## 7 Methodological Considerations for Optimizing the Results from 3M-VAS Scans  
Based on our experience learned from scanning experiments, we offer some hints below meant to aid researchers wishing to extend our own results. These are simple techniques for avoiding artifacts produced by the software, which could complicate the architectural interpretation of the resulting heatmaps. These suggestions do not speak against the 3M-VAS software, but are simply helpful aids to applying it in its current implementation. Perhaps some of these artifacts will be easier to control in future releases. Such complications do not appear in real eye-tracking studies. 1. **Clouds**: In Figures 1 and 15, clouds attract a certain amount of pre-attentive gaze because of their fractal outline. The cloud outline in an image is likely to divert pre-attentive gaze from the building of interest; this is in addition to reducing the contrast of the outline of a white or very light-colored building. Replacing the clouds with a homogeneous blue background sampled from nearby clear sky areas is the best way to avoid both of these issues (Figure 15B). Dispelling that this is in any way a limitation of the software, the photographer could wait until the clouds have cleared. Our suggestion is meant to facilitate scanning experiments with an existing picture. 2. **Regions in shadow**: The shaded area of the building in Figure 1 does not attract much attention, not because the building is lacking in detail, but because of the low contrasts created by those details, as visualized in the image under these lighting conditions. Shadows on any part of a building cause that area to be incorrectly scanned. This point is also illustrated in Figure 2: increasing the brightness and contrast of a wing that is in the shade makes its details more visible to the software, resulting in its inclusion in the heat map. (Or one could seek a picture taken at a different time of day when the region in question is not in shadow). These two images (Figures 1 and 2) illustrate an important point that should be kept in mind by an investigator using the software: the dynamic range of digital sensors, and also the dynamic range of film, in case of scanned film images, is lower than that of the human visual system, and only images acquired using the high dynamic range (HDR) method approach the eye's dynamic range [36]. In a real-life scene, the observer will have no difficulty looking at either shaded or illuminated parts of a building, perceptually counterbalancing the luminosity difference and perceiving the form as a whole. The software, on the other hand, sees an area of considerably lower intensity, and registers it as a dark area. Therefore, there should ideally be a bright, even illumination on an examined building, and when two or more buildings are compared in a scene, that they are all equally well illuminated. 3. **Contrast**: Contrast, either in intensity or color, is important for the software to correctly register the building's forms. This point partially overlaps with the shading issue, discussed above. Color saturation is another factor, which promotes clear distinction of forms of different colors. In Figure 14, increasing the saturation of a sharp but low-contrast image makes the states readily discernible. In Figure 15, removing the clouds and replacing them with a uniform blue background color, sampled from areas of blue sky, better reveals the outline of the white University of London tower. 4. **Framing**: If the building or structure of interest is tightly framed in the image, a new, monochrome frame could be added, preferably using a color sampled from the sky, to avoid peripheral parts of the building receiving less \"attention\" by the software (Figure 16). This intervention actually resembles the real-life situation in which we are viewing a building more faithfully, where it normally does not occupy all of our visual field. 5. **People**: People are always looking for people, and this is illustrated in Figures 1, 2, 7-9 and 14.