The Correlational Structure of Natural Images and the Calibration of Spatial Representations

Physiologists have long proposed that correlated input activity is important in normal sensory development. Here it is postulated that the visual system is sensitive to the correlation in image intensity across the visual field, and that these correlations are used to help calibrate spatial representations. Since measurements made near to each other in the visual field are more correlated than measurements made at a distance, the degree of correlation can be used as an estimate of the distance between two measurements and can therefore be used to calibrate a roughly organized spatial representation. We therefore explored the hypothesis that low level spatial representations are calibrated using a signal based on image intensity correlation. If the visual system uses input statistics to calibrate its spatial representation, then any distortions and anisotropies in these input statistics should be mirrored by distortions in the representation of space. To test the psychological implications of this hypothesis, a collection of 81 images of open and urban landscapes were used to estimate the degree of correlation between image intensity measurement pairs as a function of both distance and orientation. Doing this we show that a system that used the statistics measured to calibrate its representation would show: (1) a horizontal-vertical illusion;(2) the magnitude of this illusion would depend on the amount of open and urban landscapes in the environment;(3) there would be a nontrivial relationship between line orientation and judged length. Analogues of all these distortions and regularities can be found in the psychophysical literature on distance estimation. This gives strength to the proposal that spatial representations are calibrated using input statistics.