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Cognitive Clusters and Attractors in Remote Viewing: a Pilot Study Lian Sidorov, Julie Reeung, Dan Bailey, John Cook, Linda Lazarus, Gerry Zeitlin, Bill Stroud
Abstract The present study was an attempt by a group of non-professional remote viewers to answer two questions about the nature of RV information: 1. Do specific target characteristics (such as motion, emotional load, energy, repetitive patterns, etc.) statistically correlate with an increase in the amount of correct RV data? 2. Are there any correlations between certain classes of remote viewing perceptions, regardless of target or viewer characteristics? A pool of double targets was pre-assembled in sealed envelopes, each consisting of two unrelated pictures that were as similar to each other as possible, except for a single, isolated aspect - the experimental variable (for this pilot study, the experimental variable was Motion, with randomly interspersed targets from other category pools in order to neutralize viewer expectations). The viewers, who used individual RV methods, were blind to the nature of the targets or the variable. Once thirty sessions were collected in the Motion category, each of the two pictures in a target pair was scored against the session data, using a modified Buchanan perceptual grid. One important feature of this experimental design was that the viewer’s quality or performance during any given task affected both scores simultaneously – providing a highly desirable self-calibration mechanism for evaluation purposes. On the other hand, for our second hypothesis the nature of individual targets was not taken into account: the question being asked referred to potential correlations across viewers and across sessions, therefore each pair of images was treated as a single, complex target. Using MS Excel, t-scores were calculated for each of the 23 perceptual categories, as well as for the sum-totals of kinesthetic impressions, 1st and 2nd order line angles, total line angles and visual and 3rd order total scores. No significant scores were obtained at a probability level of 0.1 or higher, which means that the presence of motion at the target did not appear to generate an increased amount of correct remote viewing information in any of the perceptual categories that we tested for. However, 30 strong and very strong correlations (p<0.01 and p<0.005) were found between various perceptual categories, with only 2 out of the total of 231 expected to occur by chance at this significance level. Visual and conceptual data formed large correlation basins, as expected based on normal cognitive processes and logical inferences. Most interestingly, though, we also found many strong correlations between perceptual categories which would not typically be bound by known physiological pathways. This unexpected result raises the possibility that there may be anatomically identifiable RV-interface areas in the brain that would account for these particular correlation basin configurations - points that may represent a seldom-looked for intersection of, say, smell-sound-temperature processing pathways and which may have common physiological characteristics which allow them to act as RV data transducers. Given the small size of our experiment, it is impossible to tell whether these intriguing results represent a promising lead or a mere artifact. Nevertheless, in our opinion they warrant the effort of a replication on a larger scale which, should it confirm these conclusions, would cast new light on the nature of RV information.
Physical universes are user interfaces for minds. Just as the virtual worlds experienced in VR arcades are interfaces that allow the arcade user to interact effectively with an unseen world of computers and software, so also the physical world one experiences daily is a species-specific user interface that allows one to survive while interacting with a world of which one may be substantially ignorant. Hoffman’s Second Law*
I. INTRODUCTION
Over three decades have passed since the term remote viewing, meaning "the acquisition and description, by mental means, of information blocked from ordinary perception by distance, shielding or time” has entered modern consciousness. Like practically every other branch of parapsychology, the phenomenon has met with considerable resistance, passed through numerous proof-of existence experiments, engendered a plethora of speculative explanations, then settled in a scientific limbo where those who believe in its validity continue to focus on demonstrations and practical applications, while those who dismiss it a priori as scientific heresy see no reason to learn more about the mechanism of such "coincidences". However, the modern roots of remote viewing - Rene Warcollier's experiments in group telepathy at the beginning of the 20th century - point to a far more exciting time in the history of this young science: a time when questions were asked about the nature of the information being transmitted between target and recipient, and the ways in which our memory, analytical blueprints and personal interactions were capable of modifying this information. The aim of the present study (a very modest effort by a small group of remote viewers), is simply to get back to Warcollier’s phenomenological way of thinking about this intriguing phenomenon. Our purpose is not to prove that correct data can be obtained about a target under double-blind conditions, but to look at the type of data being produced, and see whether the target's intrinsic nature affects the amount and quality of information being produced, and whether any patterns can be observed in the resulting information matrix. In doing so, we hope to contribute to a small but important pool of empirical knowledge about RV - such as the observation that highly energetic targets, especially ones involving nuclear processes, are “seldom missed” [1] and Ed May’s formal studies [2,3] showing that the amount of data produced increases with a target’s Shannon entropy. These are questions with obvious navigational significance for a viewer: for example, what if we could formally prove that certain target features are universally more “loaded”, regardless of the viewer’s background (say – that an older structure, or a rapidly moving object, or a repetitive pattern, are more readily perceived than other aspects)? What if we could find certain target characteristics for which the accuracy can be shown to exceed expectations? If we knew that such aspects were part of the known set about an operational target, we could then task things accordingly – perhaps starting with these target aspects, or assigning better probabilities to the data which relates to these elements. Furthermore, knowledge about such universal cognitive attractors might assist us in navigating the temporal and geographic basin of a given target – where the well-known slide toward features or events of greater interest would be tempered by an awareness of these potential magnets. Finally, such observations could cast some additional light on the fundamental mystery of remote viewing: what is it about the composition and structure of a target that makes it “knowable”, that allows us to perceive its various elements in the order in which we perceive them, at the angles from which we perceive them – in other words, what is the natural vocabulary and syntax of remote viewing information, before it is translated into our awareness?
II. HYPOTHESES
There were two questions we tried to address in this study: 1. Do certain target characteristics statistically correlate with an increase in the amount of correct RV data? If so, does the increase affect specific perceptual modalities (i.e. shapes, textures, movement, conceptual data)? For the first phase of our study, we chose Motion as the target variable - therefore our first hypotheses could be stated as follows: "If all else is equal, targets which possess a higher degree of perceivable motion will yield more correct information under remote viewing conditions than control targets." Note that the first caveats of our experiment are the terms "being equal" and "perceivable motion": first, there is no absolute way to ensure that the experimental and control targets are perfectly matched for everything except the motion variable - although every effort has been made to choose targets that are of similar emotional impact, chromatic variation and size. Secondly, one must recognize that motion is intrinsic to every material particle in the universe - hence no target could be considered "static". However, RV data is generally reported from the perspective of a human being observing the target at human resolution (unless otherwise cued), so "perceived motion" is simply the motion that would be reported, should a person be present at the target.
2. The other question we tried to answer was whether there were any correlations between RV perceptual modalities in terms of the amount of correct information produced by different viewers. Our second hypothesis could therefore be stated as: “A statistically significant correlation exists between certain classes of remote viewing perceptions, regardless of target or viewer characteristics.” Note that for our second hypothesis, the nature of individual targets is not taken into account: the question being asked refers to potential correlations across viewers and across sessions, therefore each pair of images is treated as a single, complex target.
III. METHODOLOGY
For the purpose of this study, we decided to base our perceptual category definitions on Lyn Buchanan’s CRV scoring matrix [see Buchanan 2003, p 284; also see Frequently Asked Questions on the CRV website at www.crviewer.com ]. However, it must be recognized from the beginning that these terms (both as originally defined by Buchanan and in our interpretation) are mere approximations: while they make a reasonable effort to circumscribe various perceptual and conceptual classes, there is a considerable amount of potential overlap and ambiguity which, from the beginning, compromise the significance of any statistical result we may obtain. Our warning to the reader is therefore to treat this study with a critical eye and realize that our statistical approach is only a road sign pointing toward possible conclusions about the nature of RV information. The perceptual matrix is as follows:
SCORING CATEGORY DEFINITIONS AND EXAMPLES (modified from Buchanan list and regrouped into perceptual classes)
* Perceptual classes: 1st order perceptions: no reference to other elements in the target or to associative cognitive categories 2nd order perceptions: involve comparison of two target percepts 3rd order perceptions: involve reference to abstract, associative cognitive categories
I. VISUAL DATA A. LINE-ANGLE PERCEPTIONS 1st order perceptions: Visual Patterns Descriptors that refer to a visual arrangement which repeats. E.g.: dotted, laced, weaved, striped, alternating, repeating, crisscrossing, blotchy, spotted Structures Terms or descriptors relating to the structural makeup of something. E.g.: frameworked, propped up, webbed, honeycombed, strutted. Shapes Descriptors of basic shape and topological aspects. E.g.: flat, round, triangular, terraced, concave, convex, pointed, irregular, hexagonal, tubular, stick-like, box-like, spherical, rectangular
2-order perceptions Alignment Spatial relationships between two (or more) things or directional descriptors related to static objects. E.g.: arrayed (one after another), aligned (with each other), stacked, intertwined, arranged ('in a row', 'in a circle', 'in a square', etc.), perpendicular to something else. Positions Descriptors that relate to the way something is physically configured (relative to background) E.g.: sitting, squatting, laying, upside-down, right-side up, standing, arched, crooked
B. COLORS AND BRIGHTNESS 1st order perceptions Colors Color descriptors. E.g.: blue, white, multicolored, monochromatic 2nd order perceptions Luminance (*typically is relative to background) Descriptors relating to levels of brightness. E.g.: bright, weakly (lit), glowing, brilliant, dull, dark, sunny, blinding, moonlit, pitch black (dark). _____________________________________________________________________ II. KINESTHETIC/ MOTION 1st order perceptions *Motion/Vibration unaccompanied by perceived sound Descriptors which relate to the type or quality of motion E.g.: rotating, twirling, twisting, undulating, tunneling, rocking, slowing, accelerating, burst of…
2nd order perceptions Directions Directional descriptors related to motion. E.g.: forward, up, upward, random, clockwise, circular, northerly, outwardly, left, right, front, below *Subjective Sense of Balance (typically relates self to motion or orientation in the surroundings) E.g.: feeling dizzy, vertigo, sense of tipping over
________________________________________________________________________ III. OTHER
Other 1st order perceptions Smells General and specific smell descriptors. E.g.: vanilla, pungent, pleasant, fragrant, chemical Taste General and specific taste descriptors. E.g.: tart, bitter, sweet, sour, unpleasant, chemical, artificial, vanilla, dirt taste Sounds General and specific sound descriptors. E.g.: buzzing, swooshing sound, loud, cacophonous, musical, ringing, clicking, rumbling, people sounds, bird sounds, raspy, high pitched *Temperature (typically these are reported relative to normal body temperature; in some situations a relative temperature may be obtained, as when the viewer “moves” from a very cold to a less cold aspect of the target) General and specific temperature descriptors. E.g.: hot, cold, cool, warm, burning hot, ice cold, freezing, body temperature *Textures (can be tactile or visual) Descriptors relating to the physical and visual surface quality of something. E.g.: rough, smooth, bumpy, papery, silken, gravelly, gritty, wet, dry, shiny
Other 2nd order perceptions * “SCALE” (Size, Speed, Weight, Rate of Change) Non-quantitative or comparative descriptors relating to physical size, weight, speed or rate of change (descriptors involving specific quantities are to be listed under Measures – i.e. 100 lb, 15 degrees, 2 meters); also temporal distribution of event(s) E.g.: larger, smaller, slender, massive, thin, huge, tiny, heavy, light, weightless, faster, occasional *Density (typically combination of visual and proprioception: must compare mass and volume, or shape and resistance to motion) Descriptors dealing with the distribution of mass throughout the volume of something. E.g.: dense, sparse, crowded, empty, hollow, viscous, feathery, solid, porous ________________________________________________________________________ IV. EMOTIONS
Descriptors relating to mental states and mental processes. E.g.: sad, joyous, angry, awe-struck, confused, tongue-tied, analytical, sleeping, unaware, sickened, focused, painful, laughing, suffering, reflecting, thinking, concentrating, remembering. ________________________________________________________________________ V. 3rd ORDER PERCEPTIONS: ABSTRACT/ GENERAL ASSOCIATION CATEGORIES
Composition Descriptors dealing with the material composition of something. E.g.: metallic, plastic, gel-like, liquid, stone, natural, artificial, fibrous. Ambience General “feel” or “mood” descriptors associated with a person, place or thing. E.g.: indoors, outdoors, old, new, spacious, peaceful, tense, regal, ancient, sterile, sacred, somber, celebratory, nurturing, vast, sparse, dense, roomy, hallowed, hushed, daunting, heaviness, European feel. *Conceptuals/Purposes Terms describing the overall nature, purpose, function, etc. of persons, places or things. E.g.: cooperation, control, deception, religious, political, industrial, transport, weaponry, communication, transportation, religious, attacking, comfort, recreational. Also, specific identification of target elements (noun, gestalts) E.g.: disk, building, vehicle, frame, monument, ocean, valley, rock, mountain, tool, weapon. *Measures and Numbers Terms identifying numbers or quantities. E.g.: few, many, multiple, singular, dozen, 163, 4/11/03. *Relationships Terms or descriptors which relate to logical or conceptual relationships between two or more things (compares two or more elements via reference to abstract, non-physical category) E.g.: happier, less potent, [parent/child], [cause/effect] ________________________________________________________________________ Notes:
1. Patterns in physical descriptors are to be listed under the appropriate “sense” (visual, auditory, temperature, motion, etc) as they merely represent ways in which something is perceived, not a separate perception. Patterns in conceptual and emotional data are also to be listed under these respective categories. 2. Energy: where type specified (i.e. heat, motion, brightness), they are listed under that category; if only a “sense of energy” is perceived, it is to be listed as a gestalt 3. Gestalts should be listed under Conceptuals: i.e. water, man-made, natural, energy, organic, etc; ____________________________________________________________________________________________ Note : items with * are slightly different from, or absent in the CRV original matrix.
General approach A pool of double targets was assembled, each consisting of two unrelated pictures that were as similar to each other as possible, except for a single, isolated aspect - the experimental variable. For example, these could represent two fields - one empty, one filled with soldiers engaged in battle; or two crowds - one marching peacefully, one engaged in riots; or two ships - one sailing uneventfully, the other under construction; or two planets - one much larger than the other; etc. For each double target, picture A was designated as the one in which the specific variable was represented by a greater absolute value (i.e. greater size, temperature, emotional impact) or as the one in which the experimental variable was consistently isolated (i.e. repetitive patterns): this was to ensure uniformity in the statistical analysis of these effects. Examples of possible variables: 1. high vs. low movement
To avoid additional biasing influences, these double targets were not loaded with any specific tasking questions, but assembled ahead of time under neutral conditions, enclosed in sealed, numbered envelopes and assigned to one global pool, from which daily targets were chosen blindly by the experiment coordinator. (Under ideal circumstances, this pool would be quite extensive and analysis of the results will not be carried out until all the targets in the pool have been exhausted and all the sessions collected. A separate record should be kept of the particular feature isolated for each target, but not consulted on a routine basis. Unless this precaution is taken, there is a possibility that, by knowing the class a given target belongs to, the person posting the target might insert his own expectations into the outcome of the group's sessions. There is also a possibility that the viewers themselves might identify a pattern in the type of targets they receive and develop undesirable expectations about the nature of future targets. Target pools should therefore consist of an assortment of several experimental samples - that is, several groups of variables - from which daily assignments are chosen at random.) Unfortunately, due to time and subject limitations, our pool consisted of only two types of target variables, one of which was Motion. The envelopes were assigned a specific mm/dd/yy coordinate which was posted on our website together with the date when the feedback would become available. If no sessions were submitted by the deadline, the target was re-posted with a different deadline – therefore ensuring that the requisite number of sessions would eventually be reached. (Note: due to practical considerations, there was no attempt made to obtain an equal number of sessions from each viewer - however this, too, is a departure from ideal experimental conditions.) A sample size of 30 sessions was chosen for this test, consisting of sessions submitted by 5 volunteers using their own remote viewing methods. (One important feature of this experimental design is that the viewer’s quality or performance during any given task affects both A and B scores simultaneously – which provides a highly desirable self-calibration mechanism for evaluation purposes.) Once all the targets in our pool were exhausted, the sessions were separated according to their experimental variable, then each data point provided by the viewers was scored and assigned to one of 4 categories: A. relevant only to picture A. B. relevant only to picture B C. relevant to either the A or B components of the double-target D. not relevant according to available information (incorrect data). A score difference M can be thus calculated for each session as (A-B) and the mean of M over the entire sample of 30 sessions can be designated as [M]. Using a Student t table, we can then test a number of hypotheses with variable degrees of confidence. 1. [M] = 0 (there is no statistically significant difference between the number of correct perceptions relevant to targets of type A versus targets of types B: the experimental variable does not represent an attractor) 2. [M]>0 (targets of type A are more likely to be correctly perceived by the viewer) 3. [M]<0 (targets of type A are less likely to be correctly perceived by the viewer) A similar process can be followed by calculating (A-B) scores across all sessions for each individual target category in order to determine whether particular RV modalities (i.e. shapes, textures, motion), are enhanced in targets with specific features (i.e. Motion). Our scoring sheets looked as follows:
*********************************************************************** DTPI SCORING SHEET
CATEGORY 1ST ORDER 2ND ORDER 3RD ORDER I A. VISUAL LINE-ANGLES Visual Patterns __________ Structures __________ Shapes __________ Alignment __________ Positions __________
I B. COLORS AND BRIGHTNESS Colors __________ Luminance __________
Visual Total ____________________________________________________
II. KINESTHETIC/ MOTION Motion/Vibration ___________ Motion Direction ___________ Subjective Sense of Balance ___________
Kinesthetic Total _________________________________________________
III. OTHER
SMELLS ___________ TASTE ___________ SOUNDS ___________ TEMPERATURE ___________ TEXTURES ___________ “SCALE” (Size, Weight, Speed, Rate of Change) __________ Density __________ _________________________________________________________________ IV. EMOTIONS ___________ _________________________________________________________________ V. 3rd ORDER PERCEPTIONS: ABSTRACT ASSOCIATION CATEGORIES
Composition _________ Ambience _________ Conceptuals/ Purposes/Object ID _________ Measures and Numbers _________ Relationships _________
3rd Order Total _____________________________________________________
********************************************************************
Two scores were compiled for each of the 30 submitted sessions: an M-score, tabulating the difference in correct perceptions (by category) between A and B; and a C-score, calculating how many correct perceptions (by category) were reported that were relevant to either A or B.
Example: “there is a manmade which is octagonal, red, metallic, and has an educational purpose” (assume picture A shows a Stop sign, while B shows a red apple)
Perception Category Y for A Y for B M=Y(A) – Y(B) C=Y for A or B
manmade______conceptual___1_________0_________1_____________1_______ octagonal _______shape______1_________0_________1_____________1_______ red_____________color______1_________1_________0_____________1_______ metallic_______composition__ 1_________0_________1_____________1_______ educational _____purpose ____1 ________ 0_________1 ____________ 1_______
where Y for A = percept is correct for image A; value can be 0 or 1 Y for B= percept is correct for image B; value can be 0 or 1 C= percept is correct for A or B; entered as 1 if correct for either or both, or 0 if correct for none M= difference between first and second column: M can be 1, 0 or -1
Example of M-SCORE SHEET [M=Y(A) – Y(B)] for above session
CATEGORY 1ST ORDER 2ND ORDER 3RD ORDER I A. VISUAL LINE-ANGLES Visual Patterns __________ Structures __________ Shapes ____1______ Alignment __________ Positions __________
I B. COLORS AND BRIGHTNESS Colors ____0______ Luminance __________
Visual Total ____________________________________________________
II. KINESTHETIC/ MOTION Motion/Vibration ___________ Motion Direction ___________ Subjective Sense of Balance ___________
Kinesthetic Total _________________________________________________
III. OTHER
SMELLS ___________ TASTE ___________ SOUNDS ___________ TEMPERATURE ___________ TEXTURES ___________ “SCALE” (Size, Weight, Speed, Rate of Change) __________ Density __________ _________________________________________________________________ IV. EMOTIONS ___________ _________________________________________________________________ V. 3rd ORDER PERCEPTIONS: ABSTRACT ASSOCIATION CATEGORIES
Composition ___1_____ Ambience _________ Conceptuals/ Purposes/Object ID ____2____ Measures and Numbers _________ Relationships _________
3rd Order Total _______________________________________________3______
The C-score for this session would show a value of 1 for each of the five different perceptual categories. All unmarked categories were entered with an M (or C) value of 0 into the statistical analysis.
IV. RESULTS **
Hypothesis I
Using MS Excel, t-scores were calculated for each of the 23 perceptual categories, as well as for the sum-totals of kinesthetic impressions, 1st and 2nd order line angles, total line angles and visual and 3rd order total scores (see Table 1). No significant scores were obtained at a probability level of 0.1 or higher, which means that our first null hypothesis has to be accepted: that is, the presence of motion at the target does not appear to generate an increased amount of correct remote viewing information in any of the perceptual categories that we tested for.
Table 1
Number of correct RV perceptions and t-scores* for Motion target pool (over 30 sessions)
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___________________________________________ * t=(x-μ)/s*SQR(n) where x is the sample average, μ is 0 as per the null hypothesis, s is the sample’s standard deviation and n is 30.
Hypothesis II
The top categories as far as number of correct perceptions were, in decreasing order, Concepts, Textures, Colors, Shape, Motion, SCALE and Alignment. Making allowance for the atypical (but necessary) breadth of the Conceptual category, the predominant type of data coming through was clearly visual, and overwhelmingly 1st order. 30 strong and very strong correlations (p<0.01 and p<0.005) were found between various perceptual categories, with only 2 out of the total of 231 expected to occur by chance at this significance level (see Tables 2 and 3 below).
Table 2
Pearson Correlations Between Perceptual Category
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Structure & Alignment 0.658 Position 0.469
Shape Colors 0.569 SCALE 0.539
Alignment Position 0.446
Color Luminance 0.541 Texture 0.699 SCALE 0.433 Concepts 0.574
Luminance Taste 0.487 Sounds 0.683 Temps 0.650 Texture 0.578 Emotions 0.561
Direction Ambience 0.473 Concepts 0.454
Smells Sounds 0.489 Temps 0.686
Taste Ambience 0.430
Sounds Temps 0.709 Textures 0.519
Temps Textures 0.607 Concepts 0.476
Textures Ambience 0.507 Concepts 0.636
SCALE Concepts 0.593
Density Emotions 0.477 Composition 0.461
Ambience Concepts 0.485 Measures Relations 0.522
______________________________________
Table 3
Statistically Significant Perceptual Correlation Basins (p <0.01)
___________________________________________________
Luminance Taste Sounds Temps Texture Emotions Color
Concepts Color Direction Temps Texture SCALE Ambience
Textures Ambience Concepts Temps Sounds Luminance Color
Color Shape Luminance Texture SCALE Concepts
Temps Textures Concepts Sounds Smells Luminance
Sounds Temps Textures Smells Luminance
Ambience Concepts Direction Taste Texture
SCALE Concepts Color Shape
Structure Alignment Position
Shape Colors SCALE
Direction Ambience Concepts
Smells Sounds Temps
Taste Ambience Luminance
Alignment Position Structure
Density Emotions Composition
Emotions Luminance Density
Composition Density
Measures Relations
_______________________________________________
V. DISCUSSION
The findings summarized above suggest several possibilities: 1. once target contact is established, certain perceptual categories are (either innately or by virtue of common training methods) more easily decoded by the viewer’s mind; given that approximately 80% of our normal perceptual input is visual, one could indeed expect a similar focus on RV data as the viewer attempts to decode the target 2. RV perceptions may occur in “cognitive clusters”, such that target contact simultaneously activates correlated processing modules. Unfortunately we have no current understanding of what “target contact” might consist of, nor of what levels of cognitive processing may be involved, beyond the obvious final step of cortical activation necessary for the recognition and linguistic expression of this data. Hopefully the identification of such correlations may begin to assist us in looking for earlier stages of cognitive processing.
As expected, the majority of correct perceptions was in the visual category (33%; if we include in this class perceptions with possible but not definite visual components, such as motion, direction, textures, scale, density, composition and measures, the total reaches 65%). Marked correlations were noted between target aspects like color, shape, luminance, texture, and relative size (SCALE) – which may indicate common perceptual processing pathways, as in the normal sensory processes, but may additionally reflect the relatively arbitrary nature of such categorical distinctions among visual data. Does RV visual information, like sensory visual information, come in one package simultaneously triggering color- and line specific neurons? Interestingly enough, the cumulative wisdom of over three decades of remote viewing appears to suggest that colors, shapes and textures perceived within one moment of target contact may not be all accurate but reflect some degree of fragmentation and “filling in the blanks” by our conscious brain; while colors, shapes and textures perceived while attempting to repeatedly focus on the same target aspect may in fact correspond to different target elements, at least for all but the most proficient viewers… For a relatively inexperienced subject, scanning the target is far from systematic, and perceptions typically occur in very basic, unimodal categories – that is, a patch of color, a sense of texture, a simple line angle (Warcollier 2001 p xxxvii, xl, 31; Warcollier 1927, pp 355-373; McMoneagle 1997, 2000; Targ & Katra; IEEE Symposia p 29, 46; McMoneagle 2002, p. 99, 191). To what can we then attribute such strong correlations between these categories of perception in our group of novice to intermediate viewers? The most likely explanation seems to be that visual elements of RV perception do indeed interface with our minds as an information cluster, even though the “syntax” of such a cluster may be scrambled in the process of translating it into conscious awareness. Conceptual data was also at the center of a large correlation basin, which seems to reflect the ability of viewers to correctly interpret a multitude of lower-order perceptions and correctly draw abstract, very specific conclusions about the target. More interesting, however, are a number of correlations which do not seem to make immediate sense – such as those between sounds and temperatures (r = 0.709); smells and temperatures (r = 0.686); luminance and sounds (r = 0.683); temperatures and textures (r = 0.607); sounds and textures (r = 0.519), etc. While some correlations (such as between colors and textures, or SCALE and concepts) lend themselves to certain logical arguments (see Appendix), others are utterly perplexing from the perspective of our normal sensory pathways.
What is the significance of these correlations? There are two possible explanations: 1. one type of perception is causally linked to the other; for example, various kinds of visual and other 1st or 2nd order perceptions are likely the source of most conceptual data in this study, in the same way they are for sensory pathways. These correlations are indeed most likely the product of analytical processing in our brains, an NOT a matter of RV perception. 2. both perception types in a correlation pair are initiated by the same root event, without being causally sequential. But what could account for these root events ? One possibility is that the RV information arrives at our cognitive interface in data bundles that are coding for the clusters of sensory perception identified in our correlation basins. These bundles act as a vocabulary of primary information codes, a basic spectrum of cognitive “features” characterizing all objects in the universe, and which may be a function of quantum-like building blocks coming together in various formations or the result of some other, yet unknown substrate and its recombinant laws. From the perspective of our normal sensory modality, it would appear that the characteristics of the target initially come through as “phonemes” into which several perceptual “letters” are folded. Alternatively, it could be that the information is always the same, available in its entirety at all times, and there is no “signal”, only target contact; but the way in which we view the target, the way in which we focus attention on it through different cues, determines what part of the overall information we are receptive to – what sector of our cognitive map is primed to interface with the RV information set. We can then talk about a correct syntax of RV cues – the proper way to focus on a target in order to filter in information of a particular nature… In such a case the existence of strong correlations suggests that there are sensitive points at the intersection of the various sensory processing pathways which are triggered by contact with the target – and therefore that it may be worth looking for such potential anatomical areas in future brain imaging studies. It could be that making target contact activates a bundle of correlated sensory pathways, but this physiological correlation lies outside the geometrical space of normal brain function (since temperature, sounds and smells are not typically thought of as sharing an afferent pathway). One could turn the problem on its head and say that perhaps the interface with our brain is at the top associative level – that target contact activates a certain cognitive map/association basin, and then the left brain automatically recognizes the main components of this map, such as temperature, sound and smell. Perhaps that is why ambience plays such an important role in CRV training – why one is taught to always practice recognizing the “feel” of a place, wherever one is. Thus one could suggest that the way in which we survey a target ends up activating different but overlapping high-level associative maps, so that we end up with both correct, often reinforced data, and incorrect associations which just happen to lie in that cognitive basin.
On the basis of the above discussion, we would like to suggest that: 1. There may be anatomically identifiable “apices”, or trigger areas in the brain, that would account for these particular correlation basin configurations (points that are at the intersection of, say, smell-sound-temperature normal processing pathways). To reiterate, while some of the 30 strongest correlations identified in this study make immediate sense due to their joint participation in major sensory processing modules (i.e. visual), others are distinctly odd – and these, in our opinion, are the ones that warrant further investigation. 2. These “apex points” may have common anatomical or physiological characteristics which account for their ability to act as RV data interfaces. Finally, there is of course another, more trivial but possible interpretation, given the very limited size of our subject pool – namely, that individuals who are better at perceiving one type of data (say sounds) are also better at perceiving the other modality of its correlation pair (i.e. temperatures). A very strong distortion of the data might be present in our results due to the small number of viewers we had available for this study. As mentioned earlier, our experiment can only be treated as a starting point and none of its results should be taken at face value unless replicated by a considerably larger study. Are these correlations merely an artifact of this particular experiment? Clearly, the first step we have to take is to replicate this pilot study with different types of targets and an increased number of subjects and sessions. Should the correlations hold in subsequent studies, the next challenge will be to identify the basic phonemes of this extrasensory vocabulary on the basis of correlation basins and other observations.
CONCLUSION
In a recent survey published by the New York Times (January 4, 2005) Donald Hoffman, a cognitive scientist at the University of California-Irvine, said the following: “I believe that consciousness and its contents are all that exists. Space-time, matter and fields never were the fundamental denizens of the universe but have always been, from their beginning, among the humbler contents of consciousness, dependent on it for their very being. The world of our daily experience - the world of tables, chairs, stars and people, with their attendant shapes, smells, feels and sounds - is a species-specific user interface to a realm far more complex, a realm whose essential character is conscious. It is unlikely that the contents of our interface in any way resemble that realm. Indeed the usefulness of an interface requires, in general, that they do not. For the point of an interface, such as the Windows interface on a computer, is simplification and ease of use. We click icons because this is quicker and less prone to error than editing megabytes of software or toggling voltages in circuits. Evolutionary pressures dictate that our species-specific interface, this world of our daily experience, should itself be a radical simplification, selected not for the exhaustive depiction of truth but for the mutable pragmatics of survival. If this is right, if consciousness is fundamental, then we should not be surprised that, despite centuries of effort by the most brilliant of minds, there is as yet no physicalist theory of consciousness, no theory that explains how mindless matter or energy or fields could be, or cause, conscious experience.”
How can we make sense of the very strong correlations appearing in this study between seemingly unrelated perceptual categories, such as sounds and temperatures, or luminance and sound? If RV information were a physical quantity, we should perhaps conclude that these stimuli share some commonality in their energy frequency distribution or other parameters of their physical interface with our physiology… But information is the great Unknown in the fabric of reality: all we can say about it at the present time is that it appears to play a crucial but less-than-well-understood role at the smallest physical scale (see quantum uncertainty principle) and that it also seems to transcend “the arrow of time and causality”, at least as evidenced by a number of parapsychology experiments (McMoneagle 2002 p. 257; Radin p.103). Information may prove to be an emergent property of physical energy and its organization, or a co-existing fundamental substrate of nature – but irrespective of the choice, there is no denying that information is a characteristic of everything in existence or conceivable. It is therefore natural to ask – are there no basic laws for the hierarchical organization of information in the universe, as there are for the organization of matter? Are there no rules of natural affinity and exclusion, no forms toward which information evolves as a result of first principles, no “syntax”? And if such forms exist, transparent as they may be behind the façade of material organization, then could they account for the particular way in which information manifests to a remote viewer? In other words – could the study of remote viewing sessions, which deals exclusively in information, teach us something about the invisible information endoskeleton which lies within every physical manifestation in the universe?
** Note: for target pool, raw data and statistical analysis spreadsheet, please contact Lian Sidorov at lian@emergentmind.org
REFERENCES
* as quoted in The Edge (www.edge.org)
1. The New Explorers: Joseph McMoneagle on the Stargate Program, the
Science Behind Remote Viewing and the Need for Coordinated Research 2. May E., Spottiswoode J., James C. (1994). Shannon entropy as an intrinsic target property: toward a reductionistic model of anomalous cognition. www.jsasoc.com/docs/entropy.pdf
3. May EC and Spottiswoode SJS (1998) The Correlation of the Gradient of Shannon Entropy and Anomalous Cognition: Towards an AC Sensory System. See Online Proceedings of the Parapsychological Association, 1998 at http://www.parapsych.org/pa_convention_proceedings.html. Full paper available
4. [2] Buchanan, Lyn (2003) The Seventh Sense. Paraview, 191 Seventh Avenue, New York, NY 10011
IEEE Symposia on the Nature of Extrasensory Perception, Tart, Puthoff and Targ Eds. Hampton Roads Publishing, Charlottesville, VA 2002 McMoneagle, Joseph (1997). Mind Trek: exploring consciousness, time and space through remote viewing. Hampton Roads Publishing, Charlottesville, VA 1997 McMoneagle, Joseph (2000). Remote Viewing Secrets: A Handbook. Hampton Roads Publishing, Charlottesville, VA 2000 McMoneagle, Joseph (2002) The Stargate chronicles: memoirs of a psychic spy. Hampton Roads Publishing, Charlottesville, VA 2002 Radin, D. (1997) The conscious universe. HarperCollins Publishers, NY, 1997 Targ, R. and Katra, J. (1998) What we know about remote viewing. (from Miracles of Mind). International Remote Viewing Association Archives. URL: http://www.irva.org/papers/doc-whatweknow.shtml Warcollier, R. (2001) Mind to Mind. Russell Targ Editions, Hampton Roads Publishing, Charlottesville, VA 2001 Warcollier, R (1927) Ce qui se transmet. Revue Metapsychique, 5, pp 355-373, Septembre-Octobre 1927, Alcan. Ed. URL: http://auriol.free.fr/parapsychologie/Warcollier/Warcollier27RM5.htm
Appendix
Top 30 correlations (in decreasing order):
1. r = 0.709 sounds – temps 2. r = 0.699 color – texture: texture may be cognitively derived from subtle variations in color shades (hence behaving as a 2nd order perception) but in that case we must question why there are more correct perceptions in the texture than in the color category 3. r = 0.686 smells – temps 4. r = 0.683 luminance – sounds 5. r = 0.658 structure-alignment: alignment of various elements may lead to conclusions about the structure that is being perceived 6. r = 0.650 luminance – temps: it is possible that both categories are a function of perceived target energy 7. r = 0.636 textures – concepts: very likely texture is a major factor in the identification of a target element’s nature or purpose 8. r = 0.607 temps – textures 9. r = 0.593 SCALE – concepts: a target element’s relative size is very probably one of the key filters in identifying its nature or purpose 10. r = 0.578 luminance – texture: as with color, texture is probably identified at least in part on the basis of subtle variations in an element’s luminance, or brightness 11. r = 0.574 color – concepts: as in 7. above – though it’s interesting to note that texture appears to be more strongly correlated with concepts than color – perhaps a more powerful determinant 12. r = 0.569 shape – colors: shapes may be perceived as simple patches of color 13. r = 0.561 luminance – emotions 14. r = 0.541 color- luminance 15. r = 0.539 shape-SCALE: in normal sensory mode relative size perception would require awareness of an object’s borders, therefore its shape 16. r = 0.522 measures- relations: logical relations often involve a comparison of measured quantities 17. r = 0.519 sounds- textures 18. r = 0.507 texture – ambience: ambience may sometimes be derived on the basis of textures 19. r = 0.489 smells – sounds 20. r = 0.487 luminance – taste 21. r = 0.485 ambience – concepts: both are derived “conclusions”, with ambience carrying a greater emotional charge 22. r = 0.477 density – emotions 23. r = 0.476 temperature – concepts: as in 7, 11 24. r = 0.473 direction – ambience 25. r = 0.469 structure – position: structure is often perceived against a background and the target’s orientation relative to this background tends to feed into logical conclusions regarding the nature of the perceived structure 26. r = 0.461 density – composition: composition is likely derived in part on the basis of perceived density 27. r = 0.454 direction – concepts: as in 7, 11, 23 28. r = 0.446 alignment – position: similar to 25 29. r = 0.433 color – SCALE: SCALE assessment requires target elements to differentiate themselves and that is commonly in terms of color variation 30. r = 0.430 taste – ambiance
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