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Abstract
BackgroundThe statistical structure of the visual world offers many useful clues for understanding how biological visual systems may understand natural scenes. One particularly important early process in visual object recognition is that of grouping together edges which belong to the same contour. The layout of edges in natural scenes have strong statistical structure. One such statistical property is that edges tend to lie on a common circle, and this 'co-circularity' can predict human performance at contour grouping. We therefore tested the hypothesis that long-range excitatory lateral connections in the primary visual cortex, which are believed to be involved in contour grouping, display a similar co-circular structure.ResultsBy analyzing data from tree shrews, where information on both lateral connectivity and the overall structure of the orientation map was available, we found a surprising diversity in the relevant statistical structure of the connections. In particular, the extent to which co-circularity was displayed varied significantly.ConclusionsOverall, these data suggest the intriguing possibility that V1 may contain both co-circular and anti-cocircular connections.
Highlights
The statistical structure of the visual world offers many useful clues for understanding how biological visual systems may understand natural scenes
A more recently discovered generalization of co-linearity displayed by natural scenes is co-circularity: edges tend to be tangent to a common circle more often than would be expected by chance [6]
In order to test whether the co-circularity we found in the horizontal connections was due to this co-linearity, we recalculated Ddiff(r), but excluded all points lying near the axis of the injection site
Summary
The statistical structure of the visual world offers many useful clues for understanding how biological visual systems may understand natural scenes. The layout of edges in natural scenes have strong statistical structure One such statistical property is that edges tend to lie on a common circle, and this ‘co-circularity’ can predict human performance at contour grouping. The layout of edges in natural scenes has strong statistical structure. A more recently discovered generalization of co-linearity displayed by natural scenes is co-circularity: edges tend to be tangent to a common circle more often than would be expected by chance [6]. The degree of co-circularity in a contour can be used to predict human contour detection performance [7]. This raises the question of what biological substrate underlies this effect of co-circularity on performance
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