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Geometrica, the Technology and Methodology


Geometrica was invented in the Information Age for the Information Age. It satisfies all the criteria for information display and visualization. We have to contend with information overload, safety issues, Human Machine Interface (HMI) and it's design issues that stem from the Stone- and Industrial ages.Current state of the art Instruments unchanged from the industrial age. When most of the HMI's in existence today were designed, there was a poor understanding of the issues involved. Even today we battle with newly designed HMI's that are not optimally designed or implemented. Menu driven designs are a prime example of how easily the HMI can be badly designed. Having a button associated with a specific function is easy to learn and remember. Having multiple functions assigned to a button is often confusing and lead to errors. These problems are well documented but unfortunately the solutions are seldom adhered to. Geometrica was designed by going back to fundamentals of vision science and cognition and the way they relate to HMI and computing. A study was made of the human eye, the way it works, how humans see and process information to form a mental picture in our brain. This mental picture is crucial in our "sensemaking" process.

If we understand how the brain makes sense and understands information it will help us to design better interfaces. Most of what we as humans experience is perceived visually. This means that we can focus on this channel to make huge improvements to our designs. Lets take a closer look.

Understanding Vision

When we look at something, like reading this information for example, we use a specific part of our eye, namely the central high resolution part. This part is referred to as the Focal channel. It is a small cone shaped section covering roughly 25 degrees of our vision. The rest is referred to as the ambient channel, closely related to peripheral vision and covering about 180 degrees (TURANO, Herdman and Danelie 1993; Simoneau, Leiboitz, Ulbrecht, Tyrell and Cavanagh 1992). The successful concurrent performance of walking using peripheral vision and reading street addresses using focal vision is normal, since they tap different visual resources. Focal vision is directed towards the street names and numbers while peripheral vision properly monitors the peripheral flow field. We keep on walking without a problem, even while we are looking at street names and numbers.

From this it is clear that we have two channels between our eyes and brain and that the information flows via these channels in a parallel mode, i.e. simultaneously. The dual channels are merged seamlessly in the brain and it looks like we see only one picture. In part this fact is to blame for the instrument designs we must read today. All current instruments require us to look directly at them and use the high fidelity focal channel to read numbers and descriptions. We need to do this to allow our brain to form the mental picture and interpret and make sense of what we see.

When we consider human evolution we find that the natural selection processes favored the skills of the surviving humans. Eon's ago humans survived by spotting prey and avoiding predators. This depended mostly on vision, which gave us the ability to distinguish between the shapes of the predators and prey. Especially in the predator case, if you failed to recognize the shape quickly and avoid the predator, you were killed. The surviving humans, us in other words, are the best shape processors. This illustrates why we as humans are so good at shape recognition. This process in the brain happens very fast and with high fidelity.

Traditionally it was thought that peripheral vision was not of a high enough resolution to be used as an acquiring channel. This was due to the fact that the data researchers fed this channel information, as required by the focal channel, at the same resolution as the focal channel. If we manipulate the data and send the same data displayed in a different format via the peripheral vision channel, we find it is as fast and accurate as reading with the focal channel. The key to this is to leverage the brain's shape processing powers.

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