Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment
2. Using the 3M-VAS Software
3. Neurological Background
4. Materials and Methods
- Areas of Interest. These can be specified by the user, and each of them has a numeric score which is the probability that a person will look somewhere within that area during the pre-attentive period. We did not use this feature.
- Heatmap. This is a color-coded probability map that a certain part of the image will attract the gaze during the pre-attentive period. We used this feature in all the scans, adopting it as the most direct and useful diagnostic tool for our analysis.
- Hotspots. A simplified version of the heatmap results shows only the areas that are most likely to be seen during the pre-attentive scan, with a numeric score indicating the probability that a person will look somewhere in that region during the pre-attentive period.
- Gaze Sequence. This indicates the four most likely gaze locations, in their most probable viewing order.
- Visual Features. This visualization gives an insight to how the algorithm works, by extracting those same features that drive pre-attentive processing in our visual system ; namely edges, intensity, red/green color contrast, blue/yellow color contrast, and faces. We used this feature only in the first scan, for demonstration purposes.
- Framing: Adding a surrounding frame, when the building of interest occupies most of the photographic field.
- Brightness/contrast/saturation: Adjusting these parameters, either overall or locally, influences the way that the software registers the examined structures.
- Distance from the building: Examining whether approaching a structure reveals more details, resulting in sustained coherence, or if the coherence disintegrates.
- A high degree of organized complexity, defined through nested symmetries, engages the viewer in pre-attentive, unconscious interest.
- Visual engagement distributes uniformly throughout a complex, highly ordered composition, with no gaps and few hotspots in the heatmap.
- Hotspots in a successful composition’s heatmap coincide with points of functional interest such as the main entry, or prominent windows and other central features.
- An unsuccessful (disengaging) composition will show hotspots in irrelevant places such as the building’s corner or edge, or away from the building altogether.
- The way that engagement depends upon the distance of approach is correlated to organized complexity. The most successful examples show fractal scaling, i.e., organized complexity at every magnification, and thus retain engagement in a scale-free manner.
- Plainness and monotonous repetition fail to engage the viewer, resulting in a heatmap with large empty areas.
- A non-trivial structure that lacks fractality results in a fragmented heatmap with gaps, which loses the viewer’s attention.
- The software used here is sensitive to artifacts that might confuse the architectural results. Our suggestions for circumventing those are discussed in the next section.
7. Methodological Considerations for Optimizing the Results from 3M-VAS Scans
- Clouds: In Figure 1 and Figure 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.
- 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 (Figure 1 and Figure 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 . 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.
- 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 statues 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.
- 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.
- People: People are always looking for people, and this is illustrated in Figure 1, Figure 2, Figure 7, Figure 8, Figure 9 and Figure 14. This attention bias prioritizes persons in a scan. While possibly interesting for some analyses, it is worth keeping in mind if one is interested in recording pre-attentive reactions to a building without any hotspots specifically related to human presence.
- Distance: This paper drew attention to the effect of distance, and how, when there is enough complexity, moving closer just reveals more of that complexity. A comprehensive analysis of a single building should ideally use a sequence of image scans taken at different approaches. In buildings that do not have enough organized complexity, then the closer you are, the less coherently they will register. The corollary is: the more organized complexity the building possesses, the more difficult it is to see it break down at close distances.
8. Christopher Alexander’s “Field of Centers”
9. Coherence, Disconnection, and Threat
Conflicts of Interest
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Lavdas, A.A.; Salingaros, N.A.; Sussman, A. Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment. Appl. Sci. 2021, 11, 6197. https://doi.org/10.3390/app11136197
Lavdas AA, Salingaros NA, Sussman A. Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment. Applied Sciences. 2021; 11(13):6197. https://doi.org/10.3390/app11136197Chicago/Turabian Style
Lavdas, Alexandros A., Nikos A. Salingaros, and Ann Sussman. 2021. "Visual Attention Software: A New Tool for Understanding the “Subliminal” Experience of the Built Environment" Applied Sciences 11, no. 13: 6197. https://doi.org/10.3390/app11136197