Jonah Bossewitch	Prof. Johnson-Laird

Cog 200	10/8/93

Problem 1

How does the brain process a scene in a way that allows us to distinguish discrete objects from one another? How are patterns in visual images recognized? A study performed informally in class, provides us with some important clues that may help us penetrate the mystery of these phenomenon.

We were all shown two pictures that consisted primarily of lots of little triangles all congruent, oriented the same way, and lined up in rows and columns. Each picture also had a cluster of shapes that differed from the other triangles. The elements of the cluster each occupied the same area as the majority elements and were aligned with the rows and columns of the rest of the picture. The experiment tested the relative time it took for the cluster to "jump out" of the page, or in other words for the subject to recognize a discrepancy in a visual pattern. A brief period of time after being shown the first picture, it was apparent to most of us that not all of the triangles were oriented the same way, and a cluster of triangles were actually rotated 90 degrees with respect to the majority triangles. We were then shown a second picture and it was immediately apparent to most people that this picture had a cluster of rotated 'L' shaped forms in place of some of the triangles. Why did most people notice the rotated 'L's so much faster than they noticed the rotated triangles?

From the results of the experiment it seems that we can hypothesize that the rate at which the brain detects pattern disruptions is a function of the type of disruption of the pattern. The fact that it takes us longer to notice the rotated triangles than it does to notice the 'L' shapes indicates that the brain is processing the scene in a particular order. We must hypothesize a representation of pattern recognition that accounts for this order.

One possible way to interpret the experiment is by positing that pattern recognition involves the processing of zero crossings. The brain chunks an image by grouping together the blocks of an image with the same density of zero crossings. The algorithm for chunking a scene would involve iterativly chunking the scene at higher and higher resolutions (smaller scales). If pattern recognition was actually implemented using zero crossing density information the explanation of the phenomena in question would go something like this:

At a first glance, both images look uniform since the large area blocks (i.e. rows and columns) are homogeneous. On the next resolution level, on the order of the area of an element of the picture, the concentration of zero crossings is much lower in the region of the 'L' shapes that in the region of the triangles. However, at just this level, the rotated triangles produce the same density of zero crossings as the rest of the picture. This is because an area the size of an element the rotated triangles have as much black in them as the majority triangles, while the 'L' shapes do not. This would cause the 'L' shapes to be chunked at this stage while the triangles have not yet been differentiated. However, at the next level of resolution even the rotated triangles are detected to have different zero crossing densities, since they are oriented differently.

This hypothesis suggests a number of different predictions whose validation would help confirm it. First, if the idea of iterative chunking is correct, testing subjects on a similar picture with a cluster of misaligned rows of triangles should yield very fast recognition of the discrepancy. Second, altering the size of the cluster objects while keeping the shape constant should also yield fast recognition. The concept of iterative chunking may already be employed in children's puzzles that ask the child to find the differences between two pictures, to decide which picture is different from the others, or to find hidden objects is a picture. These children's games could be easily adapted to test for iterative chinking. The idea of pattern recognition relying on zero crossing density would be more difficult to test thoroughly, especially since it is based on the unconfirmed theory of zero crossings. However, a series of experiments that tested subjects ability to recognize patterns in clusters of dots with a deliberate density distribution might lead to some interesting further questions and explanations.