“The design challenge is to use each kind of design device to its greatest advantage in providing efficient access to visual queries”.

The efficiency of visual queries can be enhanced by exploiting the easily apprehended patterns. The book chapter gives enough details for understanding how brain interprets pattern and objects (what pathway) from basic features. The main challenge for a designer is now to effectively assign the semantic meanings to these patterns pertaining to its problem domain.

It also seems that what makes “patterns” distinct from “features” is that patterns are invariant to some types of warping, and to a lesser extent (if you ask me) that the underlying features are interchangeable. (e.g. generalized contours.)

The discussion of silhouetting brought about another interesting point. A lot of current visualization literature focuses on clutter reduction: essentially removing extraneous data from a visualization. At the basis of this theory lies the idea that simpler is better. The idea that an object is most recognizable by it’s silhouette seems to provide a perceptual argument to the theory of simplicity, at least in part due to the fact that a silhouette is such an extreme abstraction from the potential complexity of the surface it represents. I am curious, however, how it is the concave features that make the silhouette most distinguishable.

This idea of silhouetting and simplicity appears to draw on the principles behind FACS and Chernoff Faces. Combining this theory on simplicity with the strength of innate human facial recognition appears to further the idea of the strongest perceptual techniques are those which will present the user with the simplest image.

On a slightly divergent note, I found it interesting that Ware discusses contour, lighting and texturing as three basic perceptual depth cues. Although I’d seen lighting in such a context before, texturing and contour are far less obvious choices. However, Ware brings up interesting examples to which both apply and make me wonder if the illusion of depth in 2D images is really possible without at least some pairing amongst these properties.

**Comments with respect to Ch 7 of Information Visualization (my best guess at the overlapping data)

For more complex patterns, what pathway is used from V1 to IT. As the processing moves up to IT cortex, individual difference becomes large. Other than finding contours and regions, we perceive a pattern by grouping objects by spatial proximity. When the pattern is complex, we see them in multiple chunks, therefore we do a series of fixations.

Overall, when designing, we can implement connecting contour, enclosing contour, common color region, proximity grouping, alignment, common texture region, common movement to make a relationship between graphical entities. We can also synchronize meanings (semantics) and graphical code.

I really like the 2.5 D space discription of the visual property of human eye. There are difinitely large difference between the “away” (in our domain, we call it ‘longitudinal’ dimension or the ‘z’ direction) dimension and the “up-down” (tranverse in our domain) and “side-way” (lateral) (or X-Y plane) dimensions. However, I think the ability of our perception for the “away” dimension is not as bad as the author declared. A good experiment might be put 3 identical simple objects (circular or rectangle boards) in different positions along the “away” dimension, and let our visual system determine the relative positions. One might argue that our visual system might determine the position by the size of the boards in the x-y plane, but we can modify the experiment as we can adjust the size of the boards so they seems the same size for the viewer even at different positions. If we can still tell the correct position relationship, our sight in the ‘z’ direction might not be so bad.

I really like that the author gives some suggestions and methods in design for proper pattern finding, especially the line graph examples. The way to use uncertainty as the shadows surrounding the center line is a very good way to show the difference and relationship between the two sets of data. However, I’m a little bit confused by how they construct the graph. The pattern of the uncertainty is obviously not identical to the weather tempareture data, so what did they do to generate the fourth graph in the bottom left figure?

Since we have so much more information in the up-down and sideways dimensions, our brains have grown to interpret certain types of visual discontinuities as depth cues. If it weren’t for this, we probably would not be able to perceive things being “3D” when drawn or photographed.

He also talks about how there are different types of patterns that are easily recognized, and as the complexity goes up, the region of the brain that does the recognition moves from V1 to V2 and V4, IT. Also, we are able to recognize patterns at several different scales.

Another point is that it is important to use patterns that have semantic meanings that relate to our actual experience of the world. There is a nice list of some such semantics at the end of the chapter.

One of the ideas in this chapter is about the patterns for design. Since most designs are mixed by learned symbolic meanings, apprehended patterns can make a design visually efficient. Two types of patterns are discussed, graphic ones and relationship-defining ones. I do not quite understand why graphic patterns are believed to be better in expressing abstract ideas, since it seems to me that relationship-defining patterns can be sometimes more clear.

I like the examples about Line Graph, which expresses trend, comparison and uncertainty in a simple and understandable way. My first glance at the figures match the queries listed below and I believe such technique can be used to present data analysis with certain emphasis.

Ware starts off by noting that we see the world mostly in 2.5D instead of 3D. Image plane sampling is orders of magnitude more efficient than depth sampling. This stunted view of depth sampling is what merits less than one D. The ‘what’ pathway identifies the object. ‘Binding’ makes disconnected pieces of information into a connected whole. I was a bit disappointed that Ware didn’t provide a more extended treatment of depth. My intuitive sense is that depth would be very important for our survival because of the stereoscopic quality that it would bring to our vision.

The generalized contour mechanism enables us to identify discontinuity in edges. This is a very efficient mechanism, explaining why it is that we are able to recognize shapes represented by line drawings so effectively.

Orientation, size, contrast and color are the properties that allow us to discriminate between textures.

Motion patterns are just as important as static patterns, as they tell us about the layout of the objects in space.

Patterns discerned in V1 and V2 are common to most everyone, while patterns discerned higher up (V4 and IT) are more likely to be learned.

When a pattern can be recognized in a single, apprehendable chunk, it can be recognized easily. However, when it has to be broken up into multiple, smaller chunks, then each chunk has to be recognized and kept in visual working memory.

Our ability to recognize patterns in spite of distortions is fundamental to visual intelligence.

Spatial metaphors are fundamental to the way language works.