cpenannon Enironooe ar Urtrane JargebPrenety CIA-RDP96-00789RO0Z2N2RDOPM 1994 or understanding these sources, interpreting the results from process-oriented research is problemati- cal, if not impossible. . Transmission Detector AC Response Figure 1. Information-transfer Metaphor For example, suppose that the quality of an AC 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 register 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 of AC 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 high jumpers; 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 2002/05/17 : CIA-RDP96-00789R003200240001-0 3
a TLC - CIA- - -0 CPYStairifion Eiopy ag an intinsle Target Proporty yee eo BORO 0 320073 008 Koril 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 j such property, then all process-oriented AC 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 i 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 AC 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 analysis techniques. Shannon Entropy: A Short Description Building upon the pioneering work of Leo Szilard (1925, 1929), Shannon and Weaver (1949) developed what is now called information theory. This theory formalizes the intuitive idea of information that there is more “information” in rare events, such as winning the lottery, than in common ones, such as taking a breath. Shannon defined the entropy for a given system as the logarithmic average of the prob- ability of occurrence of all possible events in the system. Entropy, used in this sense, is defined as a measure of our uncertainty, or lack of information, about a system. Suppose, for example, we had an octagonal fair die (i.c., each of the eight sides is equally likely to come up). Applying Equation 1, below, to this system gives an entropy of three bits, which is in fact the maximum possible for this system. If, on the other hand, the die were completely biased so that the same side always came up, the entropy would be zero. In other words, if each outcome is equally Jikely then each event-has the maximum surprise. Conversely, there is no surprise if the same side always lands facing up. In the case of images, a similar analysis can be used to calculate the entropy. For simplicity, consider a i black and white image in which the brightness, or luminance, of each picture element, or pixel, is mea- sured on a scale from zero to 255, that is, with an eight bit binary number. Equation 1 can again be used to arrive at a measure of the picture’s entropy. As with the other sensory systems were gradients are more easily detected, we shall show that the gradient of Shannon’s entropy is correlated with AC perfor- mance far better than the entropy itself. In other sensory systems, receptor cells are sensitive to incident energy regardless of “meaning”, which is ascribed as a later cognitive function. Shannon entropy is also devoid of meaning. The pixel analysis ignores anything to do with cognitive features. From this point of view, a photograph of a nuclear blast is, perhaps, no more Shannon-entropic than a photograph of a kitten; it all depends on the intensities, which were used to create the photographs. Approved For Release 2002/05/17 : CIA-RDP96-00789R003200240001-0 4
cpy Shannon Entfipy ayant FetrifiekeTscga0BReErty : CIA-RDP96-00789R03 bo AOE D 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: Me Se = — >, Pmal0BPms)s (1) m= where px is the probability of finding image intensity m of color k. Ina 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, Ny is 255 (i.e., 28—1) for each k,k =7, 8 5. For color, k, the total change of the entropy in differential form is given by: ds, = |VS, + do] + Har (2) 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, py. In Lantz, Luke, and May’s 1993 experiment, the targets were all video clips that met the following criteria: @ Topic 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 segment was digitized (i.e., 640 x 480 pixels) for eight bits of information of red, green, and blue intensity. The video image conformed to the NTSC standard aspect ratio of 4x3, so we arbitrarily assumed an area (i.e., macro-pixel) of 16 x12 = 192 pix- els from which we calculated the pm. 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 20x 15 cm 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 pr, For this macro-pixel size, the target frame was divided into a 40x40 array. The entropy for the (jy th macro-pixel was computed as: N-1 Si; =n > Pm login ), m=O where Pp iS computed empirically only from the pixels in the (i, /) 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. Approved For Release 2002/05/17 : CIA-RDP96-00789R003200240001-0 5
op ‘Shannon Enrapyane dif tntdRediza Bar QOO2TOpPFTY: CIA-RDP96-00789R0032002F000RH 1994 is 3 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.¢., no intensity of green in this case). Ina similar fashion, the Sj are calculated for the entire scene. Since: and j range from zero to 40, each frame contains a total of 1,600 macro-pixels. Probability Dens! Intensity (m) Figure 3. Green Intensity Distribution for the City Target (Macro-pixel 3,3). 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. Approved For Release 2002/05/17 : CIA-RDP96-00789R003200240001-0 6
Wel lod Shannon EntrOppeavarliintidkiciesgetO2NEMW7 - CIA-RDP9G-00FEORBGSYEOSAANNT- 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, Sis we Bi) = Sit + 49 — SiO (3) ot At At , where At is one over the digitizing frame rate. We can see immediately that the dynamic targets will have a larger 4S 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 for AC; however, this procedure does not usually indicate the absolute quality of the AC. 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. Table 1 shows the rating scale that we used to assess the quality of the AC responses, regardless of their rank. To apply this subjective scale to an AC 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. Table 1. 0-7 Point Assessment Scale Description incorrect information incorrect information. some incorrect information. Ta | Soferegonicasninsoeninschascenesnemsie | incorrect information. receiver has made contact with the site. 2 | Sane Semen aE ESS SIPONASIS expectation. ttle correspondence. Approved For Release 2002/05/17 : CIA-RDP96-00789R003200240001-0 7