider the possibilities if we were able to construct a target pool and eliminate a known source of variance. Psychological and physiological factors would be much easier to detect. Giv- en the availability of inexpensive video digitizing boards for personal computers, replication attempts are easily within the grasp of research groups with modest operating budgets. Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-~2 Approved For Release 2000/08/08 : CIA-RDP96-00789ROO3000490006-0 Shannon Entropy as an Intrinsic Target Property V2.22 April 1994 References Bern, D. J. and Honorton, C. (1994). Does psi exist? Replicable evidence for an anomalous process of information transfer. Psychological Bulletin. 115, No. 1, 4-18. Delanoy, D. L. (1988), Characteristics of successful free-response targets: Experimental findings and observations. Proceedings of Presented Papers, The Parapsychological Association 31st Annual Convention, Montreal, Canada, 230-246. Watt, C. (1988). Characteristics of successful free-response targets: Theoretical considerations. Proceedings of Presented Papers, The Parapsychological Association 31st Annual Convention, Montreal, Canada, 247-263. Lantz, N. D. and Luke, W L. W, and May, E. C. (1994). Thrget and sender dependencies in anomalous cognition experiments. Submitted for publication in the Journal of Parapsychology. May, E. C., Luke, W L. W, Trask, V V, and Frivold, T J. (1990). Observation of neuromagnetic fields in response to remote stimuli. Proceedings ofPresented Papers, The Parapsychological Association 33rd Annual Convention, National 4-H Center, Chevy Chase, MD, 168-185. May, E. C., Spottiswoode, S. J. P, and James, C. L. (1994). Managing the target pool bandwidth: Noise reduction in anomalous cognition experiments.Submitted for publication in the Journal of Parapsychology. May, E. C., Utts, J. M., Humphrey, B. S., Luke, W L. W, Frivold, T J., and'aask, V V (1990). Advances in remote-viewing analysis. Journal of ParapsycholpAy, 54, 193-228. Reichert, H. (1992). Introduction to Neurobiology, Oxford University Press, New York, NY. Approved For Release 2000/08/08 : CIA-RDP96-00789ROO300049000e--,O