NTB: What are your goals for 2013, as far as these models are concerned?
Watson: The task-based performance model for electronic imaging systems is really my major goal right now. We’ve recently submitted one paper on the optical performance model that I described a moment ago. We’re in the process of completing a paper on the complete human pattern identification model, which will be completed quite soon, and then the third product will be an actual paper on the technological applications of this model to quantify the performance of imaging systems.
Again, trying to give you a sense of the effort that goes into developing these models, another feature of this identification model, beyond the optics, is processing in the human retina. So, one feature of the retina is that the density of neural cells declines as you go away from the point of fixation. We’re all familiar with the fact that an image gets blurry as you move away from the point where you’re looking. But that’s not an optical effect. That’s an effect of the neural machinery in the retina, and so we’re doing a very careful job of modeling that quantitative change in resolution as you go across the retina, and that’s part of what produces accurate performance for human observers.
I’ll give you one example: One set of data that we’re modeling is letter identification. Letters are a wonderful pattern stimulus for human experiments because people are extremely well practiced at identifying letters of the Roman alphabet. You don’t have to worry about training the observers. The data that we’re looking at are how much contrast you need — that is, how much difference there is between the white background and the black letter, let’s say, as a function of the size of the letter.
As you might imagine: In order to identify a letter when it’s very tiny, near your letter acuity limit, you need a lot of contrast. You need essentially 100% contrast, which means a black letter on a white background. But if the letter’s larger, you can manage with less contrast. It can be a light grey letter on a white background.
Now the curious thing is that you might think that as letters get larger and larger, they would get easier and easier to see – that is, you would need less and less contrast. But that is not what happens. After they get larger than the size of about one degree — and a degree of visual angle is a unit we use in vision science, which is about the size of the width of your thumb at arm’s length — their performance no longer improves. Now why is that? Well, it really has to do with the fact that as the letters get larger, they necessarily impinge upon areas of the retina that have fewer and fewer neurons, and consequently the resolution goes down and the performance also does not improve. That’s an example of the kind of neuroscience result that we have to introduce into our models in order to make them accurate predictors of human performance.
NTB: What is your favorite part of the job?
Watson: My colleagues. I really enjoy working with other people, and we have an excellent group here at NASA Ames Research Center, who constantly challenge me and improve my work. I very much value their collaboration.
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